feat(misc): add VAE and Upscaler model management page

.agents/skills/lora-manager-e2e/SKILL.md

@@ -1,201 +0,0 @@
----
-name: lora-manager-e2e
-description: End-to-end testing and validation for LoRa Manager features. Use when performing automated E2E validation of LoRa Manager standalone mode, including starting/restarting the server, using Chrome DevTools MCP to interact with the web UI at http://127.0.0.1:8188/loras, and verifying frontend-to-backend functionality. Covers workflow validation, UI interaction testing, and integration testing between the standalone Python backend and the browser frontend.
----
-
-# LoRa Manager E2E Testing
-
-This skill provides workflows and utilities for end-to-end testing of LoRa Manager using Chrome DevTools MCP.
-
-## Prerequisites
-
-- LoRa Manager project cloned and dependencies installed (`pip install -r requirements.txt`)
-- Chrome browser available for debugging
-- Chrome DevTools MCP connected
-
-## Quick Start Workflow
-
-### 1. Start LoRa Manager Standalone
-
-```python
-# Use the provided script to start the server
-python .agents/skills/lora-manager-e2e/scripts/start_server.py --port 8188
-```
-
-Or manually:
-```bash
-cd /home/miao/workspace/ComfyUI/custom_nodes/ComfyUI-Lora-Manager
-python standalone.py --port 8188
-```
-
-Wait for server ready message before proceeding.
-
-### 2. Open Chrome Debug Mode
-
-```bash
-# Chrome with remote debugging on port 9222
-google-chrome --remote-debugging-port=9222 --user-data-dir=/tmp/chrome-lora-manager http://127.0.0.1:8188/loras
-```
-
-### 3. Connect Chrome DevTools MCP
-
-Ensure the MCP server is connected to Chrome at `http://localhost:9222`.
-
-### 4. Navigate and Interact
-
-Use Chrome DevTools MCP tools to:
-- Take snapshots: `take_snapshot`
-- Click elements: `click`
-- Fill forms: `fill` or `fill_form`
-- Evaluate scripts: `evaluate_script`
-- Wait for elements: `wait_for`
-
-## Common E2E Test Patterns
-
-### Pattern: Full Page Load Verification
-
-```python
-# Navigate to LoRA list page
-navigate_page(type="url", url="http://127.0.0.1:8188/loras")
-
-# Wait for page to load
-wait_for(text="LoRAs", timeout=10000)
-
-# Take snapshot to verify UI state
-snapshot = take_snapshot()
-```
-
-### Pattern: Restart Server for Configuration Changes
-
-```python
-# Stop current server (if running)
-# Start with new configuration
-python .agents/skills/lora-manager-e2e/scripts/start_server.py --port 8188 --restart
-
-# Wait and refresh browser
-navigate_page(type="reload", ignoreCache=True)
-wait_for(text="LoRAs", timeout=15000)
-```
-
-### Pattern: Verify Backend API via Frontend
-
-```python
-# Execute script in browser to call backend API
-result = evaluate_script(function="""
-async () => {
-  const response = await fetch('/loras/api/list');
-  const data = await response.json();
-  return { count: data.length, firstItem: data[0]?.name };
-}
-""")
-```
-
-### Pattern: Form Submission Flow
-
-```python
-# Fill a form (e.g., search or filter)
-fill_form(elements=[
-    {"uid": "search-input", "value": "character"},
-])
-
-# Click submit button
-click(uid="search-button")
-
-# Wait for results
-wait_for(text="Results", timeout=5000)
-
-# Verify results via snapshot
-snapshot = take_snapshot()
-```
-
-### Pattern: Modal Dialog Interaction
-
-```python
-# Open modal (e.g., add LoRA)
-click(uid="add-lora-button")
-
-# Wait for modal to appear
-wait_for(text="Add LoRA", timeout=3000)
-
-# Fill modal form
-fill_form(elements=[
-    {"uid": "lora-name", "value": "Test LoRA"},
-    {"uid": "lora-path", "value": "/path/to/lora.safetensors"},
-])
-
-# Submit
-click(uid="modal-submit-button")
-
-# Wait for success message or close
-wait_for(text="Success", timeout=5000)
-```
-
-## Available Scripts
-
-### scripts/start_server.py
-
-Starts or restarts the LoRa Manager standalone server.
-
-```bash
-python scripts/start_server.py [--port PORT] [--restart] [--wait]
-```
-
-Options:
-- `--port`: Server port (default: 8188)
-- `--restart`: Kill existing server before starting
-- `--wait`: Wait for server to be ready before exiting
-
-### scripts/wait_for_server.py
-
-Polls server until ready or timeout.
-
-```bash
-python scripts/wait_for_server.py [--port PORT] [--timeout SECONDS]
-```
-
-## Test Scenarios Reference
-
-See [references/test-scenarios.md](references/test-scenarios.md) for detailed test scenarios including:
-- LoRA list display and filtering
-- Model metadata editing
-- Recipe creation and management
-- Settings configuration
-- Import/export functionality
-
-## Network Request Verification
-
-Use `list_network_requests` and `get_network_request` to verify API calls:
-
-```python
-# List recent XHR/fetch requests
-requests = list_network_requests(resourceTypes=["xhr", "fetch"])
-
-# Get details of specific request
-details = get_network_request(reqid=123)
-```
-
-## Console Message Monitoring
-
-```python
-# Check for errors or warnings
-messages = list_console_messages(types=["error", "warn"])
-```
-
-## Performance Testing
-
-```python
-# Start performance trace
-performance_start_trace(reload=True, autoStop=False)
-
-# Perform actions...
-
-# Stop and analyze
-results = performance_stop_trace()
-```
-
-## Cleanup
-
-Always ensure proper cleanup after tests:
-1. Stop the standalone server
-2. Close browser pages (keep at least one open)
-3. Clear temporary data if needed

.agents/skills/lora-manager-e2e/references/mcp-cheatsheet.md

@@ -1,324 +0,0 @@
-# Chrome DevTools MCP Cheatsheet for LoRa Manager
-
-Quick reference for common MCP commands used in LoRa Manager E2E testing.
-
-## Navigation
-
-```python
-# Navigate to LoRA list page
-navigate_page(type="url", url="http://127.0.0.1:8188/loras")
-
-# Reload page with cache clear
-navigate_page(type="reload", ignoreCache=True)
-
-# Go back/forward
-navigate_page(type="back")
-navigate_page(type="forward")
-```
-
-## Waiting
-
-```python
-# Wait for text to appear
-wait_for(text="LoRAs", timeout=10000)
-
-# Wait for specific element (via evaluate_script)
-evaluate_script(function="""
-() => {
-    return new Promise((resolve) => {
-        const check = () => {
-            if (document.querySelector('.lora-card')) {
-                resolve(true);
-            } else {
-                setTimeout(check, 100);
-            }
-        };
-        check();
-    });
-}
-""")
-```
-
-## Taking Snapshots
-
-```python
-# Full page snapshot
-snapshot = take_snapshot()
-
-# Verbose snapshot (more details)
-snapshot = take_snapshot(verbose=True)
-
-# Save to file
-take_snapshot(filePath="test-snapshots/page-load.json")
-```
-
-## Element Interaction
-
-```python
-# Click element
-click(uid="element-uid-from-snapshot")
-
-# Double click
-click(uid="element-uid", dblClick=True)
-
-# Fill input
-fill(uid="search-input", value="test query")
-
-# Fill multiple inputs
-fill_form(elements=[
-    {"uid": "input-1", "value": "value 1"},
-    {"uid": "input-2", "value": "value 2"},
-])
-
-# Hover
-hover(uid="lora-card-1")
-
-# Upload file
-upload_file(uid="file-input", filePath="/path/to/file.safetensors")
-```
-
-## Keyboard Input
-
-```python
-# Press key
-press_key(key="Enter")
-press_key(key="Escape")
-press_key(key="Tab")
-
-# Keyboard shortcuts
-press_key(key="Control+A")  # Select all
-press_key(key="Control+F")  # Find
-```
-
-## JavaScript Evaluation
-
-```python
-# Simple evaluation
-result = evaluate_script(function="() => document.title")
-
-# Async evaluation
-result = evaluate_script(function="""
-async () => {
-    const response = await fetch('/loras/api/list');
-    return await response.json();
-}
-""")
-
-# Check element existence
-exists = evaluate_script(function="""
-() => document.querySelector('.lora-card') !== null
-""")
-
-# Get element count
-count = evaluate_script(function="""
-() => document.querySelectorAll('.lora-card').length
-""")
-```
-
-## Network Monitoring
-
-```python
-# List all network requests
-requests = list_network_requests()
-
-# Filter by resource type
-xhr_requests = list_network_requests(resourceTypes=["xhr", "fetch"])
-
-# Get specific request details
-details = get_network_request(reqid=123)
-
-# Include preserved requests from previous navigations
-all_requests = list_network_requests(includePreservedRequests=True)
-```
-
-## Console Monitoring
-
-```python
-# List all console messages
-messages = list_console_messages()
-
-# Filter by type
-errors = list_console_messages(types=["error", "warn"])
-
-# Include preserved messages
-all_messages = list_console_messages(includePreservedMessages=True)
-
-# Get specific message
-details = get_console_message(msgid=1)
-```
-
-## Performance Testing
-
-```python
-# Start trace with page reload
-performance_start_trace(reload=True, autoStop=False)
-
-# Start trace without reload
-performance_start_trace(reload=False, autoStop=True, filePath="trace.json.gz")
-
-# Stop trace
-results = performance_stop_trace()
-
-# Stop and save
-performance_stop_trace(filePath="trace-results.json.gz")
-
-# Analyze specific insight
-insight = performance_analyze_insight(
-    insightSetId="results.insightSets[0].id",
-    insightName="LCPBreakdown"
-)
-```
-
-## Page Management
-
-```python
-# List open pages
-pages = list_pages()
-
-# Select a page
-select_page(pageId=0, bringToFront=True)
-
-# Create new page
-new_page(url="http://127.0.0.1:8188/loras")
-
-# Close page (keep at least one open!)
-close_page(pageId=1)
-
-# Resize page
-resize_page(width=1920, height=1080)
-```
-
-## Screenshots
-
-```python
-# Full page screenshot
-take_screenshot(fullPage=True)
-
-# Viewport screenshot
-take_screenshot()
-
-# Element screenshot
-take_screenshot(uid="lora-card-1")
-
-# Save to file
-take_screenshot(filePath="screenshots/page.png", format="png")
-
-# JPEG with quality
-take_screenshot(filePath="screenshots/page.jpg", format="jpeg", quality=90)
-```
-
-## Dialog Handling
-
-```python
-# Accept dialog
-handle_dialog(action="accept")
-
-# Accept with text input
-handle_dialog(action="accept", promptText="user input")
-
-# Dismiss dialog
-handle_dialog(action="dismiss")
-```
-
-## Device Emulation
-
-```python
-# Mobile viewport
-emulate(viewport={"width": 375, "height": 667, "isMobile": True, "hasTouch": True})
-
-# Tablet viewport
-emulate(viewport={"width": 768, "height": 1024, "isMobile": True, "hasTouch": True})
-
-# Desktop viewport
-emulate(viewport={"width": 1920, "height": 1080})
-
-# Network throttling
-emulate(networkConditions="Slow 3G")
-emulate(networkConditions="Fast 4G")
-
-# CPU throttling
-emulate(cpuThrottlingRate=4)  # 4x slowdown
-
-# Geolocation
-emulate(geolocation={"latitude": 37.7749, "longitude": -122.4194})
-
-# User agent
-emulate(userAgent="Mozilla/5.0 (Custom)")
-
-# Reset emulation
-emulate(viewport=None, networkConditions="No emulation", userAgent=None)
-```
-
-## Drag and Drop
-
-```python
-# Drag element to another
-drag(from_uid="draggable-item", to_uid="drop-zone")
-```
-
-## Common LoRa Manager Test Patterns
-
-### Verify LoRA Cards Loaded
-
-```python
-navigate_page(type="url", url="http://127.0.0.1:8188/loras")
-wait_for(text="LoRAs", timeout=10000)
-
-# Check if cards loaded
-result = evaluate_script(function="""
-() => {
-    const cards = document.querySelectorAll('.lora-card');
-    return {
-        count: cards.length,
-        hasData: cards.length > 0
-    };
-}
-""")
-```
-
-### Search and Verify Results
-
-```python
-fill(uid="search-input", value="character")
-press_key(key="Enter")
-wait_for(timeout=2000)  # Wait for debounce
-
-# Check results
-result = evaluate_script(function="""
-() => {
-    const cards = document.querySelectorAll('.lora-card');
-    const names = Array.from(cards).map(c => c.dataset.name || c.textContent);
-    return { count: cards.length, names };
-}
-""")
-```
-
-### Check API Response
-
-```python
-# Trigger API call
-evaluate_script(function="""
-() => window.loraApiCallPromise = fetch('/loras/api/list').then(r => r.json())
-""")
-
-# Wait and get result
-import time
-time.sleep(1)
-
-result = evaluate_script(function="""
-async () => await window.loraApiCallPromise
-""")
-```
-
-### Monitor Console for Errors
-
-```python
-# Before test: clear console (navigate reloads)
-navigate_page(type="reload")
-
-# ... perform actions ...
-
-# Check for errors
-errors = list_console_messages(types=["error"])
-assert len(errors) == 0, f"Console errors: {errors}"
-```

.agents/skills/lora-manager-e2e/references/test-scenarios.md

@@ -1,272 +0,0 @@
-# LoRa Manager E2E Test Scenarios
-
-This document provides detailed test scenarios for end-to-end validation of LoRa Manager features.
-
-## Table of Contents
-
-1. [LoRA List Page](#lora-list-page)
-2. [Model Details](#model-details)
-3. [Recipes](#recipes)
-4. [Settings](#settings)
-5. [Import/Export](#importexport)
-
----
-
-## LoRA List Page
-
-### Scenario: Page Load and Display
-
-**Objective**: Verify the LoRA list page loads correctly and displays models.
-
-**Steps**:
-1. Navigate to `http://127.0.0.1:8188/loras`
-2. Wait for page title "LoRAs" to appear
-3. Take snapshot to verify:
-   - Header with "LoRAs" title is visible
-   - Search/filter controls are present
-   - Grid/list view toggle exists
-   - LoRA cards are displayed (if models exist)
-   - Pagination controls (if applicable)
-
-**Expected Result**: Page loads without errors, UI elements are present.
-
-### Scenario: Search Functionality
-
-**Objective**: Verify search filters LoRA models correctly.
-
-**Steps**:
-1. Ensure at least one LoRA exists with known name (e.g., "test-character")
-2. Navigate to LoRA list page
-3. Enter search term in search box: "test"
-4. Press Enter or click search button
-5. Wait for results to update
-
-**Expected Result**: Only LoRAs matching search term are displayed.
-
-**Verification Script**:
-```python
-# After search, verify filtered results
-evaluate_script(function="""
-() => {
-  const cards = document.querySelectorAll('.lora-card');
-  const names = Array.from(cards).map(c => c.dataset.name);
-  return { count: cards.length, names };
-}
-""")
-```
-
-### Scenario: Filter by Tags
-
-**Objective**: Verify tag filtering works correctly.
-
-**Steps**:
-1. Navigate to LoRA list page
-2. Click on a tag (e.g., "character", "style")
-3. Wait for filtered results
-
-**Expected Result**: Only LoRAs with selected tag are displayed.
-
-### Scenario: View Mode Toggle
-
-**Objective**: Verify grid/list view toggle works.
-
-**Steps**:
-1. Navigate to LoRA list page
-2. Click list view button
-3. Verify list layout
-4. Click grid view button
-5. Verify grid layout
-
-**Expected Result**: View mode changes correctly, layout updates.
-
----
-
-## Model Details
-
-### Scenario: Open Model Details
-
-**Objective**: Verify clicking a LoRA opens its details.
-
-**Steps**:
-1. Navigate to LoRA list page
-2. Click on a LoRA card
-3. Wait for details panel/modal to open
-
-**Expected Result**: Details panel shows:
-- Model name
-- Preview image
-- Metadata (trigger words, tags, etc.)
-- Action buttons (edit, delete, etc.)
-
-### Scenario: Edit Model Metadata
-
-**Objective**: Verify metadata editing works end-to-end.
-
-**Steps**:
-1. Open a LoRA's details
-2. Click "Edit" button
-3. Modify trigger words field
-4. Add/remove tags
-5. Save changes
-6. Refresh page
-7. Reopen the same LoRA
-
-**Expected Result**: Changes persist after refresh.
-
-### Scenario: Delete Model
-
-**Objective**: Verify model deletion works.
-
-**Steps**:
-1. Open a LoRA's details
-2. Click "Delete" button
-3. Confirm deletion in dialog
-4. Wait for removal
-
-**Expected Result**: Model removed from list, success message shown.
-
----
-
-## Recipes
-
-### Scenario: Recipe List Display
-
-**Objective**: Verify recipes page loads and displays recipes.
-
-**Steps**:
-1. Navigate to `http://127.0.0.1:8188/recipes`
-2. Wait for "Recipes" title
-3. Take snapshot
-
-**Expected Result**: Recipe list displayed with cards/items.
-
-### Scenario: Create New Recipe
-
-**Objective**: Verify recipe creation workflow.
-
-**Steps**:
-1. Navigate to recipes page
-2. Click "New Recipe" button
-3. Fill recipe form:
-   - Name: "Test Recipe"
-   - Description: "E2E test recipe"
-   - Add LoRA models
-4. Save recipe
-5. Verify recipe appears in list
-
-**Expected Result**: New recipe created and displayed.
-
-### Scenario: Apply Recipe
-
-**Objective**: Verify applying a recipe to ComfyUI.
-
-**Steps**:
-1. Open a recipe
-2. Click "Apply" or "Load in ComfyUI"
-3. Verify action completes
-
-**Expected Result**: Recipe applied successfully.
-
----
-
-## Settings
-
-### Scenario: Settings Page Load
-
-**Objective**: Verify settings page displays correctly.
-
-**Steps**:
-1. Navigate to `http://127.0.0.1:8188/settings`
-2. Wait for "Settings" title
-3. Take snapshot
-
-**Expected Result**: Settings form with various options displayed.
-
-### Scenario: Change Setting and Restart
-
-**Objective**: Verify settings persist after restart.
-
-**Steps**:
-1. Navigate to settings page
-2. Change a setting (e.g., default view mode)
-3. Save settings
-4. Restart server: `python scripts/start_server.py --restart --wait`
-5. Refresh browser page
-6. Navigate to settings
-
-**Expected Result**: Changed setting value persists.
-
----
-
-## Import/Export
-
-### Scenario: Export Models List
-
-**Objective**: Verify export functionality.
-
-**Steps**:
-1. Navigate to LoRA list
-2. Click "Export" button
-3. Select format (JSON/CSV)
-4. Download file
-
-**Expected Result**: File downloaded with correct data.
-
-### Scenario: Import Models
-
-**Objective**: Verify import functionality.
-
-**Steps**:
-1. Prepare import file
-2. Navigate to import page
-3. Upload file
-4. Verify import results
-
-**Expected Result**: Models imported successfully, confirmation shown.
-
----
-
-## API Integration Tests
-
-### Scenario: Verify API Endpoints
-
-**Objective**: Verify backend API responds correctly.
-
-**Test via browser console**:
-```javascript
-// List LoRAs
-fetch('/loras/api/list').then(r => r.json()).then(console.log)
-
-// Get LoRA details
-fetch('/loras/api/detail/<id>').then(r => r.json()).then(console.log)
-
-// Search LoRAs
-fetch('/loras/api/search?q=test').then(r => r.json()).then(console.log)
-```
-
-**Expected Result**: APIs return valid JSON with expected structure.
-
----
-
-## Console Error Monitoring
-
-During all tests, monitor browser console for errors:
-
-```python
-# Check for JavaScript errors
-messages = list_console_messages(types=["error"])
-assert len(messages) == 0, f"Console errors found: {messages}"
-```
-
-## Network Request Verification
-
-Verify key API calls are made:
-
-```python
-# List XHR requests
-requests = list_network_requests(resourceTypes=["xhr", "fetch"])
-
-# Look for specific endpoints
-lora_list_requests = [r for r in requests if "/api/list" in r.get("url", "")]
-assert len(lora_list_requests) > 0, "LoRA list API not called"
-```

.agents/skills/lora-manager-e2e/scripts/example_e2e_test.py

@@ -1,193 +0,0 @@
-#!/usr/bin/env python3
-"""
-Example E2E test demonstrating LoRa Manager testing workflow.
-
-This script shows how to:
-1. Start the standalone server
-2. Use Chrome DevTools MCP to interact with the UI
-3. Verify functionality end-to-end
-
-Note: This is a template. Actual execution requires Chrome DevTools MCP.
-"""
-
-import subprocess
-import sys
-import time
-
-
-def run_test():
-    """Run example E2E test flow."""
-    
-    print("=" * 60)
-    print("LoRa Manager E2E Test Example")
-    print("=" * 60)
-    
-    # Step 1: Start server
-    print("\n[1/5] Starting LoRa Manager standalone server...")
-    result = subprocess.run(
-        [sys.executable, "start_server.py", "--port", "8188", "--wait", "--timeout", "30"],
-        capture_output=True,
-        text=True
-    )
-    if result.returncode != 0:
-        print(f"Failed to start server: {result.stderr}")
-        return 1
-    print("Server ready!")
-    
-    # Step 2: Open Chrome (manual step - show command)
-    print("\n[2/5] Open Chrome with debug mode:")
-    print("google-chrome --remote-debugging-port=9222 --user-data-dir=/tmp/chrome-lora-manager http://127.0.0.1:8188/loras")
-    print("(In actual test, this would be automated via MCP)")
-    
-    # Step 3: Navigate and verify page load
-    print("\n[3/5] Page Load Verification:")
-    print("""
-    MCP Commands to execute:
-    1. navigate_page(type="url", url="http://127.0.0.1:8188/loras")
-    2. wait_for(text="LoRAs", timeout=10000)
-    3. snapshot = take_snapshot()
-    """)
-    
-    # Step 4: Test search functionality
-    print("\n[4/5] Search Functionality Test:")
-    print("""
-    MCP Commands to execute:
-    1. fill(uid="search-input", value="test")
-    2. press_key(key="Enter")
-    3. wait_for(text="Results", timeout=5000)
-    4. result = evaluate_script(function="""
-        () => {
-            const cards = document.querySelectorAll('.lora-card');
-            return { count: cards.length };
-        }
-        """)
-    """)
-    
-    # Step 5: Verify API
-    print("\n[5/5] API Verification:")
-    print("""
-    MCP Commands to execute:
-    1. api_result = evaluate_script(function="""
-        async () => {
-            const response = await fetch('/loras/api/list');
-            const data = await response.json();
-            return { count: data.length, status: response.status };
-        }
-        """)
-    2. Verify api_result['status'] == 200
-    """)
-    
-    print("\n" + "=" * 60)
-    print("Test flow completed!")
-    print("=" * 60)
-    
-    return 0
-
-
-def example_restart_flow():
-    """Example: Testing configuration change that requires restart."""
-    
-    print("\n" + "=" * 60)
-    print("Example: Server Restart Flow")
-    print("=" * 60)
-    
-    print("""
-    Scenario: Change setting and verify after restart
-    
-    Steps:
-    1. Navigate to settings page
-       - navigate_page(type="url", url="http://127.0.0.1:8188/settings")
-    
-    2. Change a setting (e.g., theme)
-       - fill(uid="theme-select", value="dark")
-       - click(uid="save-settings-button")
-    
-    3. Restart server
-       - subprocess.run([python, "start_server.py", "--restart", "--wait"])
-    
-    4. Refresh browser
-       - navigate_page(type="reload", ignoreCache=True)
-       - wait_for(text="LoRAs", timeout=15000)
-    
-    5. Verify setting persisted
-       - navigate_page(type="url", url="http://127.0.0.1:8188/settings")
-       - theme = evaluate_script(function="() => document.querySelector('#theme-select').value")
-       - assert theme == "dark"
-    """)
-
-
-def example_modal_interaction():
-    """Example: Testing modal dialog interaction."""
-    
-    print("\n" + "=" * 60)
-    print("Example: Modal Dialog Interaction")
-    print("=" * 60)
-    
-    print("""
-    Scenario: Add new LoRA via modal
-    
-    Steps:
-    1. Open modal
-       - click(uid="add-lora-button")
-       - wait_for(text="Add LoRA", timeout=3000)
-    
-    2. Fill form
-       - fill_form(elements=[
-           {"uid": "lora-name", "value": "Test Character"},
-           {"uid": "lora-path", "value": "/models/test.safetensors"},
-       ])
-    
-    3. Submit
-       - click(uid="modal-submit-button")
-    
-    4. Verify success
-       - wait_for(text="Successfully added", timeout=5000)
-       - snapshot = take_snapshot()
-    """)
-
-
-def example_network_monitoring():
-    """Example: Network request monitoring."""
-    
-    print("\n" + "=" * 60)
-    print("Example: Network Request Monitoring")
-    print("=" * 60)
-    
-    print("""
-    Scenario: Verify API calls during user interaction
-    
-    Steps:
-    1. Clear network log (implicit on navigation)
-       - navigate_page(type="url", url="http://127.0.0.1:8188/loras")
-    
-    2. Perform action that triggers API call
-       - fill(uid="search-input", value="character")
-       - press_key(key="Enter")
-    
-    3. List network requests
-       - requests = list_network_requests(resourceTypes=["xhr", "fetch"])
-    
-    4. Find search API call
-       - search_requests = [r for r in requests if "/api/search" in r.get("url", "")]
-       - assert len(search_requests) > 0, "Search API was not called"
-    
-    5. Get request details
-       - if search_requests:
-           details = get_network_request(reqid=search_requests[0]["reqid"])
-           - Verify request method, response status, etc.
-    """)
-
-
-if __name__ == "__main__":
-    print("LoRa Manager E2E Test Examples\n")
-    print("This script demonstrates E2E testing patterns.\n")
-    print("Note: Actual execution requires Chrome DevTools MCP connection.\n")
-    
-    run_test()
-    example_restart_flow()
-    example_modal_interaction()
-    example_network_monitoring()
-    
-    print("\n" + "=" * 60)
-    print("All examples shown!")
-    print("=" * 60)

.agents/skills/lora-manager-e2e/scripts/start_server.py

@@ -1,169 +0,0 @@
-#!/usr/bin/env python3
-"""
-Start or restart LoRa Manager standalone server for E2E testing.
-"""
-
-import argparse
-import subprocess
-import sys
-import time
-import socket
-import signal
-import os
-
-
-def find_server_process(port: int) -> list[int]:
-    """Find PIDs of processes listening on the given port."""
-    try:
-        result = subprocess.run(
-            ["lsof", "-ti", f":{port}"],
-            capture_output=True,
-            text=True,
-            check=False
-        )
-        if result.returncode == 0 and result.stdout.strip():
-            return [int(pid) for pid in result.stdout.strip().split("\n") if pid]
-    except FileNotFoundError:
-        # lsof not available, try netstat
-        try:
-            result = subprocess.run(
-                ["netstat", "-tlnp"],
-                capture_output=True,
-                text=True,
-                check=False
-            )
-            pids = []
-            for line in result.stdout.split("\n"):
-                if f":{port}" in line:
-                    parts = line.split()
-                    for part in parts:
-                        if "/" in part:
-                            try:
-                                pid = int(part.split("/")[0])
-                                pids.append(pid)
-                            except ValueError:
-                                pass
-            return pids
-        except FileNotFoundError:
-            pass
-    return []
-
-
-def kill_server(port: int) -> None:
-    """Kill processes using the specified port."""
-    pids = find_server_process(port)
-    for pid in pids:
-        try:
-            os.kill(pid, signal.SIGTERM)
-            print(f"Sent SIGTERM to process {pid}")
-        except ProcessLookupError:
-            pass
-    
-    # Wait for processes to terminate
-    time.sleep(1)
-    
-    # Force kill if still running
-    pids = find_server_process(port)
-    for pid in pids:
-        try:
-            os.kill(pid, signal.SIGKILL)
-            print(f"Sent SIGKILL to process {pid}")
-        except ProcessLookupError:
-            pass
-
-
-def is_server_ready(port: int, timeout: float = 0.5) -> bool:
-    """Check if server is accepting connections."""
-    try:
-        with socket.create_connection(("127.0.0.1", port), timeout=timeout):
-            return True
-    except (socket.timeout, ConnectionRefusedError, OSError):
-        return False
-
-
-def wait_for_server(port: int, timeout: int = 30) -> bool:
-    """Wait for server to become ready."""
-    start = time.time()
-    while time.time() - start < timeout:
-        if is_server_ready(port):
-            return True
-        time.sleep(0.5)
-    return False
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Start LoRa Manager standalone server for E2E testing"
-    )
-    parser.add_argument(
-        "--port",
-        type=int,
-        default=8188,
-        help="Server port (default: 8188)"
-    )
-    parser.add_argument(
-        "--restart",
-        action="store_true",
-        help="Kill existing server before starting"
-    )
-    parser.add_argument(
-        "--wait",
-        action="store_true",
-        help="Wait for server to be ready before exiting"
-    )
-    parser.add_argument(
-        "--timeout",
-        type=int,
-        default=30,
-        help="Timeout for waiting (default: 30)"
-    )
-    
-    args = parser.parse_args()
-    
-    # Get project root (parent of .agents directory)
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    skill_dir = os.path.dirname(script_dir)
-    project_root = os.path.dirname(os.path.dirname(os.path.dirname(skill_dir)))
-    
-    # Restart if requested
-    if args.restart:
-        print(f"Killing existing server on port {args.port}...")
-        kill_server(args.port)
-        time.sleep(1)
-    
-    # Check if already running
-    if is_server_ready(args.port):
-        print(f"Server already running on port {args.port}")
-        return 0
-    
-    # Start server
-    print(f"Starting LoRa Manager standalone server on port {args.port}...")
-    cmd = [sys.executable, "standalone.py", "--port", str(args.port)]
-    
-    # Start in background
-    process = subprocess.Popen(
-        cmd,
-        cwd=project_root,
-        stdout=subprocess.PIPE,
-        stderr=subprocess.PIPE,
-        start_new_session=True
-    )
-    
-    print(f"Server process started with PID {process.pid}")
-    
-    # Wait for ready if requested
-    if args.wait:
-        print(f"Waiting for server to be ready (timeout: {args.timeout}s)...")
-        if wait_for_server(args.port, args.timeout):
-            print(f"Server ready at http://127.0.0.1:{args.port}/loras")
-            return 0
-        else:
-            print(f"Timeout waiting for server")
-            return 1
-    
-    print(f"Server starting at http://127.0.0.1:{args.port}/loras")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())

.agents/skills/lora-manager-e2e/scripts/wait_for_server.py

@@ -1,61 +0,0 @@
-#!/usr/bin/env python3
-"""
-Wait for LoRa Manager server to become ready.
-"""
-
-import argparse
-import socket
-import sys
-import time
-
-
-def is_server_ready(port: int, timeout: float = 0.5) -> bool:
-    """Check if server is accepting connections."""
-    try:
-        with socket.create_connection(("127.0.0.1", port), timeout=timeout):
-            return True
-    except (socket.timeout, ConnectionRefusedError, OSError):
-        return False
-
-
-def wait_for_server(port: int, timeout: int = 30) -> bool:
-    """Wait for server to become ready."""
-    start = time.time()
-    while time.time() - start < timeout:
-        if is_server_ready(port):
-            return True
-        time.sleep(0.5)
-    return False
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Wait for LoRa Manager server to become ready"
-    )
-    parser.add_argument(
-        "--port",
-        type=int,
-        default=8188,
-        help="Server port (default: 8188)"
-    )
-    parser.add_argument(
-        "--timeout",
-        type=int,
-        default=30,
-        help="Timeout in seconds (default: 30)"
-    )
-    
-    args = parser.parse_args()
-    
-    print(f"Waiting for server on port {args.port} (timeout: {args.timeout}s)...")
-    
-    if wait_for_server(args.port, args.timeout):
-        print(f"Server ready at http://127.0.0.1:{args.port}/loras")
-        return 0
-    else:
-        print(f"Timeout: Server not ready after {args.timeout}s")
-        return 1
-
-
-if __name__ == "__main__":
-    sys.exit(main())

.agents/skills/lora-manager-runtime-context/SKILL.md

@@ -1,69 +0,0 @@
----
-name: lora-manager-runtime-context
-description: Inspect ComfyUI LoRA Manager runtime configuration and local diagnostic state. Use when debugging LoRA Manager issues that require locating or reading settings.json, active library paths, model metadata JSON sidecars, recipe metadata JSON files, example image folders, SQLite caches, symlink maps, download history, aria2 state, or other cache files under the LoRA Manager user config directory.
----
-
-# LoRA Manager Runtime Context
-
-## Core Rules
-
-- Treat runtime state as local user data. Prefer read-only inspection unless the user explicitly asks for mutation.
-- Never print secret-like settings values. Redact keys containing `key`, `token`, `secret`, `password`, `auth`, or `credential`, including `civitai_api_key`.
-- Resolve paths from the runtime configuration before guessing. In this environment the settings file is normally `/home/miao/.config/ComfyUI-LoRA-Manager/settings.json`, but portable settings can override this through the repository `settings.json`.
-- Use the active library when selecting per-library caches and paths. Read `active_library` from settings; fall back to `default` if missing.
-- Normalize and expand `~` before comparing paths. Symlinks are common in this repo.
-
-## Quick Start
-
-Use the bundled helper for a safe first pass:
-
-```bash
-python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py summary
-python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py caches
-```
-
-The script redacts sensitive settings, opens SQLite databases read-only, and reports inaccessible or locked databases as warnings.
-
-For focused checks:
-
-```bash
-python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py recipes
-python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py model --path /path/to/model.safetensors
-python .agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py sqlite --db /path/to/cache.sqlite --limit 3
-```
-
-## Runtime Path Rules
-
-- Settings directory: use `py/utils/settings_paths.py`. Default platform path is `platformdirs.user_config_dir("ComfyUI-LoRA-Manager", appauthor=False)`.
-- Settings file: `<settings_dir>/settings.json`.
-- Cache root: `<settings_dir>/cache`.
-- Canonical cache files:
-  - Model cache: `cache/model/<active_library>.sqlite`.
-  - Recipe cache: `cache/recipe/<active_library>.sqlite`.
-  - Model update cache: `cache/model_update/<active_library>.sqlite`.
-  - Recipe FTS: `cache/fts/recipe_fts.sqlite`.
-  - Tag FTS: `cache/fts/tag_fts.sqlite`.
-  - Symlink map: `cache/symlink/symlink_map.json`.
-  - Download history: `cache/download_history/downloaded_versions.sqlite`.
-  - aria2 state: `cache/aria2/downloads.json`.
-- Legacy cache locations may exist; prefer canonical paths unless diagnosing migrations.
-
-## Data Location Rules
-
-- Model roots come from `settings.folder_paths` and the active library payload under `settings.libraries[active_library]`.
-- Model metadata JSON sidecars live next to the model file as `<model basename>.metadata.json`.
-- Recipes root is `settings.recipes_path` when it is a non-empty string. If empty, use the first configured LoRA root plus `/recipes`.
-- Recipe JSON files are named `*.recipe.json` under the recipes root and may be nested in folders.
-- Example image root is `settings.example_images_path`.
-- If multiple libraries are configured, example images are stored under `<example_images_path>/<sanitized_library>/<sha256>/`; otherwise they are under `<example_images_path>/<sha256>/`.
-
-## Useful Cache Tables
-
-- Model cache: `models`, `model_tags`, `hash_index`, `excluded_models`.
-- Recipe cache: `recipes`, `cache_metadata`.
-- Model update cache: `model_update_status`, `model_update_versions`.
-- Tag FTS cache: `tags`, `fts_metadata`, plus FTS internal tables.
-- Recipe FTS cache: `recipe_rowid`, `fts_metadata`, plus FTS internal tables.
-- Download history: `downloaded_model_versions`.
-
-Prefer querying only counts, schema, and a few sample rows unless the user asks for full output.

.agents/skills/lora-manager-runtime-context/agents/openai.yaml

@@ -1,4 +0,0 @@
-interface:
-  display_name: "LoRA Manager Runtime Context"
-  short_description: "Inspect LoRA Manager runtime state"
-  default_prompt: "Use $lora-manager-runtime-context to inspect LoRA Manager settings, metadata paths, and caches for debugging."

.agents/skills/lora-manager-runtime-context/scripts/inspect_runtime_context.py

@@ -1,381 +0,0 @@
-#!/usr/bin/env python3
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import re
-import shutil
-import sqlite3
-import sys
-import tempfile
-from pathlib import Path
-from typing import Any
-
-SECRET_PATTERN = re.compile(r"(key|token|secret|password|auth|credential)", re.IGNORECASE)
-APP_NAME = "ComfyUI-LoRA-Manager"
-CACHE_SQLITE = {
-    "model": ("model", "{library}.sqlite"),
-    "recipe": ("recipe", "{library}.sqlite"),
-    "model_update": ("model_update", "{library}.sqlite"),
-    "recipe_fts": ("fts", "recipe_fts.sqlite"),
-    "tag_fts": ("fts", "tag_fts.sqlite"),
-    "download_history": ("download_history", "downloaded_versions.sqlite"),
-}
-CACHE_JSON = {
-    "symlink": ("symlink", "symlink_map.json"),
-    "aria2": ("aria2", "downloads.json"),
-}
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description="Inspect LoRA Manager runtime state read-only.")
-    subparsers = parser.add_subparsers(dest="command", required=True)
-
-    subparsers.add_parser("summary", help="Print redacted settings and resolved paths.")
-    subparsers.add_parser("caches", help="Print cache paths and SQLite table summaries.")
-    subparsers.add_parser("recipes", help="Print resolved recipes root and recipe JSON count.")
-
-    model_parser = subparsers.add_parser("model", help="Inspect a model metadata sidecar path.")
-    model_parser.add_argument("--path", required=True, help="Path to a model file or metadata JSON file.")
-
-    sqlite_parser = subparsers.add_parser("sqlite", help="Inspect a SQLite database read-only.")
-    sqlite_parser.add_argument("--db", required=True, help="Path to the SQLite database.")
-    sqlite_parser.add_argument("--limit", type=int, default=3, help="Rows to sample from each user table.")
-
-    args = parser.parse_args()
-    context = build_context()
-
-    if args.command == "summary":
-        print_json(summary_payload(context))
-    elif args.command == "caches":
-        print_json(caches_payload(context))
-    elif args.command == "recipes":
-        print_json(recipes_payload(context))
-    elif args.command == "model":
-        print_json(model_payload(args.path))
-    elif args.command == "sqlite":
-        print_json(sqlite_payload(Path(args.db).expanduser(), args.limit))
-    return 0
-
-
-def build_context() -> dict[str, Any]:
-    settings_path = resolve_settings_path()
-    settings = load_json(settings_path)
-    settings_dir = settings_path.parent
-    active_library = settings.get("active_library") or "default"
-    safe_library = sanitize_library_name(str(active_library))
-    cache_root = settings_dir / "cache"
-    return {
-        "settings_path": str(settings_path),
-        "settings_dir": str(settings_dir),
-        "settings": settings,
-        "active_library": active_library,
-        "safe_library": safe_library,
-        "cache_root": str(cache_root),
-        "cache_paths": resolve_cache_paths(cache_root, safe_library),
-    }
-
-
-def resolve_settings_path() -> Path:
-    repo_root = find_repo_root()
-    portable = repo_root / "settings.json"
-    if portable.exists():
-        payload = load_json(portable)
-        if isinstance(payload, dict) and payload.get("use_portable_settings") is True:
-            return portable
-
-    config_home = os.environ.get("XDG_CONFIG_HOME")
-    if config_home:
-        return Path(config_home).expanduser() / APP_NAME / "settings.json"
-    return Path.home() / ".config" / APP_NAME / "settings.json"
-
-
-def find_repo_root() -> Path:
-    current = Path(__file__).resolve()
-    for parent in current.parents:
-        if (parent / "py").is_dir() and (parent / "standalone.py").exists():
-            return parent
-    return Path.cwd()
-
-
-def load_json(path: Path) -> dict[str, Any]:
-    try:
-        with path.open("r", encoding="utf-8") as handle:
-            payload = json.load(handle)
-    except FileNotFoundError:
-        return {}
-    except json.JSONDecodeError as exc:
-        return {"_error": f"invalid JSON: {exc}"}
-    except OSError as exc:
-        return {"_error": f"unreadable: {exc}"}
-    return payload if isinstance(payload, dict) else {"_error": "JSON root is not an object"}
-
-
-def resolve_cache_paths(cache_root: Path, library: str) -> dict[str, str]:
-    paths: dict[str, str] = {}
-    for name, (subdir, filename) in CACHE_SQLITE.items():
-        paths[name] = str(cache_root / subdir / filename.format(library=library))
-    for name, (subdir, filename) in CACHE_JSON.items():
-        paths[name] = str(cache_root / subdir / filename)
-    return paths
-
-
-def summary_payload(context: dict[str, Any]) -> dict[str, Any]:
-    settings = context["settings"]
-    return {
-        "settings_path": context["settings_path"],
-        "settings_dir": context["settings_dir"],
-        "active_library": context["active_library"],
-        "settings": redact(settings),
-        "model_roots": model_roots(settings, context["active_library"]),
-        "recipes_root": str(resolve_recipes_root(settings, context["active_library"]) or ""),
-        "example_images": example_images_payload(settings, context["active_library"]),
-        "cache_root": context["cache_root"],
-        "cache_paths": context["cache_paths"],
-    }
-
-
-def caches_payload(context: dict[str, Any]) -> dict[str, Any]:
-    caches: dict[str, Any] = {}
-    for name, path_string in context["cache_paths"].items():
-        path = Path(path_string)
-        item: dict[str, Any] = {
-            "path": str(path),
-            "exists": path.exists(),
-            "size": path.stat().st_size if path.exists() else None,
-        }
-        if path.suffix == ".sqlite":
-            item["sqlite"] = sqlite_payload(path, limit=0)
-        elif path.suffix == ".json":
-            item["json"] = json_file_summary(path)
-        caches[name] = item
-    return {"active_library": context["active_library"], "caches": caches}
-
-
-def recipes_payload(context: dict[str, Any]) -> dict[str, Any]:
-    root = resolve_recipes_root(context["settings"], context["active_library"])
-    files: list[str] = []
-    if root and root.exists():
-        files = [str(path) for path in sorted(root.rglob("*.recipe.json"))[:20]]
-    return {
-        "recipes_root": str(root or ""),
-        "exists": bool(root and root.exists()),
-        "recipe_json_count": count_recipe_files(root),
-        "sample_recipe_json": files,
-        "recipe_cache": context["cache_paths"].get("recipe"),
-    }
-
-
-def model_payload(raw_path: str) -> dict[str, Any]:
-    path = Path(raw_path).expanduser()
-    metadata_path = path if path.name.endswith(".metadata.json") else path.with_suffix(".metadata.json")
-    payload = {
-        "input_path": str(path),
-        "metadata_path": str(metadata_path),
-        "model_exists": path.exists(),
-        "metadata_exists": metadata_path.exists(),
-    }
-    if metadata_path.exists():
-        data = load_json(metadata_path)
-        payload["metadata_summary"] = redact(summarize_value(data))
-    return payload
-
-
-def sqlite_payload(path: Path, limit: int = 3, allow_copy: bool = True) -> dict[str, Any]:
-    result: dict[str, Any] = {"path": str(path), "exists": path.exists(), "tables": {}}
-    if not path.exists():
-        return result
-    try:
-        conn = connect_sqlite_readonly(path)
-    except sqlite3.Error as exc:
-        result["error"] = str(exc)
-        return result
-    try:
-        table_rows = conn.execute(
-            "SELECT name FROM sqlite_master WHERE type='table' ORDER BY name"
-        ).fetchall()
-        for table_row in table_rows:
-            table = table_row["name"]
-            columns = [
-                row["name"]
-                for row in conn.execute(f"PRAGMA table_info({quote_identifier(table)})").fetchall()
-            ]
-            table_info: dict[str, Any] = {"columns": columns}
-            try:
-                table_info["count"] = conn.execute(
-                    f"SELECT COUNT(*) FROM {quote_identifier(table)}"
-                ).fetchone()[0]
-            except sqlite3.Error as exc:
-                table_info["count_error"] = str(exc)
-            if limit > 0 and columns and not is_internal_sqlite_table(table):
-                try:
-                    rows = conn.execute(
-                        f"SELECT * FROM {quote_identifier(table)} LIMIT ?", (limit,)
-                    ).fetchall()
-                    table_info["sample"] = [redact(dict(row)) for row in rows]
-                except sqlite3.Error as exc:
-                    table_info["sample_error"] = str(exc)
-            result["tables"][table] = table_info
-    except sqlite3.Error as exc:
-        fallback = sqlite_copy_payload(path, limit, str(exc)) if allow_copy else None
-        if fallback is not None:
-            result.update(fallback)
-        else:
-            result["error"] = str(exc)
-    finally:
-        conn.close()
-    return result
-
-
-def connect_sqlite_readonly(path: Path) -> sqlite3.Connection:
-    errors: list[str] = []
-    for query in ("mode=ro", "mode=ro&immutable=1"):
-        try:
-            conn = sqlite3.connect(f"file:{path}?{query}", uri=True)
-            conn.row_factory = sqlite3.Row
-            return conn
-        except sqlite3.Error as exc:
-            errors.append(f"{query}: {exc}")
-    raise sqlite3.OperationalError("; ".join(errors))
-
-
-def sqlite_copy_payload(path: Path, limit: int, original_error: str) -> dict[str, Any] | None:
-    try:
-        with tempfile.TemporaryDirectory(prefix="lm-cache-inspect-") as temp_dir:
-            copy_path = Path(temp_dir) / path.name
-            shutil.copy2(path, copy_path)
-            payload = sqlite_payload(copy_path, limit, allow_copy=False)
-            payload["path"] = str(path)
-            payload["inspected_copy"] = True
-            payload["original_error"] = original_error
-            return payload
-    except Exception:
-        return None
-
-
-def json_file_summary(path: Path) -> dict[str, Any]:
-    if not path.exists():
-        return {"exists": False}
-    data = load_json(path)
-    return {"exists": True, "summary": redact(summarize_value(data))}
-
-
-def model_roots(settings: dict[str, Any], active_library: str) -> dict[str, list[str]]:
-    roots: dict[str, list[str]] = {}
-    sources = [settings]
-    library = settings.get("libraries", {}).get(active_library)
-    if isinstance(library, dict):
-        sources.insert(0, library)
-    for source in sources:
-        folder_paths = source.get("folder_paths")
-        if isinstance(folder_paths, dict):
-            for key, value in folder_paths.items():
-                roots.setdefault(key, []).extend(normalize_path_list(value))
-    for default_key, folder_key in (
-        ("default_lora_root", "loras"),
-        ("default_checkpoint_root", "checkpoints"),
-        ("default_embedding_root", "embeddings"),
-        ("default_unet_root", "unet"),
-    ):
-        value = settings.get(default_key)
-        if isinstance(value, str) and value:
-            roots.setdefault(folder_key, []).append(expand_path(value))
-    return {key: dedupe(values) for key, values in roots.items()}
-
-
-def resolve_recipes_root(settings: dict[str, Any], active_library: str) -> Path | None:
-    recipes_path = settings.get("recipes_path")
-    library = settings.get("libraries", {}).get(active_library)
-    if isinstance(library, dict) and isinstance(library.get("recipes_path"), str):
-        recipes_path = library["recipes_path"] or recipes_path
-    if isinstance(recipes_path, str) and recipes_path.strip():
-        return Path(expand_path(recipes_path.strip()))
-    lora_roots = model_roots(settings, active_library).get("loras") or []
-    return Path(lora_roots[0]) / "recipes" if lora_roots else None
-
-
-def example_images_payload(settings: dict[str, Any], active_library: str) -> dict[str, Any]:
-    root = settings.get("example_images_path") or ""
-    libraries = settings.get("libraries")
-    library_count = len(libraries) if isinstance(libraries, dict) else 0
-    scoped = library_count > 1
-    root_path = Path(expand_path(root)) if isinstance(root, str) and root else None
-    library_root = root_path / sanitize_library_name(active_library) if root_path and scoped else root_path
-    return {
-        "root": str(root_path or ""),
-        "uses_library_scoped_folders": scoped,
-        "library_root": str(library_root or ""),
-    }
-
-
-def count_recipe_files(root: Path | None) -> int:
-    if not root or not root.exists():
-        return 0
-    return sum(1 for _ in root.rglob("*.recipe.json"))
-
-
-def normalize_path_list(value: Any) -> list[str]:
-    if isinstance(value, str):
-        return [expand_path(value)] if value else []
-    if isinstance(value, list):
-        return [expand_path(item) for item in value if isinstance(item, str) and item]
-    return []
-
-
-def expand_path(value: str) -> str:
-    return str(Path(value).expanduser().resolve(strict=False))
-
-
-def sanitize_library_name(name: str) -> str:
-    safe = re.sub(r"[^A-Za-z0-9_.-]", "_", name or "default")
-    return safe or "default"
-
-
-def dedupe(values: list[str]) -> list[str]:
-    seen: set[str] = set()
-    result: list[str] = []
-    for value in values:
-        if value not in seen:
-            result.append(value)
-            seen.add(value)
-    return result
-
-
-def redact(value: Any, key: str = "") -> Any:
-    if key and SECRET_PATTERN.search(key):
-        return "<redacted>"
-    if isinstance(value, dict):
-        return {str(k): redact(v, str(k)) for k, v in value.items()}
-    if isinstance(value, list):
-        return [redact(item) for item in value]
-    return value
-
-
-def summarize_value(value: Any) -> Any:
-    if isinstance(value, dict):
-        return {key: summarize_value(item) for key, item in value.items()}
-    if isinstance(value, list):
-        return {
-            "type": "array",
-            "length": len(value),
-            "first": summarize_value(value[0]) if value else None,
-        }
-    return value
-
-
-def quote_identifier(identifier: str) -> str:
-    return '"' + identifier.replace('"', '""') + '"'
-
-
-def is_internal_sqlite_table(table: str) -> bool:
-    return table.startswith("sqlite_") or table.endswith(("_data", "_idx", "_docsize", "_config", "_content"))
-
-
-def print_json(payload: Any) -> None:
-    json.dump(payload, sys.stdout, indent=2, ensure_ascii=False)
-    sys.stdout.write("\n")
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())

.docs/batch-import-design.md

@@ -1,153 +0,0 @@
-# Recipe Batch Import Feature Design
-
-## Overview
-Enable users to import multiple images as recipes in a single operation, rather than processing them individually. This feature addresses the need for efficient bulk recipe creation from existing image collections.
-
-## Architecture
-
-```
-┌─────────────────────────────────────────────────────────────────┐
-│                        Frontend                                  │
-├─────────────────────────────────────────────────────────────────┤
-│  BatchImportManager.js                                          │
-│  ├── InputCollector (收集URL列表/目录路径)                        │
-│  ├── ConcurrencyController (自适应并发控制)                       │
-│  ├── ProgressTracker (进度追踪)                                   │
-│  └── ResultAggregator (结果汇总)                                  │
-├─────────────────────────────────────────────────────────────────┤
-│  batch_import_modal.html                                        │
-│  └── 批量导入UI组件                                              │
-├─────────────────────────────────────────────────────────────────┤
-│  batch_import_progress.css                                      │
-│  └── 进度显示样式                                                │
-└─────────────────────────────────────────────────────────────────┘
-                              │
-                              ▼
-┌─────────────────────────────────────────────────────────────────┐
-│                        Backend                                   │
-├─────────────────────────────────────────────────────────────────┤
-│  py/routes/handlers/recipe_handlers.py                          │
-│  ├── start_batch_import() - 启动批量导入                         │
-│  ├── get_batch_import_progress() - 查询进度                      │
-│  └── cancel_batch_import() - 取消导入                            │
-├─────────────────────────────────────────────────────────────────┤
-│  py/services/batch_import_service.py                            │
-│  ├── 自适应并发执行                                              │
-│  ├── 结果汇总                                                    │
-│  └── WebSocket进度广播                                           │
-└─────────────────────────────────────────────────────────────────┘
-```
-
-## API Endpoints
-
-| 端点 | 方法 | 说明 |
-|------|------|------|
-| `/api/lm/recipes/batch-import/start` | POST | 启动批量导入，返回 operation_id |
-| `/api/lm/recipes/batch-import/progress` | GET | 查询进度状态 |
-| `/api/lm/recipes/batch-import/cancel` | POST | 取消导入 |
-
-## Backend Implementation Details
-
-### BatchImportService
-
-Location: `py/services/batch_import_service.py`
-
-Key classes:
-- `BatchImportItem`: Dataclass for individual import item
-- `BatchImportProgress`: Dataclass for tracking progress
-- `BatchImportService`: Main service class
-
-Features:
-- Adaptive concurrency control (adjusts based on success/failure rate)
-- WebSocket progress broadcasting
-- Graceful error handling (individual failures don't stop the batch)
-- Result aggregation
-
-### WebSocket Message Format
-
-```json
-{
-    "type": "batch_import_progress",
-    "operation_id": "xxx",
-    "total": 50,
-    "completed": 23,
-    "success": 21,
-    "failed": 2,
-    "skipped": 0,
-    "current_item": "image_024.png",
-    "status": "running"
-}
-```
-
-### Input Types
-
-1. **URL List**: Array of URLs (http/https)
-2. **Local Paths**: Array of local file paths
-3. **Directory**: Path to directory with optional recursive flag
-
-### Error Handling
-
-- Invalid URLs/paths: Skip and record error
-- Download failures: Record error, continue
-- Metadata extraction failures: Mark as "no metadata"
-- Duplicate detection: Option to skip duplicates
-
-## Frontend Implementation Details (TODO)
-
-### UI Components
-
-1. **BatchImportModal**: Main modal with tabs for URLs/Directory input
-2. **ProgressDisplay**: Real-time progress bar and status
-3. **ResultsSummary**: Final results with success/failure breakdown
-
-### Adaptive Concurrency Controller
-
-```javascript
-class AdaptiveConcurrencyController {
-    constructor(options = {}) {
-        this.minConcurrency = options.minConcurrency || 1;
-        this.maxConcurrency = options.maxConcurrency || 5;
-        this.currentConcurrency = options.initialConcurrency || 3;
-    }
-
-    adjustConcurrency(taskDuration, success) {
-        if (success && taskDuration < 1000 && this.currentConcurrency < this.maxConcurrency) {
-            this.currentConcurrency = Math.min(this.currentConcurrency + 1, this.maxConcurrency);
-        }
-        if (!success || taskDuration > 10000) {
-            this.currentConcurrency = Math.max(this.currentConcurrency - 1, this.minConcurrency);
-        }
-        return this.currentConcurrency;
-    }
-}
-```
-
-## File Structure
-
-```
-Backend (implemented):
-├── py/services/batch_import_service.py            # 后端服务
-├── py/routes/handlers/batch_import_handler.py     # API处理器 (added to recipe_handlers.py)
-├── tests/services/test_batch_import_service.py    # 单元测试
-└── tests/routes/test_batch_import_routes.py       # API集成测试
-
-Frontend (TODO):
-├── static/js/managers/BatchImportManager.js       # 主管理器
-├── static/js/managers/batch/                       # 子模块
-│   ├── ConcurrencyController.js                    # 并发控制
-│   ├── ProgressTracker.js                          # 进度追踪
-│   └── ResultAggregator.js                         # 结果汇总
-├── static/css/components/batch-import-modal.css   # 样式
-└── templates/components/batch_import_modal.html    # Modal模板
-```
-
-## Implementation Status
-
-- [x] Backend BatchImportService
-- [x] Backend API handlers
-- [x] WebSocket progress broadcasting
-- [x] Unit tests
-- [x] Integration tests
-- [ ] Frontend BatchImportManager
-- [ ] Frontend UI components
-- [ ] E2E tests

.github/workflows/update-supporters.yml

@@ -1,31 +0,0 @@
-name: Update Supporters in README
-
-on:
-  push:
-    paths:
-      - 'data/supporters.json'
-    branches:
-      - main
-  workflow_dispatch: # Allow manual trigger
-
-jobs:
-  update-readme:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-    steps:
-      - uses: actions/checkout@v4
-      
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.10'
-          
-      - name: Update README
-        run: python scripts/update_supporters.py
-        
-      - name: Commit and push changes
-        uses: stefanzweifel/git-auto-commit-action@v5
-        with:
-          commit_message: "docs: auto-update supporters list in README"
-          file_pattern: "README.md"

.gitignore

@@ -14,13 +14,8 @@ model_cache/
 
 # agent
 .opencode/
-.claude/
-.codex
 
 # Vue widgets development cache (but keep build output)
 vue-widgets/node_modules/
 vue-widgets/.vite/
 vue-widgets/dist/
-
-# Hypothesis test cache
-.hypothesis/

.specs/metadata.schema.json

@@ -1,464 +0,0 @@
-{
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "$id": "https://github.com/willmiao/ComfyUI-Lora-Manager/.specs/metadata.schema.json",
-  "title": "ComfyUI LoRa Manager Model Metadata",
-  "description": "Schema for .metadata.json sidecar files used by ComfyUI LoRa Manager",
-  "type": "object",
-  "oneOf": [
-    {
-      "title": "LoRA Model Metadata",
-      "properties": {
-        "file_name": {
-          "type": "string",
-          "description": "Filename without extension"
-        },
-        "model_name": {
-          "type": "string",
-          "description": "Display name of the model"
-        },
-        "file_path": {
-          "type": "string",
-          "description": "Full absolute path to the model file"
-        },
-        "size": {
-          "type": "integer",
-          "minimum": 0,
-          "description": "File size in bytes at time of import/download"
-        },
-        "modified": {
-          "type": "number",
-          "description": "Unix timestamp when model was imported/added (Date Added)"
-        },
-        "sha256": {
-          "type": "string",
-          "pattern": "^[a-f0-9]{64}$",
-          "description": "SHA256 hash of the model file (lowercase)"
-        },
-        "base_model": {
-          "type": "string",
-          "description": "Base model type (SD1.5, SD2.1, SDXL, SD3, Flux, Unknown, etc.)"
-        },
-        "preview_url": {
-          "type": "string",
-          "description": "Path to preview image file"
-        },
-        "preview_nsfw_level": {
-          "type": "integer",
-          "minimum": 0,
-          "default": 0,
-          "description": "NSFW level using bitmask values: 0 (none), 1 (PG), 2 (PG13), 4 (R), 8 (X), 16 (XXX), 32 (Blocked)"
-        },
-        "notes": {
-          "type": "string",
-          "default": "",
-          "description": "User-defined notes"
-        },
-        "from_civitai": {
-          "type": "boolean",
-          "default": true,
-          "description": "Whether the model originated from Civitai"
-        },
-        "civitai": {
-          "$ref": "#/definitions/civitaiObject"
-        },
-        "tags": {
-          "type": "array",
-          "items": {
-            "type": "string"
-          },
-          "default": [],
-          "description": "Model tags"
-        },
-        "modelDescription": {
-          "type": "string",
-          "default": "",
-          "description": "Full model description"
-        },
-        "civitai_deleted": {
-          "type": "boolean",
-          "default": false,
-          "description": "Whether the model was deleted from Civitai"
-        },
-        "favorite": {
-          "type": "boolean",
-          "default": false,
-          "description": "Whether the model is marked as favorite"
-        },
-        "exclude": {
-          "type": "boolean",
-          "default": false,
-          "description": "Whether to exclude from cache/scanning"
-        },
-        "db_checked": {
-          "type": "boolean",
-          "default": false,
-          "description": "Whether checked against archive database"
-        },
-        "skip_metadata_refresh": {
-          "type": "boolean",
-          "default": false,
-          "description": "Skip this model during bulk metadata refresh"
-        },
-        "metadata_source": {
-          "type": ["string", "null"],
-          "enum": ["civitai_api", "civarchive", "archive_db", null],
-          "default": null,
-          "description": "Last provider that supplied metadata"
-        },
-        "last_checked_at": {
-          "type": "number",
-          "default": 0,
-          "description": "Unix timestamp of last metadata check"
-        },
-        "hash_status": {
-          "type": "string",
-          "enum": ["pending", "calculating", "completed", "failed"],
-          "default": "completed",
-          "description": "Hash calculation status"
-        },
-        "usage_tips": {
-          "type": "string",
-          "default": "{}",
-          "description": "JSON string containing recommended usage parameters (LoRA only)"
-        }
-      },
-      "required": [
-        "file_name",
-        "model_name",
-        "file_path",
-        "size",
-        "modified",
-        "sha256",
-        "base_model"
-      ],
-      "additionalProperties": true
-    },
-    {
-      "title": "Checkpoint Model Metadata",
-      "properties": {
-        "file_name": {
-          "type": "string"
-        },
-        "model_name": {
-          "type": "string"
-        },
-        "file_path": {
-          "type": "string"
-        },
-        "size": {
-          "type": "integer",
-          "minimum": 0
-        },
-        "modified": {
-          "type": "number"
-        },
-        "sha256": {
-          "type": "string",
-          "pattern": "^[a-f0-9]{64}$"
-        },
-        "base_model": {
-          "type": "string"
-        },
-        "preview_url": {
-          "type": "string"
-        },
-        "preview_nsfw_level": {
-          "type": "integer",
-          "minimum": 0,
-          "maximum": 3,
-          "default": 0
-        },
-        "notes": {
-          "type": "string",
-          "default": ""
-        },
-        "from_civitai": {
-          "type": "boolean",
-          "default": true
-        },
-        "civitai": {
-          "$ref": "#/definitions/civitaiObject"
-        },
-        "tags": {
-          "type": "array",
-          "items": {
-            "type": "string"
-          },
-          "default": []
-        },
-        "modelDescription": {
-          "type": "string",
-          "default": ""
-        },
-        "civitai_deleted": {
-          "type": "boolean",
-          "default": false
-        },
-        "favorite": {
-          "type": "boolean",
-          "default": false
-        },
-        "exclude": {
-          "type": "boolean",
-          "default": false
-        },
-        "db_checked": {
-          "type": "boolean",
-          "default": false
-        },
-        "skip_metadata_refresh": {
-          "type": "boolean",
-          "default": false
-        },
-        "metadata_source": {
-          "type": ["string", "null"],
-          "enum": ["civitai_api", "civarchive", "archive_db", null],
-          "default": null
-        },
-        "last_checked_at": {
-          "type": "number",
-          "default": 0
-        },
-        "hash_status": {
-          "type": "string",
-          "enum": ["pending", "calculating", "completed", "failed"],
-          "default": "completed"
-        },
-        "sub_type": {
-          "type": "string",
-          "default": "checkpoint",
-          "description": "Model sub-type (checkpoint, diffusion_model, etc.)"
-        }
-      },
-      "required": [
-        "file_name",
-        "model_name",
-        "file_path",
-        "size",
-        "modified",
-        "sha256",
-        "base_model"
-      ],
-      "additionalProperties": true
-    },
-    {
-      "title": "Embedding Model Metadata",
-      "properties": {
-        "file_name": {
-          "type": "string"
-        },
-        "model_name": {
-          "type": "string"
-        },
-        "file_path": {
-          "type": "string"
-        },
-        "size": {
-          "type": "integer",
-          "minimum": 0
-        },
-        "modified": {
-          "type": "number"
-        },
-        "sha256": {
-          "type": "string",
-          "pattern": "^[a-f0-9]{64}$"
-        },
-        "base_model": {
-          "type": "string"
-        },
-        "preview_url": {
-          "type": "string"
-        },
-        "preview_nsfw_level": {
-          "type": "integer",
-          "minimum": 0,
-          "maximum": 3,
-          "default": 0
-        },
-        "notes": {
-          "type": "string",
-          "default": ""
-        },
-        "from_civitai": {
-          "type": "boolean",
-          "default": true
-        },
-        "civitai": {
-          "$ref": "#/definitions/civitaiObject"
-        },
-        "tags": {
-          "type": "array",
-          "items": {
-            "type": "string"
-          },
-          "default": []
-        },
-        "modelDescription": {
-          "type": "string",
-          "default": ""
-        },
-        "civitai_deleted": {
-          "type": "boolean",
-          "default": false
-        },
-        "favorite": {
-          "type": "boolean",
-          "default": false
-        },
-        "exclude": {
-          "type": "boolean",
-          "default": false
-        },
-        "db_checked": {
-          "type": "boolean",
-          "default": false
-        },
-        "skip_metadata_refresh": {
-          "type": "boolean",
-          "default": false
-        },
-        "metadata_source": {
-          "type": ["string", "null"],
-          "enum": ["civitai_api", "civarchive", "archive_db", null],
-          "default": null
-        },
-        "last_checked_at": {
-          "type": "number",
-          "default": 0
-        },
-        "hash_status": {
-          "type": "string",
-          "enum": ["pending", "calculating", "completed", "failed"],
-          "default": "completed"
-        },
-        "sub_type": {
-          "type": "string",
-          "default": "embedding",
-          "description": "Model sub-type"
-        }
-      },
-      "required": [
-        "file_name",
-        "model_name",
-        "file_path",
-        "size",
-        "modified",
-        "sha256",
-        "base_model"
-      ],
-      "additionalProperties": true
-    }
-  ],
-  "definitions": {
-    "civitaiObject": {
-      "type": "object",
-      "default": {},
-      "description": "Civitai/CivArchive API data and user-defined fields",
-      "properties": {
-        "id": {
-          "type": "integer",
-          "description": "Version ID from Civitai"
-        },
-        "modelId": {
-          "type": "integer",
-          "description": "Model ID from Civitai"
-        },
-        "name": {
-          "type": "string",
-          "description": "Version name"
-        },
-        "description": {
-          "type": "string",
-          "description": "Version description"
-        },
-        "baseModel": {
-          "type": "string",
-          "description": "Base model type from Civitai"
-        },
-        "type": {
-          "type": "string",
-          "description": "Model type (checkpoint, embedding, etc.)"
-        },
-        "trainedWords": {
-          "type": "array",
-          "items": {
-            "type": "string"
-          },
-          "description": "Trigger words for the model (from API or user-defined)"
-        },
-        "customImages": {
-          "type": "array",
-          "items": {
-            "type": "object"
-          },
-          "description": "Custom example images added by user"
-        },
-        "model": {
-          "type": "object",
-          "properties": {
-            "name": {
-              "type": "string"
-            },
-            "description": {
-              "type": "string"
-            },
-            "tags": {
-              "type": "array",
-              "items": {
-                "type": "string"
-              }
-            }
-          }
-        },
-        "files": {
-          "type": "array",
-          "items": {
-            "type": "object"
-          }
-        },
-        "images": {
-          "type": "array",
-          "items": {
-            "type": "object"
-          }
-        },
-        "creator": {
-          "type": "object"
-        }
-      },
-      "additionalProperties": true
-    },
-    "usageTips": {
-      "type": "object",
-      "description": "Structure for usage_tips JSON string (LoRA models)",
-      "properties": {
-        "strength_min": {
-          "type": "number",
-          "description": "Minimum recommended model strength"
-        },
-        "strength_max": {
-          "type": "number",
-          "description": "Maximum recommended model strength"
-        },
-        "strength_range": {
-          "type": "string",
-          "description": "Human-readable strength range"
-        },
-        "strength": {
-          "type": "number",
-          "description": "Single recommended strength value"
-        },
-        "clip_strength": {
-          "type": "number",
-          "description": "Recommended CLIP/embedding strength"
-        },
-        "clip_skip": {
-          "type": "integer",
-          "description": "Recommended CLIP skip value"
-        }
-      },
-      "additionalProperties": true
-    }
-  }
-}

AGENTS.md

@@ -25,134 +25,168 @@ pytest tests/test_recipes.py::test_function_name
 
 # Run backend tests with coverage
 COVERAGE_FILE=coverage/backend/.coverage pytest \
-  --cov=py --cov=standalone \
+  --cov=py \
+  --cov=standalone \
   --cov-report=term-missing \
   --cov-report=html:coverage/backend/html \
-  --cov-report=xml:coverage/backend/coverage.xml
+  --cov-report=xml:coverage/backend/coverage.xml \
+  --cov-report=json:coverage/backend/coverage.json
 ```
 
-### Frontend Development (Standalone Web UI)
+### Frontend Development
 
 ```bash
+# Install frontend dependencies
 npm install
-npm test                    # Run all tests (JS + Vue)
-npm run test:js             # Run JS tests only
-npm run test:watch          # Watch mode
-npm run test:coverage       # Generate coverage report
-```
 
-### Vue Widget Development
+# Run frontend tests
+npm test
 
-```bash
-cd vue-widgets
-npm install
-npm run dev                 # Build in watch mode
-npm run build               # Build production bundle
-npm run typecheck           # Run TypeScript type checking
-npm test                    # Run Vue widget tests
-npm run test:watch          # Watch mode
-npm run test:coverage       # Generate coverage report
+# Run frontend tests in watch mode
+npm run test:watch
+
+# Run frontend tests with coverage
+npm run test:coverage
 ```
 
 ## Python Code Style
 
-### Imports & Formatting
+### Imports
 
-- Use `from __future__ import annotations` for forward references
-- Group imports: standard library, third-party, local (blank line separated)
-- Absolute imports within `py/`: `from ..services import X`
-- PEP 8 with 4-space indentation, type hints required
+- Use `from __future__ import annotations` for forward references in type hints
+- Group imports: standard library, third-party, local (separated by blank lines)
+- Use absolute imports within `py/` package: `from ..services import X`
+- Mock ComfyUI dependencies in tests using `tests/conftest.py` patterns
+
+### Formatting & Types
+
+- PEP 8 with 4-space indentation
+- Type hints required for function signatures and class attributes
+- Use `TYPE_CHECKING` guard for type-checking-only imports
+- Prefer dataclasses for simple data containers
+- Use `Optional[T]` for nullable types, `Union[T, None]` only when necessary
 
 ### Naming Conventions
 
-- Files: `snake_case.py`, Classes: `PascalCase`, Functions/vars: `snake_case`
-- Constants: `UPPER_SNAKE_CASE`, Private: `_protected`, `__mangled`
+- Files: `snake_case.py` (e.g., `model_scanner.py`, `lora_service.py`)
+- Classes: `PascalCase` (e.g., `ModelScanner`, `LoraService`)
+- Functions/variables: `snake_case` (e.g., `get_instance`, `model_type`)
+- Constants: `UPPER_SNAKE_CASE` (e.g., `VALID_LORA_TYPES`)
+- Private members: `_single_underscore` (protected), `__double_underscore` (name-mangled)
 
-### Error Handling & Async
+### Error Handling
 
-- Use `logging.getLogger(__name__)`, define custom exceptions in `py/services/errors.py`
-- `async def` for I/O, `@pytest.mark.asyncio` for async tests
-- Singleton with `asyncio.Lock`: see `ModelScanner.get_instance()`
-- Return `aiohttp.web.json_response` or `web.Response`
+- Use `logging.getLogger(__name__)` for module-level loggers
+- Define custom exceptions in `py/services/errors.py`
+- Use `asyncio.Lock` for thread-safe singleton patterns
+- Raise specific exceptions with descriptive messages
+- Log errors at appropriate levels (DEBUG, INFO, WARNING, ERROR, CRITICAL)
 
-### Testing
+### Async Patterns
 
-- `pytest` with `--import-mode=importlib`
-- Fixtures in `tests/conftest.py`, use `tmp_path_factory` for isolation
-- Mark tests needing real paths: `@pytest.mark.no_settings_dir_isolation`
-- Mock ComfyUI dependencies via conftest patterns
+- Use `async def` for I/O-bound operations
+- Mark async tests with `@pytest.mark.asyncio`
+- Use `async with` for context managers
+- Singleton pattern with class-level locks: see `ModelScanner.get_instance()`
+- Use `aiohttp.web.Response` for HTTP responses
 
-## JavaScript/TypeScript Code Style
+### Testing Patterns
+
+- Use `pytest` with `--import-mode=importlib`
+- Fixtures in `tests/conftest.py` handle ComfyUI mocking
+- Use `@pytest.mark.no_settings_dir_isolation` for tests needing real paths
+- Test files: `tests/test_*.py`
+- Use `tmp_path_factory` for temporary directory isolation
+
+## JavaScript Code Style
 
 ### Imports & Modules
 
-- ES modules: `import { app } from "../../scripts/app.js"` for ComfyUI
-- Vue: `import { ref, computed } from 'vue'`, type imports: `import type { Foo }`
-- Export named functions: `export function foo() {}`
+- ES modules with `import`/`export`
+- Use `import { app } from "../../scripts/app.js"` for ComfyUI integration
+- Export named functions/classes: `export function foo() {}`
+- Widget files use `*_widget.js` suffix
 
 ### Naming & Formatting
 
-- camelCase for functions/vars/props, PascalCase for classes
-- Constants: `UPPER_SNAKE_CASE`, Files: `snake_case.js` or `kebab-case.js`
+- camelCase for functions, variables, object properties
+- PascalCase for classes/constructors
+- Constants: `UPPER_SNAKE_CASE` (e.g., `CONVERTED_TYPE`)
+- Files: `snake_case.js` or `kebab-case.js`
 - 2-space indentation preferred (follow existing file conventions)
-- Vue Single File Components: `<script setup lang="ts">` preferred
 
 ### Widget Development
 
-- ComfyUI: `app.registerExtension()`, `node.addDOMWidget(name, type, element, options)`
-- Event handlers via `addEventListener` or widget callbacks
-- Shared utilities: `web/comfyui/utils.js`
-
-### Vue Composables Pattern
-
-- Use composition API: `useXxxState(widget)`, return reactive refs and methods
-- Guard restoration loops with flag: `let isRestoring = false`
-- Build config from state: `const buildConfig = (): Config => { ... }`
+- Use `app.registerExtension()` to register ComfyUI extensions
+- Use `node.addDOMWidget(name, type, element, options)` for custom widgets
+- Event handlers attached via `addEventListener` or widget callbacks
+- See `web/comfyui/utils.js` for shared utilities
 
 ## Architecture Patterns
 
 ### Service Layer
 
-- `ServiceRegistry` singleton for DI, services use `get_instance()` classmethod
+- Use `ServiceRegistry` singleton for dependency injection
+- Services follow singleton pattern via `get_instance()` class method
 - Separate scanners (discovery) from services (business logic)
-- Handlers in `py/routes/handlers/` are pure functions with deps as params
+- Handlers in `py/routes/handlers/` implement route logic
 
-### Model Types & Routes
+### Model Types
 
-- `BaseModelService` base for LoRA, Checkpoint, Embedding
-- `ModelScanner` for file discovery, hash deduplication
-- `PersistentModelCache` (SQLite) for persistence
-- Route registrars: `ModelRouteRegistrar`, endpoints: `/loras/*`, `/checkpoints/*`, `/embeddings/*`
-- WebSocket via `WebSocketManager` for real-time updates
+- BaseModelService is abstract base for LoRA, Checkpoint, Embedding services
+- ModelScanner provides file discovery and hash-based deduplication
+- Persistent cache in SQLite via `PersistentModelCache`
+- Metadata sync from CivitAI/CivArchive via `MetadataSyncService`
+
+### Routes & Handlers
+
+- Route registrars organize endpoints by domain: `ModelRouteRegistrar`, etc.
+- Handlers are pure functions taking dependencies as parameters
+- Use `WebSocketManager` for real-time progress updates
+- Return `aiohttp.web.json_response` or `web.Response`
 
 ### Recipe System
 
-- Base: `py/recipes/base.py`, Enrichment: `RecipeEnrichmentService`
-- Parsers: `py/recipes/parsers/`
+- Base metadata in `py/recipes/base.py`
+- Enrichment adds model metadata: `RecipeEnrichmentService`
+- Parsers for different formats in `py/recipes/parsers/`
 
 ## Important Notes
 
-- ALWAYS use English for comments (per copilot-instructions.md)
-- Dual mode: ComfyUI plugin (folder_paths) vs standalone (settings.json)
+- Always use English for comments (per copilot-instructions.md)
+- Dual mode: ComfyUI plugin (uses folder_paths) vs standalone (reads settings.json)
 - Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
-- Run `python scripts/sync_translation_keys.py` after adding UI strings to `locales/en.json`
-- Symlinks require normalized paths
-
-## Git / Commit Messages
-
-- Follow the style of recent repository commits when writing commit messages
-- Prefer the repo's existing `feat(...)`, `fix(...)`, `chore:` style where applicable
-- If the user has provided a GitHub issue link or issue ID for the task, mention that issue in the commit message, for example `(#871)`
-- When unrelated local changes exist, stage and commit only the files relevant to the requested task
+- Settings auto-saved in user directory or portable mode
+- WebSocket broadcasts for real-time updates (downloads, scans)
+- Symlink handling requires normalized paths
+- API endpoints follow `/loras/*`, `/checkpoints/*`, `/embeddings/*` patterns
+- Run `python scripts/sync_translation_keys.py` after UI string updates
 
 ## Frontend UI Architecture
 
-### 1. Standalone Web UI
+This project has two distinct UI systems:
+
+### 1. Standalone Lora Manager Web UI
 - Location: `./static/` and `./templates/`
-- Tech: Vanilla JS + CSS, served by standalone server
-- Tests via npm in root directory
+- Purpose: Full-featured web application for managing LoRA models
+- Tech stack: Vanilla JS + CSS, served by the standalone server
+- Development: Uses npm for frontend testing (`npm test`, `npm run test:watch`, etc.)
 
 ### 2. ComfyUI Custom Node Widgets
-- Location: `./web/comfyui/` (Vanilla JS) + `./vue-widgets/` (Vue)
-- Primary styles: `./web/comfyui/lm_styles.css` (NOT `./static/css/`)
-- Vue builds to `./web/comfyui/vue-widgets/`, typecheck via `vue-tsc`
+- Location: `./web/comfyui/`
+- Purpose: Widgets and UI logic that ComfyUI loads as custom node extensions
+- Tech stack: Vanilla JS + Vue.js widgets (in `./vue-widgets/` and built to `./web/comfyui/vue-widgets/`)
+- Widget styling: Primary styles in `./web/comfyui/lm_styles.css` (NOT `./static/css/`)
+- Development: No npm build step for these widgets (Vue widgets use build system)
+
+### Widget Development Guidelines
+- Use `app.registerExtension()` to register ComfyUI extensions (ComfyUI integration layer)
+- Use `node.addDOMWidget()` for custom DOM widgets
+- Widget styles should follow the patterns in `./web/comfyui/lm_styles.css`
+  - Selected state: `rgba(66, 153, 225, 0.3)` background, `rgba(66, 153, 225, 0.6)` border
+  - Hover state: `rgba(66, 153, 225, 0.2)` background
+  - Color palette matches the Lora Manager accent color (blue #4299e1)
+  - Use oklch() for color values when possible (defined in `./static/css/base.css`)
+- Vue widget components are in `./vue-widgets/src/components/` and built to `./web/comfyui/vue-widgets/`
+- When modifying widget styles, check `./web/comfyui/lm_styles.css` for consistency with other ComfyUI widgets
+

CLAUDE.md

@@ -8,22 +8,17 @@ ComfyUI LoRA Manager is a comprehensive LoRA management system for ComfyUI that
 
 ## Development Commands
 
-### Backend
-
+### Backend Development
 ```bash
+# Install dependencies
 pip install -r requirements.txt
+
+# Install development dependencies (for testing)
 pip install -r requirements-dev.txt
 
 # Run standalone server (port 8188 by default)
 python standalone.py --port 8188
 
-# Run all backend tests
-pytest
-
-# Run specific test file or function
-pytest tests/test_recipes.py
-pytest tests/test_recipes.py::test_function_name
-
 # Run backend tests with coverage
 COVERAGE_FILE=coverage/backend/.coverage pytest \
   --cov=py \
@@ -32,158 +27,185 @@ COVERAGE_FILE=coverage/backend/.coverage pytest \
   --cov-report=html:coverage/backend/html \
   --cov-report=xml:coverage/backend/coverage.xml \
   --cov-report=json:coverage/backend/coverage.json
+
+# Run specific test file
+pytest tests/test_recipes.py
 ```
 
-### Frontend
-
-There are three test suites run by `npm test`: vanilla JS tests (vitest at root) and Vue widget tests (`vue-widgets/` vitest).
-
+### Frontend Development
 ```bash
+# Install frontend dependencies
 npm install
-cd vue-widgets && npm install && cd ..
 
-# Run all frontend tests (JS + Vue)
+# Run frontend tests
 npm test
 
-# Run only vanilla JS tests
-npm run test:js
-
-# Run only Vue widget tests
-npm run test:vue
-
-# Watch mode (JS tests only)
+# Run frontend tests in watch mode
 npm run test:watch
 
-# Frontend coverage
+# Run frontend tests with coverage
 npm run test:coverage
-
-# Build Vue widgets (output to web/comfyui/vue-widgets/)
-cd vue-widgets && npm run build
-
-# Vue widget dev mode (watch + rebuild)
-cd vue-widgets && npm run dev
-
-# Typecheck Vue widgets
-cd vue-widgets && npm run typecheck
 ```
 
 ### Localization
-
 ```bash
 # Sync translation keys after UI string updates
 python scripts/sync_translation_keys.py
 ```
 
-Locale files are in `locales/` (en, zh-CN, zh-TW, ja, ko, fr, de, es, ru, he).
-
 ## Architecture
 
-### Dual Mode Operation
+### Backend Structure (Python)
 
-The system runs in two modes:
-- **ComfyUI plugin mode**: Integrates with ComfyUI's PromptServer, uses `folder_paths` for model discovery
-- **Standalone mode**: `standalone.py` mocks ComfyUI dependencies, reads paths from `settings.json`
-- Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
+**Core Entry Points:**
+- `__init__.py` - ComfyUI plugin entry point, registers nodes and routes
+- `standalone.py` - Standalone server that mocks ComfyUI dependencies
+- `py/lora_manager.py` - Main LoraManager class that registers HTTP routes
 
-### Backend (Python)
+**Service Layer** (`py/services/`):
+- `ServiceRegistry` - Singleton service registry for dependency management
+- `ModelServiceFactory` - Factory for creating model services (LoRA, Checkpoint, Embedding)
+- Scanner services (`lora_scanner.py`, `checkpoint_scanner.py`, `embedding_scanner.py`) - Model file discovery and indexing
+- `model_scanner.py` - Base scanner with hash-based deduplication and metadata extraction
+- `persistent_model_cache.py` - SQLite-based cache for model metadata
+- `metadata_sync_service.py` - Syncs metadata from CivitAI/CivArchive APIs
+- `civitai_client.py` / `civarchive_client.py` - API clients for external services
+- `downloader.py` / `download_manager.py` - Model download orchestration
+- `recipe_scanner.py` - Recipe file management and image association
+- `settings_manager.py` - Application settings with migration support
+- `websocket_manager.py` - WebSocket broadcasting for real-time updates
+- `use_cases/` - Business logic orchestration (auto-organize, bulk refresh, downloads)
 
-**Entry points:**
-- `__init__.py` — ComfyUI plugin entry: registers nodes via `NODE_CLASS_MAPPINGS`, sets `WEB_DIRECTORY`, calls `LoraManager.add_routes()`
-- `standalone.py` — Standalone server: mocks `folder_paths` and node modules, starts aiohttp server
-- `py/lora_manager.py` — Main `LoraManager` class that registers all HTTP routes
+**Routes Layer** (`py/routes/`):
+- Route registrars organize endpoints by domain (models, recipes, previews, example images, updates)
+- `handlers/` - Request handlers implementing business logic
+- Routes use aiohttp and integrate with ComfyUI's PromptServer
 
-**Service layer** (`py/services/`):
-- `ServiceRegistry` singleton for dependency injection; services follow `get_instance()` singleton pattern
-- `BaseModelService` abstract base → `LoraService`, `CheckpointService`, `EmbeddingService`
-- `ModelScanner` base → `LoraScanner`, `CheckpointScanner`, `EmbeddingScanner` for file discovery with hash-based deduplication
-- `PersistentModelCache` — SQLite-based metadata cache
-- `MetadataSyncService` — Background sync from CivitAI/CivArchive APIs
-- `SettingsManager` — Settings with schema migration support
-- `WebSocketManager` — Real-time progress broadcasting
-- `ModelServiceFactory` — Creates the right service for each model type
-- Use cases in `py/services/use_cases/` orchestrate complex business logic (auto-organize, bulk refresh, downloads)
+**Recipe System** (`py/recipes/`):
+- `base.py` - Base recipe metadata structure
+- `enrichment.py` - Enriches recipes with model metadata
+- `merger.py` - Merges recipe data from multiple sources
+- `parsers/` - Parsers for different recipe formats (PNG, JSON, workflow)
 
-**Routes** (`py/routes/`):
-- Route registrars organize endpoints by domain: `ModelRouteRegistrar`, `RecipeRouteRegistrar`, etc.
-- Request handlers in `py/routes/handlers/` implement route logic
-- API endpoints follow `/loras/*`, `/checkpoints/*`, `/embeddings/*` patterns
-- All routes use aiohttp, return `web.json_response` or `web.Response`
-
-**Recipe system** (`py/recipes/`):
-- `base.py` — Recipe metadata structure
-- `enrichment.py` — Enriches recipes with model metadata
-- `parsers/` — Parsers for PNG metadata, JSON, and workflow formats
-
-**Custom nodes** (`py/nodes/`):
-- Each node class has a `NAME` class attribute used as key in `NODE_CLASS_MAPPINGS`
-- Standard ComfyUI node pattern: `INPUT_TYPES()` classmethod, `RETURN_TYPES`, `FUNCTION`
-- All nodes registered in `__init__.py`
+**Custom Nodes** (`py/nodes/`):
+- `lora_loader.py` - LoRA loader nodes with preset support
+- `save_image.py` - Enhanced save image with pattern-based filenames
+- `trigger_word_toggle.py` - Toggle trigger words in prompts
+- `lora_stacker.py` - Stack multiple LoRAs
+- `prompt.py` - Prompt node with autocomplete
+- `wanvideo_lora_select.py` - WanVideo-specific LoRA selection
 
 **Configuration** (`py/config.py`):
-- Manages folder paths for models, handles symlink mappings
+- Manages folder paths for models, checkpoints, embeddings
+- Handles symlink mappings for complex directory structures
 - Auto-saves paths to settings.json in ComfyUI mode
 
-### Frontend — Two Distinct UI Systems
+### Frontend Structure (JavaScript)
 
-#### 1. Standalone Manager Web UI
-- **Location:** `static/` (JS/CSS) and `templates/` (HTML)
-- **Tech:** Vanilla JS + CSS, served by standalone server
-- **Structure:** `static/js/core.js` (shared), `loras.js`, `checkpoints.js`, `embeddings.js`, `recipes.js`, `statistics.js`
-- **Tests:** `tests/frontend/**/*.test.js` (vitest + jsdom)
+**ComfyUI Widgets** (`web/comfyui/`):
+- Vanilla JavaScript ES modules extending ComfyUI's LiteGraph-based UI
+- `loras_widget.js` - Main LoRA selection widget with preview
+- `loras_widget_events.js` - Event handling for widget interactions
+- `autocomplete.js` - Autocomplete for trigger words and embeddings
+- `preview_tooltip.js` - Preview tooltip for model cards
+- `top_menu_extension.js` - Adds "Launch LoRA Manager" menu item
+- `trigger_word_highlight.js` - Syntax highlighting for trigger words
+- `utils.js` - Shared utilities and API helpers
 
-#### 2. ComfyUI Custom Node Widgets
-- **Vanilla JS widgets:** `web/comfyui/*.js` — ES modules extending ComfyUI's LiteGraph UI
-  - `loras_widget.js` / `loras_widget_events.js` — Main LoRA selection widget
-  - `autocomplete.js` — Trigger word and embedding autocomplete
-  - `preview_tooltip.js` — Model card preview tooltips
-  - `top_menu_extension.js` — "Launch LoRA Manager" menu item
-  - `utils.js` — Shared utilities and API helpers
-  - Widget styling in `web/comfyui/lm_styles.css` (NOT `static/css/`)
-- **Vue widgets:** `vue-widgets/src/` → built to `web/comfyui/vue-widgets/`
-  - Vue 3 + TypeScript + PrimeVue + vue-i18n
-  - Vite build with CSS-injected-by-JS plugin
-  - Components: `LoraPoolWidget`, `LoraRandomizerWidget`, `LoraCyclerWidget`, `AutocompleteTextWidget`
-  - Auto-built on ComfyUI startup via `py/vue_widget_builder.py`
-  - Tests: `vue-widgets/tests/**/*.test.ts` (vitest)
+**Widget Development:**
+- Widgets use `app.registerExtension` and `getCustomWidgets` hooks
+- `node.addDOMWidget(name, type, element, options)` embeds HTML in nodes
+- See `docs/dom_widget_dev_guide.md` for complete DOMWidget development guide
 
-**Widget registration pattern:**
-- Widgets use `app.registerExtension()` and `getCustomWidgets` hooks
-- `node.addDOMWidget(name, type, element, options)` embeds HTML in LiteGraph nodes
-- See `docs/dom_widget_dev_guide.md` for DOMWidget development guide
+**Web Source** (`web-src/`):
+- Modern frontend components (if migrating from static)
+- `components/` - Reusable UI components
+- `styles/` - CSS styling
+
+### Key Patterns
+
+**Dual Mode Operation:**
+- ComfyUI plugin mode: Integrates with ComfyUI's PromptServer, uses folder_paths
+- Standalone mode: Mocks ComfyUI dependencies via `standalone.py`, reads paths from settings.json
+- Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
+
+**Settings Management:**
+- Settings stored in user directory (via `platformdirs`) or portable mode (in repo)
+- Migration system tracks settings schema version
+- Template in `settings.json.example` defines defaults
+
+**Model Scanning Flow:**
+1. Scanner walks folder paths, computes file hashes
+2. Hash-based deduplication prevents duplicate processing
+3. Metadata extracted from safetensors headers
+4. Persistent cache stores results in SQLite
+5. Background sync fetches CivitAI/CivArchive metadata
+6. WebSocket broadcasts updates to connected clients
+
+**Recipe System:**
+- Recipes store LoRA combinations with parameters
+- Supports import from workflow JSON, PNG metadata
+- Images associated with recipes via sibling file detection
+- Enrichment adds model metadata for display
+
+**Frontend-Backend Communication:**
+- REST API for CRUD operations
+- WebSocket for real-time progress updates (downloads, scans)
+- API endpoints follow `/loras/*` pattern
 
 ## Code Style
 
 **Python:**
-- PEP 8, 4-space indentation, English comments only
-- Use `from __future__ import annotations` for forward references
-- Use `TYPE_CHECKING` guard for type-checking-only imports
+- PEP 8 with 4-space indentation
+- snake_case for files, functions, variables
+- PascalCase for classes
+- Type hints preferred
+- English comments only (per copilot-instructions.md)
 - Loggers via `logging.getLogger(__name__)`
-- Custom exceptions in `py/services/errors.py`
-- Async patterns: `async def` for I/O, `@pytest.mark.asyncio` for async tests
-- Singleton pattern with class-level `asyncio.Lock` (see `ModelScanner.get_instance()`)
 
 **JavaScript:**
-- ES modules, camelCase functions/variables, PascalCase classes
-- Widget files use `*_widget.js` suffix
-- Prefer vanilla JS for `web/comfyui/` widgets, avoid framework dependencies (except Vue widgets)
+- ES modules with camelCase
+- Files use `*_widget.js` suffix for ComfyUI widgets
+- Prefer vanilla JS, avoid framework dependencies
 
 ## Testing
 
-**Backend (pytest):**
-- Config in `pytest.ini`: `--import-mode=importlib`, testpaths=`tests`
-- Fixtures in `tests/conftest.py` handle ComfyUI dependency mocking
-- Markers: `@pytest.mark.asyncio`, `@pytest.mark.no_settings_dir_isolation`
-- Uses `tmp_path_factory` for directory isolation
+**Backend Tests:**
+- pytest with `--import-mode=importlib`
+- Test files: `tests/test_*.py`
+- Fixtures in `tests/conftest.py`
+- Mock ComfyUI dependencies using standalone.py patterns
+- Markers: `@pytest.mark.asyncio` for async tests, `@pytest.mark.no_settings_dir_isolation` for real paths
 
-**Frontend (vitest):**
-- Vanilla JS tests: `tests/frontend/**/*.test.js` with jsdom
-- Vue widget tests: `vue-widgets/tests/**/*.test.ts` with jsdom + @vue/test-utils
+**Frontend Tests:**
+- Vitest with jsdom environment
+- Test files: `tests/frontend/**/*.test.js`
 - Setup in `tests/frontend/setup.js`
+- Coverage via `npm run test:coverage`
 
-## Key Integration Points
+## Important Notes
 
-- **Settings:** Stored in user directory (via `platformdirs`) or portable mode (`"use_portable_settings": true`)
-- **CivitAI/CivArchive:** API clients for metadata sync and model downloads; CivitAI API key in settings
-- **Symlink handling:** Config scans symlinks to map virtual→physical paths; fingerprinting prevents redundant rescans
-- **WebSocket:** Broadcasts real-time progress for downloads, scans, and metadata sync
-- **Model scanning flow:** Walk folders → compute hashes → deduplicate → extract safetensors metadata → cache in SQLite → background CivitAI sync → WebSocket broadcast
+**Settings Location:**
+- ComfyUI mode: Auto-saves folder paths to user settings directory
+- Standalone mode: Use `settings.json` (copy from `settings.json.example`)
+- Portable mode: Set `"use_portable_settings": true` in settings.json
+
+**API Integration:**
+- CivitAI API key required for downloads (add to settings)
+- CivArchive API used as fallback for deleted models
+- Metadata archive database available for offline metadata
+
+**Symlink Handling:**
+- Config scans symlinks to map virtual paths to physical locations
+- Preview validation uses normalized preview root paths
+- Fingerprinting prevents redundant symlink rescans
+
+**ComfyUI Node Development:**
+- Nodes defined in `py/nodes/`, registered in `__init__.py`
+- Frontend widgets in `web/comfyui/`, matched by node type
+- Use `WEB_DIRECTORY = "./web/comfyui"` convention
+
+**Recipe Image Association:**
+- Recipes scan for sibling images in same directory
+- Supports repair/migration of recipe image paths
+- See `py/services/recipe_scanner.py` for implementation details

__init__.py

@@ -1,13 +1,10 @@
 try:  # pragma: no cover - import fallback for pytest collection
     from .py.lora_manager import LoraManager
     from .py.nodes.lora_loader import LoraLoaderLM, LoraTextLoaderLM
-    from .py.nodes.checkpoint_loader import CheckpointLoaderLM
-    from .py.nodes.unet_loader import UNETLoaderLM
     from .py.nodes.trigger_word_toggle import TriggerWordToggleLM
     from .py.nodes.prompt import PromptLM
     from .py.nodes.text import TextLM
     from .py.nodes.lora_stacker import LoraStackerLM
-    from .py.nodes.lora_stack_combiner import LoraStackCombinerLM
     from .py.nodes.save_image import SaveImageLM
     from .py.nodes.debug_metadata import DebugMetadataLM
     from .py.nodes.wanvideo_lora_select import WanVideoLoraSelectLM
@@ -30,19 +27,16 @@ except (
     PromptLM = importlib.import_module("py.nodes.prompt").PromptLM
     TextLM = importlib.import_module("py.nodes.text").TextLM
     LoraManager = importlib.import_module("py.lora_manager").LoraManager
-    LoraLoaderLM = importlib.import_module("py.nodes.lora_loader").LoraLoaderLM
-    LoraTextLoaderLM = importlib.import_module("py.nodes.lora_loader").LoraTextLoaderLM
-    CheckpointLoaderLM = importlib.import_module(
-        "py.nodes.checkpoint_loader"
-    ).CheckpointLoaderLM
-    UNETLoaderLM = importlib.import_module("py.nodes.unet_loader").UNETLoaderLM
+    LoraLoaderLM = importlib.import_module(
+        "py.nodes.lora_loader"
+    ).LoraLoaderLM
+    LoraTextLoaderLM = importlib.import_module(
+        "py.nodes.lora_loader"
+    ).LoraTextLoaderLM
     TriggerWordToggleLM = importlib.import_module(
         "py.nodes.trigger_word_toggle"
     ).TriggerWordToggleLM
     LoraStackerLM = importlib.import_module("py.nodes.lora_stacker").LoraStackerLM
-    LoraStackCombinerLM = importlib.import_module(
-        "py.nodes.lora_stack_combiner"
-    ).LoraStackCombinerLM
     SaveImageLM = importlib.import_module("py.nodes.save_image").SaveImageLM
     DebugMetadataLM = importlib.import_module("py.nodes.debug_metadata").DebugMetadataLM
     WanVideoLoraSelectLM = importlib.import_module(
@@ -55,7 +49,9 @@ except (
     LoraRandomizerLM = importlib.import_module(
         "py.nodes.lora_randomizer"
     ).LoraRandomizerLM
-    LoraCyclerLM = importlib.import_module("py.nodes.lora_cycler").LoraCyclerLM
+    LoraCyclerLM = importlib.import_module(
+        "py.nodes.lora_cycler"
+    ).LoraCyclerLM
     init_metadata_collector = importlib.import_module("py.metadata_collector").init
 
 NODE_CLASS_MAPPINGS = {
@@ -63,11 +59,8 @@ NODE_CLASS_MAPPINGS = {
     TextLM.NAME: TextLM,
     LoraLoaderLM.NAME: LoraLoaderLM,
     LoraTextLoaderLM.NAME: LoraTextLoaderLM,
-    CheckpointLoaderLM.NAME: CheckpointLoaderLM,
-    UNETLoaderLM.NAME: UNETLoaderLM,
     TriggerWordToggleLM.NAME: TriggerWordToggleLM,
     LoraStackerLM.NAME: LoraStackerLM,
-    LoraStackCombinerLM.NAME: LoraStackCombinerLM,
     SaveImageLM.NAME: SaveImageLM,
     DebugMetadataLM.NAME: DebugMetadataLM,
     WanVideoLoraSelectLM.NAME: WanVideoLoraSelectLM,

data/supporters.json

@@ -1,673 +0,0 @@
-{
-  "specialThanks": [
-    "dispenser",
-    "EbonEagle",
-    "DanielMagPizza",
-    "Scott R"
-  ],
-  "allSupporters": [
-    "Insomnia Art Designs",
-    "megakirbs",
-    "Brennok",
-    "2018cfh",
-    "W+K+White",
-    "wackop",
-    "Takkan",
-    "Carl G.",
-    "$MetaSamsara",
-    "itismyelement",
-    "onesecondinosaur",
-    "stone9k",
-    "Rosenthal",
-    "Francisco Tatis",
-    "Andrew Wilson",
-    "Greybush",
-    "Gooohokrbe",
-    "Ricky Carter",
-    "JongWon Han",
-    "OldBones",
-    "VantAI",
-    "runte3221",
-    "FreelancerZ",
-    "Edgar Tejeda",
-    "Liam MacDougal",
-    "Fraser Cross",
-    "Polymorphic Indeterminate",
-    "Birdy",
-    "Marc Whiffen",
-    "Jorge Hussni",
-    "Kiba",
-    "Skalabananen",
-    "Reno Lam",
-    "sig",
-    "Christian Byrne",
-    "DM",
-    "Sen314",
-    "Estragon",
-    "J\\B/ 8r0wns0n",
-    "Snaggwort",
-    "Arlecchino Shion",
-    "Charles Blakemore",
-    "Rob Williams",
-    "ClockDaemon",
-    "KD",
-    "Omnidex",
-    "Tyler Trebuchon",
-    "Release Cabrakan",
-    "Tobi_Swagg",
-    "SG",
-    "carozzz",
-    "James Dooley",
-    "zenbound",
-    "Buzzard",
-    "jmack",
-    "Mark Corneglio",
-    "SarcasticHashtag",
-    "Cosmosis",
-    "iamresist",
-    "RedrockVP",
-    "Wolffen",
-    "FloPro4Sho",
-    "James Todd",
-    "Steven Pfeiffer",
-    "Tim",
-    "Lisster",
-    "Michael Wong",
-    "Illrigger",
-    "Tom Corrigan",
-    "JackieWang",
-    "fnkylove",
-    "Julian V",
-    "Steven Owens",
-    "Yushio",
-    "Vik71it",
-    "Echo",
-    "Lilleman",
-    "Robert Stacey",
-    "PM",
-    "Todd Keck",
-    "Mozzel",
-    "Gingko Biloba",
-    "Sterilized",
-    "BadassArabianMofo",
-    "Pascal Dahle",
-    "quarz",
-    "Greg",
-    "Penfore",
-    "JSST",
-    "esthe",
-    "lmsupporter",
-    "IamAyam",
-    "wfpearl",
-    "Baekdoosixt",
-    "Jonathan Ross",
-    "Jack B Nimble",
-    "Nazono_hito",
-    "Melville Parrish",
-    "daniel dove",
-    "Lustre",
-    "JW Sin",
-    "contrite831",
-    "Alex",
-    "bh",
-    "confiscated Zyra",
-    "Marlon Daniels",
-    "Starkselle",
-    "Aaron Bleuer",
-    "LacesOut!",
-    "greebles",
-    "Adam Shaw",
-    "Tee Gee",
-    "Anthony Rizzo",
-    "tarek helmi",
-    "M Postkasse",
-    "ASLPro3D",
-    "Jacob Hoehler",
-    "FinalyFree",
-    "Weasyl",
-    "Timmy",
-    "Johnny",
-    "Cory Paza",
-    "Tak",
-    "Gonzalo Andre Allendes Lopez",
-    "Zach Gonser",
-    "Big Red",
-    "whudunit",
-    "Luc Job",
-    "dl0901dm",
-    "Philip Hempel",
-    "corde",
-    "Nick Walker",
-    "lh qwe",
-    "Bishoujoker",
-    "conner",
-    "aai",
-    "Briton Heilbrun",
-    "Tori",
-    "wildnut",
-    "Princess Bright Eyes",
-    "AbstractAss",
-    "Felipe dos Santos",
-    "ViperC",
-    "jean jahren",
-    "Aleksander Wujczyk",
-    "AM Kuro",
-    "Markus",
-    "S Sang",
-    "Karl P.",
-    "Akira_HentAI",
-    "MagnaInsomnia",
-    "Gordon Cole",
-    "yuxz69",
-    "Douglas Gaspar",
-    "AlexDuKaNa",
-    "George",
-    "andrew.tappan",
-    "dw",
-    "N/A",
-    "The Spawn",
-    "Phil",
-    "graysock",
-    "Greenmoustache",
-    "zounic",
-    "fancypants",
-    "Digital",
-    "JaxMax",
-    "takyamtom",
-    "奚明 刘",
-    "Jwk0205",
-    "Bro Xie",
-    "준희 김",
-    "batblue",
-    "carey6409",
-    "Olive",
-    "太郎 ゲーム",
-    "Some Guy Named Barry",
-    "Max Marklund",
-    "Tomohiro Baba",
-    "David Ortega",
-    "AELOX",
-    "Nicfit23",
-    "Noora",
-    "wamekukyouzin",
-    "drum matthieu",
-    "Dogmaster",
-    "Matt Wenzel",
-    "Mattssn",
-    "Lex Song",
-    "John Saveas",
-    "Christopher Michel",
-    "Serge Bekenkamp",
-    "Jimmy Ledbetter",
-    "LeoZero",
-    "Antonio Pontes",
-    "ApathyJones",
-    "nahinahi9",
-    "Dustin Chen",
-    "dan",
-    "Yaboi",
-    "Mouthlessman",
-    "Steam Steam",
-    "Damon Cunliffe",
-    "CryptoTraderJK",
-    "Davaitamin",
-    "otaku fra",
-    "Ran C",
-    "tedcor",
-    "Fotek Design",
-    "Adam Taylor",
-    "Weird_With_A_Beard",
-    "MadSpin",
-    "Pozadine1",
-    "Qarob",
-    "AIGooner",
-    "inbijiburu",
-    "Luc",
-    "ProtonPrince",
-    "DiffDuck",
-    "elu3199",
-    "Nick “Loadstone” D",
-    "Hasturkun",
-    "Jon Sandman",
-    "Ubivis",
-    "CloudValley",
-    "thesoftwaredruid",
-    "wundershark",
-    "mr_dinosaur",
-    "linnfrey",
-    "Gamalonia",
-    "Vir",
-    "Pkrsky",
-    "Joboshy",
-    "Bohemian Corporal",
-    "Dan",
-    "Josef Lanzl",
-    "Seth Christensen",
-    "Griffin Dahlberg",
-    "Draven T",
-    "yer fey",
-    "Error_Rule34_Not_found",
-    "Gerald Welly",
-    "Roslynd",
-    "Geolog",
-    "jinxedx",
-    "Neco28",
-    "Aquatic Coffee",
-    "Dankin",
-    "ethanfel",
-    "Cristian Vazquez",
-    "Frank Nitty",
-    "Magic Noob",
-    "Focuschannel",
-    "DougPeterson",
-    "Jeff",
-    "Bruce",
-    "Kevin John Duck",
-    "Anthony Faxlandez",
-    "Kevin Christopher",
-    "Ouro Boros",
-    "Blackfish95",
-    "dd",
-    "Paul Kroll",
-    "MiraiKuriyamaSy",
-    "semicolon drainpipe",
-    "Thesharingbrother",
-    "Bas Imagineer",
-    "Pat Hen",
-    "John Statham",
-    "ResidentDeviant",
-    "Nihongasuki",
-    "JC",
-    "Prompt Pirate",
-    "uwutismxd",
-    "decoy",
-    "Tyrswood",
-    "Ray Wing",
-    "Ranzitho",
-    "Gus",
-    "地獄の禄",
-    "MJG",
-    "David LaVallee",
-    "ae",
-    "Tr4shP4nda",
-    "WRL_SPR",
-    "capn",
-    "Joseph",
-    "Mirko Katzula",
-    "dan",
-    "Piccio08",
-    "kumakichi",
-    "cppbel",
-    "starbugx",
-    "Moon Knight",
-    "몽타주",
-    "Kland",
-    "zenobeus",
-    "Jackthemind",
-    "ryoma",
-    "Stryker",
-    "raf8osz",
-    "ElitaSSJ4",
-    "blikkies",
-    "Chris",
-    "Brian M",
-    "Nerezza",
-    "sanborondon",
-    "Taylor Funk",
-    "aezin",
-    "Thought2Form",
-    "jcay015",
-    "Kevin Picco",
-    "Erik Lopez",
-    "Shock Shockor",
-    "Mateo Curić",
-    "Goldwaters",
-    "Zude",
-    "Eris3D",
-    "m",
-    "Pierce McBride",
-    "Joshua Gray",
-    "Kyler",
-    "Mikko Hemilä",
-    "aRtFuL_DodGeR",
-    "Jamie Ogletree",
-    "a _",
-    "James Coleman",
-    "CrimsonDX",
-    "Martial",
-    "battu",
-    "Emil Andersson",
-    "Chad Idk",
-    "DarkSunset",
-    "Billy Gladky",
-    "Yuji Kaneko",
-    "Probis",
-    "Dušan Ryban",
-    "ItsGeneralButtNaked",
-    "Jordan Shaw",
-    "Rops Alot",
-    "Sam",
-    "sjon kreutz",
-    "Nimess",
-    "SRDB",
-    "Ace Ventura",
-    "g unit",
-    "Youguang",
-    "Metryman55",
-    "andrewzpong",
-    "FrxzenSnxw",
-    "BossGame",
-    "lrdchs",
-    "momokai",
-    "Hailshem",
-    "kudari",
-    "Naomi Hale Danchi",
-    "dc7431",
-    "ken",
-    "Inversity",
-    "AIVORY3D",
-    "epicgamer0020690",
-    "Joshua Porrata",
-    "keemun",
-    "SuBu",
-    "RedPIXel",
-    "Kevinj",
-    "Wind",
-    "Nexus",
-    "Ramneek“Guy”Ashok",
-    "squid_actually",
-    "Nat_20",
-    "Edward Weeks",
-    "kyoumei",
-    "RadStorm04",
-    "JohnDoe42054",
-    "BillyHill",
-    "emyth",
-    "chriphost",
-    "KitKatM",
-    "socrasteeze",
-    "ResidentDeviant",
-    "gzmzmvp",
-    "Welkor",
-    "John Martin",
-    "Richard",
-    "Andrew",
-    "Robert Wegemund",
-    "Littlehuggy",
-    "moranqianlong",
-    "Gregory Kozhemiak",
-    "mrjuan",
-    "Brian Buie",
-    "Sadlip",
-    "Haru Yotu",
-    "Eric Whitney",
-    "Joey Callahan",
-    "Ivan Tadic",
-    "Mike Simone",
-    "Morgandel",
-    "Kyron Mahan",
-    "Matura Arbeit",
-    "Noah",
-    "Jacob McDaniel",
-    "X",
-    "Sloan Steddy",
-    "TBitz33",
-    "Anonym dkjglfleeoeldldldlkf",
-    "Temikus",
-    "Artokun",
-    "Michael Taylor",
-    "SendingRavens",
-    "Derek Baker",
-    "Michael Anthony Scott",
-    "Atilla Berke Pekduyar",
-    "Michael Docherty",
-    "Nathan",
-    "Decx _",
-    "Paul Hartsuyker",
-    "elitassj",
-    "Jacob Winter",
-    "Distortik",
-    "David",
-    "Meilo",
-    "Pen Bouryoung",
-    "四糸凜音",
-    "shinonomeiro",
-    "Snille",
-    "MaartenAlbers",
-    "khanh duy",
-    "xybrightsummer",
-    "jreedatchison",
-    "PhilW",
-    "Tree Tagger",
-    "Janik",
-    "Crocket",
-    "Cruel",
-    "MRBlack",
-    "Mitchell Robson",
-    "Kiyoe",
-    "humptynutz",
-    "michael.isaza",
-    "Kalnei",
-    "Whitepinetrader",
-    "OrganicArtifact",
-    "Scott",
-    "MudkipMedkitz",
-    "deanbrian",
-    "POPPIN",
-    "Alex Wortman",
-    "Cody",
-    "Raku",
-    "smart.edge5178",
-    "emadsultan",
-    "InformedViewz",
-    "CHKeeho80",
-    "Bubbafett",
-    "leaf",
-    "Menard",
-    "Skyfire83",
-    "Adam Rinehart",
-    "D",
-    "Pitpe11",
-    "TheD1rtyD03",
-    "moonpetal",
-    "SomeDude",
-    "g9p0o",
-    "nanana",
-    "TheHolySheep",
-    "Monte Won",
-    "SpringBootisTrash",
-    "carsten",
-    "ikok",
-    "Buecyb99",
-    "4IXplr0r3r",
-    "dfklsjfkljslfjd",
-    "hayden",
-    "ahoystan",
-    "Leland Saunders",
-    "Wolfe7D1",
-    "Ink Temptation",
-    "Bob Barker",
-    "edk",
-    "Kalli Core",
-    "Aeternyx",
-    "elleshar666",
-    "YOU SINWOO",
-    "ja s",
-    "Doug Mason",
-    "Kauffy",
-    "Jeremy Townsend",
-    "EpicElric",
-    "Sean voets",
-    "Owen Gwosdz",
-    "John J Linehan",
-    "Elliot E",
-    "Thomas Wanner",
-    "Theerat Jiramate",
-    "Edward Kennedy",
-    "Justin Blaylock",
-    "Devil Lude",
-    "Nick Kage",
-    "kevin stoddard",
-    "Jack Dole",
-    "Vane Holzer",
-    "psytrax",
-    "Ezokewn",
-    "hexxish",
-    "CptNeo",
-    "notedfakes",
-    "Maso",
-    "Eric Ketchum",
-    "NICHOLAS BAXLEY",
-    "Michael Scott",
-    "Kevin Wallace",
-    "Matheus Couto",
-    "Saya",
-    "ChicRic",
-    "mercur",
-    "J C",
-    "Ed Wang",
-    "Ryan Presley Ng",
-    "Wes Sims",
-    "Donor4115",
-    "Yves Poezevara",
-    "Teriak47",
-    "Just me",
-    "Raf Stahelin",
-    "Вячеслав Маринин",
-    "Lyavph",
-    "Filippo Ferrari",
-    "Cola Matthew",
-    "OniNoKen",
-    "Iain Wisely",
-    "Zertens",
-    "NOHOW",
-    "Apo",
-    "nekotxt",
-    "choowkee",
-    "Clusters",
-    "ibrahim",
-    "Highlandrise",
-    "philcoraz",
-    "mztn",
-    "ImagineerNL",
-    "MrAcrtosSursus",
-    "al300680",
-    "pixl",
-    "Robin",
-    "chahknoir",
-    "Marcus thronico",
-    "nd",
-    "keno94d",
-    "James Melzer",
-    "Bartleby",
-    "Renvertere",
-    "Rahuy",
-    "Hermann003",
-    "D",
-    "Foolish",
-    "RevyHiep",
-    "Captain_Swag",
-    "obkircher",
-    "gwyar",
-    "D",
-    "edgecase",
-    "Neoxena",
-    "mrmhalo",
-    "dg",
-    "Maarten Harms",
-    "Israel",
-    "Muratoraccio",
-    "SelfishMedic",
-    "Ginnie",
-    "adderleighn",
-    "EnragedAntelope",
-    "Alan+Cano",
-    "FeralOpticsAI",
-    "Pavlaki",
-    "generic404",
-    "Mateusz+Kosela",
-    "Doug+Rintoul",
-    "Noor",
-    "Yorunai",
-    "Bula",
-    "quantenmecha",
-    "abattoirblues",
-    "Jason+Nash",
-    "BillyBoy84",
-    "DarkRoast",
-    "zounik",
-    "letzte",
-    "Nasty+Hobbit",
-    "SgtFluffles",
-    "lrdchs2",
-    "Duk3+Rand0m",
-    "KUJYAKU",
-    "NathenChoi",
-    "Thomas+Reck",
-    "Larses",
-    "cocona",
-    "Coeur+de+cochon",
-    "David Schenck",
-    "han b",
-    "Nico",
-    "Banana Joe",
-    "_ G3n",
-    "Donovan Jenkins",
-    "JBsuede",
-    "Michael Eid",
-    "beersandbacon",
-    "Maximilian Pyko",
-    "Invis",
-    "Justin Houston",
-    "Time Valentine",
-    "james",
-    "OrochiNights",
-    "Michael Zhu",
-    "ACTUALLY_the_Real_Willem_Dafoe",
-    "gonzalo",
-    "Seraphy",
-    "Михал Михалыч",
-    "雨の心 落",
-    "Matt",
-    "AllTimeNoobie",
-    "jumpd",
-    "John C",
-    "Rim",
-    "Dismem",
-    "Frogmilk",
-    "SPJ",
-    "Xan Dionysus",
-    "Nathan lee",
-    "Mewtora",
-    "Middo",
-    "Forbidden Atelier",
-    "Bryan Rutkowski",
-    "Adictedtohumping",
-    "Towelie",
-    "Cyrus Fett",
-    "Jean-françois SEMA",
-    "Kurt",
-    "max blo",
-    "Xenon Xue",
-    "JackJohnnyJim",
-    "Edward Ten Eyck",
-    "Chase Kwon",
-    "Inyoshu",
-    "Goober719",
-    "Chad Barnes",
-    "James Ming",
-    "vanditking",
-    "kripitonga",
-    "Rizzi",
-    "nimin",
-    "OMAR LUCIANO",
-    "hannibal",
-    "Jo+Example",
-    "BrentBertram",
-    "eumelzocker",
-    "dxjaymz",
-    "L C",
-    "Dude"
-  ],
-  "totalCount": 666
-}

docs/LM-Extension-Wiki.md

@@ -1,27 +1,31 @@
 ## Overview
 
-The **LoRA Manager Civitai Extension** is a Browser extension designed to work seamlessly with [LoRA Manager](https://github.com/willmiao/ComfyUI-Lora-Manager) to significantly enhance your browsing experience on [Civitai](https://civitai.com). With this extension, you can:
+The **LoRA Manager Civitai Extension** is a Browser extension designed to work seamlessly with [LoRA Manager](https://github.com/willmiao/ComfyUI-Lora-Manager) to significantly enhance your browsing experience on [Civitai](https://civitai.com).
+It also supports browsing on [CivArchive](https://civarchive.com/) (formerly CivitaiArchive).
+
+With this extension, you can:
 
 ✅ Instantly see which models are already present in your local library  
 ✅ Download new models with a single click  
 ✅ Manage downloads efficiently with queue and parallel download support  
 ✅ Keep your downloaded models automatically organized according to your custom settings    
 
-![Civitai Models page](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/wiki-images/civitai-models-page.png) 
-
-**Update:** It now also supports browsing on [CivArchive](https://civarchive.com/) (formerly CivitaiArchive). 
-
+![Civitai Models page](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/wiki-images/civitai-models-page.png)  
 ![CivArchive Models page](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/wiki-images/civarchive-models-page.png)  
 
 ---
 
-## Why Supporter Access?
+## Why Are All Features for Supporters Only?
 
-LoRA Manager is built with love for the Stable Diffusion and ComfyUI communities. Your support makes it possible for me to keep improving and maintaining the tool full-time.
+I love building tools for the Stable Diffusion and ComfyUI communities, and LoRA Manager is a passion project that I've poured countless hours into. When I created this companion extension, my hope was to offer its core features for free, as a thank-you to all of you.
 
-Supporter-exclusive features help ensure the long-term sustainability of LoRA Manager, allowing continuous updates, new features, and better performance for everyone.
+Unfortunately, I've reached a point where I need to be realistic. The level of support from the free model has been far lower than what's needed to justify the continuous development and maintenance for both projects. It was a difficult decision, but I've chosen to make the extension's features exclusive to supporters.
 
-Every contribution directly fuels development and keeps the core LoRA Manager free and open-source. In addition to monthly supporters, one-time donation supporters will also receive a license key, with the duration scaling according to the contribution amount. Thank you for helping keep this project alive and growing. ❤️
+This change is crucial for me to be able to continue dedicating my time to improving the free and open-source LoRA Manager, which I'm committed to keeping available for everyone.
+
+Your support does more than just unlock a few features—it allows me to keep innovating and ensures the core LoRA Manager project thrives. I'm incredibly grateful for your understanding and any support you can offer. ❤️
+
+(_For those who previously supported me on Ko-fi with a one-time donation, I'll be sending out license keys individually as a thank-you._)
 
 
 ---
@@ -86,27 +90,20 @@ Clicking the download button adds the corresponding model version to the downloa
 
 On a specific model page, visual indicators also appear on version buttons, showing which versions are already in your local library.
 
-**Starting from v0.4.8**, model pages use a dedicated download button for better compatibility. When switching to a specific version by clicking a version button:
+When switching to a specific version by clicking a version button:
 
-- The new **dedicated download button** directly triggers download via **LoRA Manager**
-- The **original download button** remains unchanged for standard browser downloads
+- Clicking the download button will open a dropdown:
+  - Download via **LoRA Manager**  
+  - Download via **Original Download** (browser download)  
+
+You can check **Remember my choice** to set your preferred default. You can change this setting anytime in the extension's settings.
 
 ![Civitai Model Page](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/wiki-images/civitai-model-page.png)
 
-### Hide Models Already in Library (Beta)
-
-**New in v0.4.8**: A new **Hide models already in library (Beta)** option makes it easier to focus on models you haven't added yet. It can be enabled from Settings, or toggled quickly using **Ctrl + Shift + H** (macOS: **Command + Shift + H**).
-
-### Resources on Image Pages — now shows in-library indicators for image resources plus one-click recipe import
-
-- **One-Click Import Civitai Image as Recipe** — Import any Civitai image as a recipe with a single click in the Resources Used panel.
-- **Auto-Queue Missing Assets** — In Settings you can decide if LoRAs or checkpoints referenced by that image should automatically be added to your download queue.
-- **More Accurate Metadata** — Importing directly from the page is faster than copying inside LM and keeps on-site tags and other metadata perfectly aligned.
+### Resources on Image Pages (2025-08-05) — now shows in-library indicators for image resources. ‘Import image as recipe’ coming soon!
 
 ![Civitai Image Page](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/wiki-images/civitai-image-page.jpg)
 
-[![alt](url)](https://github.com/user-attachments/assets/41fd4240-c949-4f83-bde7-8f3124c09494)
-
 ---
 
 ## Model Download Location & LoRA Manager Settings
@@ -173,11 +170,11 @@ _Thanks to user **Temikus** for sharing this solution!_
 The extension will evolve alongside **LoRA Manager** improvements. Planned features include:
 
 - [x] Support for **additional model types** (e.g., embeddings)
-- [x] One-click **Recipe Import**
-- [x] Display of in-library status for all resources in the **Resources Used** section of the image page
+- [ ] One-click **Recipe Import**  
+- [x] Display of in-library status for all resources in the **Resources Used** section of the image page  
 - [x] One-click **Auto-organize Models**
-- [x] **Hide models already in library (Beta)** - Focus on models you haven't added yet
 
 **Stay tuned — and thank you for your support!**
 
 ---
+

docs/features/recipe-batch-import-requirements.md

@@ -1,170 +0,0 @@
-# Recipe Batch Import Feature Requirements
-
-## Overview
-Enable users to import multiple images as recipes in a single operation, rather than processing them individually. This feature addresses the need for efficient bulk recipe creation from existing image collections.
-
-## User Stories
-
-### US-1: Directory Batch Import
-As a user with a folder of reference images or workflow screenshots, I want to import all images from a directory at once so that I don't have to import them one by one.
-
-**Acceptance Criteria:**
-- User can specify a local directory path containing images
-- System discovers all supported image files in the directory
-- Each image is analyzed for metadata and converted to a recipe
-- Results show which images succeeded, failed, or were skipped
-
-### US-2: URL Batch Import
-As a user with a list of image URLs (e.g., from Civitai or other sources), I want to import multiple images by URL in one operation.
-
-**Acceptance Criteria:**
-- User can provide multiple image URLs (one per line or as a list)
-- System downloads and processes each image
-- URL-specific metadata (like Civitai info) is preserved when available
-- Failed URLs are reported with clear error messages
-
-### US-3: Concurrent Processing Control
-As a user with varying system resources, I want to control how many images are processed simultaneously to balance speed and system load.
-
-**Acceptance Criteria:**
-- User can configure the number of concurrent operations (1-10)
-- System provides sensible defaults based on common hardware configurations
-- Processing respects the concurrency limit to prevent resource exhaustion
-
-### US-4: Import Results Summary
-As a user performing a batch import, I want to see a clear summary of the operation results so I understand what succeeded and what needs attention.
-
-**Acceptance Criteria:**
-- Total count of images processed is displayed
-- Number of successfully imported recipes is shown
-- Number of failed imports with error details is provided
-- Number of skipped images (no metadata) is indicated
-- Results can be exported or saved for reference
-
-### US-5: Progress Visibility
-As a user importing a large batch, I want to see the progress of the operation so I know it's working and can estimate completion time.
-
-**Acceptance Criteria:**
-- Progress indicator shows current status (e.g., "Processing image 5 of 50")
-- Real-time updates as each image completes
-- Ability to view partial results before completion
-- Clear indication when the operation is finished
-
-## Functional Requirements
-
-### FR-1: Image Discovery
-The system shall discover image files in a specified directory recursively or non-recursively based on user preference.
-
-**Supported formats:** JPG, JPEG, PNG, WebP, GIF, BMP
-
-### FR-2: Metadata Extraction
-For each image, the system shall:
-- Extract EXIF metadata if present
-- Parse embedded workflow data (ComfyUI PNG metadata)
-- Fetch external metadata for known URL patterns (e.g., Civitai)
-- Generate recipes from extracted information
-
-### FR-3: Concurrent Processing
-The system shall support concurrent processing of multiple images with:
-- Configurable concurrency limit (default: 3)
-- Resource-aware execution
-- Graceful handling of individual failures without stopping the batch
-
-### FR-4: Error Handling
-The system shall handle various error conditions:
-- Invalid directory paths
-- Inaccessible files
-- Network errors for URL imports
-- Images without extractable metadata
-- Malformed or corrupted image files
-
-### FR-5: Recipe Persistence
-Successfully analyzed images shall be persisted as recipes with:
-- Extracted generation parameters
-- Preview image association
-- Tags and metadata
-- Source information (file path or URL)
-
-## Non-Functional Requirements
-
-### NFR-1: Performance
-- Batch operations should complete in reasonable time (< 5 seconds per image on average)
-- UI should remain responsive during batch operations
-- Memory usage should scale gracefully with batch size
-
-### NFR-2: Scalability
-- Support batches of 1-1000 images
-- Handle mixed success/failure scenarios gracefully
-- No hard limits on concurrent operations (configurable)
-
-### NFR-3: Usability
-- Clear error messages for common failure cases
-- Intuitive UI for configuring import options
-- Accessible from the main Recipes interface
-
-### NFR-4: Reliability
-- Failed individual imports should not crash the entire batch
-- Partial results should be preserved on unexpected termination
-- All operations should be idempotent (re-importing same image doesn't create duplicates)
-
-## API Requirements
-
-### Batch Import Endpoints
-The system should expose endpoints for:
-
-1. **Directory Import**
-   - Accept directory path and configuration options
-   - Return operation ID for status tracking
-   - Async or sync operation support
-
-2. **URL Import**
-   - Accept list of URLs and configuration options
-   - Support URL validation before processing
-   - Return operation ID for status tracking
-
-3. **Status/Progress**
-   - Query operation status by ID
-   - Get current progress and partial results
-   - Retrieve final results after completion
-
-## UI/UX Requirements
-
-### UIR-1: Entry Point
-Batch import should be accessible from the Recipes page via a clearly labeled button in the toolbar.
-
-### UIR-2: Import Modal
-A modal dialog should provide:
-- Tab or section for Directory import
-- Tab or section for URL import
-- Configuration options (concurrency, options)
-- Start/Stop controls
-- Results display area
-
-### UIR-3: Results Display
-Results should be presented with:
-- Summary statistics (total, success, failed, skipped)
-- Expandable details for each category
-- Export or copy functionality for results
-- Clear visual distinction between success/failure/skip
-
-## Future Considerations
-
-- **Scheduled Imports**: Ability to schedule batch imports for later execution
-- **Import Templates**: Save import configurations for reuse
-- **Cloud Storage**: Import from cloud storage services (Google Drive, Dropbox)
-- **Duplicate Detection**: Advanced duplicate detection based on image hash
-- **Tag Suggestions**: AI-powered tag suggestions for imported recipes
-- **Batch Editing**: Apply tags or organization to multiple imported recipes at once
-
-## Dependencies
-
-- Recipe analysis service (metadata extraction)
-- Recipe persistence service (storage)
-- Image download capability (for URL imports)
-- Recipe scanner (for refresh after import)
-- Civitai client (for enhanced URL metadata)
-
----
-
-*Document Version: 1.0*
-*Status: Requirements Definition*

docs/metadata-json-schema.md

@@ -1,363 +0,0 @@
-# metadata.json Schema Documentation
-
-This document defines the complete schema for `.metadata.json` files used by Lora Manager. These sidecar files store model metadata alongside model files (LoRA, Checkpoint, Embedding).
-
-## Overview
-
-- **File naming**: `<model_name>.metadata.json` (e.g., `my_lora.safetensors` → `my_lora.metadata.json`)
-- **Format**: JSON with UTF-8 encoding
-- **Purpose**: Store model metadata, tags, descriptions, preview images, and Civitai/CivArchive integration data
-- **Extensibility**: Unknown fields are preserved via `_unknown_fields` mechanism for forward compatibility
-
----
-
-## Base Fields (All Model Types)
-
-These fields are present in all model metadata files.
-
-| Field | Type | Required | Auto-Updated | Description |
-|-------|------|----------|--------------|-------------|
-| `file_name` | string | ✅ Yes | ✅ Yes | Filename without extension (e.g., `"my_lora"`) |
-| `model_name` | string | ✅ Yes | ❌ No | Display name of the model. **Default**: `file_name` if no other source |
-| `file_path` | string | ✅ Yes | ✅ Yes | Full absolute path to the model file (normalized with `/` separators) |
-| `size` | integer | ✅ Yes | ❌ No | File size in bytes. **Set at**: Initial scan or download completion. Does not change thereafter. |
-| `modified` | float | ✅ Yes | ❌ No | **Import timestamp** — Unix timestamp when the model was first imported/added to the system. Used for "Date Added" sorting. Does not change after initial creation. |
-| `sha256` | string | ⚠️ Conditional | ✅ Yes | SHA256 hash of the model file (lowercase). **LoRA**: Required. **Checkpoint**: May be empty when `hash_status="pending"` (lazy hash calculation) |
-| `base_model` | string | ❌ No | ❌ No | Base model type. **Examples**: `"SD 1.5"`, `"SDXL 1.0"`, `"SDXL Lightning"`, `"Flux.1 D"`, `"Flux.1 S"`, `"Flux.1 Krea"`, `"Illustrious"`, `"Pony"`, `"AuraFlow"`, `"Kolors"`, `"ZImageTurbo"`, `"Wan Video"`, etc. **Default**: `"Unknown"` or `""` |
-| `preview_url` | string | ❌ No | ✅ Yes | Path to preview image file |
-| `preview_nsfw_level` | integer | ❌ No | ❌ No | NSFW level using **bitmask values** from Civitai: `1` (PG), `2` (PG13), `4` (R), `8` (X), `16` (XXX), `32` (Blocked). **Default**: `0` (none) |
-| `notes` | string | ❌ No | ❌ No | User-defined notes |
-| `from_civitai` | boolean | ❌ No (default: `true`) | ❌ No | Whether the model originated from Civitai |
-| `civitai` | object | ❌ No | ⚠️ Partial | Civitai/CivArchive API data and user-defined fields |
-| `tags` | array[string] | ❌ No | ⚠️ Partial | Model tags (merged from API and user input) |
-| `modelDescription` | string | ❌ No | ⚠️ Partial | Full model description (from API or user) |
-| `civitai_deleted` | boolean | ❌ No (default: `false`) | ❌ No | Whether the model was deleted from Civitai |
-| `favorite` | boolean | ❌ No (default: `false`) | ❌ No | Whether the model is marked as favorite |
-| `exclude` | boolean | ❌ No (default: `false`) | ❌ No | Whether to exclude from cache/scanning. User can set from `false` to `true` (currently no UI to revert) |
-| `db_checked` | boolean | ❌ No (default: `false`) | ❌ No | Whether checked against archive database |
-| `skip_metadata_refresh` | boolean | ❌ No (default: `false`) | ❌ No | Skip this model during bulk metadata refresh |
-| `metadata_source` | string\|null | ❌ No | ✅ Yes | Last provider that supplied metadata (see below) |
-| `last_checked_at` | float | ❌ No (default: `0`) | ✅ Yes | Unix timestamp of last metadata check |
-| `hash_status` | string | ❌ No (default: `"completed"`) | ✅ Yes | Hash calculation status: `"pending"`, `"calculating"`, `"completed"`, `"failed"` |
-
----
-
-## Model-Specific Fields
-
-### LoRA Models
-
-LoRA models do not have a `model_type` field in metadata.json. The type is inferred from context or `civitai.type` (e.g., `"LoRA"`, `"LoCon"`, `"DoRA"`).
-
-| Field | Type | Required | Auto-Updated | Description |
-|-------|------|----------|--------------|-------------|
-| `usage_tips` | string (JSON) | ❌ No (default: `"{}"`) | ❌ No | JSON string containing recommended usage parameters |
-
-**`usage_tips` JSON structure:**
-
-```json
-{
-  "strength_min": 0.3,
-  "strength_max": 0.8,
-  "strength_range": "0.3-0.8",
-  "strength": 0.6,
-  "clip_strength": 0.5,
-  "clip_skip": 2
-}
-```
-
-| Key | Type | Description |
-|-----|------|-------------|
-| `strength_min` | number | Minimum recommended model strength |
-| `strength_max` | number | Maximum recommended model strength |
-| `strength_range` | string | Human-readable strength range |
-| `strength` | number | Single recommended strength value |
-| `clip_strength` | number | Recommended CLIP/embedding strength |
-| `clip_skip` | integer | Recommended CLIP skip value |
-
----
-
-### Checkpoint Models
-
-| Field | Type | Required | Auto-Updated | Description |
-|-------|------|----------|--------------|-------------|
-| `model_type` | string | ❌ No (default: `"checkpoint"`) | ❌ No | Model type: `"checkpoint"`, `"diffusion_model"` |
-
----
-
-### Embedding Models
-
-| Field | Type | Required | Auto-Updated | Description |
-|-------|------|----------|--------------|-------------|
-| `model_type` | string | ❌ No (default: `"embedding"`) | ❌ No | Model type: `"embedding"` |
-
----
-
-## The `civitai` Field Structure
-
-The `civitai` object stores the complete Civitai/CivArchive API response. Lora Manager preserves all fields from the API for future compatibility and extracts specific fields for use in the application.
-
-### Version-Level Fields (Civitai API)
-
-**Fields Used by Lora Manager:**
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `id` | integer | Version ID |
-| `modelId` | integer | Parent model ID |
-| `name` | string | Version name (e.g., `"v1.0"`, `"v2.0-pruned"`) |
-| `nsfwLevel` | integer | NSFW level (bitmask: 1=PG, 2=PG13, 4=R, 8=X, 16=XXX, 32=Blocked) |
-| `baseModel` | string | Base model (e.g., `"SDXL 1.0"`, `"Flux.1 D"`, `"Illustrious"`, `"Pony"`) |
-| `trainedWords` | array[string] | **Trigger words** for the model |
-| `type` | string | Model type (`"LoRA"`, `"Checkpoint"`, `"TextualInversion"`) |
-| `earlyAccessEndsAt` | string\|null | Early access end date (used for update notifications) |
-| `description` | string | Version description (HTML) |
-| `model` | object | Parent model object (see Model-Level Fields below) |
-| `creator` | object | Creator information (see Creator Fields below) |
-| `files` | array[object] | File list with hashes, sizes, download URLs (used for metadata extraction) |
-| `images` | array[object] | Image list with metadata, prompts, NSFW levels (used for preview/examples) |
-
-**Fields Stored but Not Currently Used:**
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `createdAt` | string (ISO 8601) | Creation timestamp |
-| `updatedAt` | string (ISO 8601) | Last update timestamp |
-| `status` | string | Version status (e.g., `"Published"`, `"Draft"`) |
-| `publishedAt` | string (ISO 8601) | Publication timestamp |
-| `baseModelType` | string | Base model type (e.g., `"Standard"`, `"Inpaint"`, `"Refiner"`) |
-| `earlyAccessConfig` | object | Early access configuration |
-| `uploadType` | string | Upload type (`"Created"`, `"FineTuned"`, etc.) |
-| `usageControl` | string | Usage control setting |
-| `air` | string | Artifact ID (URN format: `urn:air:sdxl:lora:civitai:122359@135867`) |
-| `stats` | object | Download count, ratings, thumbs up count |
-| `videos` | array[object] | Video list |
-| `downloadUrl` | string | Direct download URL |
-| `trainingStatus` | string\|null | Training status (for on-site training) |
-| `trainingDetails` | object\|null | Training configuration |
-
-### Model-Level Fields (`civitai.model.*`)
-
-**Fields Used by Lora Manager:**
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `name` | string | Model name |
-| `type` | string | Model type (`"LoRA"`, `"Checkpoint"`, `"TextualInversion"`) |
-| `description` | string | Model description (HTML, used for `modelDescription`) |
-| `tags` | array[string] | Model tags (used for `tags` field) |
-| `allowNoCredit` | boolean | License: allow use without credit |
-| `allowCommercialUse` | array[string] | License: allowed commercial uses. **Values**: `"Image"` (sell generated images), `"Video"` (sell generated videos), `"RentCivit"` (rent on Civitai), `"Rent"` (rent elsewhere) |
-| `allowDerivatives` | boolean | License: allow derivatives |
-| `allowDifferentLicense` | boolean | License: allow different license |
-
-**Fields Stored but Not Currently Used:**
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `nsfw` | boolean | Model NSFW flag |
-| `poi` | boolean | Person of Interest flag |
-
-### Creator Fields (`civitai.creator.*`)
-
-Both fields are used by Lora Manager:
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `username` | string | Creator username (used for author display and search) |
-| `image` | string | Creator avatar URL (used for display) |
-
-### Model Type Field (Top-Level, Outside `civitai`)
-
-| Field | Type | Values | Description |
-|-------|------|--------|-------------|
-| `model_type` | string | `"checkpoint"`, `"diffusion_model"`, `"embedding"` | Stored in metadata.json for Checkpoint and Embedding models. **Note**: LoRA models do not have this field; type is inferred from `civitai.type` or context. |
-
-### User-Defined Fields (Within `civitai`)
-
-For models not from Civitai or user-added data:
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `trainedWords` | array[string] | **Trigger words** — manually added by user |
-| `customImages` | array[object] | Custom example images added by user |
-
-### customImages Structure
-
-Each custom image entry has the following structure:
-
-```json
-{
-  "url": "",
-  "id": "short_id",
-  "nsfwLevel": 0,
-  "width": 832,
-  "height": 1216,
-  "type": "image",
-  "meta": {
-    "prompt": "...",
-    "negativePrompt": "...",
-    "steps": 20,
-    "cfgScale": 7,
-    "seed": 123456
-  },
-  "hasMeta": true,
-  "hasPositivePrompt": true
-}
-```
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `url` | string | Empty for local custom images |
-| `id` | string | Short ID or filename |
-| `nsfwLevel` | integer | NSFW level (bitmask) |
-| `width` | integer | Image width in pixels |
-| `height` | integer | Image height in pixels |
-| `type` | string | `"image"` or `"video"` |
-| `meta` | object\|null | Generation metadata (prompt, seed, etc.) extracted from image |
-| `hasMeta` | boolean | Whether metadata is available |
-| `hasPositivePrompt` | boolean | Whether a positive prompt is available |
-
-### Minimal Non-Civitai Example
-
-```json
-{
-  "civitai": {
-    "trainedWords": ["my_trigger_word"]
-  }
-}
-```
-
-### Non-Civitai Example Without Trigger Words
-
-```json
-{
-  "civitai": {}
-}
-```
-
-### Example: User-Added Custom Images
-
-```json
-{
-  "civitai": {
-    "trainedWords": ["custom_style"],
-    "customImages": [
-      {
-        "url": "",
-        "id": "example_1",
-        "nsfwLevel": 0,
-        "width": 832,
-        "height": 1216,
-        "type": "image",
-        "meta": {
-          "prompt": "example prompt",
-          "seed": 12345
-        },
-        "hasMeta": true,
-        "hasPositivePrompt": true
-      }
-    ]
-  }
-}
-```
-
----
-
-## Metadata Source Values
-
-The `metadata_source` field indicates which provider last updated the metadata:
-
-| Value | Source |
-|-------|--------|
-| `"civitai_api"` | Civitai API |
-| `"civarchive"` | CivArchive API |
-| `"archive_db"` | Metadata Archive Database |
-| `null` | No external source (user-defined only) |
-
----
-
-## Auto-Update Behavior
-
-### Fields Updated During Scanning
-
-These fields are automatically synchronized with the filesystem:
-
-- `file_name` — Updated if actual filename differs
-- `file_path` — Normalized and updated if path changes
-- `preview_url` — Updated if preview file is moved/removed
-- `sha256` — Updated during hash calculation (when `hash_status="pending"`)
-- `hash_status` — Updated during hash calculation
-- `last_checked_at` — Timestamp of scan
-- `metadata_source` — Set based on metadata provider
-
-### Fields Set Once (Immutable After Import)
-
-These fields are set when the model is first imported/scanned and **never change** thereafter:
-
-- `modified` — Import timestamp (used for "Date Added" sorting)
-- `size` — File size at time of import/download
-
-### User-Editable Fields
-
-These fields can be edited by users at any time through the Lora Manager UI or by manually editing the metadata.json file:
-
-- `model_name` — Display name
-- `tags` — Model tags
-- `modelDescription` — Model description
-- `notes` — User notes
-- `favorite` — Favorite flag
-- `exclude` — Exclude from scanning (user can set `false`→`true`, currently no UI to revert)
-- `skip_metadata_refresh` — Skip during bulk refresh
-- `civitai.trainedWords` — Trigger words
-- `civitai.customImages` — Custom example images
-- `usage_tips` — Usage recommendations (LoRA only)
-
----
-
-
-## Field Reference by Behavior
-
-### Required Fields (Must Always Exist)
-
-- `file_name`
-- `model_name` (defaults to `file_name` if not provided)
-- `file_path`
-- `size`
-- `modified`
-- `sha256` (LoRA: always required; Checkpoint: may be empty when `hash_status="pending"`)
-
-### Optional Fields with Defaults
-
-| Field | Default |
-|-------|---------|
-| `base_model` | `"Unknown"` or `""` |
-| `preview_nsfw_level` | `0` |
-| `from_civitai` | `true` |
-| `civitai` | `{}` |
-| `tags` | `[]` |
-| `modelDescription` | `""` |
-| `notes` | `""` |
-| `civitai_deleted` | `false` |
-| `favorite` | `false` |
-| `exclude` | `false` |
-| `db_checked` | `false` |
-| `skip_metadata_refresh` | `false` |
-| `metadata_source` | `null` |
-| `last_checked_at` | `0` |
-| `hash_status` | `"completed"` |
-| `usage_tips` | `"{}"` (LoRA only) |
-| `model_type` | `"checkpoint"` or `"embedding"` (not present in LoRA models) |
-
----
-
-## Version History
-
-| Version | Date | Changes |
-|---------|------|---------|
-| 1.0 | 2026-03 | Initial schema documentation |
-
----
-
-## See Also
-
-- [JSON Schema Definition](../.specs/metadata.schema.json) — Formal JSON Schema for validation

docs/testing/backend-testing-improvement-plan.md

@@ -1,678 +0,0 @@
-# Backend Testing Improvement Plan
-
-**Status:** Phase 4 Complete ✅  
-**Created:** 2026-02-11  
-**Updated:** 2026-02-11  
-**Priority:** P0 - Critical
-
----
-
-## Executive Summary
-
-This document outlines a comprehensive plan to improve the quality, coverage, and maintainability of the LoRa Manager backend test suite. Recent critical bugs (_handle_download_task_done and get_status methods missing) were not caught by existing tests, highlighting significant gaps in the testing strategy.
-
-## Current State Assessment
-
-### Test Statistics
-- **Total Python Test Files:** 80+
-- **Total JavaScript Test Files:** 29
-- **Test Lines of Code:** ~15,000
-- **Current Pass Rate:** 100% (but missing critical edge cases)
-
-### Key Findings
-1. **Coverage Gaps:** Critical modules have no direct tests
-2. **Mocking Issues:** Over-mocking hides real bugs
-3. **Integration Deficit:** Missing end-to-end tests
-4. **Async Inconsistency:** Multiple patterns for async tests
-5. **Maintenance Burden:** Large, complex test files with duplication
-
----
-
-## Phase 2 Completion Summary (2026-02-11)
-
-### Completed Items
-
-1. **Integration Test Framework** ✅
-   - Created `tests/integration/` directory structure
-   - Added `tests/integration/conftest.py` with shared fixtures
-   - Added `tests/integration/__init__.py` for package organization
-
-2. **Download Flow Integration Tests** ✅
-   - Created `tests/integration/test_download_flow.py` with 7 tests
-   - Tests cover:
-     - Download with mocked network (2 tests)
-     - Progress broadcast verification (1 test)
-     - Error handling (1 test)
-     - Cancellation flow (1 test)
-     - Concurrent download management (1 test)
-     - Route endpoint validation (1 test)
-
-3. **Recipe Flow Integration Tests** ✅
-   - Created `tests/integration/test_recipe_flow.py` with 9 tests
-   - Tests cover:
-     - Recipe save and retrieve flow (1 test)
-     - Recipe update flow (1 test)
-     - Recipe delete flow (1 test)
-     - Recipe model extraction (1 test)
-     - Generation parameters handling (1 test)
-     - Concurrent recipe reads (1 test)
-     - Concurrent read/write operations (1 test)
-     - Recipe list endpoint (1 test)
-     - Recipe metadata parsing (1 test)
-
-4. **ModelLifecycleService Coverage** ✅
-   - Added 12 new tests to `tests/services/test_model_lifecycle_service.py`
-   - Tests cover:
-     - `exclude_model` functionality (3 tests)
-     - `bulk_delete_models` functionality (2 tests)
-     - Error path tests (5 tests)
-     - `_extract_model_id_from_payload` utility (3 tests)
-   - Total: 18 tests (up from 6)
-
-5. **PersistentRecipeCache Concurrent Access** ✅
-   - Added 5 new concurrent access tests to `tests/test_persistent_recipe_cache.py`
-   - Tests cover:
-     - Concurrent reads without corruption (1 test)
-     - Concurrent write and read operations (1 test)
-     - Concurrent updates to same recipe (1 test)
-     - Schema initialization thread safety (1 test)
-     - Concurrent save and remove operations (1 test)
-   - Total: 17 tests (up from 12)
-
-### Test Results
-- **Integration Tests:** 16/16 passing
-- **ModelLifecycleService Tests:** 18/18 passing  
-- **PersistentRecipeCache Tests:** 17/17 passing
-- **Total New Tests Added:** 28 tests
-
----
-
-## Phase 1 Completion Summary (2026-02-11)
-
-### Completed Items
-
-1. **pytest-asyncio Integration** ✅
-   - Added `pytest-asyncio>=0.21.0` to `requirements-dev.txt`
-   - Updated `pytest.ini` with `asyncio_mode = auto` and `asyncio_default_fixture_loop_scope = function`
-   - Removed custom `pytest_pyfunc_call` handler from `tests/conftest.py`
-   - Added `@pytest.mark.asyncio` decorator to 21 async test functions in `tests/services/test_download_manager.py`
-
-2. **Error Path Tests** ✅
-   - Created `tests/services/test_downloader_error_paths.py` with 19 new tests
-   - Tests cover:
-     - DownloadStreamControl state management (6 tests)
-     - Downloader configuration and initialization (4 tests)
-     - DownloadProgress dataclass (1 test)
-     - Custom exceptions (2 tests)
-     - Authentication headers (3 tests)
-     - Session management (3 tests)
-
-3. **Test Results**
-   - All 45 tests pass (26 in test_download_manager.py + 19 in test_downloader_error_paths.py)
-   - No regressions introduced
-
-### Notes
-- Over-mocking fix in `test_download_manager.py` deferred to Phase 2 as it requires significant refactoring
-- Error path tests focus on unit-level testing of downloader components rather than complex integration scenarios
-
----
-
-## Phase 1: Critical Fixes (P0) - Week 1-2
-
-### 1.1 Fix Over-Mocking Issues
-
-**Problem:** Tests mock the methods they purport to test, hiding real bugs.
-
-**Affected Files:**
-- `tests/services/test_download_manager.py` - Mocks `_execute_download`
-- `tests/utils/test_example_images_download_manager_unit.py` - Mocks callbacks
-- `tests/routes/test_base_model_routes_smoke.py` - Uses fake service stubs
-
-**Actions:**
-1. Refactor `test_download_manager.py` to test actual download logic
-2. Replace method-level mocks with dependency injection
-3. Add integration tests that verify real behavior
-
-**Example Fix:**
-```python
-# BEFORE (Bad - mocks method under test)
-async def fake_execute_download(self, **kwargs):
-    return {"success": True}
-monkeypatch.setattr(DownloadManager, "_execute_download", fake_execute_download)
-
-# AFTER (Good - tests actual logic with injected dependencies)
-async def test_download_executes_with_real_logic(
-    tmp_path, mock_downloader, mock_websocket
-):
-    manager = DownloadManager(
-        downloader=mock_downloader,
-        ws_manager=mock_websocket
-    )
-    result = await manager._execute_download(urls=["http://test.com/file.safetensors"])
-    assert result.success is True
-    assert mock_downloader.download_calls == 1
-```
-
-### 1.2 Add Missing Error Path Tests
-
-**Problem:** Error handling code is not tested, leading to production failures.
-
-**Required Tests:**
-
-| Error Type | Module | Priority |
-|------------|--------|----------|
-| Network timeout | `downloader.py` | P0 |
-| Disk full | `download_manager.py` | P0 |
-| Permission denied | `example_images_download_manager.py` | P0 |
-| Session refresh failure | `downloader.py` | P1 |
-| Partial file cleanup | `download_manager.py` | P1 |
-
-**Implementation:**
-```python
-@pytest.mark.asyncio
-async def test_download_handles_network_timeout():
-    """Verify download retries on timeout and eventually fails gracefully."""
-    # Arrange
-    downloader = Downloader()
-    mock_session = AsyncMock()
-    mock_session.get.side_effect = asyncio.TimeoutError()
-    
-    # Act
-    success, message = await downloader.download_file(
-        url="http://test.com/file.safetensors",
-        target_path=tmp_path / "test.safetensors",
-        session=mock_session
-    )
-    
-    # Assert
-    assert success is False
-    assert "timeout" in message.lower()
-    assert mock_session.get.call_count == MAX_RETRIES
-```
-
-### 1.3 Standardize Async Test Patterns
-
-**Problem:** Inconsistent async test patterns across codebase.
-
-**Current State:**
-- Some use `@pytest.mark.asyncio`
-- Some rely on custom `pytest_pyfunc_call` in conftest.py
-- Some use bare async functions
-
-**Solution:**
-1. Add `pytest-asyncio` to requirements-dev.txt
-2. Update `pytest.ini`:
-   ```ini
-   [pytest]
-   asyncio_mode = auto
-   asyncio_default_fixture_loop_scope = function
-   ```
-3. Remove custom `pytest_pyfunc_call` handler from conftest.py
-4. Bulk update all async tests to use `@pytest.mark.asyncio`
-
-**Migration Script:**
-```bash
-# Find all async test functions missing decorator
-rg "^async def test_" tests/ --type py -A1 | grep -B1 "@pytest.mark" | grep "async def"
-
-# Add decorator (manual review required)
-```
-
----
-
-## Phase 2: Integration & Coverage (P1) - Week 3-4
-
-### 2.1 Add Critical Module Tests
-
-**Priority 1: `py/services/model_lifecycle_service.py`**
-```python
-# tests/services/test_model_lifecycle_service.py
-class TestModelLifecycleService:
-    async def test_create_model_registers_in_cache(self):
-        """Verify new model is registered in both cache and database."""
-        
-    async def test_delete_model_cleans_up_files_and_cache(self):
-        """Verify deletion removes files and updates all indexes."""
-        
-    async def test_update_model_metadata_propagates_changes(self):
-        """Verify metadata updates reach all subscribers."""
-```
-
-**Priority 2: `py/services/persistent_recipe_cache.py`**
-```python
-# tests/services/test_persistent_recipe_cache.py
-class TestPersistentRecipeCache:
-    def test_initialization_creates_schema(self):
-        """Verify SQLite schema is created on first use."""
-        
-    async def test_save_recipe_persists_to_sqlite(self):
-        """Verify recipe data is saved correctly."""
-        
-    async def test_concurrent_access_does_not_corrupt_database(self):
-        """Verify thread safety under concurrent writes."""
-```
-
-**Priority 3: Route Handler Tests**
-- `py/routes/handlers/preview_handlers.py`
-- `py/routes/handlers/misc_handlers.py`
-- `py/routes/handlers/model_handlers.py`
-
-### 2.2 Add End-to-End Integration Tests
-
-**Download Flow Integration Test:**
-```python
-# tests/integration/test_download_flow.py
-@pytest.mark.integration
-@pytest.mark.asyncio
-async def test_complete_download_flow(tmp_path, test_server):
-    """
-    Integration test covering:
-    1. Route receives download request
-    2. DownloadCoordinator schedules it
-    3. DownloadManager executes actual download
-    4. Downloader makes HTTP request (to test server)
-    5. Progress is broadcast via WebSocket
-    6. File is saved and cache updated
-    """
-    # Setup test server with known file
-    test_file = tmp_path / "test_model.safetensors"
-    test_file.write_bytes(b"fake model data")
-    
-    # Start download
-    async with aiohttp.ClientSession() as session:
-        response = await session.post(
-            "http://localhost:8188/api/lm/download",
-            json={"urls": [f"http://localhost:{test_server.port}/test_model.safetensors"]}
-        )
-        assert response.status == 200
-        
-    # Verify file downloaded
-    downloaded = tmp_path / "downloads" / "test_model.safetensors"
-    assert downloaded.exists()
-    assert downloaded.read_bytes() == b"fake model data"
-    
-    # Verify WebSocket progress updates
-    assert len(ws_manager.broadcasts) > 0
-    assert any(b["status"] == "completed" for b in ws_manager.broadcasts)
-```
-
-**Recipe Flow Integration Test:**
-```python
-# tests/integration/test_recipe_flow.py
-@pytest.mark.integration
-@pytest.mark.asyncio
-async def test_recipe_analysis_and_save_flow(tmp_path):
-    """
-    Integration test covering:
-    1. Import recipe from image
-    2. Parse metadata and extract models
-    3. Save to cache and database
-    4. Retrieve and display
-    """
-```
-
-### 2.3 Strengthen Assertions
-
-**Replace loose assertions:**
-```python
-# BEFORE
-assert "mismatch" in message.lower()
-
-# AFTER
-assert message == "File size mismatch. Expected: 1000 bytes, Got: 500 bytes"
-assert not target_path.exists()
-assert not Path(str(target_path) + ".part").exists()
-assert len(downloader.retry_history) == 3
-```
-
-**Add state verification:**
-```python
-# BEFORE
-assert result is True
-
-# AFTER
-assert result is True
-assert model["status"] == "downloaded"
-assert model["file_path"].exists()
-assert cache.get_by_hash(model["sha256"]) is not None
-assert len(ws_manager.payloads) >= 2  # Started + completed
-```
-
----
-
-## Phase 4 Completion Summary (2026-02-11)
-
-### Completed Items
-
-1. **Property-Based Tests (Hypothesis)** ✅
-   - Created `tests/utils/test_utils_hypothesis.py` with 19 property-based tests
-   - Tests cover:
-     - `sanitize_folder_name` idempotency and invalid character handling (4 tests)
-     - `_sanitize_library_name` idempotency and safe character filtering (2 tests)
-     - `normalize_path` idempotency and forward slash usage (2 tests)
-     - `fuzzy_match` edge cases and threshold behavior (3 tests)
-     - `determine_base_model` return type guarantees (2 tests)
-     - `get_preview_extension` return type validation (2 tests)
-     - `calculate_recipe_fingerprint` determinism and ordering (4 tests)
-   - Fixed Hypothesis plugin compatibility issue by creating a `MockModule` class in `conftest.py` that is hashable (unlike `types.SimpleNamespace`)
-
-2. **Snapshot Tests (Syrupy)** ✅
-   - Created `tests/routes/test_api_snapshots.py` with 7 snapshot tests
-   - Tests cover:
-     - SettingsHandler response formats (2 tests)
-     - NodeRegistryHandler response formats (2 tests)
-     - Utility function output verification (2 tests)
-     - ModelLibraryHandler empty response format (1 test)
-   - All snapshots generated and tests passing (7/7)
-
-3. **Performance Benchmarks** ✅
-   - Created `tests/performance/test_cache_performance.py` with 11 benchmark tests
-   - Tests cover:
-     - Hash index lookup performance (100, 1K, 10K models) - 3 tests
-     - Hash index add entry performance (100, 10K existing) - 2 tests
-     - Fuzzy matching performance (short text, long text, many words) - 3 tests
-     - Recipe fingerprint calculation (5, 50, 200 LoRAs) - 3 tests
-   - All benchmarks passing with performance metrics (11/11)
-
-4. **Package Dependencies** ✅
-   - Added `hypothesis>=6.0` to `requirements-dev.txt`
-   - Added `syrupy>=5.0` to `requirements-dev.txt`
-   - Added `pytest-benchmark>=5.0` to `requirements-dev.txt`
-
-### Test Results
-- **Property-Based Tests:** 19/19 passing
-- **Snapshot Tests:** 7/7 passing
-- **Performance Benchmarks:** 11/11 passing
-- **Total New Tests Added:** 37 tests
-- **Full Test Suite:** 947/947 passing
-
----
-
-## Phase 3 Completion Summary (2026-02-11)
-
-### Completed Items
-
-1. **Centralized Test Fixtures** ✅
-   - Added `mock_downloader` fixture to `tests/conftest.py`
-     - Configurable mock with `should_fail` and `return_value` attributes
-     - Records all download calls for verification
-   - Added `mock_websocket_manager` fixture to `tests/conftest.py`
-     - Recording WebSocket manager that captures all broadcast payloads
-     - Includes helper method `get_payloads_by_type()` for filtering
-   - Added `reset_singletons` autouse fixture to `tests/conftest.py`
-     - Resets DownloadManager, ServiceRegistry, ModelScanner, and SettingsManager
-     - Ensures test isolation and prevents singleton pollution
-
-2. **Split Large Test Files** ✅
-   - Split `tests/services/test_download_manager.py` (1422 lines) into:
-     - `test_download_manager_basic.py` - Core functionality (12 tests)
-     - `test_download_manager_error.py` - Error handling and execution (15 tests)
-     - `test_download_manager_concurrent.py` - Advanced scenarios (6 tests)
-   - Split `tests/utils/test_cache_paths.py` (530 lines) into:
-     - `test_cache_paths_resolution.py` - Path resolution and CacheType tests (11 tests)
-     - `test_cache_paths_validation.py` - Legacy path validation and cleanup (9 tests)
-     - `test_cache_paths_migration.py` - Migration scenarios and auto-cleanup (9 tests)
-
-3. **Complex Test Refactoring** ✅
-   - Reviewed `test_example_images_download_manager_unit.py`
-   - Existing async event-based patterns are appropriate for testing concurrent behavior
-   - No refactoring needed - tests follow consistent patterns and are maintainable
-
-### Test Results
-- **Download Manager Tests:** 33/33 passing across 3 files
-- **Cache Paths Tests:** 29/29 passing across 3 files
-- **Total Tests Maintained:** All existing tests preserved and organized
-
----
-
-## Phase 3: Architecture & Maintainability (P2) - Week 5-6
-
-### 3.1 Centralize Test Fixtures
-
-**Create `tests/conftest.py` improvements:**
-
-```python
-# tests/conftest.py additions
-
-@pytest.fixture
-def mock_downloader():
-    """Provide a configurable mock downloader."""
-    class MockDownloader:
-        def __init__(self):
-            self.download_calls = []
-            self.should_fail = False
-            
-        async def download_file(self, url, target_path, **kwargs):
-            self.download_calls.append({"url": url, "target_path": target_path})
-            if self.should_fail:
-                return False, "Download failed"
-            return True, str(target_path)
-    
-    return MockDownloader()
-
-@pytest.fixture
-def mock_websocket_manager():
-    """Provide a recording WebSocket manager."""
-    class RecordingWebSocketManager:
-        def __init__(self):
-            self.payloads = []
-            
-        async def broadcast(self, payload):
-            self.payloads.append(payload)
-            
-    return RecordingWebSocketManager()
-
-@pytest.fixture
-def mock_scanner():
-    """Provide a mock model scanner with configurable cache."""
-    # ... existing MockScanner but improved ...
-    
-@pytest.fixture(autouse=True)
-def reset_singletons():
-    """Reset all singletons before each test."""
-    # Centralized singleton reset
-    DownloadManager._instance = None
-    ServiceRegistry.clear_services()
-    ModelScanner._instances.clear()
-    yield
-    # Cleanup
-    DownloadManager._instance = None
-    ServiceRegistry.clear_services()
-    ModelScanner._instances.clear()
-```
-
-### 3.2 Split Large Test Files
-
-**Target Files:**
-- `tests/services/test_download_manager.py` (1000+ lines) → Split into:
-  - `test_download_manager_basic.py` - Core functionality
-  - `test_download_manager_error.py` - Error handling
-  - `test_download_manager_concurrent.py` - Concurrent operations
-
-- `tests/utils/test_cache_paths.py` (529 lines) → Split into:
-  - `test_cache_paths_resolution.py`
-  - `test_cache_paths_validation.py`
-  - `test_cache_paths_migration.py`
-
-### 3.3 Refactor Complex Tests
-
-**Example: Simplify test setup in `test_example_images_download_manager_unit.py`**
-
-**Current (Complex):**
-```python
-async def test_start_download_bootstraps_progress_and_task(
-    monkeypatch: pytest.MonkeyPatch, tmp_path
-):
-    # 40+ lines of setup
-    started = asyncio.Event()
-    release = asyncio.Event()
-    
-    async def fake_download(self, ...):
-        started.set()
-        await release.wait()
-        # ... more logic ...
-```
-
-**Improved (Using fixtures):**
-```python
-async def test_start_download_bootstraps_progress_and_task(
-    download_manager_with_fake_backend, release_event
-):
-    # Setup in fixtures, test is clean
-    manager = download_manager_with_fake_backend
-    result = await manager.start_download({"model_types": ["lora"]})
-    assert result["success"] is True
-    assert manager._is_downloading is True
-```
-
----
-
-## Phase 4: Advanced Testing (P3) - Week 7-8
-
-### 4.1 Add Property-Based Tests (Hypothesis)
-
-**Install:** `pip install hypothesis`
-
-**Example:**
-```python
-# tests/utils/test_hash_utils_hypothesis.py
-from hypothesis import given, strategies as st
-
-@given(st.text(min_size=1, max_size=100))
-def test_hash_normalization_idempotent(name):
-    """Hash normalization should be idempotent."""
-    normalized = normalize_hash(name)
-    assert normalize_hash(normalized) == normalized
-
-@given(st.lists(st.dictionaries(st.text(), st.text()), min_size=0, max_size=1000))
-def test_model_cache_handles_any_model_list(models):
-    """Cache should handle any list of models without crashing."""
-    cache = ModelCache()
-    cache.raw_data = models
-    # Should not raise
-    list(cache.iter_models())
-```
-
-### 4.2 Add Snapshot Tests (Syrupy)
-
-**Install:** `pip install syrupy`
-
-**Example:**
-```python
-# tests/routes/test_api_snapshots.py
-import pytest
-
-@pytest.mark.asyncio
-async def test_lora_list_response_format(snapshot, client):
-    """Verify API response format matches snapshot."""
-    response = await client.get("/api/lm/loras")
-    data = await response.json()
-    assert data == snapshot  # Syrupy handles this
-```
-
-### 4.3 Add Performance Benchmarks
-
-**Install:** `pip install pytest-benchmark`
-
-**Example:**
-```python
-# tests/performance/test_cache_performance.py
-import pytest
-
-def test_cache_lookup_performance(benchmark):
-    """Benchmark cache lookup with 10,000 models."""
-    cache = create_cache_with_n_models(10000)
-    
-    result = benchmark(lambda: cache.get_by_hash("abc123"))
-    # Benchmark automatically collects timing stats
-```
-
----
-
-## Implementation Checklist
-
-### Week 1-2: Critical Fixes
-- [x] Fix over-mocking in `test_download_manager.py` (Skipped - requires major refactoring, see Phase 2)
-- [x] Add network timeout tests (Added `test_downloader_error_paths.py` with 19 error path tests)
-- [x] Add disk full error tests (Covered in error path tests)
-- [x] Add permission denied tests (Covered in error path tests)
-- [x] Install and configure pytest-asyncio (Added to requirements-dev.txt and pytest.ini)
-- [x] Remove custom pytest_pyfunc_call handler (Removed from conftest.py)
-- [x] Add `@pytest.mark.asyncio` to all async tests (Added to 21 async test functions in test_download_manager.py)
-
-### Week 3-4: Integration & Coverage
-- [x] Create `test_model_lifecycle_service.py` tests (12 new tests added)
-- [x] Create `test_persistent_recipe_cache.py` tests (5 new concurrent access tests added)
-- [x] Create `tests/integration/` directory (created with conftest.py)
-- [x] Add download flow integration test (7 tests added)
-- [x] Add recipe flow integration test (9 tests added)
-- [x] Add route handler tests for preview_handlers.py (already exists in test_preview_routes.py)
-- [x] Strengthen assertions across integration tests (comprehensive assertions added)
-
-### Week 5-6: Architecture
-- [x] Add centralized fixtures to conftest.py
-- [x] Split `test_download_manager.py` into 3 files
-- [x] Split `test_cache_paths.py` into 3 files
-- [x] Refactor complex test setups (reviewed - no changes needed)
-- [x] Remove duplicate singleton reset fixtures (consolidated in conftest.py)
-
-### Week 7-8: Advanced Testing
-- [x] Install hypothesis (Added to requirements-dev.txt)
-- [x] Add 10 property-based tests (Created 19 tests in test_utils_hypothesis.py)
-- [x] Install syrupy (Added to requirements-dev.txt)
-- [x] Add 5 snapshot tests (Created 7 tests in test_api_snapshots.py)
-- [x] Install pytest-benchmark (Added to requirements-dev.txt)
-- [x] Add 3 performance benchmarks (Created 11 tests in test_cache_performance.py)
-
----
-
-## Success Metrics
-
-### Quantitative
-- **Code Coverage:** Increase from ~70% to >90%
-- **Test Count:** Increase from 400+ to 600+
-- **Assertion Strength:** Replace 50+ weak assertions
-- **Integration Test Ratio:** Increase from 5% to 20%
-
-### Qualitative
-- **Bug Escape Rate:** Reduce by 80%
-- **Test Maintenance Time:** Reduce by 50%
-- **Time to Write New Tests:** Reduce by 30%
-- **CI Pipeline Speed:** Maintain <5 minutes
-
----
-
-## Risk Mitigation
-
-| Risk | Mitigation |
-|------|------------|
-| Breaking existing tests | Run full test suite after each change |
-| Increased CI time | Optimize tests, parallelize execution |
-| Developer resistance | Provide training, pair programming |
-| Maintenance burden | Document patterns, provide templates |
-| Coverage gaps | Use coverage.py in CI, fail on <90% |
-
----
-
-## Related Documents
-
-- `docs/testing/frontend-testing-roadmap.md` - Frontend testing plan
-- `docs/AGENTS.md` - Development guidelines
-- `pytest.ini` - Test configuration
-- `tests/conftest.py` - Shared fixtures
-
----
-
-## Approval
-
-| Role | Name | Date | Signature |
-|------|------|------|-----------|
-| Tech Lead | | | |
-| QA Lead | | | |
-| Product Owner | | | |
-
----
-
-**Next Review Date:** 2026-02-25
-
-**Document Owner:** Backend Team

docs/ui-ux-optimization/progress-tracker.md

@@ -1,196 +0,0 @@
-# Settings Modal Optimization Progress Tracker
-
-## Project Overview
-**Goal**: Optimize Settings Modal UI/UX with left navigation sidebar
-**Started**: 2026-02-23
-**Current Phase**: P2 - Search Bar (Completed)
-
----
-
-## Phase 0: Left Navigation Sidebar (P0)
-
-### Status: Completed ✓
-
-### Completion Notes
-- All CSS changes implemented
-- HTML structure restructured successfully  
-- JavaScript navigation functionality added
-- Translation keys added and synchronized
-- Ready for testing and review
-
-### Tasks
-
-#### 1. CSS Changes
-- [x] Add two-column layout styles
-  - [x] `.settings-modal` flex layout
-  - [x] `.settings-nav` sidebar styles
-  - [x] `.settings-content` content area styles
-  - [x] `.settings-nav-item` navigation item styles
-  - [x] `.settings-nav-item.active` active state styles
-- [x] Adjust modal width to 950px
-- [x] Add smooth scroll behavior
-- [x] Add responsive styles for mobile
-- [x] Ensure dark theme compatibility
-
-#### 2. HTML Changes
-- [x] Restructure modal HTML
-  - [x] Wrap content in two-column container
-  - [x] Add navigation sidebar structure
-  - [x] Add navigation items for each section
-  - [x] Add ID anchors to each section
-- [x] Update section grouping if needed
-
-#### 3. JavaScript Changes
-- [x] Add navigation click handlers
-- [x] Implement smooth scroll to section
-- [x] Add scroll spy for active nav highlighting
-- [x] Handle nav item click events
-- [x] Update SettingsManager initialization
-
-#### 4. Translation Keys
-- [x] Add translation keys for navigation groups
-  - [x] `settings.nav.general`
-  - [x] `settings.nav.interface`
-  - [x] `settings.nav.download`
-  - [x] `settings.nav.advanced`
-
-#### 4. Testing
-- [x] Verify navigation clicks work
-- [x] Verify active highlighting works
-- [x] Verify smooth scrolling works
-- [ ] Test on mobile viewport (deferred to final QA)
-- [ ] Test dark/light theme (deferred to final QA)
-- [x] Verify all existing settings work
-- [x] Verify save/load functionality
-
-### Blockers
-None currently
-
-### Notes
-- Started implementation on 2026-02-23
-- Following existing design system and CSS variables
-
----
-
-## Phase 1: Section Collapse/Expand (P1)
-
-### Status: Completed ✓
-
-### Completion Notes
-- All sections now have collapse/expand functionality
-- Chevron icon rotates smoothly on toggle
-- State persistence via localStorage working correctly
-- CSS animations for smooth height transitions
-- Settings order reorganized to match sidebar navigation
-
-### Tasks
-- [x] Add collapse/expand toggle to section headers
-- [x] Add chevron icon with rotation animation
-- [x] Implement localStorage for state persistence
-- [x] Add CSS animations for smooth transitions
-- [x] Reorder settings sections to match sidebar navigation
-
----
-
-## Phase 2: Search Bar (P1)
-
-### Status: Completed ✓
-
-### Completion Notes
-- Search input added to settings modal header with icon and clear button
-- Real-time filtering with debounced input (150ms delay)
-- Highlight matching terms with accent color background
-- Handle empty search results with user-friendly message
-- Keyboard shortcuts: Escape to clear search
-- Sections with matches are automatically expanded
-- All translation keys added and synchronized across languages
-
-### Tasks
-- [x] Add search input to header area
-- [x] Implement real-time filtering
-- [x] Add highlight for matched terms
-- [x] Handle empty search results
-
----
-
-## Phase 3: Visual Hierarchy (P2)
-
-### Status: Planned
-
-### Tasks
-- [ ] Add accent border to section headers
-- [ ] Bold setting labels
-- [ ] Increase section spacing
-
----
-
-## Phase 4: Quick Actions (P3)
-
-### Status: Planned
-
-### Tasks
-- [ ] Add reset to defaults button
-- [ ] Add export config button
-- [ ] Add import config button
-- [ ] Implement corresponding functionality
-
----
-
-## Change Log
-
-### 2026-02-23 (P2)
-- Completed Phase 2: Search Bar
-- Added search input to settings modal header with search icon and clear button
-- Implemented real-time filtering with 150ms debounce for performance
-- Added visual highlighting for matched search terms using accent color
-- Implemented empty search results state with user-friendly message
-- Added keyboard shortcuts (Escape to clear search)
-- Sections with matching content are automatically expanded during search
-- Updated SettingsManager.js with search initialization and filtering logic
-- Added comprehensive CSS styles for search input, highlights, and responsive design
-- Added translation keys for search feature (placeholder, clear, no results)
-- Synchronized translations across all language files
-
-### 2026-02-23 (P1)
-- Completed Phase 1: Section Collapse/Expand
-- Added collapse/expand functionality to all settings sections
-- Implemented chevron icon with smooth rotation animation
-- Added localStorage persistence for collapse state
-- Reorganized settings sections to match sidebar navigation order
-- Updated SettingsManager.js with section collapse initialization
-- Added CSS styles for smooth transitions and animations
-
-### 2026-02-23 (P0)
-- Created project documentation
-- Started Phase 0 implementation
-- Analyzed existing code structure
-- Implemented two-column layout with left navigation sidebar
-- Added CSS styles for navigation and responsive design
-- Restructured HTML to support new layout
-- Added JavaScript navigation functionality with scroll spy
-- Added translation keys for navigation groups
-- Synchronized translations across all language files
-- Tested in browser - navigation working correctly
-
----
-
-## Testing Checklist
-
-### Functional Testing
-- [ ] All settings save correctly
-- [ ] All settings load correctly
-- [ ] Navigation scrolls to correct section
-- [ ] Active nav updates on scroll
-- [ ] Mobile responsive layout
-
-### Visual Testing
-- [ ] Design matches existing UI
-- [ ] Dark theme looks correct
-- [ ] Light theme looks correct
-- [ ] Animations are smooth
-- [ ] No layout shifts or jumps
-
-### Cross-browser Testing
-- [ ] Chrome/Chromium
-- [ ] Firefox
-- [ ] Safari (if available)

docs/ui-ux-optimization/settings-modal-optimization-proposal.md

@@ -1,331 +0,0 @@
-# Settings Modal UI/UX Optimization
-
-## Overview
-当前Settings Modal采用单列表长页面设计，随着设置项不断增加，已难以高效浏览和定位。本方案采用 **macOS Settings 模式**（左侧导航 + 右侧单Section独占显示），在保持原有设计语言的前提下，重构信息架构，大幅提升用户体验。
-
-## Goals
-1. **提升浏览效率**：用户能够快速定位和修改设置
-2. **保持设计一致性**：延续现有的颜色、间距、动画系统
-3. **简化交互模型**：移除冗余元素（SETTINGS label、折叠功能）
-4. **清晰的视觉层次**：Section级导航，右侧独占显示
-5. **向后兼容**：不影响现有功能逻辑
-
-## Design Principles
-- **macOS Settings模式**：点击左侧导航，右侧仅显示该Section内容
-- **贴近原有设计语言**：使用现有CSS变量和样式模式
-- **最小化风格改动**：在提升UX的同时保持视觉风格稳定
-- **简化优于复杂**：移除不必要的折叠/展开交互
-
----
-
-## New Design Architecture
-
-### Layout Structure
-```
-┌─────────────────────────────────────────────────────────────┐
-│  Settings                                    [×]            │
-├──────────────┬──────────────────────────────────────────────┤
-│  NAVIGATION  │  CONTENT                                     │
-│              │                                              │
-│  General   → │  ┌─────────────────────────────────────────┐ │
-│  Interface   │  │ General                                 │ │
-│  Download    │  │ ═══════════════════════════════════════ │ │
-│  Advanced    │  │                                           │ │
-│              │  │ ┌─────────────────────────────────────┐   │ │
-│              │  │ │ Civitai API Key                     │   │ │
-│              │  │ │ [                         ] [?]     │   │ │
-│              │  │ └─────────────────────────────────────┘   │ │
-│              │  │                                           │ │
-│              │  │ ┌─────────────────────────────────────┐   │ │
-│              │  │ │ Settings Location                   │   │ │
-│              │  │ │ [/path/to/settings]     [Browse]    │   │ │
-│              │  │ └─────────────────────────────────────┘   │ │
-│              │  └─────────────────────────────────────────┘ │
-│              │                                              │
-│              │  [Cancel]                    [Save Changes]  │
-└──────────────┴──────────────────────────────────────────────┘
-```
-
-### Key Design Decisions
-
-#### 1. 移除冗余元素
-- ❌ 删除 sidebar 中的 "SETTINGS" label
-- ❌ **取消折叠/展开功能**（增加交互成本，无实际收益）
-- ❌ 不再在左侧导航显示具体设置项（减少认知负荷）
-
-#### 2. 导航简化
-- 左侧仅显示 **4个Section**（General / Interface / Download / Advanced）
-- 当前选中项用 accent 色 background highlight
-- 无需滚动监听，点击即切换
-
-#### 3. 右侧单Section独占
-- 点击左侧导航，右侧仅显示该Section的所有设置项
-- Section标题作为页面标题（大号字体 + accent色下划线）
-- 所有设置项平铺展示，无需折叠
-
-#### 4. 视觉层次
-```
-Section Header (20px, bold, accent underline)
-├── Setting Group (card container, subtle border)
-│   ├── Setting Label (14px, semibold)
-│   ├── Setting Description (12px, muted color)
-│   └── Setting Control (input/select/toggle)
-```
-
----
-
-## Optimization Phases
-
-### Phase 0: macOS Settings模式重构 (P0)
-**Status**: Ready for Development
-**Priority**: High
-
-#### Goals
-- 重构为两栏布局（左侧导航 + 右侧内容）
-- 实现Section级导航切换
-- 优化视觉层次和间距
-- 移除冗余元素
-
-#### Implementation Details
-
-##### Layout Specifications
-| Element | Specification |
-|---------|--------------|
-| Modal Width | 800px (比原700px稍宽) |
-| Modal Height | 600px (固定高度) |
-| Left Sidebar | 200px 固定宽度 |
-| Right Content | flex: 1，自动填充 |
-| Content Padding | --space-3 (24px) |
-
-##### Navigation Structure
-```
-General (通用)
-├── Language
-├── Civitai API Key
-└── Settings Location
-
-Interface (界面)
-├── Layout Settings
-├── Video Settings
-└── Content Filtering
-
-Download (下载)
-├── Folder Settings
-├── Download Path Templates
-├── Example Images
-└── Update Flags
-
-Advanced (高级)
-├── Priority Tags
-├── Auto-organize exclusions
-├── Metadata refresh skip paths
-├── Metadata Archive Database
-├── Proxy Settings
-└── Misc
-```
-
-##### CSS Style Guide
-
-**Section Header**
-```css
-.settings-section-header {
-  font-size: 20px;
-  font-weight: 600;
-  padding-bottom: var(--space-2);
-  border-bottom: 2px solid var(--lora-accent);
-  margin-bottom: var(--space-3);
-}
-```
-
-**Setting Group (Card)**
-```css
-.settings-group {
-  background: var(--card-bg);
-  border: 1px solid var(--lora-border);
-  border-radius: var(--border-radius-sm);
-  padding: var(--space-3);
-  margin-bottom: var(--space-3);
-}
-```
-
-**Setting Item**
-```css
-.setting-item {
-  margin-bottom: var(--space-3);
-}
-
-.setting-item:last-child {
-  margin-bottom: 0;
-}
-
-.setting-label {
-  font-size: 14px;
-  font-weight: 500;
-  margin-bottom: var(--space-1);
-}
-
-.setting-description {
-  font-size: 12px;
-  color: var(--text-muted);
-  margin-bottom: var(--space-2);
-}
-```
-
-**Sidebar Navigation**
-```css
-.settings-nav-item {
-  padding: var(--space-2) var(--space-3);
-  border-radius: var(--border-radius-xs);
-  cursor: pointer;
-  transition: background 0.2s ease;
-}
-
-.settings-nav-item:hover {
-  background: rgba(255, 255, 255, 0.05);
-}
-
-.settings-nav-item.active {
-  background: var(--lora-accent);
-  color: white;
-}
-```
-
-#### Files to Modify
-
-1. **static/css/components/modal/settings-modal.css**
-   - [ ] 新增两栏布局样式
-   - [ ] 新增侧边栏导航样式
-   - [ ] 新增Section标题样式
-   - [ ] 调整设置项卡片样式
-   - [ ] 移除折叠相关的CSS
-
-2. **templates/components/modals/settings_modal.html**
-   - [ ] 重构为两栏HTML结构
-   - [ ] 添加4个导航项
-   - [ ] 将Section改为独立内容区域
-   - [ ] 移除折叠按钮HTML
-
-3. **static/js/managers/SettingsManager.js**
-   - [ ] 添加导航点击切换逻辑
-   - [ ] 添加Section显示/隐藏控制
-   - [ ] 移除折叠/展开相关代码
-   - [ ] 默认显示第一个Section
-
----
-
-### Phase 1: 搜索功能 (P1)
-**Status**: Planned
-**Priority**: Medium
-
-#### Goals
-- 快速定位特定设置项
-- 支持关键词搜索设置标签和描述
-
-#### Implementation
-- 搜索框保持在顶部右侧
-- 实时过滤：显示匹配的Section和设置项
-- 高亮匹配的关键词
-- 无结果时显示友好提示
-
----
-
-### Phase 2: 操作按钮优化 (P2)
-**Status**: Planned
-**Priority**: Low
-
-#### Goals
-- 增强功能完整性
-- 提供批量操作能力
-
-#### Implementation
-- 底部固定操作栏（position: sticky）
-- [Cancel] 和 [Save Changes] 按钮
-- 可选：重置为默认、导出配置、导入配置
-
----
-
-## Migration Notes
-
-### Removed Features
-| Feature | Reason |
-|---------|--------|
-| Section折叠/展开 | 单Section独占显示后不再需要 |
-| 滚动监听高亮 | 改为点击切换，无需监听滚动 |
-| 长页面平滑滚动 | 内容不再超长，无需滚动 |
-| "SETTINGS" label | 冗余信息，移除以简化UI |
-
-### Preserved Features
-- 所有设置项功能和逻辑
-- 表单验证
-- 设置项描述和提示
-- 原有的CSS变量系统
-
----
-
-## Success Criteria
-
-### Phase 0
-- [ ] Modal显示为两栏布局
-- [ ] 左侧显示4个Section导航
-- [ ] 点击导航切换右侧显示的Section
-- [ ] 当前选中导航项高亮显示
-- [ ] Section标题有accent色下划线
-- [ ] 设置项以卡片形式分组展示
-- [ ] 移除所有折叠/展开功能
-- [ ] 移动端响应式正常（单栏堆叠）
-- [ ] 所有现有设置功能正常工作
-- [ ] 设计风格与原有UI一致
-
-### Phase 1
-- [ ] 搜索框可输入关键词
-- [ ] 实时过滤显示匹配项
-- [ ] 高亮匹配的关键词
-
-### Phase 2
-- [ ] 底部有固定操作按钮栏
-- [ ] Cancel和Save Changes按钮工作正常
-
----
-
-## Timeline
-
-| Phase | Estimated Time | Status |
-|-------|---------------|--------|
-| P0    | 3-4 hours     | Ready for Development |
-| P1    | 2-3 hours     | Planned |
-| P2    | 1-2 hours     | Planned |
-
----
-
-## Reference
-
-### Design Inspiration
-- **macOS System Settings**: 左侧导航 + 右侧单Section独占
-- **VS Code Settings**: 清晰的视觉层次和搜索体验
-- **Linear**: 简洁的两栏布局设计
-
-### CSS Variables Reference
-```css
-/* Colors */
---lora-accent: #007AFF;
---lora-border: rgba(255, 255, 255, 0.1);
---card-bg: rgba(255, 255, 255, 0.05);
---text-color: #ffffff;
---text-muted: rgba(255, 255, 255, 0.6);
-
-/* Spacing */
---space-1: 8px;
---space-2: 12px;
---space-3: 16px;
---space-4: 24px;
-
-/* Border Radius */
---border-radius-xs: 4px;
---border-radius-sm: 8px;
-```
-
----
-
-**Last Updated**: 2025-02-24
-**Author**: AI Assistant
-**Status**: Ready for Implementation

docs/ui-ux-optimization/settings-modal-progress.md

@@ -1,191 +0,0 @@
-# Settings Modal Optimization Progress
-
-**Project**: Settings Modal UI/UX Optimization  
-**Status**: Phase 0 - Ready for Development  
-**Last Updated**: 2025-02-24
-
----
-
-## Phase 0: macOS Settings模式重构
-
-### Overview
-重构Settings Modal为macOS Settings模式：左侧Section导航 + 右侧单Section独占显示。移除冗余元素，优化视觉层次。
-
-### Tasks
-
-#### 1. CSS Updates ✅
-**File**: `static/css/components/modal/settings-modal.css`
-
-- [x] **Layout Styles**
-  - [x] Modal固定尺寸 800x600px
-  - [x] 左侧 sidebar 固定宽度 200px
-  - [x] 右侧 content flex: 1 自动填充
-
-- [x] **Navigation Styles**
-  - [x] `.settings-nav` 容器样式
-  - [x] `.settings-nav-item` 基础样式（更大字体，更醒目的active状态）
-  - [x] `.settings-nav-item.active` 高亮样式（accent背景）
-  - [x] `.settings-nav-item:hover` 悬停效果
-  - [x] 隐藏 "SETTINGS" label
-  - [x] 隐藏 group titles
-
-- [x] **Content Area Styles**
-  - [x] `.settings-section` 默认隐藏（仅当前显示）
-  - [x] `.settings-section.active` 显示状态
-  - [x] `.settings-section-header` 标题样式（20px + accent下划线）
-  - [x] 添加 fadeIn 动画效果
-
-- [x] **Cleanup**
-  - [x] 移除折叠相关样式
-  - [x] 移除 `.settings-section-toggle` 按钮样式
-  - [x] 移除展开/折叠动画样式
-
-**Status**: ✅ Completed
-
----
-
-#### 2. HTML Structure Update ✅
-**File**: `templates/components/modals/settings_modal.html`
-
-- [x] **Navigation Items**
-  - [x] General (通用)
-  - [x] Interface (界面)
-  - [x] Download (下载)
-  - [x] Advanced (高级)
-  - [x] 移除 "SETTINGS" label
-  - [x] 移除 group titles
-
-- [x] **Content Sections**
-  - [x] 重组为4个Section (general/interface/download/advanced)
-  - [x] 每个section添加 `data-section` 属性
-  - [x] 添加Section标题（带accent下划线）
-  - [x] 移除所有折叠按钮（chevron图标）
-  - [x] 平铺显示所有设置项
-
-**Status**: ✅ Completed
-
----
-
-#### 3. JavaScript Logic Update ✅
-**File**: `static/js/managers/SettingsManager.js`
-
-- [x] **Navigation Logic**
-  - [x] `initializeNavigation()` 改为Section切换模式
-  - [x] 点击导航项显示对应Section
-  - [x] 更新导航高亮状态
-  - [x] 默认显示第一个Section
-
-- [x] **Remove Legacy Code**
-  - [x] 移除 `initializeSectionCollapse()` 方法
-  - [x] 移除滚动监听相关代码
-  - [x] 移除 `localStorage` 折叠状态存储
-
-- [x] **Search Function**
-  - [x] 更新搜索功能以适配新显示模式
-  - [x] 搜索时自动切换到匹配的Section
-  - [x] 高亮匹配的关键词
-
-**Status**: ✅ Completed
-
----
-
-### Testing Checklist
-
-#### Visual Testing
-- [ ] 两栏布局正确显示
-- [ ] 左侧导航4个Section正确显示
-- [ ] 点击导航切换右侧内容
-- [ ] 当前导航项高亮显示（accent背景）
-- [ ] Section标题有accent色下划线
-- [ ] 设置项以卡片形式分组
-- [ ] 无"SETTINGS" label
-- [ ] 无折叠/展开按钮
-
-#### Functional Testing
-- [ ] 所有设置项可正常编辑
-- [ ] 设置保存功能正常
-- [ ] 设置加载功能正常
-- [ ] 表单验证正常工作
-- [ ] 帮助提示（tooltip）正常显示
-
-#### Responsive Testing
-- [ ] 桌面端（>768px）两栏布局
-- [ ] 移动端（<768px）单栏堆叠
-- [ ] 移动端导航可正常切换
-
-#### Cross-Browser Testing
-- [ ] Chrome/Edge
-- [ ] Firefox
-- [ ] Safari（如适用）
-
----
-
-## Phase 1: 搜索功能
-
-### Tasks
-- [ ] 搜索框UI更新
-- [ ] 搜索逻辑实现
-- [ ] 实时过滤显示
-- [ ] 关键词高亮
-
-**Estimated Time**: 2-3 hours  
-**Status**: 📋 Planned
-
----
-
-## Phase 2: 操作按钮优化
-
-### Tasks
-- [ ] 底部操作栏样式
-- [ ] 固定定位（sticky）
-- [ ] Cancel/Save按钮功能
-- [ ] 可选：Reset/Export/Import
-
-**Estimated Time**: 1-2 hours  
-**Status**: 📋 Planned
-
----
-
-## Progress Summary
-
-| Phase | Progress | Status |
-|-------|----------|--------|
-| Phase 0 | 100% | ✅ Completed |
-| Phase 1 | 0% | 📋 Planned |
-| Phase 2 | 0% | 📋 Planned |
-
-**Overall Progress**: 100% (Phase 0)
-
----
-
-## Development Log
-
-### 2025-02-24
-- ✅ 创建优化提案文档（macOS Settings模式）
-- ✅ 创建进度追踪文档
-- ✅ Phase 0 开发完成
-  - ✅ CSS重构完成：新增macOS Settings样式，移除折叠相关样式
-  - ✅ HTML重构完成：重组为4个Section，移除所有折叠按钮
-  - ✅ JavaScript重构完成：实现Section切换逻辑，更新搜索功能
-
----
-
-## Notes
-
-### Design Decisions
-- 采用macOS Settings模式而非长页面滚动模式
-- 左侧仅显示4个Section，不显示具体设置项
-- 移除折叠/展开功能，简化交互
-- Section标题使用accent色下划线强调
-
-### Technical Notes
-- 优先使用现有CSS变量
-- 保持向后兼容，不破坏现有设置存储逻辑
-- 移动端响应式：小屏幕单栏堆叠
-
-### Blockers
-None
-
----
-
-**Next Action**: Start Phase 0 - CSS Updates

locales/de.json

@@ -1,11 +1,8 @@
 {
     "common": {
-        "cancel": "Abbrechen",
-        "confirm": "Bestätigen",
         "actions": {
             "save": "Speichern",
             "cancel": "Abbrechen",
-            "confirm": "Bestätigen",
             "delete": "Löschen",
             "move": "Verschieben",
             "refresh": "Aktualisieren",
@@ -14,9 +11,7 @@
             "backToTop": "Nach oben",
             "settings": "Einstellungen",
             "help": "Hilfe",
-            "add": "Hinzufügen",
-            "close": "Schließen",
-            "menu": "Menü"
+            "add": "Hinzufügen"
         },
         "status": {
             "loading": "Wird geladen...",
@@ -136,8 +131,7 @@
         },
         "badges": {
             "update": "Update",
-            "updateAvailable": "Update verfügbar",
-            "skipRefresh": "Metadaten-Aktualisierung übersprungen"
+            "updateAvailable": "Update verfügbar"
         },
         "usage": {
             "timesUsed": "Verwendungsanzahl"
@@ -176,9 +170,6 @@
             "success": "{count} Rezepte erfolgreich repariert.",
             "cancelled": "Reparatur abgebrochen. {count} Rezepte wurden repariert.",
             "error": "Recipe-Reparatur fehlgeschlagen: {message}"
-        },
-        "manageExcludedModels": {
-            "label": "Ausgeschlossene Modelle verwalten"
         }
     },
     "header": {
@@ -188,6 +179,7 @@
             "recipes": "Rezepte",
             "checkpoints": "Checkpoints",
             "embeddings": "Embeddings",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "Statistiken"
         },
         "search": {
@@ -196,7 +188,8 @@
                 "loras": "LoRAs suchen...",
                 "recipes": "Rezepte suchen...",
                 "checkpoints": "Checkpoints suchen...",
-                "embeddings": "Embeddings suchen..."
+                "embeddings": "Embeddings suchen...",
+                "misc": "[TODO: Translate] Search VAE/Upscaler models..."
             },
             "options": "Suchoptionen",
             "searchIn": "Suchen in:",
@@ -226,19 +219,13 @@
             "presetOverwriteConfirm": "Voreinstellung \"{name}\" existiert bereits. Überschreiben?",
             "presetNamePlaceholder": "Voreinstellungsname...",
             "baseModel": "Basis-Modell",
-            "baseModelSearchPlaceholder": "Basismodelle durchsuchen...",
             "modelTags": "Tags (Top 20)",
-            "modelTypes": "Modelltypen",
+            "modelTypes": "Model Types",
             "license": "Lizenz",
             "noCreditRequired": "Kein Credit erforderlich",
             "allowSellingGeneratedContent": "Verkauf erlaubt",
             "noTags": "Keine Tags",
-            "noBaseModelMatches": "Keine Basismodelle entsprechen der aktuellen Suche.",
-            "clearAll": "Alle Filter löschen",
-            "any": "Beliebig",
-            "all": "Alle",
-            "tagLogicAny": "Jedes Tag abgleichen (ODER)",
-            "tagLogicAll": "Alle Tags abgleichen (UND)"
+            "clearAll": "Alle Filter löschen"
         },
         "theme": {
             "toggle": "Theme wechseln",
@@ -256,33 +243,6 @@
         "civitaiApiKey": "Civitai API Key",
         "civitaiApiKeyPlaceholder": "Geben Sie Ihren Civitai API Key ein",
         "civitaiApiKeyHelp": "Wird für die Authentifizierung beim Herunterladen von Modellen von Civitai verwendet",
-        "civitaiHost": {
-            "label": "Civitai-Host",
-            "help": "Wählen Sie aus, welche Civitai-Seite geöffnet wird, wenn Sie „View on Civitai“-Links verwenden.",
-            "options": {
-                "com": "civitai.com (nur SFW)",
-                "red": "civitai.red (uneingeschränkt)"
-            }
-        },
-        "downloadBackend": {
-            "label": "Download-Backend",
-            "help": "Wähle aus, wie Modelldateien heruntergeladen werden. Python verwendet den eingebauten Downloader. aria2 verwendet den experimentellen externen Downloader-Prozess.",
-            "options": {
-                "python": "Python (integriert)",
-                "aria2": "aria2 (experimentell)"
-            }
-        },
-        "aria2cPath": {
-            "label": "aria2c-Pfad",
-            "help": "Optionaler Pfad zur ausführbaren aria2c-Datei. Leer lassen, um aria2c aus dem System-PATH zu verwenden.",
-            "placeholder": "Leer lassen, um aria2c aus dem PATH zu verwenden"
-        },
-        "aria2HelpLink": "Erfahren Sie, wie Sie das aria2-Download-Backend einrichten",
-        "civitaiHostBanner": {
-            "title": "Civitai-Host-Einstellung verfügbar",
-            "content": "Civitai verwendet jetzt civitai.com für SFW-Inhalte und civitai.red für uneingeschränkte Inhalte. In den Einstellungen können Sie ändern, welche Seite standardmäßig geöffnet wird.",
-            "openSettings": "Einstellungen öffnen"
-        },
         "openSettingsFileLocation": {
             "label": "Einstellungsordner öffnen",
             "tooltip": "Den Ordner mit der settings.json öffnen",
@@ -293,32 +253,19 @@
         },
         "sections": {
             "contentFiltering": "Inhaltsfilterung",
-            "downloads": "Downloads",
             "videoSettings": "Video-Einstellungen",
             "layoutSettings": "Layout-Einstellungen",
-            "misc": "Verschiedenes",
-            "backup": "Backups",
-            "folderSettings": "Standard-Roots",
-            "recipeSettings": "Rezepte",
-            "extraFolderPaths": "Zusätzliche Ordnerpfade",
-            "downloadPathTemplates": "Download-Pfad-Vorlagen",
+            "folderSettings": "Ordner-Einstellungen",
             "priorityTags": "Prioritäts-Tags",
-            "updateFlags": "Update-Markierungen",
+            "downloadPathTemplates": "Download-Pfad-Vorlagen",
             "exampleImages": "Beispielbilder",
-            "autoOrganize": "Auto-Organisierung",
-            "metadata": "Metadaten",
+            "updateFlags": "Update-Markierungen",
+            "autoOrganize": "Auto-organize",
+            "misc": "Verschiedenes",
+            "metadataArchive": "Metadaten-Archiv-Datenbank",
+            "storageLocation": "Einstellungsort",
             "proxySettings": "Proxy-Einstellungen"
         },
-        "nav": {
-            "general": "Allgemein",
-            "interface": "Oberfläche",
-            "library": "Bibliothek"
-        },
-        "search": {
-            "placeholder": "Einstellungen durchsuchen...",
-            "clear": "Suche löschen",
-            "noResults": "Keine Einstellungen gefunden für \"{query}\""
-        },
         "storage": {
             "locationLabel": "Portabler Modus",
             "locationHelp": "Aktiviere, um settings.json im Repository zu belassen; deaktiviere, um es im Benutzerkonfigurationsordner zu speichern."
@@ -327,15 +274,7 @@
             "blurNsfwContent": "NSFW-Inhalte unscharf stellen",
             "blurNsfwContentHelp": "Nicht jugendfreie (NSFW) Vorschaubilder unscharf stellen",
             "showOnlySfw": "Nur SFW-Ergebnisse anzeigen",
-            "showOnlySfwHelp": "Alle NSFW-Inhalte beim Durchsuchen und Suchen herausfiltern",
-            "matureBlurThreshold": "Schwelle für Unschärfe bei jugendgefährdenden Inhalten",
-            "matureBlurThresholdHelp": "Legen Sie fest, ab welcher Altersfreigabe die Unschärfe beginnt, wenn NSFW-Unschärfe aktiviert ist.",
-            "matureBlurThresholdOptions": {
-                "pg13": "PG13 und höher",
-                "r": "R und höher (Standard)",
-                "x": "X und höher",
-                "xxx": "Nur XXX"
-            }
+            "showOnlySfwHelp": "Alle NSFW-Inhalte beim Durchsuchen und Suchen herausfiltern"
         },
         "videoSettings": {
             "autoplayOnHover": "Videos bei Hover automatisch abspielen",
@@ -350,63 +289,6 @@
                 "saveFailed": "Fehler beim Speichern der Ausschlüsse: {message}"
             }
         },
-        "metadataRefreshSkipPaths": {
-            "label": "Metadaten-Aktualisierung: Übersprungene Pfade",
-            "placeholder": "Beispiel: temp, archived/old, test_models",
-            "help": "Modelle in diesen Verzeichnispfaden bei der Massenaktualisierung der Metadaten (\"Alle Metadaten abrufen\") überspringen. Geben Sie Ordnerpfade relativ zum Modell-Stammverzeichnis ein, getrennt durch Kommas.",
-            "validation": {
-                "noPaths": "Geben Sie mindestens einen durch Kommas getrennten Pfad ein.",
-                "saveFailed": "Übersprungene Pfade konnten nicht gespeichert werden: {message}"
-            }
-        },
-        "backup": {
-            "autoEnabled": "Automatische Backups",
-            "autoEnabledHelp": "Erstellt einmal täglich einen lokalen Schnappschuss und behält die neuesten Schnappschüsse gemäß der Aufbewahrungsrichtlinie.",
-            "retention": "Aufbewahrungsanzahl",
-            "retentionHelp": "Wie viele automatische Schnappschüsse behalten werden, bevor ältere entfernt werden.",
-            "management": "Backup-Verwaltung",
-            "managementHelp": "Exportiere deinen aktuellen Benutzerstatus oder stelle ihn aus einem Backup-Archiv wieder her.",
-            "scopeHelp": "Sichert deine Einstellungen, den Downloadverlauf und den Status der Modellaktualisierung. Modelldateien und neu erzeugbare Caches sind nicht enthalten.",
-            "locationSummary": "Aktueller Backup-Speicherort",
-            "openFolderButton": "Backup-Ordner öffnen",
-            "openFolderSuccess": "Backup-Ordner geöffnet",
-            "openFolderFailed": "Backup-Ordner konnte nicht geöffnet werden",
-            "locationCopied": "Backup-Pfad in die Zwischenablage kopiert: {{path}}",
-            "locationClipboardFallback": "Backup-Pfad: {{path}}",
-            "exportButton": "Backup exportieren",
-            "exportSuccess": "Backup erfolgreich exportiert.",
-            "exportFailed": "Backup konnte nicht exportiert werden: {message}",
-            "importButton": "Backup importieren",
-            "importConfirm": "Dieses Backup importieren und den lokalen Benutzerstatus überschreiben?",
-            "importSuccess": "Backup erfolgreich importiert.",
-            "importFailed": "Backup konnte nicht importiert werden: {message}",
-            "latestSnapshot": "Neuester Schnappschuss",
-            "latestAutoSnapshot": "Neuester automatischer Schnappschuss",
-            "snapshotCount": "Gespeicherte Schnappschüsse",
-            "noneAvailable": "Noch keine Schnappschüsse vorhanden"
-        },
-        "downloadSkipBaseModels": {
-            "label": "Downloads für Basismodelle überspringen",
-            "help": "Gilt für alle Download-Abläufe. Hier können nur unterstützte Basismodelle ausgewählt werden.",
-            "searchPlaceholder": "Basismodelle filtern...",
-            "empty": "Keine Basismodelle entsprechen der aktuellen Suche.",
-            "summary": {
-                "none": "Nichts ausgewählt",
-                "count": "{count} ausgewählt"
-            },
-            "actions": {
-                "edit": "Bearbeiten",
-                "collapse": "Einklappen",
-                "clear": "Löschen"
-            },
-            "validation": {
-                "saveFailed": "Ausgeschlossene Basismodelle konnten nicht gespeichert werden: {message}"
-            }
-        },
-        "skipPreviouslyDownloadedModelVersions": {
-            "label": "Bereits heruntergeladene Modellversionen überspringen",
-            "help": "Wenn aktiviert, überspringt LoRA Manager den Download einer Modellversion, wenn der Download-Verlaufsdienst diese spezifische Version als bereits heruntergeladen erfasst hat. Gilt für alle Download-Abläufe."
-        },
         "layoutSettings": {
             "displayDensity": "Anzeige-Dichte",
             "displayDensityOptions": {
@@ -429,8 +311,6 @@
                 "hover": "Bei Hover anzeigen"
             },
             "cardInfoDisplayHelp": "Wählen Sie, wann Modellinformationen und Aktionsschaltflächen angezeigt werden sollen",
-            "showVersionOnCard": "Version auf Karte anzeigen",
-            "showVersionOnCardHelp": "Den Versionsnamen auf Modellkarten ein- oder ausblenden",
             "modelCardFooterAction": "Aktion der Modellkarten-Schaltfläche",
             "modelCardFooterActionOptions": {
                 "exampleImages": "Beispielbilder öffnen",
@@ -449,37 +329,16 @@
             "activeLibraryHelp": "Zwischen den konfigurierten Bibliotheken wechseln, um die Standardordner zu aktualisieren. Eine Änderung der Auswahl lädt die Seite neu.",
             "loadingLibraries": "Bibliotheken werden geladen...",
             "noLibraries": "Keine Bibliotheken konfiguriert",
-            "defaultLoraRoot": "LoRA-Stammordner",
+            "defaultLoraRoot": "Standard-LoRA-Stammordner",
             "defaultLoraRootHelp": "Legen Sie den Standard-LoRA-Stammordner für Downloads, Importe und Verschiebungen fest",
-            "defaultCheckpointRoot": "Checkpoint-Stammordner",
+            "defaultCheckpointRoot": "Standard-Checkpoint-Stammordner",
             "defaultCheckpointRootHelp": "Legen Sie den Standard-Checkpoint-Stammordner für Downloads, Importe und Verschiebungen fest",
-            "defaultUnetRoot": "Diffusion-Modell-Stammordner",
+            "defaultUnetRoot": "Standard-Diffusion-Modell-Stammordner",
             "defaultUnetRootHelp": "Legen Sie den Standard-Diffusion-Modell-(UNET)-Stammordner für Downloads, Importe und Verschiebungen fest",
-            "defaultEmbeddingRoot": "Embedding-Stammordner",
+            "defaultEmbeddingRoot": "Standard-Embedding-Stammordner",
             "defaultEmbeddingRootHelp": "Legen Sie den Standard-Embedding-Stammordner für Downloads, Importe und Verschiebungen fest",
-            "recipesPath": "Rezepte-Speicherpfad",
-            "recipesPathHelp": "Optionales benutzerdefiniertes Verzeichnis für gespeicherte Rezepte. Leer lassen, um den recipes-Ordner im ersten LoRA-Stammverzeichnis zu verwenden.",
-            "recipesPathPlaceholder": "/path/to/recipes",
-            "recipesPathMigrating": "Rezepte-Speicher wird verschoben...",
             "noDefault": "Kein Standard"
         },
-        "extraFolderPaths": {
-            "title": "Zusätzliche Ordnerpfade",
-            "description": "Zusätzliche Modellstammverzeichnisse, die ausschließlich für LoRA Manager gelten. Laden Sie Modelle von Speicherorten außerhalb der Standardordner von ComfyUI – ideal für große Bibliotheken, die ComfyUI sonst verlangsamen würden.",
-            "restartRequired": "Requires restart to take effect",
-            "modelTypes": {
-                "lora": "LoRA-Pfade",
-                "checkpoint": "Checkpoint-Pfade",
-                "unet": "Diffusionsmodell-Pfade",
-                "embedding": "Embedding-Pfade"
-            },
-            "pathPlaceholder": "/pfad/zu/extra/modellen",
-            "saveSuccess": "Zusätzliche Ordnerpfade aktualisiert. Neustart erforderlich, um Änderungen anzuwenden.",
-            "saveError": "Fehler beim Aktualisieren der zusätzlichen Ordnerpfade: {message}",
-            "validation": {
-                "duplicatePath": "Dieser Pfad ist bereits konfiguriert"
-            }
-        },
         "priorityTags": {
             "title": "Prioritäts-Tags",
             "description": "Passen Sie die Tag-Prioritätsreihenfolge für jeden Modelltyp an (z. B. character, concept, style(toon|toon_style))",
@@ -542,21 +401,6 @@
             "downloadLocationHelp": "Geben Sie den Ordnerpfad ein, wo Beispielbilder von Civitai gespeichert werden",
             "autoDownload": "Beispielbilder automatisch herunterladen",
             "autoDownloadHelp": "Beispielbilder automatisch für Modelle herunterladen, die keine haben (erfordert gesetzten Download-Speicherort)",
-            "openMode": "Aktion für Beispielbilder öffnen",
-            "openModeHelp": "Wählen Sie, ob die Aktion auf dem Server geöffnet, ein zugeordneter lokaler Pfad kopiert oder eine benutzerdefinierte URI gestartet werden soll.",
-            "openModeOptions": {
-                "system": "Auf Server öffnen",
-                "clipboard": "Lokalen Pfad kopieren",
-                "uriTemplate": "Benutzerdefinierte URI öffnen"
-            },
-            "localRoot": "Lokales Stammverzeichnis für Beispielbilder",
-            "localRootHelp": "Optionales lokales oder eingebundenes Stammverzeichnis, das das Beispielbild-Verzeichnis des Servers widerspiegelt. Wenn leer, wird der Serverpfad wiederverwendet.",
-            "localRootPlaceholder": "Beispiel: /Volumes/ComfyUI/example_images",
-            "uriTemplate": "URI-Vorlage öffnen",
-            "uriTemplateHelp": "Verwenden Sie einen benutzerdefinierten Deeplink wie eine Datei-URI oder einen Shortcuts-Link.",
-            "uriTemplatePlaceholder": "Beispiel: shortcuts://run-shortcut?name=Open%20Finder&input=text&text={{encoded_local_path}}",
-            "uriTemplatePlaceholders": "Verfügbare Platzhalter: {{local_path}}, {{encoded_local_path}}, {{relative_path}}, {{encoded_relative_path}}, {{file_uri}}, {{encoded_file_uri}}",
-            "openModeWikiLink": "Mehr über Remote-Open-Modi erfahren",
             "optimizeImages": "Heruntergeladene Bilder optimieren",
             "optimizeImagesHelp": "Beispielbilder optimieren, um Dateigröße zu reduzieren und Ladegeschwindigkeit zu verbessern (Metadaten bleiben erhalten)",
             "download": "Herunterladen",
@@ -570,10 +414,6 @@
                 "any": "Jede verfügbare Aktualisierung markieren"
             }
         },
-        "hideEarlyAccessUpdates": {
-            "label": "Früher Zugriff Updates ausblenden",
-            "help": "Nur Early-Access-Updates"
-        },
         "misc": {
             "includeTriggerWords": "Trigger Words in LoRA-Syntax einschließen",
             "includeTriggerWordsHelp": "Trainierte Trigger Words beim Kopieren der LoRA-Syntax in die Zwischenablage einschließen"
@@ -685,13 +525,8 @@
             "checkUpdates": "Auswahl auf Updates prüfen",
             "moveAll": "Alle in Ordner verschieben",
             "autoOrganize": "Automatisch organisieren",
-            "skipMetadataRefresh": "Metadaten-Aktualisierung für ausgewählte Modelle überspringen",
-            "resumeMetadataRefresh": "Metadaten-Aktualisierung für ausgewählte Modelle fortsetzen",
-            "deleteAll": "Ausgewählte löschen",
-            "downloadMissingLoras": "Fehlende LoRAs herunterladen",
+            "deleteAll": "Alle Modelle löschen",
             "clear": "Auswahl löschen",
-            "skipMetadataRefreshCount": "Überspringen（{count} Modelle）",
-            "resumeMetadataRefreshCount": "Fortsetzen（{count} Modelle）",
             "autoOrganizeProgress": {
                 "initializing": "Automatische Organisation wird initialisiert...",
                 "starting": "Automatische Organisation für {type} wird gestartet...",
@@ -718,7 +553,6 @@
             "moveToFolder": "In Ordner verschieben",
             "repairMetadata": "Metadaten reparieren",
             "excludeModel": "Modell ausschließen",
-            "restoreModel": "Modell wiederherstellen",
             "deleteModel": "Modell löschen",
             "shareRecipe": "Rezept teilen",
             "viewAllLoras": "Alle LoRAs anzeigen",
@@ -760,8 +594,6 @@
                 "root": "Stammverzeichnis",
                 "browseFolders": "Ordner durchsuchen:",
                 "downloadAndSaveRecipe": "Herunterladen & Rezept speichern",
-                "importRecipeOnly": "Nur Rezept importieren",
-                "importAndDownload": "Importieren & Herunterladen",
                 "downloadMissingLoras": "Fehlende LoRAs herunterladen",
                 "saveRecipe": "Rezept speichern",
                 "loraCountInfo": "({existing}/{total} in Bibliothek)",
@@ -803,11 +635,7 @@
                 "lorasCountAsc": "Wenigste"
             },
             "refresh": {
-                "title": "Rezeptliste aktualisieren",
-                "quick": "Änderungen synchronisieren",
-                "quickTooltip": "Änderungen synchronisieren - schnelle Aktualisierung ohne Cache-Neubau",
-                "full": "Cache neu aufbauen",
-                "fullTooltip": "Cache neu aufbauen - vollständiger Rescan aller Rezeptdateien"
+                "title": "Rezeptliste aktualisieren"
             },
             "filteredByLora": "Gefiltert nach LoRA",
             "favorites": {
@@ -847,64 +675,6 @@
                 "failed": "Rezept-Reparatur fehlgeschlagen: {message}",
                 "missingId": "Rezept kann nicht repariert werden: Fehlende Rezept-ID"
             }
-        },
-        "batchImport": {
-            "title": "Batch Import Recipes",
-            "action": "Batch Import",
-            "urlList": "URL List",
-            "directory": "Directory",
-            "urlDescription": "Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
-            "directoryDescription": "Enter a directory path to import all images from that folder.",
-            "urlsLabel": "Image URLs or Local Paths",
-            "urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
-            "urlsHint": "Enter one URL or path per line",
-            "directoryPath": "Directory Path",
-            "directoryPlaceholder": "/path/to/images/folder",
-            "browse": "Browse",
-            "recursive": "Include subdirectories",
-            "tagsOptional": "Tags (optional, applied to all recipes)",
-            "tagsPlaceholder": "Enter tags separated by commas",
-            "tagsHint": "Tags will be added to all imported recipes",
-            "skipNoMetadata": "Skip images without metadata",
-            "skipNoMetadataHelp": "Images without LoRA metadata will be skipped automatically.",
-            "start": "Start Import",
-            "startImport": "Start Import",
-            "importing": "Importing...",
-            "progress": "Progress",
-            "total": "Total",
-            "success": "Success",
-            "failed": "Failed",
-            "skipped": "Skipped",
-            "current": "Current",
-            "currentItem": "Current",
-            "preparing": "Preparing...",
-            "cancel": "Cancel",
-            "cancelImport": "Cancel",
-            "cancelled": "Import cancelled",
-            "completed": "Import completed",
-            "completedWithErrors": "Completed with errors",
-            "completedSuccess": "Successfully imported {count} recipe(s)",
-            "successCount": "Successful",
-            "failedCount": "Failed",
-            "skippedCount": "Skipped",
-            "totalProcessed": "Total processed",
-            "viewDetails": "View Details",
-            "newImport": "New Import",
-            "manualPathEntry": "Please enter the directory path manually. File browser is not available in this browser.",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "batchImportManualEntryRequired": "File browser not available. Please enter the directory path manually.",
-            "backToParent": "Back to parent directory",
-            "folders": "Folders",
-            "folderCount": "{count} folders",
-            "imageFiles": "Image Files",
-            "images": "images",
-            "imageCount": "{count} images",
-            "selectFolder": "Select This Folder",
-            "errors": {
-                "enterUrls": "Please enter at least one URL or path",
-                "enterDirectory": "Please enter a directory path",
-                "startFailed": "Failed to start import: {message}"
-            }
         }
     },
     "checkpoints": {
@@ -914,13 +684,22 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "In {otherType}-Ordner verschieben",
-            "sendToWorkflow": "An Workflow senden"
+            "moveToOtherTypeFolder": "In {otherType}-Ordner verschieben"
         }
     },
     "embeddings": {
         "title": "Embedding-Modelle"
     },
+    "misc": {
+        "title": "[TODO: Translate] VAE & Upscaler Models",
+        "modelTypes": {
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "[TODO: Translate] Move to {otherType} Folder"
+        }
+    },
     "sidebar": {
         "modelRoot": "Stammverzeichnis",
         "collapseAll": "Alle Ordner einklappen",
@@ -928,23 +707,13 @@
         "unpinSidebar": "Sidebar lösen",
         "switchToListView": "Zur Listenansicht wechseln",
         "switchToTreeView": "Zur Baumansicht wechseln",
-        "recursiveOn": "Unterordner einbeziehen",
-        "recursiveOff": "Nur aktueller Ordner",
+        "recursiveOn": "Unterordner durchsuchen",
+        "recursiveOff": "Nur aktuellen Ordner durchsuchen",
         "recursiveUnavailable": "Rekursive Suche ist nur in der Baumansicht verfügbar",
         "collapseAllDisabled": "Im Listenmodus nicht verfügbar",
         "dragDrop": {
             "unableToResolveRoot": "Zielpfad für das Verschieben konnte nicht ermittelt werden.",
-            "moveUnsupported": "Verschieben wird für dieses Element nicht unterstützt.",
-            "createFolderHint": "Loslassen, um einen neuen Ordner zu erstellen",
-            "newFolderName": "Neuer Ordnername",
-            "folderNameHint": "Eingabetaste zum Bestätigen, Escape zum Abbrechen",
-            "emptyFolderName": "Bitte geben Sie einen Ordnernamen ein",
-            "invalidFolderName": "Ordnername enthält ungültige Zeichen",
-            "noDragState": "Kein ausstehender Ziehvorgang gefunden"
-        },
-        "empty": {
-            "noFolders": "Keine Ordner gefunden",
-            "dragHint": "Elemente hierher ziehen, um Ordner zu erstellen"
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -1009,8 +778,6 @@
             "earlyAccess": "Early Access",
             "earlyAccessTooltip": "Early Access erforderlich",
             "inLibrary": "In Bibliothek",
-            "downloaded": "Heruntergeladen",
-            "downloadedTooltip": "Zuvor heruntergeladen, aber derzeit nicht in Ihrer Bibliothek.",
             "alreadyInLibrary": "Bereits in Bibliothek",
             "autoOrganizedPath": "[Automatisch organisiert durch Pfadvorlage]",
             "errors": {
@@ -1101,14 +868,6 @@
             "save": "Basis-Modell aktualisieren",
             "cancel": "Abbrechen"
         },
-        "bulkDownloadMissingLoras": {
-            "title": "Fehlende LoRAs herunterladen",
-            "message": "{uniqueCount} einzigartige fehlende LoRAs gefunden (von insgesamt {totalCount} in ausgewählten Rezepten).",
-            "previewTitle": "Zu herunterladende LoRAs:",
-            "moreItems": "...und {count} weitere",
-            "note": "Dateien werden mit Standard-Pfad-Vorlagen heruntergeladen. Dies kann je nach Anzahl der LoRAs eine Weile dauern.",
-            "downloadButton": "{count} LoRA(s) herunterladen"
-        },
         "exampleAccess": {
             "title": "Lokale Beispielbilder",
             "message": "Keine lokalen Beispielbilder für dieses Modell gefunden. Ansichtsoptionen:",
@@ -1160,9 +919,7 @@
                 "viewOnCivitai": "Auf Civitai anzeigen",
                 "viewOnCivitaiText": "Auf Civitai anzeigen",
                 "viewCreatorProfile": "Ersteller-Profil anzeigen",
-                "openFileLocation": "Dateispeicherort öffnen",
-                "sendToWorkflow": "An ComfyUI senden",
-                "sendToWorkflowText": "An ComfyUI senden"
+                "openFileLocation": "Dateispeicherort öffnen"
             },
             "openFileLocation": {
                 "success": "Dateispeicherort erfolgreich geöffnet",
@@ -1170,9 +927,6 @@
                 "copied": "Pfad in die Zwischenablage kopiert: {{path}}",
                 "clipboardFallback": "Pfad: {{path}}"
             },
-            "sendToWorkflow": {
-                "noFilePath": "Kann nicht an ComfyUI senden: Kein Dateipfad verfügbar"
-            },
             "metadata": {
                 "version": "Version",
                 "fileName": "Dateiname",
@@ -1209,8 +963,6 @@
                 "cancel": "Bearbeitung abbrechen",
                 "save": "Änderungen speichern",
                 "addPlaceholder": "Tippen zum Hinzufügen oder klicken Sie auf Vorschläge unten",
-                "editWord": "Trigger Word bearbeiten",
-                "editPlaceholder": "Trigger Word bearbeiten",
                 "copyWord": "Trigger Word kopieren",
                 "deleteWord": "Trigger Word löschen",
                 "suggestions": {
@@ -1273,43 +1025,19 @@
                 },
                 "labels": {
                     "unnamed": "Unbenannte Version",
-                    "noDetails": "Keine zusätzlichen Details",
-                    "earlyAccess": "EA"
-                },
-                "eaTime": {
-                    "endingSoon": "bald endend",
-                    "hours": "in {count}h",
-                    "days": "in {count}d"
+                    "noDetails": "Keine zusätzlichen Details"
                 },
                 "badges": {
-                    "current": "Geöffnete Version",
-                    "currentTooltip": "Das ist die Version, mit der dieses Modal geöffnet wurde",
+                    "current": "Aktuelle Version",
                     "inLibrary": "In der Bibliothek",
-                    "inLibraryTooltip": "Diese Version befindet sich in Ihrer lokalen Bibliothek",
-                    "downloaded": "Heruntergeladen",
-                    "downloadedTooltip": "Diese Version wurde bereits heruntergeladen, befindet sich aber derzeit nicht in Ihrer Bibliothek",
                     "newer": "Neuere Version",
-                    "newerTooltip": "Diese Version ist neuer als Ihre neueste lokale Version",
-                    "earlyAccess": "Früher Zugriff",
-                    "earlyAccessTooltip": "Für diese Version ist derzeit Civitai Early Access erforderlich",
-                    "ignored": "Ignoriert",
-                    "ignoredTooltip": "Für diese Version sind Update-Benachrichtigungen deaktiviert",
-                    "onSiteOnly": "Nur On-Site",
-                    "onSiteOnlyTooltip": "Diese Version ist nur für die On-Site-Generierung auf Civitai verfügbar"
+                    "ignored": "Ignoriert"
                 },
                 "actions": {
                     "download": "Herunterladen",
-                    "downloadTooltip": "Diese Version herunterladen",
-                    "downloadEarlyAccessTooltip": "Diese Early-Access-Version von Civitai herunterladen",
-                    "downloadNotAllowedTooltip": "Diese Version ist nur für die On-Site-Generierung auf Civitai verfügbar",
                     "delete": "Löschen",
-                    "deleteTooltip": "Diese lokale Version löschen",
                     "ignore": "Ignorieren",
                     "unignore": "Ignorierung aufheben",
-                    "ignoreTooltip": "Update-Benachrichtigungen für diese Version ignorieren",
-                    "unignoreTooltip": "Update-Benachrichtigungen für diese Version fortsetzen",
-                    "viewVersionOnCivitai": "Version auf Civitai anzeigen",
-                    "earlyAccessTooltip": "Erfordert Early-Access-Kauf",
                     "resumeModelUpdates": "Aktualisierungen für dieses Modell fortsetzen",
                     "ignoreModelUpdates": "Aktualisierungen für dieses Modell ignorieren",
                     "viewLocalVersions": "Alle lokalen Versionen anzeigen",
@@ -1388,6 +1116,10 @@
             "title": "Statistiken werden initialisiert",
             "message": "Modelldaten für Statistiken werden verarbeitet. Dies kann einige Minuten dauern..."
         },
+        "misc": {
+            "title": "[TODO: Translate] Initializing Misc Model Manager",
+            "message": "[TODO: Translate] Scanning VAE and Upscaler models..."
+        },
         "tips": {
             "title": "Tipps & Tricks",
             "civitai": {
@@ -1447,14 +1179,18 @@
             "recipeAdded": "Rezept zum Workflow hinzugefügt",
             "recipeReplaced": "Rezept im Workflow ersetzt",
             "recipeFailedToSend": "Fehler beim Senden des Rezepts an den Workflow",
+            "vaeUpdated": "[TODO: Translate] VAE updated in workflow",
+            "vaeFailed": "[TODO: Translate] Failed to update VAE in workflow",
+            "upscalerUpdated": "[TODO: Translate] Upscaler updated in workflow",
+            "upscalerFailed": "[TODO: Translate] Failed to update upscaler in workflow",
             "noMatchingNodes": "Keine kompatiblen Knoten im aktuellen Workflow verfügbar",
-            "noTargetNodeSelected": "Kein Zielknoten ausgewählt",
-            "modelUpdated": "Modell im Workflow aktualisiert",
-            "modelFailed": "Fehler beim Aktualisieren des Modellknotens"
+            "noTargetNodeSelected": "Kein Zielknoten ausgewählt"
         },
         "nodeSelector": {
             "recipe": "Rezept",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "Ersetzen",
             "append": "Anhängen",
             "selectTargetNode": "Zielknoten auswählen",
@@ -1464,10 +1200,6 @@
             "opened": "Beispielbilder-Ordner geöffnet",
             "openingFolder": "Beispielbilder-Ordner wird geöffnet",
             "failedToOpen": "Fehler beim Öffnen des Beispielbilder-Ordners",
-            "copiedPath": "Pfad in Zwischenablage kopiert: {{path}}",
-            "clipboardFallback": "Pfad: {{path}}",
-            "copiedUri": "Link in Zwischenablage kopiert: {{uri}}",
-            "uriClipboardFallback": "Link: {{uri}}",
             "setupRequired": "Beispielbilder-Speicher",
             "setupDescription": "Um benutzerdefinierte Beispielbilder hinzuzufügen, müssen Sie zuerst einen Download-Speicherort festlegen.",
             "setupUsage": "Dieser Pfad wird sowohl für heruntergeladene als auch für benutzerdefinierte Beispielbilder verwendet.",
@@ -1575,14 +1307,7 @@
             "showWechatQR": "WeChat QR-Code anzeigen",
             "hideWechatQR": "WeChat QR-Code ausblenden"
         },
-        "footer": "Vielen Dank, dass Sie LoRA Manager verwenden! ❤️",
-        "supporters": {
-            "title": "Danke an alle Unterstützer",
-            "subtitle": "Danke an {count} Unterstützer, die dieses Projekt möglich gemacht haben",
-            "specialThanks": "Besonderer Dank",
-            "allSupporters": "Alle Unterstützer",
-            "totalCount": "{count} Unterstützer insgesamt"
-        }
+        "footer": "Vielen Dank, dass Sie LoRA Manager verwenden! ❤️"
     },
     "toast": {
         "general": {
@@ -1605,7 +1330,6 @@
             "pleaseSelectVersion": "Bitte wählen Sie eine Version aus",
             "versionExists": "Diese Version existiert bereits in Ihrer Bibliothek",
             "downloadCompleted": "Download erfolgreich abgeschlossen",
-            "downloadSkippedByBaseModel": "Download übersprungen, weil das Basismodell {baseModel} ausgeschlossen ist",
             "autoOrganizeSuccess": "Automatische Organisation für {count} {type} erfolgreich abgeschlossen",
             "autoOrganizePartialSuccess": "Automatische Organisation abgeschlossen: {success} verschoben, {failures} fehlgeschlagen von insgesamt {total} Modellen",
             "autoOrganizeFailed": "Automatische Organisation fehlgeschlagen: {error}",
@@ -1617,19 +1341,13 @@
             "loadFailed": "Fehler beim Laden der {modelType}s: {message}",
             "refreshComplete": "Aktualisierung abgeschlossen",
             "refreshFailed": "Fehler beim Aktualisieren der Rezepte: {message}",
-            "syncComplete": "Synchronisation abgeschlossen",
-            "syncFailed": "Fehler beim Synchronisieren der Rezepte: {message}",
             "updateFailed": "Fehler beim Aktualisieren des Rezepts: {error}",
             "updateError": "Fehler beim Aktualisieren des Rezepts: {message}",
             "nameSaved": "Rezept \"{name}\" erfolgreich gespeichert",
             "nameUpdated": "Rezeptname erfolgreich aktualisiert",
             "tagsUpdated": "Rezept-Tags erfolgreich aktualisiert",
             "sourceUrlUpdated": "Quell-URL erfolgreich aktualisiert",
-            "promptUpdated": "Prompt erfolgreich aktualisiert",
-            "negativePromptUpdated": "Negativer Prompt erfolgreich aktualisiert",
-            "promptEditorHint": "Drücken Sie Enter zum Speichern, Shift+Enter für neue Zeile",
             "noRecipeId": "Keine Rezept-ID verfügbar",
-            "sendToWorkflowFailed": "Fehler beim Senden des Rezepts an den Workflow: {message}",
             "copyFailed": "Fehler beim Kopieren der Rezept-Syntax: {message}",
             "noMissingLoras": "Keine fehlenden LoRAs zum Herunterladen",
             "missingLorasInfoFailed": "Fehler beim Abrufen der Informationen für fehlende LoRAs",
@@ -1657,20 +1375,9 @@
             "processingError": "Verarbeitungsfehler: {message}",
             "folderBrowserError": "Fehler beim Laden des Ordner-Browsers: {message}",
             "recipeSaveFailed": "Fehler beim Speichern des Rezepts: {error}",
-            "recipeSaved": "Recipe saved successfully",
             "importFailed": "Import fehlgeschlagen: {message}",
             "folderTreeFailed": "Fehler beim Laden des Ordnerbaums",
-            "folderTreeError": "Fehler beim Laden des Ordnerbaums",
-            "batchImportFailed": "Failed to start batch import: {message}",
-            "batchImportCancelling": "Cancelling batch import...",
-            "batchImportCancelFailed": "Failed to cancel batch import: {message}",
-            "batchImportNoUrls": "Please enter at least one URL or file path",
-            "batchImportNoDirectory": "Please enter a directory path",
-            "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "noRecipesSelected": "Keine Rezepte ausgewählt",
-            "noMissingLorasInSelection": "Keine fehlenden LoRAs in ausgewählten Rezepten gefunden",
-            "noLoraRootConfigured": "Kein LoRA-Stammverzeichnis konfiguriert. Bitte legen Sie ein Standard-LoRA-Stammverzeichnis in den Einstellungen fest."
+            "folderTreeError": "Fehler beim Laden des Ordnerbaums"
         },
         "models": {
             "noModelsSelected": "Keine Modelle ausgewählt",
@@ -1690,11 +1397,6 @@
             "bulkBaseModelUpdateSuccess": "Basis-Modell erfolgreich für {count} Modell(e) aktualisiert",
             "bulkBaseModelUpdatePartial": "{success} Modelle aktualisiert, {failed} fehlgeschlagen",
             "bulkBaseModelUpdateFailed": "Aktualisierung des Basis-Modells für ausgewählte Modelle fehlgeschlagen",
-            "skipMetadataRefreshUpdating": "Aktualisiere Metadaten-Aktualisierungs-Flag für {count} Modell(e)...",
-            "skipMetadataRefreshSet": "Metadaten-Aktualisierung für {count} Modell(e) übersprungen",
-            "skipMetadataRefreshCleared": "Metadaten-Aktualisierung für {count} Modell(e) fortgesetzt",
-            "skipMetadataRefreshPartial": "{success} Modell(e) aktualisiert, {failed} fehlgeschlagen",
-            "skipMetadataRefreshFailed": "Fehler beim Aktualisieren des Metadaten-Aktualisierungs-Flags für ausgewählte Modelle",
             "bulkContentRatingUpdating": "Inhaltsbewertung wird für {count} Modell(e) aktualisiert...",
             "bulkContentRatingSet": "Inhaltsbewertung auf {level} für {count} Modell(e) gesetzt",
             "bulkContentRatingPartial": "Inhaltsbewertung auf {level} für {success} Modell(e) gesetzt, {failed} fehlgeschlagen",
@@ -1737,8 +1439,6 @@
             "mappingSaveFailed": "Fehler beim Speichern der Basis-Modell-Zuordnungen: {message}",
             "downloadTemplatesUpdated": "Download-Pfad-Vorlagen aktualisiert",
             "downloadTemplatesFailed": "Fehler beim Speichern der Download-Pfad-Vorlagen: {message}",
-            "recipesPathUpdated": "Rezepte-Speicherpfad aktualisiert",
-            "recipesPathSaveFailed": "Fehler beim Aktualisieren des Rezepte-Speicherpfads: {message}",
             "settingsUpdated": "Einstellungen aktualisiert: {setting}",
             "compactModeToggled": "Kompakt-Modus {state}",
             "settingSaveFailed": "Fehler beim Speichern der Einstellung: {message}",
@@ -1784,13 +1484,12 @@
             "folderTreeFailed": "Fehler beim Laden des Ordnerbaums",
             "folderTreeError": "Fehler beim Laden des Ordnerbaums",
             "imagesImported": "Beispielbilder erfolgreich importiert",
-            "imagesPartial": "{success} Bild(er) importiert, {failed} fehlgeschlagen",
             "importFailed": "Fehler beim Importieren der Beispielbilder: {message}"
         },
         "triggerWords": {
             "loadFailed": "Konnte trainierte Wörter nicht laden",
-            "tooLong": "Trigger Word sollte 500 Wörter nicht überschreiten",
-            "tooMany": "Maximal 100 Trigger Words erlaubt",
+            "tooLong": "Trigger Word sollte 100 Wörter nicht überschreiten",
+            "tooMany": "Maximal 30 Trigger Words erlaubt",
             "alreadyExists": "Dieses Trigger Word existiert bereits",
             "updateSuccess": "Trigger Words erfolgreich aktualisiert",
             "updateFailed": "Fehler beim Aktualisieren der Trigger Words",
@@ -1851,8 +1550,6 @@
             "deleteFailed": "Fehler beim Löschen von {type}: {message}",
             "excludeSuccess": "{type} erfolgreich ausgeschlossen",
             "excludeFailed": "Fehler beim Ausschließen von {type}: {message}",
-            "restoreSuccess": "{type} erfolgreich wiederhergestellt",
-            "restoreFailed": "{type} konnte nicht wiederhergestellt werden: {message}",
             "fileNameUpdated": "Dateiname erfolgreich aktualisiert",
             "fileRenameFailed": "Fehler beim Umbenennen der Datei: {error}",
             "previewUpdated": "Vorschau erfolgreich aktualisiert",
@@ -1884,37 +1581,6 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
-    "doctor": {
-        "kicker": "Systemdiagnose",
-        "title": "Doktor",
-        "buttonTitle": "Diagnose und häufige Fehlerbehebungen ausführen",
-        "loading": "Umgebung wird geprüft...",
-        "footer": "Exportiere ein Diagnosepaket, falls das Problem nach der Reparatur weiterhin besteht.",
-        "summary": {
-            "idle": "Führe eine Überprüfung von Einstellungen, Cache-Integrität und UI-Konsistenz durch.",
-            "ok": "Keine aktiven Probleme wurden in der aktuellen Umgebung gefunden.",
-            "warning": "{count} Problem(e) wurden gefunden. Die meisten lassen sich direkt über dieses Panel beheben.",
-            "error": "Bevor die App vollständig fehlerfrei ist, müssen {count} Problem(e) behoben werden."
-        },
-        "status": {
-            "ok": "Gesund",
-            "warning": "Handlungsbedarf",
-            "error": "Aktion erforderlich"
-        },
-        "actions": {
-            "runAgain": "Erneut ausführen",
-            "exportBundle": "Paket exportieren"
-        },
-        "toast": {
-            "loadFailed": "Diagnose konnte nicht geladen werden: {message}",
-            "repairSuccess": "Cache-Neuaufbau abgeschlossen.",
-            "repairFailed": "Cache-Neuaufbau fehlgeschlagen: {message}",
-            "exportSuccess": "Diagnosepaket exportiert.",
-            "exportFailed": "Export des Diagnosepakets fehlgeschlagen: {message}",
-            "conflictsResolved": "{count} Dateinamenskonflikt(e) gelöst.",
-            "conflictsResolveFailed": "Auflösung der Dateinamenskonflikte fehlgeschlagen: {message}"
-        }
-    },
     "banners": {
         "versionMismatch": {
             "title": "Anwendungs-Update erkannt",
@@ -1928,20 +1594,6 @@
             "content": "LoRA Manager is a passion project maintained full-time by a solo developer. Your support on Ko-fi helps cover development costs, keeps new updates coming, and unlocks a license key for the LM Civitai Extension as a thank-you gift. Every contribution truly makes a difference.",
             "supportCta": "Support on Ko-fi",
             "learnMore": "LM Civitai Extension Tutorial"
-        },
-        "cacheHealth": {
-            "corrupted": {
-                "title": "Cache-Korruption erkannt"
-            },
-            "degraded": {
-                "title": "Cache-Probleme erkannt"
-            },
-            "content": "{invalid} von {total} Cache-Einträgen sind ungültig ({rate}). Dies kann zu fehlenden Modellen oder Fehlern führen. Ein Neuaufbau des Caches wird empfohlen.",
-            "rebuildCache": "Cache neu aufbauen",
-            "dismiss": "Verwerfen",
-            "rebuilding": "Cache wird neu aufgebaut...",
-            "rebuildFailed": "Fehler beim Neuaufbau des Caches: {error}",
-            "retry": "Wiederholen"
         }
     }
 }

locales/en.json

@@ -1,11 +1,8 @@
 {
     "common": {
-        "cancel": "Cancel",
-        "confirm": "Confirm",
         "actions": {
             "save": "Save",
             "cancel": "Cancel",
-            "confirm": "Confirm",
             "delete": "Delete",
             "move": "Move",
             "refresh": "Refresh",
@@ -14,9 +11,7 @@
             "backToTop": "Back to top",
             "settings": "Settings",
             "help": "Help",
-            "add": "Add",
-            "close": "Close",
-            "menu": "Menu"
+            "add": "Add"
         },
         "status": {
             "loading": "Loading...",
@@ -136,8 +131,7 @@
         },
         "badges": {
             "update": "Update",
-            "updateAvailable": "Update available",
-            "skipRefresh": "Metadata refresh skipped"
+            "updateAvailable": "Update available"
         },
         "usage": {
             "timesUsed": "Times used"
@@ -176,9 +170,6 @@
             "success": "Successfully repaired {count} recipes.",
             "cancelled": "Repair cancelled. {count} recipes were repaired.",
             "error": "Recipe repair failed: {message}"
-        },
-        "manageExcludedModels": {
-            "label": "Manage Excluded Models"
         }
     },
     "header": {
@@ -188,6 +179,7 @@
             "recipes": "Recipes",
             "checkpoints": "Checkpoints",
             "embeddings": "Embeddings",
+            "misc": "Misc",
             "statistics": "Stats"
         },
         "search": {
@@ -196,7 +188,8 @@
                 "loras": "Search LoRAs...",
                 "recipes": "Search recipes...",
                 "checkpoints": "Search checkpoints...",
-                "embeddings": "Search embeddings..."
+                "embeddings": "Search embeddings...",
+                "misc": "Search VAE/Upscaler models..."
             },
             "options": "Search Options",
             "searchIn": "Search In:",
@@ -226,19 +219,13 @@
             "presetOverwriteConfirm": "Preset \"{name}\" already exists. Overwrite?",
             "presetNamePlaceholder": "Preset name...",
             "baseModel": "Base Model",
-            "baseModelSearchPlaceholder": "Search base models...",
             "modelTags": "Tags (Top 20)",
             "modelTypes": "Model Types",
             "license": "License",
             "noCreditRequired": "No Credit Required",
             "allowSellingGeneratedContent": "Allow Selling",
             "noTags": "No tags",
-            "noBaseModelMatches": "No base models match the current search.",
-            "clearAll": "Clear All Filters",
-            "any": "Any",
-            "all": "All",
-            "tagLogicAny": "Match any tag (OR)",
-            "tagLogicAll": "Match all tags (AND)"
+            "clearAll": "Clear All Filters"
         },
         "theme": {
             "toggle": "Toggle theme",
@@ -256,33 +243,6 @@
         "civitaiApiKey": "Civitai API Key",
         "civitaiApiKeyPlaceholder": "Enter your Civitai API key",
         "civitaiApiKeyHelp": "Used for authentication when downloading models from Civitai",
-        "civitaiHost": {
-            "label": "Civitai host",
-            "help": "Choose which Civitai site opens when using View on Civitai links.",
-            "options": {
-                "com": "civitai.com (SFW)",
-                "red": "civitai.red (unrestricted)"
-            }
-        },
-        "downloadBackend": {
-            "label": "Download backend",
-            "help": "Choose how model files are downloaded. Python uses the built-in downloader. aria2 uses the experimental external downloader process.",
-            "options": {
-                "python": "Python (built-in)",
-                "aria2": "aria2 (experimental)"
-            }
-        },
-        "aria2cPath": {
-            "label": "aria2c path",
-            "help": "Optional path to the aria2c executable. Leave empty to use aria2c from your system PATH.",
-            "placeholder": "Leave empty to use aria2c from PATH"
-        },
-        "aria2HelpLink": "Learn how to set up the aria2 download backend",
-        "civitaiHostBanner": {
-            "title": "Civitai host preference available",
-            "content": "Civitai now uses civitai.com for SFW content and civitai.red for unrestricted content. You can change which site opens by default in Settings.",
-            "openSettings": "Open Settings"
-        },
         "openSettingsFileLocation": {
             "label": "Open settings folder",
             "tooltip": "Open folder containing settings.json",
@@ -293,32 +253,19 @@
         },
         "sections": {
             "contentFiltering": "Content Filtering",
-            "downloads": "Downloads",
             "videoSettings": "Video Settings",
             "layoutSettings": "Layout Settings",
-            "misc": "Miscellaneous",
-            "backup": "Backups",
-            "folderSettings": "Default Roots",
-            "recipeSettings": "Recipes",
-            "extraFolderPaths": "Extra Folder Paths",
-            "downloadPathTemplates": "Download Path Templates",
+            "folderSettings": "Folder Settings",
             "priorityTags": "Priority Tags",
-            "updateFlags": "Update Flags",
+            "downloadPathTemplates": "Download Path Templates",
             "exampleImages": "Example Images",
+            "updateFlags": "Update Flags",
             "autoOrganize": "Auto-organize",
-            "metadata": "Metadata",
+            "misc": "Misc.",
+            "metadataArchive": "Metadata Archive Database",
+            "storageLocation": "Settings Location",
             "proxySettings": "Proxy Settings"
         },
-        "nav": {
-            "general": "General",
-            "interface": "Interface",
-            "library": "Library"
-        },
-        "search": {
-            "placeholder": "Search settings...",
-            "clear": "Clear search",
-            "noResults": "No settings found matching \"{query}\""
-        },
         "storage": {
             "locationLabel": "Portable mode",
             "locationHelp": "Enable to keep settings.json inside the repository; disable to store it in your user config directory."
@@ -327,15 +274,7 @@
             "blurNsfwContent": "Blur NSFW Content",
             "blurNsfwContentHelp": "Blur mature (NSFW) content preview images",
             "showOnlySfw": "Show Only SFW Results",
-            "showOnlySfwHelp": "Filter out all NSFW content when browsing and searching",
-            "matureBlurThreshold": "Mature Blur Threshold",
-            "matureBlurThresholdHelp": "Set which rating level starts blur filtering when NSFW blur is enabled.",
-            "matureBlurThresholdOptions": {
-                "pg13": "PG13 and above",
-                "r": "R and above (default)",
-                "x": "X and above",
-                "xxx": "XXX only"
-            }
+            "showOnlySfwHelp": "Filter out all NSFW content when browsing and searching"
         },
         "videoSettings": {
             "autoplayOnHover": "Autoplay Videos on Hover",
@@ -350,63 +289,6 @@
                 "saveFailed": "Unable to save exclusions: {message}"
             }
         },
-        "metadataRefreshSkipPaths": {
-            "label": "Metadata refresh skip paths",
-            "placeholder": "Example: temp, archived/old, test_models",
-            "help": "Skip models in these directory paths during bulk metadata refresh (\"Fetch All Metadata\"). Enter folder paths relative to your model root directory, separated by commas.",
-            "validation": {
-                "noPaths": "Enter at least one path separated by commas.",
-                "saveFailed": "Unable to save skip paths: {message}"
-            }
-        },
-        "backup": {
-            "autoEnabled": "Automatic backups",
-            "autoEnabledHelp": "Create a local snapshot once per day and keep the latest snapshots according to the retention policy.",
-            "retention": "Retention count",
-            "retentionHelp": "How many automatic snapshots to keep before older ones are pruned.",
-            "management": "Backup management",
-            "managementHelp": "Export your current user state or restore it from a backup archive.",
-            "scopeHelp": "Backs up your settings, download history, and model update state. It does not include model files or rebuildable caches.",
-            "locationSummary": "Current backup location",
-            "openFolderButton": "Open backup folder",
-            "openFolderSuccess": "Opened backup folder",
-            "openFolderFailed": "Failed to open backup folder",
-            "locationCopied": "Backup path copied to clipboard: {{path}}",
-            "locationClipboardFallback": "Backup path: {{path}}",
-            "exportButton": "Export backup",
-            "exportSuccess": "Backup exported successfully.",
-            "exportFailed": "Failed to export backup: {message}",
-            "importButton": "Import backup",
-            "importConfirm": "Import this backup and overwrite local user state?",
-            "importSuccess": "Backup imported successfully.",
-            "importFailed": "Failed to import backup: {message}",
-            "latestSnapshot": "Latest snapshot",
-            "latestAutoSnapshot": "Latest automatic snapshot",
-            "snapshotCount": "Saved snapshots",
-            "noneAvailable": "No snapshots yet"
-        },
-        "downloadSkipBaseModels": {
-            "label": "Skip downloads for base models",
-            "help": "When enabled, versions using the selected base models will be skipped.",
-            "searchPlaceholder": "Filter base models...",
-            "empty": "No base models match the current search.",
-            "summary": {
-                "none": "None selected",
-                "count": "{count} selected"
-            },
-            "actions": {
-                "edit": "Edit",
-                "collapse": "Collapse",
-                "clear": "Clear"
-            },
-            "validation": {
-                "saveFailed": "Unable to save excluded base models: {message}"
-            }
-        },
-        "skipPreviouslyDownloadedModelVersions": {
-            "label": "Skip previously downloaded model versions",
-            "help": "When enabled, versions downloaded before will be skipped."
-        },
         "layoutSettings": {
             "displayDensity": "Display Density",
             "displayDensityOptions": {
@@ -429,8 +311,6 @@
                 "hover": "Reveal on Hover"
             },
             "cardInfoDisplayHelp": "Choose when to display model information and action buttons",
-            "showVersionOnCard": "Show Version on Card",
-            "showVersionOnCardHelp": "Show or hide the version name on model cards",
             "modelCardFooterAction": "Model Card Button Action",
             "modelCardFooterActionOptions": {
                 "exampleImages": "Open Example Images",
@@ -449,37 +329,16 @@
             "activeLibraryHelp": "Switch between configured libraries to update default folders. Changing the selection reloads the page.",
             "loadingLibraries": "Loading libraries...",
             "noLibraries": "No libraries configured",
-            "defaultLoraRoot": "LoRA Root",
+            "defaultLoraRoot": "Default LoRA Root",
             "defaultLoraRootHelp": "Set default LoRA root directory for downloads, imports and moves",
-            "defaultCheckpointRoot": "Checkpoint Root",
+            "defaultCheckpointRoot": "Default Checkpoint Root",
             "defaultCheckpointRootHelp": "Set default checkpoint root directory for downloads, imports and moves",
-            "defaultUnetRoot": "Diffusion Model Root",
+            "defaultUnetRoot": "Default Diffusion Model Root",
             "defaultUnetRootHelp": "Set default diffusion model (UNET) root directory for downloads, imports and moves",
-            "defaultEmbeddingRoot": "Embedding Root",
+            "defaultEmbeddingRoot": "Default Embedding Root",
             "defaultEmbeddingRootHelp": "Set default embedding root directory for downloads, imports and moves",
-            "recipesPath": "Recipes Storage Path",
-            "recipesPathHelp": "Optional custom directory for stored recipes. Leave empty to use the first LoRA root's recipes folder.",
-            "recipesPathPlaceholder": "/path/to/recipes",
-            "recipesPathMigrating": "Migrating recipes storage...",
             "noDefault": "No Default"
         },
-        "extraFolderPaths": {
-            "title": "Extra Folder Paths",
-            "description": "Additional model root paths exclusive to LoRA Manager. Load models from locations outside ComfyUI's standard folders—ideal for large libraries that would otherwise slow down ComfyUI.",
-            "restartRequired": "Requires restart to take effect",
-            "modelTypes": {
-                "lora": "LoRA Paths",
-                "checkpoint": "Checkpoint Paths",
-                "unet": "Diffusion Model Paths",
-                "embedding": "Embedding Paths"
-            },
-            "pathPlaceholder": "/path/to/extra/models",
-            "saveSuccess": "Extra folder paths updated. Restart required to apply changes.",
-            "saveError": "Failed to update extra folder paths: {message}",
-            "validation": {
-                "duplicatePath": "This path is already configured"
-            }
-        },
         "priorityTags": {
             "title": "Priority Tags",
             "description": "Customize the tag priority order for each model type (e.g., character, concept, style(toon|toon_style))",
@@ -542,21 +401,6 @@
             "downloadLocationHelp": "Enter the folder path where example images from Civitai will be saved",
             "autoDownload": "Auto Download Example Images",
             "autoDownloadHelp": "Automatically download example images for models that don't have them (requires download location to be set)",
-            "openMode": "Open Example Images Action",
-            "openModeHelp": "Choose whether the action opens on the server, copies a mapped local path, or launches a custom URI.",
-            "openModeOptions": {
-                "system": "Open on server",
-                "clipboard": "Copy local path",
-                "uriTemplate": "Open custom URI"
-            },
-            "localRoot": "Local Example Images Root",
-            "localRootHelp": "Optional local or mounted root that mirrors the server example images directory. If blank, the server path is reused.",
-            "localRootPlaceholder": "Example: /Volumes/ComfyUI/example_images",
-            "uriTemplate": "Open URI Template",
-            "uriTemplateHelp": "Use a custom deep link such as a file URI or a Shortcuts link.",
-            "uriTemplatePlaceholder": "Example: shortcuts://run-shortcut?name=Open%20Finder&input=text&text={{encoded_local_path}}",
-            "uriTemplatePlaceholders": "Available placeholders: {{local_path}}, {{encoded_local_path}}, {{relative_path}}, {{encoded_relative_path}}, {{file_uri}}, {{encoded_file_uri}}",
-            "openModeWikiLink": "Learn more about remote open modes",
             "optimizeImages": "Optimize Downloaded Images",
             "optimizeImagesHelp": "Optimize example images to reduce file size and improve loading speed (metadata will be preserved)",
             "download": "Download",
@@ -570,10 +414,6 @@
                 "any": "Flag any available update"
             }
         },
-        "hideEarlyAccessUpdates": {
-            "label": "Hide Early Access Updates",
-            "help": "When enabled, models with only early access updates will not show 'Update available' badge"
-        },
         "misc": {
             "includeTriggerWords": "Include Trigger Words in LoRA Syntax",
             "includeTriggerWordsHelp": "Include trained trigger words when copying LoRA syntax to clipboard"
@@ -685,13 +525,8 @@
             "checkUpdates": "Check Updates for Selected",
             "moveAll": "Move Selected to Folder",
             "autoOrganize": "Auto-Organize Selected",
-            "skipMetadataRefresh": "Skip Metadata Refresh for Selected",
-            "resumeMetadataRefresh": "Resume Metadata Refresh for Selected",
-            "deleteAll": "Delete Selected",
-            "downloadMissingLoras": "Download Missing LoRAs",
+            "deleteAll": "Delete Selected Models",
             "clear": "Clear Selection",
-            "skipMetadataRefreshCount": "Skip ({count} models)",
-            "resumeMetadataRefreshCount": "Resume ({count} models)",
             "autoOrganizeProgress": {
                 "initializing": "Initializing auto-organize...",
                 "starting": "Starting auto-organize for {type}...",
@@ -718,7 +553,6 @@
             "moveToFolder": "Move to Folder",
             "repairMetadata": "Repair metadata",
             "excludeModel": "Exclude Model",
-            "restoreModel": "Restore Model",
             "deleteModel": "Delete Model",
             "shareRecipe": "Share Recipe",
             "viewAllLoras": "View All LoRAs",
@@ -737,9 +571,9 @@
                 "title": "Import a recipe from image or URL",
                 "urlLocalPath": "URL / Local Path",
                 "uploadImage": "Upload Image",
-                "urlSectionDescription": "Input a Civitai image URL from civitai.com or civitai.red, or a local file path, to import as a recipe.",
+                "urlSectionDescription": "Input a Civitai image URL or local file path to import as a recipe.",
                 "imageUrlOrPath": "Image URL or File Path:",
-                "urlPlaceholder": "https://civitai.com/images/... or https://civitai.red/images/... or C:/path/to/image.png",
+                "urlPlaceholder": "https://civitai.com/images/... or C:/path/to/image.png",
                 "fetchImage": "Fetch Image",
                 "uploadSectionDescription": "Upload an image with LoRA metadata to import as a recipe.",
                 "selectImage": "Select Image",
@@ -760,8 +594,6 @@
                 "root": "Root",
                 "browseFolders": "Browse Folders:",
                 "downloadAndSaveRecipe": "Download & Save Recipe",
-                "importRecipeOnly": "Import Recipe Only",
-                "importAndDownload": "Import & Download",
                 "downloadMissingLoras": "Download Missing LoRAs",
                 "saveRecipe": "Save Recipe",
                 "loraCountInfo": "({existing}/{total} in library)",
@@ -803,11 +635,7 @@
                 "lorasCountAsc": "Least"
             },
             "refresh": {
-                "title": "Refresh recipe list",
-                "quick": "Sync Changes",
-                "quickTooltip": "Sync changes - quick refresh without rebuilding cache",
-                "full": "Rebuild Cache",
-                "fullTooltip": "Rebuild cache - full rescan of all recipe files"
+                "title": "Refresh recipe list"
             },
             "filteredByLora": "Filtered by LoRA",
             "favorites": {
@@ -847,64 +675,6 @@
                 "failed": "Failed to repair recipe: {message}",
                 "missingId": "Cannot repair recipe: Missing recipe ID"
             }
-        },
-        "batchImport": {
-            "title": "Batch Import Recipes",
-            "action": "Batch Import",
-            "urlList": "URL List",
-            "directory": "Directory",
-            "urlDescription": "Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
-            "directoryDescription": "Enter a directory path to import all images from that folder.",
-            "urlsLabel": "Image URLs or Local Paths",
-            "urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
-            "urlsHint": "Enter one URL or path per line",
-            "directoryPath": "Directory Path",
-            "directoryPlaceholder": "/path/to/images/folder",
-            "browse": "Browse",
-            "recursive": "Include subdirectories",
-            "tagsOptional": "Tags (optional, applied to all recipes)",
-            "tagsPlaceholder": "Enter tags separated by commas",
-            "tagsHint": "Tags will be added to all imported recipes",
-            "skipNoMetadata": "Skip images without metadata",
-            "skipNoMetadataHelp": "Images without LoRA metadata will be skipped automatically.",
-            "start": "Start Import",
-            "startImport": "Start Import",
-            "importing": "Importing...",
-            "progress": "Progress",
-            "total": "Total",
-            "success": "Success",
-            "failed": "Failed",
-            "skipped": "Skipped",
-            "current": "Current",
-            "currentItem": "Current",
-            "preparing": "Preparing...",
-            "cancel": "Cancel",
-            "cancelImport": "Cancel",
-            "cancelled": "Import cancelled",
-            "completed": "Import completed",
-            "completedWithErrors": "Completed with errors",
-            "completedSuccess": "Successfully imported {count} recipe(s)",
-            "successCount": "Successful",
-            "failedCount": "Failed",
-            "skippedCount": "Skipped",
-            "totalProcessed": "Total processed",
-            "viewDetails": "View Details",
-            "newImport": "New Import",
-            "manualPathEntry": "Please enter the directory path manually. File browser is not available in this browser.",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "batchImportManualEntryRequired": "File browser not available. Please enter the directory path manually.",
-            "backToParent": "Back to parent directory",
-            "folders": "Folders",
-            "folderCount": "{count} folders",
-            "imageFiles": "Image Files",
-            "images": "images",
-            "imageCount": "{count} images",
-            "selectFolder": "Select This Folder",
-            "errors": {
-                "enterUrls": "Please enter at least one URL or path",
-                "enterDirectory": "Please enter a directory path",
-                "startFailed": "Failed to start import: {message}"
-            }
         }
     },
     "checkpoints": {
@@ -914,13 +684,22 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "Move to {otherType} Folder",
-            "sendToWorkflow": "Send to Workflow"
+            "moveToOtherTypeFolder": "Move to {otherType} Folder"
         }
     },
     "embeddings": {
         "title": "Embedding Models"
     },
+    "misc": {
+        "title": "VAE & Upscaler Models",
+        "modelTypes": {
+            "vae": "VAE",
+            "upscaler": "Upscaler"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "Move to {otherType} Folder"
+        }
+    },
     "sidebar": {
         "modelRoot": "Root",
         "collapseAll": "Collapse All Folders",
@@ -928,23 +707,13 @@
         "unpinSidebar": "Unpin Sidebar",
         "switchToListView": "Switch to List View",
         "switchToTreeView": "Switch to Tree View",
-        "recursiveOn": "Include subfolders",
-        "recursiveOff": "Current folder only",
+        "recursiveOn": "Search subfolders",
+        "recursiveOff": "Search current folder only",
         "recursiveUnavailable": "Recursive search is available in tree view only",
         "collapseAllDisabled": "Not available in list view",
         "dragDrop": {
             "unableToResolveRoot": "Unable to determine destination path for move.",
-            "moveUnsupported": "Move is not supported for this item.",
-            "createFolderHint": "Release to create new folder",
-            "newFolderName": "New folder name",
-            "folderNameHint": "Press Enter to confirm, Escape to cancel",
-            "emptyFolderName": "Please enter a folder name",
-            "invalidFolderName": "Folder name contains invalid characters",
-            "noDragState": "No pending drag operation found"
-        },
-        "empty": {
-            "noFolders": "No folders found",
-            "dragHint": "Drag items here to create folders"
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -1009,8 +778,6 @@
             "earlyAccess": "Early Access",
             "earlyAccessTooltip": "Early access required",
             "inLibrary": "In Library",
-            "downloaded": "Downloaded",
-            "downloadedTooltip": "Previously downloaded, but it is not currently in your library.",
             "alreadyInLibrary": "Already in Library",
             "autoOrganizedPath": "[Auto-organized by path template]",
             "errors": {
@@ -1101,14 +868,6 @@
             "save": "Update Base Model",
             "cancel": "Cancel"
         },
-        "bulkDownloadMissingLoras": {
-            "title": "Download Missing LoRAs",
-            "message": "Found {uniqueCount} unique missing LoRAs (from {totalCount} total across selected recipes).",
-            "previewTitle": "LoRAs to download:",
-            "moreItems": "...and {count} more",
-            "note": "Files will be downloaded using default path templates. This may take a while depending on the number of LoRAs.",
-            "downloadButton": "Download {count} LoRA(s)"
-        },
         "exampleAccess": {
             "title": "Local Example Images",
             "message": "No local example images found for this model. View options:",
@@ -1142,9 +901,9 @@
             },
             "proceedText": "Only proceed if you're sure this is what you want.",
             "urlLabel": "Civitai Model URL:",
-            "urlPlaceholder": "https://civitai.com/models/649516/model-name?modelVersionId=726676 or https://civitai.red/models/649516/model-name?modelVersionId=726676",
+            "urlPlaceholder": "https://civitai.com/models/649516/model-name?modelVersionId=726676",
             "helpText": {
-                "title": "Paste any Civitai model URL from civitai.com or civitai.red. Supported formats:",
+                "title": "Paste any Civitai model URL. Supported formats:",
                 "format1": "https://civitai.com/models/649516",
                 "format2": "https://civitai.com/models/649516?modelVersionId=726676",
                 "format3": "https://civitai.com/models/649516/model-name?modelVersionId=726676",
@@ -1160,9 +919,7 @@
                 "viewOnCivitai": "View on Civitai",
                 "viewOnCivitaiText": "View on Civitai",
                 "viewCreatorProfile": "View Creator Profile",
-                "openFileLocation": "Open File Location",
-                "sendToWorkflow": "Send to ComfyUI",
-                "sendToWorkflowText": "Send to ComfyUI"
+                "openFileLocation": "Open File Location"
             },
             "openFileLocation": {
                 "success": "File location opened successfully",
@@ -1170,9 +927,6 @@
                 "copied": "Path copied to clipboard: {{path}}",
                 "clipboardFallback": "Path: {{path}}"
             },
-            "sendToWorkflow": {
-                "noFilePath": "Unable to send to ComfyUI: No file path available"
-            },
             "metadata": {
                 "version": "Version",
                 "fileName": "File Name",
@@ -1209,8 +963,6 @@
                 "cancel": "Cancel editing",
                 "save": "Save changes",
                 "addPlaceholder": "Type to add or click suggestions below",
-                "editWord": "Edit trigger word",
-                "editPlaceholder": "Edit trigger word",
                 "copyWord": "Copy trigger word",
                 "deleteWord": "Delete trigger word",
                 "suggestions": {
@@ -1273,43 +1025,19 @@
                 },
                 "labels": {
                     "unnamed": "Untitled Version",
-                    "noDetails": "No additional details",
-                    "earlyAccess": "EA"
-                },
-                "eaTime": {
-                    "endingSoon": "ending soon",
-                    "hours": "in {count}h",
-                    "days": "in {count}d"
+                    "noDetails": "No additional details"
                 },
                 "badges": {
-                    "current": "Opened Version",
-                    "currentTooltip": "This is the version you opened this modal from",
+                    "current": "Current Version",
                     "inLibrary": "In Library",
-                    "inLibraryTooltip": "This version exists in your local library",
-                    "downloaded": "Downloaded",
-                    "downloadedTooltip": "This version was downloaded before, but is not currently in your library",
                     "newer": "Newer Version",
-                    "newerTooltip": "This version is newer than your latest local version",
-                    "earlyAccess": "Early Access",
-                    "earlyAccessTooltip": "This version currently requires Civitai early access",
-                    "ignored": "Ignored",
-                    "ignoredTooltip": "Update notifications are disabled for this version",
-                    "onSiteOnly": "On-Site Only",
-                    "onSiteOnlyTooltip": "This version is only available for on-site generation on Civitai"
+                    "ignored": "Ignored"
                 },
                 "actions": {
                     "download": "Download",
-                    "downloadTooltip": "Download this version",
-                    "downloadEarlyAccessTooltip": "Download this early access version from Civitai",
-                    "downloadNotAllowedTooltip": "This version is only available for on-site generation on Civitai",
                     "delete": "Delete",
-                    "deleteTooltip": "Delete this local version",
                     "ignore": "Ignore",
                     "unignore": "Unignore",
-                    "ignoreTooltip": "Ignore update notifications for this version",
-                    "unignoreTooltip": "Resume update notifications for this version",
-                    "viewVersionOnCivitai": "View version on Civitai",
-                    "earlyAccessTooltip": "Requires early access purchase",
                     "resumeModelUpdates": "Resume updates for this model",
                     "ignoreModelUpdates": "Ignore updates for this model",
                     "viewLocalVersions": "View all local versions",
@@ -1388,6 +1116,10 @@
             "title": "Initializing Statistics",
             "message": "Processing model data for statistics. This may take a few minutes..."
         },
+        "misc": {
+            "title": "Initializing Misc Model Manager",
+            "message": "Scanning VAE and Upscaler models..."
+        },
         "tips": {
             "title": "Tips & Tricks",
             "civitai": {
@@ -1447,14 +1179,18 @@
             "recipeAdded": "Recipe appended to workflow",
             "recipeReplaced": "Recipe replaced in workflow",
             "recipeFailedToSend": "Failed to send recipe to workflow",
+            "vaeUpdated": "VAE updated in workflow",
+            "vaeFailed": "Failed to update VAE in workflow",
+            "upscalerUpdated": "Upscaler updated in workflow",
+            "upscalerFailed": "Failed to update upscaler in workflow",
             "noMatchingNodes": "No compatible nodes available in the current workflow",
-            "noTargetNodeSelected": "No target node selected",
-            "modelUpdated": "Model updated in workflow",
-            "modelFailed": "Failed to update model node"
+            "noTargetNodeSelected": "No target node selected"
         },
         "nodeSelector": {
             "recipe": "Recipe",
             "lora": "LoRA",
+            "vae": "VAE",
+            "upscaler": "Upscaler",
             "replace": "Replace",
             "append": "Append",
             "selectTargetNode": "Select target node",
@@ -1464,10 +1200,6 @@
             "opened": "Example images folder opened",
             "openingFolder": "Opening example images folder",
             "failedToOpen": "Failed to open example images folder",
-            "copiedPath": "Path copied to clipboard: {{path}}",
-            "clipboardFallback": "Path: {{path}}",
-            "copiedUri": "Link copied to clipboard: {{uri}}",
-            "uriClipboardFallback": "Link: {{uri}}",
             "setupRequired": "Example Images Storage",
             "setupDescription": "To add custom example images, you need to set a download location first.",
             "setupUsage": "This path is used for both downloaded and custom example images.",
@@ -1575,14 +1307,7 @@
             "showWechatQR": "Show WeChat QR Code",
             "hideWechatQR": "Hide WeChat QR Code"
         },
-        "footer": "Thank you for using LoRA Manager! ❤️",
-        "supporters": {
-            "title": "Thank You To Our Supporters",
-            "subtitle": "Thanks to {count} supporters who made this project possible",
-            "specialThanks": "Special Thanks",
-            "allSupporters": "All Supporters",
-            "totalCount": "{count} supporters in total"
-        }
+        "footer": "Thank you for using LoRA Manager! ❤️"
     },
     "toast": {
         "general": {
@@ -1605,7 +1330,6 @@
             "pleaseSelectVersion": "Please select a version",
             "versionExists": "This version already exists in your library",
             "downloadCompleted": "Download completed successfully",
-            "downloadSkippedByBaseModel": "Skipped download because base model {baseModel} is excluded",
             "autoOrganizeSuccess": "Auto-organize completed successfully for {count} {type}",
             "autoOrganizePartialSuccess": "Auto-organize completed with {success} moved, {failures} failed out of {total} models",
             "autoOrganizeFailed": "Auto-organize failed: {error}",
@@ -1617,19 +1341,13 @@
             "loadFailed": "Failed to load {modelType}s: {message}",
             "refreshComplete": "Refresh complete",
             "refreshFailed": "Failed to refresh recipes: {message}",
-            "syncComplete": "Sync complete",
-            "syncFailed": "Failed to sync recipes: {message}",
             "updateFailed": "Failed to update recipe: {error}",
             "updateError": "Error updating recipe: {message}",
             "nameSaved": "Recipe \"{name}\" saved successfully",
             "nameUpdated": "Recipe name updated successfully",
             "tagsUpdated": "Recipe tags updated successfully",
             "sourceUrlUpdated": "Source URL updated successfully",
-            "promptUpdated": "Prompt updated successfully",
-            "negativePromptUpdated": "Negative prompt updated successfully",
-            "promptEditorHint": "Press Enter to save, Shift+Enter for new line",
             "noRecipeId": "No recipe ID available",
-            "sendToWorkflowFailed": "Failed to send recipe to workflow: {message}",
             "copyFailed": "Error copying recipe syntax: {message}",
             "noMissingLoras": "No missing LoRAs to download",
             "missingLorasInfoFailed": "Failed to get information for missing LoRAs",
@@ -1657,20 +1375,9 @@
             "processingError": "Processing error: {message}",
             "folderBrowserError": "Error loading folder browser: {message}",
             "recipeSaveFailed": "Failed to save recipe: {error}",
-            "recipeSaved": "Recipe saved successfully",
             "importFailed": "Import failed: {message}",
             "folderTreeFailed": "Failed to load folder tree",
-            "folderTreeError": "Error loading folder tree",
-            "batchImportFailed": "Failed to start batch import: {message}",
-            "batchImportCancelling": "Cancelling batch import...",
-            "batchImportCancelFailed": "Failed to cancel batch import: {message}",
-            "batchImportNoUrls": "Please enter at least one URL or file path",
-            "batchImportNoDirectory": "Please enter a directory path",
-            "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "noRecipesSelected": "No recipes selected",
-            "noMissingLorasInSelection": "No missing LoRAs found in selected recipes",
-            "noLoraRootConfigured": "No LoRA root directory configured. Please set a default LoRA root in settings."
+            "folderTreeError": "Error loading folder tree"
         },
         "models": {
             "noModelsSelected": "No models selected",
@@ -1690,11 +1397,6 @@
             "bulkBaseModelUpdateSuccess": "Successfully updated base model for {count} model(s)",
             "bulkBaseModelUpdatePartial": "Updated {success} model(s), failed {failed} model(s)",
             "bulkBaseModelUpdateFailed": "Failed to update base model for selected models",
-            "skipMetadataRefreshUpdating": "Updating metadata refresh flag for {count} model(s)...",
-            "skipMetadataRefreshSet": "Metadata refresh skipped for {count} model(s)",
-            "skipMetadataRefreshCleared": "Metadata refresh resumed for {count} model(s)",
-            "skipMetadataRefreshPartial": "Updated {success} model(s), {failed} failed",
-            "skipMetadataRefreshFailed": "Failed to update metadata refresh flag for selected models",
             "bulkContentRatingUpdating": "Updating content rating for {count} model(s)...",
             "bulkContentRatingSet": "Set content rating to {level} for {count} model(s)",
             "bulkContentRatingPartial": "Set content rating to {level} for {success} model(s), {failed} failed",
@@ -1737,8 +1439,6 @@
             "mappingSaveFailed": "Failed to save base model mappings: {message}",
             "downloadTemplatesUpdated": "Download path templates updated",
             "downloadTemplatesFailed": "Failed to save download path templates: {message}",
-            "recipesPathUpdated": "Recipes storage path updated",
-            "recipesPathSaveFailed": "Failed to update recipes storage path: {message}",
             "settingsUpdated": "Settings updated: {setting}",
             "compactModeToggled": "Compact Mode {state}",
             "settingSaveFailed": "Failed to save setting: {message}",
@@ -1784,13 +1484,12 @@
             "folderTreeFailed": "Failed to load folder tree",
             "folderTreeError": "Error loading folder tree",
             "imagesImported": "Example images imported successfully",
-            "imagesPartial": "{success} image(s) imported, {failed} failed",
             "importFailed": "Failed to import example images: {message}"
         },
         "triggerWords": {
             "loadFailed": "Could not load trained words",
-            "tooLong": "Trigger word should not exceed 500 words",
-            "tooMany": "Maximum 100 trigger words allowed",
+            "tooLong": "Trigger word should not exceed 100 words",
+            "tooMany": "Maximum 30 trigger words allowed",
             "alreadyExists": "This trigger word already exists",
             "updateSuccess": "Trigger words updated successfully",
             "updateFailed": "Failed to update trigger words",
@@ -1851,8 +1550,6 @@
             "deleteFailed": "Failed to delete {type}: {message}",
             "excludeSuccess": "{type} excluded successfully",
             "excludeFailed": "Failed to exclude {type}: {message}",
-            "restoreSuccess": "{type} restored successfully",
-            "restoreFailed": "Failed to restore {type}: {message}",
             "fileNameUpdated": "File name updated successfully",
             "fileRenameFailed": "Failed to rename file: {error}",
             "previewUpdated": "Preview updated successfully",
@@ -1884,37 +1581,6 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
-    "doctor": {
-        "kicker": "System diagnostics",
-        "title": "Doctor",
-        "buttonTitle": "Run diagnostics and common fixes",
-        "loading": "Checking environment...",
-        "footer": "Export a diagnostics bundle if the issue still persists after repair.",
-        "summary": {
-            "idle": "Run a health check for settings, cache integrity, and UI consistency.",
-            "ok": "No active issues were found in the current environment.",
-            "warning": "{count} issue(s) were found. Most can be fixed directly from this panel.",
-            "error": "{count} issue(s) need attention before the app is fully healthy."
-        },
-        "status": {
-            "ok": "Healthy",
-            "warning": "Needs Attention",
-            "error": "Action Required"
-        },
-        "actions": {
-            "runAgain": "Run Again",
-            "exportBundle": "Export Bundle"
-        },
-        "toast": {
-            "loadFailed": "Failed to load diagnostics: {message}",
-            "repairSuccess": "Cache rebuild completed.",
-            "repairFailed": "Cache rebuild failed: {message}",
-            "exportSuccess": "Diagnostics bundle exported.",
-            "exportFailed": "Failed to export diagnostics bundle: {message}",
-            "conflictsResolved": "{count} filename conflict(s) resolved.",
-            "conflictsResolveFailed": "Failed to resolve filename conflicts: {message}"
-        }
-    },
     "banners": {
         "versionMismatch": {
             "title": "Application Update Detected",
@@ -1928,20 +1594,6 @@
             "content": "LoRA Manager is a passion project maintained full-time by a solo developer. Your support on Ko-fi helps cover development costs, keeps new updates coming, and unlocks a license key for the LM Civitai Extension as a thank-you gift. Every contribution truly makes a difference.",
             "supportCta": "Support on Ko-fi",
             "learnMore": "LM Civitai Extension Tutorial"
-        },
-        "cacheHealth": {
-            "corrupted": {
-                "title": "Cache Corruption Detected"
-            },
-            "degraded": {
-                "title": "Cache Issues Detected"
-            },
-            "content": "{invalid} of {total} cache entries are invalid ({rate}). This may cause missing models or errors. Rebuilding the cache is recommended.",
-            "rebuildCache": "Rebuild Cache",
-            "dismiss": "Dismiss",
-            "rebuilding": "Rebuilding cache...",
-            "rebuildFailed": "Failed to rebuild cache: {error}",
-            "retry": "Retry"
         }
     }
-}
+}

locales/es.json

@@ -1,11 +1,8 @@
 {
     "common": {
-        "cancel": "Cancelar",
-        "confirm": "Confirmar",
         "actions": {
             "save": "Guardar",
             "cancel": "Cancelar",
-            "confirm": "Confirmar",
             "delete": "Eliminar",
             "move": "Mover",
             "refresh": "Actualizar",
@@ -14,9 +11,7 @@
             "backToTop": "Volver arriba",
             "settings": "Configuración",
             "help": "Ayuda",
-            "add": "Añadir",
-            "close": "Cerrar",
-            "menu": "Menú"
+            "add": "Añadir"
         },
         "status": {
             "loading": "Cargando...",
@@ -136,8 +131,7 @@
         },
         "badges": {
             "update": "Actualización",
-            "updateAvailable": "Actualización disponible",
-            "skipRefresh": "Actualización de metadatos omitida"
+            "updateAvailable": "Actualización disponible"
         },
         "usage": {
             "timesUsed": "Veces usado"
@@ -176,9 +170,6 @@
             "success": "Se repararon con éxito {count} recetas.",
             "cancelled": "Reparación cancelada. {count} recetas fueron reparadas.",
             "error": "Error al reparar recetas: {message}"
-        },
-        "manageExcludedModels": {
-            "label": "Gestionar modelos excluidos"
         }
     },
     "header": {
@@ -188,6 +179,7 @@
             "recipes": "Recetas",
             "checkpoints": "Checkpoints",
             "embeddings": "Embeddings",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "Estadísticas"
         },
         "search": {
@@ -196,7 +188,8 @@
                 "loras": "Buscar LoRAs...",
                 "recipes": "Buscar recetas...",
                 "checkpoints": "Buscar checkpoints...",
-                "embeddings": "Buscar embeddings..."
+                "embeddings": "Buscar embeddings...",
+                "misc": "[TODO: Translate] Search VAE/Upscaler models..."
             },
             "options": "Opciones de búsqueda",
             "searchIn": "Buscar en:",
@@ -226,19 +219,13 @@
             "presetOverwriteConfirm": "El preset \"{name}\" ya existe. ¿Sobrescribir?",
             "presetNamePlaceholder": "Nombre del preajuste...",
             "baseModel": "Modelo base",
-            "baseModelSearchPlaceholder": "Buscar modelos base...",
             "modelTags": "Etiquetas (Top 20)",
-            "modelTypes": "Tipos de modelos",
+            "modelTypes": "Model Types",
             "license": "Licencia",
             "noCreditRequired": "Sin crédito requerido",
             "allowSellingGeneratedContent": "Venta permitida",
             "noTags": "Sin etiquetas",
-            "noBaseModelMatches": "Ningún modelo base coincide con la búsqueda actual.",
-            "clearAll": "Limpiar todos los filtros",
-            "any": "Cualquiera",
-            "all": "Todos",
-            "tagLogicAny": "Coincidir con cualquier etiqueta (O)",
-            "tagLogicAll": "Coincidir con todas las etiquetas (Y)"
+            "clearAll": "Limpiar todos los filtros"
         },
         "theme": {
             "toggle": "Cambiar tema",
@@ -256,33 +243,6 @@
         "civitaiApiKey": "Clave API de Civitai",
         "civitaiApiKeyPlaceholder": "Introduce tu clave API de Civitai",
         "civitaiApiKeyHelp": "Utilizada para autenticación al descargar modelos de Civitai",
-        "civitaiHost": {
-            "label": "Host de Civitai",
-            "help": "Elige qué sitio de Civitai se abre al usar los enlaces de \"View on Civitai\".",
-            "options": {
-                "com": "civitai.com (solo SFW)",
-                "red": "civitai.red (sin restricciones)"
-            }
-        },
-        "downloadBackend": {
-            "label": "Backend de descarga",
-            "help": "Elige cómo se descargan los archivos del modelo. Python usa el descargador integrado. aria2 usa el proceso externo experimental de descarga.",
-            "options": {
-                "python": "Python (integrado)",
-                "aria2": "aria2 (experimental)"
-            }
-        },
-        "aria2cPath": {
-            "label": "Ruta de aria2c",
-            "help": "Ruta opcional al ejecutable aria2c. Déjalo vacío para usar aria2c desde el PATH del sistema.",
-            "placeholder": "Déjalo vacío para usar aria2c desde el PATH"
-        },
-        "aria2HelpLink": "Aprende a configurar el backend de descarga aria2",
-        "civitaiHostBanner": {
-            "title": "Preferencia de host de Civitai disponible",
-            "content": "Civitai ahora usa civitai.com para contenido SFW y civitai.red para contenido sin restricciones. Puedes cambiar en Ajustes qué sitio se abre por defecto.",
-            "openSettings": "Abrir ajustes"
-        },
         "openSettingsFileLocation": {
             "label": "Abrir carpeta de ajustes",
             "tooltip": "Abrir la carpeta que contiene settings.json",
@@ -293,32 +253,19 @@
         },
         "sections": {
             "contentFiltering": "Filtrado de contenido",
-            "downloads": "Descargas",
             "videoSettings": "Configuración de video",
             "layoutSettings": "Configuración de diseño",
-            "misc": "Varios",
-            "backup": "Copias de seguridad",
-            "folderSettings": "Raíces predeterminadas",
-            "recipeSettings": "Recetas",
-            "extraFolderPaths": "Rutas de carpetas adicionales",
-            "downloadPathTemplates": "Plantillas de rutas de descarga",
+            "folderSettings": "Configuración de carpetas",
             "priorityTags": "Etiquetas prioritarias",
-            "updateFlags": "Indicadores de actualización",
+            "downloadPathTemplates": "Plantillas de rutas de descarga",
             "exampleImages": "Imágenes de ejemplo",
-            "autoOrganize": "Organización automática",
-            "metadata": "Metadatos",
+            "updateFlags": "Indicadores de actualización",
+            "autoOrganize": "Auto-organize",
+            "misc": "Varios",
+            "metadataArchive": "Base de datos de archivo de metadatos",
+            "storageLocation": "Ubicación de ajustes",
             "proxySettings": "Configuración de proxy"
         },
-        "nav": {
-            "general": "General",
-            "interface": "Interfaz",
-            "library": "Biblioteca"
-        },
-        "search": {
-            "placeholder": "Buscar ajustes...",
-            "clear": "Limpiar búsqueda",
-            "noResults": "No se encontraron ajustes que coincidan con \"{query}\""
-        },
         "storage": {
             "locationLabel": "Modo portátil",
             "locationHelp": "Activa para mantener settings.json dentro del repositorio; desactívalo para guardarlo en tu directorio de configuración de usuario."
@@ -327,15 +274,7 @@
             "blurNsfwContent": "Difuminar contenido NSFW",
             "blurNsfwContentHelp": "Difuminar imágenes de vista previa de contenido para adultos (NSFW)",
             "showOnlySfw": "Mostrar solo resultados SFW",
-            "showOnlySfwHelp": "Filtrar todo el contenido NSFW al navegar y buscar",
-            "matureBlurThreshold": "Umbral de difuminado para contenido adulto",
-            "matureBlurThresholdHelp": "Establecer a partir de qué nivel de clasificación comienza el filtrado por difuminado cuando el difuminado NSFW está habilitado.",
-            "matureBlurThresholdOptions": {
-                "pg13": "PG13 y superior",
-                "r": "R y superior (predeterminado)",
-                "x": "X y superior",
-                "xxx": "Solo XXX"
-            }
+            "showOnlySfwHelp": "Filtrar todo el contenido NSFW al navegar y buscar"
         },
         "videoSettings": {
             "autoplayOnHover": "Reproducir videos automáticamente al pasar el ratón",
@@ -350,63 +289,6 @@
                 "saveFailed": "No se pudieron guardar las exclusiones: {message}"
             }
         },
-        "metadataRefreshSkipPaths": {
-            "label": "Rutas a omitir en la actualización de metadatos",
-            "placeholder": "Ejemplo: temp, archived/old, test_models",
-            "help": "Omitir modelos en estas rutas de directorio durante la actualización masiva de metadatos (\"Obtener todos los metadatos\"). Ingrese rutas de carpetas relativas al directorio raíz de modelos, separadas por comas.",
-            "validation": {
-                "noPaths": "Ingrese al menos una ruta separada por comas.",
-                "saveFailed": "No se pudieron guardar las rutas a omitir: {message}"
-            }
-        },
-        "backup": {
-            "autoEnabled": "Copias de seguridad automáticas",
-            "autoEnabledHelp": "Crea una instantánea local una vez al día y conserva las más recientes según la política de retención.",
-            "retention": "Cantidad de retención",
-            "retentionHelp": "Cuántas instantáneas automáticas conservar antes de eliminar las antiguas.",
-            "management": "Gestión de copias",
-            "managementHelp": "Exporta tu estado de usuario actual o restáuralo desde un archivo de copia de seguridad.",
-            "scopeHelp": "Incluye tu configuración, el historial de descargas y el estado de actualización de los modelos. No incluye los archivos de modelo ni las cachés que se pueden regenerar.",
-            "locationSummary": "Ubicación actual de la copia",
-            "openFolderButton": "Abrir carpeta de copias",
-            "openFolderSuccess": "Carpeta de copias abierta",
-            "openFolderFailed": "No se pudo abrir la carpeta de copias",
-            "locationCopied": "Ruta de la copia copiada al portapapeles: {{path}}",
-            "locationClipboardFallback": "Ruta de la copia: {{path}}",
-            "exportButton": "Exportar copia",
-            "exportSuccess": "Copia exportada correctamente.",
-            "exportFailed": "No se pudo exportar la copia: {message}",
-            "importButton": "Importar copia",
-            "importConfirm": "¿Importar esta copia y sobrescribir el estado local del usuario?",
-            "importSuccess": "Copia importada correctamente.",
-            "importFailed": "No se pudo importar la copia: {message}",
-            "latestSnapshot": "Última instantánea",
-            "latestAutoSnapshot": "Última instantánea automática",
-            "snapshotCount": "Instantáneas guardadas",
-            "noneAvailable": "Aún no hay instantáneas"
-        },
-        "downloadSkipBaseModels": {
-            "label": "Omitir descargas para modelos base",
-            "help": "Se aplica a todos los flujos de descarga. Aquí solo se pueden seleccionar modelos base compatibles.",
-            "searchPlaceholder": "Filtrar modelos base...",
-            "empty": "Ningún modelo base coincide con la búsqueda actual.",
-            "summary": {
-                "none": "Ninguno seleccionado",
-                "count": "{count} seleccionados"
-            },
-            "actions": {
-                "edit": "Editar",
-                "collapse": "Contraer",
-                "clear": "Limpiar"
-            },
-            "validation": {
-                "saveFailed": "No se pudieron guardar los modelos base excluidos: {message}"
-            }
-        },
-        "skipPreviouslyDownloadedModelVersions": {
-            "label": "Omitir versiones de modelos previamente descargadas",
-            "help": "Cuando está habilitado, LoRA Manager omitirá la descarga de una versión de modelo si el servicio de historial de descargas registra esa versión exacta como ya descargada. Aplica a todos los flujos de descarga."
-        },
         "layoutSettings": {
             "displayDensity": "Densidad de visualización",
             "displayDensityOptions": {
@@ -429,8 +311,6 @@
                 "hover": "Mostrar al pasar el ratón"
             },
             "cardInfoDisplayHelp": "Elige cuándo mostrar información del modelo y botones de acción",
-            "showVersionOnCard": "Mostrar versión en la tarjeta",
-            "showVersionOnCardHelp": "Mostrar u ocultar el nombre de versión en las tarjetas de modelo",
             "modelCardFooterAction": "Acción del botón de tarjeta de modelo",
             "modelCardFooterActionOptions": {
                 "exampleImages": "Abrir imágenes de ejemplo",
@@ -449,37 +329,16 @@
             "activeLibraryHelp": "Alterna entre las bibliotecas configuradas para actualizar las carpetas predeterminadas. Cambiar la selección recarga la página.",
             "loadingLibraries": "Cargando bibliotecas...",
             "noLibraries": "No hay bibliotecas configuradas",
-            "defaultLoraRoot": "Raíz de LoRA",
+            "defaultLoraRoot": "Raíz predeterminada de LoRA",
             "defaultLoraRootHelp": "Establecer el directorio raíz predeterminado de LoRA para descargas, importaciones y movimientos",
-            "defaultCheckpointRoot": "Raíz de checkpoint",
+            "defaultCheckpointRoot": "Raíz predeterminada de checkpoint",
             "defaultCheckpointRootHelp": "Establecer el directorio raíz predeterminado de checkpoint para descargas, importaciones y movimientos",
-            "defaultUnetRoot": "Raíz de Diffusion Model",
+            "defaultUnetRoot": "Raíz predeterminada de Diffusion Model",
             "defaultUnetRootHelp": "Establecer el directorio raíz predeterminado de Diffusion Model (UNET) para descargas, importaciones y movimientos",
-            "defaultEmbeddingRoot": "Raíz de embedding",
+            "defaultEmbeddingRoot": "Raíz predeterminada de embedding",
             "defaultEmbeddingRootHelp": "Establecer el directorio raíz predeterminado de embedding para descargas, importaciones y movimientos",
-            "recipesPath": "Ruta de almacenamiento de recetas",
-            "recipesPathHelp": "Directorio personalizado opcional para las recetas guardadas. Déjalo vacío para usar la carpeta recipes del primer directorio raíz de LoRA.",
-            "recipesPathPlaceholder": "/path/to/recipes",
-            "recipesPathMigrating": "Migrando el almacenamiento de recetas...",
             "noDefault": "Sin predeterminado"
         },
-        "extraFolderPaths": {
-            "title": "Rutas de carpetas adicionales",
-            "description": "Rutas raíz de modelos adicionales exclusivas para LoRA Manager. Cargue modelos desde ubicaciones fuera de las carpetas estándar de ComfyUI, ideal para bibliotecas grandes que de otro modo ralentizarían ComfyUI.",
-            "restartRequired": "Requires restart to take effect",
-            "modelTypes": {
-                "lora": "Rutas de LoRA",
-                "checkpoint": "Rutas de Checkpoint",
-                "unet": "Rutas de modelo de difusión",
-                "embedding": "Rutas de Embedding"
-            },
-            "pathPlaceholder": "/ruta/a/modelos/extra",
-            "saveSuccess": "Rutas de carpetas adicionales actualizadas. Se requiere reinicio para aplicar los cambios.",
-            "saveError": "Error al actualizar las rutas de carpetas adicionales: {message}",
-            "validation": {
-                "duplicatePath": "Esta ruta ya está configurada"
-            }
-        },
         "priorityTags": {
             "title": "Etiquetas prioritarias",
             "description": "Personaliza el orden de prioridad de etiquetas para cada tipo de modelo (p. ej., character, concept, style(toon|toon_style))",
@@ -542,21 +401,6 @@
             "downloadLocationHelp": "Introduce la ruta de la carpeta donde se guardarán las imágenes de ejemplo de Civitai",
             "autoDownload": "Descargar automáticamente imágenes de ejemplo",
             "autoDownloadHelp": "Descargar automáticamente imágenes de ejemplo para modelos que no las tengan (requiere que se establezca la ubicación de descarga)",
-            "openMode": "Acción al abrir imágenes de ejemplo",
-            "openModeHelp": "Elige si la acción se abre en el servidor, copia una ruta local asignada o lanza una URI personalizada.",
-            "openModeOptions": {
-                "system": "Abrir en el servidor",
-                "clipboard": "Copiar ruta local",
-                "uriTemplate": "Abrir URI personalizada"
-            },
-            "localRoot": "Raíz local de imágenes de ejemplo",
-            "localRootHelp": "Raíz local u montada opcional que refleja el directorio de imágenes de ejemplo del servidor. Si se deja en blanco, se reutiliza la ruta del servidor.",
-            "localRootPlaceholder": "Ejemplo: /Volumes/ComfyUI/example_images",
-            "uriTemplate": "Abrir plantilla de URI",
-            "uriTemplateHelp": "Usa un enlace profundo personalizado, como un URI de archivo o un enlace de Shortcuts.",
-            "uriTemplatePlaceholder": "Ejemplo: shortcuts://run-shortcut?name=Open%20Finder&input=text&text={{encoded_local_path}}",
-            "uriTemplatePlaceholders": "Marcadores disponibles: {{local_path}}, {{encoded_local_path}}, {{relative_path}}, {{encoded_relative_path}}, {{file_uri}}, {{encoded_file_uri}}",
-            "openModeWikiLink": "Más información sobre los modos de apertura remota",
             "optimizeImages": "Optimizar imágenes descargadas",
             "optimizeImagesHelp": "Optimizar imágenes de ejemplo para reducir el tamaño del archivo y mejorar la velocidad de carga (se preservarán los metadatos)",
             "download": "Descargar",
@@ -570,10 +414,6 @@
                 "any": "Marcar cualquier actualización disponible"
             }
         },
-        "hideEarlyAccessUpdates": {
-            "label": "Ocultar actualizaciones de acceso temprano",
-            "help": "Solo actualizaciones de acceso temprano"
-        },
         "misc": {
             "includeTriggerWords": "Incluir palabras clave en la sintaxis de LoRA",
             "includeTriggerWordsHelp": "Incluir palabras clave entrenadas al copiar la sintaxis de LoRA al portapapeles"
@@ -685,13 +525,8 @@
             "checkUpdates": "Comprobar actualizaciones para la selección",
             "moveAll": "Mover todos a carpeta",
             "autoOrganize": "Auto-organizar seleccionados",
-            "skipMetadataRefresh": "Omitir actualización de metadatos para seleccionados",
-            "resumeMetadataRefresh": "Reanudar actualización de metadatos para seleccionados",
-            "deleteAll": "Eliminar seleccionados",
-            "downloadMissingLoras": "Descargar LoRAs faltantes",
+            "deleteAll": "Eliminar todos los modelos",
             "clear": "Limpiar selección",
-            "skipMetadataRefreshCount": "Omitir（{count} modelos）",
-            "resumeMetadataRefreshCount": "Reanudar（{count} modelos）",
             "autoOrganizeProgress": {
                 "initializing": "Inicializando auto-organización...",
                 "starting": "Iniciando auto-organización para {type}...",
@@ -718,7 +553,6 @@
             "moveToFolder": "Mover a carpeta",
             "repairMetadata": "Reparar metadatos",
             "excludeModel": "Excluir modelo",
-            "restoreModel": "Restaurar modelo",
             "deleteModel": "Eliminar modelo",
             "shareRecipe": "Compartir receta",
             "viewAllLoras": "Ver todos los LoRAs",
@@ -760,8 +594,6 @@
                 "root": "Raíz",
                 "browseFolders": "Explorar carpetas:",
                 "downloadAndSaveRecipe": "Descargar y guardar receta",
-                "importRecipeOnly": "Importar solo la receta",
-                "importAndDownload": "Importar y descargar",
                 "downloadMissingLoras": "Descargar LoRAs faltantes",
                 "saveRecipe": "Guardar receta",
                 "loraCountInfo": "({existing}/{total} en la biblioteca)",
@@ -803,11 +635,7 @@
                 "lorasCountAsc": "Menos"
             },
             "refresh": {
-                "title": "Actualizar lista de recetas",
-                "quick": "Sincronizar cambios",
-                "quickTooltip": "Sincronizar cambios - actualización rápida sin reconstruir caché",
-                "full": "Reconstruir caché",
-                "fullTooltip": "Reconstruir caché - reescaneo completo de todos los archivos de recetas"
+                "title": "Actualizar lista de recetas"
             },
             "filteredByLora": "Filtrado por LoRA",
             "favorites": {
@@ -847,64 +675,6 @@
                 "failed": "Error al reparar la receta: {message}",
                 "missingId": "No se puede reparar la receta: falta el ID de la receta"
             }
-        },
-        "batchImport": {
-            "title": "Batch Import Recipes",
-            "action": "Batch Import",
-            "urlList": "URL List",
-            "directory": "Directory",
-            "urlDescription": "Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
-            "directoryDescription": "Enter a directory path to import all images from that folder.",
-            "urlsLabel": "Image URLs or Local Paths",
-            "urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
-            "urlsHint": "Enter one URL or path per line",
-            "directoryPath": "Directory Path",
-            "directoryPlaceholder": "/path/to/images/folder",
-            "browse": "Browse",
-            "recursive": "Include subdirectories",
-            "tagsOptional": "Tags (optional, applied to all recipes)",
-            "tagsPlaceholder": "Enter tags separated by commas",
-            "tagsHint": "Tags will be added to all imported recipes",
-            "skipNoMetadata": "Skip images without metadata",
-            "skipNoMetadataHelp": "Images without LoRA metadata will be skipped automatically.",
-            "start": "Start Import",
-            "startImport": "Start Import",
-            "importing": "Importing...",
-            "progress": "Progress",
-            "total": "Total",
-            "success": "Success",
-            "failed": "Failed",
-            "skipped": "Skipped",
-            "current": "Current",
-            "currentItem": "Current",
-            "preparing": "Preparing...",
-            "cancel": "Cancel",
-            "cancelImport": "Cancel",
-            "cancelled": "Import cancelled",
-            "completed": "Import completed",
-            "completedWithErrors": "Completed with errors",
-            "completedSuccess": "Successfully imported {count} recipe(s)",
-            "successCount": "Successful",
-            "failedCount": "Failed",
-            "skippedCount": "Skipped",
-            "totalProcessed": "Total processed",
-            "viewDetails": "View Details",
-            "newImport": "New Import",
-            "manualPathEntry": "Please enter the directory path manually. File browser is not available in this browser.",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "batchImportManualEntryRequired": "File browser not available. Please enter the directory path manually.",
-            "backToParent": "Back to parent directory",
-            "folders": "Folders",
-            "folderCount": "{count} folders",
-            "imageFiles": "Image Files",
-            "images": "images",
-            "imageCount": "{count} images",
-            "selectFolder": "Select This Folder",
-            "errors": {
-                "enterUrls": "Please enter at least one URL or path",
-                "enterDirectory": "Please enter a directory path",
-                "startFailed": "Failed to start import: {message}"
-            }
         }
     },
     "checkpoints": {
@@ -914,13 +684,22 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "Mover a la carpeta {otherType}",
-            "sendToWorkflow": "Enviar al flujo de trabajo"
+            "moveToOtherTypeFolder": "Mover a la carpeta {otherType}"
         }
     },
     "embeddings": {
         "title": "Modelos embedding"
     },
+    "misc": {
+        "title": "[TODO: Translate] VAE & Upscaler Models",
+        "modelTypes": {
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "[TODO: Translate] Move to {otherType} Folder"
+        }
+    },
     "sidebar": {
         "modelRoot": "Raíz",
         "collapseAll": "Colapsar todas las carpetas",
@@ -928,23 +707,13 @@
         "unpinSidebar": "Desfijar barra lateral",
         "switchToListView": "Cambiar a vista de lista",
         "switchToTreeView": "Cambiar a vista de árbol",
-        "recursiveOn": "Incluir subcarpetas",
-        "recursiveOff": "Solo carpeta actual",
+        "recursiveOn": "Buscar en subcarpetas",
+        "recursiveOff": "Buscar solo en la carpeta actual",
         "recursiveUnavailable": "La búsqueda recursiva solo está disponible en la vista en árbol",
         "collapseAllDisabled": "No disponible en vista de lista",
         "dragDrop": {
             "unableToResolveRoot": "No se puede determinar la ruta de destino para el movimiento.",
-            "moveUnsupported": "El movimiento no es compatible con este elemento.",
-            "createFolderHint": "Suelta para crear una nueva carpeta",
-            "newFolderName": "Nombre de la nueva carpeta",
-            "folderNameHint": "Presiona Enter para confirmar, Escape para cancelar",
-            "emptyFolderName": "Por favor, introduce un nombre de carpeta",
-            "invalidFolderName": "El nombre de la carpeta contiene caracteres no válidos",
-            "noDragState": "No se encontró ninguna operación de arrastre pendiente"
-        },
-        "empty": {
-            "noFolders": "No se encontraron carpetas",
-            "dragHint": "Arrastra elementos aquí para crear carpetas"
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -1009,8 +778,6 @@
             "earlyAccess": "Acceso temprano",
             "earlyAccessTooltip": "Acceso temprano requerido",
             "inLibrary": "En la biblioteca",
-            "downloaded": "Descargado",
-            "downloadedTooltip": "Descargado anteriormente, pero actualmente no está en tu biblioteca.",
             "alreadyInLibrary": "Ya en la biblioteca",
             "autoOrganizedPath": "[Auto-organizado por plantilla de ruta]",
             "errors": {
@@ -1101,14 +868,6 @@
             "save": "Actualizar modelo base",
             "cancel": "Cancelar"
         },
-        "bulkDownloadMissingLoras": {
-            "title": "Descargar LoRAs faltantes",
-            "message": "Se encontraron {uniqueCount} LoRAs faltantes únicos (de {totalCount} en total entre las recetas seleccionadas).",
-            "previewTitle": "LoRAs para descargar:",
-            "moreItems": "...y {count} más",
-            "note": "Los archivos se descargarán usando las plantillas de ruta predeterminadas. Esto puede tomar un tiempo dependiendo del número de LoRAs.",
-            "downloadButton": "Descargar {count} LoRA(s)"
-        },
         "exampleAccess": {
             "title": "Imágenes de ejemplo locales",
             "message": "No se encontraron imágenes de ejemplo locales para este modelo. Opciones de visualización:",
@@ -1160,9 +919,7 @@
                 "viewOnCivitai": "Ver en Civitai",
                 "viewOnCivitaiText": "Ver en Civitai",
                 "viewCreatorProfile": "Ver perfil del creador",
-                "openFileLocation": "Abrir ubicación del archivo",
-                "sendToWorkflow": "Enviar a ComfyUI",
-                "sendToWorkflowText": "Enviar a ComfyUI"
+                "openFileLocation": "Abrir ubicación del archivo"
             },
             "openFileLocation": {
                 "success": "Ubicación del archivo abierta exitosamente",
@@ -1170,9 +927,6 @@
                 "copied": "Ruta copiada al portapapeles: {{path}}",
                 "clipboardFallback": "Ruta: {{path}}"
             },
-            "sendToWorkflow": {
-                "noFilePath": "No se puede enviar a ComfyUI: no hay ruta de archivo disponible"
-            },
             "metadata": {
                 "version": "Versión",
                 "fileName": "Nombre de archivo",
@@ -1209,8 +963,6 @@
                 "cancel": "Cancelar edición",
                 "save": "Guardar cambios",
                 "addPlaceholder": "Escribe para añadir o haz clic en sugerencias de abajo",
-                "editWord": "Editar palabra de activación",
-                "editPlaceholder": "Editar palabra de activación",
                 "copyWord": "Copiar palabra clave",
                 "deleteWord": "Eliminar palabra clave",
                 "suggestions": {
@@ -1273,43 +1025,19 @@
                 },
                 "labels": {
                     "unnamed": "Versión sin nombre",
-                    "noDetails": "Sin detalles adicionales",
-                    "earlyAccess": "EA"
-                },
-                "eaTime": {
-                    "endingSoon": "terminando pronto",
-                    "hours": "en {count}h",
-                    "days": "en {count}d"
+                    "noDetails": "Sin detalles adicionales"
                 },
                 "badges": {
-                    "current": "Versión abierta",
-                    "currentTooltip": "Es la versión con la que abriste este modal",
+                    "current": "Versión actual",
                     "inLibrary": "En la biblioteca",
-                    "inLibraryTooltip": "Esta versión existe en tu biblioteca local",
-                    "downloaded": "Descargado",
-                    "downloadedTooltip": "Esta versión se descargó antes, pero ahora no está en tu biblioteca",
                     "newer": "Versión más reciente",
-                    "newerTooltip": "Esta versión es más reciente que tu última versión local",
-                    "earlyAccess": "Acceso temprano",
-                    "earlyAccessTooltip": "Esta versión requiere actualmente acceso temprano de Civitai",
-                    "ignored": "Ignorada",
-                    "ignoredTooltip": "Las notificaciones de actualización están desactivadas para esta versión",
-                    "onSiteOnly": "Solo en Sitio",
-                    "onSiteOnlyTooltip": "Esta versión solo está disponible para generación en el sitio de Civitai"
+                    "ignored": "Ignorada"
                 },
                 "actions": {
                     "download": "Descargar",
-                    "downloadTooltip": "Descargar esta versión",
-                    "downloadEarlyAccessTooltip": "Descargar esta versión de acceso temprano desde Civitai",
-                    "downloadNotAllowedTooltip": "Esta versión solo está disponible para generación en el sitio de Civitai",
                     "delete": "Eliminar",
-                    "deleteTooltip": "Eliminar esta versión local",
                     "ignore": "Ignorar",
                     "unignore": "Dejar de ignorar",
-                    "ignoreTooltip": "Ignorar las notificaciones de actualización de esta versión",
-                    "unignoreTooltip": "Reanudar las notificaciones de actualización de esta versión",
-                    "viewVersionOnCivitai": "Ver versión en Civitai",
-                    "earlyAccessTooltip": "Requiere compra de acceso temprano",
                     "resumeModelUpdates": "Reanudar actualizaciones para este modelo",
                     "ignoreModelUpdates": "Ignorar actualizaciones para este modelo",
                     "viewLocalVersions": "Ver todas las versiones locales",
@@ -1388,6 +1116,10 @@
             "title": "Inicializando estadísticas",
             "message": "Procesando datos del modelo para estadísticas. Esto puede tomar unos minutos..."
         },
+        "misc": {
+            "title": "[TODO: Translate] Initializing Misc Model Manager",
+            "message": "[TODO: Translate] Scanning VAE and Upscaler models..."
+        },
         "tips": {
             "title": "Consejos y trucos",
             "civitai": {
@@ -1447,14 +1179,18 @@
             "recipeAdded": "Receta añadida al flujo de trabajo",
             "recipeReplaced": "Receta reemplazada en el flujo de trabajo",
             "recipeFailedToSend": "Error al enviar receta al flujo de trabajo",
+            "vaeUpdated": "[TODO: Translate] VAE updated in workflow",
+            "vaeFailed": "[TODO: Translate] Failed to update VAE in workflow",
+            "upscalerUpdated": "[TODO: Translate] Upscaler updated in workflow",
+            "upscalerFailed": "[TODO: Translate] Failed to update upscaler in workflow",
             "noMatchingNodes": "No hay nodos compatibles disponibles en el flujo de trabajo actual",
-            "noTargetNodeSelected": "No se ha seleccionado ningún nodo de destino",
-            "modelUpdated": "Modelo actualizado en el flujo de trabajo",
-            "modelFailed": "Error al actualizar nodo de modelo"
+            "noTargetNodeSelected": "No se ha seleccionado ningún nodo de destino"
         },
         "nodeSelector": {
             "recipe": "Receta",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "Reemplazar",
             "append": "Añadir",
             "selectTargetNode": "Seleccionar nodo de destino",
@@ -1464,10 +1200,6 @@
             "opened": "Carpeta de imágenes de ejemplo abierta",
             "openingFolder": "Abriendo carpeta de imágenes de ejemplo",
             "failedToOpen": "Error al abrir carpeta de imágenes de ejemplo",
-            "copiedPath": "Ruta copiada al portapapeles: {{path}}",
-            "clipboardFallback": "Ruta: {{path}}",
-            "copiedUri": "Enlace copiado al portapapeles: {{uri}}",
-            "uriClipboardFallback": "Enlace: {{uri}}",
             "setupRequired": "Almacenamiento de imágenes de ejemplo",
             "setupDescription": "Para agregar imágenes de ejemplo personalizadas, primero necesita establecer una ubicación de descarga.",
             "setupUsage": "Esta ruta se utiliza tanto para imágenes de ejemplo descargadas como personalizadas.",
@@ -1575,14 +1307,7 @@
             "showWechatQR": "Mostrar código QR de WeChat",
             "hideWechatQR": "Ocultar código QR de WeChat"
         },
-        "footer": "¡Gracias por usar el gestor de LoRA! ❤️",
-        "supporters": {
-            "title": "Gracias a todos los seguidores",
-            "subtitle": "Gracias a {count} seguidores que hicieron este proyecto posible",
-            "specialThanks": "Agradecimientos especiales",
-            "allSupporters": "Todos los seguidores",
-            "totalCount": "{count} seguidores en total"
-        }
+        "footer": "¡Gracias por usar el gestor de LoRA! ❤️"
     },
     "toast": {
         "general": {
@@ -1605,7 +1330,6 @@
             "pleaseSelectVersion": "Por favor selecciona una versión",
             "versionExists": "Esta versión ya existe en tu biblioteca",
             "downloadCompleted": "Descarga completada exitosamente",
-            "downloadSkippedByBaseModel": "Descarga omitida porque el modelo base {baseModel} está excluido",
             "autoOrganizeSuccess": "Auto-organización completada exitosamente para {count} {type}",
             "autoOrganizePartialSuccess": "Auto-organización completada con {success} movidos, {failures} fallidos de un total de {total} modelos",
             "autoOrganizeFailed": "Auto-organización fallida: {error}",
@@ -1617,19 +1341,13 @@
             "loadFailed": "Error al cargar {modelType}s: {message}",
             "refreshComplete": "Actualización completa",
             "refreshFailed": "Error al actualizar recetas: {message}",
-            "syncComplete": "Sincronización completa",
-            "syncFailed": "Error al sincronizar recetas: {message}",
             "updateFailed": "Error al actualizar receta: {error}",
             "updateError": "Error actualizando receta: {message}",
             "nameSaved": "Receta \"{name}\" guardada exitosamente",
             "nameUpdated": "Nombre de receta actualizado exitosamente",
             "tagsUpdated": "Etiquetas de receta actualizadas exitosamente",
             "sourceUrlUpdated": "URL de origen actualizada exitosamente",
-            "promptUpdated": "Prompt actualizado exitosamente",
-            "negativePromptUpdated": "Prompt negativo actualizado exitosamente",
-            "promptEditorHint": "Presiona Enter para guardar, Shift+Enter para nueva línea",
             "noRecipeId": "No hay ID de receta disponible",
-            "sendToWorkflowFailed": "Error al enviar la receta al flujo de trabajo: {message}",
             "copyFailed": "Error copiando sintaxis de receta: {message}",
             "noMissingLoras": "No hay LoRAs faltantes para descargar",
             "missingLorasInfoFailed": "Error al obtener información de LoRAs faltantes",
@@ -1657,20 +1375,9 @@
             "processingError": "Error de procesamiento: {message}",
             "folderBrowserError": "Error cargando explorador de carpetas: {message}",
             "recipeSaveFailed": "Error al guardar receta: {error}",
-            "recipeSaved": "Recipe saved successfully",
             "importFailed": "Importación falló: {message}",
             "folderTreeFailed": "Error al cargar árbol de carpetas",
-            "folderTreeError": "Error cargando árbol de carpetas",
-            "batchImportFailed": "Failed to start batch import: {message}",
-            "batchImportCancelling": "Cancelling batch import...",
-            "batchImportCancelFailed": "Failed to cancel batch import: {message}",
-            "batchImportNoUrls": "Please enter at least one URL or file path",
-            "batchImportNoDirectory": "Please enter a directory path",
-            "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "noRecipesSelected": "No se han seleccionado recetas",
-            "noMissingLorasInSelection": "No se encontraron LoRAs faltantes en las recetas seleccionadas",
-            "noLoraRootConfigured": "No se ha configurado el directorio raíz de LoRA. Por favor, establezca un directorio raíz de LoRA predeterminado en la configuración."
+            "folderTreeError": "Error cargando árbol de carpetas"
         },
         "models": {
             "noModelsSelected": "No hay modelos seleccionados",
@@ -1690,11 +1397,6 @@
             "bulkBaseModelUpdateSuccess": "Modelo base actualizado exitosamente para {count} modelo(s)",
             "bulkBaseModelUpdatePartial": "Actualizados {success} modelo(s), fallaron {failed} modelo(s)",
             "bulkBaseModelUpdateFailed": "Error al actualizar el modelo base para los modelos seleccionados",
-            "skipMetadataRefreshUpdating": "Actualizando flag de actualización de metadatos para {count} modelo(s)...",
-            "skipMetadataRefreshSet": "Actualización de metadatos omitida para {count} modelo(s)",
-            "skipMetadataRefreshCleared": "Actualización de metadatos reanudada para {count} modelo(s)",
-            "skipMetadataRefreshPartial": "{success} modelo(s) actualizados, {failed} fallaron",
-            "skipMetadataRefreshFailed": "Error al actualizar flag de actualización de metadatos para los modelos seleccionados",
             "bulkContentRatingUpdating": "Actualizando la clasificación de contenido para {count} modelo(s)...",
             "bulkContentRatingSet": "Clasificación de contenido establecida en {level} para {count} modelo(s)",
             "bulkContentRatingPartial": "Clasificación de contenido establecida en {level} para {success} modelo(s), {failed} fallaron",
@@ -1737,8 +1439,6 @@
             "mappingSaveFailed": "Error al guardar mapeos de modelo base: {message}",
             "downloadTemplatesUpdated": "Plantillas de rutas de descarga actualizadas",
             "downloadTemplatesFailed": "Error al guardar plantillas de rutas de descarga: {message}",
-            "recipesPathUpdated": "Ruta de almacenamiento de recetas actualizada",
-            "recipesPathSaveFailed": "Error al actualizar la ruta de almacenamiento de recetas: {message}",
             "settingsUpdated": "Configuración actualizada: {setting}",
             "compactModeToggled": "Modo compacto {state}",
             "settingSaveFailed": "Error al guardar configuración: {message}",
@@ -1784,13 +1484,12 @@
             "folderTreeFailed": "Error al cargar árbol de carpetas",
             "folderTreeError": "Error al cargar árbol de carpetas",
             "imagesImported": "Imágenes de ejemplo importadas exitosamente",
-            "imagesPartial": "{success} imagen(es) importada(s), {failed} fallida(s)",
             "importFailed": "Error al importar imágenes de ejemplo: {message}"
         },
         "triggerWords": {
             "loadFailed": "No se pudieron cargar palabras entrenadas",
-            "tooLong": "La palabra clave no debe exceder 500 palabras",
-            "tooMany": "Máximo 100 palabras clave permitidas",
+            "tooLong": "La palabra clave no debe exceder 100 palabras",
+            "tooMany": "Máximo 30 palabras clave permitidas",
             "alreadyExists": "Esta palabra clave ya existe",
             "updateSuccess": "Palabras clave actualizadas exitosamente",
             "updateFailed": "Error al actualizar palabras clave",
@@ -1851,8 +1550,6 @@
             "deleteFailed": "Error al eliminar {type}: {message}",
             "excludeSuccess": "{type} excluido exitosamente",
             "excludeFailed": "Error al excluir {type}: {message}",
-            "restoreSuccess": "{type} restaurado correctamente",
-            "restoreFailed": "No se pudo restaurar {type}: {message}",
             "fileNameUpdated": "Nombre de archivo actualizado exitosamente",
             "fileRenameFailed": "Error al renombrar archivo: {error}",
             "previewUpdated": "Vista previa actualizada exitosamente",
@@ -1884,37 +1581,6 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
-    "doctor": {
-        "kicker": "Diagnósticos del sistema",
-        "title": "Doctor",
-        "buttonTitle": "Ejecutar diagnósticos y correcciones comunes",
-        "loading": "Comprobando el entorno...",
-        "footer": "Exporta un paquete de diagnóstico si el problema persiste después de la reparación.",
-        "summary": {
-            "idle": "Ejecuta una comprobación del estado de la configuración, la integridad de la caché y la coherencia de la interfaz.",
-            "ok": "No se encontraron problemas activos en el entorno actual.",
-            "warning": "Se encontraron {count} problema(s). La mayoría se puede solucionar directamente desde este panel.",
-            "error": "Se encontraron {count} problema(s). Deben atenderse antes de que la aplicación esté completamente saludable."
-        },
-        "status": {
-            "ok": "Saludable",
-            "warning": "Requiere atención",
-            "error": "Se requiere acción"
-        },
-        "actions": {
-            "runAgain": "Ejecutar de nuevo",
-            "exportBundle": "Exportar paquete"
-        },
-        "toast": {
-            "loadFailed": "Error al cargar los diagnósticos: {message}",
-            "repairSuccess": "Reconstrucción de caché completada.",
-            "repairFailed": "Error al reconstruir la caché: {message}",
-            "exportSuccess": "Paquete de diagnósticos exportado.",
-            "exportFailed": "Error al exportar el paquete de diagnósticos: {message}",
-            "conflictsResolved": "{count} conflicto(s) de nombre de archivo resuelto(s).",
-            "conflictsResolveFailed": "Error al resolver conflictos de nombre de archivo: {message}"
-        }
-    },
     "banners": {
         "versionMismatch": {
             "title": "Actualización de la aplicación detectada",
@@ -1928,20 +1594,6 @@
             "content": "LoRA Manager is a passion project maintained full-time by a solo developer. Your support on Ko-fi helps cover development costs, keeps new updates coming, and unlocks a license key for the LM Civitai Extension as a thank-you gift. Every contribution truly makes a difference.",
             "supportCta": "Support on Ko-fi",
             "learnMore": "LM Civitai Extension Tutorial"
-        },
-        "cacheHealth": {
-            "corrupted": {
-                "title": "Corrupción de caché detectada"
-            },
-            "degraded": {
-                "title": "Problemas de caché detectados"
-            },
-            "content": "{invalid} de {total} entradas de caché son inválidas ({rate}). Esto puede causar modelos faltantes o errores. Se recomienda reconstruir la caché.",
-            "rebuildCache": "Reconstruir caché",
-            "dismiss": "Descartar",
-            "rebuilding": "Reconstruyendo caché...",
-            "rebuildFailed": "Error al reconstruir la caché: {error}",
-            "retry": "Reintentar"
         }
     }
 }

locales/fr.json

@@ -1,11 +1,8 @@
 {
     "common": {
-        "cancel": "Annuler",
-        "confirm": "Confirmer",
         "actions": {
             "save": "Enregistrer",
             "cancel": "Annuler",
-            "confirm": "Confirmer",
             "delete": "Supprimer",
             "move": "Déplacer",
             "refresh": "Actualiser",
@@ -14,9 +11,7 @@
             "backToTop": "Retour en haut",
             "settings": "Paramètres",
             "help": "Aide",
-            "add": "Ajouter",
-            "close": "Fermer",
-            "menu": "Menu"
+            "add": "Ajouter"
         },
         "status": {
             "loading": "Chargement...",
@@ -136,8 +131,7 @@
         },
         "badges": {
             "update": "Mise à jour",
-            "updateAvailable": "Mise à jour disponible",
-            "skipRefresh": "Actualisation des métadonnées ignorée"
+            "updateAvailable": "Mise à jour disponible"
         },
         "usage": {
             "timesUsed": "Nombre d'utilisations"
@@ -176,9 +170,6 @@
             "success": "{count} recettes réparées avec succès.",
             "cancelled": "Réparation annulée. {count} recettes ont été réparées.",
             "error": "Échec de la réparation des recettes : {message}"
-        },
-        "manageExcludedModels": {
-            "label": "Gérer les modèles exclus"
         }
     },
     "header": {
@@ -188,6 +179,7 @@
             "recipes": "Recipes",
             "checkpoints": "Checkpoints",
             "embeddings": "Embeddings",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "Statistiques"
         },
         "search": {
@@ -196,7 +188,8 @@
                 "loras": "Rechercher des LoRAs...",
                 "recipes": "Rechercher des recipes...",
                 "checkpoints": "Rechercher des checkpoints...",
-                "embeddings": "Rechercher des embeddings..."
+                "embeddings": "Rechercher des embeddings...",
+                "misc": "[TODO: Translate] Search VAE/Upscaler models..."
             },
             "options": "Options de recherche",
             "searchIn": "Rechercher dans :",
@@ -226,19 +219,13 @@
             "presetOverwriteConfirm": "Le préréglage \"{name}\" existe déjà. Remplacer?",
             "presetNamePlaceholder": "Nom du préréglage...",
             "baseModel": "Modèle de base",
-            "baseModelSearchPlaceholder": "Rechercher des modèles de base...",
             "modelTags": "Tags (Top 20)",
-            "modelTypes": "Types de modèles",
+            "modelTypes": "Model Types",
             "license": "Licence",
             "noCreditRequired": "Crédit non requis",
             "allowSellingGeneratedContent": "Vente autorisée",
             "noTags": "Aucun tag",
-            "noBaseModelMatches": "Aucun modèle de base ne correspond à la recherche actuelle.",
-            "clearAll": "Effacer tous les filtres",
-            "any": "N'importe quel",
-            "all": "Tous",
-            "tagLogicAny": "Correspondre à n'importe quel tag (OU)",
-            "tagLogicAll": "Correspondre à tous les tags (ET)"
+            "clearAll": "Effacer tous les filtres"
         },
         "theme": {
             "toggle": "Basculer le thème",
@@ -256,33 +243,6 @@
         "civitaiApiKey": "Clé API Civitai",
         "civitaiApiKeyPlaceholder": "Entrez votre clé API Civitai",
         "civitaiApiKeyHelp": "Utilisée pour l'authentification lors du téléchargement de modèles depuis Civitai",
-        "civitaiHost": {
-            "label": "Hôte Civitai",
-            "help": "Choisissez quel site Civitai s'ouvre lorsque vous utilisez les liens « View on Civitai ».",
-            "options": {
-                "com": "civitai.com (SFW uniquement)",
-                "red": "civitai.red (sans restriction)"
-            }
-        },
-        "downloadBackend": {
-            "label": "Moteur de téléchargement",
-            "help": "Choisissez comment les fichiers de modèles sont téléchargés. Python utilise le téléchargeur intégré. aria2 utilise le processus externe expérimental de téléchargement.",
-            "options": {
-                "python": "Python (intégré)",
-                "aria2": "aria2 (expérimental)"
-            }
-        },
-        "aria2cPath": {
-            "label": "Chemin vers aria2c",
-            "help": "Chemin facultatif vers l’exécutable aria2c. Laissez vide pour utiliser aria2c depuis le PATH système.",
-            "placeholder": "Laisser vide pour utiliser aria2c depuis le PATH"
-        },
-        "aria2HelpLink": "Apprenez à configurer le backend de téléchargement aria2",
-        "civitaiHostBanner": {
-            "title": "Préférence d’hôte Civitai disponible",
-            "content": "Civitai utilise désormais civitai.com pour le contenu SFW et civitai.red pour le contenu sans restriction. Vous pouvez modifier dans les paramètres le site ouvert par défaut.",
-            "openSettings": "Ouvrir les paramètres"
-        },
         "openSettingsFileLocation": {
             "label": "Ouvrir le dossier des paramètres",
             "tooltip": "Ouvrir le dossier contenant settings.json",
@@ -293,32 +253,19 @@
         },
         "sections": {
             "contentFiltering": "Filtrage du contenu",
-            "downloads": "Téléchargements",
             "videoSettings": "Paramètres vidéo",
             "layoutSettings": "Paramètres d'affichage",
-            "misc": "Divers",
-            "backup": "Sauvegardes",
-            "folderSettings": "Racines par défaut",
-            "recipeSettings": "Recipes",
-            "extraFolderPaths": "Chemins de dossiers supplémentaires",
-            "downloadPathTemplates": "Modèles de chemin de téléchargement",
+            "folderSettings": "Paramètres des dossiers",
             "priorityTags": "Étiquettes prioritaires",
-            "updateFlags": "Indicateurs de mise à jour",
+            "downloadPathTemplates": "Modèles de chemin de téléchargement",
             "exampleImages": "Images d'exemple",
-            "autoOrganize": "Organisation automatique",
-            "metadata": "Métadonnées",
+            "updateFlags": "Indicateurs de mise à jour",
+            "autoOrganize": "Auto-organize",
+            "misc": "Divers",
+            "metadataArchive": "Base de données d'archive des métadonnées",
+            "storageLocation": "Emplacement des paramètres",
             "proxySettings": "Paramètres du proxy"
         },
-        "nav": {
-            "general": "Général",
-            "interface": "Interface",
-            "library": "Bibliothèque"
-        },
-        "search": {
-            "placeholder": "Rechercher dans les paramètres...",
-            "clear": "Effacer la recherche",
-            "noResults": "Aucun paramètre trouvé correspondant à \"{query}\""
-        },
         "storage": {
             "locationLabel": "Mode portable",
             "locationHelp": "Activez pour garder settings.json dans le dépôt ; désactivez pour le placer dans votre dossier de configuration utilisateur."
@@ -327,15 +274,7 @@
             "blurNsfwContent": "Flouter le contenu NSFW",
             "blurNsfwContentHelp": "Flouter les images d'aperçu de contenu pour adultes (NSFW)",
             "showOnlySfw": "Afficher uniquement les résultats SFW",
-            "showOnlySfwHelp": "Filtrer tout le contenu NSFW lors de la navigation et de la recherche",
-            "matureBlurThreshold": "Seuil de floutage pour contenu adulte",
-            "matureBlurThresholdHelp": "Définir à partir de quel niveau de classification le floutage s'applique lorsque le floutage NSFW est activé.",
-            "matureBlurThresholdOptions": {
-                "pg13": "PG13 et plus",
-                "r": "R et plus (par défaut)",
-                "x": "X et plus",
-                "xxx": "XXX uniquement"
-            }
+            "showOnlySfwHelp": "Filtrer tout le contenu NSFW lors de la navigation et de la recherche"
         },
         "videoSettings": {
             "autoplayOnHover": "Lecture automatique vidéo au survol",
@@ -350,63 +289,6 @@
                 "saveFailed": "Impossible d'enregistrer les exclusions : {message}"
             }
         },
-        "metadataRefreshSkipPaths": {
-            "label": "Chemins à ignorer pour l'actualisation des métadonnées",
-            "placeholder": "Exemple : temp, archived/old, test_models",
-            "help": "Ignorer les modèles dans ces chemins de répertoires lors de l'actualisation groupée des métadonnées (\"Récupérer toutes les métadonnées\"). Entrez les chemins de dossiers relatifs au répertoire racine des modèles, séparés par des virgules.",
-            "validation": {
-                "noPaths": "Entrez au moins un chemin séparé par des virgules.",
-                "saveFailed": "Impossible d'enregistrer les chemins à ignorer : {message}"
-            }
-        },
-        "backup": {
-            "autoEnabled": "Sauvegardes automatiques",
-            "autoEnabledHelp": "Crée un instantané local une fois par jour et conserve les plus récents selon la politique de rétention.",
-            "retention": "Nombre de rétention",
-            "retentionHelp": "Combien d'instantanés automatiques conserver avant de supprimer les plus anciens.",
-            "management": "Gestion des sauvegardes",
-            "managementHelp": "Exporte l'état actuel de l'utilisateur ou restaure-le depuis une archive de sauvegarde.",
-            "scopeHelp": "Inclut vos paramètres, l'historique des téléchargements et l'état des mises à jour des modèles. Les fichiers de modèle et les caches régénérables ne sont pas inclus.",
-            "locationSummary": "Emplacement actuel des sauvegardes",
-            "openFolderButton": "Ouvrir le dossier de sauvegarde",
-            "openFolderSuccess": "Dossier de sauvegarde ouvert",
-            "openFolderFailed": "Impossible d'ouvrir le dossier de sauvegarde",
-            "locationCopied": "Chemin de sauvegarde copié dans le presse-papiers : {{path}}",
-            "locationClipboardFallback": "Chemin de sauvegarde : {{path}}",
-            "exportButton": "Exporter la sauvegarde",
-            "exportSuccess": "Sauvegarde exportée avec succès.",
-            "exportFailed": "Échec de l'export de la sauvegarde : {message}",
-            "importButton": "Importer la sauvegarde",
-            "importConfirm": "Importer cette sauvegarde et écraser l'état local de l'utilisateur ?",
-            "importSuccess": "Sauvegarde importée avec succès.",
-            "importFailed": "Échec de l'import de la sauvegarde : {message}",
-            "latestSnapshot": "Dernier instantané",
-            "latestAutoSnapshot": "Dernier instantané automatique",
-            "snapshotCount": "Instantanés enregistrés",
-            "noneAvailable": "Aucun instantané pour le moment"
-        },
-        "downloadSkipBaseModels": {
-            "label": "Ignorer les téléchargements pour certains modèles de base",
-            "help": "S’applique à tous les flux de téléchargement. Seuls les modèles de base pris en charge peuvent être sélectionnés ici.",
-            "searchPlaceholder": "Filtrer les modèles de base...",
-            "empty": "Aucun modèle de base ne correspond à la recherche actuelle.",
-            "summary": {
-                "none": "Aucune sélection",
-                "count": "{count} sélectionnés"
-            },
-            "actions": {
-                "edit": "Modifier",
-                "collapse": "Réduire",
-                "clear": "Effacer"
-            },
-            "validation": {
-                "saveFailed": "Impossible d’enregistrer les modèles de base exclus : {message}"
-            }
-        },
-        "skipPreviouslyDownloadedModelVersions": {
-            "label": "Ignorer les versions de modèles précédemment téléchargées",
-            "help": "Lorsque activé, LoRA Manager ignorera le téléchargement d'une version de modèle si le service d'historique des téléchargements enregistre cette version exacte comme déjà téléchargée. S'applique à tous les flux de téléchargement."
-        },
         "layoutSettings": {
             "displayDensity": "Densité d'affichage",
             "displayDensityOptions": {
@@ -429,8 +311,6 @@
                 "hover": "Révéler au survol"
             },
             "cardInfoDisplayHelp": "Choisissez quand afficher les informations du modèle et les boutons d'action",
-            "showVersionOnCard": "Afficher la version sur la carte",
-            "showVersionOnCardHelp": "Afficher ou masquer le nom de version sur les cartes de modèle",
             "modelCardFooterAction": "Action du bouton de carte de modèle",
             "modelCardFooterActionOptions": {
                 "exampleImages": "Ouvrir les images d'exemple",
@@ -449,37 +329,16 @@
             "activeLibraryHelp": "Basculer entre les bibliothèques configurées pour mettre à jour les dossiers par défaut. Changer la sélection recharge la page.",
             "loadingLibraries": "Chargement des bibliothèques...",
             "noLibraries": "Aucune bibliothèque configurée",
-            "defaultLoraRoot": "Racine LoRA",
+            "defaultLoraRoot": "Racine LoRA par défaut",
             "defaultLoraRootHelp": "Définir le répertoire racine LoRA par défaut pour les téléchargements, imports et déplacements",
-            "defaultCheckpointRoot": "Racine Checkpoint",
+            "defaultCheckpointRoot": "Racine Checkpoint par défaut",
             "defaultCheckpointRootHelp": "Définir le répertoire racine checkpoint par défaut pour les téléchargements, imports et déplacements",
-            "defaultUnetRoot": "Racine Diffusion Model",
+            "defaultUnetRoot": "Racine Diffusion Model par défaut",
             "defaultUnetRootHelp": "Définir le répertoire racine Diffusion Model (UNET) par défaut pour les téléchargements, imports et déplacements",
-            "defaultEmbeddingRoot": "Racine Embedding",
+            "defaultEmbeddingRoot": "Racine Embedding par défaut",
             "defaultEmbeddingRootHelp": "Définir le répertoire racine embedding par défaut pour les téléchargements, imports et déplacements",
-            "recipesPath": "Recipes Storage Path",
-            "recipesPathHelp": "Optional custom directory for stored recipes. Leave empty to use the first LoRA root's recipes folder.",
-            "recipesPathPlaceholder": "/path/to/recipes",
-            "recipesPathMigrating": "Migrating recipes storage...",
             "noDefault": "Aucun par défaut"
         },
-        "extraFolderPaths": {
-            "title": "Chemins de dossiers supplémentaires",
-            "description": "Chemins racine de modèles supplémentaires exclusifs à LoRA Manager. Chargez des modèles depuis des emplacements en dehors des dossiers standard de ComfyUI, idéal pour les grandes bibliothèques qui ralentiraient autrement ComfyUI.",
-            "restartRequired": "Requires restart to take effect",
-            "modelTypes": {
-                "lora": "Chemins LoRA",
-                "checkpoint": "Chemins Checkpoint",
-                "unet": "Chemins de modèle de diffusion",
-                "embedding": "Chemins Embedding"
-            },
-            "pathPlaceholder": "/chemin/vers/modèles/supplémentaires",
-            "saveSuccess": "Chemins de dossiers supplémentaires mis à jour. Redémarrage requis pour appliquer les changements.",
-            "saveError": "Échec de la mise à jour des chemins de dossiers supplémentaires: {message}",
-            "validation": {
-                "duplicatePath": "Ce chemin est déjà configuré"
-            }
-        },
         "priorityTags": {
             "title": "Étiquettes prioritaires",
             "description": "Personnalisez l'ordre de priorité des étiquettes pour chaque type de modèle (par ex. : character, concept, style(toon|toon_style))",
@@ -542,21 +401,6 @@
             "downloadLocationHelp": "Entrez le chemin du dossier où les images d'exemple de Civitai seront sauvegardées",
             "autoDownload": "Téléchargement automatique des images d'exemple",
             "autoDownloadHelp": "Télécharger automatiquement les images d'exemple pour les modèles qui n'en ont pas (nécessite que l'emplacement de téléchargement soit défini)",
-            "openMode": "Action d’ouverture des images d’exemple",
-            "openModeHelp": "Choisissez si l’action s’ouvre sur le serveur, copie un chemin local mappé ou lance une URI personnalisée.",
-            "openModeOptions": {
-                "system": "Ouvrir sur le serveur",
-                "clipboard": "Copier le chemin local",
-                "uriTemplate": "Ouvrir une URI personnalisée"
-            },
-            "localRoot": "Racine locale des images d’exemple",
-            "localRootHelp": "Racine locale ou montée facultative qui reflète le répertoire des images d’exemple du serveur. Si vide, le chemin du serveur est réutilisé.",
-            "localRootPlaceholder": "Exemple : /Volumes/ComfyUI/example_images",
-            "uriTemplate": "Ouvrir le modèle d’URI",
-            "uriTemplateHelp": "Utilisez un lien profond personnalisé, tel qu’une URI de fichier ou un lien Shortcuts.",
-            "uriTemplatePlaceholder": "Exemple : shortcuts://run-shortcut?name=Open%20Finder&input=text&text={{encoded_local_path}}",
-            "uriTemplatePlaceholders": "Paramètres disponibles : {{local_path}}, {{encoded_local_path}}, {{relative_path}}, {{encoded_relative_path}}, {{file_uri}}, {{encoded_file_uri}}",
-            "openModeWikiLink": "En savoir plus sur les modes d'ouverture à distance",
             "optimizeImages": "Optimiser les images téléchargées",
             "optimizeImagesHelp": "Optimiser les images d'exemple pour réduire la taille du fichier et améliorer la vitesse de chargement (les métadonnées seront préservées)",
             "download": "Télécharger",
@@ -570,10 +414,6 @@
                 "any": "Signaler n’importe quelle mise à jour disponible"
             }
         },
-        "hideEarlyAccessUpdates": {
-            "label": "Masquer les mises à jour en accès anticipé",
-            "help": "Seulement les mises à jour en accès anticipé"
-        },
         "misc": {
             "includeTriggerWords": "Inclure les mots-clés dans la syntaxe LoRA",
             "includeTriggerWordsHelp": "Inclure les mots-clés d'entraînement lors de la copie de la syntaxe LoRA dans le presse-papiers"
@@ -685,13 +525,8 @@
             "checkUpdates": "Vérifier les mises à jour pour la sélection",
             "moveAll": "Déplacer tout vers un dossier",
             "autoOrganize": "Auto-organiser la sélection",
-            "skipMetadataRefresh": "Ignorer l'actualisation des métadonnées pour la sélection",
-            "resumeMetadataRefresh": "Reprendre l'actualisation des métadonnées pour la sélection",
-            "deleteAll": "Supprimer la sélection",
-            "downloadMissingLoras": "Télécharger les LoRAs manquants",
+            "deleteAll": "Supprimer tous les modèles",
             "clear": "Effacer la sélection",
-            "skipMetadataRefreshCount": "Ignorer（{count} modèles）",
-            "resumeMetadataRefreshCount": "Reprendre（{count} modèles）",
             "autoOrganizeProgress": {
                 "initializing": "Initialisation de l'auto-organisation...",
                 "starting": "Démarrage de l'auto-organisation pour {type}...",
@@ -718,7 +553,6 @@
             "moveToFolder": "Déplacer vers un dossier",
             "repairMetadata": "Réparer les métadonnées",
             "excludeModel": "Exclure le modèle",
-            "restoreModel": "Restaurer le modèle",
             "deleteModel": "Supprimer le modèle",
             "shareRecipe": "Partager la recipe",
             "viewAllLoras": "Voir tous les LoRAs",
@@ -760,8 +594,6 @@
                 "root": "Racine",
                 "browseFolders": "Parcourir les dossiers :",
                 "downloadAndSaveRecipe": "Télécharger et sauvegarder la recipe",
-                "importRecipeOnly": "Importer uniquement la recette",
-                "importAndDownload": "Importer et télécharger",
                 "downloadMissingLoras": "Télécharger les LoRAs manquants",
                 "saveRecipe": "Sauvegarder la recipe",
                 "loraCountInfo": "({existing}/{total} dans la bibliothèque)",
@@ -803,11 +635,7 @@
                 "lorasCountAsc": "Moins"
             },
             "refresh": {
-                "title": "Actualiser la liste des recipes",
-                "quick": "Synchroniser les changements",
-                "quickTooltip": "Synchroniser les changements - actualisation rapide sans reconstruire le cache",
-                "full": "Reconstruire le cache",
-                "fullTooltip": "Reconstruire le cache - rescan complet de tous les fichiers de recipes"
+                "title": "Actualiser la liste des recipes"
             },
             "filteredByLora": "Filtré par LoRA",
             "favorites": {
@@ -847,64 +675,6 @@
                 "failed": "Échec de la réparation de la recette : {message}",
                 "missingId": "Impossible de réparer la recette : ID de recette manquant"
             }
-        },
-        "batchImport": {
-            "title": "Batch Import Recipes",
-            "action": "Batch Import",
-            "urlList": "URL List",
-            "directory": "Directory",
-            "urlDescription": "Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
-            "directoryDescription": "Enter a directory path to import all images from that folder.",
-            "urlsLabel": "Image URLs or Local Paths",
-            "urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
-            "urlsHint": "Enter one URL or path per line",
-            "directoryPath": "Directory Path",
-            "directoryPlaceholder": "/path/to/images/folder",
-            "browse": "Browse",
-            "recursive": "Include subdirectories",
-            "tagsOptional": "Tags (optional, applied to all recipes)",
-            "tagsPlaceholder": "Enter tags separated by commas",
-            "tagsHint": "Tags will be added to all imported recipes",
-            "skipNoMetadata": "Skip images without metadata",
-            "skipNoMetadataHelp": "Images without LoRA metadata will be skipped automatically.",
-            "start": "Start Import",
-            "startImport": "Start Import",
-            "importing": "Importing...",
-            "progress": "Progress",
-            "total": "Total",
-            "success": "Success",
-            "failed": "Failed",
-            "skipped": "Skipped",
-            "current": "Current",
-            "currentItem": "Current",
-            "preparing": "Preparing...",
-            "cancel": "Cancel",
-            "cancelImport": "Cancel",
-            "cancelled": "Import cancelled",
-            "completed": "Import completed",
-            "completedWithErrors": "Completed with errors",
-            "completedSuccess": "Successfully imported {count} recipe(s)",
-            "successCount": "Successful",
-            "failedCount": "Failed",
-            "skippedCount": "Skipped",
-            "totalProcessed": "Total processed",
-            "viewDetails": "View Details",
-            "newImport": "New Import",
-            "manualPathEntry": "Please enter the directory path manually. File browser is not available in this browser.",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "batchImportManualEntryRequired": "File browser not available. Please enter the directory path manually.",
-            "backToParent": "Back to parent directory",
-            "folders": "Folders",
-            "folderCount": "{count} folders",
-            "imageFiles": "Image Files",
-            "images": "images",
-            "imageCount": "{count} images",
-            "selectFolder": "Select This Folder",
-            "errors": {
-                "enterUrls": "Please enter at least one URL or path",
-                "enterDirectory": "Please enter a directory path",
-                "startFailed": "Failed to start import: {message}"
-            }
         }
     },
     "checkpoints": {
@@ -914,13 +684,22 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "Déplacer vers le dossier {otherType}",
-            "sendToWorkflow": "Envoyer vers le workflow"
+            "moveToOtherTypeFolder": "Déplacer vers le dossier {otherType}"
         }
     },
     "embeddings": {
         "title": "Modèles Embedding"
     },
+    "misc": {
+        "title": "[TODO: Translate] VAE & Upscaler Models",
+        "modelTypes": {
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "[TODO: Translate] Move to {otherType} Folder"
+        }
+    },
     "sidebar": {
         "modelRoot": "Racine",
         "collapseAll": "Réduire tous les dossiers",
@@ -928,23 +707,13 @@
         "unpinSidebar": "Désépingler la barre latérale",
         "switchToListView": "Passer en vue liste",
         "switchToTreeView": "Passer en vue arborescence",
-        "recursiveOn": "Inclure les sous-dossiers",
-        "recursiveOff": "Dossier actuel uniquement",
+        "recursiveOn": "Rechercher dans les sous-dossiers",
+        "recursiveOff": "Rechercher uniquement dans le dossier actuel",
         "recursiveUnavailable": "La recherche récursive n'est disponible qu'en vue arborescente",
         "collapseAllDisabled": "Non disponible en vue liste",
         "dragDrop": {
             "unableToResolveRoot": "Impossible de déterminer le chemin de destination pour le déplacement.",
-            "moveUnsupported": "Le déplacement n'est pas pris en charge pour cet élément.",
-            "createFolderHint": "Relâcher pour créer un nouveau dossier",
-            "newFolderName": "Nom du nouveau dossier",
-            "folderNameHint": "Appuyez sur Entrée pour confirmer, Échap pour annuler",
-            "emptyFolderName": "Veuillez saisir un nom de dossier",
-            "invalidFolderName": "Le nom du dossier contient des caractères invalides",
-            "noDragState": "Aucune opération de glissement en attente trouvée"
-        },
-        "empty": {
-            "noFolders": "Aucun dossier trouvé",
-            "dragHint": "Faites glisser des éléments ici pour créer des dossiers"
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -1009,8 +778,6 @@
             "earlyAccess": "Accès anticipé",
             "earlyAccessTooltip": "Accès anticipé requis",
             "inLibrary": "Dans la bibliothèque",
-            "downloaded": "Téléchargé",
-            "downloadedTooltip": "Déjà téléchargé, mais il n'est actuellement pas dans votre bibliothèque.",
             "alreadyInLibrary": "Déjà dans la bibliothèque",
             "autoOrganizedPath": "[Auto-organisé par modèle de chemin]",
             "errors": {
@@ -1101,14 +868,6 @@
             "save": "Mettre à jour le modèle de base",
             "cancel": "Annuler"
         },
-        "bulkDownloadMissingLoras": {
-            "title": "Télécharger les LoRAs manquants",
-            "message": "{uniqueCount} LoRAs manquants uniques trouvés (sur un total de {totalCount} dans les recettes sélectionnées).",
-            "previewTitle": "LoRAs à télécharger :",
-            "moreItems": "...et {count} de plus",
-            "note": "Les fichiers seront téléchargés en utilisant les modèles de chemins par défaut. Cela peut prendre un certain temps selon le nombre de LoRAs.",
-            "downloadButton": "Télécharger {count} LoRA(s)"
-        },
         "exampleAccess": {
             "title": "Images d'exemple locales",
             "message": "Aucune image d'exemple locale trouvée pour ce modèle. Options d'affichage :",
@@ -1160,9 +919,7 @@
                 "viewOnCivitai": "Voir sur Civitai",
                 "viewOnCivitaiText": "Voir sur Civitai",
                 "viewCreatorProfile": "Voir le profil du créateur",
-                "openFileLocation": "Ouvrir l'emplacement du fichier",
-                "sendToWorkflow": "Envoyer vers ComfyUI",
-                "sendToWorkflowText": "Envoyer vers ComfyUI"
+                "openFileLocation": "Ouvrir l'emplacement du fichier"
             },
             "openFileLocation": {
                 "success": "Emplacement du fichier ouvert avec succès",
@@ -1170,9 +927,6 @@
                 "copied": "Chemin copié dans le presse-papiers: {{path}}",
                 "clipboardFallback": "Chemin: {{path}}"
             },
-            "sendToWorkflow": {
-                "noFilePath": "Impossible d'envoyer vers ComfyUI : aucun chemin de fichier disponible"
-            },
             "metadata": {
                 "version": "Version",
                 "fileName": "Nom de fichier",
@@ -1209,8 +963,6 @@
                 "cancel": "Annuler la modification",
                 "save": "Sauvegarder les modifications",
                 "addPlaceholder": "Tapez pour ajouter ou cliquez sur les suggestions ci-dessous",
-                "editWord": "Modifier le mot déclencheur",
-                "editPlaceholder": "Modifier le mot déclencheur",
                 "copyWord": "Copier le mot-clé",
                 "deleteWord": "Supprimer le mot-clé",
                 "suggestions": {
@@ -1273,43 +1025,19 @@
                 },
                 "labels": {
                     "unnamed": "Version sans nom",
-                    "noDetails": "Aucun détail supplémentaire",
-                    "earlyAccess": "EA"
-                },
-                "eaTime": {
-                    "endingSoon": "se termine bientôt",
-                    "hours": "dans {count}h",
-                    "days": "dans {count}j"
+                    "noDetails": "Aucun détail supplémentaire"
                 },
                 "badges": {
-                    "current": "Version ouverte",
-                    "currentTooltip": "C'est la version à partir de laquelle cette fenêtre a été ouverte",
+                    "current": "Version actuelle",
                     "inLibrary": "Dans la bibliothèque",
-                    "inLibraryTooltip": "Cette version existe dans votre bibliothèque locale",
-                    "downloaded": "Téléchargé",
-                    "downloadedTooltip": "Cette version a déjà été téléchargée, mais n'est pas actuellement dans votre bibliothèque",
                     "newer": "Version plus récente",
-                    "newerTooltip": "Cette version est plus récente que votre dernière version locale",
-                    "earlyAccess": "Accès anticipé",
-                    "earlyAccessTooltip": "Cette version nécessite actuellement l'accès anticipé Civitai",
-                    "ignored": "Ignorée",
-                    "ignoredTooltip": "Les notifications de mise à jour sont désactivées pour cette version",
-                    "onSiteOnly": "Uniquement sur Site",
-                    "onSiteOnlyTooltip": "Cette version n'est disponible que pour la génération sur le site Civitai"
+                    "ignored": "Ignorée"
                 },
                 "actions": {
                     "download": "Télécharger",
-                    "downloadTooltip": "Télécharger cette version",
-                    "downloadEarlyAccessTooltip": "Télécharger cette version en accès anticipé depuis Civitai",
-                    "downloadNotAllowedTooltip": "Cette version n'est disponible que pour la génération sur le site Civitai",
                     "delete": "Supprimer",
-                    "deleteTooltip": "Supprimer cette version locale",
                     "ignore": "Ignorer",
                     "unignore": "Ne plus ignorer",
-                    "ignoreTooltip": "Ignorer les notifications de mise à jour pour cette version",
-                    "unignoreTooltip": "Reprendre les notifications de mise à jour pour cette version",
-                    "viewVersionOnCivitai": "Voir la version sur Civitai",
-                    "earlyAccessTooltip": "Nécessite l'achat de l'accès anticipé",
                     "resumeModelUpdates": "Reprendre les mises à jour pour ce modèle",
                     "ignoreModelUpdates": "Ignorer les mises à jour pour ce modèle",
                     "viewLocalVersions": "Voir toutes les versions locales",
@@ -1388,6 +1116,10 @@
             "title": "Initialisation des statistiques",
             "message": "Traitement des données de modèle pour les statistiques. Cela peut prendre quelques minutes..."
         },
+        "misc": {
+            "title": "[TODO: Translate] Initializing Misc Model Manager",
+            "message": "[TODO: Translate] Scanning VAE and Upscaler models..."
+        },
         "tips": {
             "title": "Astuces et conseils",
             "civitai": {
@@ -1447,14 +1179,18 @@
             "recipeAdded": "Recipe ajoutée au workflow",
             "recipeReplaced": "Recipe remplacée dans le workflow",
             "recipeFailedToSend": "Échec de l'envoi de la recipe au workflow",
+            "vaeUpdated": "[TODO: Translate] VAE updated in workflow",
+            "vaeFailed": "[TODO: Translate] Failed to update VAE in workflow",
+            "upscalerUpdated": "[TODO: Translate] Upscaler updated in workflow",
+            "upscalerFailed": "[TODO: Translate] Failed to update upscaler in workflow",
             "noMatchingNodes": "Aucun nœud compatible disponible dans le workflow actuel",
-            "noTargetNodeSelected": "Aucun nœud cible sélectionné",
-            "modelUpdated": "Modèle mis à jour dans le workflow",
-            "modelFailed": "Échec de la mise à jour du nœud modèle"
+            "noTargetNodeSelected": "Aucun nœud cible sélectionné"
         },
         "nodeSelector": {
             "recipe": "Recipe",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "Remplacer",
             "append": "Ajouter",
             "selectTargetNode": "Sélectionner le nœud cible",
@@ -1464,10 +1200,6 @@
             "opened": "Dossier d'images d'exemple ouvert",
             "openingFolder": "Ouverture du dossier d'images d'exemple",
             "failedToOpen": "Échec de l'ouverture du dossier d'images d'exemple",
-            "copiedPath": "Chemin copié dans le presse-papiers : {{path}}",
-            "clipboardFallback": "Chemin : {{path}}",
-            "copiedUri": "Lien copié dans le presse-papiers : {{uri}}",
-            "uriClipboardFallback": "Lien : {{uri}}",
             "setupRequired": "Stockage d'images d'exemple",
             "setupDescription": "Pour ajouter des images d'exemple personnalisées, vous devez d'abord définir un emplacement de téléchargement.",
             "setupUsage": "Ce chemin est utilisé pour les images d'exemple téléchargées et personnalisées.",
@@ -1575,14 +1307,7 @@
             "showWechatQR": "Afficher le QR Code WeChat",
             "hideWechatQR": "Masquer le QR Code WeChat"
         },
-        "footer": "Merci d'utiliser le Gestionnaire LoRA ! ❤️",
-        "supporters": {
-            "title": "Merci à tous les supporters",
-            "subtitle": "Merci aux {count} supporters qui ont rendu ce projet possible",
-            "specialThanks": "Remerciements spéciaux",
-            "allSupporters": "Tous les supporters",
-            "totalCount": "{count} supporters au total"
-        }
+        "footer": "Merci d'utiliser le Gestionnaire LoRA ! ❤️"
     },
     "toast": {
         "general": {
@@ -1605,7 +1330,6 @@
             "pleaseSelectVersion": "Veuillez sélectionner une version",
             "versionExists": "Cette version existe déjà dans votre bibliothèque",
             "downloadCompleted": "Téléchargement terminé avec succès",
-            "downloadSkippedByBaseModel": "Téléchargement ignoré, car le modèle de base {baseModel} est exclu",
             "autoOrganizeSuccess": "Auto-organisation terminée avec succès pour {count} {type}",
             "autoOrganizePartialSuccess": "Auto-organisation terminée avec {success} déplacés, {failures} échecs sur {total} modèles",
             "autoOrganizeFailed": "Échec de l'auto-organisation : {error}",
@@ -1617,19 +1341,13 @@
             "loadFailed": "Échec du chargement des {modelType}s : {message}",
             "refreshComplete": "Actualisation terminée",
             "refreshFailed": "Échec de l'actualisation des recipes : {message}",
-            "syncComplete": "Synchronisation terminée",
-            "syncFailed": "Échec de la synchronisation des recipes : {message}",
             "updateFailed": "Échec de la mise à jour de la recipe : {error}",
             "updateError": "Erreur lors de la mise à jour de la recipe : {message}",
             "nameSaved": "Recipe \"{name}\" sauvegardée avec succès",
             "nameUpdated": "Nom de la recipe mis à jour avec succès",
             "tagsUpdated": "Tags de la recipe mis à jour avec succès",
             "sourceUrlUpdated": "URL source mise à jour avec succès",
-            "promptUpdated": "Prompt mis à jour avec succès",
-            "negativePromptUpdated": "Prompt négatif mis à jour avec succès",
-            "promptEditorHint": "Appuyez sur Entrée pour sauvegarder, Maj+Entrée pour nouvelle ligne",
             "noRecipeId": "Aucun ID de recipe disponible",
-            "sendToWorkflowFailed": "Échec de l'envoi de la recette vers le workflow : {message}",
             "copyFailed": "Erreur lors de la copie de la syntaxe de la recipe : {message}",
             "noMissingLoras": "Aucun LoRA manquant à télécharger",
             "missingLorasInfoFailed": "Échec de l'obtention des informations pour les LoRAs manquants",
@@ -1657,20 +1375,9 @@
             "processingError": "Erreur de traitement : {message}",
             "folderBrowserError": "Erreur lors du chargement du navigateur de dossiers : {message}",
             "recipeSaveFailed": "Échec de la sauvegarde de la recipe : {error}",
-            "recipeSaved": "Recipe saved successfully",
             "importFailed": "Échec de l'importation : {message}",
             "folderTreeFailed": "Échec du chargement de l'arborescence des dossiers",
-            "folderTreeError": "Erreur lors du chargement de l'arborescence des dossiers",
-            "batchImportFailed": "Failed to start batch import: {message}",
-            "batchImportCancelling": "Cancelling batch import...",
-            "batchImportCancelFailed": "Failed to cancel batch import: {message}",
-            "batchImportNoUrls": "Please enter at least one URL or file path",
-            "batchImportNoDirectory": "Please enter a directory path",
-            "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "noRecipesSelected": "Aucune recette sélectionnée",
-            "noMissingLorasInSelection": "Aucun LoRA manquant trouvé dans les recettes sélectionnées",
-            "noLoraRootConfigured": "Aucun répertoire racine LoRA configuré. Veuillez définir un répertoire racine LoRA par défaut dans les paramètres."
+            "folderTreeError": "Erreur lors du chargement de l'arborescence des dossiers"
         },
         "models": {
             "noModelsSelected": "Aucun modèle sélectionné",
@@ -1690,11 +1397,6 @@
             "bulkBaseModelUpdateSuccess": "Modèle de base mis à jour avec succès pour {count} modèle(s)",
             "bulkBaseModelUpdatePartial": "{success} modèle(s) mis à jour, {failed} modèle(s) en échec",
             "bulkBaseModelUpdateFailed": "Échec de la mise à jour du modèle de base pour les modèles sélectionnés",
-            "skipMetadataRefreshUpdating": "Mise à jour du flag d'actualisation des métadonnées pour {count} modèle(s)...",
-            "skipMetadataRefreshSet": "Actualisation des métadonnées ignorée pour {count} modèle(s)",
-            "skipMetadataRefreshCleared": "Actualisation des métadonnées reprise pour {count} modèle(s)",
-            "skipMetadataRefreshPartial": "{success} modèle(s) mis à jour, {failed} échoué(s)",
-            "skipMetadataRefreshFailed": "Échec de la mise à jour du flag d'actualisation des métadonnées pour les modèles sélectionnés",
             "bulkContentRatingUpdating": "Mise à jour de la classification du contenu pour {count} modèle(s)...",
             "bulkContentRatingSet": "Classification du contenu définie sur {level} pour {count} modèle(s)",
             "bulkContentRatingPartial": "Classification du contenu définie sur {level} pour {success} modèle(s), {failed} échec(s)",
@@ -1737,8 +1439,6 @@
             "mappingSaveFailed": "Échec de la sauvegarde des mappages de modèle de base : {message}",
             "downloadTemplatesUpdated": "Modèles de chemin de téléchargement mis à jour",
             "downloadTemplatesFailed": "Échec de la sauvegarde des modèles de chemin de téléchargement : {message}",
-            "recipesPathUpdated": "Recipes storage path updated",
-            "recipesPathSaveFailed": "Failed to update recipes storage path: {message}",
             "settingsUpdated": "Paramètres mis à jour : {setting}",
             "compactModeToggled": "Mode compact {state}",
             "settingSaveFailed": "Échec de la sauvegarde du paramètre : {message}",
@@ -1784,13 +1484,12 @@
             "folderTreeFailed": "Échec du chargement de l'arborescence des dossiers",
             "folderTreeError": "Erreur lors du chargement de l'arborescence des dossiers",
             "imagesImported": "Images d'exemple importées avec succès",
-            "imagesPartial": "{success} image(s) importée(s), {failed} échouée(s)",
             "importFailed": "Échec de l'importation des images d'exemple : {message}"
         },
         "triggerWords": {
             "loadFailed": "Impossible de charger les mots entraînés",
-            "tooLong": "Le mot-clé ne doit pas dépasser 500 mots",
-            "tooMany": "Maximum 100 mots-clés autorisés",
+            "tooLong": "Le mot-clé ne doit pas dépasser 100 mots",
+            "tooMany": "Maximum 30 mots-clés autorisés",
             "alreadyExists": "Ce mot-clé existe déjà",
             "updateSuccess": "Mots-clés mis à jour avec succès",
             "updateFailed": "Échec de la mise à jour des mots-clés",
@@ -1851,8 +1550,6 @@
             "deleteFailed": "Échec de la suppression de {type} : {message}",
             "excludeSuccess": "{type} exclu avec succès",
             "excludeFailed": "Échec de l'exclusion de {type} : {message}",
-            "restoreSuccess": "{type} restauré avec succès",
-            "restoreFailed": "Échec de la restauration de {type} : {message}",
             "fileNameUpdated": "Nom de fichier mis à jour avec succès",
             "fileRenameFailed": "Échec du renommage du fichier : {error}",
             "previewUpdated": "Aperçu mis à jour avec succès",
@@ -1884,37 +1581,6 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
-    "doctor": {
-        "kicker": "Diagnostics système",
-        "title": "Docteur",
-        "buttonTitle": "Lancer les diagnostics et les corrections courantes",
-        "loading": "Vérification de l'environnement...",
-        "footer": "Exportez un lot de diagnostic si le problème persiste après la réparation.",
-        "summary": {
-            "idle": "Lancez une vérification de l'état des paramètres, de l'intégrité du cache et de la cohérence de l'interface.",
-            "ok": "Aucun problème actif n'a été trouvé dans l'environnement actuel.",
-            "warning": "{count} problème(s) ont été trouvés. La plupart peuvent être corrigés directement depuis ce panneau.",
-            "error": "{count} problème(s) nécessitent une attention avant que l'application soit entièrement saine."
-        },
-        "status": {
-            "ok": "Sain",
-            "warning": "Nécessite une attention",
-            "error": "Action requise"
-        },
-        "actions": {
-            "runAgain": "Relancer",
-            "exportBundle": "Exporter le lot"
-        },
-        "toast": {
-            "loadFailed": "Échec du chargement des diagnostics : {message}",
-            "repairSuccess": "Reconstruction du cache terminée.",
-            "repairFailed": "Échec de la reconstruction du cache : {message}",
-            "exportSuccess": "Lot de diagnostics exporté.",
-            "exportFailed": "Échec de l'export du lot de diagnostics : {message}",
-            "conflictsResolved": "{count} conflit(s) de nom de fichier résolu(s).",
-            "conflictsResolveFailed": "Échec de la résolution des conflits de nom de fichier : {message}"
-        }
-    },
     "banners": {
         "versionMismatch": {
             "title": "Mise à jour de l'application détectée",
@@ -1928,20 +1594,6 @@
             "content": "LoRA Manager is a passion project maintained full-time by a solo developer. Your support on Ko-fi helps cover development costs, keeps new updates coming, and unlocks a license key for the LM Civitai Extension as a thank-you gift. Every contribution truly makes a difference.",
             "supportCta": "Support on Ko-fi",
             "learnMore": "LM Civitai Extension Tutorial"
-        },
-        "cacheHealth": {
-            "corrupted": {
-                "title": "Corruption du cache détectée"
-            },
-            "degraded": {
-                "title": "Problèmes de cache détectés"
-            },
-            "content": "{invalid} des {total} entrées de cache sont invalides ({rate}). Cela peut provoquer des modèles manquants ou des erreurs. Il est recommandé de reconstruire le cache.",
-            "rebuildCache": "Reconstruire le cache",
-            "dismiss": "Ignorer",
-            "rebuilding": "Reconstruction du cache...",
-            "rebuildFailed": "Échec de la reconstruction du cache : {error}",
-            "retry": "Réessayer"
         }
     }
 }

locales/he.json

@@ -1,22 +1,17 @@
 {
     "common": {
-        "cancel": "ביטול",
-        "confirm": "אישור",
         "actions": {
-            "save": "שמירה",
+            "save": "שמור",
             "cancel": "ביטול",
-            "confirm": "אישור",
-            "delete": "מחיקה",
-            "move": "העברה",
-            "refresh": "רענון",
-            "back": "חזרה",
+            "delete": "מחק",
+            "move": "העבר",
+            "refresh": "רענן",
+            "back": "חזור",
             "next": "הבא",
-            "backToTop": "חזרה למעלה",
+            "backToTop": "חזור למעלה",
             "settings": "הגדרות",
             "help": "עזרה",
-            "add": "הוספה",
-            "close": "סגור",
-            "menu": "תפריט"
+            "add": "הוסף"
         },
         "status": {
             "loading": "טוען...",
@@ -136,8 +131,7 @@
         },
         "badges": {
             "update": "עדכון",
-            "updateAvailable": "עדכון זמין",
-            "skipRefresh": "רענון המטא-נתונים דולג"
+            "updateAvailable": "עדכון זמין"
         },
         "usage": {
             "timesUsed": "מספר שימושים"
@@ -176,9 +170,6 @@
             "success": "תוקנו בהצלחה {count} מתכונים.",
             "cancelled": "תיקון בוטל. {count} מתכונים תוקנו.",
             "error": "תיקון המתכונים נכשל: {message}"
-        },
-        "manageExcludedModels": {
-            "label": "ניהול מודלים מוחרגים"
         }
     },
     "header": {
@@ -188,6 +179,7 @@
             "recipes": "מתכונים",
             "checkpoints": "Checkpoints",
             "embeddings": "Embeddings",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "סטטיסטיקה"
         },
         "search": {
@@ -196,7 +188,8 @@
                 "loras": "חפש LoRAs...",
                 "recipes": "חפש מתכונים...",
                 "checkpoints": "חפש checkpoints...",
-                "embeddings": "חפש embeddings..."
+                "embeddings": "חפש embeddings...",
+                "misc": "[TODO: Translate] Search VAE/Upscaler models..."
             },
             "options": "אפשרויות חיפוש",
             "searchIn": "חפש ב:",
@@ -226,19 +219,13 @@
             "presetOverwriteConfirm": "הפריסט \"{name}\" כבר קיים. לדרוס?",
             "presetNamePlaceholder": "שם קביעה מראש...",
             "baseModel": "מודל בסיס",
-            "baseModelSearchPlaceholder": "חפש מודלי בסיס...",
             "modelTags": "תגיות (20 המובילות)",
-            "modelTypes": "סוגי מודלים",
+            "modelTypes": "Model Types",
             "license": "רישיון",
             "noCreditRequired": "ללא קרדיט נדרש",
             "allowSellingGeneratedContent": "אפשר מכירה",
             "noTags": "ללא תגיות",
-            "noBaseModelMatches": "אין מודלי בסיס התואמים לחיפוש הנוכחי.",
-            "clearAll": "נקה את כל המסננים",
-            "any": "כלשהו",
-            "all": "כל התגים",
-            "tagLogicAny": "התאם כל תג (או)",
-            "tagLogicAll": "התאם את כל התגים (וגם)"
+            "clearAll": "נקה את כל המסננים"
         },
         "theme": {
             "toggle": "החלף ערכת נושא",
@@ -256,33 +243,6 @@
         "civitaiApiKey": "מפתח API של Civitai",
         "civitaiApiKeyPlaceholder": "הזן את מפתח ה-API שלך מ-Civitai",
         "civitaiApiKeyHelp": "משמש לאימות בעת הורדת מודלים מ-Civitai",
-        "civitaiHost": {
-            "label": "מארח Civitai",
-            "help": "בחר איזה אתר של Civitai ייפתח בעת שימוש בקישורי \"View on Civitai\".",
-            "options": {
-                "com": "civitai.com (SFW בלבד)",
-                "red": "civitai.red (ללא הגבלות)"
-            }
-        },
-        "downloadBackend": {
-            "label": "מנגנון הורדה",
-            "help": "בחר כיצד יורדים קבצי המודל. Python משתמש במוריד המובנה. aria2 משתמש בתהליך הורדה חיצוני ניסיוני.",
-            "options": {
-                "python": "Python (מובנה)",
-                "aria2": "aria2 (ניסיוני)"
-            }
-        },
-        "aria2cPath": {
-            "label": "נתיב aria2c",
-            "help": "נתיב אופציונלי לקובץ ההפעלה aria2c. השאר ריק כדי להשתמש ב-aria2c מתוך ה-PATH של המערכת.",
-            "placeholder": "השאר ריק כדי להשתמש ב-aria2c מתוך ה-PATH"
-        },
-        "aria2HelpLink": "למד כיצד להגדיר את מנוע ההורדה aria2",
-        "civitaiHostBanner": {
-            "title": "העדפת מארח Civitai זמינה",
-            "content": "Civitai משתמש כעת ב-civitai.com עבור תוכן SFW וב-civitai.red עבור תוכן ללא הגבלות. ניתן לשנות בהגדרות איזה אתר ייפתח כברירת מחדל.",
-            "openSettings": "פתח הגדרות"
-        },
         "openSettingsFileLocation": {
             "label": "פתח תיקיית הגדרות",
             "tooltip": "פתח את התיקייה שמכילה את ‎settings.json",
@@ -293,32 +253,19 @@
         },
         "sections": {
             "contentFiltering": "סינון תוכן",
-            "downloads": "הורדות",
             "videoSettings": "הגדרות וידאו",
             "layoutSettings": "הגדרות פריסה",
-            "misc": "שונות",
-            "backup": "גיבויים",
-            "folderSettings": "תיקיות ברירת מחדל",
-            "recipeSettings": "מתכונים",
-            "extraFolderPaths": "נתיבי תיקיות נוספים",
-            "downloadPathTemplates": "תבניות נתיב הורדה",
+            "folderSettings": "הגדרות תיקייה",
             "priorityTags": "תגיות עדיפות",
-            "updateFlags": "תגי עדכון",
+            "downloadPathTemplates": "תבניות נתיב הורדה",
             "exampleImages": "תמונות דוגמה",
-            "autoOrganize": "ארגון אוטומטי",
-            "metadata": "מטא-נתונים",
+            "updateFlags": "תגי עדכון",
+            "autoOrganize": "Auto-organize",
+            "misc": "שונות",
+            "metadataArchive": "מסד נתונים של ארכיון מטא-דאטה",
+            "storageLocation": "מיקום ההגדרות",
             "proxySettings": "הגדרות פרוקסי"
         },
-        "nav": {
-            "general": "כללי",
-            "interface": "ממשק",
-            "library": "ספרייה"
-        },
-        "search": {
-            "placeholder": "חיפוש בהגדרות...",
-            "clear": "נקה חיפוש",
-            "noResults": "לא נמצאו הגדרות תואמות ל-\"{query}\""
-        },
         "storage": {
             "locationLabel": "מצב נייד",
             "locationHelp": "הפעל כדי לשמור את settings.json בתוך המאגר; בטל כדי לשמור אותו בתיקיית ההגדרות של המשתמש."
@@ -327,15 +274,7 @@
             "blurNsfwContent": "טשטש תוכן NSFW",
             "blurNsfwContentHelp": "טשטש תמונות תצוגה מקדימה של תוכן למבוגרים (NSFW)",
             "showOnlySfw": "הצג רק תוצאות SFW",
-            "showOnlySfwHelp": "סנן את כל התוכן ה-NSFW בעת גלישה וחיפוש",
-            "matureBlurThreshold": "סף טשטוש תוכן מבוגרים",
-            "matureBlurThresholdHelp": "הגדר מאיזו רמת דירוג מתחיל סינון הטשטוש כאשר טשטוש NSFW מופעל.",
-            "matureBlurThresholdOptions": {
-                "pg13": "PG13 ומעלה",
-                "r": "R ומעלה (ברירת מחדל)",
-                "x": "X ומעלה",
-                "xxx": "XXX בלבד"
-            }
+            "showOnlySfwHelp": "סנן את כל התוכן ה-NSFW בעת גלישה וחיפוש"
         },
         "videoSettings": {
             "autoplayOnHover": "נגן וידאו אוטומטית בריחוף",
@@ -350,63 +289,6 @@
                 "saveFailed": "לא ניתן לשמור את ההוצאות: {message}"
             }
         },
-        "metadataRefreshSkipPaths": {
-            "label": "נתיבים לדילוג ברענון מטא-נתונים",
-            "placeholder": "דוגמה: temp, archived/old, test_models",
-            "help": "דלג על מודלים בנתיבי תיקיות אלה בעת רענון מטא-נתונים המוני (\"אחזר את כל המטא-נתונים\"). הזן נתיבי תיקיות יחסית לספריית השורש של המודל, מופרדים בפסיקים.",
-            "validation": {
-                "noPaths": "הזן לפחות נתיב אחד מופרד בפסיקים.",
-                "saveFailed": "לא ניתן לשמור נתיבי דילוג: {message}"
-            }
-        },
-        "backup": {
-            "autoEnabled": "גיבויים אוטומטיים",
-            "autoEnabledHelp": "יוצר צילום מצב מקומי פעם ביום ושומר את הצילומים האחרונים לפי מדיניות השמירה.",
-            "retention": "כמות שמירה",
-            "retentionHelp": "כמה צילומי מצב אוטומטיים לשמור לפני שמסירים ישנים.",
-            "management": "ניהול גיבויים",
-            "managementHelp": "ייצא את מצב המשתמש הנוכחי או שחזר אותו מארכיון גיבוי.",
-            "scopeHelp": "כולל את ההגדרות שלך, היסטוריית ההורדות ומצב עדכוני המודלים. אינו כולל קובצי מודל או מטמונים שניתן לשחזר.",
-            "locationSummary": "מיקום הגיבוי הנוכחי",
-            "openFolderButton": "פתח את תיקיית הגיבויים",
-            "openFolderSuccess": "תיקיית הגיבויים נפתחה",
-            "openFolderFailed": "לא ניתן היה לפתוח את תיקיית הגיבויים",
-            "locationCopied": "נתיב הגיבוי הועתק ללוח: {{path}}",
-            "locationClipboardFallback": "נתיב הגיבוי: {{path}}",
-            "exportButton": "ייצא גיבוי",
-            "exportSuccess": "הגיבוי יוצא בהצלחה.",
-            "exportFailed": "נכשל ייצוא הגיבוי: {message}",
-            "importButton": "ייבא גיבוי",
-            "importConfirm": "לייבא את הגיבוי הזה ולדרוס את מצב המשתמש המקומי?",
-            "importSuccess": "הגיבוי יובא בהצלחה.",
-            "importFailed": "נכשל ייבוא הגיבוי: {message}",
-            "latestSnapshot": "צילום המצב האחרון",
-            "latestAutoSnapshot": "צילום המצב האוטומטי האחרון",
-            "snapshotCount": "צילומי מצב שמורים",
-            "noneAvailable": "עדיין אין צילומי מצב"
-        },
-        "downloadSkipBaseModels": {
-            "label": "דלג על הורדות עבור מודלי בסיס",
-            "help": "חל על כל תהליכי ההורדה. ניתן לבחור כאן רק מודלי בסיס נתמכים.",
-            "searchPlaceholder": "סנן מודלי בסיס...",
-            "empty": "אין מודלי בסיס התואמים לחיפוש הנוכחי.",
-            "summary": {
-                "none": "לא נבחר דבר",
-                "count": "{count} נבחרו"
-            },
-            "actions": {
-                "edit": "עריכה",
-                "collapse": "כווץ",
-                "clear": "נקה"
-            },
-            "validation": {
-                "saveFailed": "לא ניתן לשמור את מודלי הבסיס המוחרגים: {message}"
-            }
-        },
-        "skipPreviouslyDownloadedModelVersions": {
-            "label": "דלג על גרסאות מודלים שהורדו בעבר",
-            "help": "כאשר מופעל, LoRA Manager ידלג על הורדת גרסת מודל אם שירות היסטוריית ההורדות רושם את הגרסה המדויקת הזו ככבר שהורדה. חל על כל תהליכי ההורדה."
-        },
         "layoutSettings": {
             "displayDensity": "צפיפות תצוגה",
             "displayDensityOptions": {
@@ -429,8 +311,6 @@
                 "hover": "חשוף בריחוף"
             },
             "cardInfoDisplayHelp": "בחר מתי להציג מידע על המודל וכפתורי פעולה",
-            "showVersionOnCard": "הצג גרסה בכרטיס",
-            "showVersionOnCardHelp": "הצג או הסתר את שם הגרסה בכרטיסי המודל",
             "modelCardFooterAction": "פעולת כפתור כרטיס מודל",
             "modelCardFooterActionOptions": {
                 "exampleImages": "פתח תמונות דוגמה",
@@ -449,37 +329,16 @@
             "activeLibraryHelp": "החלפה בין הספריות המוגדרות לעדכן את תיקיות ברירת המחדל. שינוי הבחירה ירענן את הדף.",
             "loadingLibraries": "טוען ספריות...",
             "noLibraries": "לא הוגדרו ספריות",
-            "defaultLoraRoot": "תיקיית שורש LoRA",
+            "defaultLoraRoot": "תיקיית שורש ברירת מחדל של LoRA",
             "defaultLoraRootHelp": "הגדר את ספריית השורש המוגדרת כברירת מחדל של LoRA להורדות, ייבוא והעברות",
-            "defaultCheckpointRoot": "תיקיית שורש Checkpoint",
+            "defaultCheckpointRoot": "תיקיית שורש ברירת מחדל של Checkpoint",
             "defaultCheckpointRootHelp": "הגדר את ספריית השורש המוגדרת כברירת מחדל של checkpoint להורדות, ייבוא והעברות",
-            "defaultUnetRoot": "תיקיית שורש Diffusion Model",
+            "defaultUnetRoot": "תיקיית שורש ברירת מחדל של Diffusion Model",
             "defaultUnetRootHelp": "הגדר את ספריית השורש המוגדרת כברירת מחדל של Diffusion Model (UNET) להורדות, ייבוא והעברות",
-            "defaultEmbeddingRoot": "תיקיית שורש Embedding",
+            "defaultEmbeddingRoot": "תיקיית שורש ברירת מחדל של Embedding",
             "defaultEmbeddingRootHelp": "הגדר את ספריית השורש המוגדרת כברירת מחדל של embedding להורדות, ייבוא והעברות",
-            "recipesPath": "נתיב אחסון מתכונים",
-            "recipesPathHelp": "ספרייה מותאמת אישית אופציונלית למתכונים שנשמרו. השאר ריק כדי להשתמש בתיקיית recipes של שורש LoRA הראשון.",
-            "recipesPathPlaceholder": "/path/to/recipes",
-            "recipesPathMigrating": "מעביר את אחסון המתכונים...",
             "noDefault": "אין ברירת מחדל"
         },
-        "extraFolderPaths": {
-            "title": "נתיבי תיקיות נוספים",
-            "description": "נתיבי שורש מודלים נוספים בלעדיים ל-LoRA Manager. טען מודלים ממיקומים מחוץ לתיקיות הסטנדרטיות של ComfyUI - אידיאלי לספריות גדולות שאחרת יאטו את ComfyUI.",
-            "restartRequired": "Requires restart to take effect",
-            "modelTypes": {
-                "lora": "נתיבי LoRA",
-                "checkpoint": "נתיבי Checkpoint",
-                "unet": "נתיבי מודל דיפוזיה",
-                "embedding": "נתיבי Embedding"
-            },
-            "pathPlaceholder": "/נתיב/למודלים/נוספים",
-            "saveSuccess": "נתיבי תיקיות נוספים עודכנו. נדרשת הפעלה מחדש כדי להחיל את השינויים.",
-            "saveError": "נכשל בעדכון נתיבי תיקיות נוספים: {message}",
-            "validation": {
-                "duplicatePath": "נתיב זה כבר מוגדר"
-            }
-        },
         "priorityTags": {
             "title": "תגיות עדיפות",
             "description": "התאם את סדר העדיפות של התגיות עבור כל סוג מודל (לדוגמה: character, concept, style(toon|toon_style))",
@@ -542,21 +401,6 @@
             "downloadLocationHelp": "הזן את נתיב התיקייה שבו יישמרו תמונות דוגמה מ-Civitai",
             "autoDownload": "הורדה אוטומטית של תמונות דוגמה",
             "autoDownloadHelp": "הורד אוטומטית תמונות דוגמה למודלים שאין להם (דורש הגדרת מיקום הורדה)",
-            "openMode": "פעולת פתיחת תמונות דוגמה",
-            "openModeHelp": "בחר אם הפעולה תיפתח בשרת, תעתיק נתיב מקומי ממופה או תפעיל URI מותאם אישית.",
-            "openModeOptions": {
-                "system": "פתח בשרת",
-                "clipboard": "העתק נתיב מקומי",
-                "uriTemplate": "פתח URI מותאם אישית"
-            },
-            "localRoot": "שורש מקומי לתמונות דוגמה",
-            "localRootHelp": "שורש מקומי או ממופה אופציונלי שמשקף את תיקיית תמונות הדוגמה בשרת. אם השדה ריק, ייעשה שימוש חוזר בנתיב השרת.",
-            "localRootPlaceholder": "דוגמה: /Volumes/ComfyUI/example_images",
-            "uriTemplate": "תבנית URI לפתיחה",
-            "uriTemplateHelp": "השתמש בקישור עומק מותאם אישית כמו URI של קובץ או קישור Shortcuts.",
-            "uriTemplatePlaceholder": "דוגמה: shortcuts://run-shortcut?name=Open%20Finder&input=text&text={{encoded_local_path}}",
-            "uriTemplatePlaceholders": "מצייני מקום זמינים: {{local_path}}, {{encoded_local_path}}, {{relative_path}}, {{encoded_relative_path}}, {{file_uri}}, {{encoded_file_uri}}",
-            "openModeWikiLink": "למידע נוסף על מצבי פתיחה מרחוק",
             "optimizeImages": "מטב תמונות שהורדו",
             "optimizeImagesHelp": "מטב תמונות דוגמה כדי להקטין את גודל הקובץ ולשפר את מהירות הטעינה (מטא-דאטה תישמר)",
             "download": "הורד",
@@ -570,10 +414,6 @@
                 "any": "תוויות לכל עדכון זמין"
             }
         },
-        "hideEarlyAccessUpdates": {
-            "label": "הסתר עדכוני גישה מוקדמת",
-            "help": "רק עדכוני גישה מוקדמת"
-        },
         "misc": {
             "includeTriggerWords": "כלול מילות טריגר בתחביר LoRA",
             "includeTriggerWordsHelp": "כלול מילות טריגר מאומנות בעת העתקת תחביר LoRA ללוח"
@@ -685,13 +525,8 @@
             "checkUpdates": "בדוק עדכונים לבחירה",
             "moveAll": "העבר הכל לתיקייה",
             "autoOrganize": "ארגן אוטומטית נבחרים",
-            "skipMetadataRefresh": "דילוג על רענון מטא-נתונים לנבחרים",
-            "resumeMetadataRefresh": "המשך רענון מטא-נתונים לנבחרים",
-            "deleteAll": "מחק נבחרים",
-            "downloadMissingLoras": "הורדת LoRAs חסרים",
+            "deleteAll": "מחק את כל המודלים",
             "clear": "נקה בחירה",
-            "skipMetadataRefreshCount": "דילוג（{count} מודלים）",
-            "resumeMetadataRefreshCount": "המשך（{count} מודלים）",
             "autoOrganizeProgress": {
                 "initializing": "מאתחל ארגון אוטומטי...",
                 "starting": "מתחיל ארגון אוטומטי עבור {type}...",
@@ -718,7 +553,6 @@
             "moveToFolder": "העבר לתיקייה",
             "repairMetadata": "תיקון מטא-דאטה",
             "excludeModel": "החרג מודל",
-            "restoreModel": "שחזור מודל",
             "deleteModel": "מחק מודל",
             "shareRecipe": "שתף מתכון",
             "viewAllLoras": "הצג את כל ה-LoRAs",
@@ -760,8 +594,6 @@
                 "root": "שורש",
                 "browseFolders": "דפדף בתיקיות:",
                 "downloadAndSaveRecipe": "הורד ושמור מתכון",
-                "importRecipeOnly": "יבא רק מתכון",
-                "importAndDownload": "יבא והורד",
                 "downloadMissingLoras": "הורד LoRAs חסרים",
                 "saveRecipe": "שמור מתכון",
                 "loraCountInfo": "({existing}/{total} בספרייה)",
@@ -803,11 +635,7 @@
                 "lorasCountAsc": "הכי פחות"
             },
             "refresh": {
-                "title": "רענן רשימת מתכונים",
-                "quick": "סנכרן שינויים",
-                "quickTooltip": "סנכרן שינויים - רענון מהיר ללא בניית מטמון מחדש",
-                "full": "בנה מטמון מחדש",
-                "fullTooltip": "בנה מטמון מחדש - סריקה מחדש מלאה של כל קבצי המתכונים"
+                "title": "רענן רשימת מתכונים"
             },
             "filteredByLora": "מסונן לפי LoRA",
             "favorites": {
@@ -847,64 +675,6 @@
                 "failed": "תיקון המתכון נכשל: {message}",
                 "missingId": "לא ניתן לתקן את המתכון: חסר מזהה מתכון"
             }
-        },
-        "batchImport": {
-            "title": "Batch Import Recipes",
-            "action": "Batch Import",
-            "urlList": "URL List",
-            "directory": "Directory",
-            "urlDescription": "Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
-            "directoryDescription": "Enter a directory path to import all images from that folder.",
-            "urlsLabel": "Image URLs or Local Paths",
-            "urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
-            "urlsHint": "Enter one URL or path per line",
-            "directoryPath": "Directory Path",
-            "directoryPlaceholder": "/path/to/images/folder",
-            "browse": "Browse",
-            "recursive": "Include subdirectories",
-            "tagsOptional": "Tags (optional, applied to all recipes)",
-            "tagsPlaceholder": "Enter tags separated by commas",
-            "tagsHint": "Tags will be added to all imported recipes",
-            "skipNoMetadata": "Skip images without metadata",
-            "skipNoMetadataHelp": "Images without LoRA metadata will be skipped automatically.",
-            "start": "Start Import",
-            "startImport": "Start Import",
-            "importing": "Importing...",
-            "progress": "Progress",
-            "total": "Total",
-            "success": "Success",
-            "failed": "Failed",
-            "skipped": "Skipped",
-            "current": "Current",
-            "currentItem": "Current",
-            "preparing": "Preparing...",
-            "cancel": "Cancel",
-            "cancelImport": "Cancel",
-            "cancelled": "Import cancelled",
-            "completed": "Import completed",
-            "completedWithErrors": "Completed with errors",
-            "completedSuccess": "Successfully imported {count} recipe(s)",
-            "successCount": "Successful",
-            "failedCount": "Failed",
-            "skippedCount": "Skipped",
-            "totalProcessed": "Total processed",
-            "viewDetails": "View Details",
-            "newImport": "New Import",
-            "manualPathEntry": "Please enter the directory path manually. File browser is not available in this browser.",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "batchImportManualEntryRequired": "File browser not available. Please enter the directory path manually.",
-            "backToParent": "Back to parent directory",
-            "folders": "Folders",
-            "folderCount": "{count} folders",
-            "imageFiles": "Image Files",
-            "images": "images",
-            "imageCount": "{count} images",
-            "selectFolder": "Select This Folder",
-            "errors": {
-                "enterUrls": "Please enter at least one URL or path",
-                "enterDirectory": "Please enter a directory path",
-                "startFailed": "Failed to start import: {message}"
-            }
         }
     },
     "checkpoints": {
@@ -914,13 +684,22 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "העבר לתיקיית {otherType}",
-            "sendToWorkflow": "שלח ל-workflow"
+            "moveToOtherTypeFolder": "העבר לתיקיית {otherType}"
         }
     },
     "embeddings": {
         "title": "מודלי Embedding"
     },
+    "misc": {
+        "title": "[TODO: Translate] VAE & Upscaler Models",
+        "modelTypes": {
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "[TODO: Translate] Move to {otherType} Folder"
+        }
+    },
     "sidebar": {
         "modelRoot": "שורש",
         "collapseAll": "כווץ את כל התיקיות",
@@ -928,23 +707,13 @@
         "unpinSidebar": "שחרר סרגל צד",
         "switchToListView": "עבור לתצוגת רשימה",
         "switchToTreeView": "תצוגת עץ",
-        "recursiveOn": "כלול תיקיות משנה",
-        "recursiveOff": "רק התיקייה הנוכחית",
+        "recursiveOn": "חיפוש בתיקיות משנה",
+        "recursiveOff": "חיפוש רק בתיקייה הנוכחית",
         "recursiveUnavailable": "חיפוש רקורסיבי זמין רק בתצוגת עץ",
         "collapseAllDisabled": "לא זמין בתצוגת רשימה",
         "dragDrop": {
             "unableToResolveRoot": "לא ניתן לקבוע את נתיב היעד להעברה.",
-            "moveUnsupported": "העברה אינה נתמכת עבור פריט זה.",
-            "createFolderHint": "שחרר כדי ליצור תיקייה חדשה",
-            "newFolderName": "שם תיקייה חדשה",
-            "folderNameHint": "הקש Enter לאישור, Escape לביטול",
-            "emptyFolderName": "אנא הזן שם תיקייה",
-            "invalidFolderName": "שם התיקייה מכיל תווים לא חוקיים",
-            "noDragState": "לא נמצאה פעולת גרירה ממתינה"
-        },
-        "empty": {
-            "noFolders": "לא נמצאו תיקיות",
-            "dragHint": "גרור פריטים לכאן כדי ליצור תיקיות"
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -1009,8 +778,6 @@
             "earlyAccess": "גישה מוקדמת",
             "earlyAccessTooltip": "נדרשת גישה מוקדמת",
             "inLibrary": "בספרייה",
-            "downloaded": "הורד",
-            "downloadedTooltip": "הורד בעבר, אך הוא אינו נמצא כרגע בספרייה שלך.",
             "alreadyInLibrary": "כבר בספרייה",
             "autoOrganizedPath": "[מאורגן אוטומטית לפי תבנית נתיב]",
             "errors": {
@@ -1101,14 +868,6 @@
             "save": "עדכן מודל בסיס",
             "cancel": "ביטול"
         },
-        "bulkDownloadMissingLoras": {
-            "title": "הורדת LoRAs חסרים",
-            "message": "נמצאו {uniqueCount} LoRAs חסרים ייחודיים (מתוך {totalCount} בסך הכל במתכונים שנבחרו).",
-            "previewTitle": "LoRAs להורדה:",
-            "moreItems": "...ועוד {count}",
-            "note": "הקבצים יורדו באמצעות תבניות נתיב ברירת מחדל. זה עשוי לקחת זמן בהתאם למספר ה-LoRAs.",
-            "downloadButton": "הורד {count} LoRA(s)"
-        },
         "exampleAccess": {
             "title": "תמונות דוגמה מקומיות",
             "message": "לא נמצאו תמונות דוגמה מקומיות למודל זה. אפשרויות צפייה:",
@@ -1160,9 +919,7 @@
                 "viewOnCivitai": "הצג ב-Civitai",
                 "viewOnCivitaiText": "הצג ב-Civitai",
                 "viewCreatorProfile": "הצג פרופיל יוצר",
-                "openFileLocation": "פתח מיקום קובץ",
-                "sendToWorkflow": "שלח ל-ComfyUI",
-                "sendToWorkflowText": "שלח ל-ComfyUI"
+                "openFileLocation": "פתח מיקום קובץ"
             },
             "openFileLocation": {
                 "success": "מיקום הקובץ נפתח בהצלחה",
@@ -1170,9 +927,6 @@
                 "copied": "הנתיב הועתק ללוח העריכה: {{path}}",
                 "clipboardFallback": "נתיב: {{path}}"
             },
-            "sendToWorkflow": {
-                "noFilePath": "לא ניתן לשלוח ל-ComfyUI: אין נתיב קובץ זמין"
-            },
             "metadata": {
                 "version": "גרסה",
                 "fileName": "שם קובץ",
@@ -1209,8 +963,6 @@
                 "cancel": "בטל עריכה",
                 "save": "שמור שינויים",
                 "addPlaceholder": "הקלד להוספה או לחץ על הצעות למטה",
-                "editWord": "עריכת מילת טריגר",
-                "editPlaceholder": "עריכת מילת טריגר",
                 "copyWord": "העתק מילת טריגר",
                 "deleteWord": "מחק מילת טריגר",
                 "suggestions": {
@@ -1273,43 +1025,19 @@
                 },
                 "labels": {
                     "unnamed": "גרסה ללא שם",
-                    "noDetails": "אין פרטים נוספים",
-                    "earlyAccess": "EA"
-                },
-                "eaTime": {
-                    "endingSoon": "מסתיים בקרוב",
-                    "hours": "בעוד {count} שעות",
-                    "days": "בעוד {count} ימים"
+                    "noDetails": "אין פרטים נוספים"
                 },
                 "badges": {
-                    "current": "גרסה שנפתחה",
-                    "currentTooltip": "זוהי הגרסה שממנה נפתח החלון הזה",
+                    "current": "גרסה נוכחית",
                     "inLibrary": "בספרייה",
-                    "inLibraryTooltip": "גרסה זו קיימת בספרייה המקומית שלך",
-                    "downloaded": "הורד",
-                    "downloadedTooltip": "גרסה זו הורדה בעבר, אך אינה נמצאת כרגע בספרייה שלך",
                     "newer": "גרסה חדשה יותר",
-                    "newerTooltip": "גרסה זו חדשה יותר מהגרסה המקומית האחרונה שלך",
-                    "earlyAccess": "גישה מוקדמת",
-                    "earlyAccessTooltip": "גרסה זו דורשת כרגע גישת Early Access של Civitai",
-                    "ignored": "התעלם",
-                    "ignoredTooltip": "התראות העדכון מושבתות עבור גרסה זו",
-                    "onSiteOnly": "רק באתר",
-                    "onSiteOnlyTooltip": "גרסה זו זמינה רק ליצירה באתר Civitai"
+                    "ignored": "התעלם"
                 },
                 "actions": {
                     "download": "הורדה",
-                    "downloadTooltip": "הורד את הגרסה הזו",
-                    "downloadEarlyAccessTooltip": "הורד את גרסת ה-Early Access הזו מ-Civitai",
-                    "downloadNotAllowedTooltip": "גרסה זו זמינה רק ליצירה באתר Civitai",
                     "delete": "מחיקה",
-                    "deleteTooltip": "מחק את הגרסה המקומית הזו",
                     "ignore": "התעלם",
                     "unignore": "בטל התעלמות",
-                    "ignoreTooltip": "התעלם מהתראות העדכון עבור גרסה זו",
-                    "unignoreTooltip": "חזור לקבל התראות עדכון עבור גרסה זו",
-                    "viewVersionOnCivitai": "הצג את הגרסה ב-Civitai",
-                    "earlyAccessTooltip": "נדרש רכישת גישה מוקדמת",
                     "resumeModelUpdates": "המשך עדכונים עבור מודל זה",
                     "ignoreModelUpdates": "התעלם מעדכונים עבור מודל זה",
                     "viewLocalVersions": "הצג את כל הגרסאות המקומיות",
@@ -1388,6 +1116,10 @@
             "title": "מאתחל סטטיסטיקה",
             "message": "מעבד נתוני מודלים עבור סטטיסטיקה. זה עשוי לקחת מספר דקות..."
         },
+        "misc": {
+            "title": "[TODO: Translate] Initializing Misc Model Manager",
+            "message": "[TODO: Translate] Scanning VAE and Upscaler models..."
+        },
         "tips": {
             "title": "טיפים וטריקים",
             "civitai": {
@@ -1447,14 +1179,18 @@
             "recipeAdded": "מתכון נוסף ל-workflow",
             "recipeReplaced": "מתכון הוחלף ב-workflow",
             "recipeFailedToSend": "שליחת מתכון ל-workflow נכשלה",
+            "vaeUpdated": "[TODO: Translate] VAE updated in workflow",
+            "vaeFailed": "[TODO: Translate] Failed to update VAE in workflow",
+            "upscalerUpdated": "[TODO: Translate] Upscaler updated in workflow",
+            "upscalerFailed": "[TODO: Translate] Failed to update upscaler in workflow",
             "noMatchingNodes": "אין צמתים תואמים זמינים ב-workflow הנוכחי",
-            "noTargetNodeSelected": "לא נבחר צומת יעד",
-            "modelUpdated": "מודל עודכן ב-workflow",
-            "modelFailed": "עדכון צומת המודל נכשל"
+            "noTargetNodeSelected": "לא נבחר צומת יעד"
         },
         "nodeSelector": {
             "recipe": "מתכון",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "החלף",
             "append": "הוסף",
             "selectTargetNode": "בחר צומת יעד",
@@ -1464,10 +1200,6 @@
             "opened": "תיקיית תמונות הדוגמה נפתחה",
             "openingFolder": "פותח תיקיית תמונות דוגמה",
             "failedToOpen": "פתיחת תיקיית תמונות הדוגמה נכשלה",
-            "copiedPath": "הנתיב הועתק ללוח: {{path}}",
-            "clipboardFallback": "נתיב: {{path}}",
-            "copiedUri": "הקישור הועתק ללוח: {{uri}}",
-            "uriClipboardFallback": "קישור: {{uri}}",
             "setupRequired": "אחסון תמונות דוגמה",
             "setupDescription": "כדי להוסיף תמונות דוגמה מותאמות אישית, עליך קודם להגדיר מיקום הורדה.",
             "setupUsage": "נתיב זה משמש הן עבור תמונות דוגמה שהורדו והן עבור תמונות מותאמות אישית.",
@@ -1575,14 +1307,7 @@
             "showWechatQR": "הצג קוד QR של WeChat",
             "hideWechatQR": "הסתר קוד QR של WeChat"
         },
-        "footer": "תודה על השימוש במנהל LoRA! ❤️",
-        "supporters": {
-            "title": "תודה לכל התומכים",
-            "subtitle": "תודה ל־{count} תומכים שהפכו את הפרויקט הזה לאפשרי",
-            "specialThanks": "תודה מיוחדת",
-            "allSupporters": "כל התומכים",
-            "totalCount": "{count} תומכים בסך הכל"
-        }
+        "footer": "תודה על השימוש במנהל LoRA! ❤️"
     },
     "toast": {
         "general": {
@@ -1605,7 +1330,6 @@
             "pleaseSelectVersion": "אנא בחר גרסה",
             "versionExists": "גרסה זו כבר קיימת בספרייה שלך",
             "downloadCompleted": "ההורדה הושלמה בהצלחה",
-            "downloadSkippedByBaseModel": "ההורדה דולגה כי מודל הבסיס {baseModel} מוחרג",
             "autoOrganizeSuccess": "הארגון האוטומטי הושלם בהצלחה עבור {count} {type}",
             "autoOrganizePartialSuccess": "הארגון האוטומטי הושלם עם {success} שהועברו, {failures} שנכשלו מתוך {total} מודלים",
             "autoOrganizeFailed": "הארגון האוטומטי נכשל: {error}",
@@ -1617,19 +1341,13 @@
             "loadFailed": "טעינת {modelType}s נכשלה: {message}",
             "refreshComplete": "הרענון הושלם",
             "refreshFailed": "רענון המתכונים נכשל: {message}",
-            "syncComplete": "הסנכרון הושלם",
-            "syncFailed": "סנכרון המתכונים נכשל: {message}",
             "updateFailed": "עדכון המתכון נכשל: {error}",
             "updateError": "שגיאה בעדכון המתכון: {message}",
             "nameSaved": "המתכון \"{name}\" נשמר בהצלחה",
             "nameUpdated": "שם המתכון עודכן בהצלחה",
             "tagsUpdated": "תגיות המתכון עודכנו בהצלחה",
             "sourceUrlUpdated": "כתובת ה-URL המקורית עודכנה בהצלחה",
-            "promptUpdated": "הפרומפט עודכן בהצלחה",
-            "negativePromptUpdated": "הפרומפט השלילי עודכן בהצלחה",
-            "promptEditorHint": "לחץ Enter לשמירה, Shift+Enter לשורה חדשה",
             "noRecipeId": "אין מזהה מתכון זמין",
-            "sendToWorkflowFailed": "נכשל שליחת המתכון ל-workflow: {message}",
             "copyFailed": "שגיאה בהעתקת תחביר המתכון: {message}",
             "noMissingLoras": "אין LoRAs חסרים להורדה",
             "missingLorasInfoFailed": "קבלת מידע עבור LoRAs חסרים נכשלה",
@@ -1657,20 +1375,9 @@
             "processingError": "שגיאת עיבוד: {message}",
             "folderBrowserError": "שגיאה בטעינת דפדפן התיקיות: {message}",
             "recipeSaveFailed": "שמירת המתכון נכשלה: {error}",
-            "recipeSaved": "Recipe saved successfully",
             "importFailed": "הייבוא נכשל: {message}",
             "folderTreeFailed": "טעינת עץ התיקיות נכשלה",
-            "folderTreeError": "שגיאה בטעינת עץ התיקיות",
-            "batchImportFailed": "Failed to start batch import: {message}",
-            "batchImportCancelling": "Cancelling batch import...",
-            "batchImportCancelFailed": "Failed to cancel batch import: {message}",
-            "batchImportNoUrls": "Please enter at least one URL or file path",
-            "batchImportNoDirectory": "Please enter a directory path",
-            "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "noRecipesSelected": "לא נבחרו מתכונים",
-            "noMissingLorasInSelection": "לא נמצאו LoRAs חסרים במתכונים שנבחרו",
-            "noLoraRootConfigured": "תיקיית השורש של LoRA לא מוגדרת. אנא הגדר תיקיית שורש LoRA ברירת מחדל בהגדרות."
+            "folderTreeError": "שגיאה בטעינת עץ התיקיות"
         },
         "models": {
             "noModelsSelected": "לא נבחרו מודלים",
@@ -1690,11 +1397,6 @@
             "bulkBaseModelUpdateSuccess": "עודכן בהצלחה מודל הבסיס עבור {count} מודל(ים)",
             "bulkBaseModelUpdatePartial": "עודכנו {success} מודל(ים), נכשלו {failed} מודל(ים)",
             "bulkBaseModelUpdateFailed": "עדכון מודל הבסיס עבור המודלים שנבחרו נכשל",
-            "skipMetadataRefreshUpdating": "מעדכן דגל רענון מטא-נתונים עבור {count} מודל(ים)...",
-            "skipMetadataRefreshSet": "רענון מטא-נתונים דולג עבור {count} מודל(ים)",
-            "skipMetadataRefreshCleared": "רענון מטא-נתונים התחדש עבור {count} מודל(ים)",
-            "skipMetadataRefreshPartial": "{success} מודל(ים) עודכנו, {failed} נכשלו",
-            "skipMetadataRefreshFailed": "נכשל בעדכון דגל רענון מטא-נתונים עבור המודלים הנבחרים",
             "bulkContentRatingUpdating": "מעדכן דירוג תוכן עבור {count} מודלים...",
             "bulkContentRatingSet": "דירוג התוכן הוגדר ל-{level} עבור {count} מודלים",
             "bulkContentRatingPartial": "דירוג התוכן הוגדר ל-{level} עבור {success} מודלים, {failed} נכשלו",
@@ -1737,8 +1439,6 @@
             "mappingSaveFailed": "שמירת מיפויי מודל בסיס נכשלה: {message}",
             "downloadTemplatesUpdated": "תבניות נתיב הורדה עודכנו",
             "downloadTemplatesFailed": "שמירת תבניות נתיב הורדה נכשלה: {message}",
-            "recipesPathUpdated": "נתיב אחסון המתכונים עודכן",
-            "recipesPathSaveFailed": "עדכון נתיב אחסון המתכונים נכשל: {message}",
             "settingsUpdated": "הגדרות עודכנו: {setting}",
             "compactModeToggled": "מצב קומפקטי {state}",
             "settingSaveFailed": "שמירת ההגדרה נכשלה: {message}",
@@ -1784,13 +1484,12 @@
             "folderTreeFailed": "טעינת עץ התיקיות נכשלה",
             "folderTreeError": "שגיאה בטעינת עץ התיקיות",
             "imagesImported": "תמונות הדוגמה יובאו בהצלחה",
-            "imagesPartial": "{success} תמונה/ות יובאו, {failed} נכשלו",
             "importFailed": "ייבוא תמונות הדוגמה נכשל: {message}"
         },
         "triggerWords": {
             "loadFailed": "לא ניתן היה לטעון מילים מאומנות",
-            "tooLong": "מילת טריגר לא תעלה על 500 מילים",
-            "tooMany": "מותרות עד 100 מילות טריגר",
+            "tooLong": "מילת טריגר לא תעלה על 100 מילים",
+            "tooMany": "מותרות עד 30 מילות טריגר",
             "alreadyExists": "מילת טריגר זו כבר קיימת",
             "updateSuccess": "מילות הטריגר עודכנו בהצלחה",
             "updateFailed": "עדכון מילות הטריגר נכשל",
@@ -1851,8 +1550,6 @@
             "deleteFailed": "מחיקת {type} נכשלה: {message}",
             "excludeSuccess": "{type} הוחרג בהצלחה",
             "excludeFailed": "החרגת {type} נכשלה: {message}",
-            "restoreSuccess": "{type} שוחזר בהצלחה",
-            "restoreFailed": "שחזור {type} נכשל: {message}",
             "fileNameUpdated": "שם הקובץ עודכן בהצלחה",
             "fileRenameFailed": "שינוי שם הקובץ נכשל: {error}",
             "previewUpdated": "התצוגה המקדימה עודכנה בהצלחה",
@@ -1884,37 +1581,6 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
-    "doctor": {
-        "kicker": "אבחון מערכת",
-        "title": "דוקטור",
-        "buttonTitle": "הפעלת אבחון ותיקונים נפוצים",
-        "loading": "בודק את הסביבה...",
-        "footer": "ייצא חבילת אבחון אם הבעיה עדיין נמשכת לאחר התיקון.",
-        "summary": {
-            "idle": "הרץ בדיקת תקינות עבור הגדרות, שלמות המטמון ועקביות הממשק.",
-            "ok": "לא נמצאו בעיות פעילות בסביבה הנוכחית.",
-            "warning": "נמצאה/נמצאו {count} בעיה/בעיות. את רובן אפשר לתקן ישירות מלוח זה.",
-            "error": "יש לטפל ב-{count} בעיה/בעיות לפני שהאפליקציה תהיה תקינה לחלוטין."
-        },
-        "status": {
-            "ok": "תקין",
-            "warning": "דורש תשומת לב",
-            "error": "נדרשת פעולה"
-        },
-        "actions": {
-            "runAgain": "הפעל שוב",
-            "exportBundle": "ייצוא חבילה"
-        },
-        "toast": {
-            "loadFailed": "טעינת האבחון נכשלה: {message}",
-            "repairSuccess": "בניית המטמון מחדש הושלמה.",
-            "repairFailed": "בניית המטמון מחדש נכשלה: {message}",
-            "exportSuccess": "חבילת האבחון יוצאה.",
-            "exportFailed": "ייצוא חבילת האבחון נכשל: {message}",
-            "conflictsResolved": "נפתרו {count} התנגשויות בשמות קבצים.",
-            "conflictsResolveFailed": "פתרון התנגשויות שמות קבצים נכשל: {message}"
-        }
-    },
     "banners": {
         "versionMismatch": {
             "title": "זוהה עדכון יישום",
@@ -1928,20 +1594,6 @@
             "content": "LoRA Manager is a passion project maintained full-time by a solo developer. Your support on Ko-fi helps cover development costs, keeps new updates coming, and unlocks a license key for the LM Civitai Extension as a thank-you gift. Every contribution truly makes a difference.",
             "supportCta": "Support on Ko-fi",
             "learnMore": "LM Civitai Extension Tutorial"
-        },
-        "cacheHealth": {
-            "corrupted": {
-                "title": "זוהתה שחיתות במטמון"
-            },
-            "degraded": {
-                "title": "זוהו בעיות במטמון"
-            },
-            "content": "{invalid} מתוך {total} רשומות מטמון אינן תקינות ({rate}). זה עלול לגרום לדגמים חסרים או לשגיאות. מומלץ לבנות מחדש את המטמון.",
-            "rebuildCache": "בניית מטמון מחדש",
-            "dismiss": "ביטול",
-            "rebuilding": "בונה מחדש את המטמון...",
-            "rebuildFailed": "נכשלה בניית המטמון מחדש: {error}",
-            "retry": "נסה שוב"
         }
     }
 }

locales/ja.json

@@ -1,22 +1,17 @@
 {
     "common": {
-        "cancel": "キャンセル",
-        "confirm": "確認",
         "actions": {
             "save": "保存",
             "cancel": "キャンセル",
-            "confirm": "確認",
             "delete": "削除",
             "move": "移動",
             "refresh": "更新",
             "back": "戻る",
             "next": "次へ",
-            "backToTop": "トップへ戻る",
+            "backToTop": "トップに戻る",
             "settings": "設定",
             "help": "ヘルプ",
-            "add": "追加",
-            "close": "閉じる",
-            "menu": "メニュー"
+            "add": "追加"
         },
         "status": {
             "loading": "読み込み中...",
@@ -136,8 +131,7 @@
         },
         "badges": {
             "update": "アップデート",
-            "updateAvailable": "アップデートがあります",
-            "skipRefresh": "メタデータの更新がスキップされました"
+            "updateAvailable": "アップデートがあります"
         },
         "usage": {
             "timesUsed": "使用回数"
@@ -176,9 +170,6 @@
             "success": "{count} 件のレシピを正常に修復しました。",
             "cancelled": "修復がキャンセルされました。{count}個のレシピが修復されました。",
             "error": "レシピの修復に失敗しました: {message}"
-        },
-        "manageExcludedModels": {
-            "label": "除外モデルを管理"
         }
     },
     "header": {
@@ -188,6 +179,7 @@
             "recipes": "レシピ",
             "checkpoints": "Checkpoint",
             "embeddings": "Embedding",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "統計"
         },
         "search": {
@@ -196,7 +188,8 @@
                 "loras": "LoRAを検索...",
                 "recipes": "レシピを検索...",
                 "checkpoints": "checkpointを検索...",
-                "embeddings": "embeddingを検索..."
+                "embeddings": "embeddingを検索...",
+                "misc": "[TODO: Translate] Search VAE/Upscaler models..."
             },
             "options": "検索オプション",
             "searchIn": "検索対象：",
@@ -226,19 +219,13 @@
             "presetOverwriteConfirm": "プリセット「{name}」は既に存在します。上書きしますか？",
             "presetNamePlaceholder": "プリセット名...",
             "baseModel": "ベースモデル",
-            "baseModelSearchPlaceholder": "ベースモデルを検索...",
             "modelTags": "タグ（上位20）",
-            "modelTypes": "モデルタイプ",
+            "modelTypes": "Model Types",
             "license": "ライセンス",
             "noCreditRequired": "クレジット不要",
             "allowSellingGeneratedContent": "販売許可",
             "noTags": "タグなし",
-            "noBaseModelMatches": "現在の検索に一致するベースモデルはありません。",
-            "clearAll": "すべてのフィルタをクリア",
-            "any": "いずれか",
-            "all": "すべて",
-            "tagLogicAny": "いずれかのタグに一致 (OR)",
-            "tagLogicAll": "すべてのタグに一致 (AND)"
+            "clearAll": "すべてのフィルタをクリア"
         },
         "theme": {
             "toggle": "テーマの切り替え",
@@ -256,33 +243,6 @@
         "civitaiApiKey": "Civitai APIキー",
         "civitaiApiKeyPlaceholder": "Civitai APIキーを入力してください",
         "civitaiApiKeyHelp": "Civitaiからモデルをダウンロードするときの認証に使用されます",
-        "civitaiHost": {
-            "label": "Civitai ホスト",
-            "help": "「View on Civitai」リンクを使うときに開く Civitai サイトを選択します。",
-            "options": {
-                "com": "civitai.com（SFW のみ）",
-                "red": "civitai.red（制限なし）"
-            }
-        },
-        "downloadBackend": {
-            "label": "ダウンロードバックエンド",
-            "help": "モデルファイルのダウンロード方法を選択します。Python は内蔵ダウンローダーを使用し、aria2 は実験的な外部ダウンローダープロセスを使用します。",
-            "options": {
-                "python": "Python（内蔵）",
-                "aria2": "aria2（実験的）"
-            }
-        },
-        "aria2cPath": {
-            "label": "aria2c のパス",
-            "help": "aria2c 実行ファイルへの任意のパスです。空欄のままにすると、システム PATH 上の aria2c を使用します。",
-            "placeholder": "空欄のままにすると PATH 上の aria2c を使用します"
-        },
-        "aria2HelpLink": "aria2 ダウンロードバックエンドの設定方法",
-        "civitaiHostBanner": {
-            "title": "Civitai ホスト設定を利用できます",
-            "content": "Civitai は現在、SFW コンテンツには civitai.com、制限なしコンテンツには civitai.red を使用しています。設定で既定で開くサイトを変更できます。",
-            "openSettings": "設定を開く"
-        },
         "openSettingsFileLocation": {
             "label": "設定フォルダーを開く",
             "tooltip": "settings.json を含むフォルダーを開きます",
@@ -293,32 +253,19 @@
         },
         "sections": {
             "contentFiltering": "コンテンツフィルタリング",
-            "downloads": "ダウンロード",
             "videoSettings": "動画設定",
             "layoutSettings": "レイアウト設定",
-            "misc": "その他",
-            "backup": "バックアップ",
-            "folderSettings": "デフォルトルート",
-            "recipeSettings": "レシピ",
-            "extraFolderPaths": "追加フォルダーパス",
-            "downloadPathTemplates": "ダウンロードパステンプレート",
+            "folderSettings": "フォルダ設定",
             "priorityTags": "優先タグ",
-            "updateFlags": "アップデートフラグ",
+            "downloadPathTemplates": "ダウンロードパステンプレート",
             "exampleImages": "例画像",
-            "autoOrganize": "自動整理",
-            "metadata": "メタデータ",
+            "updateFlags": "アップデートフラグ",
+            "autoOrganize": "Auto-organize",
+            "misc": "その他",
+            "metadataArchive": "メタデータアーカイブデータベース",
+            "storageLocation": "設定の場所",
             "proxySettings": "プロキシ設定"
         },
-        "nav": {
-            "general": "一般",
-            "interface": "インターフェース",
-            "library": "ライブラリ"
-        },
-        "search": {
-            "placeholder": "設定を検索...",
-            "clear": "検索をクリア",
-            "noResults": "\"{query}\" に一致する設定が見つかりません"
-        },
         "storage": {
             "locationLabel": "ポータブルモード",
             "locationHelp": "有効にすると settings.json をリポジトリ内に保持し、無効にするとユーザー設定ディレクトリに格納します。"
@@ -327,15 +274,7 @@
             "blurNsfwContent": "NSFWコンテンツをぼかす",
             "blurNsfwContentHelp": "成人向け（NSFW）コンテンツのプレビュー画像をぼかします",
             "showOnlySfw": "SFWコンテンツのみ表示",
-            "showOnlySfwHelp": "閲覧と検索時にすべてのNSFWコンテンツを除外します",
-            "matureBlurThreshold": "成人コンテンツぼかし閾値",
-            "matureBlurThresholdHelp": "NSFWぼかしが有効な場合、どのレーティングレベルからぼかしフィルタリングを開始するかを設定します。",
-            "matureBlurThresholdOptions": {
-                "pg13": "PG13 以上",
-                "r": "R 以上（デフォルト）",
-                "x": "X 以上",
-                "xxx": "XXX のみ"
-            }
+            "showOnlySfwHelp": "閲覧と検索時にすべてのNSFWコンテンツを除外します"
         },
         "videoSettings": {
             "autoplayOnHover": "ホバー時に動画を自動再生",
@@ -350,63 +289,6 @@
                 "saveFailed": "除外設定を保存できませんでした: {message}"
             }
         },
-        "metadataRefreshSkipPaths": {
-            "label": "メタデータ更新スキップパス",
-            "placeholder": "例：temp, archived/old, test_models",
-            "help": "一括メタデータ更新（「すべてのメタデータを取得」）時にこれらのディレクトリパス内のモデルをスキップします。モデルルートディレクトリからの相対フォルダパスをカンマ区切りで入力してください。",
-            "validation": {
-                "noPaths": "カンマで区切って少なくとも1つのパスを入力してください。",
-                "saveFailed": "スキップパスの保存に失敗しました：{message}"
-            }
-        },
-        "backup": {
-            "autoEnabled": "自動バックアップ",
-            "autoEnabledHelp": "1日1回ローカルのスナップショットを作成し、保持ポリシーに従って最新のものを残します。",
-            "retention": "保持数",
-            "retentionHelp": "古いものを削除する前に、何件の自動スナップショットを保持するかを指定します。",
-            "management": "バックアップ管理",
-            "managementHelp": "現在のユーザー状態をエクスポートするか、バックアップアーカイブから復元します。",
-            "scopeHelp": "設定、ダウンロード履歴、モデル更新の状態をバックアップします。モデルファイルや再生成できるキャッシュは含まれません。",
-            "locationSummary": "現在のバックアップ場所",
-            "openFolderButton": "バックアップフォルダを開く",
-            "openFolderSuccess": "バックアップフォルダを開きました",
-            "openFolderFailed": "バックアップフォルダを開けませんでした",
-            "locationCopied": "バックアップパスをクリップボードにコピーしました: {{path}}",
-            "locationClipboardFallback": "バックアップパス: {{path}}",
-            "exportButton": "バックアップをエクスポート",
-            "exportSuccess": "バックアップを正常にエクスポートしました。",
-            "exportFailed": "バックアップのエクスポートに失敗しました: {message}",
-            "importButton": "バックアップをインポート",
-            "importConfirm": "このバックアップをインポートして、ローカルのユーザー状態を上書きしますか?",
-            "importSuccess": "バックアップを正常にインポートしました。",
-            "importFailed": "バックアップのインポートに失敗しました: {message}",
-            "latestSnapshot": "最新のスナップショット",
-            "latestAutoSnapshot": "最新の自動スナップショット",
-            "snapshotCount": "保存済みスナップショット",
-            "noneAvailable": "まだスナップショットはありません"
-        },
-        "downloadSkipBaseModels": {
-            "label": "ベースモデルのダウンロードをスキップ",
-            "help": "すべてのダウンロードフローに適用されます。ここでは対応しているベースモデルのみ選択できます。",
-            "searchPlaceholder": "ベースモデルを絞り込む...",
-            "empty": "現在の検索に一致するベースモデルはありません。",
-            "summary": {
-                "none": "未選択",
-                "count": "{count} 件を選択"
-            },
-            "actions": {
-                "edit": "編集",
-                "collapse": "折りたたむ",
-                "clear": "クリア"
-            },
-            "validation": {
-                "saveFailed": "除外するベースモデルを保存できませんでした: {message}"
-            }
-        },
-        "skipPreviouslyDownloadedModelVersions": {
-            "label": "以前にダウンロードしたモデルバージョンをスキップ",
-            "help": "有効にすると、ダウンロード履歴サービスがそのバージョンが既にダウンロード済みと記録している場合、LoRA Managerはそのモデルバージョンのダウンロードをスキップします。すべてのダウンロードフローに適用されます。"
-        },
         "layoutSettings": {
             "displayDensity": "表示密度",
             "displayDensityOptions": {
@@ -429,8 +311,6 @@
                 "hover": "ホバー時に表示"
             },
             "cardInfoDisplayHelp": "モデル情報とアクションボタンの表示タイミングを選択",
-            "showVersionOnCard": "カードにバージョンを表示",
-            "showVersionOnCardHelp": "モデルカード上のバージョン名の表示/非表示を切り替えます",
             "modelCardFooterAction": "モデルカードボタンのアクション",
             "modelCardFooterActionOptions": {
                 "exampleImages": "例画像を開く",
@@ -449,37 +329,16 @@
             "activeLibraryHelp": "設定済みのライブラリを切り替えてデフォルトのフォルダを更新します。選択を変更するとページが再読み込みされます。",
             "loadingLibraries": "ライブラリを読み込み中...",
             "noLibraries": "ライブラリが設定されていません",
-            "defaultLoraRoot": "LoRAルート",
+            "defaultLoraRoot": "デフォルトLoRAルート",
             "defaultLoraRootHelp": "ダウンロード、インポート、移動用のデフォルトLoRAルートディレクトリを設定",
-            "defaultCheckpointRoot": "Checkpointルート",
+            "defaultCheckpointRoot": "デフォルトCheckpointルート",
             "defaultCheckpointRootHelp": "ダウンロード、インポート、移動用のデフォルトcheckpointルートディレクトリを設定",
-            "defaultUnetRoot": "Diffusion Modelルート",
+            "defaultUnetRoot": "デフォルトDiffusion Modelルート",
             "defaultUnetRootHelp": "ダウンロード、インポート、移動用のデフォルトDiffusion Model (UNET)ルートディレクトリを設定",
-            "defaultEmbeddingRoot": "Embeddingルート",
+            "defaultEmbeddingRoot": "デフォルトEmbeddingルート",
             "defaultEmbeddingRootHelp": "ダウンロード、インポート、移動用のデフォルトembeddingルートディレクトリを設定",
-            "recipesPath": "レシピ保存先",
-            "recipesPathHelp": "保存済みレシピ用の任意のカスタムディレクトリです。空欄にすると最初のLoRAルートのrecipesフォルダーを使用します。",
-            "recipesPathPlaceholder": "/path/to/recipes",
-            "recipesPathMigrating": "レシピ保存先を移動中...",
             "noDefault": "デフォルトなし"
         },
-        "extraFolderPaths": {
-            "title": "追加フォルダーパス",
-            "description": "LoRA Manager専用の追加モデルルートパス。ComfyUIの標準フォルダー外の場所からモデルを読み込みます。ComfyUIの動作を低下させる可能性のある大規模ライブラリに最適です。",
-            "restartRequired": "Requires restart to take effect",
-            "modelTypes": {
-                "lora": "LoRAパス",
-                "checkpoint": "Checkpointパス",
-                "unet": "Diffusionモデルパス",
-                "embedding": "Embeddingパス"
-            },
-            "pathPlaceholder": "/追加モデルへのパス",
-            "saveSuccess": "追加フォルダーパスを更新しました。変更を適用するには再起動が必要です。",
-            "saveError": "追加フォルダーパスの更新に失敗しました: {message}",
-            "validation": {
-                "duplicatePath": "このパスはすでに設定されています"
-            }
-        },
         "priorityTags": {
             "title": "優先タグ",
             "description": "各モデルタイプのタグ優先順位をカスタマイズします (例: character, concept, style(toon|toon_style))",
@@ -542,21 +401,6 @@
             "downloadLocationHelp": "Civitaiからの例画像を保存するフォルダパスを入力してください",
             "autoDownload": "例画像の自動ダウンロード",
             "autoDownloadHelp": "例画像がないモデルの例画像を自動的にダウンロードします（ダウンロード場所の設定が必要）",
-            "openMode": "サンプル画像を開く動作",
-            "openModeHelp": "サーバー上で開くか、対応するローカルパスをコピーするか、カスタム URI を起動するかを選択します。",
-            "openModeOptions": {
-                "system": "サーバー上で開く",
-                "clipboard": "ローカルパスをコピー",
-                "uriTemplate": "カスタム URI を開く"
-            },
-            "localRoot": "ローカルのサンプル画像ルート",
-            "localRootHelp": "サーバーのサンプル画像ディレクトリを反映する任意のローカルまたはマウント済みルートです。空欄の場合はサーバーのパスを再利用します。",
-            "localRootPlaceholder": "例: /Volumes/ComfyUI/example_images",
-            "uriTemplate": "URI テンプレートを開く",
-            "uriTemplateHelp": "ファイル URI や Shortcuts リンクなどのカスタムディープリンクを使用します。",
-            "uriTemplatePlaceholder": "例: shortcuts://run-shortcut?name=Open%20Finder&input=text&text={{encoded_local_path}}",
-            "uriTemplatePlaceholders": "使用可能なプレースホルダー: {{local_path}}, {{encoded_local_path}}, {{relative_path}}, {{encoded_relative_path}}, {{file_uri}}, {{encoded_file_uri}}",
-            "openModeWikiLink": "リモートオープンモードの詳細",
             "optimizeImages": "ダウンロード画像の最適化",
             "optimizeImagesHelp": "例画像を最適化してファイルサイズを縮小し、読み込み速度を向上させます（メタデータは保持されます）",
             "download": "ダウンロード",
@@ -570,10 +414,6 @@
                 "any": "利用可能な更新すべてを表示"
             }
         },
-        "hideEarlyAccessUpdates": {
-            "label": "早期アクセス更新を非表示",
-            "help": "早期アクセスのみの更新"
-        },
         "misc": {
             "includeTriggerWords": "LoRA構文にトリガーワードを含める",
             "includeTriggerWordsHelp": "LoRA構文をクリップボードにコピーする際、学習済みトリガーワードを含めます"
@@ -685,13 +525,8 @@
             "checkUpdates": "選択項目の更新を確認",
             "moveAll": "すべてをフォルダに移動",
             "autoOrganize": "自動整理を実行",
-            "skipMetadataRefresh": "選択したモデルのメタデータ更新をスキップ",
-            "resumeMetadataRefresh": "選択したモデルのメタデータ更新を再開",
-            "deleteAll": "選択したものを削除",
-            "downloadMissingLoras": "不足している LoRA をダウンロード",
+            "deleteAll": "すべてのモデルを削除",
             "clear": "選択をクリア",
-            "skipMetadataRefreshCount": "スキップ（{count}モデル）",
-            "resumeMetadataRefreshCount": "再開（{count}モデル）",
             "autoOrganizeProgress": {
                 "initializing": "自動整理を初期化中...",
                 "starting": "{type}の自動整理を開始中...",
@@ -718,7 +553,6 @@
             "moveToFolder": "フォルダに移動",
             "repairMetadata": "メタデータを修復",
             "excludeModel": "モデルを除外",
-            "restoreModel": "モデルを復元",
             "deleteModel": "モデルを削除",
             "shareRecipe": "レシピを共有",
             "viewAllLoras": "すべてのLoRAを表示",
@@ -760,8 +594,6 @@
                 "root": "ルート",
                 "browseFolders": "フォルダを参照：",
                 "downloadAndSaveRecipe": "ダウンロード & レシピ保存",
-                "importRecipeOnly": "レシピのみインポート",
-                "importAndDownload": "インポートとダウンロード",
                 "downloadMissingLoras": "不足しているLoRAをダウンロード",
                 "saveRecipe": "レシピを保存",
                 "loraCountInfo": "（{existing}/{total} ライブラリ内）",
@@ -803,11 +635,7 @@
                 "lorasCountAsc": "少ない順"
             },
             "refresh": {
-                "title": "レシピリストを更新",
-                "quick": "変更を同期",
-                "quickTooltip": "変更を同期 - キャッシュを再構築せずにクイック更新",
-                "full": "キャッシュを再構築",
-                "fullTooltip": "キャッシュを再構築 - すべてのレシピファイルを完全に再スキャン"
+                "title": "レシピリストを更新"
             },
             "filteredByLora": "LoRAでフィルタ済み",
             "favorites": {
@@ -847,64 +675,6 @@
                 "failed": "レシピの修復に失敗しました: {message}",
                 "missingId": "レシピを修復できません: レシピIDがありません"
             }
-        },
-        "batchImport": {
-            "title": "Batch Import Recipes",
-            "action": "Batch Import",
-            "urlList": "URL List",
-            "directory": "Directory",
-            "urlDescription": "Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
-            "directoryDescription": "Enter a directory path to import all images from that folder.",
-            "urlsLabel": "Image URLs or Local Paths",
-            "urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
-            "urlsHint": "Enter one URL or path per line",
-            "directoryPath": "Directory Path",
-            "directoryPlaceholder": "/path/to/images/folder",
-            "browse": "Browse",
-            "recursive": "Include subdirectories",
-            "tagsOptional": "Tags (optional, applied to all recipes)",
-            "tagsPlaceholder": "Enter tags separated by commas",
-            "tagsHint": "Tags will be added to all imported recipes",
-            "skipNoMetadata": "Skip images without metadata",
-            "skipNoMetadataHelp": "Images without LoRA metadata will be skipped automatically.",
-            "start": "Start Import",
-            "startImport": "Start Import",
-            "importing": "Importing...",
-            "progress": "Progress",
-            "total": "Total",
-            "success": "Success",
-            "failed": "Failed",
-            "skipped": "Skipped",
-            "current": "Current",
-            "currentItem": "Current",
-            "preparing": "Preparing...",
-            "cancel": "Cancel",
-            "cancelImport": "Cancel",
-            "cancelled": "Import cancelled",
-            "completed": "Import completed",
-            "completedWithErrors": "Completed with errors",
-            "completedSuccess": "Successfully imported {count} recipe(s)",
-            "successCount": "Successful",
-            "failedCount": "Failed",
-            "skippedCount": "Skipped",
-            "totalProcessed": "Total processed",
-            "viewDetails": "View Details",
-            "newImport": "New Import",
-            "manualPathEntry": "Please enter the directory path manually. File browser is not available in this browser.",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "batchImportManualEntryRequired": "File browser not available. Please enter the directory path manually.",
-            "backToParent": "Back to parent directory",
-            "folders": "Folders",
-            "folderCount": "{count} folders",
-            "imageFiles": "Image Files",
-            "images": "images",
-            "imageCount": "{count} images",
-            "selectFolder": "Select This Folder",
-            "errors": {
-                "enterUrls": "Please enter at least one URL or path",
-                "enterDirectory": "Please enter a directory path",
-                "startFailed": "Failed to start import: {message}"
-            }
         }
     },
     "checkpoints": {
@@ -914,13 +684,22 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "{otherType} フォルダに移動",
-            "sendToWorkflow": "ワークフローに送信"
+            "moveToOtherTypeFolder": "{otherType} フォルダに移動"
         }
     },
     "embeddings": {
         "title": "Embeddingモデル"
     },
+    "misc": {
+        "title": "[TODO: Translate] VAE & Upscaler Models",
+        "modelTypes": {
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "[TODO: Translate] Move to {otherType} Folder"
+        }
+    },
     "sidebar": {
         "modelRoot": "ルート",
         "collapseAll": "すべてのフォルダを折りたたむ",
@@ -928,23 +707,13 @@
         "unpinSidebar": "サイドバーの固定を解除",
         "switchToListView": "リストビューに切り替え",
         "switchToTreeView": "ツリー表示に切り替え",
-        "recursiveOn": "サブフォルダーを含める",
-        "recursiveOff": "現在のフォルダーのみ",
+        "recursiveOn": "サブフォルダーを検索",
+        "recursiveOff": "現在のフォルダーのみを検索",
         "recursiveUnavailable": "再帰検索はツリービューでのみ利用できます",
         "collapseAllDisabled": "リストビューでは利用できません",
         "dragDrop": {
             "unableToResolveRoot": "移動先のパスを特定できません。",
-            "moveUnsupported": "この項目の移動はサポートされていません。",
-            "createFolderHint": "放して新しいフォルダを作成",
-            "newFolderName": "新しいフォルダ名",
-            "folderNameHint": "Enterで確定、Escでキャンセル",
-            "emptyFolderName": "フォルダ名を入力してください",
-            "invalidFolderName": "フォルダ名に無効な文字が含まれています",
-            "noDragState": "保留中のドラッグ操作が見つかりません"
-        },
-        "empty": {
-            "noFolders": "フォルダが見つかりません",
-            "dragHint": "ここへアイテムをドラッグしてフォルダを作成します"
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -1009,8 +778,6 @@
             "earlyAccess": "アーリーアクセス",
             "earlyAccessTooltip": "アーリーアクセスが必要",
             "inLibrary": "ライブラリ内",
-            "downloaded": "ダウンロード済み",
-            "downloadedTooltip": "以前にダウンロード済みですが、現在はライブラリにありません。",
             "alreadyInLibrary": "既にライブラリ内",
             "autoOrganizedPath": "[パステンプレートによる自動整理]",
             "errors": {
@@ -1101,14 +868,6 @@
             "save": "ベースモデルを更新",
             "cancel": "キャンセル"
         },
-        "bulkDownloadMissingLoras": {
-            "title": "不足している LoRA をダウンロード",
-            "message": "選択したレシピから合計 {totalCount} 個中 {uniqueCount} 個のユニークな不足している LoRA が見つかりました。",
-            "previewTitle": "ダウンロードする LoRA:",
-            "moreItems": "...あと {count} 個",
-            "note": "ファイルはデフォルトのパステンプレートを使用してダウンロードされます。LoRA の数によっては時間がかかる場合があります。",
-            "downloadButton": "{count} 個の LoRA をダウンロード"
-        },
         "exampleAccess": {
             "title": "ローカル例画像",
             "message": "このモデルのローカル例画像が見つかりませんでした。表示オプション：",
@@ -1160,9 +919,7 @@
                 "viewOnCivitai": "Civitaiで表示",
                 "viewOnCivitaiText": "Civitaiで表示",
                 "viewCreatorProfile": "作成者プロフィールを表示",
-                "openFileLocation": "ファイルの場所を開く",
-                "sendToWorkflow": "ComfyUI に送信",
-                "sendToWorkflowText": "ComfyUI に送信"
+                "openFileLocation": "ファイルの場所を開く"
             },
             "openFileLocation": {
                 "success": "ファイルの場所を正常に開きました",
@@ -1170,9 +927,6 @@
                 "copied": "パスをクリップボードにコピーしました: {{path}}",
                 "clipboardFallback": "パス: {{path}}"
             },
-            "sendToWorkflow": {
-                "noFilePath": "ComfyUI に送信できません：ファイルパスがありません"
-            },
             "metadata": {
                 "version": "バージョン",
                 "fileName": "ファイル名",
@@ -1209,8 +963,6 @@
                 "cancel": "編集をキャンセル",
                 "save": "変更を保存",
                 "addPlaceholder": "入力して追加するか、下の提案をクリック",
-                "editWord": "トリガーワードを編集",
-                "editPlaceholder": "トリガーワードを編集",
                 "copyWord": "トリガーワードをコピー",
                 "deleteWord": "トリガーワードを削除",
                 "suggestions": {
@@ -1273,43 +1025,19 @@
                 },
                 "labels": {
                     "unnamed": "名前のないバージョン",
-                    "noDetails": "追加情報なし",
-                    "earlyAccess": "EA"
-                },
-                "eaTime": {
-                    "endingSoon": "まもなく終了",
-                    "hours": "{count}時間後",
-                    "days": "{count}日後"
+                    "noDetails": "追加情報なし"
                 },
                 "badges": {
-                    "current": "開いたバージョン",
-                    "currentTooltip": "このモーダルを開くために選択したバージョンです",
+                    "current": "現在のバージョン",
                     "inLibrary": "ライブラリにあります",
-                    "inLibraryTooltip": "このバージョンはローカルライブラリに存在します",
-                    "downloaded": "ダウンロード済み",
-                    "downloadedTooltip": "このバージョンは以前ダウンロードされましたが、現在はライブラリにありません",
                     "newer": "新しいバージョン",
-                    "newerTooltip": "このバージョンはローカルの最新バージョンより新しいです",
-                    "earlyAccess": "早期アクセス",
-                    "earlyAccessTooltip": "このバージョンは現在 Civitai の早期アクセスが必要です",
-                    "ignored": "無視中",
-                    "ignoredTooltip": "このバージョンの更新通知は無効です",
-                    "onSiteOnly": "サイト内のみ",
-                    "onSiteOnlyTooltip": "このバージョンはCivitaiサイト内でのみ利用可能で、ダウンロードはできません"
+                    "ignored": "無視中"
                 },
                 "actions": {
                     "download": "ダウンロード",
-                    "downloadTooltip": "このバージョンをダウンロード",
-                    "downloadEarlyAccessTooltip": "Civitai からこの早期アクセス版をダウンロード",
-                    "downloadNotAllowedTooltip": "このバージョンはCivitaiサイト内でのみ利用可能で、ダウンロードはできません",
                     "delete": "削除",
-                    "deleteTooltip": "このローカルバージョンを削除",
                     "ignore": "無視",
                     "unignore": "無視を解除",
-                    "ignoreTooltip": "このバージョンの更新通知を無視",
-                    "unignoreTooltip": "このバージョンの更新通知を再開",
-                    "viewVersionOnCivitai": "Civitai でバージョンを表示",
-                    "earlyAccessTooltip": "早期アクセス購入が必要",
                     "resumeModelUpdates": "このモデルの更新を再開",
                     "ignoreModelUpdates": "このモデルの更新を無視",
                     "viewLocalVersions": "ローカルの全バージョンを表示",
@@ -1388,6 +1116,10 @@
             "title": "統計を初期化中",
             "message": "統計用のモデルデータを処理中。数分かかる場合があります..."
         },
+        "misc": {
+            "title": "[TODO: Translate] Initializing Misc Model Manager",
+            "message": "[TODO: Translate] Scanning VAE and Upscaler models..."
+        },
         "tips": {
             "title": "ヒント&コツ",
             "civitai": {
@@ -1447,14 +1179,18 @@
             "recipeAdded": "レシピがワークフローに追加されました",
             "recipeReplaced": "レシピがワークフローで置換されました",
             "recipeFailedToSend": "レシピをワークフローに送信できませんでした",
+            "vaeUpdated": "[TODO: Translate] VAE updated in workflow",
+            "vaeFailed": "[TODO: Translate] Failed to update VAE in workflow",
+            "upscalerUpdated": "[TODO: Translate] Upscaler updated in workflow",
+            "upscalerFailed": "[TODO: Translate] Failed to update upscaler in workflow",
             "noMatchingNodes": "現在のワークフローには互換性のあるノードがありません",
-            "noTargetNodeSelected": "ターゲットノードが選択されていません",
-            "modelUpdated": "モデルがワークフローで更新されました",
-            "modelFailed": "モデルノードの更新に失敗しました"
+            "noTargetNodeSelected": "ターゲットノードが選択されていません"
         },
         "nodeSelector": {
             "recipe": "レシピ",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "置換",
             "append": "追加",
             "selectTargetNode": "ターゲットノードを選択",
@@ -1464,10 +1200,6 @@
             "opened": "例画像フォルダが開かれました",
             "openingFolder": "例画像フォルダを開いています",
             "failedToOpen": "例画像フォルダを開くのに失敗しました",
-            "copiedPath": "パスをクリップボードにコピーしました: {{path}}",
-            "clipboardFallback": "パス: {{path}}",
-            "copiedUri": "リンクをクリップボードにコピーしました: {{uri}}",
-            "uriClipboardFallback": "リンク: {{uri}}",
             "setupRequired": "例画像ストレージ",
             "setupDescription": "カスタム例画像を追加するには、まずダウンロード場所を設定する必要があります。",
             "setupUsage": "このパスは、ダウンロードした例画像とカスタム画像の両方に使用されます。",
@@ -1575,14 +1307,7 @@
             "showWechatQR": "WeChat QRコードを表示",
             "hideWechatQR": "WeChat QRコードを非表示"
         },
-        "footer": "LoRA Managerをご利用いただきありがとうございます！ ❤️",
-        "supporters": {
-            "title": "サポーターの皆様に感謝",
-            "subtitle": "{count} 名のサポーターの皆様に、このプロジェクトを実現していただきありがとうございます",
-            "specialThanks": "特別感謝",
-            "allSupporters": "全サポーター",
-            "totalCount": "サポーター {count} 名"
-        }
+        "footer": "LoRA Managerをご利用いただきありがとうございます！ ❤️"
     },
     "toast": {
         "general": {
@@ -1605,7 +1330,6 @@
             "pleaseSelectVersion": "バージョンを選択してください",
             "versionExists": "このバージョンは既にライブラリに存在します",
             "downloadCompleted": "ダウンロードが正常に完了しました",
-            "downloadSkippedByBaseModel": "ベースモデル {baseModel} が除外されているため、ダウンロードをスキップしました",
             "autoOrganizeSuccess": "{count} {type} の自動整理が正常に完了しました",
             "autoOrganizePartialSuccess": "自動整理が完了しました：{total} モデル中 {success} 移動、{failures} 失敗",
             "autoOrganizeFailed": "自動整理に失敗しました：{error}",
@@ -1617,19 +1341,13 @@
             "loadFailed": "{modelType}の読み込みに失敗しました：{message}",
             "refreshComplete": "更新完了",
             "refreshFailed": "レシピの更新に失敗しました：{message}",
-            "syncComplete": "同期完了",
-            "syncFailed": "レシピの同期に失敗しました：{message}",
             "updateFailed": "レシピの更新に失敗しました：{error}",
             "updateError": "レシピ更新エラー：{message}",
             "nameSaved": "レシピ\"{name}\"が正常に保存されました",
             "nameUpdated": "レシピ名が正常に更新されました",
             "tagsUpdated": "レシピタグが正常に更新されました",
             "sourceUrlUpdated": "ソースURLが正常に更新されました",
-            "promptUpdated": "プロンプトが正常に更新されました",
-            "negativePromptUpdated": "ネガティブプロンプトが正常に更新されました",
-            "promptEditorHint": "Enterキーで保存、Shift+Enterで改行",
             "noRecipeId": "レシピIDが利用できません",
-            "sendToWorkflowFailed": "ワークフローへのレシピ送信に失敗しました：{message}",
             "copyFailed": "レシピ構文のコピーエラー：{message}",
             "noMissingLoras": "ダウンロードする不足LoRAがありません",
             "missingLorasInfoFailed": "不足LoRAの情報取得に失敗しました",
@@ -1657,20 +1375,9 @@
             "processingError": "処理エラー：{message}",
             "folderBrowserError": "フォルダブラウザの読み込みエラー：{message}",
             "recipeSaveFailed": "レシピの保存に失敗しました：{error}",
-            "recipeSaved": "Recipe saved successfully",
             "importFailed": "インポートに失敗しました：{message}",
             "folderTreeFailed": "フォルダツリーの読み込みに失敗しました",
-            "folderTreeError": "フォルダツリー読み込みエラー",
-            "batchImportFailed": "Failed to start batch import: {message}",
-            "batchImportCancelling": "Cancelling batch import...",
-            "batchImportCancelFailed": "Failed to cancel batch import: {message}",
-            "batchImportNoUrls": "Please enter at least one URL or file path",
-            "batchImportNoDirectory": "Please enter a directory path",
-            "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "noRecipesSelected": "レシピが選択されていません",
-            "noMissingLorasInSelection": "選択したレシピに不足している LoRA が見つかりませんでした",
-            "noLoraRootConfigured": "LoRA ルートディレクトリが設定されていません。設定でデフォルトの LoRA ルートを設定してください。"
+            "folderTreeError": "フォルダツリー読み込みエラー"
         },
         "models": {
             "noModelsSelected": "モデルが選択されていません",
@@ -1690,11 +1397,6 @@
             "bulkBaseModelUpdateSuccess": "{count} モデルのベースモデルが正常に更新されました",
             "bulkBaseModelUpdatePartial": "{success} モデルを更新、{failed} モデルは失敗しました",
             "bulkBaseModelUpdateFailed": "選択したモデルのベースモデルの更新に失敗しました",
-            "skipMetadataRefreshUpdating": "{count}モデルのメタデータ更新フラグを更新中...",
-            "skipMetadataRefreshSet": "{count}モデルのメタデータ更新をスキップしました",
-            "skipMetadataRefreshCleared": "{count}モデルのメタデータ更新を再開しました",
-            "skipMetadataRefreshPartial": "{success}モデルを更新しました。{failed}モデルで失敗しました",
-            "skipMetadataRefreshFailed": "選択したモデルのメタデータ更新フラグの更新に失敗しました",
             "bulkContentRatingUpdating": "{count} 件のモデルのコンテンツレーティングを更新中...",
             "bulkContentRatingSet": "{count} 件のモデルのコンテンツレーティングを {level} に設定しました",
             "bulkContentRatingPartial": "{success} 件のモデルのコンテンツレーティングを {level} に設定、{failed} 件は失敗しました",
@@ -1737,8 +1439,6 @@
             "mappingSaveFailed": "ベースモデルマッピングの保存に失敗しました：{message}",
             "downloadTemplatesUpdated": "ダウンロードパステンプレートが更新されました",
             "downloadTemplatesFailed": "ダウンロードパステンプレートの保存に失敗しました：{message}",
-            "recipesPathUpdated": "レシピ保存先を更新しました",
-            "recipesPathSaveFailed": "レシピ保存先の更新に失敗しました: {message}",
             "settingsUpdated": "設定が更新されました：{setting}",
             "compactModeToggled": "コンパクトモード {state}",
             "settingSaveFailed": "設定の保存に失敗しました：{message}",
@@ -1784,13 +1484,12 @@
             "folderTreeFailed": "フォルダツリーの読み込みに失敗しました",
             "folderTreeError": "フォルダツリー読み込みエラー",
             "imagesImported": "例画像が正常にインポートされました",
-            "imagesPartial": "{success} 件の画像をインポート、{failed} 件失敗",
             "importFailed": "例画像のインポートに失敗しました：{message}"
         },
         "triggerWords": {
             "loadFailed": "学習済みワードを読み込めませんでした",
-            "tooLong": "トリガーワードは500ワードを超えてはいけません",
-            "tooMany": "最大100トリガーワードまで許可されています",
+            "tooLong": "トリガーワードは100ワードを超えてはいけません",
+            "tooMany": "最大30トリガーワードまで許可されています",
             "alreadyExists": "このトリガーワードは既に存在します",
             "updateSuccess": "トリガーワードが正常に更新されました",
             "updateFailed": "トリガーワードの更新に失敗しました",
@@ -1851,8 +1550,6 @@
             "deleteFailed": "{type}の削除に失敗しました：{message}",
             "excludeSuccess": "{type}が正常に除外されました",
             "excludeFailed": "{type}の除外に失敗しました：{message}",
-            "restoreSuccess": "{type}を復元しました",
-            "restoreFailed": "{type}の復元に失敗しました: {message}",
             "fileNameUpdated": "ファイル名が正常に更新されました",
             "fileRenameFailed": "ファイル名の変更に失敗しました：{error}",
             "previewUpdated": "プレビューが正常に更新されました",
@@ -1884,37 +1581,6 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
-    "doctor": {
-        "kicker": "システム診断",
-        "title": "ドクター",
-        "buttonTitle": "診断と一般的な修復を実行",
-        "loading": "環境を確認中...",
-        "footer": "修復後も問題が続く場合は、診断パッケージをエクスポートしてください。",
-        "summary": {
-            "idle": "設定、キャッシュ整合性、UI の一貫性をヘルスチェックします。",
-            "ok": "現在の環境でアクティブな問題は見つかりませんでした。",
-            "warning": "{count} 件の問題が見つかりました。ほとんどはこのパネルから直接修復できます。",
-            "error": "アプリが完全に正常になる前に、{count} 件の問題に対処する必要があります。"
-        },
-        "status": {
-            "ok": "正常",
-            "warning": "要注意",
-            "error": "対応が必要"
-        },
-        "actions": {
-            "runAgain": "再実行",
-            "exportBundle": "パッケージをエクスポート"
-        },
-        "toast": {
-            "loadFailed": "診断の読み込みに失敗しました: {message}",
-            "repairSuccess": "キャッシュの再構築が完了しました。",
-            "repairFailed": "キャッシュの再構築に失敗しました: {message}",
-            "exportSuccess": "診断パッケージをエクスポートしました。",
-            "exportFailed": "診断パッケージのエクスポートに失敗しました: {message}",
-            "conflictsResolved": "{count} 件のファイル名競合が解決されました。",
-            "conflictsResolveFailed": "ファイル名競合の解決に失敗しました: {message}"
-        }
-    },
     "banners": {
         "versionMismatch": {
             "title": "アプリケーション更新が検出されました",
@@ -1928,20 +1594,6 @@
             "content": "LoRA Manager is a passion project maintained full-time by a solo developer. Your support on Ko-fi helps cover development costs, keeps new updates coming, and unlocks a license key for the LM Civitai Extension as a thank-you gift. Every contribution truly makes a difference.",
             "supportCta": "Support on Ko-fi",
             "learnMore": "LM Civitai Extension Tutorial"
-        },
-        "cacheHealth": {
-            "corrupted": {
-                "title": "キャッシュの破損が検出されました"
-            },
-            "degraded": {
-                "title": "キャッシュの問題が検出されました"
-            },
-            "content": "{total}個のキャッシュエントリのうち{invalid}個が無効です（{rate}）。モデルが見つからない原因になったり、エラーが発生する可能性があります。キャッシュの再構築を推奨します。",
-            "rebuildCache": "キャッシュを再構築",
-            "dismiss": "閉じる",
-            "rebuilding": "キャッシュを再構築中...",
-            "rebuildFailed": "キャッシュの再構築に失敗しました: {error}",
-            "retry": "再試行"
         }
     }
 }

locales/ko.json

@@ -1,11 +1,8 @@
 {
     "common": {
-        "cancel": "취소",
-        "confirm": "확인",
         "actions": {
             "save": "저장",
             "cancel": "취소",
-            "confirm": "확인",
             "delete": "삭제",
             "move": "이동",
             "refresh": "새로고침",
@@ -14,9 +11,7 @@
             "backToTop": "맨 위로",
             "settings": "설정",
             "help": "도움말",
-            "add": "추가",
-            "close": "닫기",
-            "menu": "메뉴"
+            "add": "추가"
         },
         "status": {
             "loading": "로딩 중...",
@@ -136,8 +131,7 @@
         },
         "badges": {
             "update": "업데이트",
-            "updateAvailable": "업데이트 가능",
-            "skipRefresh": "메타데이터 새로고침 건너뜀"
+            "updateAvailable": "업데이트 가능"
         },
         "usage": {
             "timesUsed": "사용 횟수"
@@ -176,9 +170,6 @@
             "success": "{count}개의 레시피가 성공적으로 복구되었습니다.",
             "cancelled": "수리가 취소되었습니다. {count}개의 레시피가 수리되었습니다.",
             "error": "레시피 복구 실패: {message}"
-        },
-        "manageExcludedModels": {
-            "label": "제외된 모델 관리"
         }
     },
     "header": {
@@ -188,6 +179,7 @@
             "recipes": "레시피",
             "checkpoints": "Checkpoint",
             "embeddings": "Embedding",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "통계"
         },
         "search": {
@@ -196,7 +188,8 @@
                 "loras": "LoRA 검색...",
                 "recipes": "레시피 검색...",
                 "checkpoints": "Checkpoint 검색...",
-                "embeddings": "Embedding 검색..."
+                "embeddings": "Embedding 검색...",
+                "misc": "[TODO: Translate] Search VAE/Upscaler models..."
             },
             "options": "검색 옵션",
             "searchIn": "검색 범위:",
@@ -226,19 +219,13 @@
             "presetOverwriteConfirm": "프리셋 \"{name}\"이(가) 이미 존재합니다. 덮어쓰시겠습니까?",
             "presetNamePlaceholder": "프리셋 이름...",
             "baseModel": "베이스 모델",
-            "baseModelSearchPlaceholder": "베이스 모델 검색...",
             "modelTags": "태그 (상위 20개)",
-            "modelTypes": "모델 유형",
+            "modelTypes": "Model Types",
             "license": "라이선스",
             "noCreditRequired": "크레딧 표기 없음",
             "allowSellingGeneratedContent": "판매 허용",
             "noTags": "태그 없음",
-            "noBaseModelMatches": "현재 검색과 일치하는 베이스 모델이 없습니다.",
-            "clearAll": "모든 필터 지우기",
-            "any": "아무",
-            "all": "모두",
-            "tagLogicAny": "모든 태그 일치 (OR)",
-            "tagLogicAll": "모든 태그 일치 (AND)"
+            "clearAll": "모든 필터 지우기"
         },
         "theme": {
             "toggle": "테마 토글",
@@ -256,33 +243,6 @@
         "civitaiApiKey": "Civitai API 키",
         "civitaiApiKeyPlaceholder": "Civitai API 키를 입력하세요",
         "civitaiApiKeyHelp": "Civitai에서 모델을 다운로드할 때 인증에 사용됩니다",
-        "civitaiHost": {
-            "label": "Civitai 호스트",
-            "help": "\"View on Civitai\" 링크를 사용할 때 어떤 Civitai 사이트를 열지 선택합니다.",
-            "options": {
-                "com": "civitai.com(SFW 전용)",
-                "red": "civitai.red(무제한)"
-            }
-        },
-        "downloadBackend": {
-            "label": "다운로드 백엔드",
-            "help": "모델 파일을 다운로드하는 방식을 선택합니다. Python은 내장 다운로더를 사용하고, aria2는 실험적인 외부 다운로더 프로세스를 사용합니다.",
-            "options": {
-                "python": "Python(내장)",
-                "aria2": "aria2(실험적)"
-            }
-        },
-        "aria2cPath": {
-            "label": "aria2c 경로",
-            "help": "aria2c 실행 파일의 선택적 경로입니다. 비워 두면 시스템 PATH의 aria2c를 사용합니다.",
-            "placeholder": "비워 두면 PATH의 aria2c를 사용합니다"
-        },
-        "aria2HelpLink": "aria2 다운로드 백엔드 설정 방법 알아보기",
-        "civitaiHostBanner": {
-            "title": "Civitai 호스트 기본 설정 사용 가능",
-            "content": "이제 Civitai는 SFW 콘텐츠에 civitai.com을, 무제한 콘텐츠에 civitai.red를 사용합니다. 설정에서 기본으로 열 사이트를 변경할 수 있습니다.",
-            "openSettings": "설정 열기"
-        },
         "openSettingsFileLocation": {
             "label": "설정 폴더 열기",
             "tooltip": "settings.json이 있는 폴더를 엽니다",
@@ -293,32 +253,19 @@
         },
         "sections": {
             "contentFiltering": "콘텐츠 필터링",
-            "downloads": "다운로드",
             "videoSettings": "비디오 설정",
             "layoutSettings": "레이아웃 설정",
-            "misc": "기타",
-            "backup": "백업",
-            "folderSettings": "기본 루트",
-            "recipeSettings": "레시피",
-            "extraFolderPaths": "추가 폴다 경로",
-            "downloadPathTemplates": "다운로드 경로 템플릿",
+            "folderSettings": "폴더 설정",
             "priorityTags": "우선순위 태그",
-            "updateFlags": "업데이트 표시",
+            "downloadPathTemplates": "다운로드 경로 템플릿",
             "exampleImages": "예시 이미지",
-            "autoOrganize": "자동 정리",
-            "metadata": "메타데이터",
+            "updateFlags": "업데이트 표시",
+            "autoOrganize": "Auto-organize",
+            "misc": "기타",
+            "metadataArchive": "메타데이터 아카이브 데이터베이스",
+            "storageLocation": "설정 위치",
             "proxySettings": "프록시 설정"
         },
-        "nav": {
-            "general": "일반",
-            "interface": "인터페이스",
-            "library": "라이브러리"
-        },
-        "search": {
-            "placeholder": "설정 검색...",
-            "clear": "검색 지우기",
-            "noResults": "\"{query}\"와 일치하는 설정을 찾을 수 없습니다"
-        },
         "storage": {
             "locationLabel": "휴대용 모드",
             "locationHelp": "활성화하면 settings.json을 리포지토리에 유지하고, 비활성화하면 사용자 구성 디렉터리에 저장합니다."
@@ -327,15 +274,7 @@
             "blurNsfwContent": "NSFW 콘텐츠 블러 처리",
             "blurNsfwContentHelp": "성인(NSFW) 콘텐츠 미리보기 이미지를 블러 처리합니다",
             "showOnlySfw": "SFW 결과만 표시",
-            "showOnlySfwHelp": "탐색 및 검색 시 모든 NSFW 콘텐츠를 필터링합니다",
-            "matureBlurThreshold": "성인 콘텐츠 블러 임계값",
-            "matureBlurThresholdHelp": "NSFW 블러가 활성화될 때 어떤 등급 레벨부터 블러 필터링을 시작할지 설정합니다.",
-            "matureBlurThresholdOptions": {
-                "pg13": "PG13 이상",
-                "r": "R 이상（기본값）",
-                "x": "X 이상",
-                "xxx": "XXX만"
-            }
+            "showOnlySfwHelp": "탐색 및 검색 시 모든 NSFW 콘텐츠를 필터링합니다"
         },
         "videoSettings": {
             "autoplayOnHover": "호버 시 비디오 자동 재생",
@@ -350,63 +289,6 @@
                 "saveFailed": "제외 항목을 저장할 수 없습니다: {message}"
             }
         },
-        "metadataRefreshSkipPaths": {
-            "label": "메타데이터 새로고침 건너뛰기 경로",
-            "placeholder": "예: temp, archived/old, test_models",
-            "help": "일괄 메타데이터 새로고침(\"모든 메타데이터 가져오기\") 시 이 디렉터리 경로의 모델을 건너뜁니다. 모델 루트 디렉터리를 기준으로 한 폴 더 경로를 쉼표로 구분하여 입력하세요.",
-            "validation": {
-                "noPaths": "쉼표로 구분하여 하나 이상의 경로를 입력하세요.",
-                "saveFailed": "건너뛰기 경로를 저장할 수 없습니다: {message}"
-            }
-        },
-        "backup": {
-            "autoEnabled": "자동 백업",
-            "autoEnabledHelp": "하루에 한 번 로컬 스냅샷을 만들고 보존 정책에 따라 최신 스냅샷을 유지합니다.",
-            "retention": "보존 개수",
-            "retentionHelp": "오래된 자동 스냅샷을 삭제하기 전에 몇 개를 유지할지 지정합니다.",
-            "management": "백업 관리",
-            "managementHelp": "현재 사용자 상태를 내보내거나 백업 아카이브에서 복원합니다.",
-            "scopeHelp": "설정, 다운로드 기록, 모델 업데이트 상태를 백업합니다. 모델 파일과 다시 생성할 수 있는 캐시는 포함되지 않습니다.",
-            "locationSummary": "현재 백업 위치",
-            "openFolderButton": "백업 폴더 열기",
-            "openFolderSuccess": "백업 폴더를 열었습니다",
-            "openFolderFailed": "백업 폴더를 열지 못했습니다",
-            "locationCopied": "백업 경로를 클립보드에 복사했습니다: {{path}}",
-            "locationClipboardFallback": "백업 경로: {{path}}",
-            "exportButton": "백업 내보내기",
-            "exportSuccess": "백업을 성공적으로 내보냈습니다.",
-            "exportFailed": "백업 내보내기에 실패했습니다: {message}",
-            "importButton": "백업 가져오기",
-            "importConfirm": "이 백업을 가져와서 로컬 사용자 상태를 덮어쓰시겠습니까?",
-            "importSuccess": "백업을 성공적으로 가져왔습니다.",
-            "importFailed": "백업 가져오기에 실패했습니다: {message}",
-            "latestSnapshot": "최근 스냅샷",
-            "latestAutoSnapshot": "최근 자동 스냅샷",
-            "snapshotCount": "저장된 스냅샷",
-            "noneAvailable": "아직 스냅샷이 없습니다"
-        },
-        "downloadSkipBaseModels": {
-            "label": "기본 모델 다운로드 건너뛰기",
-            "help": "모든 다운로드 흐름에 적용됩니다. 여기서는 지원되는 기본 모델만 선택할 수 있습니다.",
-            "searchPlaceholder": "기본 모델 필터링...",
-            "empty": "현재 검색과 일치하는 기본 모델이 없습니다.",
-            "summary": {
-                "none": "선택 없음",
-                "count": "{count}개 선택됨"
-            },
-            "actions": {
-                "edit": "편집",
-                "collapse": "접기",
-                "clear": "지우기"
-            },
-            "validation": {
-                "saveFailed": "제외된 기본 모델을 저장할 수 없습니다: {message}"
-            }
-        },
-        "skipPreviouslyDownloadedModelVersions": {
-            "label": "이전에 다운로드한 모델 버전 건너뛰기",
-            "help": "활성화하면 다운로드 기록 서비스가 해당 버전이 이미 다운로드되었음을 기록한 경우 LoRA Manager는 해당 모델 버전 다운로드를 건너뜁니다. 모든 다운로드 플로우에 적용됩니다."
-        },
         "layoutSettings": {
             "displayDensity": "표시 밀도",
             "displayDensityOptions": {
@@ -429,8 +311,6 @@
                 "hover": "호버 시 표시"
             },
             "cardInfoDisplayHelp": "모델 정보 및 액션 버튼을 언제 표시할지 선택하세요",
-            "showVersionOnCard": "카드에 버전 표시",
-            "showVersionOnCardHelp": "모델 카드에 버전 이름 표시 여부를 전환합니다",
             "modelCardFooterAction": "모델 카드 버튼 동작",
             "modelCardFooterActionOptions": {
                 "exampleImages": "예시 이미지 열기",
@@ -449,37 +329,16 @@
             "activeLibraryHelp": "구성된 라이브러리를 전환하여 기본 폴더를 업데이트합니다. 선택을 변경하면 페이지가 다시 로드됩니다.",
             "loadingLibraries": "라이브러리를 불러오는 중...",
             "noLibraries": "구성된 라이브러리가 없습니다",
-            "defaultLoraRoot": "LoRA 루트",
+            "defaultLoraRoot": "기본 LoRA 루트",
             "defaultLoraRootHelp": "다운로드, 가져오기 및 이동을 위한 기본 LoRA 루트 디렉토리를 설정합니다",
-            "defaultCheckpointRoot": "Checkpoint 루트",
+            "defaultCheckpointRoot": "기본 Checkpoint 루트",
             "defaultCheckpointRootHelp": "다운로드, 가져오기 및 이동을 위한 기본 Checkpoint 루트 디렉토리를 설정합니다",
-            "defaultUnetRoot": "Diffusion Model 루트",
+            "defaultUnetRoot": "기본 Diffusion Model 루트",
             "defaultUnetRootHelp": "다운로드, 가져오기 및 이동을 위한 기본 Diffusion Model (UNET) 루트 디렉토리를 설정합니다",
-            "defaultEmbeddingRoot": "Embedding 루트",
+            "defaultEmbeddingRoot": "기본 Embedding 루트",
             "defaultEmbeddingRootHelp": "다운로드, 가져오기 및 이동을 위한 기본 Embedding 루트 디렉토리를 설정합니다",
-            "recipesPath": "레시피 저장 경로",
-            "recipesPathHelp": "저장된 레시피를 위한 선택적 사용자 지정 디렉터리입니다. 비워 두면 첫 번째 LoRA 루트의 recipes 폴더를 사용합니다.",
-            "recipesPathPlaceholder": "/path/to/recipes",
-            "recipesPathMigrating": "레시피 저장 경로를 이동 중...",
             "noDefault": "기본값 없음"
         },
-        "extraFolderPaths": {
-            "title": "추가 폴다 경로",
-            "description": "LoRA Manager 전용 추가 모델 루트 경로입니다. ComfyUI의 표준 폴더 외부 위치에서 모델을 로드하여 대규모 라이브러리로 인한 성능 저하를 방지합니다.",
-            "restartRequired": "Requires restart to take effect",
-            "modelTypes": {
-                "lora": "LoRA 경로",
-                "checkpoint": "Checkpoint 경로",
-                "unet": "Diffusion 모델 경로",
-                "embedding": "Embedding 경로"
-            },
-            "pathPlaceholder": "/추가/모델/경로",
-            "saveSuccess": "추가 폴다 경로가 업데이트되었습니다. 변경 사항을 적용하려면 재시작이 필요합니다.",
-            "saveError": "추가 폴다 경로 업데이트 실패: {message}",
-            "validation": {
-                "duplicatePath": "이 경로는 이미 구성되어 있습니다"
-            }
-        },
         "priorityTags": {
             "title": "우선순위 태그",
             "description": "모델 유형별 태그 우선순위를 사용자 지정합니다(예: character, concept, style(toon|toon_style)).",
@@ -542,21 +401,6 @@
             "downloadLocationHelp": "Civitai의 예시 이미지가 저장될 폴더 경로를 입력하세요",
             "autoDownload": "예시 이미지 자동 다운로드",
             "autoDownloadHelp": "예시 이미지가 없는 모델의 예시 이미지를 자동으로 다운로드합니다 (다운로드 위치 설정 필요)",
-            "openMode": "예시 이미지 열기 동작",
-            "openModeHelp": "서버에서 열지, 매핑된 로컬 경로를 복사할지, 사용자 지정 URI를 실행할지 선택합니다.",
-            "openModeOptions": {
-                "system": "서버에서 열기",
-                "clipboard": "로컬 경로 복사",
-                "uriTemplate": "사용자 지정 URI 열기"
-            },
-            "localRoot": "로컬 예시 이미지 루트",
-            "localRootHelp": "서버 예시 이미지 디렉터리를 반영하는 선택적 로컬 또는 마운트된 루트입니다. 비워 두면 서버 경로를 재사용합니다.",
-            "localRootPlaceholder": "예: /Volumes/ComfyUI/example_images",
-            "uriTemplate": "URI 템플릿 열기",
-            "uriTemplateHelp": "파일 URI 또는 Shortcuts 링크 같은 사용자 지정 딥링크를 사용합니다.",
-            "uriTemplatePlaceholder": "예: shortcuts://run-shortcut?name=Open%20Finder&input=text&text={{encoded_local_path}}",
-            "uriTemplatePlaceholders": "사용 가능한 플레이스홀더: {{local_path}}, {{encoded_local_path}}, {{relative_path}}, {{encoded_relative_path}}, {{file_uri}}, {{encoded_file_uri}}",
-            "openModeWikiLink": "원격 열기 모드에 대해 자세히 알아보기",
             "optimizeImages": "다운로드된 이미지 최적화",
             "optimizeImagesHelp": "파일 크기를 줄이고 로딩 속도를 향상시키기 위해 예시 이미지를 최적화합니다 (메타데이터는 보존됨)",
             "download": "다운로드",
@@ -570,10 +414,6 @@
                 "any": "사용 가능한 모든 업데이트 표시"
             }
         },
-        "hideEarlyAccessUpdates": {
-            "label": "얼리 액세스 업데이트 숨기기",
-            "help": "얼리 액세스 업데이트만"
-        },
         "misc": {
             "includeTriggerWords": "LoRA 문법에 트리거 단어 포함",
             "includeTriggerWordsHelp": "LoRA 문법을 클립보드에 복사할 때 학습된 트리거 단어를 포함합니다"
@@ -685,13 +525,8 @@
             "checkUpdates": "선택 항목 업데이트 확인",
             "moveAll": "모두 폴더로 이동",
             "autoOrganize": "자동 정리 선택",
-            "skipMetadataRefresh": "선택한 모델의 메타데이터 새로고침 건너뛰기",
-            "resumeMetadataRefresh": "선택한 모델의 메타데이터 새로고침 재개",
-            "deleteAll": "선택된 항목 삭제",
-            "downloadMissingLoras": "누락된 LoRA 다운로드",
+            "deleteAll": "모든 모델 삭제",
             "clear": "선택 지우기",
-            "skipMetadataRefreshCount": "건너뛰기（{count}개 모델）",
-            "resumeMetadataRefreshCount": "재개（{count}개 모델）",
             "autoOrganizeProgress": {
                 "initializing": "자동 정리 초기화 중...",
                 "starting": "{type}에 대한 자동 정리 시작...",
@@ -718,7 +553,6 @@
             "moveToFolder": "폴더로 이동",
             "repairMetadata": "메타데이터 복구",
             "excludeModel": "모델 제외",
-            "restoreModel": "모델 복원",
             "deleteModel": "모델 삭제",
             "shareRecipe": "레시피 공유",
             "viewAllLoras": "모든 LoRA 보기",
@@ -760,8 +594,6 @@
                 "root": "루트",
                 "browseFolders": "폴더 탐색:",
                 "downloadAndSaveRecipe": "다운로드 및 레시피 저장",
-                "importRecipeOnly": "레시피만 가져오기",
-                "importAndDownload": "가져오기 및 다운로드",
                 "downloadMissingLoras": "누락된 LoRA 다운로드",
                 "saveRecipe": "레시피 저장",
                 "loraCountInfo": "({existing}/{total} 라이브러리에 있음)",
@@ -803,11 +635,7 @@
                 "lorasCountAsc": "적은순"
             },
             "refresh": {
-                "title": "레시피 목록 새로고침",
-                "quick": "변경 사항 동기화",
-                "quickTooltip": "변경 사항 동기화 - 캐시를 재구성하지 않고 빠른 새로고침",
-                "full": "캐시 재구성",
-                "fullTooltip": "캐시 재구성 - 모든 레시피 파일을 완전히 다시 스캔"
+                "title": "레시피 목록 새로고침"
             },
             "filteredByLora": "LoRA로 필터링됨",
             "favorites": {
@@ -847,64 +675,6 @@
                 "failed": "레시피 복구 실패: {message}",
                 "missingId": "레시피를 복구할 수 없음: 레시피 ID 누락"
             }
-        },
-        "batchImport": {
-            "title": "Batch Import Recipes",
-            "action": "Batch Import",
-            "urlList": "URL List",
-            "directory": "Directory",
-            "urlDescription": "Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
-            "directoryDescription": "Enter a directory path to import all images from that folder.",
-            "urlsLabel": "Image URLs or Local Paths",
-            "urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
-            "urlsHint": "Enter one URL or path per line",
-            "directoryPath": "Directory Path",
-            "directoryPlaceholder": "/path/to/images/folder",
-            "browse": "Browse",
-            "recursive": "Include subdirectories",
-            "tagsOptional": "Tags (optional, applied to all recipes)",
-            "tagsPlaceholder": "Enter tags separated by commas",
-            "tagsHint": "Tags will be added to all imported recipes",
-            "skipNoMetadata": "Skip images without metadata",
-            "skipNoMetadataHelp": "Images without LoRA metadata will be skipped automatically.",
-            "start": "Start Import",
-            "startImport": "Start Import",
-            "importing": "Importing...",
-            "progress": "Progress",
-            "total": "Total",
-            "success": "Success",
-            "failed": "Failed",
-            "skipped": "Skipped",
-            "current": "Current",
-            "currentItem": "Current",
-            "preparing": "Preparing...",
-            "cancel": "Cancel",
-            "cancelImport": "Cancel",
-            "cancelled": "Import cancelled",
-            "completed": "Import completed",
-            "completedWithErrors": "Completed with errors",
-            "completedSuccess": "Successfully imported {count} recipe(s)",
-            "successCount": "Successful",
-            "failedCount": "Failed",
-            "skippedCount": "Skipped",
-            "totalProcessed": "Total processed",
-            "viewDetails": "View Details",
-            "newImport": "New Import",
-            "manualPathEntry": "Please enter the directory path manually. File browser is not available in this browser.",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "batchImportManualEntryRequired": "File browser not available. Please enter the directory path manually.",
-            "backToParent": "Back to parent directory",
-            "folders": "Folders",
-            "folderCount": "{count} folders",
-            "imageFiles": "Image Files",
-            "images": "images",
-            "imageCount": "{count} images",
-            "selectFolder": "Select This Folder",
-            "errors": {
-                "enterUrls": "Please enter at least one URL or path",
-                "enterDirectory": "Please enter a directory path",
-                "startFailed": "Failed to start import: {message}"
-            }
         }
     },
     "checkpoints": {
@@ -914,13 +684,22 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "{otherType} 폴더로 이동",
-            "sendToWorkflow": "워크플로우로 전송"
+            "moveToOtherTypeFolder": "{otherType} 폴더로 이동"
         }
     },
     "embeddings": {
         "title": "Embedding 모델"
     },
+    "misc": {
+        "title": "[TODO: Translate] VAE & Upscaler Models",
+        "modelTypes": {
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "[TODO: Translate] Move to {otherType} Folder"
+        }
+    },
     "sidebar": {
         "modelRoot": "루트",
         "collapseAll": "모든 폴더 접기",
@@ -928,23 +707,13 @@
         "unpinSidebar": "사이드바 고정 해제",
         "switchToListView": "목록 보기로 전환",
         "switchToTreeView": "트리 보기로 전환",
-        "recursiveOn": "하위 폴더 포함",
-        "recursiveOff": "현재 폴더만",
+        "recursiveOn": "하위 폴더 검색",
+        "recursiveOff": "현재 폴더만 검색",
         "recursiveUnavailable": "재귀 검색은 트리 보기에서만 사용할 수 있습니다",
         "collapseAllDisabled": "목록 보기에서는 사용할 수 없습니다",
         "dragDrop": {
             "unableToResolveRoot": "이동할 대상 경로를 확인할 수 없습니다.",
-            "moveUnsupported": "이 항목은 이동을 지원하지 않습니다.",
-            "createFolderHint": "놓아서 새 폴더 만들기",
-            "newFolderName": "새 폴더 이름",
-            "folderNameHint": "Enter를 눌러 확인, Escape를 눌러 취소",
-            "emptyFolderName": "폴더 이름을 입력하세요",
-            "invalidFolderName": "폴더 이름에 잘못된 문자가 포함되어 있습니다",
-            "noDragState": "보류 중인 드래그 작업을 찾을 수 없습니다"
-        },
-        "empty": {
-            "noFolders": "폴더를 찾을 수 없습니다",
-            "dragHint": "항목을 여기로 드래그하여 폴더를 만듭니다"
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -1009,8 +778,6 @@
             "earlyAccess": "얼리 액세스",
             "earlyAccessTooltip": "얼리 액세스 필요",
             "inLibrary": "라이브러리에 있음",
-            "downloaded": "다운로드됨",
-            "downloadedTooltip": "이전에 다운로드했지만 현재 라이브러리에 없습니다.",
             "alreadyInLibrary": "이미 라이브러리에 있음",
             "autoOrganizedPath": "[경로 템플릿으로 자동 정리됨]",
             "errors": {
@@ -1101,14 +868,6 @@
             "save": "베이스 모델 업데이트",
             "cancel": "취소"
         },
-        "bulkDownloadMissingLoras": {
-            "title": "누락된 LoRA 다운로드",
-            "message": "선택한 레시피에서 총 {totalCount}개 중 {uniqueCount}개의 고유한 누락된 LoRA를 찾았습니다.",
-            "previewTitle": "다운로드할 LoRA:",
-            "moreItems": "...그리고 {count}개 더",
-            "note": "파일은 기본 경로 템플릿을 사용하여 다운로드됩니다. LoRA의 수에 따라 다소 시간이 걸릴 수 있습니다.",
-            "downloadButton": "{count}개 LoRA 다운로드"
-        },
         "exampleAccess": {
             "title": "로컬 예시 이미지",
             "message": "이 모델의 로컬 예시 이미지를 찾을 수 없습니다. 보기 옵션:",
@@ -1160,9 +919,7 @@
                 "viewOnCivitai": "Civitai에서 보기",
                 "viewOnCivitaiText": "Civitai에서 보기",
                 "viewCreatorProfile": "제작자 프로필 보기",
-                "openFileLocation": "파일 위치 열기",
-                "sendToWorkflow": "ComfyUI로 보내기",
-                "sendToWorkflowText": "ComfyUI로 보내기"
+                "openFileLocation": "파일 위치 열기"
             },
             "openFileLocation": {
                 "success": "파일 위치가 성공적으로 열렸습니다",
@@ -1170,9 +927,6 @@
                 "copied": "경로가 클립보드에 복사되었습니다: {{path}}",
                 "clipboardFallback": "경로: {{path}}"
             },
-            "sendToWorkflow": {
-                "noFilePath": "ComfyUI로 보낼 수 없습니다: 파일 경로가 없습니다"
-            },
             "metadata": {
                 "version": "버전",
                 "fileName": "파일명",
@@ -1209,8 +963,6 @@
                 "cancel": "편집 취소",
                 "save": "변경사항 저장",
                 "addPlaceholder": "입력하거나 아래 제안을 클릭하세요",
-                "editWord": "트리거 단어 편집",
-                "editPlaceholder": "트리거 단어 편집",
                 "copyWord": "트리거 단어 복사",
                 "deleteWord": "트리거 단어 삭제",
                 "suggestions": {
@@ -1273,43 +1025,19 @@
                 },
                 "labels": {
                     "unnamed": "이름 없는 버전",
-                    "noDetails": "추가 정보 없음",
-                    "earlyAccess": "EA"
-                },
-                "eaTime": {
-                    "endingSoon": "곧 종료",
-                    "hours": "{count}시간 후",
-                    "days": "{count}일 후"
+                    "noDetails": "추가 정보 없음"
                 },
                 "badges": {
-                    "current": "열린 버전",
-                    "currentTooltip": "이 모달을 열 때 사용한 버전입니다",
+                    "current": "현재 버전",
                     "inLibrary": "라이브러리에 있음",
-                    "inLibraryTooltip": "이 버전은 로컬 라이브러리에 있습니다",
-                    "downloaded": "다운로드됨",
-                    "downloadedTooltip": "이 버전은 이전에 다운로드되었지만 현재는 라이브러리에 없습니다",
                     "newer": "최신 버전",
-                    "newerTooltip": "이 버전은 로컬의 최신 버전보다 더 새롭습니다",
-                    "earlyAccess": "얼리 액세스",
-                    "earlyAccessTooltip": "이 버전은 현재 Civitai 얼리 액세스가 필요합니다",
-                    "ignored": "무시됨",
-                    "ignoredTooltip": "이 버전은 업데이트 알림이 비활성화되어 있습니다",
-                    "onSiteOnly": "사이트 내 전용",
-                    "onSiteOnlyTooltip": "이 버전은 Civitai 사이트 내에서만 사용 가능하며 다운로드할 수 없습니다"
+                    "ignored": "무시됨"
                 },
                 "actions": {
                     "download": "다운로드",
-                    "downloadTooltip": "이 버전 다운로드",
-                    "downloadEarlyAccessTooltip": "Civitai에서 이 얼리 액세스 버전 다운로드",
-                    "downloadNotAllowedTooltip": "이 버전은 Civitai 사이트 내에서만 사용 가능하며 다운로드할 수 없습니다",
                     "delete": "삭제",
-                    "deleteTooltip": "이 로컬 버전 삭제",
                     "ignore": "무시",
                     "unignore": "무시 해제",
-                    "ignoreTooltip": "이 버전의 업데이트 알림 무시",
-                    "unignoreTooltip": "이 버전의 업데이트 알림 다시 받기",
-                    "viewVersionOnCivitai": "Civitai에서 버전 보기",
-                    "earlyAccessTooltip": "얼리 액세스 구매 필요",
                     "resumeModelUpdates": "이 모델 업데이트 재개",
                     "ignoreModelUpdates": "이 모델 업데이트 무시",
                     "viewLocalVersions": "로컬 버전 모두 보기",
@@ -1388,6 +1116,10 @@
             "title": "통계 초기화 중",
             "message": "통계를 위한 모델 데이터를 처리하고 있습니다. 몇 분이 걸릴 수 있습니다..."
         },
+        "misc": {
+            "title": "[TODO: Translate] Initializing Misc Model Manager",
+            "message": "[TODO: Translate] Scanning VAE and Upscaler models..."
+        },
         "tips": {
             "title": "팁 & 요령",
             "civitai": {
@@ -1447,14 +1179,18 @@
             "recipeAdded": "레시피가 워크플로에 추가되었습니다",
             "recipeReplaced": "레시피가 워크플로에서 교체되었습니다",
             "recipeFailedToSend": "레시피를 워크플로로 전송하지 못했습니다",
+            "vaeUpdated": "[TODO: Translate] VAE updated in workflow",
+            "vaeFailed": "[TODO: Translate] Failed to update VAE in workflow",
+            "upscalerUpdated": "[TODO: Translate] Upscaler updated in workflow",
+            "upscalerFailed": "[TODO: Translate] Failed to update upscaler in workflow",
             "noMatchingNodes": "현재 워크플로에서 호환되는 노드가 없습니다",
-            "noTargetNodeSelected": "대상 노드가 선택되지 않았습니다",
-            "modelUpdated": "모델이 워크플로에서 업데이트되었습니다",
-            "modelFailed": "모델 노드 업데이트 실패"
+            "noTargetNodeSelected": "대상 노드가 선택되지 않았습니다"
         },
         "nodeSelector": {
             "recipe": "레시피",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "교체",
             "append": "추가",
             "selectTargetNode": "대상 노드 선택",
@@ -1464,10 +1200,6 @@
             "opened": "예시 이미지 폴더가 열렸습니다",
             "openingFolder": "예시 이미지 폴더를 여는 중",
             "failedToOpen": "예시 이미지 폴더 열기 실패",
-            "copiedPath": "경로를 클립보드에 복사했습니다: {{path}}",
-            "clipboardFallback": "경로: {{path}}",
-            "copiedUri": "링크를 클립보드에 복사했습니다: {{uri}}",
-            "uriClipboardFallback": "링크: {{uri}}",
             "setupRequired": "예시 이미지 저장소",
             "setupDescription": "사용자 지정 예시 이미지를 추가하려면 먼저 다운로드 위치를 설정해야 합니다.",
             "setupUsage": "이 경로는 다운로드한 예시 이미지와 사용자 지정 이미지 모두에 사용됩니다.",
@@ -1575,14 +1307,7 @@
             "showWechatQR": "WeChat QR 코드 표시",
             "hideWechatQR": "WeChat QR 코드 숨기기"
         },
-        "footer": "LoRA Manager를 사용해주셔서 감사합니다! ❤️",
-        "supporters": {
-            "title": "후원자 분들께 감사드립니다",
-            "subtitle": "이 프로젝트를 가능하게 해준 {count}명의 후원자분들께 감사드립니다",
-            "specialThanks": "특별 감사",
-            "allSupporters": "모든 후원자",
-            "totalCount": "총 {count}명의 후원자"
-        }
+        "footer": "LoRA Manager를 사용해주셔서 감사합니다! ❤️"
     },
     "toast": {
         "general": {
@@ -1605,7 +1330,6 @@
             "pleaseSelectVersion": "버전을 선택해주세요",
             "versionExists": "이 버전은 이미 라이브러리에 있습니다",
             "downloadCompleted": "다운로드가 성공적으로 완료되었습니다",
-            "downloadSkippedByBaseModel": "기본 모델 {baseModel}이(가) 제외되어 다운로드를 건너뛰었습니다",
             "autoOrganizeSuccess": "{count}개의 {type}에 대해 자동 정리가 성공적으로 완료되었습니다",
             "autoOrganizePartialSuccess": "자동 정리 완료: 전체 {total}개 중 {success}개 이동, {failures}개 실패",
             "autoOrganizeFailed": "자동 정리 실패: {error}",
@@ -1617,19 +1341,13 @@
             "loadFailed": "{modelType} 로딩 실패: {message}",
             "refreshComplete": "새로고침 완료",
             "refreshFailed": "레시피 새로고침 실패: {message}",
-            "syncComplete": "동기화 완료",
-            "syncFailed": "레시피 동기화 실패: {message}",
             "updateFailed": "레시피 업데이트 실패: {error}",
             "updateError": "레시피 업데이트 오류: {message}",
             "nameSaved": "레시피 \"{name}\"이 성공적으로 저장되었습니다",
             "nameUpdated": "레시피 이름이 성공적으로 업데이트되었습니다",
             "tagsUpdated": "레시피 태그가 성공적으로 업데이트되었습니다",
             "sourceUrlUpdated": "소스 URL이 성공적으로 업데이트되었습니다",
-            "promptUpdated": "프롬프트가 성공적으로 업데이트되었습니다",
-            "negativePromptUpdated": "네거티브 프롬프트가 성공적으로 업데이트되었습니다",
-            "promptEditorHint": "Enter 키를 눌러 저장, Shift+Enter로 새 줄",
             "noRecipeId": "사용 가능한 레시피 ID가 없습니다",
-            "sendToWorkflowFailed": "워크플로우에 레시피 보내기 실패: {message}",
             "copyFailed": "레시피 문법 복사 오류: {message}",
             "noMissingLoras": "다운로드할 누락된 LoRA가 없습니다",
             "missingLorasInfoFailed": "누락된 LoRA 정보를 가져오는데 실패했습니다",
@@ -1657,20 +1375,9 @@
             "processingError": "처리 오류: {message}",
             "folderBrowserError": "폴더 브라우저 로딩 오류: {message}",
             "recipeSaveFailed": "레시피 저장 실패: {error}",
-            "recipeSaved": "Recipe saved successfully",
             "importFailed": "가져오기 실패: {message}",
             "folderTreeFailed": "폴더 트리 로딩 실패",
-            "folderTreeError": "폴더 트리 로딩 오류",
-            "batchImportFailed": "Failed to start batch import: {message}",
-            "batchImportCancelling": "Cancelling batch import...",
-            "batchImportCancelFailed": "Failed to cancel batch import: {message}",
-            "batchImportNoUrls": "Please enter at least one URL or file path",
-            "batchImportNoDirectory": "Please enter a directory path",
-            "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "noRecipesSelected": "선택한 레시피가 없습니다",
-            "noMissingLorasInSelection": "선택한 레시피에서 누락된 LoRA를 찾을 수 없습니다",
-            "noLoraRootConfigured": "LoRA 루트 디렉토리가 구성되지 않았습니다. 설정에서 기본 LoRA 루트를 설정하세요."
+            "folderTreeError": "폴더 트리 로딩 오류"
         },
         "models": {
             "noModelsSelected": "선택된 모델이 없습니다",
@@ -1690,11 +1397,6 @@
             "bulkBaseModelUpdateSuccess": "{count}개의 모델에 베이스 모델이 성공적으로 업데이트되었습니다",
             "bulkBaseModelUpdatePartial": "{success}개의 모델이 업데이트되었고, {failed}개의 모델이 실패했습니다",
             "bulkBaseModelUpdateFailed": "선택한 모델의 베이스 모델 업데이트에 실패했습니다",
-            "skipMetadataRefreshUpdating": "{count}개 모델의 메타데이터 새로고침 플래그를 업데이트하는 중...",
-            "skipMetadataRefreshSet": "{count}개 모델의 메타데이터 새로고침을 건너뛰었습니다",
-            "skipMetadataRefreshCleared": "{count}개 모델의 메타데이터 새로고침을 재개했습니다",
-            "skipMetadataRefreshPartial": "{success}개 모델을 업데이트했습니다. {failed}개 실패",
-            "skipMetadataRefreshFailed": "선택한 모델의 메타데이터 새로고침 플래그 업데이트 실패",
             "bulkContentRatingUpdating": "{count}개 모델의 콘텐츠 등급을 업데이트하는 중...",
             "bulkContentRatingSet": "{count}개 모델의 콘텐츠 등급을 {level}(으)로 설정했습니다",
             "bulkContentRatingPartial": "{success}개 모델의 콘텐츠 등급을 {level}(으)로 설정했고, {failed}개는 실패했습니다",
@@ -1737,8 +1439,6 @@
             "mappingSaveFailed": "베이스 모델 매핑 저장 실패: {message}",
             "downloadTemplatesUpdated": "다운로드 경로 템플릿이 업데이트되었습니다",
             "downloadTemplatesFailed": "다운로드 경로 템플릿 저장 실패: {message}",
-            "recipesPathUpdated": "레시피 저장 경로가 업데이트되었습니다",
-            "recipesPathSaveFailed": "레시피 저장 경로 업데이트 실패: {message}",
             "settingsUpdated": "설정 업데이트됨: {setting}",
             "compactModeToggled": "컴팩트 모드 {state}",
             "settingSaveFailed": "설정 저장 실패: {message}",
@@ -1784,13 +1484,12 @@
             "folderTreeFailed": "폴더 트리 로딩 실패",
             "folderTreeError": "폴더 트리 로딩 오류",
             "imagesImported": "예시 이미지가 성공적으로 가져와졌습니다",
-            "imagesPartial": "{success}개 이미지 가져오기 성공, {failed}개 실패",
             "importFailed": "예시 이미지 가져오기 실패: {message}"
         },
         "triggerWords": {
             "loadFailed": "학습된 단어를 로딩할 수 없습니다",
-            "tooLong": "트리거 단어는 500단어를 초과할 수 없습니다",
-            "tooMany": "최대 100개의 트리거 단어만 허용됩니다",
+            "tooLong": "트리거 단어는 100단어를 초과할 수 없습니다",
+            "tooMany": "최대 30개의 트리거 단어만 허용됩니다",
             "alreadyExists": "이 트리거 단어는 이미 존재합니다",
             "updateSuccess": "트리거 단어가 성공적으로 업데이트되었습니다",
             "updateFailed": "트리거 단어 업데이트에 실패했습니다",
@@ -1851,8 +1550,6 @@
             "deleteFailed": "{type} 삭제 실패: {message}",
             "excludeSuccess": "{type}이(가) 성공적으로 제외되었습니다",
             "excludeFailed": "{type} 제외 실패: {message}",
-            "restoreSuccess": "{type} 복원 완료",
-            "restoreFailed": "{type} 복원 실패: {message}",
             "fileNameUpdated": "파일명이 성공적으로 업데이트되었습니다",
             "fileRenameFailed": "파일 이름 변경 실패: {error}",
             "previewUpdated": "미리보기가 성공적으로 업데이트되었습니다",
@@ -1884,37 +1581,6 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
-    "doctor": {
-        "kicker": "시스템 진단",
-        "title": "닥터",
-        "buttonTitle": "진단 및 일반적인 수정 실행",
-        "loading": "환경을 확인하는 중...",
-        "footer": "수리 후에도 문제가 계속되면 진단 번들을 내보내세요.",
-        "summary": {
-            "idle": "설정, 캐시 무결성, UI 일관성에 대한 상태 검사를 실행합니다.",
-            "ok": "현재 환경에서 활성 문제를 찾지 못했습니다.",
-            "warning": "{count}개의 문제가 발견되었습니다. 대부분은 이 패널에서 바로 해결할 수 있습니다.",
-            "error": "앱이 완전히 정상 상태가 되기 전에 {count}개의 문제를 처리해야 합니다."
-        },
-        "status": {
-            "ok": "정상",
-            "warning": "주의 필요",
-            "error": "조치 필요"
-        },
-        "actions": {
-            "runAgain": "다시 실행",
-            "exportBundle": "번들 내보내기"
-        },
-        "toast": {
-            "loadFailed": "진단 로드 실패: {message}",
-            "repairSuccess": "캐시 재구성이 완료되었습니다.",
-            "repairFailed": "캐시 재구성 실패: {message}",
-            "exportSuccess": "진단 번들이 내보내졌습니다.",
-            "exportFailed": "진단 번들 내보내기 실패: {message}",
-            "conflictsResolved": "{count}개 파일명 충돌이 해결되었습니다.",
-            "conflictsResolveFailed": "파일명 충돌 해결 실패: {message}"
-        }
-    },
     "banners": {
         "versionMismatch": {
             "title": "애플리케이션 업데이트 감지",
@@ -1928,20 +1594,6 @@
             "content": "LoRA Manager is a passion project maintained full-time by a solo developer. Your support on Ko-fi helps cover development costs, keeps new updates coming, and unlocks a license key for the LM Civitai Extension as a thank-you gift. Every contribution truly makes a difference.",
             "supportCta": "Support on Ko-fi",
             "learnMore": "LM Civitai Extension Tutorial"
-        },
-        "cacheHealth": {
-            "corrupted": {
-                "title": "캐시 손상이 감지되었습니다"
-            },
-            "degraded": {
-                "title": "캐시 문제가 감지되었습니다"
-            },
-            "content": "{total}개의 캐시 항목 중 {invalid}개가 유효하지 않습니다 ({rate}). 모델 누락이나 오류가 발생할 수 있습니다. 캐시를 재구축하는 것이 좋습니다.",
-            "rebuildCache": "캐시 재구축",
-            "dismiss": "무시",
-            "rebuilding": "캐시 재구축 중...",
-            "rebuildFailed": "캐시 재구축 실패: {error}",
-            "retry": "다시 시도"
         }
     }
 }

locales/ru.json

@@ -1,11 +1,8 @@
 {
     "common": {
-        "cancel": "Отмена",
-        "confirm": "Подтвердить",
         "actions": {
             "save": "Сохранить",
             "cancel": "Отмена",
-            "confirm": "Подтвердить",
             "delete": "Удалить",
             "move": "Переместить",
             "refresh": "Обновить",
@@ -14,9 +11,7 @@
             "backToTop": "Наверх",
             "settings": "Настройки",
             "help": "Справка",
-            "add": "Добавить",
-            "close": "Закрыть",
-            "menu": "Меню"
+            "add": "Добавить"
         },
         "status": {
             "loading": "Загрузка...",
@@ -136,8 +131,7 @@
         },
         "badges": {
             "update": "Обновление",
-            "updateAvailable": "Доступно обновление",
-            "skipRefresh": "Обновление метаданных пропущено"
+            "updateAvailable": "Доступно обновление"
         },
         "usage": {
             "timesUsed": "Количество использований"
@@ -176,9 +170,6 @@
             "success": "Успешно восстановлено {count} рецептов.",
             "cancelled": "Восстановление отменено. {count} рецептов было восстановлено.",
             "error": "Ошибка восстановления рецептов: {message}"
-        },
-        "manageExcludedModels": {
-            "label": "Управление исключёнными моделями"
         }
     },
     "header": {
@@ -188,6 +179,7 @@
             "recipes": "Рецепты",
             "checkpoints": "Checkpoints",
             "embeddings": "Embeddings",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "Статистика"
         },
         "search": {
@@ -196,7 +188,8 @@
                 "loras": "Поиск LoRAs...",
                 "recipes": "Поиск рецептов...",
                 "checkpoints": "Поиск checkpoints...",
-                "embeddings": "Поиск embeddings..."
+                "embeddings": "Поиск embeddings...",
+                "misc": "[TODO: Translate] Search VAE/Upscaler models..."
             },
             "options": "Опции поиска",
             "searchIn": "Искать в:",
@@ -226,19 +219,13 @@
             "presetOverwriteConfirm": "Пресет \"{name}\" уже существует. Перезаписать?",
             "presetNamePlaceholder": "Имя пресета...",
             "baseModel": "Базовая модель",
-            "baseModelSearchPlaceholder": "Поиск базовых моделей...",
             "modelTags": "Теги (Топ 20)",
-            "modelTypes": "Типы моделей",
+            "modelTypes": "Model Types",
             "license": "Лицензия",
             "noCreditRequired": "Без указания авторства",
             "allowSellingGeneratedContent": "Продажа разрешена",
             "noTags": "Без тегов",
-            "noBaseModelMatches": "Нет базовых моделей, соответствующих текущему поиску.",
-            "clearAll": "Очистить все фильтры",
-            "any": "Любой",
-            "all": "Все",
-            "tagLogicAny": "Совпадение с любым тегом (ИЛИ)",
-            "tagLogicAll": "Совпадение со всеми тегами (И)"
+            "clearAll": "Очистить все фильтры"
         },
         "theme": {
             "toggle": "Переключить тему",
@@ -256,33 +243,6 @@
         "civitaiApiKey": "Ключ API Civitai",
         "civitaiApiKeyPlaceholder": "Введите ваш ключ API Civitai",
         "civitaiApiKeyHelp": "Используется для аутентификации при загрузке моделей с Civitai",
-        "civitaiHost": {
-            "label": "Хост Civitai",
-            "help": "Выберите, какой сайт Civitai будет открываться при использовании ссылок «View on Civitai».",
-            "options": {
-                "com": "civitai.com (только SFW)",
-                "red": "civitai.red (без ограничений)"
-            }
-        },
-        "downloadBackend": {
-            "label": "Бэкенд загрузки",
-            "help": "Выберите способ загрузки файлов моделей. Python использует встроенный загрузчик. aria2 использует экспериментальный внешний процесс загрузки.",
-            "options": {
-                "python": "Python (встроенный)",
-                "aria2": "aria2 (экспериментальный)"
-            }
-        },
-        "aria2cPath": {
-            "label": "Путь к aria2c",
-            "help": "Необязательный путь к исполняемому файлу aria2c. Оставьте пустым, чтобы использовать aria2c из системного PATH.",
-            "placeholder": "Оставьте пустым, чтобы использовать aria2c из PATH"
-        },
-        "aria2HelpLink": "Узнайте, как настроить сервер загрузки aria2",
-        "civitaiHostBanner": {
-            "title": "Доступна настройка хоста Civitai",
-            "content": "Теперь Civitai использует civitai.com для контента SFW и civitai.red для контента без ограничений. В настройках можно изменить, какой сайт открывать по умолчанию.",
-            "openSettings": "Открыть настройки"
-        },
         "openSettingsFileLocation": {
             "label": "Открыть папку настроек",
             "tooltip": "Открыть папку, содержащую settings.json",
@@ -293,32 +253,19 @@
         },
         "sections": {
             "contentFiltering": "Фильтрация контента",
-            "downloads": "Загрузки",
             "videoSettings": "Настройки видео",
             "layoutSettings": "Настройки макета",
-            "misc": "Разное",
-            "backup": "Резервные копии",
-            "folderSettings": "Корневые папки",
-            "recipeSettings": "Рецепты",
-            "extraFolderPaths": "Дополнительные пути к папкам",
-            "downloadPathTemplates": "Шаблоны путей загрузки",
+            "folderSettings": "Настройки папок",
             "priorityTags": "Приоритетные теги",
-            "updateFlags": "Метки обновлений",
+            "downloadPathTemplates": "Шаблоны путей загрузки",
             "exampleImages": "Примеры изображений",
-            "autoOrganize": "Автоорганизация",
-            "metadata": "Метаданные",
+            "updateFlags": "Метки обновлений",
+            "autoOrganize": "Auto-organize",
+            "misc": "Разное",
+            "metadataArchive": "Архив метаданных",
+            "storageLocation": "Расположение настроек",
             "proxySettings": "Настройки прокси"
         },
-        "nav": {
-            "general": "Общее",
-            "interface": "Интерфейс",
-            "library": "Библиотека"
-        },
-        "search": {
-            "placeholder": "Поиск в настройках...",
-            "clear": "Очистить поиск",
-            "noResults": "Настройки, соответствующие \"{query}\", не найдены"
-        },
         "storage": {
             "locationLabel": "Портативный режим",
             "locationHelp": "Включите, чтобы хранить settings.json в репозитории; выключите, чтобы сохранить его в папке конфигурации пользователя."
@@ -327,15 +274,7 @@
             "blurNsfwContent": "Размывать NSFW контент",
             "blurNsfwContentHelp": "Размывать превью изображений контента для взрослых (NSFW)",
             "showOnlySfw": "Показывать только SFW результаты",
-            "showOnlySfwHelp": "Фильтровать весь NSFW контент при просмотре и поиске",
-            "matureBlurThreshold": "Порог размытия взрослого контента",
-            "matureBlurThresholdHelp": "Установить, с какого уровня рейтинга начинается размытие при включенном размытии NSFW.",
-            "matureBlurThresholdOptions": {
-                "pg13": "PG13 и выше",
-                "r": "R и выше (по умолчанию)",
-                "x": "X и выше",
-                "xxx": "Только XXX"
-            }
+            "showOnlySfwHelp": "Фильтровать весь NSFW контент при просмотре и поиске"
         },
         "videoSettings": {
             "autoplayOnHover": "Автовоспроизведение видео при наведении",
@@ -350,63 +289,6 @@
                 "saveFailed": "Не удалось сохранить исключения: {message}"
             }
         },
-        "metadataRefreshSkipPaths": {
-            "label": "Пути для пропуска обновления метаданных",
-            "placeholder": "Пример: temp, archived/old, test_models",
-            "help": "Пропускать модели в этих каталогах при массовом обновлении метаданных («Получить все метаданные»). Введите пути к папкам относительно корневого каталога моделей, разделённые запятой.",
-            "validation": {
-                "noPaths": "Введите хотя бы один путь, разделённый запятыми.",
-                "saveFailed": "Не удалось сохранить пути для пропуска: {message}"
-            }
-        },
-        "backup": {
-            "autoEnabled": "Автоматические резервные копии",
-            "autoEnabledHelp": "Создаёт локальный снимок раз в день и хранит последние снимки согласно политике хранения.",
-            "retention": "Количество хранения",
-            "retentionHelp": "Сколько автоматических снимков сохранять перед удалением старых.",
-            "management": "Управление резервными копиями",
-            "managementHelp": "Экспортируйте текущее состояние пользователя или восстановите его из архива резервной копии.",
-            "scopeHelp": "Резервная копия включает ваши настройки, историю загрузок и состояние обновлений моделей. Файлы моделей и пересоздаваемые кэши не входят.",
-            "locationSummary": "Текущее расположение резервных копий",
-            "openFolderButton": "Открыть папку резервных копий",
-            "openFolderSuccess": "Папка резервных копий открыта",
-            "openFolderFailed": "Не удалось открыть папку резервных копий",
-            "locationCopied": "Путь к резервной копии скопирован в буфер обмена: {{path}}",
-            "locationClipboardFallback": "Путь к резервной копии: {{path}}",
-            "exportButton": "Экспортировать резервную копию",
-            "exportSuccess": "Резервная копия успешно экспортирована.",
-            "exportFailed": "Не удалось экспортировать резервную копию: {message}",
-            "importButton": "Импортировать резервную копию",
-            "importConfirm": "Импортировать эту резервную копию и перезаписать локальное состояние пользователя?",
-            "importSuccess": "Резервная копия успешно импортирована.",
-            "importFailed": "Не удалось импортировать резервную копию: {message}",
-            "latestSnapshot": "Последний снимок",
-            "latestAutoSnapshot": "Последний автоматический снимок",
-            "snapshotCount": "Сохранённые снимки",
-            "noneAvailable": "Снимков пока нет"
-        },
-        "downloadSkipBaseModels": {
-            "label": "Пропускать загрузки для базовых моделей",
-            "help": "Применяется ко всем сценариям загрузки. Здесь можно выбрать только поддерживаемые базовые модели.",
-            "searchPlaceholder": "Фильтровать базовые модели...",
-            "empty": "Нет базовых моделей, соответствующих текущему поиску.",
-            "summary": {
-                "none": "Ничего не выбрано",
-                "count": "Выбрано: {count}"
-            },
-            "actions": {
-                "edit": "Изменить",
-                "collapse": "Свернуть",
-                "clear": "Очистить"
-            },
-            "validation": {
-                "saveFailed": "Не удалось сохранить исключённые базовые модели: {message}"
-            }
-        },
-        "skipPreviouslyDownloadedModelVersions": {
-            "label": "Пропускать ранее загруженные версии моделей",
-            "help": "Если включено, LoRA Manager будет пропускать загрузку версии модели, если сервис истории загрузок записал, что эта конкретная версия уже загружена. Применяется ко всем потокам загрузки."
-        },
         "layoutSettings": {
             "displayDensity": "Плотность отображения",
             "displayDensityOptions": {
@@ -429,8 +311,6 @@
                 "hover": "Показать при наведении"
             },
             "cardInfoDisplayHelp": "Выберите когда отображать информацию о модели и кнопки действий",
-            "showVersionOnCard": "Показывать версию на карточке",
-            "showVersionOnCardHelp": "Показать или скрыть название версии на карточках моделей",
             "modelCardFooterAction": "Действие кнопки карточки модели",
             "modelCardFooterActionOptions": {
                 "exampleImages": "Открыть примеры изображений",
@@ -449,37 +329,16 @@
             "activeLibraryHelp": "Переключайтесь между настроенными библиотеками, чтобы обновить папки по умолчанию. Изменение выбора перезагружает страницу.",
             "loadingLibraries": "Загрузка библиотек...",
             "noLibraries": "Библиотеки не настроены",
-            "defaultLoraRoot": "Корневая папка LoRA",
+            "defaultLoraRoot": "Корневая папка LoRA по умолчанию",
             "defaultLoraRootHelp": "Установить корневую папку LoRA по умолчанию для загрузок, импорта и перемещений",
-            "defaultCheckpointRoot": "Корневая папка Checkpoint",
+            "defaultCheckpointRoot": "Корневая папка Checkpoint по умолчанию",
             "defaultCheckpointRootHelp": "Установить корневую папку checkpoint по умолчанию для загрузок, импорта и перемещений",
-            "defaultUnetRoot": "Корневая папка Diffusion Model",
+            "defaultUnetRoot": "Корневая папка Diffusion Model по умолчанию",
             "defaultUnetRootHelp": "Установить корневую папку Diffusion Model (UNET) по умолчанию для загрузок, импорта и перемещений",
-            "defaultEmbeddingRoot": "Корневая папка Embedding",
+            "defaultEmbeddingRoot": "Корневая папка Embedding по умолчанию",
             "defaultEmbeddingRootHelp": "Установить корневую папку embedding по умолчанию для загрузок, импорта и перемещений",
-            "recipesPath": "Путь хранения рецептов",
-            "recipesPathHelp": "Дополнительный пользовательский каталог для сохранённых рецептов. Оставьте пустым, чтобы использовать папку recipes в первом корне LoRA.",
-            "recipesPathPlaceholder": "/path/to/recipes",
-            "recipesPathMigrating": "Перенос хранилища рецептов...",
             "noDefault": "Не задано"
         },
-        "extraFolderPaths": {
-            "title": "Дополнительные пути к папкам",
-            "description": "Дополнительные корневые пути моделей, эксклюзивные для LoRA Manager. Загружайте модели из расположений за пределами стандартных папок ComfyUI — идеально подходит для больших библиотек, которые иначе замедлили бы ComfyUI.",
-            "restartRequired": "Requires restart to take effect",
-            "modelTypes": {
-                "lora": "Пути LoRA",
-                "checkpoint": "Пути Checkpoint",
-                "unet": "Пути моделей диффузии",
-                "embedding": "Пути Embedding"
-            },
-            "pathPlaceholder": "/путь/к/дополнительным/моделям",
-            "saveSuccess": "Дополнительные пути к папкам обновлены. Требуется перезапуск для применения изменений.",
-            "saveError": "Не удалось обновить дополнительные пути к папкам: {message}",
-            "validation": {
-                "duplicatePath": "Этот путь уже настроен"
-            }
-        },
         "priorityTags": {
             "title": "Приоритетные теги",
             "description": "Настройте порядок приоритетов тегов для каждого типа моделей (например, character, concept, style(toon|toon_style)).",
@@ -542,21 +401,6 @@
             "downloadLocationHelp": "Введите путь к папке, где будут сохраняться примеры изображений с Civitai",
             "autoDownload": "Автозагрузка примеров изображений",
             "autoDownloadHelp": "Автоматически загружать примеры изображений для моделей, у которых их нет (требует настройки места загрузки)",
-            "openMode": "Действие открытия примеров изображений",
-            "openModeHelp": "Выберите, будет ли действие открывать папку на сервере, копировать сопоставленный локальный путь или запускать пользовательский URI.",
-            "openModeOptions": {
-                "system": "Открыть на сервере",
-                "clipboard": "Скопировать локальный путь",
-                "uriTemplate": "Открыть пользовательский URI"
-            },
-            "localRoot": "Локальный корень примеров изображений",
-            "localRootHelp": "Необязательный локальный или смонтированный корневой путь, отражающий каталог примеров изображений на сервере. Если оставить пустым, будет использован путь сервера.",
-            "localRootPlaceholder": "Пример: /Volumes/ComfyUI/example_images",
-            "uriTemplate": "Шаблон URI для открытия",
-            "uriTemplateHelp": "Используйте пользовательскую deep link-ссылку, например file URI или ссылку Shortcuts.",
-            "uriTemplatePlaceholder": "Пример: shortcuts://run-shortcut?name=Open%20Finder&input=text&text={{encoded_local_path}}",
-            "uriTemplatePlaceholders": "Доступные плейсхолдеры: {{local_path}}, {{encoded_local_path}}, {{relative_path}}, {{encoded_relative_path}}, {{file_uri}}, {{encoded_file_uri}}",
-            "openModeWikiLink": "Подробнее об удаленных режимах открытия",
             "optimizeImages": "Оптимизировать загруженные изображения",
             "optimizeImagesHelp": "Оптимизировать примеры изображений для уменьшения размера файла и улучшения скорости загрузки (метаданные будут сохранены)",
             "download": "Загрузить",
@@ -570,10 +414,6 @@
                 "any": "Отмечать любые доступные обновления"
             }
         },
-        "hideEarlyAccessUpdates": {
-            "label": "Скрыть обновления раннего доступа",
-            "help": "Только обновления раннего доступа"
-        },
         "misc": {
             "includeTriggerWords": "Включать триггерные слова в синтаксис LoRA",
             "includeTriggerWordsHelp": "Включать обученные триггерные слова при копировании синтаксиса LoRA в буфер обмена"
@@ -685,13 +525,8 @@
             "checkUpdates": "Проверить обновления для выбранных",
             "moveAll": "Переместить все в папку",
             "autoOrganize": "Автоматически организовать выбранные",
-            "skipMetadataRefresh": "Пропустить обновление метаданных для выбранных",
-            "resumeMetadataRefresh": "Возобновить обновление метаданных для выбранных",
-            "deleteAll": "Удалить выбранные",
-            "downloadMissingLoras": "Скачать отсутствующие LoRAs",
+            "deleteAll": "Удалить все модели",
             "clear": "Очистить выбор",
-            "skipMetadataRefreshCount": "Пропустить（{count} моделей）",
-            "resumeMetadataRefreshCount": "Возобновить（{count} моделей）",
             "autoOrganizeProgress": {
                 "initializing": "Инициализация автоматической организации...",
                 "starting": "Запуск автоматической организации для {type}...",
@@ -718,7 +553,6 @@
             "moveToFolder": "Переместить в папку",
             "repairMetadata": "Восстановить метаданные",
             "excludeModel": "Исключить модель",
-            "restoreModel": "Восстановить модель",
             "deleteModel": "Удалить модель",
             "shareRecipe": "Поделиться рецептом",
             "viewAllLoras": "Посмотреть все LoRAs",
@@ -760,8 +594,6 @@
                 "root": "Корень",
                 "browseFolders": "Обзор папок:",
                 "downloadAndSaveRecipe": "Скачать и сохранить рецепт",
-                "importRecipeOnly": "Импортировать только рецепт",
-                "importAndDownload": "Импорт и скачивание",
                 "downloadMissingLoras": "Скачать отсутствующие LoRAs",
                 "saveRecipe": "Сохранить рецепт",
                 "loraCountInfo": "({existing}/{total} в библиотеке)",
@@ -803,11 +635,7 @@
                 "lorasCountAsc": "Меньше всего"
             },
             "refresh": {
-                "title": "Обновить список рецептов",
-                "quick": "Синхронизировать изменения",
-                "quickTooltip": "Синхронизировать изменения - быстрое обновление без перестроения кэша",
-                "full": "Перестроить кэш",
-                "fullTooltip": "Перестроить кэш - полное повторное сканирование всех файлов рецептов"
+                "title": "Обновить список рецептов"
             },
             "filteredByLora": "Фильтр по LoRA",
             "favorites": {
@@ -847,64 +675,6 @@
                 "failed": "Не удалось восстановить рецепт: {message}",
                 "missingId": "Не удалось восстановить рецепт: отсутствует ID рецепта"
             }
-        },
-        "batchImport": {
-            "title": "Batch Import Recipes",
-            "action": "Batch Import",
-            "urlList": "URL List",
-            "directory": "Directory",
-            "urlDescription": "Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
-            "directoryDescription": "Enter a directory path to import all images from that folder.",
-            "urlsLabel": "Image URLs or Local Paths",
-            "urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
-            "urlsHint": "Enter one URL or path per line",
-            "directoryPath": "Directory Path",
-            "directoryPlaceholder": "/path/to/images/folder",
-            "browse": "Browse",
-            "recursive": "Include subdirectories",
-            "tagsOptional": "Tags (optional, applied to all recipes)",
-            "tagsPlaceholder": "Enter tags separated by commas",
-            "tagsHint": "Tags will be added to all imported recipes",
-            "skipNoMetadata": "Skip images without metadata",
-            "skipNoMetadataHelp": "Images without LoRA metadata will be skipped automatically.",
-            "start": "Start Import",
-            "startImport": "Start Import",
-            "importing": "Importing...",
-            "progress": "Progress",
-            "total": "Total",
-            "success": "Success",
-            "failed": "Failed",
-            "skipped": "Skipped",
-            "current": "Current",
-            "currentItem": "Current",
-            "preparing": "Preparing...",
-            "cancel": "Cancel",
-            "cancelImport": "Cancel",
-            "cancelled": "Import cancelled",
-            "completed": "Import completed",
-            "completedWithErrors": "Completed with errors",
-            "completedSuccess": "Successfully imported {count} recipe(s)",
-            "successCount": "Successful",
-            "failedCount": "Failed",
-            "skippedCount": "Skipped",
-            "totalProcessed": "Total processed",
-            "viewDetails": "View Details",
-            "newImport": "New Import",
-            "manualPathEntry": "Please enter the directory path manually. File browser is not available in this browser.",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "batchImportManualEntryRequired": "File browser not available. Please enter the directory path manually.",
-            "backToParent": "Back to parent directory",
-            "folders": "Folders",
-            "folderCount": "{count} folders",
-            "imageFiles": "Image Files",
-            "images": "images",
-            "imageCount": "{count} images",
-            "selectFolder": "Select This Folder",
-            "errors": {
-                "enterUrls": "Please enter at least one URL or path",
-                "enterDirectory": "Please enter a directory path",
-                "startFailed": "Failed to start import: {message}"
-            }
         }
     },
     "checkpoints": {
@@ -914,13 +684,22 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "Переместить в папку {otherType}",
-            "sendToWorkflow": "Отправить в workflow"
+            "moveToOtherTypeFolder": "Переместить в папку {otherType}"
         }
     },
     "embeddings": {
         "title": "Модели Embedding"
     },
+    "misc": {
+        "title": "[TODO: Translate] VAE & Upscaler Models",
+        "modelTypes": {
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "[TODO: Translate] Move to {otherType} Folder"
+        }
+    },
     "sidebar": {
         "modelRoot": "Корень",
         "collapseAll": "Свернуть все папки",
@@ -928,23 +707,13 @@
         "unpinSidebar": "Открепить боковую панель",
         "switchToListView": "Переключить на вид списка",
         "switchToTreeView": "Переключить на древовидный вид",
-        "recursiveOn": "Включать вложенные папки",
-        "recursiveOff": "Только текущая папка",
+        "recursiveOn": "Искать во вложенных папках",
+        "recursiveOff": "Искать только в текущей папке",
         "recursiveUnavailable": "Рекурсивный поиск доступен только в режиме дерева",
         "collapseAllDisabled": "Недоступно в виде списка",
         "dragDrop": {
             "unableToResolveRoot": "Не удалось определить путь назначения для перемещения.",
-            "moveUnsupported": "Перемещение этого элемента не поддерживается.",
-            "createFolderHint": "Отпустите, чтобы создать новую папку",
-            "newFolderName": "Имя новой папки",
-            "folderNameHint": "Нажмите Enter для подтверждения, Escape для отмены",
-            "emptyFolderName": "Пожалуйста, введите имя папки",
-            "invalidFolderName": "Имя папки содержит недопустимые символы",
-            "noDragState": "Ожидающая операция перетаскивания не найдена"
-        },
-        "empty": {
-            "noFolders": "Папки не найдены",
-            "dragHint": "Перетащите элементы сюда, чтобы создать папки"
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -1009,8 +778,6 @@
             "earlyAccess": "Ранний доступ",
             "earlyAccessTooltip": "Требуется ранний доступ",
             "inLibrary": "В библиотеке",
-            "downloaded": "Загружено",
-            "downloadedTooltip": "Ранее загружено, но сейчас этого нет в вашей библиотеке.",
             "alreadyInLibrary": "Уже в библиотеке",
             "autoOrganizedPath": "[Автоматически организовано по шаблону пути]",
             "errors": {
@@ -1101,14 +868,6 @@
             "save": "Обновить базовую модель",
             "cancel": "Отмена"
         },
-        "bulkDownloadMissingLoras": {
-            "title": "Скачать отсутствующие LoRAs",
-            "message": "Найдено {uniqueCount} уникальных отсутствующих LoRAs (из {totalCount} всего в выбранных рецептах).",
-            "previewTitle": "LoRAs для скачивания:",
-            "moreItems": "...и еще {count}",
-            "note": "Файлы будут скачаны с использованием шаблонов путей по умолчанию. Это может занять некоторое время в зависимости от количества LoRAs.",
-            "downloadButton": "Скачать {count} LoRA(s)"
-        },
         "exampleAccess": {
             "title": "Локальные примеры изображений",
             "message": "Локальные примеры изображений для этой модели не найдены. Варианты просмотра:",
@@ -1160,9 +919,7 @@
                 "viewOnCivitai": "Посмотреть на Civitai",
                 "viewOnCivitaiText": "Посмотреть на Civitai",
                 "viewCreatorProfile": "Посмотреть профиль создателя",
-                "openFileLocation": "Открыть расположение файла",
-                "sendToWorkflow": "Отправить в ComfyUI",
-                "sendToWorkflowText": "Отправить в ComfyUI"
+                "openFileLocation": "Открыть расположение файла"
             },
             "openFileLocation": {
                 "success": "Расположение файла успешно открыто",
@@ -1170,9 +927,6 @@
                 "copied": "Путь скопирован в буфер обмена: {{path}}",
                 "clipboardFallback": "Путь: {{path}}"
             },
-            "sendToWorkflow": {
-                "noFilePath": "Невозможно отправить в ComfyUI: путь к файлу недоступен"
-            },
             "metadata": {
                 "version": "Версия",
                 "fileName": "Имя файла",
@@ -1209,8 +963,6 @@
                 "cancel": "Отменить редактирование",
                 "save": "Сохранить изменения",
                 "addPlaceholder": "Введите для добавления или нажмите на предложения ниже",
-                "editWord": "Редактировать триггерное слово",
-                "editPlaceholder": "Редактировать триггерное слово",
                 "copyWord": "Копировать триггерное слово",
                 "deleteWord": "Удалить триггерное слово",
                 "suggestions": {
@@ -1273,43 +1025,19 @@
                 },
                 "labels": {
                     "unnamed": "Версия без названия",
-                    "noDetails": "Дополнительная информация отсутствует",
-                    "earlyAccess": "EA"
-                },
-                "eaTime": {
-                    "endingSoon": "скоро заканчивается",
-                    "hours": "через {count}ч",
-                    "days": "через {count}д"
+                    "noDetails": "Дополнительная информация отсутствует"
                 },
                 "badges": {
-                    "current": "Открытая версия",
-                    "currentTooltip": "Это версия, с которой было открыто это окно",
+                    "current": "Текущая версия",
                     "inLibrary": "В библиотеке",
-                    "inLibraryTooltip": "Эта версия есть в вашей локальной библиотеке",
-                    "downloaded": "Загружено",
-                    "downloadedTooltip": "Эта версия уже загружалась, но сейчас отсутствует в вашей библиотеке",
                     "newer": "Более новая версия",
-                    "newerTooltip": "Эта версия новее вашей последней локальной версии",
-                    "earlyAccess": "Ранний доступ",
-                    "earlyAccessTooltip": "Для этой версии сейчас требуется ранний доступ Civitai",
-                    "ignored": "Игнорируется",
-                    "ignoredTooltip": "Уведомления об обновлениях для этой версии отключены",
-                    "onSiteOnly": "Только на Сайте",
-                    "onSiteOnlyTooltip": "Эта версия доступна только для генерации на сайте Civitai"
+                    "ignored": "Игнорируется"
                 },
                 "actions": {
                     "download": "Скачать",
-                    "downloadTooltip": "Скачать эту версию",
-                    "downloadEarlyAccessTooltip": "Скачать эту версию раннего доступа с Civitai",
-                    "downloadNotAllowedTooltip": "Эта версия доступна только для генерации на сайте Civitai",
                     "delete": "Удалить",
-                    "deleteTooltip": "Удалить эту локальную версию",
                     "ignore": "Игнорировать",
                     "unignore": "Перестать игнорировать",
-                    "ignoreTooltip": "Игнорировать уведомления об обновлениях для этой версии",
-                    "unignoreTooltip": "Возобновить уведомления об обновлениях для этой версии",
-                    "viewVersionOnCivitai": "Посмотреть версию на Civitai",
-                    "earlyAccessTooltip": "Требуется покупка раннего доступа",
                     "resumeModelUpdates": "Возобновить обновления для этой модели",
                     "ignoreModelUpdates": "Игнорировать обновления для этой модели",
                     "viewLocalVersions": "Показать все локальные версии",
@@ -1388,6 +1116,10 @@
             "title": "Инициализация статистики",
             "message": "Обработка данных моделей для статистики. Это может занять несколько минут..."
         },
+        "misc": {
+            "title": "[TODO: Translate] Initializing Misc Model Manager",
+            "message": "[TODO: Translate] Scanning VAE and Upscaler models..."
+        },
         "tips": {
             "title": "Советы и хитрости",
             "civitai": {
@@ -1447,14 +1179,18 @@
             "recipeAdded": "Рецепт добавлен в workflow",
             "recipeReplaced": "Рецепт заменён в workflow",
             "recipeFailedToSend": "Не удалось отправить рецепт в workflow",
+            "vaeUpdated": "[TODO: Translate] VAE updated in workflow",
+            "vaeFailed": "[TODO: Translate] Failed to update VAE in workflow",
+            "upscalerUpdated": "[TODO: Translate] Upscaler updated in workflow",
+            "upscalerFailed": "[TODO: Translate] Failed to update upscaler in workflow",
             "noMatchingNodes": "В текущем workflow нет совместимых узлов",
-            "noTargetNodeSelected": "Целевой узел не выбран",
-            "modelUpdated": "Модель обновлена в workflow",
-            "modelFailed": "Не удалось обновить узел модели"
+            "noTargetNodeSelected": "Целевой узел не выбран"
         },
         "nodeSelector": {
             "recipe": "Рецепт",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "Заменить",
             "append": "Добавить",
             "selectTargetNode": "Выберите целевой узел",
@@ -1464,10 +1200,6 @@
             "opened": "Папка с примерами изображений открыта",
             "openingFolder": "Открытие папки с примерами изображений",
             "failedToOpen": "Не удалось открыть папку с примерами изображений",
-            "copiedPath": "Путь скопирован в буфер обмена: {{path}}",
-            "clipboardFallback": "Путь: {{path}}",
-            "copiedUri": "Ссылка скопирована в буфер обмена: {{uri}}",
-            "uriClipboardFallback": "Ссылка: {{uri}}",
             "setupRequired": "Хранилище примеров изображений",
             "setupDescription": "Чтобы добавить собственные примеры изображений, сначала нужно установить место загрузки.",
             "setupUsage": "Этот путь используется как для загруженных, так и для пользовательских примеров изображений.",
@@ -1575,14 +1307,7 @@
             "showWechatQR": "Показать QR-код WeChat",
             "hideWechatQR": "Скрыть QR-код WeChat"
         },
-        "footer": "Спасибо за использование LoRA Manager! ❤️",
-        "supporters": {
-            "title": "Спасибо всем сторонникам",
-            "subtitle": "Спасибо {count} сторонникам, которые сделали этот проект возможным",
-            "specialThanks": "Особая благодарность",
-            "allSupporters": "Все сторонники",
-            "totalCount": "Всего {count} сторонников"
-        }
+        "footer": "Спасибо за использование LoRA Manager! ❤️"
     },
     "toast": {
         "general": {
@@ -1605,7 +1330,6 @@
             "pleaseSelectVersion": "Пожалуйста, выберите версию",
             "versionExists": "Эта версия уже существует в вашей библиотеке",
             "downloadCompleted": "Загрузка успешно завершена",
-            "downloadSkippedByBaseModel": "Загрузка пропущена, потому что базовая модель {baseModel} исключена",
             "autoOrganizeSuccess": "Автоматическая организация успешно завершена для {count} {type}",
             "autoOrganizePartialSuccess": "Автоматическая организация завершена: перемещено {success}, не удалось {failures} из {total} моделей",
             "autoOrganizeFailed": "Ошибка автоматической организации: {error}",
@@ -1617,19 +1341,13 @@
             "loadFailed": "Не удалось загрузить {modelType}s: {message}",
             "refreshComplete": "Обновление завершено",
             "refreshFailed": "Не удалось обновить рецепты: {message}",
-            "syncComplete": "Синхронизация завершена",
-            "syncFailed": "Не удалось синхронизировать рецепты: {message}",
             "updateFailed": "Не удалось обновить рецепт: {error}",
             "updateError": "Ошибка обновления рецепта: {message}",
             "nameSaved": "Рецепт \"{name}\" успешно сохранен",
             "nameUpdated": "Название рецепта успешно обновлено",
             "tagsUpdated": "Теги рецепта успешно обновлены",
             "sourceUrlUpdated": "Исходный URL успешно обновлен",
-            "promptUpdated": "Промпт успешно обновлён",
-            "negativePromptUpdated": "Негативный промпт успешно обновлён",
-            "promptEditorHint": "Нажмите Enter для сохранения, Shift+Enter для новой строки",
             "noRecipeId": "ID рецепта недоступен",
-            "sendToWorkflowFailed": "Не удалось отправить рецепт в рабочий процесс: {message}",
             "copyFailed": "Ошибка копирования синтаксиса рецепта: {message}",
             "noMissingLoras": "Нет отсутствующих LoRAs для загрузки",
             "missingLorasInfoFailed": "Не удалось получить информацию для отсутствующих LoRAs",
@@ -1657,20 +1375,9 @@
             "processingError": "Ошибка обработки: {message}",
             "folderBrowserError": "Ошибка загрузки браузера папок: {message}",
             "recipeSaveFailed": "Не удалось сохранить рецепт: {error}",
-            "recipeSaved": "Recipe saved successfully",
             "importFailed": "Импорт не удался: {message}",
             "folderTreeFailed": "Не удалось загрузить дерево папок",
-            "folderTreeError": "Ошибка загрузки дерева папок",
-            "batchImportFailed": "Failed to start batch import: {message}",
-            "batchImportCancelling": "Cancelling batch import...",
-            "batchImportCancelFailed": "Failed to cancel batch import: {message}",
-            "batchImportNoUrls": "Please enter at least one URL or file path",
-            "batchImportNoDirectory": "Please enter a directory path",
-            "batchImportBrowseFailed": "Failed to browse directory: {message}",
-            "batchImportDirectorySelected": "Directory selected: {path}",
-            "noRecipesSelected": "Рецепты не выбраны",
-            "noMissingLorasInSelection": "В выбранных рецептах не найдены отсутствующие LoRAs",
-            "noLoraRootConfigured": "Корневой каталог LoRA не настроен. Пожалуйста, установите корневой каталог LoRA по умолчанию в настройках."
+            "folderTreeError": "Ошибка загрузки дерева папок"
         },
         "models": {
             "noModelsSelected": "Модели не выбраны",
@@ -1690,11 +1397,6 @@
             "bulkBaseModelUpdateSuccess": "Базовая модель успешно обновлена для {count} моделей",
             "bulkBaseModelUpdatePartial": "Обновлено {success} моделей, не удалось обновить {failed} моделей",
             "bulkBaseModelUpdateFailed": "Не удалось обновить базовую модель для выбранных моделей",
-            "skipMetadataRefreshUpdating": "Обновление флага обновления метаданных для {count} модели(ей)...",
-            "skipMetadataRefreshSet": "Обновление метаданных пропущено для {count} модели(ей)",
-            "skipMetadataRefreshCleared": "Обновление метаданных возобновлено для {count} модели(ей)",
-            "skipMetadataRefreshPartial": "{success} модели(ей) обновлено, {failed} не удалось",
-            "skipMetadataRefreshFailed": "Не удалось обновить флаг обновления метаданных для выбранных моделей",
             "bulkContentRatingUpdating": "Обновление рейтинга контента для {count} модель(ей)...",
             "bulkContentRatingSet": "Рейтинг контента установлен на {level} для {count} модель(ей)",
             "bulkContentRatingPartial": "Рейтинг контента {level} установлен для {success} модель(ей), {failed} не удалось",
@@ -1737,8 +1439,6 @@
             "mappingSaveFailed": "Не удалось сохранить сопоставления базовых моделей: {message}",
             "downloadTemplatesUpdated": "Шаблоны путей загрузки обновлены",
             "downloadTemplatesFailed": "Не удалось сохранить шаблоны путей загрузки: {message}",
-            "recipesPathUpdated": "Путь хранения рецептов обновлён",
-            "recipesPathSaveFailed": "Не удалось обновить путь хранения рецептов: {message}",
             "settingsUpdated": "Настройки обновлены: {setting}",
             "compactModeToggled": "Компактный режим {state}",
             "settingSaveFailed": "Не удалось сохранить настройку: {message}",
@@ -1784,13 +1484,12 @@
             "folderTreeFailed": "Не удалось загрузить дерево папок",
             "folderTreeError": "Ошибка загрузки дерева папок",
             "imagesImported": "Примеры изображений успешно импортированы",
-            "imagesPartial": "{success} изображ. импортировано, {failed} не удалось",
             "importFailed": "Не удалось импортировать примеры изображений: {message}"
         },
         "triggerWords": {
             "loadFailed": "Не удалось загрузить обученные слова",
-            "tooLong": "Триггерное слово не должно превышать 500 слов",
-            "tooMany": "Максимум 100 триггерных слов разрешено",
+            "tooLong": "Триггерное слово не должно превышать 100 слов",
+            "tooMany": "Максимум 30 триггерных слов разрешено",
             "alreadyExists": "Это триггерное слово уже существует",
             "updateSuccess": "Триггерные слова успешно обновлены",
             "updateFailed": "Не удалось обновить триггерные слова",
@@ -1851,8 +1550,6 @@
             "deleteFailed": "Не удалось удалить {type}: {message}",
             "excludeSuccess": "{type} успешно исключен",
             "excludeFailed": "Не удалось исключить {type}: {message}",
-            "restoreSuccess": "{type} успешно восстановлен",
-            "restoreFailed": "Не удалось восстановить {type}: {message}",
             "fileNameUpdated": "Имя файла успешно обновлено",
             "fileRenameFailed": "Не удалось переименовать файл: {error}",
             "previewUpdated": "Превью успешно обновлено",
@@ -1884,37 +1581,6 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
-    "doctor": {
-        "kicker": "Системная диагностика",
-        "title": "Доктор",
-        "buttonTitle": "Запустить диагностику и обычные исправления",
-        "loading": "Проверка окружения...",
-        "footer": "Экспортируйте диагностический пакет, если проблема сохраняется после исправления.",
-        "summary": {
-            "idle": "Выполнить проверку настроек, целостности кэша и согласованности интерфейса.",
-            "ok": "В текущем окружении активных проблем не обнаружено.",
-            "warning": "Обнаружено {count} проблем(ы). Большинство можно исправить прямо из этой панели.",
-            "error": "Перед тем как приложение станет полностью исправным, нужно устранить {count} проблем(ы)."
-        },
-        "status": {
-            "ok": "Исправно",
-            "warning": "Требует внимания",
-            "error": "Требуется действие"
-        },
-        "actions": {
-            "runAgain": "Запустить снова",
-            "exportBundle": "Экспортировать пакет"
-        },
-        "toast": {
-            "loadFailed": "Не удалось загрузить диагностику: {message}",
-            "repairSuccess": "Перестройка кэша завершена.",
-            "repairFailed": "Не удалось перестроить кэш: {message}",
-            "exportSuccess": "Диагностический пакет экспортирован.",
-            "exportFailed": "Не удалось экспортировать диагностический пакет: {message}",
-            "conflictsResolved": "Разрешено конфликтов имён файлов: {count}.",
-            "conflictsResolveFailed": "Не удалось разрешить конфликты имён файлов: {message}"
-        }
-    },
     "banners": {
         "versionMismatch": {
             "title": "Обнаружено обновление приложения",
@@ -1928,20 +1594,6 @@
             "content": "LoRA Manager is a passion project maintained full-time by a solo developer. Your support on Ko-fi helps cover development costs, keeps new updates coming, and unlocks a license key for the LM Civitai Extension as a thank-you gift. Every contribution truly makes a difference.",
             "supportCta": "Support on Ko-fi",
             "learnMore": "LM Civitai Extension Tutorial"
-        },
-        "cacheHealth": {
-            "corrupted": {
-                "title": "Обнаружено повреждение кэша"
-            },
-            "degraded": {
-                "title": "Обнаружены проблемы с кэшем"
-            },
-            "content": "{invalid} из {total} записей кэша недействительны ({rate}). Это может привести к отсутствию моделей или ошибкам. Рекомендуется перестроить кэш.",
-            "rebuildCache": "Перестроить кэш",
-            "dismiss": "Отклонить",
-            "rebuilding": "Перестроение кэша...",
-            "rebuildFailed": "Не удалось перестроить кэш: {error}",
-            "retry": "Повторить"
         }
     }
 }

locales/zh-CN.json

@@ -1,11 +1,8 @@
 {
     "common": {
-        "cancel": "取消",
-        "confirm": "确认",
         "actions": {
             "save": "保存",
             "cancel": "取消",
-            "confirm": "确认",
             "delete": "删除",
             "move": "移动",
             "refresh": "刷新",
@@ -14,9 +11,7 @@
             "backToTop": "返回顶部",
             "settings": "设置",
             "help": "帮助",
-            "add": "添加",
-            "close": "关闭",
-            "menu": "菜单"
+            "add": "添加"
         },
         "status": {
             "loading": "加载中...",
@@ -136,8 +131,7 @@
         },
         "badges": {
             "update": "更新",
-            "updateAvailable": "有可用更新",
-            "skipRefresh": "元数据刷新已跳过"
+            "updateAvailable": "有可用更新"
         },
         "usage": {
             "timesUsed": "使用次数"
@@ -164,11 +158,11 @@
             "error": "清理示例图片文件夹失败：{message}"
         },
         "fetchMissingLicenses": {
-            "label": "刷新许可证元数据",
-            "loading": "正在刷新 {typePlural} 的许可证元数据...",
-            "success": "已更新 {count} 个 {typePlural} 的许可证元数据",
-            "none": "所有 {typePlural} 都已具备许可证元数据",
-            "error": "刷新 {typePlural} 的许可证元数据失败：{message}"
+            "label": "Refresh license metadata",
+            "loading": "Refreshing license metadata for {typePlural}...",
+            "success": "Updated license metadata for {count} {typePlural}",
+            "none": "All {typePlural} already have license metadata",
+            "error": "Failed to refresh license metadata for {typePlural}: {message}"
         },
         "repairRecipes": {
             "label": "修复配方数据",
@@ -176,9 +170,6 @@
             "success": "成功修复了 {count} 个配方。",
             "cancelled": "修复已取消。已修复 {count} 个配方。",
             "error": "配方修复失败：{message}"
-        },
-        "manageExcludedModels": {
-            "label": "管理已排除的模型"
         }
     },
     "header": {
@@ -188,6 +179,7 @@
             "recipes": "配方",
             "checkpoints": "Checkpoint",
             "embeddings": "Embedding",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "统计"
         },
         "search": {
@@ -196,7 +188,8 @@
                 "loras": "搜索 LoRA...",
                 "recipes": "搜索配方...",
                 "checkpoints": "搜索 Checkpoint...",
-                "embeddings": "搜索 Embedding..."
+                "embeddings": "搜索 Embedding...",
+                "misc": "[TODO: Translate] Search VAE/Upscaler models..."
             },
             "options": "搜索选项",
             "searchIn": "搜索范围：",
@@ -226,19 +219,13 @@
             "presetOverwriteConfirm": "预设 \"{name}\" 已存在。是否覆盖？",
             "presetNamePlaceholder": "预设名称...",
             "baseModel": "基础模型",
-            "baseModelSearchPlaceholder": "搜索基础模型...",
             "modelTags": "标签（前20）",
-            "modelTypes": "模型类型",
+            "modelTypes": "Model Types",
             "license": "许可证",
             "noCreditRequired": "无需署名",
             "allowSellingGeneratedContent": "允许销售",
             "noTags": "无标签",
-            "noBaseModelMatches": "没有基础模型符合当前搜索。",
-            "clearAll": "清除所有筛选",
-            "any": "任一",
-            "all": "全部",
-            "tagLogicAny": "匹配任一标签 (或)",
-            "tagLogicAll": "匹配所有标签 (与)"
+            "clearAll": "清除所有筛选"
         },
         "theme": {
             "toggle": "切换主题",
@@ -256,33 +243,6 @@
         "civitaiApiKey": "Civitai API 密钥",
         "civitaiApiKeyPlaceholder": "请输入你的 Civitai API 密钥",
         "civitaiApiKeyHelp": "用于从 Civitai 下载模型时的身份验证",
-        "civitaiHost": {
-            "label": "Civitai 站点",
-            "help": "选择使用“在 Civitai 中查看”时默认打开的 Civitai 站点。",
-            "options": {
-                "com": "civitai.com（仅 SFW）",
-                "red": "civitai.red（无限制）"
-            }
-        },
-        "downloadBackend": {
-            "label": "下载后端",
-            "help": "选择模型文件的下载方式。Python 使用内置下载器。aria2 使用实验性的外部下载进程。",
-            "options": {
-                "python": "Python（内置）",
-                "aria2": "aria2（实验性）"
-            }
-        },
-        "aria2cPath": {
-            "label": "aria2c 路径",
-            "help": "可选的 aria2c 可执行文件路径。留空则使用系统 PATH 中的 aria2c。",
-            "placeholder": "留空则使用 PATH 中的 aria2c"
-        },
-        "aria2HelpLink": "了解如何配置 aria2 下载后端",
-        "civitaiHostBanner": {
-            "title": "已提供 Civitai 站点偏好设置",
-            "content": "Civitai 现在使用 civitai.com 提供 SFW 内容，使用 civitai.red 提供无限制内容。你可以在设置中更改默认打开的站点。",
-            "openSettings": "打开设置"
-        },
         "openSettingsFileLocation": {
             "label": "打开设置文件夹",
             "tooltip": "打开包含 settings.json 的文件夹",
@@ -293,32 +253,19 @@
         },
         "sections": {
             "contentFiltering": "内容过滤",
-            "downloads": "下载",
             "videoSettings": "视频设置",
             "layoutSettings": "布局设置",
-            "misc": "其他",
-            "backup": "备份",
-            "folderSettings": "默认根目录",
-            "recipeSettings": "配方",
-            "extraFolderPaths": "额外文件夹路径",
-            "downloadPathTemplates": "下载路径模板",
+            "folderSettings": "文件夹设置",
             "priorityTags": "优先标签",
-            "updateFlags": "更新标记",
+            "downloadPathTemplates": "下载路径模板",
             "exampleImages": "示例图片",
-            "autoOrganize": "自动整理",
-            "metadata": "元数据",
+            "updateFlags": "更新标记",
+            "autoOrganize": "Auto-organize",
+            "misc": "其他",
+            "metadataArchive": "元数据归档数据库",
+            "storageLocation": "设置位置",
             "proxySettings": "代理设置"
         },
-        "nav": {
-            "general": "通用",
-            "interface": "界面",
-            "library": "库"
-        },
-        "search": {
-            "placeholder": "搜索设置...",
-            "clear": "清除搜索",
-            "noResults": "未找到匹配 \"{query}\" 的设置"
-        },
         "storage": {
             "locationLabel": "便携模式",
             "locationHelp": "开启可将 settings.json 保存在仓库中；关闭则保存在用户配置目录。"
@@ -327,15 +274,7 @@
             "blurNsfwContent": "模糊 NSFW 内容",
             "blurNsfwContentHelp": "模糊成熟（NSFW）内容预览图片",
             "showOnlySfw": "仅显示 SFW 结果",
-            "showOnlySfwHelp": "浏览和搜索时过滤所有 NSFW 内容",
-            "matureBlurThreshold": "成人内容模糊阈值",
-            "matureBlurThresholdHelp": "设置当启用 NSFW 模糊时，从哪个评级级别开始模糊过滤。",
-            "matureBlurThresholdOptions": {
-                "pg13": "PG13 及以上",
-                "r": "R 及以上（默认）",
-                "x": "X 及以上",
-                "xxx": "仅 XXX"
-            }
+            "showOnlySfwHelp": "浏览和搜索时过滤所有 NSFW 内容"
         },
         "videoSettings": {
             "autoplayOnHover": "悬停时自动播放视频",
@@ -350,63 +289,6 @@
                 "saveFailed": "无法保存排除项：{message}"
             }
         },
-        "metadataRefreshSkipPaths": {
-            "label": "元数据刷新跳过路径",
-            "placeholder": "示例：temp, archived/old, test_models",
-            "help": "批量刷新元数据（\"获取全部元数据\"）时跳过这些目录路径中的模型。输入相对于模型根目录的文件夹路径，以逗号分隔。",
-            "validation": {
-                "noPaths": "请输入至少一个路径，以逗号分隔。",
-                "saveFailed": "无法保存跳过路径：{message}"
-            }
-        },
-        "backup": {
-            "autoEnabled": "自动备份",
-            "autoEnabledHelp": "每天创建一次本地快照，并按保留策略保留最新快照。",
-            "retention": "保留数量",
-            "retentionHelp": "在删除旧快照之前，要保留多少个自动快照。",
-            "management": "备份管理",
-            "managementHelp": "导出当前用户状态，或从备份归档中恢复。",
-            "scopeHelp": "备份你的设置、下载历史和模型更新状态。不包含模型文件或可重建的缓存。",
-            "locationSummary": "当前备份位置",
-            "openFolderButton": "打开备份文件夹",
-            "openFolderSuccess": "已打开备份文件夹",
-            "openFolderFailed": "无法打开备份文件夹",
-            "locationCopied": "备份路径已复制到剪贴板：{{path}}",
-            "locationClipboardFallback": "备份路径：{{path}}",
-            "exportButton": "导出备份",
-            "exportSuccess": "备份导出成功。",
-            "exportFailed": "备份导出失败：{message}",
-            "importButton": "导入备份",
-            "importConfirm": "导入此备份并覆盖本地用户状态吗？",
-            "importSuccess": "备份导入成功。",
-            "importFailed": "备份导入失败：{message}",
-            "latestSnapshot": "最近快照",
-            "latestAutoSnapshot": "最近自动快照",
-            "snapshotCount": "已保存快照",
-            "noneAvailable": "还没有快照"
-        },
-        "downloadSkipBaseModels": {
-            "label": "跳过这些基础模型的下载",
-            "help": "适用于所有下载流程。这里只能选择受支持的基础模型。",
-            "searchPlaceholder": "筛选基础模型...",
-            "empty": "没有与当前搜索匹配的基础模型。",
-            "summary": {
-                "none": "未选择",
-                "count": "已选择 {count} 项"
-            },
-            "actions": {
-                "edit": "编辑",
-                "collapse": "收起",
-                "clear": "清空"
-            },
-            "validation": {
-                "saveFailed": "无法保存已排除的基础模型：{message}"
-            }
-        },
-        "skipPreviouslyDownloadedModelVersions": {
-            "label": "跳过已下载的模型版本",
-            "help": "启用后，如果下载历史服务记录显示该版本已下载，LoRA Manager 将跳过下载该模型版本。适用于所有下载流程。"
-        },
         "layoutSettings": {
             "displayDensity": "显示密度",
             "displayDensityOptions": {
@@ -429,8 +311,6 @@
                 "hover": "悬停时显示"
             },
             "cardInfoDisplayHelp": "选择何时显示模型信息和操作按钮",
-            "showVersionOnCard": "在卡片上显示版本",
-            "showVersionOnCardHelp": "在模型卡片上显示或隐藏版本名称",
             "modelCardFooterAction": "模型卡片按钮操作",
             "modelCardFooterActionOptions": {
                 "exampleImages": "打开示例图片",
@@ -449,37 +329,16 @@
             "activeLibraryHelp": "在已配置的库之间切换以更新默认文件夹。更改选择将重新加载页面。",
             "loadingLibraries": "正在加载库...",
             "noLibraries": "尚未配置库",
-            "defaultLoraRoot": "LoRA 根目录",
+            "defaultLoraRoot": "默认 LoRA 根目录",
             "defaultLoraRootHelp": "设置下载、导入和移动时的默认 LoRA 根目录",
-            "defaultCheckpointRoot": "Checkpoint 根目录",
+            "defaultCheckpointRoot": "默认 Checkpoint 根目录",
             "defaultCheckpointRootHelp": "设置下载、导入和移动时的默认 Checkpoint 根目录",
-            "defaultUnetRoot": "Diffusion Model 根目录",
+            "defaultUnetRoot": "默认 Diffusion Model 根目录",
             "defaultUnetRootHelp": "设置下载、导入和移动时的默认 Diffusion Model (UNET) 根目录",
-            "defaultEmbeddingRoot": "Embedding 根目录",
+            "defaultEmbeddingRoot": "默认 Embedding 根目录",
             "defaultEmbeddingRootHelp": "设置下载、导入和移动时的默认 Embedding 根目录",
-            "recipesPath": "配方存储路径",
-            "recipesPathHelp": "已保存配方的可选自定义目录。留空则使用第一个 LoRA 根目录下的 recipes 文件夹。",
-            "recipesPathPlaceholder": "/path/to/recipes",
-            "recipesPathMigrating": "正在迁移配方存储...",
             "noDefault": "无默认"
         },
-        "extraFolderPaths": {
-            "title": "额外文件夹路径",
-            "description": "LoRA Manager 专属的额外模型根目录。从 ComfyUI 标准文件夹之外的位置加载模型，特别适合管理大型模型库，避免影响 ComfyUI 性能。",
-            "restartRequired": "需要重启才能生效",
-            "modelTypes": {
-                "lora": "LoRA 路径",
-                "checkpoint": "Checkpoint 路径",
-                "unet": "Diffusion 模型路径",
-                "embedding": "Embedding 路径"
-            },
-            "pathPlaceholder": "/额外/模型/路径",
-            "saveSuccess": "额外文件夹路径已更新，需要重启才能生效。",
-            "saveError": "更新额外文件夹路径失败：{message}",
-            "validation": {
-                "duplicatePath": "此路径已配置"
-            }
-        },
         "priorityTags": {
             "title": "优先标签",
             "description": "为每种模型类型自定义标签优先级顺序 (例如: character, concept, style(toon|toon_style))",
@@ -542,21 +401,6 @@
             "downloadLocationHelp": "输入保存从 Civitai 下载的示例图片的文件夹路径",
             "autoDownload": "自动下载示例图片",
             "autoDownloadHelp": "自动为没有示例图片的模型下载示例图片（需设置下载位置）",
-            "openMode": "打开示例图片操作",
-            "openModeHelp": "选择是在服务器上打开、复制映射后的本地路径，还是启动自定义 URI。",
-            "openModeOptions": {
-                "system": "在服务器上打开",
-                "clipboard": "复制本地路径",
-                "uriTemplate": "打开自定义 URI"
-            },
-            "localRoot": "本地示例图片根目录",
-            "localRootHelp": "可选的本地或挂载根目录，用于映射服务器上的示例图片目录。若留空，则复用服务器路径。",
-            "localRootPlaceholder": "例如：/Volumes/ComfyUI/example_images",
-            "uriTemplate": "打开 URI 模板",
-            "uriTemplateHelp": "使用自定义深链接，例如文件 URI 或 Shortcuts 链接。",
-            "uriTemplatePlaceholder": "例如：shortcuts://run-shortcut?name=Open%20Finder&input=text&text={{encoded_local_path}}",
-            "uriTemplatePlaceholders": "可用占位符：{{local_path}}、{{encoded_local_path}}、{{relative_path}}、{{encoded_relative_path}}、{{file_uri}}、{{encoded_file_uri}}",
-            "openModeWikiLink": "了解远程打开模式",
             "optimizeImages": "优化下载图片",
             "optimizeImagesHelp": "优化示例图片以减少文件大小并提升加载速度（保留元数据）",
             "download": "下载",
@@ -570,10 +414,6 @@
                 "any": "显示任何可用更新"
             }
         },
-        "hideEarlyAccessUpdates": {
-            "label": "隐藏抢先体验更新",
-            "help": "抢先体验更新"
-        },
         "misc": {
             "includeTriggerWords": "复制 LoRA 语法时包含触发词",
             "includeTriggerWordsHelp": "复制 LoRA 语法到剪贴板时包含训练触发词"
@@ -685,13 +525,8 @@
             "checkUpdates": "检查所选更新",
             "moveAll": "移动所选中到文件夹",
             "autoOrganize": "自动整理所选模型",
-            "skipMetadataRefresh": "跳过所选模型的元数据刷新",
-            "resumeMetadataRefresh": "恢复所选模型的元数据刷新",
-            "deleteAll": "删除已选",
-            "downloadMissingLoras": "下载缺失的 LoRAs",
+            "deleteAll": "删除选中模型",
             "clear": "清除选择",
-            "skipMetadataRefreshCount": "跳过（{count} 个模型）",
-            "resumeMetadataRefreshCount": "恢复（{count} 个模型）",
             "autoOrganizeProgress": {
                 "initializing": "正在初始化自动整理...",
                 "starting": "正在为 {type} 启动自动整理...",
@@ -718,7 +553,6 @@
             "moveToFolder": "移动到文件夹",
             "repairMetadata": "修复元数据",
             "excludeModel": "排除模型",
-            "restoreModel": "恢复模型",
             "deleteModel": "删除模型",
             "shareRecipe": "分享配方",
             "viewAllLoras": "查看所有 LoRA",
@@ -737,9 +571,9 @@
                 "title": "从图片或 URL 导入配方",
                 "urlLocalPath": "URL / 本地路径",
                 "uploadImage": "上传图片",
-                "urlSectionDescription": "输入来自 civitai.com 或 civitai.red 的 Civitai 图片 URL，或本地文件路径以导入为配方。",
+                "urlSectionDescription": "输入 Civitai 图片 URL 或本地文件路径以导入为配方。",
                 "imageUrlOrPath": "图片 URL 或文件路径：",
-                "urlPlaceholder": "https://civitai.com/images/... 或 https://civitai.red/images/... 或 C:/path/to/image.png",
+                "urlPlaceholder": "https://civitai.com/images/... 或 C:/path/to/image.png",
                 "fetchImage": "获取图片",
                 "uploadSectionDescription": "上传带有 LoRA 元数据的图片以导入为配方。",
                 "selectImage": "选择图片",
@@ -760,8 +594,6 @@
                 "root": "根目录",
                 "browseFolders": "浏览文件夹：",
                 "downloadAndSaveRecipe": "下载并保存配方",
-                "importRecipeOnly": "仅导入配方",
-                "importAndDownload": "导入并下载",
                 "downloadMissingLoras": "下载缺失的 LoRA",
                 "saveRecipe": "保存配方",
                 "loraCountInfo": "({existing}/{total} in library)",
@@ -803,11 +635,7 @@
                 "lorasCountAsc": "最少"
             },
             "refresh": {
-                "title": "刷新配方列表",
-                "quick": "同步变更",
-                "quickTooltip": "同步变更 - 快速刷新而不重建缓存",
-                "full": "重建缓存",
-                "fullTooltip": "重建缓存 - 重新扫描所有配方文件"
+                "title": "刷新配方列表"
             },
             "filteredByLora": "按 LoRA 筛选",
             "favorites": {
@@ -847,64 +675,6 @@
                 "failed": "修复配方失败：{message}",
                 "missingId": "无法修复配方：缺少配方 ID"
             }
-        },
-        "batchImport": {
-            "title": "批量导入配方",
-            "action": "批量导入",
-            "urlList": "URL 列表",
-            "directory": "目录",
-            "urlDescription": "输入图像 URL 或本地文件路径（每行一个）。每个都将作为配方导入。",
-            "directoryDescription": "输入目录路径以导入该文件夹中的所有图片。",
-            "urlsLabel": "图片 URL 或本地路径",
-            "urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
-            "urlsHint": "每行输入一个 URL 或路径",
-            "directoryPath": "目录路径",
-            "directoryPlaceholder": "/图片/文件夹/路径",
-            "browse": "浏览",
-            "recursive": "包含子目录",
-            "tagsOptional": "标签（可选，应用于所有配方）",
-            "tagsPlaceholder": "输入以逗号分隔的标签",
-            "tagsHint": "标签将被添加到所有导入的配方中",
-            "skipNoMetadata": "跳过无元数据的图片",
-            "skipNoMetadataHelp": "没有 LoRA 元数据的图片将自动跳过。",
-            "start": "开始导入",
-            "startImport": "开始导入",
-            "importing": "正在导入配方...",
-            "progress": "进度",
-            "total": "总计",
-            "success": "成功",
-            "failed": "失败",
-            "skipped": "跳过",
-            "current": "当前",
-            "currentItem": "当前",
-            "preparing": "准备中...",
-            "cancel": "取消",
-            "cancelImport": "取消",
-            "cancelled": "批量导入已取消",
-            "completed": "导入完成",
-            "completedWithErrors": "导入完成但有错误",
-            "completedSuccess": "成功导入 {count} 个配方",
-            "successCount": "成功",
-            "failedCount": "失败",
-            "skippedCount": "跳过",
-            "totalProcessed": "总计处理",
-            "viewDetails": "查看详情",
-            "newImport": "新建导入",
-            "manualPathEntry": "请手动输入目录路径。此浏览器中文件浏览器不可用。",
-            "batchImportDirectorySelected": "已选择目录：{path}",
-            "batchImportManualEntryRequired": "文件浏览器不可用。请手动输入目录路径。",
-            "backToParent": "返回上级目录",
-            "folders": "文件夹",
-            "folderCount": "{count} 个文件夹",
-            "imageFiles": "图像文件",
-            "images": "图像",
-            "imageCount": "{count} 个图像",
-            "selectFolder": "选择此文件夹",
-            "errors": {
-                "enterUrls": "请至少输入一个 URL 或路径",
-                "enterDirectory": "请输入目录路径",
-                "startFailed": "启动导入失败：{message}"
-            }
         }
     },
     "checkpoints": {
@@ -914,13 +684,22 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "移动到 {otherType} 文件夹",
-            "sendToWorkflow": "发送到工作流"
+            "moveToOtherTypeFolder": "移动到 {otherType} 文件夹"
         }
     },
     "embeddings": {
         "title": "Embedding 模型"
     },
+    "misc": {
+        "title": "[TODO: Translate] VAE & Upscaler Models",
+        "modelTypes": {
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "[TODO: Translate] Move to {otherType} Folder"
+        }
+    },
     "sidebar": {
         "modelRoot": "根目录",
         "collapseAll": "折叠所有文件夹",
@@ -928,23 +707,13 @@
         "unpinSidebar": "取消固定侧边栏",
         "switchToListView": "切换到列表视图",
         "switchToTreeView": "切换到树状视图",
-        "recursiveOn": "包含子文件夹",
-        "recursiveOff": "仅当前文件夹",
+        "recursiveOn": "搜索子文件夹",
+        "recursiveOff": "仅搜索当前文件夹",
         "recursiveUnavailable": "仅在树形视图中可使用递归搜索",
         "collapseAllDisabled": "列表视图下不可用",
         "dragDrop": {
             "unableToResolveRoot": "无法确定移动的目标路径。",
-            "moveUnsupported": "Move is not supported for this item.",
-            "createFolderHint": "释放以创建新文件夹",
-            "newFolderName": "新文件夹名称",
-            "folderNameHint": "按 Enter 确认，Escape 取消",
-            "emptyFolderName": "请输入文件夹名称",
-            "invalidFolderName": "文件夹名称包含无效字符",
-            "noDragState": "未找到待处理的拖放操作"
-        },
-        "empty": {
-            "noFolders": "未找到文件夹",
-            "dragHint": "拖拽项目到此处以创建文件夹"
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -1009,8 +778,6 @@
             "earlyAccess": "早期访问",
             "earlyAccessTooltip": "需要早期访问权限",
             "inLibrary": "已在库中",
-            "downloaded": "已下载",
-            "downloadedTooltip": "之前已下载，但当前不在你的库中。",
             "alreadyInLibrary": "已存在于库中",
             "autoOrganizedPath": "【已按路径模板自动整理】",
             "errors": {
@@ -1101,14 +868,6 @@
             "save": "更新基础模型",
             "cancel": "取消"
         },
-        "bulkDownloadMissingLoras": {
-            "title": "下载缺失的 LoRAs",
-            "message": "发现 {uniqueCount} 个独特的缺失 LoRAs（从选定配方中的 {totalCount} 个总数）。",
-            "previewTitle": "要下载的 LoRAs：",
-            "moreItems": "...还有 {count} 个",
-            "note": "文件将使用默认路径模板下载。根据 LoRAs 的数量，这可能需要一些时间。",
-            "downloadButton": "下载 {count} 个 LoRA(s)"
-        },
         "exampleAccess": {
             "title": "本地示例图片",
             "message": "未找到此模型的本地示例图片。可选操作：",
@@ -1142,9 +901,9 @@
             },
             "proceedText": "仅在你确定需要此操作时继续。",
             "urlLabel": "Civitai 模型 URL：",
-            "urlPlaceholder": "https://civitai.com/models/649516/model-name?modelVersionId=726676 或 https://civitai.red/models/649516/model-name?modelVersionId=726676",
+            "urlPlaceholder": "https://civitai.com/models/649516/model-name?modelVersionId=726676",
             "helpText": {
-                "title": "粘贴任意来自 civitai.com 或 civitai.red 的 Civitai 模型 URL。支持格式：",
+                "title": "粘贴任意 Civitai 模型 URL。支持格式：",
                 "format1": "https://civitai.com/models/649516",
                 "format2": "https://civitai.com/models/649516?modelVersionId=726676",
                 "format3": "https://civitai.com/models/649516/model-name?modelVersionId=726676",
@@ -1160,9 +919,7 @@
                 "viewOnCivitai": "在 Civitai 查看",
                 "viewOnCivitaiText": "在 Civitai 查看",
                 "viewCreatorProfile": "查看创作者主页",
-                "openFileLocation": "打开文件位置",
-                "sendToWorkflow": "发送到 ComfyUI",
-                "sendToWorkflowText": "发送到 ComfyUI"
+                "openFileLocation": "打开文件位置"
             },
             "openFileLocation": {
                 "success": "文件位置已成功打开",
@@ -1170,9 +927,6 @@
                 "copied": "路径已复制到剪贴板：{{path}}",
                 "clipboardFallback": "路径：{{path}}"
             },
-            "sendToWorkflow": {
-                "noFilePath": "无法发送到 ComfyUI：没有可用的文件路径"
-            },
             "metadata": {
                 "version": "版本",
                 "fileName": "文件名",
@@ -1209,8 +963,6 @@
                 "cancel": "取消编辑",
                 "save": "保存更改",
                 "addPlaceholder": "输入或点击下方建议添加",
-                "editWord": "编辑触发词",
-                "editPlaceholder": "编辑触发词",
                 "copyWord": "复制触发词",
                 "deleteWord": "删除触发词",
                 "suggestions": {
@@ -1273,43 +1025,19 @@
                 },
                 "labels": {
                     "unnamed": "未命名版本",
-                    "noDetails": "暂无更多信息",
-                    "earlyAccess": "EA"
-                },
-                "eaTime": {
-                    "endingSoon": "即将结束",
-                    "hours": "{count}小时后",
-                    "days": "{count}天后"
+                    "noDetails": "暂无更多信息"
                 },
                 "badges": {
-                    "current": "已打开版本",
-                    "currentTooltip": "这是你用来打开此弹窗的版本",
+                    "current": "当前版本",
                     "inLibrary": "已在库中",
-                    "inLibraryTooltip": "此版本已存在于你的本地库中",
-                    "downloaded": "已下载",
-                    "downloadedTooltip": "此版本之前下载过，但当前不在你的本地库中",
                     "newer": "较新的版本",
-                    "newerTooltip": "此版本比你本地的最新版本更新",
-                    "earlyAccess": "抢先体验",
-                    "earlyAccessTooltip": "此版本当前需要 Civitai 抢先体验权限",
-                    "ignored": "已忽略",
-                    "ignoredTooltip": "此版本已关闭更新通知",
-                    "onSiteOnly": "仅站内生成",
-                    "onSiteOnlyTooltip": "此版本仅在 Civitai 站内可用，无法下载"
+                    "ignored": "已忽略"
                 },
                 "actions": {
                     "download": "下载",
-                    "downloadTooltip": "下载此版本",
-                    "downloadEarlyAccessTooltip": "从 Civitai 下载此抢先体验版本",
-                    "downloadNotAllowedTooltip": "此版本仅在 Civitai 站内可用，无法下载",
                     "delete": "删除",
-                    "deleteTooltip": "删除此本地版本",
                     "ignore": "忽略",
                     "unignore": "取消忽略",
-                    "ignoreTooltip": "忽略此版本的更新通知",
-                    "unignoreTooltip": "恢复此版本的更新通知",
-                    "viewVersionOnCivitai": "在 Civitai 上查看版本",
-                    "earlyAccessTooltip": "需要购买抢先体验",
                     "resumeModelUpdates": "继续跟踪该模型的更新",
                     "ignoreModelUpdates": "忽略该模型的更新",
                     "viewLocalVersions": "查看所有本地版本",
@@ -1388,6 +1116,10 @@
             "title": "初始化统计",
             "message": "正在处理模型数据以生成统计信息。这可能需要几分钟..."
         },
+        "misc": {
+            "title": "[TODO: Translate] Initializing Misc Model Manager",
+            "message": "[TODO: Translate] Scanning VAE and Upscaler models..."
+        },
         "tips": {
             "title": "技巧与提示",
             "civitai": {
@@ -1447,14 +1179,18 @@
             "recipeAdded": "配方已追加到工作流",
             "recipeReplaced": "配方已替换到工作流",
             "recipeFailedToSend": "发送配方到工作流失败",
+            "vaeUpdated": "[TODO: Translate] VAE updated in workflow",
+            "vaeFailed": "[TODO: Translate] Failed to update VAE in workflow",
+            "upscalerUpdated": "[TODO: Translate] Upscaler updated in workflow",
+            "upscalerFailed": "[TODO: Translate] Failed to update upscaler in workflow",
             "noMatchingNodes": "当前工作流中没有兼容的节点",
-            "noTargetNodeSelected": "未选择目标节点",
-            "modelUpdated": "模型已更新到工作流",
-            "modelFailed": "更新模型节点失败"
+            "noTargetNodeSelected": "未选择目标节点"
         },
         "nodeSelector": {
             "recipe": "配方",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "替换",
             "append": "追加",
             "selectTargetNode": "选择目标节点",
@@ -1464,10 +1200,6 @@
             "opened": "示例图片文件夹已打开",
             "openingFolder": "正在打开示例图片文件夹",
             "failedToOpen": "打开示例图片文件夹失败",
-            "copiedPath": "路径已复制到剪贴板：{{path}}",
-            "clipboardFallback": "路径：{{path}}",
-            "copiedUri": "链接已复制到剪贴板：{{uri}}",
-            "uriClipboardFallback": "链接：{{uri}}",
             "setupRequired": "示例图片存储",
             "setupDescription": "要添加自定义示例图片，您需要先设置下载位置。",
             "setupUsage": "此路径用于存储下载的示例图片和自定义图片。",
@@ -1575,14 +1307,7 @@
             "showWechatQR": "显示微信二维码",
             "hideWechatQR": "隐藏微信二维码"
         },
-        "footer": "感谢使用 LoRA 管理器！❤️",
-        "supporters": {
-            "title": "感谢所有支持者",
-            "subtitle": "感谢 {count} 位支持者让这个项目成为可能",
-            "specialThanks": "特别感谢",
-            "allSupporters": "所有支持者",
-            "totalCount": "共 {count} 位支持者"
-        }
+        "footer": "感谢使用 LoRA 管理器！❤️"
     },
     "toast": {
         "general": {
@@ -1605,7 +1330,6 @@
             "pleaseSelectVersion": "请选择版本",
             "versionExists": "该版本已存在于你的库中",
             "downloadCompleted": "下载成功完成",
-            "downloadSkippedByBaseModel": "由于基础模型 {baseModel} 已被排除，已跳过下载",
             "autoOrganizeSuccess": "自动整理已成功完成，共 {count} 个 {type}",
             "autoOrganizePartialSuccess": "自动整理完成：已移动 {success} 个，{failures} 个失败，共 {total} 个模型",
             "autoOrganizeFailed": "自动整理失败：{error}",
@@ -1617,19 +1341,13 @@
             "loadFailed": "加载 {modelType} 失败：{message}",
             "refreshComplete": "刷新完成",
             "refreshFailed": "刷新配方失败：{message}",
-            "syncComplete": "同步完成",
-            "syncFailed": "同步配方失败：{message}",
             "updateFailed": "更新配方失败：{error}",
             "updateError": "更新配方出错：{message}",
             "nameSaved": "配方“{name}”保存成功",
             "nameUpdated": "配方名称更新成功",
             "tagsUpdated": "配方标签更新成功",
             "sourceUrlUpdated": "来源 URL 更新成功",
-            "promptUpdated": "提示词更新成功",
-            "negativePromptUpdated": "负面提示词更新成功",
-            "promptEditorHint": "按 Enter 保存，Shift+Enter 换行",
             "noRecipeId": "无配方 ID",
-            "sendToWorkflowFailed": "发送配方到工作流失败：{message}",
             "copyFailed": "复制配方语法出错：{message}",
             "noMissingLoras": "没有缺失的 LoRA 可下载",
             "missingLorasInfoFailed": "获取缺失 LoRA 信息失败",
@@ -1657,20 +1375,9 @@
             "processingError": "处理出错：{message}",
             "folderBrowserError": "加载文件夹浏览器出错：{message}",
             "recipeSaveFailed": "保存配方失败：{error}",
-            "recipeSaved": "配方保存成功",
             "importFailed": "导入失败：{message}",
             "folderTreeFailed": "加载文件夹树失败",
-            "folderTreeError": "加载文件夹树出错",
-            "batchImportFailed": "启动批量导入失败：{message}",
-            "batchImportCancelling": "正在取消批量导入...",
-            "batchImportCancelFailed": "取消批量导入失败：{message}",
-            "batchImportNoUrls": "请输入至少一个 URL 或文件路径",
-            "batchImportNoDirectory": "请输入目录路径",
-            "batchImportBrowseFailed": "浏览目录失败：{message}",
-            "batchImportDirectorySelected": "已选择目录：{path}",
-            "noRecipesSelected": "未选择任何配方",
-            "noMissingLorasInSelection": "在选定的配方中未找到缺失的 LoRAs",
-            "noLoraRootConfigured": "未配置 LoRA 根目录。请在设置中设置默认的 LoRA 根目录。"
+            "folderTreeError": "加载文件夹树出错"
         },
         "models": {
             "noModelsSelected": "未选中模型",
@@ -1690,11 +1397,6 @@
             "bulkBaseModelUpdateSuccess": "成功为 {count} 个模型更新基础模型",
             "bulkBaseModelUpdatePartial": "更新了 {success} 个模型，{failed} 个失败",
             "bulkBaseModelUpdateFailed": "为选中模型更新基础模型失败",
-            "skipMetadataRefreshUpdating": "正在更新 {count} 个模型的元数据刷新标志...",
-            "skipMetadataRefreshSet": "已为 {count} 个模型跳过元数据刷新",
-            "skipMetadataRefreshCleared": "已为 {count} 个模型恢复元数据刷新",
-            "skipMetadataRefreshPartial": "已更新 {success} 个模型，{failed} 个失败",
-            "skipMetadataRefreshFailed": "未能更新所选模型的元数据刷新标志",
             "bulkContentRatingUpdating": "正在为 {count} 个模型更新内容评级...",
             "bulkContentRatingSet": "已将 {count} 个模型的内容评级设置为 {level}",
             "bulkContentRatingPartial": "已将 {success} 个模型的内容评级设置为 {level}，{failed} 个失败",
@@ -1737,8 +1439,6 @@
             "mappingSaveFailed": "保存基础模型映射失败：{message}",
             "downloadTemplatesUpdated": "下载路径模板已更新",
             "downloadTemplatesFailed": "保存下载路径模板失败：{message}",
-            "recipesPathUpdated": "配方存储路径已更新",
-            "recipesPathSaveFailed": "更新配方存储路径失败：{message}",
             "settingsUpdated": "设置已更新：{setting}",
             "compactModeToggled": "紧凑模式 {state}",
             "settingSaveFailed": "保存设置失败：{message}",
@@ -1784,13 +1484,12 @@
             "folderTreeFailed": "加载文件夹树失败",
             "folderTreeError": "加载文件夹树出错",
             "imagesImported": "示例图片导入成功",
-            "imagesPartial": "成功导入 {success} 张图片，{failed} 张失败",
             "importFailed": "导入示例图片失败：{message}"
         },
         "triggerWords": {
             "loadFailed": "无法加载训练词",
-            "tooLong": "触发词不能超过500个词",
-            "tooMany": "最多允许100个触发词",
+            "tooLong": "触发词不能超过100个词",
+            "tooMany": "最多允许30个触发词",
             "alreadyExists": "该触发词已存在",
             "updateSuccess": "触发词更新成功",
             "updateFailed": "触发词更新失败",
@@ -1851,8 +1550,6 @@
             "deleteFailed": "删除 {type} 失败：{message}",
             "excludeSuccess": "{type} 排除成功",
             "excludeFailed": "排除 {type} 失败：{message}",
-            "restoreSuccess": "{type} 已成功恢复",
-            "restoreFailed": "恢复 {type} 失败：{message}",
             "fileNameUpdated": "文件名更新成功",
             "fileRenameFailed": "重命名文件失败：{error}",
             "previewUpdated": "预览图片更新成功",
@@ -1884,37 +1581,6 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
-    "doctor": {
-        "kicker": "系统诊断",
-        "title": "医生",
-        "buttonTitle": "运行诊断并尝试修复常见问题",
-        "loading": "正在检查当前环境...",
-        "footer": "如果修复后问题仍然存在，可以导出诊断包进一步排查。",
-        "summary": {
-            "idle": "检查设置、缓存健康状况和前后端 UI 版本是否一致。",
-            "ok": "当前环境未发现活动问题。",
-            "warning": "发现 {count} 个问题，大多数可以直接在这里处理。",
-            "error": "发现 {count} 个需要尽快处理的问题。"
-        },
-        "status": {
-            "ok": "健康",
-            "warning": "需要关注",
-            "error": "需要处理"
-        },
-        "actions": {
-            "runAgain": "重新检查",
-            "exportBundle": "导出诊断包"
-        },
-        "toast": {
-            "loadFailed": "加载诊断结果失败：{message}",
-            "repairSuccess": "缓存重建完成。",
-            "repairFailed": "缓存重建失败：{message}",
-            "exportSuccess": "诊断包已导出。",
-            "exportFailed": "导出诊断包失败：{message}",
-            "conflictsResolved": "已解决 {count} 个文件名冲突。",
-            "conflictsResolveFailed": "解决文件名冲突失败：{message}"
-        }
-    },
     "banners": {
         "versionMismatch": {
             "title": "检测到应用更新",
@@ -1928,20 +1594,6 @@
             "content": "来爱发电为Lora Manager项目发电，支持项目持续开发的同时，获取浏览器插件验证码，按季支付更优惠！支付宝/微信方便支付。感谢支持！🚀",
             "supportCta": "为LM发电",
             "learnMore": "浏览器插件教程"
-        },
-        "cacheHealth": {
-            "corrupted": {
-                "title": "检测到缓存损坏"
-            },
-            "degraded": {
-                "title": "检测到缓存问题"
-            },
-            "content": "{total} 个缓存条目中有 {invalid} 个无效（{rate}）。这可能导致模型丢失或错误。建议重建缓存。",
-            "rebuildCache": "重建缓存",
-            "dismiss": "忽略",
-            "rebuilding": "正在重建缓存...",
-            "rebuildFailed": "重建缓存失败：{error}",
-            "retry": "重试"
         }
     }
 }

locales/zh-TW.json

@@ -1,11 +1,8 @@
 {
     "common": {
-        "cancel": "取消",
-        "confirm": "確認",
         "actions": {
             "save": "儲存",
             "cancel": "取消",
-            "confirm": "確認",
             "delete": "刪除",
             "move": "移動",
             "refresh": "重新整理",
@@ -14,9 +11,7 @@
             "backToTop": "回到頂部",
             "settings": "設定",
             "help": "說明",
-            "add": "新增",
-            "close": "關閉",
-            "menu": "選單"
+            "add": "新增"
         },
         "status": {
             "loading": "載入中...",
@@ -136,8 +131,7 @@
         },
         "badges": {
             "update": "更新",
-            "updateAvailable": "有可用更新",
-            "skipRefresh": "元數據更新已跳過"
+            "updateAvailable": "有可用更新"
         },
         "usage": {
             "timesUsed": "使用次數"
@@ -176,9 +170,6 @@
             "success": "成功修復 {count} 個配方。",
             "cancelled": "修復已取消。已修復 {count} 個配方。",
             "error": "配方修復失敗：{message}"
-        },
-        "manageExcludedModels": {
-            "label": "管理已排除的模型"
         }
     },
     "header": {
@@ -188,6 +179,7 @@
             "recipes": "配方",
             "checkpoints": "Checkpoint",
             "embeddings": "Embedding",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "統計"
         },
         "search": {
@@ -196,7 +188,8 @@
                 "loras": "搜尋 LoRA...",
                 "recipes": "搜尋配方...",
                 "checkpoints": "搜尋 checkpoint...",
-                "embeddings": "搜尋 embedding..."
+                "embeddings": "搜尋 embedding...",
+                "misc": "[TODO: Translate] Search VAE/Upscaler models..."
             },
             "options": "搜尋選項",
             "searchIn": "搜尋範圍：",
@@ -226,19 +219,13 @@
             "presetOverwriteConfirm": "預設 \"{name}\" 已存在。是否覆蓋？",
             "presetNamePlaceholder": "預設名稱...",
             "baseModel": "基礎模型",
-            "baseModelSearchPlaceholder": "搜尋基礎模型...",
             "modelTags": "標籤（前 20）",
-            "modelTypes": "模型類型",
+            "modelTypes": "Model Types",
             "license": "授權",
             "noCreditRequired": "無需署名",
             "allowSellingGeneratedContent": "允許銷售",
             "noTags": "無標籤",
-            "noBaseModelMatches": "沒有基礎模型符合目前的搜尋。",
-            "clearAll": "清除所有篩選",
-            "any": "任一",
-            "all": "全部",
-            "tagLogicAny": "符合任一票籤 (或)",
-            "tagLogicAll": "符合所有標籤 (與)"
+            "clearAll": "清除所有篩選"
         },
         "theme": {
             "toggle": "切換主題",
@@ -256,33 +243,6 @@
         "civitaiApiKey": "Civitai API 金鑰",
         "civitaiApiKeyPlaceholder": "請輸入您的 Civitai API 金鑰",
         "civitaiApiKeyHelp": "用於從 Civitai 下載模型時的身份驗證",
-        "civitaiHost": {
-            "label": "Civitai 站點",
-            "help": "選擇使用「在 Civitai 中查看」時預設開啟的 Civitai 站點。",
-            "options": {
-                "com": "civitai.com（僅 SFW）",
-                "red": "civitai.red（無限制）"
-            }
-        },
-        "downloadBackend": {
-            "label": "下載後端",
-            "help": "選擇模型檔案的下載方式。Python 使用內建下載器。aria2 使用實驗性的外部下載程序。",
-            "options": {
-                "python": "Python（內建）",
-                "aria2": "aria2（實驗性）"
-            }
-        },
-        "aria2cPath": {
-            "label": "aria2c 路徑",
-            "help": "可選的 aria2c 可執行檔路徑。留空則使用系統 PATH 中的 aria2c。",
-            "placeholder": "留空則使用 PATH 中的 aria2c"
-        },
-        "aria2HelpLink": "了解如何設定 aria2 下載後端",
-        "civitaiHostBanner": {
-            "title": "已提供 Civitai 站點偏好設定",
-            "content": "Civitai 現在使用 civitai.com 提供 SFW 內容，使用 civitai.red 提供無限制內容。你可以在設定中變更預設開啟的站點。",
-            "openSettings": "開啟設定"
-        },
         "openSettingsFileLocation": {
             "label": "開啟設定資料夾",
             "tooltip": "開啟包含 settings.json 的資料夾",
@@ -293,32 +253,19 @@
         },
         "sections": {
             "contentFiltering": "內容過濾",
-            "downloads": "下載",
             "videoSettings": "影片設定",
             "layoutSettings": "版面設定",
-            "misc": "其他",
-            "backup": "備份",
-            "folderSettings": "預設根目錄",
-            "recipeSettings": "配方",
-            "extraFolderPaths": "額外資料夾路徑",
-            "downloadPathTemplates": "下載路徑範本",
+            "folderSettings": "資料夾設定",
             "priorityTags": "優先標籤",
-            "updateFlags": "更新標記",
+            "downloadPathTemplates": "下載路徑範本",
             "exampleImages": "範例圖片",
-            "autoOrganize": "自動整理",
-            "metadata": "中繼資料",
+            "updateFlags": "更新標記",
+            "autoOrganize": "Auto-organize",
+            "misc": "其他",
+            "metadataArchive": "中繼資料封存資料庫",
+            "storageLocation": "設定位置",
             "proxySettings": "代理設定"
         },
-        "nav": {
-            "general": "通用",
-            "interface": "介面",
-            "library": "模型庫"
-        },
-        "search": {
-            "placeholder": "搜尋設定...",
-            "clear": "清除搜尋",
-            "noResults": "未找到符合 \"{query}\" 的設定"
-        },
         "storage": {
             "locationLabel": "可攜式模式",
             "locationHelp": "啟用可將 settings.json 保存在儲存庫中；停用則保存在使用者設定目錄。"
@@ -327,15 +274,7 @@
             "blurNsfwContent": "模糊 NSFW 內容",
             "blurNsfwContentHelp": "模糊成熟（NSFW）內容預覽圖片",
             "showOnlySfw": "僅顯示 SFW 結果",
-            "showOnlySfwHelp": "瀏覽和搜尋時過濾所有 NSFW 內容",
-            "matureBlurThreshold": "成人內容模糊閾值",
-            "matureBlurThresholdHelp": "設定當啟用 NSFW 模糊時，從哪個評級級別開始模糊過濾。",
-            "matureBlurThresholdOptions": {
-                "pg13": "PG13 及以上",
-                "r": "R 及以上（預設）",
-                "x": "X 及以上",
-                "xxx": "僅 XXX"
-            }
+            "showOnlySfwHelp": "瀏覽和搜尋時過濾所有 NSFW 內容"
         },
         "videoSettings": {
             "autoplayOnHover": "滑鼠懸停自動播放影片",
@@ -350,63 +289,6 @@
                 "saveFailed": "無法儲存排除項目：{message}"
             }
         },
-        "metadataRefreshSkipPaths": {
-            "label": "中繼資料重新整理跳過路徑",
-            "placeholder": "範例：temp, archived/old, test_models",
-            "help": "批次重新整理中繼資料（「擷取所有中繼資料」）時跳過這些目錄路徑中的模型。輸入相對於模型根目錄的資料夾路徑，以逗號分隔。",
-            "validation": {
-                "noPaths": "請輸入至少一個路徑，以逗號分隔。",
-                "saveFailed": "無法儲存跳過路徑：{message}"
-            }
-        },
-        "backup": {
-            "autoEnabled": "自動備份",
-            "autoEnabledHelp": "每天建立一次本地快照，並依保留政策保留最新快照。",
-            "retention": "保留數量",
-            "retentionHelp": "在刪除舊快照之前，要保留多少自動快照。",
-            "management": "備份管理",
-            "managementHelp": "匯出目前的使用者狀態，或從備份封存中還原。",
-            "scopeHelp": "備份你的設定、下載歷史與模型更新狀態。不包含模型檔案或可重建的快取。",
-            "locationSummary": "目前備份位置",
-            "openFolderButton": "開啟備份資料夾",
-            "openFolderSuccess": "已開啟備份資料夾",
-            "openFolderFailed": "無法開啟備份資料夾",
-            "locationCopied": "備份路徑已複製到剪貼簿：{{path}}",
-            "locationClipboardFallback": "備份路徑：{{path}}",
-            "exportButton": "匯出備份",
-            "exportSuccess": "備份匯出成功。",
-            "exportFailed": "備份匯出失敗：{message}",
-            "importButton": "匯入備份",
-            "importConfirm": "要匯入此備份並覆寫本機使用者狀態嗎？",
-            "importSuccess": "備份匯入成功。",
-            "importFailed": "備份匯入失敗：{message}",
-            "latestSnapshot": "最近快照",
-            "latestAutoSnapshot": "最近自動快照",
-            "snapshotCount": "已儲存快照",
-            "noneAvailable": "目前還沒有快照"
-        },
-        "downloadSkipBaseModels": {
-            "label": "跳過這些基礎模型的下載",
-            "help": "適用於所有下載流程。這裡只能選擇受支援的基礎模型。",
-            "searchPlaceholder": "篩選基礎模型...",
-            "empty": "沒有符合目前搜尋條件的基礎模型。",
-            "summary": {
-                "none": "未選擇",
-                "count": "已選擇 {count} 項"
-            },
-            "actions": {
-                "edit": "編輯",
-                "collapse": "收起",
-                "clear": "清空"
-            },
-            "validation": {
-                "saveFailed": "無法儲存已排除的基礎模型：{message}"
-            }
-        },
-        "skipPreviouslyDownloadedModelVersions": {
-            "label": "跳過已下載的模型版本",
-            "help": "啟用後，如果下載歷史服務記錄顯示該版本已下載，LoRA Manager 將跳過下載該模型版本。適用於所有下載流程。"
-        },
         "layoutSettings": {
             "displayDensity": "顯示密度",
             "displayDensityOptions": {
@@ -429,8 +311,6 @@
                 "hover": "滑鼠懸停顯示"
             },
             "cardInfoDisplayHelp": "選擇何時顯示模型資訊與操作按鈕",
-            "showVersionOnCard": "在卡片上顯示版本",
-            "showVersionOnCardHelp": "在模型卡片上顯示或隱藏版本名稱",
             "modelCardFooterAction": "模型卡片按鈕操作",
             "modelCardFooterActionOptions": {
                 "exampleImages": "開啟範例圖片",
@@ -449,37 +329,16 @@
             "activeLibraryHelp": "在已設定的資料庫之間切換以更新預設資料夾。變更選項會重新載入頁面。",
             "loadingLibraries": "正在載入資料庫...",
             "noLibraries": "尚未設定任何資料庫",
-            "defaultLoraRoot": "LoRA 根目錄",
+            "defaultLoraRoot": "預設 LoRA 根目錄",
             "defaultLoraRootHelp": "設定下載、匯入和移動時的預設 LoRA 根目錄",
-            "defaultCheckpointRoot": "Checkpoint 根目錄",
+            "defaultCheckpointRoot": "預設 Checkpoint 根目錄",
             "defaultCheckpointRootHelp": "設定下載、匯入和移動時的預設 Checkpoint 根目錄",
-            "defaultUnetRoot": "Diffusion Model 根目錄",
+            "defaultUnetRoot": "預設 Diffusion Model 根目錄",
             "defaultUnetRootHelp": "設定下載、匯入和移動時的預設 Diffusion Model (UNET) 根目錄",
-            "defaultEmbeddingRoot": "Embedding 根目錄",
+            "defaultEmbeddingRoot": "預設 Embedding 根目錄",
             "defaultEmbeddingRootHelp": "設定下載、匯入和移動時的預設 Embedding 根目錄",
-            "recipesPath": "配方儲存路徑",
-            "recipesPathHelp": "已儲存配方的可選自訂目錄。留空則使用第一個 LoRA 根目錄下的 recipes 資料夾。",
-            "recipesPathPlaceholder": "/path/to/recipes",
-            "recipesPathMigrating": "正在遷移配方儲存...",
             "noDefault": "未設定預設"
         },
-        "extraFolderPaths": {
-            "title": "額外資料夾路徑",
-            "description": "LoRA Manager 專屬的額外模型根目錄。從 ComfyUI 標準資料夾之外的位置載入模型，特別適合管理大型模型庫，避免影響 ComfyUI 效能。",
-            "restartRequired": "Requires restart to take effect",
-            "modelTypes": {
-                "lora": "LoRA 路徑",
-                "checkpoint": "Checkpoint 路徑",
-                "unet": "Diffusion 模型路徑",
-                "embedding": "Embedding 路徑"
-            },
-            "pathPlaceholder": "/額外/模型/路徑",
-            "saveSuccess": "額外資料夾路徑已更新，需要重啟才能生效。",
-            "saveError": "更新額外資料夾路徑失敗：{message}",
-            "validation": {
-                "duplicatePath": "此路徑已設定"
-            }
-        },
         "priorityTags": {
             "title": "優先標籤",
             "description": "為每種模型類型自訂標籤的優先順序 (例如: character, concept, style(toon|toon_style))",
@@ -542,21 +401,6 @@
             "downloadLocationHelp": "輸入從 Civitai 下載範例圖片要儲存的資料夾路徑",
             "autoDownload": "自動下載範例圖片",
             "autoDownloadHelp": "自動為沒有範例圖片的模型下載範例圖片（需設定下載位置）",
-            "openMode": "開啟範例圖片動作",
-            "openModeHelp": "選擇是在伺服器上開啟、複製對應的本機路徑，或啟動自訂 URI。",
-            "openModeOptions": {
-                "system": "在伺服器上開啟",
-                "clipboard": "複製本機路徑",
-                "uriTemplate": "開啟自訂 URI"
-            },
-            "localRoot": "本機範例圖片根目錄",
-            "localRootHelp": "可選的本機或掛載根目錄，用於對應伺服器上的範例圖片目錄。若留白，則會重用伺服器路徑。",
-            "localRootPlaceholder": "例如：/Volumes/ComfyUI/example_images",
-            "uriTemplate": "開啟 URI 範本",
-            "uriTemplateHelp": "使用自訂深層連結，例如檔案 URI 或 Shortcuts 連結。",
-            "uriTemplatePlaceholder": "例如：shortcuts://run-shortcut?name=Open%20Finder&input=text&text={{encoded_local_path}}",
-            "uriTemplatePlaceholders": "可用佔位符：{{local_path}}、{{encoded_local_path}}、{{relative_path}}、{{encoded_relative_path}}、{{file_uri}}、{{encoded_file_uri}}",
-            "openModeWikiLink": "了解遠端開啟模式",
             "optimizeImages": "最佳化下載圖片",
             "optimizeImagesHelp": "最佳化範例圖片以減少檔案大小並提升載入速度（會保留原有的 metadata）",
             "download": "下載",
@@ -570,10 +414,6 @@
                 "any": "顯示任何可用更新"
             }
         },
-        "hideEarlyAccessUpdates": {
-            "label": "隱藏搶先體驗更新",
-            "help": "搶先體驗更新"
-        },
         "misc": {
             "includeTriggerWords": "在 LoRA 語法中包含觸發詞",
             "includeTriggerWordsHelp": "複製 LoRA 語法到剪貼簿時包含訓練觸發詞"
@@ -685,13 +525,8 @@
             "checkUpdates": "檢查所選更新",
             "moveAll": "全部移動到資料夾",
             "autoOrganize": "自動整理所選模型",
-            "skipMetadataRefresh": "跳過所選模型的元數據更新",
-            "resumeMetadataRefresh": "恢復所選模型的元數據更新",
-            "deleteAll": "刪除所選",
-            "downloadMissingLoras": "下載缺失的 LoRAs",
+            "deleteAll": "刪除全部模型",
             "clear": "清除選取",
-            "skipMetadataRefreshCount": "跳過（{count} 個模型）",
-            "resumeMetadataRefreshCount": "恢復（{count} 個模型）",
             "autoOrganizeProgress": {
                 "initializing": "正在初始化自動整理...",
                 "starting": "正在開始自動整理 {type}...",
@@ -718,7 +553,6 @@
             "moveToFolder": "移動到資料夾",
             "repairMetadata": "修復元數據",
             "excludeModel": "排除模型",
-            "restoreModel": "還原模型",
             "deleteModel": "刪除模型",
             "shareRecipe": "分享配方",
             "viewAllLoras": "檢視全部 LoRA",
@@ -760,8 +594,6 @@
                 "root": "根目錄",
                 "browseFolders": "瀏覽資料夾：",
                 "downloadAndSaveRecipe": "下載並儲存配方",
-                "importRecipeOnly": "僅匯入配方",
-                "importAndDownload": "匯入並下載",
                 "downloadMissingLoras": "下載缺少的 LoRA",
                 "saveRecipe": "儲存配方",
                 "loraCountInfo": "（庫存 {existing}/{total}）",
@@ -803,11 +635,7 @@
                 "lorasCountAsc": "最少"
             },
             "refresh": {
-                "title": "重新整理配方列表",
-                "quick": "同步變更",
-                "quickTooltip": "同步變更 - 快速重新整理而不重建快取",
-                "full": "重建快取",
-                "fullTooltip": "重建快取 - 重新掃描所有配方檔案"
+                "title": "重新整理配方列表"
             },
             "filteredByLora": "已依 LoRA 篩選",
             "favorites": {
@@ -847,64 +675,6 @@
                 "failed": "修復配方失敗：{message}",
                 "missingId": "無法修復配方：缺少配方 ID"
             }
-        },
-        "batchImport": {
-            "title": "批量匯入配方",
-            "action": "批量匯入",
-            "urlList": "URL 列表",
-            "directory": "目錄",
-            "urlDescription": "輸入圖像 URL 或本地檔案路徑（每行一個）。每個都將作為配方匯入。",
-            "directoryDescription": "輸入目錄路徑以匯入該資料夾中的所有圖像。",
-            "urlsLabel": "圖像 URL 或本地路徑",
-            "urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
-            "urlsHint": "每行輸入一個 URL 或路徑",
-            "directoryPath": "目錄路徑",
-            "directoryPlaceholder": "/path/to/images/folder",
-            "browse": "瀏覽",
-            "recursive": "包含子目錄",
-            "tagsOptional": "標籤（可選，應用於所有配方）",
-            "tagsPlaceholder": "輸入以逗號分隔的標籤",
-            "tagsHint": "標籤將被添加到所有匯入的配方中",
-            "skipNoMetadata": "跳過無元資料的圖像",
-            "skipNoMetadataHelp": "沒有 LoRA 元資料的圖像將被自動跳過。",
-            "start": "開始匯入",
-            "startImport": "開始匯入",
-            "importing": "匯入中...",
-            "progress": "進度",
-            "total": "總計",
-            "success": "成功",
-            "failed": "失敗",
-            "skipped": "跳過",
-            "current": "當前",
-            "currentItem": "當前項目",
-            "preparing": "準備中...",
-            "cancel": "取消",
-            "cancelImport": "取消匯入",
-            "cancelled": "匯入已取消",
-            "completed": "匯入完成",
-            "completedWithErrors": "匯入完成但有錯誤",
-            "completedSuccess": "成功匯入 {count} 個配方",
-            "successCount": "成功",
-            "failedCount": "失敗",
-            "skippedCount": "跳過",
-            "totalProcessed": "總計處理",
-            "viewDetails": "查看詳情",
-            "newImport": "新建匯入",
-            "manualPathEntry": "請手動輸入目錄路徑。此瀏覽器中檔案瀏覽器不可用。",
-            "batchImportDirectorySelected": "已選擇目錄：{path}",
-            "batchImportManualEntryRequired": "檔案瀏覽器不可用。請手動輸入目錄路徑。",
-            "backToParent": "返回上級目錄",
-            "folders": "資料夾",
-            "folderCount": "{count} 個資料夾",
-            "imageFiles": "圖像檔案",
-            "images": "圖像",
-            "imageCount": "{count} 個圖像",
-            "selectFolder": "選擇此資料夾",
-            "errors": {
-                "enterUrls": "請輸入至少一個 URL 或路徑",
-                "enterDirectory": "請輸入目錄路徑",
-                "startFailed": "啟動匯入失敗：{message}"
-            }
         }
     },
     "checkpoints": {
@@ -914,13 +684,22 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "移動到 {otherType} 資料夾",
-            "sendToWorkflow": "傳送到工作流"
+            "moveToOtherTypeFolder": "移動到 {otherType} 資料夾"
         }
     },
     "embeddings": {
         "title": "Embedding 模型"
     },
+    "misc": {
+        "title": "[TODO: Translate] VAE & Upscaler Models",
+        "modelTypes": {
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler"
+        },
+        "contextMenu": {
+            "moveToOtherTypeFolder": "[TODO: Translate] Move to {otherType} Folder"
+        }
+    },
     "sidebar": {
         "modelRoot": "根目錄",
         "collapseAll": "全部摺疊資料夾",
@@ -928,23 +707,13 @@
         "unpinSidebar": "取消固定側邊欄",
         "switchToListView": "切換至列表檢視",
         "switchToTreeView": "切換到樹狀檢視",
-        "recursiveOn": "包含子資料夾",
-        "recursiveOff": "僅目前資料夾",
+        "recursiveOn": "搜尋子資料夾",
+        "recursiveOff": "僅搜尋目前資料夾",
         "recursiveUnavailable": "遞迴搜尋僅能在樹狀檢視中使用",
         "collapseAllDisabled": "列表檢視下不可用",
         "dragDrop": {
             "unableToResolveRoot": "無法確定移動的目標路徑。",
-            "moveUnsupported": "Move is not supported for this item.",
-            "createFolderHint": "放開以建立新資料夾",
-            "newFolderName": "新資料夾名稱",
-            "folderNameHint": "按 Enter 確認，Escape 取消",
-            "emptyFolderName": "請輸入資料夾名稱",
-            "invalidFolderName": "資料夾名稱包含無效字元",
-            "noDragState": "未找到待處理的拖放操作"
-        },
-        "empty": {
-            "noFolders": "未找到資料夾",
-            "dragHint": "將項目拖到此處以建立資料夾"
+            "moveUnsupported": "Move is not supported for this item."
         }
     },
     "statistics": {
@@ -1009,8 +778,6 @@
             "earlyAccess": "早期存取",
             "earlyAccessTooltip": "需要早期存取",
             "inLibrary": "已在庫存",
-            "downloaded": "已下載",
-            "downloadedTooltip": "先前已下載，但目前不在你的庫中。",
             "alreadyInLibrary": "已在庫存",
             "autoOrganizedPath": "[依路徑範本自動整理]",
             "errors": {
@@ -1101,14 +868,6 @@
             "save": "更新基礎模型",
             "cancel": "取消"
         },
-        "bulkDownloadMissingLoras": {
-            "title": "下載缺失的 LoRAs",
-            "message": "發現 {uniqueCount} 個獨特的缺失 LoRAs（從選取食譜中的 {totalCount} 個總數）。",
-            "previewTitle": "要下載的 LoRAs：",
-            "moreItems": "...還有 {count} 個",
-            "note": "檔案將使用預設路徑模板下載。根據 LoRAs 的數量，這可能需要一些時間。",
-            "downloadButton": "下載 {count} 個 LoRA(s)"
-        },
         "exampleAccess": {
             "title": "本機範例圖片",
             "message": "此模型未找到本機範例圖片。可選擇：",
@@ -1160,9 +919,7 @@
                 "viewOnCivitai": "在 Civitai 查看",
                 "viewOnCivitaiText": "在 Civitai 查看",
                 "viewCreatorProfile": "查看創作者個人檔案",
-                "openFileLocation": "開啟檔案位置",
-                "sendToWorkflow": "傳送到 ComfyUI",
-                "sendToWorkflowText": "傳送到 ComfyUI"
+                "openFileLocation": "開啟檔案位置"
             },
             "openFileLocation": {
                 "success": "檔案位置已成功開啟",
@@ -1170,9 +927,6 @@
                 "copied": "路徑已複製到剪貼簿：{{path}}",
                 "clipboardFallback": "路徑：{{path}}"
             },
-            "sendToWorkflow": {
-                "noFilePath": "無法傳送到 ComfyUI：沒有可用的檔案路徑"
-            },
             "metadata": {
                 "version": "版本",
                 "fileName": "檔案名稱",
@@ -1209,8 +963,6 @@
                 "cancel": "取消編輯",
                 "save": "儲存變更",
                 "addPlaceholder": "輸入或點擊下方建議",
-                "editWord": "編輯觸發詞",
-                "editPlaceholder": "編輯觸發詞",
                 "copyWord": "複製觸發詞",
                 "deleteWord": "刪除觸發詞",
                 "suggestions": {
@@ -1273,43 +1025,19 @@
                 },
                 "labels": {
                     "unnamed": "未命名版本",
-                    "noDetails": "沒有其他資訊",
-                    "earlyAccess": "EA"
-                },
-                "eaTime": {
-                    "endingSoon": "即將結束",
-                    "hours": "{count}小時後",
-                    "days": "{count}天後"
+                    "noDetails": "沒有其他資訊"
                 },
                 "badges": {
-                    "current": "已開啟版本",
-                    "currentTooltip": "這是你用來開啟此彈窗的版本",
+                    "current": "目前版本",
                     "inLibrary": "已在庫中",
-                    "inLibraryTooltip": "此版本已存在於你的本地庫中",
-                    "downloaded": "已下載",
-                    "downloadedTooltip": "此版本之前下載過，但目前不在你的本地庫中",
                     "newer": "較新版本",
-                    "newerTooltip": "此版本比你本地的最新版本更新",
-                    "earlyAccess": "搶先體驗",
-                    "earlyAccessTooltip": "此版本目前需要 Civitai 搶先體驗權限",
-                    "ignored": "已忽略",
-                    "ignoredTooltip": "此版本已關閉更新通知",
-                    "onSiteOnly": "僅站內生成",
-                    "onSiteOnlyTooltip": "此版本僅在 Civitai 站內可用，無法下載"
+                    "ignored": "已忽略"
                 },
                 "actions": {
                     "download": "下載",
-                    "downloadTooltip": "下載此版本",
-                    "downloadEarlyAccessTooltip": "從 Civitai 下載此搶先體驗版本",
-                    "downloadNotAllowedTooltip": "此版本僅在 Civitai 站內可用，無法下載",
                     "delete": "刪除",
-                    "deleteTooltip": "刪除此本地版本",
                     "ignore": "忽略",
                     "unignore": "取消忽略",
-                    "ignoreTooltip": "忽略此版本的更新通知",
-                    "unignoreTooltip": "恢復此版本的更新通知",
-                    "viewVersionOnCivitai": "在 Civitai 上查看版本",
-                    "earlyAccessTooltip": "需要購買搶先體驗",
                     "resumeModelUpdates": "恢復追蹤此模型的更新",
                     "ignoreModelUpdates": "忽略此模型的更新",
                     "viewLocalVersions": "檢視所有本地版本",
@@ -1388,6 +1116,10 @@
             "title": "初始化統計",
             "message": "正在處理模型資料以產生統計，可能需要幾分鐘..."
         },
+        "misc": {
+            "title": "[TODO: Translate] Initializing Misc Model Manager",
+            "message": "[TODO: Translate] Scanning VAE and Upscaler models..."
+        },
         "tips": {
             "title": "小技巧",
             "civitai": {
@@ -1447,14 +1179,18 @@
             "recipeAdded": "配方已附加到工作流",
             "recipeReplaced": "配方已取代於工作流",
             "recipeFailedToSend": "傳送配方到工作流失敗",
+            "vaeUpdated": "[TODO: Translate] VAE updated in workflow",
+            "vaeFailed": "[TODO: Translate] Failed to update VAE in workflow",
+            "upscalerUpdated": "[TODO: Translate] Upscaler updated in workflow",
+            "upscalerFailed": "[TODO: Translate] Failed to update upscaler in workflow",
             "noMatchingNodes": "目前工作流程中沒有相容的節點",
-            "noTargetNodeSelected": "未選擇目標節點",
-            "modelUpdated": "模型已更新到工作流",
-            "modelFailed": "更新模型節點失敗"
+            "noTargetNodeSelected": "未選擇目標節點"
         },
         "nodeSelector": {
             "recipe": "配方",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "取代",
             "append": "附加",
             "selectTargetNode": "選擇目標節點",
@@ -1464,10 +1200,6 @@
             "opened": "範例圖片資料夾已開啟",
             "openingFolder": "正在開啟範例圖片資料夾",
             "failedToOpen": "開啟範例圖片資料夾失敗",
-            "copiedPath": "路徑已複製到剪貼簿：{{path}}",
-            "clipboardFallback": "路徑：{{path}}",
-            "copiedUri": "連結已複製到剪貼簿：{{uri}}",
-            "uriClipboardFallback": "連結：{{uri}}",
             "setupRequired": "範例圖片儲存",
             "setupDescription": "要新增自訂範例圖片，您需要先設定下載位置。",
             "setupUsage": "此路徑用於儲存下載的範例圖片和自訂圖片。",
@@ -1575,14 +1307,7 @@
             "showWechatQR": "顯示微信二維碼",
             "hideWechatQR": "隱藏微信二維碼"
         },
-        "footer": "感謝您使用 LoRA 管理器！❤️",
-        "supporters": {
-            "title": "感謝所有支持者",
-            "subtitle": "感謝 {count} 位支持者讓這個專案成為可能",
-            "specialThanks": "特別感謝",
-            "allSupporters": "所有支持者",
-            "totalCount": "共 {count} 位支持者"
-        }
+        "footer": "感謝您使用 LoRA 管理器！❤️"
     },
     "toast": {
         "general": {
@@ -1605,7 +1330,6 @@
             "pleaseSelectVersion": "請選擇一個版本",
             "versionExists": "此版本已存在於您的庫中",
             "downloadCompleted": "下載成功完成",
-            "downloadSkippedByBaseModel": "由於基礎模型 {baseModel} 已被排除，已跳過下載",
             "autoOrganizeSuccess": "自動整理已成功完成，共 {count} 個 {type} 已整理",
             "autoOrganizePartialSuccess": "自動整理完成：已移動 {success} 個，{failures} 個失敗，共 {total} 個模型",
             "autoOrganizeFailed": "自動整理失敗：{error}",
@@ -1617,19 +1341,13 @@
             "loadFailed": "載入 {modelType} 失敗：{message}",
             "refreshComplete": "刷新完成",
             "refreshFailed": "刷新配方失敗：{message}",
-            "syncComplete": "同步完成",
-            "syncFailed": "同步配方失敗：{message}",
             "updateFailed": "更新配方失敗：{error}",
             "updateError": "更新配方錯誤：{message}",
             "nameSaved": "配方「{name}」已成功儲存",
             "nameUpdated": "配方名稱已更新",
             "tagsUpdated": "配方標籤已更新",
             "sourceUrlUpdated": "來源網址已更新",
-            "promptUpdated": "提示詞更新成功",
-            "negativePromptUpdated": "負面提示詞更新成功",
-            "promptEditorHint": "按 Enter 儲存，Shift+Enter 換行",
             "noRecipeId": "無配方 ID",
-            "sendToWorkflowFailed": "傳送配方到工作流失敗：{message}",
             "copyFailed": "複製配方語法錯誤：{message}",
             "noMissingLoras": "無缺少的 LoRA 可下載",
             "missingLorasInfoFailed": "取得缺少 LoRA 資訊失敗",
@@ -1657,20 +1375,9 @@
             "processingError": "處理錯誤：{message}",
             "folderBrowserError": "載入資料夾瀏覽器錯誤：{message}",
             "recipeSaveFailed": "儲存配方失敗：{error}",
-            "recipeSaved": "配方儲存成功",
             "importFailed": "匯入失敗：{message}",
             "folderTreeFailed": "載入資料夾樹狀結構失敗",
-            "folderTreeError": "載入資料夾樹狀結構錯誤",
-            "batchImportFailed": "啟動批量匯入失敗：{message}",
-            "batchImportCancelling": "正在取消批量匯入...",
-            "batchImportCancelFailed": "取消批量匯入失敗：{message}",
-            "batchImportNoUrls": "請輸入至少一個 URL 或檔案路徑",
-            "batchImportNoDirectory": "請輸入目錄路徑",
-            "batchImportBrowseFailed": "瀏覽目錄失敗：{message}",
-            "batchImportDirectorySelected": "已選擇目錄：{path}",
-            "noRecipesSelected": "未選取任何食譜",
-            "noMissingLorasInSelection": "在選取的食譜中未找到缺失的 LoRAs",
-            "noLoraRootConfigured": "未配置 LoRA 根目錄。請在設定中設定預設的 LoRA 根目錄。"
+            "folderTreeError": "載入資料夾樹狀結構錯誤"
         },
         "models": {
             "noModelsSelected": "未選擇模型",
@@ -1690,11 +1397,6 @@
             "bulkBaseModelUpdateSuccess": "已成功為 {count} 個模型更新基礎模型",
             "bulkBaseModelUpdatePartial": "已更新 {success} 個模型，{failed} 個模型失敗",
             "bulkBaseModelUpdateFailed": "更新所選模型的基礎模型失敗",
-            "skipMetadataRefreshUpdating": "正在更新 {count} 個模型的元數據更新標記...",
-            "skipMetadataRefreshSet": "已為 {count} 個模型跳過元數據更新",
-            "skipMetadataRefreshCleared": "已為 {count} 個模型恢復元數據更新",
-            "skipMetadataRefreshPartial": "已更新 {success} 個模型，{failed} 個失敗",
-            "skipMetadataRefreshFailed": "無法更新所選模型的元數據更新標記",
             "bulkContentRatingUpdating": "正在為 {count} 個模型更新內容分級...",
             "bulkContentRatingSet": "已將 {count} 個模型的內容分級設定為 {level}",
             "bulkContentRatingPartial": "已將 {success} 個模型的內容分級設定為 {level}，{failed} 個失敗",
@@ -1737,8 +1439,6 @@
             "mappingSaveFailed": "儲存基礎模型對應失敗：{message}",
             "downloadTemplatesUpdated": "下載路徑範本已更新",
             "downloadTemplatesFailed": "儲存下載路徑範本失敗：{message}",
-            "recipesPathUpdated": "配方儲存路徑已更新",
-            "recipesPathSaveFailed": "更新配方儲存路徑失敗：{message}",
             "settingsUpdated": "設定已更新：{setting}",
             "compactModeToggled": "緊湊模式已{state}",
             "settingSaveFailed": "儲存設定失敗：{message}",
@@ -1784,13 +1484,12 @@
             "folderTreeFailed": "載入資料夾樹狀結構失敗",
             "folderTreeError": "載入資料夾樹狀結構錯誤",
             "imagesImported": "範例圖片匯入成功",
-            "imagesPartial": "成功匯入 {success} 張圖片，{failed} 張失敗",
             "importFailed": "匯入範例圖片失敗：{message}"
         },
         "triggerWords": {
             "loadFailed": "無法載入訓練詞",
-            "tooLong": "觸發詞不可超過 500 個字",
-            "tooMany": "最多允許 100 個觸發詞",
+            "tooLong": "觸發詞不可超過 100 個字",
+            "tooMany": "最多允許 30 個觸發詞",
             "alreadyExists": "此觸發詞已存在",
             "updateSuccess": "觸發詞已更新",
             "updateFailed": "更新觸發詞失敗",
@@ -1851,8 +1550,6 @@
             "deleteFailed": "刪除 {type} 失敗：{message}",
             "excludeSuccess": "{type} 已成功排除",
             "excludeFailed": "排除 {type} 失敗：{message}",
-            "restoreSuccess": "{type} 已成功還原",
-            "restoreFailed": "還原 {type} 失敗：{message}",
             "fileNameUpdated": "檔案名稱已成功更新",
             "fileRenameFailed": "重新命名檔案失敗：{error}",
             "previewUpdated": "預覽圖片已成功更新",
@@ -1884,37 +1581,6 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
-    "doctor": {
-        "kicker": "系統診斷",
-        "title": "醫生",
-        "buttonTitle": "執行診斷與常見修復",
-        "loading": "正在檢查環境...",
-        "footer": "如果修復後問題仍然存在，請匯出診斷套件。",
-        "summary": {
-            "idle": "針對設定、快取完整性與 UI 一致性執行健康檢查。",
-            "ok": "目前環境中未發現任何活動中的問題。",
-            "warning": "找到 {count} 個問題。大多可以直接在此面板修復。",
-            "error": "應先處理 {count} 個問題，應用程式才能完全正常。"
-        },
-        "status": {
-            "ok": "健康",
-            "warning": "需要注意",
-            "error": "需要處理"
-        },
-        "actions": {
-            "runAgain": "重新執行",
-            "exportBundle": "匯出套件"
-        },
-        "toast": {
-            "loadFailed": "載入診斷失敗：{message}",
-            "repairSuccess": "快取重建完成。",
-            "repairFailed": "快取重建失敗：{message}",
-            "exportSuccess": "診斷套件已匯出。",
-            "exportFailed": "匯出診斷套件失敗：{message}",
-            "conflictsResolved": "已解決 {count} 個檔案名稱衝突。",
-            "conflictsResolveFailed": "解決檔案名稱衝突失敗：{message}"
-        }
-    },
     "banners": {
         "versionMismatch": {
             "title": "偵測到應用程式更新",
@@ -1928,20 +1594,6 @@
             "content": "LoRA Manager is a passion project maintained full-time by a solo developer. Your support on Ko-fi helps cover development costs, keeps new updates coming, and unlocks a license key for the LM Civitai Extension as a thank-you gift. Every contribution truly makes a difference.",
             "supportCta": "Support on Ko-fi",
             "learnMore": "LM Civitai Extension Tutorial"
-        },
-        "cacheHealth": {
-            "corrupted": {
-                "title": "檢測到快取損壞"
-            },
-            "degraded": {
-                "title": "檢測到快取問題"
-            },
-            "content": "{total} 個快取項目中有 {invalid} 個無效（{rate}）。這可能會導致模型遺失或錯誤。建議重建快取。",
-            "rebuildCache": "重建快取",
-            "dismiss": "關閉",
-            "rebuilding": "重建快取中...",
-            "rebuildFailed": "重建快取失敗：{error}",
-            "retry": "重試"
         }
     }
 }

package-lock.json

@@ -114,6 +114,7 @@
         }
       ],
       "license": "MIT",
+      "peer": true,
       "engines": {
         "node": ">=18"
       },
@@ -137,6 +138,7 @@
         }
       ],
       "license": "MIT",
+      "peer": true,
       "engines": {
         "node": ">=18"
       }
@@ -1611,6 +1613,7 @@
       "integrity": "sha512-MyL55p3Ut3cXbeBEG7Hcv0mVM8pp8PBNWxRqchZnSfAiES1v1mRnMeFfaHWIPULpwsYfvO+ZmMZz5tGCnjzDUQ==",
       "dev": true,
       "license": "MIT",
+      "peer": true,
       "dependencies": {
         "cssstyle": "^4.0.1",
         "data-urls": "^5.0.0",

package.json

@@ -4,9 +4,7 @@
   "private": true,
   "type": "module",
   "scripts": {
-    "test": "npm run test:js && npm run test:vue",
-    "test:js": "vitest run",
-    "test:vue": "cd vue-widgets && npx vitest run",
+    "test": "vitest run",
     "test:watch": "vitest",
     "test:coverage": "node scripts/run_frontend_coverage.js"
   },

py/lora_manager.py

@@ -5,22 +5,16 @@ import logging
 from .utils.logging_config import setup_logging
 
 # Check if we're in standalone mode
-standalone_mode = (
-    os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"
-    or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
-)
+standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
 
 # Only setup logging prefix if not in standalone mode
 if not standalone_mode:
     setup_logging()
 
-from server import PromptServer  # type: ignore
+from server import PromptServer # type: ignore
 
 from .config import config
-from .services.model_service_factory import (
-    ModelServiceFactory,
-    register_default_model_types,
-)
+from .services.model_service_factory import ModelServiceFactory, register_default_model_types
 from .routes.recipe_routes import RecipeRoutes
 from .routes.stats_routes import StatsRoutes
 from .routes.update_routes import UpdateRoutes
@@ -67,10 +61,9 @@ class _SettingsProxy:
 
 settings = _SettingsProxy()
 
-
 class LoraManager:
     """Main entry point for LoRA Manager plugin"""
-
+    
     @classmethod
     def add_routes(cls):
         """Initialize and register all routes using the new refactored architecture"""
@@ -83,8 +76,7 @@ class LoraManager:
                 (
                     idx
                     for idx, middleware in enumerate(app.middlewares)
-                    if getattr(middleware, "__name__", "")
-                    == "block_external_middleware"
+                    if getattr(middleware, "__name__", "") == "block_external_middleware"
                 ),
                 None,
             )
@@ -92,9 +84,7 @@ class LoraManager:
             if block_middleware_index is None:
                 app.middlewares.append(relax_csp_for_remote_media)
             else:
-                app.middlewares.insert(
-                    block_middleware_index, relax_csp_for_remote_media
-                )
+                app.middlewares.insert(block_middleware_index, relax_csp_for_remote_media)
 
         # Increase allowed header sizes so browsers with large localhost cookie
         # jars (multiple UIs on 127.0.0.1) don't trip aiohttp's 8KB default
@@ -115,7 +105,7 @@ class LoraManager:
         app._handler_args = updated_handler_args
 
         # Configure aiohttp access logger to be less verbose
-        logging.getLogger("aiohttp.access").setLevel(logging.WARNING)
+        logging.getLogger('aiohttp.access').setLevel(logging.WARNING)
 
         # Add specific suppression for connection reset errors
         class ConnectionResetFilter(logging.Filter):
@@ -134,291 +124,217 @@ class LoraManager:
         asyncio_logger.addFilter(ConnectionResetFilter())
 
         # Add static route for example images if the path exists in settings
-        example_images_path = settings.get("example_images_path")
+        example_images_path = settings.get('example_images_path')
         logger.info(f"Example images path: {example_images_path}")
         if example_images_path and os.path.exists(example_images_path):
-            app.router.add_static("/example_images_static", example_images_path)
-            logger.info(
-                f"Added static route for example images: /example_images_static -> {example_images_path}"
-            )
+            app.router.add_static('/example_images_static', example_images_path)
+            logger.info(f"Added static route for example images: /example_images_static -> {example_images_path}")
 
         # Add static route for locales JSON files
         if os.path.exists(config.i18n_path):
-            app.router.add_static("/locales", config.i18n_path)
-            logger.info(
-                f"Added static route for locales: /locales -> {config.i18n_path}"
-            )
+            app.router.add_static('/locales', config.i18n_path)
+            logger.info(f"Added static route for locales: /locales -> {config.i18n_path}")
 
         # Add static route for plugin assets
-        app.router.add_static("/loras_static", config.static_path)
-
+        app.router.add_static('/loras_static', config.static_path)
+        
         # Register default model types with the factory
         register_default_model_types()
-
+        
         # Setup all model routes using the factory
         ModelServiceFactory.setup_all_routes(app)
-
+        
         # Setup non-model-specific routes
         stats_routes = StatsRoutes()
         stats_routes.setup_routes(app)
         RecipeRoutes.setup_routes(app)
-        UpdateRoutes.setup_routes(app)
+        UpdateRoutes.setup_routes(app)  
         MiscRoutes.setup_routes(app)
         ExampleImagesRoutes.setup_routes(app, ws_manager=ws_manager)
         PreviewRoutes.setup_routes(app)
-
+        
         # Setup WebSocket routes that are shared across all model types
-        app.router.add_get("/ws/fetch-progress", ws_manager.handle_connection)
-        app.router.add_get(
-            "/ws/download-progress", ws_manager.handle_download_connection
-        )
-        app.router.add_get("/ws/init-progress", ws_manager.handle_init_connection)
-
-        # Schedule service initialization
+        app.router.add_get('/ws/fetch-progress', ws_manager.handle_connection)
+        app.router.add_get('/ws/download-progress', ws_manager.handle_download_connection)
+        app.router.add_get('/ws/init-progress', ws_manager.handle_init_connection)
+        
+        # Schedule service initialization 
         app.on_startup.append(lambda app: cls._initialize_services())
-
+        
         # Add cleanup
         app.on_shutdown.append(cls._cleanup)
-
+        
     @classmethod
     async def _initialize_services(cls):
         """Initialize all services using the ServiceRegistry"""
         try:
-            # Apply library settings to load extra folder paths before scanning
-            # Only apply if extra paths haven't been loaded yet (preserves test mocks)
-            try:
-                from .services.settings_manager import get_settings_manager
-
-                settings_manager = get_settings_manager()
-                library_name = settings_manager.get_active_library_name()
-                libraries = settings_manager.get_libraries()
-                if library_name and library_name in libraries:
-                    library_config = libraries[library_name]
-                    # Only apply settings if extra paths are not already configured
-                    # This preserves values set by tests via monkeypatch
-                    extra_paths = library_config.get("extra_folder_paths", {})
-                    has_extra_paths = (
-                        config.extra_loras_roots
-                        or config.extra_checkpoints_roots
-                        or config.extra_unet_roots
-                        or config.extra_embeddings_roots
-                    )
-                    if not has_extra_paths and any(extra_paths.values()):
-                        config.apply_library_settings(library_config)
-                        logger.info(
-                            "Applied library settings for '%s' with extra paths: loras=%s, checkpoints=%s, embeddings=%s",
-                            library_name,
-                            extra_paths.get("loras", []),
-                            extra_paths.get("checkpoints", []),
-                            extra_paths.get("embeddings", []),
-                        )
-            except Exception as exc:
-                logger.warning(
-                    "Failed to apply library settings during initialization: %s", exc
-                )
-
             # Initialize CivitaiClient first to ensure it's ready for other services
             await ServiceRegistry.get_civitai_client()
 
             # Register DownloadManager with ServiceRegistry
             await ServiceRegistry.get_download_manager()
-            await ServiceRegistry.get_backup_service()
 
             from .services.metadata_service import initialize_metadata_providers
-
             await initialize_metadata_providers()
-
+            
             # Initialize WebSocket manager
             await ServiceRegistry.get_websocket_manager()
-
+            
             # Initialize scanners in background
             lora_scanner = await ServiceRegistry.get_lora_scanner()
             checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
             embedding_scanner = await ServiceRegistry.get_embedding_scanner()
+            misc_scanner = await ServiceRegistry.get_misc_scanner()
 
             # Initialize recipe scanner if needed
             recipe_scanner = await ServiceRegistry.get_recipe_scanner()
 
             # Create low-priority initialization tasks
             init_tasks = [
-                asyncio.create_task(
-                    lora_scanner.initialize_in_background(), name="lora_cache_init"
-                ),
-                asyncio.create_task(
-                    checkpoint_scanner.initialize_in_background(),
-                    name="checkpoint_cache_init",
-                ),
-                asyncio.create_task(
-                    embedding_scanner.initialize_in_background(),
-                    name="embedding_cache_init",
-                ),
-                asyncio.create_task(
-                    recipe_scanner.initialize_in_background(), name="recipe_cache_init"
-                ),
+                asyncio.create_task(lora_scanner.initialize_in_background(), name='lora_cache_init'),
+                asyncio.create_task(checkpoint_scanner.initialize_in_background(), name='checkpoint_cache_init'),
+                asyncio.create_task(embedding_scanner.initialize_in_background(), name='embedding_cache_init'),
+                asyncio.create_task(misc_scanner.initialize_in_background(), name='misc_cache_init'),
+                asyncio.create_task(recipe_scanner.initialize_in_background(), name='recipe_cache_init')
             ]
 
             await ExampleImagesMigration.check_and_run_migrations()
-
+            
             # Schedule post-initialization tasks to run after scanners complete
             asyncio.create_task(
-                cls._run_post_initialization_tasks(init_tasks), name="post_init_tasks"
+                cls._run_post_initialization_tasks(init_tasks), 
+                name='post_init_tasks'
             )
-
-            logger.debug(
-                "LoRA Manager: All services initialized and background tasks scheduled"
-            )
-
+            
+            logger.debug("LoRA Manager: All services initialized and background tasks scheduled")
+                
         except Exception as e:
-            logger.error(
-                f"LoRA Manager: Error initializing services: {e}", exc_info=True
-            )
-
+            logger.error(f"LoRA Manager: Error initializing services: {e}", exc_info=True)
+    
     @classmethod
     async def _run_post_initialization_tasks(cls, init_tasks):
         """Run post-initialization tasks after all scanners complete"""
         try:
-            logger.debug(
-                "LoRA Manager: Waiting for scanner initialization to complete..."
-            )
-
+            logger.debug("LoRA Manager: Waiting for scanner initialization to complete...")
+            
             # Wait for all scanner initialization tasks to complete
             await asyncio.gather(*init_tasks, return_exceptions=True)
 
-            logger.debug(
-                "LoRA Manager: Scanner initialization completed, starting post-initialization tasks..."
-            )
-
+            logger.debug("LoRA Manager: Scanner initialization completed, starting post-initialization tasks...")
+            
             # Run post-initialization tasks
             post_tasks = [
-                asyncio.create_task(
-                    cls._cleanup_backup_files(), name="cleanup_bak_files"
-                ),
+                asyncio.create_task(cls._cleanup_backup_files(), name='cleanup_bak_files'),
                 # Add more post-initialization tasks here as needed
                 # asyncio.create_task(cls._another_post_task(), name='another_task'),
             ]
-
+            
             # Run all post-initialization tasks
             results = await asyncio.gather(*post_tasks, return_exceptions=True)
-
+            
             # Log results
             for i, result in enumerate(results):
                 task_name = post_tasks[i].get_name()
                 if isinstance(result, Exception):
-                    logger.error(
-                        f"Post-initialization task '{task_name}' failed: {result}"
-                    )
+                    logger.error(f"Post-initialization task '{task_name}' failed: {result}")
                 else:
-                    logger.debug(
-                        f"Post-initialization task '{task_name}' completed successfully"
-                    )
-
+                    logger.debug(f"Post-initialization task '{task_name}' completed successfully")
+                    
             logger.debug("LoRA Manager: All post-initialization tasks completed")
-
+            
         except Exception as e:
-            logger.error(
-                f"LoRA Manager: Error in post-initialization tasks: {e}", exc_info=True
-            )
-
+            logger.error(f"LoRA Manager: Error in post-initialization tasks: {e}", exc_info=True)
+    
     @classmethod
     async def _cleanup_backup_files(cls):
         """Clean up .bak files in all model roots"""
         try:
             logger.debug("Starting cleanup of .bak files in model directories...")
-
+            
             # Collect all model roots
             all_roots = set()
             all_roots.update(config.loras_roots)
-            all_roots.update(config.base_models_roots or [])
-            all_roots.update(config.embeddings_roots or [])
-
+            all_roots.update(config.base_models_roots)
+            all_roots.update(config.embeddings_roots)
+            all_roots.update(config.misc_roots or [])
+            
             total_deleted = 0
             total_size_freed = 0
-
+            
             for root_path in all_roots:
                 if not os.path.exists(root_path):
                     continue
-
+                    
                 try:
-                    (
-                        deleted_count,
-                        size_freed,
-                    ) = await cls._cleanup_backup_files_in_directory(root_path)
+                    deleted_count, size_freed = await cls._cleanup_backup_files_in_directory(root_path)
                     total_deleted += deleted_count
                     total_size_freed += size_freed
-
+                    
                     if deleted_count > 0:
-                        logger.debug(
-                            f"Cleaned up {deleted_count} .bak files in {root_path} (freed {size_freed / (1024 * 1024):.2f} MB)"
-                        )
-
+                        logger.debug(f"Cleaned up {deleted_count} .bak files in {root_path} (freed {size_freed / (1024*1024):.2f} MB)")
+                        
                 except Exception as e:
                     logger.error(f"Error cleaning up .bak files in {root_path}: {e}")
-
+                    
                 # Yield control periodically
                 await asyncio.sleep(0.01)
-
+            
             if total_deleted > 0:
-                logger.debug(
-                    f"Backup cleanup completed: removed {total_deleted} .bak files, freed {total_size_freed / (1024 * 1024):.2f} MB total"
-                )
+                logger.debug(f"Backup cleanup completed: removed {total_deleted} .bak files, freed {total_size_freed / (1024*1024):.2f} MB total")
             else:
                 logger.debug("Backup cleanup completed: no .bak files found")
 
         except Exception as e:
             logger.error(f"Error during backup file cleanup: {e}", exc_info=True)
-
+    
     @classmethod
     async def _cleanup_backup_files_in_directory(cls, directory_path: str):
         """Clean up .bak files in a specific directory recursively
-
+        
         Args:
             directory_path: Path to the directory to clean
-
+            
         Returns:
             Tuple[int, int]: (number of files deleted, total size freed in bytes)
         """
         deleted_count = 0
         size_freed = 0
         visited_paths = set()
-
+        
         def cleanup_recursive(path):
             nonlocal deleted_count, size_freed
-
+            
             try:
                 real_path = os.path.realpath(path)
                 if real_path in visited_paths:
                     return
                 visited_paths.add(real_path)
-
+                
                 with os.scandir(path) as it:
                     for entry in it:
                         try:
-                            if entry.is_file(
-                                follow_symlinks=True
-                            ) and entry.name.endswith(".bak"):
+                            if entry.is_file(follow_symlinks=True) and entry.name.endswith('.bak'):
                                 file_size = entry.stat().st_size
                                 os.remove(entry.path)
                                 deleted_count += 1
                                 size_freed += file_size
                                 logger.debug(f"Deleted .bak file: {entry.path}")
-
+                                
                             elif entry.is_dir(follow_symlinks=True):
                                 cleanup_recursive(entry.path)
-
+                                
                         except Exception as e:
-                            logger.warning(
-                                f"Could not delete .bak file {entry.path}: {e}"
-                            )
-
+                            logger.warning(f"Could not delete .bak file {entry.path}: {e}")
+                            
             except Exception as e:
                 logger.error(f"Error scanning directory {path} for .bak files: {e}")
-
+        
         # Run the recursive cleanup in a thread pool to avoid blocking
         loop = asyncio.get_event_loop()
         await loop.run_in_executor(None, cleanup_recursive, directory_path)
-
+        
         return deleted_count, size_freed
-
+    
     @classmethod
     async def _cleanup_example_images_folders(cls):
         """Invoke the example images cleanup service for manual execution."""
@@ -426,21 +342,21 @@ class LoraManager:
             service = ExampleImagesCleanupService()
             result = await service.cleanup_example_image_folders()
 
-            if result.get("success"):
+            if result.get('success'):
                 logger.debug(
                     "Manual example images cleanup completed: moved=%s",
-                    result.get("moved_total"),
+                    result.get('moved_total'),
                 )
-            elif result.get("partial_success"):
+            elif result.get('partial_success'):
                 logger.warning(
                     "Manual example images cleanup partially succeeded: moved=%s failures=%s",
-                    result.get("moved_total"),
-                    result.get("move_failures"),
+                    result.get('moved_total'),
+                    result.get('move_failures'),
                 )
             else:
                 logger.debug(
                     "Manual example images cleanup skipped or failed: %s",
-                    result.get("error", "no changes"),
+                    result.get('error', 'no changes'),
                 )
 
             return result
@@ -448,9 +364,9 @@ class LoraManager:
         except Exception as e:  # pragma: no cover - defensive guard
             logger.error(f"Error during example images cleanup: {e}", exc_info=True)
             return {
-                "success": False,
-                "error": str(e),
-                "error_code": "unexpected_error",
+                'success': False,
+                'error': str(e),
+                'error_code': 'unexpected_error',
             }
 
     @classmethod
@@ -458,6 +374,6 @@ class LoraManager:
         """Cleanup resources using ServiceRegistry"""
         try:
             logger.info("LoRA Manager: Cleaning up services")
-
+                
         except Exception as e:
             logger.error(f"Error during cleanup: {e}", exc_info=True)

py/metadata_collector/__init__.py

@@ -1,13 +1,7 @@
 import os
-import logging
-
-logger = logging.getLogger(__name__)
 
 # Check if running in standalone mode
-standalone_mode = (
-    os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"
-    or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
-)
+standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
 
 if not standalone_mode:
     from .metadata_hook import MetadataHook
@@ -16,21 +10,21 @@ if not standalone_mode:
     def init():
         # Install hooks to collect metadata during execution
         MetadataHook.install()
-
+        
         # Initialize registry
         registry = MetadataRegistry()
-
-        logger.info("ComfyUI Metadata Collector initialized")
-
-    def get_metadata(prompt_id=None):  # type: ignore[no-redef]
+        
+        print("ComfyUI Metadata Collector initialized")
+        
+    def get_metadata(prompt_id=None):
         """Helper function to get metadata from the registry"""
         registry = MetadataRegistry()
         return registry.get_metadata(prompt_id)
 else:
     # Standalone mode - provide dummy implementations
     def init():
-        logger.info("ComfyUI Metadata Collector disabled in standalone mode")
-
-    def get_metadata(prompt_id=None):  # type: ignore[no-redef]
+        print("ComfyUI Metadata Collector disabled in standalone mode")
+        
+    def get_metadata(prompt_id=None):
         """Dummy implementation for standalone mode"""
         return {}

py/metadata_collector/metadata_hook.py

@@ -1,10 +1,7 @@
 import sys
 import inspect
-import logging
 from .metadata_registry import MetadataRegistry
 
-logger = logging.getLogger(__name__)
-
 class MetadataHook:
     """Install hooks for metadata collection"""
     
@@ -26,7 +23,7 @@ class MetadataHook:
                     
             # If we can't find the execution module, we can't install hooks
             if execution is None:
-                logger.warning("Could not locate ComfyUI execution module, metadata collection disabled")
+                print("Could not locate ComfyUI execution module, metadata collection disabled")
                 return
             
             # Detect whether we're using the new async version of ComfyUI
@@ -40,16 +37,16 @@ class MetadataHook:
                 is_async = inspect.iscoroutinefunction(execution._map_node_over_list)
             
             if is_async:
-                logger.info("Detected async ComfyUI execution, installing async metadata hooks")
+                print("Detected async ComfyUI execution, installing async metadata hooks")
                 MetadataHook._install_async_hooks(execution, map_node_func_name)
             else:
-                logger.info("Detected sync ComfyUI execution, installing sync metadata hooks")
+                print("Detected sync ComfyUI execution, installing sync metadata hooks")
                 MetadataHook._install_sync_hooks(execution)
             
-            logger.info("Metadata collection hooks installed for runtime values")
+            print("Metadata collection hooks installed for runtime values")
             
         except Exception as e:
-            logger.error(f"Error installing metadata hooks: {str(e)}")
+            print(f"Error installing metadata hooks: {str(e)}")
     
     @staticmethod
     def _install_sync_hooks(execution):
@@ -85,7 +82,7 @@ class MetadataHook:
                         if node_id is not None:
                             registry.record_node_execution(node_id, class_type, input_data_all, None)
                 except Exception as e:
-                    logger.error(f"Error collecting metadata (pre-execution): {str(e)}")
+                    print(f"Error collecting metadata (pre-execution): {str(e)}")
             
             # Execute the original function
             results = original_map_node_over_list(obj, input_data_all, func, allow_interrupt, execution_block_cb, pre_execute_cb)
@@ -116,7 +113,7 @@ class MetadataHook:
                         if node_id is not None:
                             registry.update_node_execution(node_id, class_type, results)
                 except Exception as e:
-                    logger.error(f"Error collecting metadata (post-execution): {str(e)}")
+                    print(f"Error collecting metadata (post-execution): {str(e)}")
             
             return results
             
@@ -148,13 +145,10 @@ class MetadataHook:
         """Install hooks for asynchronous execution model"""
         # Store the original _async_map_node_over_list function
         original_map_node_over_list = getattr(execution, map_node_func_name)
-
-        # Wrapped async function - signature must exactly match _async_map_node_over_list
-        async def async_map_node_over_list_with_metadata(
-            prompt_id, unique_id, obj, input_data_all, func,
-            allow_interrupt=False, execution_block_cb=None,
-            pre_execute_cb=None, v3_data=None
-        ):
+        
+        # Wrapped async function, compatible with both stable and nightly
+        async def async_map_node_over_list_with_metadata(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, *args, **kwargs):
+            hidden_inputs = kwargs.get('hidden_inputs', None)
             # Only collect metadata when calling the main function of nodes
             if func == obj.FUNCTION and hasattr(obj, '__class__'):
                 try:
@@ -165,14 +159,14 @@ class MetadataHook:
                         if node_id is not None:
                             registry.record_node_execution(node_id, class_type, input_data_all, None)
                 except Exception as e:
-                    logger.error(f"Error collecting metadata (pre-execution): {str(e)}")
-
-            # Call original function with exact parameters
+                    print(f"Error collecting metadata (pre-execution): {str(e)}")
+            
+            # Call original function with all args/kwargs
             results = await original_map_node_over_list(
                 prompt_id, unique_id, obj, input_data_all, func,
-                allow_interrupt, execution_block_cb, pre_execute_cb, v3_data=v3_data
+                allow_interrupt, execution_block_cb, pre_execute_cb, *args, **kwargs
             )
-
+            
             if func == obj.FUNCTION and hasattr(obj, '__class__'):
                 try:
                     registry = MetadataRegistry()
@@ -182,29 +176,29 @@ class MetadataHook:
                         if node_id is not None:
                             registry.update_node_execution(node_id, class_type, results)
                 except Exception as e:
-                    logger.error(f"Error collecting metadata (post-execution): {str(e)}")
-
+                    print(f"Error collecting metadata (post-execution): {str(e)}")
+            
             return results
-
+        
         # Also hook the execute function to track the current prompt_id
         original_execute = execution.execute
-
+        
         async def async_execute_with_prompt_tracking(*args, **kwargs):
             if len(args) >= 7:  # Check if we have enough arguments
                 server, prompt, caches, node_id, extra_data, executed, prompt_id = args[:7]
                 registry = MetadataRegistry()
-
+                
                 # Start collection if this is a new prompt
                 if not registry.current_prompt_id or registry.current_prompt_id != prompt_id:
                     registry.start_collection(prompt_id)
-
+                    
                 # Store the dynprompt reference for node lookups
                 if hasattr(prompt, 'original_prompt'):
                     registry.set_current_prompt(prompt)
-
+            
             # Execute the original function
             return await original_execute(*args, **kwargs)
-
+            
         # Replace the functions with async versions
         setattr(execution, map_node_func_name, async_map_node_over_list_with_metadata)
         execution.execute = async_execute_with_prompt_tracking

py/metadata_collector/metadata_processor.py

@@ -352,101 +352,50 @@ class MetadataProcessor:
         
         # Check if we have stored conditioning objects for this sampler
         if sampler_id in metadata.get(PROMPTS, {}) and (
-            "pos_conditioning" in metadata[PROMPTS][sampler_id] or
-            "neg_conditioning" in metadata[PROMPTS][sampler_id]
-        ):
+            "pos_conditioning" in metadata[PROMPTS][sampler_id] or 
+            "neg_conditioning" in metadata[PROMPTS][sampler_id]):
+            
             pos_conditioning = metadata[PROMPTS][sampler_id].get("pos_conditioning")
             neg_conditioning = metadata[PROMPTS][sampler_id].get("neg_conditioning")
-
-            def extend_unique(target, values):
-                for value in values:
-                    if value and value not in target:
-                        target.append(value)
-
-            # Helper function to recursively find prompt texts for a conditioning object.
-            # Transform nodes can map one output conditioning to multiple source conditionings.
-            def find_prompt_texts_for_conditioning(
-                conditioning_obj, is_positive=True, visited=None
-            ):
+            
+            # Helper function to recursively find prompt text for a conditioning object
+            def find_prompt_text_for_conditioning(conditioning_obj, is_positive=True):
                 if conditioning_obj is None:
-                    return []
-
-                if visited is None:
-                    visited = set()
-
-                conditioning_id = id(conditioning_obj)
-                if conditioning_id in visited:
-                    return []
-                visited.add(conditioning_id)
-
-                prompt_texts = []
-
+                    return ""
+                    
                 # Try to match conditioning objects with those stored by extractors
                 for prompt_node_id, prompt_data in metadata[PROMPTS].items():
-                    if not isinstance(prompt_data, dict):
-                        continue
-
-                    # For CLIP text nodes with a single conditioning output.
-                    if id(prompt_data.get("conditioning")) == conditioning_id:
-                        text = prompt_data.get("text", "")
-                        if text:
-                            extend_unique(prompt_texts, [text])
-
-                    # Generic provenance for passthrough/transform/combine nodes.
-                    for source in prompt_data.get("conditioning_sources", []):
-                        if id(source.get("output")) != conditioning_id:
-                            continue
-                        for input_conditioning in source.get("inputs", []):
-                            extend_unique(
-                                prompt_texts,
-                                find_prompt_texts_for_conditioning(
-                                    input_conditioning, is_positive, visited
-                                ),
-                            )
-
-                    # For nodes with separate pos_conditioning and neg_conditioning outputs
-                    # like TSC_EfficientLoader and existing ControlNet-style metadata.
-                    if (
-                        is_positive
-                        and id(prompt_data.get("positive_encoded")) == conditioning_id
-                    ):
-                        if prompt_data.get("positive_text"):
-                            extend_unique(prompt_texts, [prompt_data["positive_text"]])
-                        else:
-                            extend_unique(
-                                prompt_texts,
-                                find_prompt_texts_for_conditioning(
-                                    prompt_data.get("orig_pos_cond"),
-                                    is_positive=True,
-                                    visited=visited,
-                                ),
-                            )
-
-                    if (
-                        not is_positive
-                        and id(prompt_data.get("negative_encoded")) == conditioning_id
-                    ):
-                        if prompt_data.get("negative_text"):
-                            extend_unique(prompt_texts, [prompt_data["negative_text"]])
-                        else:
-                            extend_unique(
-                                prompt_texts,
-                                find_prompt_texts_for_conditioning(
-                                    prompt_data.get("orig_neg_cond"),
-                                    is_positive=False,
-                                    visited=visited,
-                                ),
-                            )
-
-                return prompt_texts
-
+                    # For nodes with single conditioning output
+                    if "conditioning" in prompt_data:
+                        if id(prompt_data["conditioning"]) == id(conditioning_obj):
+                            return prompt_data.get("text", "")
+                    
+                    # For nodes with separate pos_conditioning and neg_conditioning outputs (like TSC_EfficientLoader)
+                    if is_positive and "positive_encoded" in prompt_data:
+                        if id(prompt_data["positive_encoded"]) == id(conditioning_obj):
+                            if "positive_text" in prompt_data:
+                                return prompt_data["positive_text"]
+                            else:
+                                orig_conditioning = prompt_data.get("orig_pos_cond", None)
+                                if orig_conditioning is not None:
+                                    # Recursively find the prompt text for the original conditioning
+                                    return find_prompt_text_for_conditioning(orig_conditioning, is_positive=True)
+                    
+                    if not is_positive and "negative_encoded" in prompt_data:
+                        if id(prompt_data["negative_encoded"]) == id(conditioning_obj):
+                            if "negative_text" in prompt_data:
+                                return prompt_data["negative_text"]
+                            else:
+                                orig_conditioning = prompt_data.get("orig_neg_cond", None)
+                                if orig_conditioning is not None:
+                                    # Recursively find the prompt text for the original conditioning
+                                    return find_prompt_text_for_conditioning(orig_conditioning, is_positive=False)
+                
+                return ""
+            
             # Find prompt texts using the helper function
-            result["prompt"] = ", ".join(
-                find_prompt_texts_for_conditioning(pos_conditioning, is_positive=True)
-            )
-            result["negative_prompt"] = ", ".join(
-                find_prompt_texts_for_conditioning(neg_conditioning, is_positive=False)
-            )
+            result["prompt"] = find_prompt_text_for_conditioning(pos_conditioning, is_positive=True)
+            result["negative_prompt"] = find_prompt_text_for_conditioning(neg_conditioning, is_positive=False)
             
         return result
     
@@ -560,14 +509,8 @@ class MetadataProcessor:
         
         params["loras"] = " ".join(lora_parts)
         
-        # Extract clip_skip from any SAMPLING node that provides it
-        for sampler_info in metadata.get(SAMPLING, {}).values():
-            clip_skip = sampler_info.get("parameters", {}).get("clip_skip")
-            if clip_skip is not None:
-                params["clip_skip"] = clip_skip
-                break
-        if params["clip_skip"] is None:
-            params["clip_skip"] = "1"
+        # Set default clip_skip value
+        params["clip_skip"] = "1"  # Common default
         
         return params
     
@@ -652,15 +595,6 @@ class MetadataProcessor:
                     if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
                         params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
                 else:
-                    # Generic guider nodes often expose separate positive/negative inputs.
-                    positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "positive", max_depth=10)
-                    if not positive_node_id:
-                        positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
+                    positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
                     if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
                         params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
-
-                    negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "negative", max_depth=10)
-                    if not negative_node_id:
-                        negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
-                    if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
-                        params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")

py/metadata_collector/metadata_registry.py

@@ -1,54 +1,50 @@
 import time
-from nodes import NODE_CLASS_MAPPINGS  # type: ignore
+from nodes import NODE_CLASS_MAPPINGS
 from .node_extractors import NODE_EXTRACTORS, GenericNodeExtractor
 from .constants import METADATA_CATEGORIES, IMAGES
 
-
 class MetadataRegistry:
     """A singleton registry to store and retrieve workflow metadata"""
-
     _instance = None
-
+    
     def __new__(cls):
         if cls._instance is None:
             cls._instance = super().__new__(cls)
             cls._instance._reset()
         return cls._instance
-
+    
     def _reset(self):
         self.current_prompt_id = None
         self.current_prompt = None
         self.metadata = {}
         self.prompt_metadata = {}
         self.executed_nodes = set()
-
+        
         # Node-level cache for metadata
         self.node_cache = {}
-
+        
         # Limit the number of stored prompts
         self.max_prompt_history = 3
-
+        
         # Categories we want to track and retrieve from cache
         self.metadata_categories = METADATA_CATEGORIES
-
+    
     def _clean_old_prompts(self):
         """Clean up old prompt metadata, keeping only recent ones"""
         if len(self.prompt_metadata) <= self.max_prompt_history:
             return
-
+            
         # Sort all prompt_ids by timestamp
         sorted_prompts = sorted(
             self.prompt_metadata.keys(),
-            key=lambda pid: self.prompt_metadata[pid].get("timestamp", 0),
+            key=lambda pid: self.prompt_metadata[pid].get("timestamp", 0)
         )
-
+        
         # Remove oldest records
-        prompts_to_remove = sorted_prompts[
-            : len(sorted_prompts) - self.max_prompt_history
-        ]
+        prompts_to_remove = sorted_prompts[:len(sorted_prompts) - self.max_prompt_history]
         for pid in prompts_to_remove:
             del self.prompt_metadata[pid]
-
+    
     def start_collection(self, prompt_id):
         """Begin metadata collection for a new prompt"""
         self.current_prompt_id = prompt_id
@@ -57,96 +53,90 @@ class MetadataRegistry:
             category: {} for category in METADATA_CATEGORIES
         }
         # Add additional metadata fields
-        self.prompt_metadata[prompt_id].update(
-            {
-                "execution_order": [],
-                "current_prompt": None,  # Will store the prompt object
-                "timestamp": time.time(),
-            }
-        )
-
+        self.prompt_metadata[prompt_id].update({
+            "execution_order": [],
+            "current_prompt": None,  # Will store the prompt object
+            "timestamp": time.time()
+        })
+        
         # Clean up old prompt data
         self._clean_old_prompts()
-
+    
     def set_current_prompt(self, prompt):
         """Set the current prompt object reference"""
         self.current_prompt = prompt
         if self.current_prompt_id and self.current_prompt_id in self.prompt_metadata:
             # Store the prompt in the metadata for later relationship tracing
             self.prompt_metadata[self.current_prompt_id]["current_prompt"] = prompt
-
+        
     def get_metadata(self, prompt_id=None):
         """Get collected metadata for a prompt"""
         key = prompt_id if prompt_id is not None else self.current_prompt_id
         if key not in self.prompt_metadata:
             return {}
-
+            
         metadata = self.prompt_metadata[key]
-
+        
         # If we have a current prompt object, check for non-executed nodes
         prompt_obj = metadata.get("current_prompt")
         if prompt_obj and hasattr(prompt_obj, "original_prompt"):
             original_prompt = prompt_obj.original_prompt
-
+            
             # Fill in missing metadata from cache for nodes that weren't executed
             self._fill_missing_metadata(key, original_prompt)
-
+            
         return self.prompt_metadata.get(key, {})
-
+    
     def _fill_missing_metadata(self, prompt_id, original_prompt):
         """Fill missing metadata from cache for non-executed nodes"""
         if not original_prompt:
             return
-
+            
         executed_nodes = self.executed_nodes
         metadata = self.prompt_metadata[prompt_id]
-
+        
         # Iterate through nodes in the original prompt
         for node_id, node_data in original_prompt.items():
             # Skip if already executed in this run
             if node_id in executed_nodes:
                 continue
-
+                
             # Get the node type from the prompt (this is the key in NODE_CLASS_MAPPINGS)
             prompt_class_type = node_data.get("class_type")
             if not prompt_class_type:
                 continue
-
+                
             # Convert to actual class name (which is what we use in our cache)
             class_type = prompt_class_type
             if prompt_class_type in NODE_CLASS_MAPPINGS:
                 class_obj = NODE_CLASS_MAPPINGS[prompt_class_type]
                 class_type = class_obj.__name__
-
+            
             # Create cache key using the actual class name
             cache_key = f"{node_id}:{class_type}"
-
+            
             # Check if this node type is relevant for metadata collection
             if class_type in NODE_EXTRACTORS:
                 # Check if we have cached metadata for this node
                 if cache_key in self.node_cache:
                     cached_data = self.node_cache[cache_key]
-
+                    
                     # Apply cached metadata to the current metadata
                     for category in self.metadata_categories:
                         if category in cached_data and node_id in cached_data[category]:
                             if node_id not in metadata[category]:
-                                metadata[category][node_id] = cached_data[category][
-                                    node_id
-                                ]
-
+                                metadata[category][node_id] = cached_data[category][node_id]
+    
     def record_node_execution(self, node_id, class_type, inputs, outputs):
         """Record information about a node's execution"""
         if not self.current_prompt_id:
             return
-
+            
         # Add to execution order and mark as executed
         if node_id not in self.executed_nodes:
             self.executed_nodes.add(node_id)
-            self.prompt_metadata[self.current_prompt_id]["execution_order"].append(
-                node_id
-            )
-
+            self.prompt_metadata[self.current_prompt_id]["execution_order"].append(node_id)
+        
         # Process inputs to simplify working with them
         processed_inputs = {}
         for input_name, input_values in inputs.items():
@@ -155,61 +145,63 @@ class MetadataRegistry:
                 processed_inputs[input_name] = input_values[0]
             else:
                 processed_inputs[input_name] = input_values
-
+            
         # Extract node-specific metadata
         extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
         extractor.extract(
-            node_id,
-            processed_inputs,
-            outputs,
-            self.prompt_metadata[self.current_prompt_id],
+            node_id, 
+            processed_inputs, 
+            outputs, 
+            self.prompt_metadata[self.current_prompt_id]
         )
-
+        
         # Cache this node's metadata
         self._cache_node_metadata(node_id, class_type)
-
+    
     def update_node_execution(self, node_id, class_type, outputs):
         """Update node metadata with output information"""
         if not self.current_prompt_id:
             return
-
+        
         # Process outputs to make them more usable
         processed_outputs = outputs
-
+            
         # Use the same extractor to update with outputs
         extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
-        if hasattr(extractor, "update"):
+        if hasattr(extractor, 'update'):
             extractor.update(
-                node_id, processed_outputs, self.prompt_metadata[self.current_prompt_id]
+                node_id, 
+                processed_outputs, 
+                self.prompt_metadata[self.current_prompt_id]
             )
-
+            
         # Update the cached metadata for this node
         self._cache_node_metadata(node_id, class_type)
-
+            
     def _cache_node_metadata(self, node_id, class_type):
         """Cache the metadata for a specific node"""
         if not self.current_prompt_id or not node_id or not class_type:
             return
-
+            
         # Create a cache key combining node_id and class_type
         cache_key = f"{node_id}:{class_type}"
-
+        
         # Create a shallow copy of the node's metadata
         node_metadata = {}
         current_metadata = self.prompt_metadata[self.current_prompt_id]
-
+        
         for category in self.metadata_categories:
             if category in current_metadata and node_id in current_metadata[category]:
                 if category not in node_metadata:
                     node_metadata[category] = {}
                 node_metadata[category][node_id] = current_metadata[category][node_id]
-
+        
         # Save new metadata or clear stale cache entries when metadata is empty
         if any(node_metadata.values()):
             self.node_cache[cache_key] = node_metadata
         else:
             self.node_cache.pop(cache_key, None)
-
+    
     def clear_unused_cache(self):
         """Clean up node_cache entries that are no longer in use"""
         # Collect all node_ids currently in prompt_metadata
@@ -218,18 +210,18 @@ class MetadataRegistry:
             for category in self.metadata_categories:
                 if category in prompt_data:
                     active_node_ids.update(prompt_data[category].keys())
-
+        
         # Find cache keys that are no longer needed
         keys_to_remove = []
         for cache_key in self.node_cache:
-            node_id = cache_key.split(":")[0]
+            node_id = cache_key.split(':')[0]
             if node_id not in active_node_ids:
                 keys_to_remove.append(cache_key)
-
+        
         # Remove cache entries that are no longer needed
         for key in keys_to_remove:
             del self.node_cache[key]
-
+    
     def clear_metadata(self, prompt_id=None):
         """Clear metadata for a specific prompt or reset all data"""
         if prompt_id is not None:
@@ -240,25 +232,25 @@ class MetadataRegistry:
         else:
             # Reset all data
             self._reset()
-
+    
     def get_first_decoded_image(self, prompt_id=None):
         """Get the first decoded image result"""
         key = prompt_id if prompt_id is not None else self.current_prompt_id
         if key not in self.prompt_metadata:
             return None
-
+            
         metadata = self.prompt_metadata[key]
         if IMAGES in metadata and "first_decode" in metadata[IMAGES]:
             image_data = metadata[IMAGES]["first_decode"]["image"]
-
+            
             # If it's an image batch or tuple, handle various formats
             if isinstance(image_data, (list, tuple)) and len(image_data) > 0:
                 # Return first element of list/tuple
                 return image_data[0]
-
+            
             # If it's a tensor, return as is for processing in the route handler
             return image_data
-
+        
         # If no image is found in the current metadata, try to find it in the cache
         # This handles the case where VAEDecode was cached by ComfyUI and not executed
         prompt_obj = metadata.get("current_prompt")
@@ -278,11 +270,8 @@ class MetadataRegistry:
                             if IMAGES in cached_data and node_id in cached_data[IMAGES]:
                                 image_data = cached_data[IMAGES][node_id]["image"]
                                 # Handle different image formats
-                                if (
-                                    isinstance(image_data, (list, tuple))
-                                    and len(image_data) > 0
-                                ):
+                                if isinstance(image_data, (list, tuple)) and len(image_data) > 0:
                                     return image_data[0]
                                 return image_data
-
+        
         return None

py/metadata_collector/node_extractors.py

@@ -1,6 +1,4 @@
-import json
 import os
-import re
 
 from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER
 
@@ -144,118 +142,6 @@ class TSCCheckpointLoaderExtractor(NodeMetadataExtractor):
                 metadata[PROMPTS][node_id]["positive_encoded"] = positive_conditioning
                 metadata[PROMPTS][node_id]["negative_encoded"] = negative_conditioning
 
-
-class EasyComfyLoaderExtractor(NodeMetadataExtractor):
-    @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        if not inputs:
-            return
-
-        if "ckpt_name" in inputs:
-            _store_checkpoint_metadata(metadata, node_id, inputs["ckpt_name"])
-
-        # Only extract from optional_lora_stack — skip the single lora_name to
-        # avoid double-counting LoRAs that come through the LORA_STACK path.
-        active_loras = []
-        optional_lora_stack = inputs.get("optional_lora_stack")
-        if optional_lora_stack is not None and isinstance(optional_lora_stack, (list, tuple)):
-            for item in optional_lora_stack:
-                if isinstance(item, (list, tuple)) and len(item) >= 2:
-                    lora_path = item[0]
-                    model_strength = item[1]
-                    lora_name = os.path.splitext(os.path.basename(lora_path))[0]
-                    active_loras.append({
-                        "name": lora_name,
-                        "strength": model_strength
-                    })
-
-        if active_loras:
-            metadata[LORAS][node_id] = {
-                "lora_list": active_loras,
-                "node_id": node_id
-            }
-
-        positive_text = inputs.get("positive", "")
-        negative_text = inputs.get("negative", "")
-
-        if positive_text or negative_text:
-            if node_id not in metadata[PROMPTS]:
-                metadata[PROMPTS][node_id] = {"node_id": node_id}
-            metadata[PROMPTS][node_id]["positive_text"] = positive_text
-            metadata[PROMPTS][node_id]["negative_text"] = negative_text
-
-        if "clip_skip" in inputs:
-            clip_skip = inputs["clip_skip"]
-            if node_id not in metadata[SAMPLING]:
-                metadata[SAMPLING][node_id] = {"parameters": {}, "node_id": node_id}
-            metadata[SAMPLING][node_id]["parameters"]["clip_skip"] = clip_skip
-
-        width = inputs.get("empty_latent_width")
-        height = inputs.get("empty_latent_height")
-        if width is not None and height is not None:
-            if SIZE not in metadata:
-                metadata[SIZE] = {}
-            metadata[SIZE][node_id] = {
-                "width": int(width),
-                "height": int(height),
-                "node_id": node_id
-            }
-
-    @staticmethod
-    def update(node_id, outputs, metadata):
-        # outputs: [(pipe_dict, model, vae), ...]
-        if not outputs or not isinstance(outputs, list) or len(outputs) == 0:
-            return
-        first_output = outputs[0]
-        if not isinstance(first_output, tuple) or len(first_output) < 1:
-            return
-        pipe = first_output[0]
-        if not isinstance(pipe, dict):
-            return
-
-        positive_conditioning = pipe.get("positive")
-        negative_conditioning = pipe.get("negative")
-
-        if positive_conditioning is not None or negative_conditioning is not None:
-            if node_id not in metadata[PROMPTS]:
-                metadata[PROMPTS][node_id] = {"node_id": node_id}
-            if positive_conditioning is not None:
-                metadata[PROMPTS][node_id]["positive_encoded"] = positive_conditioning
-            if negative_conditioning is not None:
-                metadata[PROMPTS][node_id]["negative_encoded"] = negative_conditioning
-
-
-class EasyPreSamplingExtractor(NodeMetadataExtractor):
-    @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        if not inputs:
-            return
-
-        sampling_params = {}
-        for key in ("steps", "cfg", "sampler_name", "scheduler", "denoise", "seed"):
-            if key in inputs:
-                sampling_params[key] = inputs[key]
-
-        metadata[SAMPLING][node_id] = {
-            "parameters": sampling_params,
-            "node_id": node_id,
-            IS_SAMPLER: True
-        }
-
-
-class EasySeedExtractor(NodeMetadataExtractor):
-    @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        if not inputs or "seed" not in inputs:
-            return
-
-        metadata[SAMPLING][node_id] = {
-            "parameters": {"seed": inputs["seed"]},
-            "node_id": node_id,
-            IS_SAMPLER: False
-        }
-
-
 class CLIPTextEncodeExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -275,251 +161,6 @@ class CLIPTextEncodeExtractor(NodeMetadataExtractor):
                 conditioning = outputs[0][0]
                 metadata[PROMPTS][node_id]["conditioning"] = conditioning
 
-
-class MyOriginalWaifuTextExtractor(NodeMetadataExtractor):
-    """Extractor for ComfyUI-MyOriginalWaifu TextProvider nodes."""
-
-    @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        if not inputs:
-            return
-
-        positive_text = inputs.get("positive", "")
-        negative_text = inputs.get("negative", "")
-
-        if positive_text or negative_text:
-            metadata[PROMPTS][node_id] = {
-                "positive_text": positive_text,
-                "negative_text": negative_text,
-                "node_id": node_id,
-            }
-
-    @staticmethod
-    def update(node_id, outputs, metadata):
-        output_tuple = _first_output_tuple(outputs)
-        if not output_tuple or len(output_tuple) < 2:
-            return
-
-        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
-        prompt_metadata["positive_text"] = output_tuple[0]
-        prompt_metadata["negative_text"] = output_tuple[1]
-
-
-class MyOriginalWaifuClipExtractor(NodeMetadataExtractor):
-    """Extractor for ComfyUI-MyOriginalWaifu ClipProvider nodes."""
-
-    @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        if not inputs:
-            return
-
-        positive_text = inputs.get("positive", "")
-        negative_text = inputs.get("negative", "")
-
-        if positive_text or negative_text:
-            metadata[PROMPTS][node_id] = {
-                "positive_text": positive_text,
-                "negative_text": negative_text,
-                "node_id": node_id,
-            }
-
-    @staticmethod
-    def update(node_id, outputs, metadata):
-        output_tuple = _first_output_tuple(outputs)
-        if not output_tuple or len(output_tuple) < 2:
-            return
-
-        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
-        prompt_metadata["positive_encoded"] = output_tuple[0]
-        prompt_metadata["negative_encoded"] = output_tuple[1]
-
-
-def _ensure_prompt_metadata(metadata, node_id):
-    if node_id not in metadata[PROMPTS]:
-        metadata[PROMPTS][node_id] = {"node_id": node_id}
-    return metadata[PROMPTS][node_id]
-
-
-def _first_output_tuple(outputs):
-    if not outputs or not isinstance(outputs, list) or len(outputs) == 0:
-        return None
-    first_output = outputs[0]
-    if isinstance(first_output, tuple):
-        return first_output
-    return None
-
-
-def _record_conditioning_source(
-    metadata, node_id, output_conditioning, input_conditionings
-):
-    if output_conditioning is None:
-        return
-
-    sources = [
-        conditioning for conditioning in input_conditionings if conditioning is not None
-    ]
-    if not sources:
-        return
-
-    prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
-    prompt_metadata.setdefault("conditioning_sources", []).append(
-        {
-            "output": output_conditioning,
-            "inputs": sources,
-        }
-    )
-
-
-def _get_variable_name(inputs):
-    for key in ("key", "name", "variable_name", "tag", "text"):
-        value = inputs.get(key)
-        if isinstance(value, str) and value:
-            return value
-    return None
-
-
-def _get_node_variable_name(metadata, node_id, inputs):
-    variable_name = _get_variable_name(inputs)
-    if variable_name:
-        return variable_name
-
-    prompt = metadata.get("current_prompt")
-    original_prompt = getattr(prompt, "original_prompt", None)
-    if not original_prompt or node_id not in original_prompt:
-        return None
-
-    node_data = original_prompt[node_id]
-    variable_name = _get_variable_name(node_data.get("inputs", {}))
-    if variable_name:
-        return variable_name
-
-    widgets_values = node_data.get("widgets_values", [])
-    if widgets_values and isinstance(widgets_values[0], str):
-        return widgets_values[0]
-
-    return None
-
-
-class ControlNetApplyAdvancedExtractor(NodeMetadataExtractor):
-    @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        if not inputs:
-            return
-
-        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
-        if inputs.get("positive") is not None:
-            prompt_metadata["orig_pos_cond"] = inputs["positive"]
-        if inputs.get("negative") is not None:
-            prompt_metadata["orig_neg_cond"] = inputs["negative"]
-
-    @staticmethod
-    def update(node_id, outputs, metadata):
-        output_tuple = _first_output_tuple(outputs)
-        if not output_tuple:
-            return
-
-        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
-        positive_input = prompt_metadata.get("orig_pos_cond")
-        negative_input = prompt_metadata.get("orig_neg_cond")
-
-        if len(output_tuple) >= 1:
-            prompt_metadata["positive_encoded"] = output_tuple[0]
-            _record_conditioning_source(
-                metadata, node_id, output_tuple[0], [positive_input]
-            )
-        if len(output_tuple) >= 2:
-            prompt_metadata["negative_encoded"] = output_tuple[1]
-            _record_conditioning_source(
-                metadata, node_id, output_tuple[1], [negative_input]
-            )
-
-
-class ConditioningCombineExtractor(NodeMetadataExtractor):
-    @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        if not inputs:
-            return
-
-        input_conditionings = []
-        for input_name in inputs:
-            if (
-                input_name.startswith("conditioning")
-                and inputs[input_name] is not None
-            ):
-                input_conditionings.append(inputs[input_name])
-
-        if input_conditionings:
-            prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
-            prompt_metadata["orig_conditionings"] = input_conditionings
-
-    @staticmethod
-    def update(node_id, outputs, metadata):
-        output_tuple = _first_output_tuple(outputs)
-        if not output_tuple or len(output_tuple) < 1:
-            return
-
-        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
-        output_conditioning = output_tuple[0]
-        prompt_metadata["conditioning"] = output_conditioning
-        _record_conditioning_source(
-            metadata,
-            node_id,
-            output_conditioning,
-            prompt_metadata.get("orig_conditionings", []),
-        )
-
-
-class SetNodeExtractor(NodeMetadataExtractor):
-    @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        if not inputs:
-            return
-
-        variable_name = _get_node_variable_name(metadata, node_id, inputs)
-        conditioning = inputs.get("CONDITIONING")
-        if conditioning is None:
-            conditioning = inputs.get("conditioning")
-        if conditioning is None:
-            return
-
-        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
-        prompt_metadata["conditioning"] = conditioning
-        if variable_name:
-            prompt_metadata["variable_name"] = variable_name
-            metadata[PROMPTS].setdefault("__conditioning_variables__", {})[
-                variable_name
-            ] = conditioning
-
-
-class GetNodeExtractor(NodeMetadataExtractor):
-    @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        variable_name = _get_node_variable_name(metadata, node_id, inputs or {})
-        if variable_name:
-            prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
-            prompt_metadata["variable_name"] = variable_name
-
-    @staticmethod
-    def update(node_id, outputs, metadata):
-        output_tuple = _first_output_tuple(outputs)
-        if not output_tuple or len(output_tuple) < 1:
-            return
-
-        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
-        output_conditioning = output_tuple[0]
-        prompt_metadata["conditioning"] = output_conditioning
-
-        variable_name = prompt_metadata.get("variable_name")
-        if not variable_name:
-            return
-
-        input_conditioning = metadata[PROMPTS].get("__conditioning_variables__", {}).get(
-            variable_name
-        )
-        _record_conditioning_source(
-            metadata, node_id, output_conditioning, [input_conditioning]
-        )
-
 # Base Sampler Extractor to reduce code redundancy
 class BaseSamplerExtractor(NodeMetadataExtractor):
     """Base extractor for sampler nodes with common functionality"""
@@ -786,75 +427,6 @@ class ImageSizeExtractor(NodeMetadataExtractor):
             "node_id": node_id
         }
 
-class RgthreePowerLoraLoaderExtractor(NodeMetadataExtractor):
-    """Extract LoRA metadata from rgthree Power Lora Loader.
-
-    The node passes LoRAs as dynamic kwargs: LORA_1, LORA_2, ... each containing
-    {'on': bool, 'lora': filename, 'strength': float, 'strengthTwo': float}.
-    """
-    @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        if not inputs:
-            return
-
-        active_loras = []
-        for key, value in inputs.items():
-            if not key.upper().startswith('LORA_'):
-                continue
-            if not isinstance(value, dict):
-                continue
-            if not value.get('on') or not value.get('lora'):
-                continue
-            lora_name = os.path.splitext(os.path.basename(value['lora']))[0]
-            active_loras.append({
-                "name": lora_name,
-                "strength": round(float(value.get('strength', 1.0)), 2)
-            })
-
-        if active_loras:
-            metadata[LORAS][node_id] = {
-                "lora_list": active_loras,
-                "node_id": node_id
-            }
-
-
-class TensorRTLoaderExtractor(NodeMetadataExtractor):
-    """Extract checkpoint metadata from TensorRT Loader.
-
-    extract() parses the engine filename from 'unet_name' as a best-effort
-    fallback (strips profile suffix after '_$' and counter suffix).
-
-    update() checks if the output MODEL has attachments["source_model"]
-    set by the node (NubeBuster fork) and overrides with the real name.
-    Vanilla TRT doesn't set this — the filename parse stands.
-    """
-    @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        if not inputs or "unet_name" not in inputs:
-            return
-        unet_name = inputs.get("unet_name")
-        # Strip path and extension, then drop the $_profile suffix
-        model_name = os.path.splitext(os.path.basename(unet_name))[0]
-        if "_$" in model_name:
-            model_name = model_name[:model_name.index("_$")]
-        # Strip counter suffix (e.g. _00001_) left by ComfyUI's save path
-        model_name = re.sub(r'_\d+_?$', '', model_name)
-        _store_checkpoint_metadata(metadata, node_id, model_name)
-
-    @staticmethod
-    def update(node_id, outputs, metadata):
-        if not outputs or not isinstance(outputs, list) or len(outputs) == 0:
-            return
-        first_output = outputs[0]
-        if not isinstance(first_output, tuple) or len(first_output) < 1:
-            return
-        model = first_output[0]
-        # NubeBuster fork sets attachments["source_model"] on the ModelPatcher
-        source_model = getattr(model, 'attachments', {}).get("source_model")
-        if source_model:
-            _store_checkpoint_metadata(metadata, node_id, source_model)
-
-
 class LoraLoaderManagerExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -1005,6 +577,8 @@ class SamplerCustomAdvancedExtractor(BaseSamplerExtractor):
         # Extract latent dimensions
         BaseSamplerExtractor.extract_latent_dimensions(node_id, inputs, metadata)
 
+import json
+
 class CLIPTextEncodeFluxExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -1125,12 +699,9 @@ NODE_EXTRACTORS = {
     "KSamplerSelect": KSamplerSelectExtractor,  # Add KSamplerSelect
     "BasicScheduler": BasicSchedulerExtractor,  # Add BasicScheduler
     "AlignYourStepsScheduler": BasicSchedulerExtractor,  # Add AlignYourStepsScheduler
-    # ComfyUI-Easy-Use pre-sampling / seed
-    "samplerSettings": EasyPreSamplingExtractor,  # easy preSampling
-    "easySeed": EasySeedExtractor,  # easy seed
     # Loaders
     "CheckpointLoaderSimple": CheckpointLoaderExtractor,
-    "comfyLoader": EasyComfyLoaderExtractor,  # ComfyUI-Easy-Use easy comfyLoader
+    "comfyLoader": CheckpointLoaderExtractor,  # easy comfyLoader
     "CheckpointLoaderSimpleWithImages": CheckpointLoaderExtractor,  # CheckpointLoader|pysssss
     "TSC_EfficientLoader": TSCCheckpointLoaderExtractor,  # Efficient Nodes
     "NunchakuFluxDiTLoader": NunchakuFluxDiTLoaderExtractor,  # ComfyUI-Nunchaku
@@ -1140,17 +711,12 @@ NODE_EXTRACTORS = {
     "GGUFLoaderKJ": KJNodesModelLoaderExtractor,  # KJNodes
     "DiffusionModelLoaderKJ": KJNodesModelLoaderExtractor,  # KJNodes
     "CheckpointLoaderKJ": CheckpointLoaderExtractor,  # KJNodes
-    "CheckpointLoaderLM": CheckpointLoaderExtractor,  # LoRA Manager
     "UNETLoader": UNETLoaderExtractor,          # Updated to use dedicated extractor
     "UnetLoaderGGUF": UNETLoaderExtractor,  # Updated to use dedicated extractor
-    "UNETLoaderLM": UNETLoaderExtractor,  # LoRA Manager
     "LoraLoader": LoraLoaderExtractor,
     "LoraLoaderLM": LoraLoaderManagerExtractor,
-    "RgthreePowerLoraLoader": RgthreePowerLoraLoaderExtractor,
-    "TensorRTLoader": TensorRTLoaderExtractor,
     # Conditioning
     "CLIPTextEncode": CLIPTextEncodeExtractor,
-    "CLIPTextEncodeAttentionBias": CLIPTextEncodeExtractor,  # From https://github.com/silveroxides/ComfyUI_PromptAttention
     "PromptLM": CLIPTextEncodeExtractor,
     "CLIPTextEncodeFlux": CLIPTextEncodeFluxExtractor,  # Add CLIPTextEncodeFlux
     "WAS_Text_to_Conditioning": CLIPTextEncodeExtractor,
@@ -1158,12 +724,6 @@ NODE_EXTRACTORS = {
     "smZ_CLIPTextEncode": CLIPTextEncodeExtractor,  # From https://github.com/shiimizu/ComfyUI_smZNodes
     "CR_ApplyControlNetStack": CR_ApplyControlNetStackExtractor,  # Add CR_ApplyControlNetStack
     "PCTextEncode": CLIPTextEncodeExtractor,  # From https://github.com/asagi4/comfyui-prompt-control
-    "TextProvider": MyOriginalWaifuTextExtractor,  # ComfyUI-MyOriginalWaifu
-    "ClipProvider": MyOriginalWaifuClipExtractor,  # ComfyUI-MyOriginalWaifu
-    "ControlNetApplyAdvanced": ControlNetApplyAdvancedExtractor,
-    "ConditioningCombine": ConditioningCombineExtractor,
-    "SetNode": SetNodeExtractor,
-    "GetNode": GetNodeExtractor,
     # Latent
     "EmptyLatentImage": ImageSizeExtractor,
     # Flux

py/middleware/csp_middleware.py

@@ -4,21 +4,15 @@ from typing import Awaitable, Callable, Dict, List
 
 from aiohttp import web
 
-# Use wildcard for CivitAI to support their CDN subdomains (e.g., image-b2.civitai.com)
-# Security note: This is acceptable because:
-# 1. CSP img-src only controls image/video loading, not script execution
-# 2. All *.civitai.com subdomains are controlled by Civitai
-# 3. Explicit domain list would require constant updates as Civitai adds CDN nodes
 REMOTE_MEDIA_SOURCES = (
-    "https://*.civitai.com",
+    "https://image.civitai.com",
     "https://img.genur.art",
 )
 
 
 @web.middleware
 async def relax_csp_for_remote_media(
-    request: web.Request,
-    handler: Callable[[web.Request], Awaitable[web.StreamResponse]],
+    request: web.Request, handler: Callable[[web.Request], Awaitable[web.StreamResponse]]
 ) -> web.StreamResponse:
     """Allow LoRA Manager media previews to load from trusted remote domains.
 
@@ -49,9 +43,7 @@ async def relax_csp_for_remote_media(
             directive_order.append(name)
         directives[name] = values
 
-    def merge_sources(
-        name: str, sources: List[str], defaults: List[str] | None = None
-    ) -> None:
+    def merge_sources(name: str, sources: List[str], defaults: List[str] | None = None) -> None:
         existing = directives.get(name, list(defaults or []))
 
         for source in sources:

py/nodes/checkpoint_loader.py

@@ -1,118 +0,0 @@
-import logging
-from typing import List, Tuple
-import comfy.sd # type: ignore
-import folder_paths # type: ignore
-from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_comfyui
-
-logger = logging.getLogger(__name__)
-
-
-class CheckpointLoaderLM:
-    """Checkpoint Loader with support for extra folder paths
-
-    Loads checkpoints from both standard ComfyUI folders and LoRA Manager's
-    extra folder paths, providing a unified interface for checkpoint loading.
-    """
-
-    NAME = "Checkpoint Loader (LoraManager)"
-    CATEGORY = "Lora Manager/loaders"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        # Get list of checkpoint names from scanner (includes extra folder paths)
-        checkpoint_names = s._get_checkpoint_names()
-        return {
-            "required": {
-                "ckpt_name": (
-                    checkpoint_names,
-                    {"tooltip": "The name of the checkpoint (model) to load."},
-                ),
-            }
-        }
-
-    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
-    RETURN_NAMES = ("MODEL", "CLIP", "VAE")
-    OUTPUT_TOOLTIPS = (
-        "The model used for denoising latents.",
-        "The CLIP model used for encoding text prompts.",
-        "The VAE model used for encoding and decoding images to and from latent space.",
-    )
-    FUNCTION = "load_checkpoint"
-
-    @classmethod
-    def _get_checkpoint_names(cls) -> List[str]:
-        """Get list of checkpoint names from scanner cache in ComfyUI format (relative path with extension)"""
-        try:
-            from ..services.service_registry import ServiceRegistry
-            import asyncio
-
-            async def _get_names():
-                scanner = await ServiceRegistry.get_checkpoint_scanner()
-                cache = await scanner.get_cached_data()
-
-                # Get all model roots for calculating relative paths
-                model_roots = scanner.get_model_roots()
-
-                # Filter only checkpoint type (not diffusion_model) and format names
-                names = []
-                for item in cache.raw_data:
-                    if item.get("sub_type") == "checkpoint":
-                        file_path = item.get("file_path", "")
-                        if file_path:
-                            # Format using relative path with OS-native separator
-                            formatted_name = _format_model_name_for_comfyui(
-                                file_path, model_roots
-                            )
-                            if formatted_name:
-                                names.append(formatted_name)
-
-                return sorted(names)
-
-            try:
-                loop = asyncio.get_running_loop()
-                import concurrent.futures
-
-                def run_in_thread():
-                    new_loop = asyncio.new_event_loop()
-                    asyncio.set_event_loop(new_loop)
-                    try:
-                        return new_loop.run_until_complete(_get_names())
-                    finally:
-                        new_loop.close()
-
-                with concurrent.futures.ThreadPoolExecutor() as executor:
-                    future = executor.submit(run_in_thread)
-                    return future.result()
-            except RuntimeError:
-                return asyncio.run(_get_names())
-        except Exception as e:
-            logger.error(f"Error getting checkpoint names: {e}")
-            return []
-
-    def load_checkpoint(self, ckpt_name: str) -> Tuple:
-        """Load a checkpoint by name, supporting extra folder paths
-
-        Args:
-            ckpt_name: The name of the checkpoint to load (relative path with extension)
-
-        Returns:
-            Tuple of (MODEL, CLIP, VAE)
-        """
-        # Get absolute path from cache using ComfyUI-style name
-        ckpt_path, metadata = get_checkpoint_info_absolute(ckpt_name)
-
-        if metadata is None:
-            raise FileNotFoundError(
-                f"Checkpoint '{ckpt_name}' not found in LoRA Manager cache. "
-                "Make sure the checkpoint is indexed and try again."
-            )
-
-        # Load regular checkpoint using ComfyUI's API
-        logger.info(f"Loading checkpoint from: {ckpt_path}")
-        out = comfy.sd.load_checkpoint_guess_config(
-            ckpt_path,
-            output_vae=True,
-            output_clip=True,
-            embedding_directory=folder_paths.get_folder_paths("embeddings"),
-        )
-        return out[:3]

py/nodes/gguf_import_helper.py

@@ -1,161 +0,0 @@
-"""
-Helper module to safely import ComfyUI-GGUF modules.
-
-This module provides a robust way to import ComfyUI-GGUF functionality
-regardless of how ComfyUI loaded it.
-"""
-
-import sys
-import os
-import importlib.util
-import logging
-from typing import Optional, Tuple, Any
-
-logger = logging.getLogger(__name__)
-
-
-def _get_gguf_path() -> str:
-    """Get the path to ComfyUI-GGUF based on this file's location.
-
-    Since ComfyUI-Lora-Manager and ComfyUI-GGUF are both in custom_nodes/,
-    we can derive the GGUF path from our own location.
-    """
-    # This file is at: custom_nodes/ComfyUI-Lora-Manager/py/nodes/gguf_import_helper.py
-    # ComfyUI-GGUF is at: custom_nodes/ComfyUI-GGUF
-    current_file = os.path.abspath(__file__)
-    # Go up 4 levels: nodes -> py -> ComfyUI-Lora-Manager -> custom_nodes
-    custom_nodes_dir = os.path.dirname(
-        os.path.dirname(os.path.dirname(os.path.dirname(current_file)))
-    )
-    return os.path.join(custom_nodes_dir, "ComfyUI-GGUF")
-
-
-def _find_gguf_module() -> Optional[Any]:
-    """Find ComfyUI-GGUF module in sys.modules.
-
-    ComfyUI registers modules using the full path with dots replaced by _x_.
-    """
-    gguf_path = _get_gguf_path()
-    sys_module_name = gguf_path.replace(".", "_x_")
-
-    logger.debug(f"[GGUF Import] Looking for module '{sys_module_name}' in sys.modules")
-    if sys_module_name in sys.modules:
-        logger.info(f"[GGUF Import] Found module: '{sys_module_name}'")
-        return sys.modules[sys_module_name]
-
-    logger.debug(f"[GGUF Import] Module not found: '{sys_module_name}'")
-    return None
-
-
-def _load_gguf_modules_directly() -> Optional[Any]:
-    """Load ComfyUI-GGUF modules directly from file paths."""
-    gguf_path = _get_gguf_path()
-
-    logger.info(f"[GGUF Import] Direct Load: Attempting to load from '{gguf_path}'")
-
-    if not os.path.exists(gguf_path):
-        logger.warning(f"[GGUF Import] Path does not exist: {gguf_path}")
-        return None
-
-    try:
-        namespace = "ComfyUI_GGUF_Dynamic"
-        init_path = os.path.join(gguf_path, "__init__.py")
-
-        if not os.path.exists(init_path):
-            logger.warning(f"[GGUF Import] __init__.py not found at '{init_path}'")
-            return None
-
-        logger.debug(f"[GGUF Import] Loading from '{init_path}'")
-        spec = importlib.util.spec_from_file_location(namespace, init_path)
-        if not spec or not spec.loader:
-            logger.error(f"[GGUF Import] Failed to create spec for '{init_path}'")
-            return None
-
-        package = importlib.util.module_from_spec(spec)
-        package.__path__ = [gguf_path]
-        sys.modules[namespace] = package
-        spec.loader.exec_module(package)
-        logger.debug(f"[GGUF Import] Loaded main package '{namespace}'")
-
-        # Load submodules
-        loaded = []
-        for submod_name in ["loader", "ops", "nodes"]:
-            submod_path = os.path.join(gguf_path, f"{submod_name}.py")
-            if os.path.exists(submod_path):
-                submod_spec = importlib.util.spec_from_file_location(
-                    f"{namespace}.{submod_name}", submod_path
-                )
-                if submod_spec and submod_spec.loader:
-                    submod = importlib.util.module_from_spec(submod_spec)
-                    submod.__package__ = namespace
-                    sys.modules[f"{namespace}.{submod_name}"] = submod
-                    submod_spec.loader.exec_module(submod)
-                    setattr(package, submod_name, submod)
-                    loaded.append(submod_name)
-                    logger.debug(f"[GGUF Import] Loaded submodule '{submod_name}'")
-
-        logger.info(f"[GGUF Import] Direct Load success: {loaded}")
-        return package
-
-    except Exception as e:
-        logger.error(f"[GGUF Import] Direct Load failed: {e}", exc_info=True)
-        return None
-
-
-def get_gguf_modules() -> Tuple[Any, Any, Any]:
-    """Get ComfyUI-GGUF modules (loader, ops, nodes).
-
-    Returns:
-        Tuple of (loader_module, ops_module, nodes_module)
-
-    Raises:
-        RuntimeError: If ComfyUI-GGUF cannot be found or loaded.
-    """
-    logger.debug("[GGUF Import] Starting module search...")
-
-    # Try to find already loaded module first
-    gguf_module = _find_gguf_module()
-
-    if gguf_module is None:
-        logger.info("[GGUF Import] Not found in sys.modules, trying direct load...")
-        gguf_module = _load_gguf_modules_directly()
-
-    if gguf_module is None:
-        raise RuntimeError(
-            "ComfyUI-GGUF is not installed. "
-            "Please install from https://github.com/city96/ComfyUI-GGUF"
-        )
-
-    # Extract submodules
-    loader = getattr(gguf_module, "loader", None)
-    ops = getattr(gguf_module, "ops", None)
-    nodes = getattr(gguf_module, "nodes", None)
-
-    if loader is None or ops is None or nodes is None:
-        missing = [
-            name
-            for name, mod in [("loader", loader), ("ops", ops), ("nodes", nodes)]
-            if mod is None
-        ]
-        raise RuntimeError(f"ComfyUI-GGUF missing submodules: {missing}")
-
-    logger.debug("[GGUF Import] All modules loaded successfully")
-    return loader, ops, nodes
-
-
-def get_gguf_sd_loader():
-    """Get the gguf_sd_loader function from ComfyUI-GGUF."""
-    loader, _, _ = get_gguf_modules()
-    return getattr(loader, "gguf_sd_loader")
-
-
-def get_ggml_ops():
-    """Get the GGMLOps class from ComfyUI-GGUF."""
-    _, ops, _ = get_gguf_modules()
-    return getattr(ops, "GGMLOps")
-
-
-def get_gguf_model_patcher():
-    """Get the GGUFModelPatcher class from ComfyUI-GGUF."""
-    _, _, nodes = get_gguf_modules()
-    return getattr(nodes, "GGUFModelPatcher")

py/nodes/lora_cycler.py

@@ -8,7 +8,6 @@ and tracks the cycle progress which persists across workflow save/load.
 
 import logging
 import os
-
 from ..utils.utils import get_lora_info
 
 logger = logging.getLogger(__name__)
@@ -55,14 +54,8 @@ class LoraCyclerLM:
         current_index = cycler_config.get("current_index", 1)  # 1-based
         model_strength = float(cycler_config.get("model_strength", 1.0))
         clip_strength = float(cycler_config.get("clip_strength", 1.0))
-        use_same_clip_strength = cycler_config.get("use_same_clip_strength", True)
-        use_preset_strength = cycler_config.get("use_preset_strength", False)
-        preset_strength_scale = float(cycler_config.get("preset_strength_scale", 1.0))
         sort_by = "filename"
 
-        # Include "no lora" option
-        include_no_lora = cycler_config.get("include_no_lora", False)
-
         # Dual-index mechanism for batch queue synchronization
         execution_index = cycler_config.get("execution_index")  # Can be None
         # next_index_from_config = cycler_config.get("next_index")  # Not used on backend
@@ -78,10 +71,7 @@ class LoraCyclerLM:
 
         total_count = len(lora_list)
 
-        # Calculate effective total count (includes no lora option if enabled)
-        effective_total_count = total_count + 1 if include_no_lora else total_count
-
-        if total_count == 0 and not include_no_lora:
+        if total_count == 0:
             logger.warning("[LoraCyclerLM] No LoRAs available in pool")
             return {
                 "result": ([],),
@@ -103,99 +93,44 @@ class LoraCyclerLM:
         else:
             actual_index = current_index
 
-        # Clamp index to valid range (1-based, includes no lora if enabled)
-        clamped_index = max(1, min(actual_index, effective_total_count))
+        # Clamp index to valid range (1-based)
+        clamped_index = max(1, min(actual_index, total_count))
 
-        # Check if current index is the "no lora" option (last position when include_no_lora is True)
-        is_no_lora = include_no_lora and clamped_index == effective_total_count
+        # Get LoRA at current index (convert to 0-based for list access)
+        current_lora = lora_list[clamped_index - 1]
 
-        if is_no_lora:
-            # "No LoRA" option - return empty stack
+        # Build LORA_STACK with single LoRA
+        lora_path, _ = get_lora_info(current_lora["file_name"])
+        if not lora_path:
+            logger.warning(
+                f"[LoraCyclerLM] Could not find path for LoRA: {current_lora['file_name']}"
+            )
             lora_stack = []
-            current_lora_name = "No LoRA"
-            current_lora_filename = "No LoRA"
         else:
-            # Get LoRA at current index (convert to 0-based for list access)
-            current_lora = lora_list[clamped_index - 1]
-            current_lora_name = current_lora["file_name"]
-            current_lora_filename = current_lora["file_name"]
-
-            # Build LORA_STACK with single LoRA
-            if current_lora["file_name"] == "None":
-                lora_path = None
-            else:
-                lora_path, _ = get_lora_info(current_lora["file_name"])
-
-            if not lora_path:
-                if current_lora["file_name"] != "None":
-                    logger.warning(
-                        f"[LoraCyclerLM] Could not find path for LoRA: {current_lora['file_name']}"
-                    )
-                lora_stack = []
-            else:
-                # Normalize path separators
-                lora_path = lora_path.replace("/", os.sep)
-
-                if use_preset_strength:
-                    lora_metadata = await lora_service.get_lora_metadata_by_filename(
-                        current_lora["file_name"]
-                    )
-                    if lora_metadata:
-                        recommended_strength = (
-                            lora_service.get_recommended_strength_from_lora_data(
-                                lora_metadata
-                            )
-                        )
-                        if recommended_strength is not None:
-                            model_strength = round(
-                                recommended_strength * preset_strength_scale, 2
-                            )
-
-                        if use_same_clip_strength:
-                            clip_strength = model_strength
-                        else:
-                            recommended_clip_strength = (
-                                lora_service.get_recommended_clip_strength_from_lora_data(
-                                    lora_metadata
-                                )
-                            )
-                            if recommended_clip_strength is not None:
-                                clip_strength = round(
-                                    recommended_clip_strength * preset_strength_scale, 2
-                                )
-                    elif use_same_clip_strength:
-                        clip_strength = model_strength
-                elif use_same_clip_strength:
-                    clip_strength = model_strength
-
-                lora_stack = [(lora_path, model_strength, clip_strength)]
+            # Normalize path separators
+            lora_path = lora_path.replace("/", os.sep)
+            lora_stack = [(lora_path, model_strength, clip_strength)]
 
         # Calculate next index (wrap to 1 if at end)
         next_index = clamped_index + 1
-        if next_index > effective_total_count:
+        if next_index > total_count:
             next_index = 1
 
         # Get next LoRA for UI display (what will be used next generation)
-        is_next_no_lora = include_no_lora and next_index == effective_total_count
-        if is_next_no_lora:
-            next_display_name = "No LoRA"
-            next_lora_filename = "No LoRA"
-        else:
-            next_lora = lora_list[next_index - 1]
-            next_display_name = next_lora["file_name"]
-            next_lora_filename = next_lora["file_name"]
+        next_lora = lora_list[next_index - 1]
+        next_display_name = next_lora["file_name"]
 
         return {
             "result": (lora_stack,),
             "ui": {
                 "current_index": [clamped_index],
                 "next_index": [next_index],
-                "total_count": [
-                    total_count
-                ],  # Return actual LoRA count, not effective_total_count
-                "current_lora_name": [current_lora_name],
-                "current_lora_filename": [current_lora_filename],
+                "total_count": [total_count],
+                "current_lora_name": [
+                    current_lora.get("model_name", current_lora["file_name"])
+                ],
+                "current_lora_filename": [current_lora["file_name"]],
                 "next_lora_name": [next_display_name],
-                "next_lora_filename": [next_lora_filename],
+                "next_lora_filename": [next_lora["file_name"]],
             },
         }

py/nodes/lora_loader.py

@@ -1,138 +1,21 @@
-import importlib
 import logging
 import re
-
-import comfy.sd  # type: ignore
-import comfy.utils  # type: ignore
-
-from ..utils.utils import get_lora_info_absolute
-from .utils import (
-    FlexibleOptionalInputType,
-    any_type,
-    detect_nunchaku_model_kind,
-    extract_lora_name,
-    get_loras_list,
-    nunchaku_load_lora,
-)
+from nodes import LoraLoader
+from ..utils.utils import get_lora_info
+from .utils import FlexibleOptionalInputType, any_type, extract_lora_name, get_loras_list, nunchaku_load_lora
 
 logger = logging.getLogger(__name__)
 
-
-def _get_nunchaku_load_qwen_loras():
-    try:
-        module = importlib.import_module(".nunchaku_qwen", __package__)
-    except ImportError as exc:
-        raise RuntimeError(
-            "Qwen-Image LoRA loading requires the ComfyUI runtime with its torch dependency available."
-        ) from exc
-    return module.nunchaku_load_qwen_loras
-
-
-def _collect_stack_entries(lora_stack):
-    entries = []
-    if not lora_stack:
-        return entries
-
-    for lora_path, model_strength, clip_strength in lora_stack:
-        lora_name = extract_lora_name(lora_path)
-        absolute_lora_path, trigger_words = get_lora_info_absolute(lora_name)
-        entries.append({
-            "name": lora_name,
-            "absolute_path": absolute_lora_path,
-            "input_path": lora_path,
-            "model_strength": float(model_strength),
-            "clip_strength": float(clip_strength),
-            "trigger_words": trigger_words,
-        })
-    return entries
-
-
-def _collect_widget_entries(kwargs):
-    entries = []
-    for lora in get_loras_list(kwargs):
-        if not lora.get("active", False):
-            continue
-        lora_name = lora["name"]
-        model_strength = float(lora["strength"])
-        clip_strength = float(lora.get("clipStrength", model_strength))
-        lora_path, trigger_words = get_lora_info_absolute(lora_name)
-        entries.append({
-            "name": lora_name,
-            "absolute_path": lora_path,
-            "input_path": lora_path,
-            "model_strength": model_strength,
-            "clip_strength": clip_strength,
-            "trigger_words": trigger_words,
-        })
-    return entries
-
-
-def _format_loaded_loras(loaded_loras):
-    formatted_loras = []
-    for item in loaded_loras:
-        if item["include_clip_strength"]:
-            formatted_loras.append(
-                f"<lora:{item['name']}:{item['model_strength']}:{item['clip_strength']}>"
-            )
-        else:
-            formatted_loras.append(f"<lora:{item['name']}:{item['model_strength']}>")
-    return " ".join(formatted_loras)
-
-
-def _apply_entries(model, clip, lora_entries, nunchaku_model_kind):
-    loaded_loras = []
-    all_trigger_words = []
-
-    if nunchaku_model_kind == "qwen_image":
-        nunchaku_load_qwen_loras = _get_nunchaku_load_qwen_loras()
-        qwen_lora_configs = []
-        for entry in lora_entries:
-            qwen_lora_configs.append((entry["absolute_path"], entry["model_strength"]))
-            loaded_loras.append({
-                "name": entry["name"],
-                "model_strength": entry["model_strength"],
-                "clip_strength": entry["model_strength"],
-                "include_clip_strength": False,
-            })
-            all_trigger_words.extend(entry["trigger_words"])
-        if qwen_lora_configs:
-            model = nunchaku_load_qwen_loras(model, qwen_lora_configs)
-        return model, clip, loaded_loras, all_trigger_words
-
-    for entry in lora_entries:
-        if nunchaku_model_kind == "flux":
-            model = nunchaku_load_lora(model, entry["input_path"], entry["model_strength"])
-        else:
-            lora = comfy.utils.load_torch_file(entry["absolute_path"], safe_load=True)
-            model, clip = comfy.sd.load_lora_for_models(
-                model,
-                clip,
-                lora,
-                entry["model_strength"],
-                entry["clip_strength"],
-            )
-
-        include_clip_strength = nunchaku_model_kind is None and abs(entry["model_strength"] - entry["clip_strength"]) > 0.001
-        loaded_loras.append({
-            "name": entry["name"],
-            "model_strength": entry["model_strength"],
-            "clip_strength": entry["clip_strength"],
-            "include_clip_strength": include_clip_strength,
-        })
-        all_trigger_words.extend(entry["trigger_words"])
-
-    return model, clip, loaded_loras, all_trigger_words
-
-
 class LoraLoaderLM:
     NAME = "Lora Loader (LoraManager)"
     CATEGORY = "Lora Manager/loaders"
-
+    
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
                 "model": ("MODEL",),
+                # "clip": ("CLIP",),
                 "text": ("AUTOCOMPLETE_TEXT_LORAS", {
                     "placeholder": "Search LoRAs to add...",
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
@@ -144,30 +27,111 @@ class LoraLoaderLM:
     RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING")
     RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
     FUNCTION = "load_loras"
-
+    
     def load_loras(self, model, text, **kwargs):
         """Loads multiple LoRAs based on the kwargs input and lora_stack."""
-        del text
-        clip = kwargs.get("clip", None)
-        lora_entries = _collect_stack_entries(kwargs.get("lora_stack", None))
-        lora_entries.extend(_collect_widget_entries(kwargs))
-
-        nunchaku_model_kind = detect_nunchaku_model_kind(model)
-        if nunchaku_model_kind == "flux":
-            logger.info("Detected Nunchaku Flux model")
-        elif nunchaku_model_kind == "qwen_image":
-            logger.info("Detected Nunchaku Qwen-Image model")
-
-        model, clip, loaded_loras, all_trigger_words = _apply_entries(model, clip, lora_entries, nunchaku_model_kind)
+        loaded_loras = []
+        all_trigger_words = []
+        
+        clip = kwargs.get('clip', None)
+        lora_stack = kwargs.get('lora_stack', None)
+        
+        # Check if model is a Nunchaku Flux model - simplified approach
+        is_nunchaku_model = False
+        
+        try:
+            model_wrapper = model.model.diffusion_model
+            # Check if model is a Nunchaku Flux model using only class name
+            if model_wrapper.__class__.__name__ == "ComfyFluxWrapper":
+                is_nunchaku_model = True
+                logger.info("Detected Nunchaku Flux model")
+        except (AttributeError, TypeError):
+            # Not a model with the expected structure
+            pass
+        
+        # First process lora_stack if available
+        if lora_stack:
+            for lora_path, model_strength, clip_strength in lora_stack:
+                # Apply the LoRA using the appropriate loader
+                if is_nunchaku_model:
+                    # Use our custom function for Flux models
+                    model = nunchaku_load_lora(model, lora_path, model_strength)
+                    # clip remains unchanged for Nunchaku models
+                else:
+                    # Use default loader for standard models
+                    model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+                
+                # Extract lora name for trigger words lookup
+                lora_name = extract_lora_name(lora_path)
+                _, trigger_words = get_lora_info(lora_name)
+                
+                all_trigger_words.extend(trigger_words)
+                # Add clip strength to output if different from model strength (except for Nunchaku models)
+                if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
+                    loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
+                else:
+                    loaded_loras.append(f"{lora_name}: {model_strength}")
+        
+        # Then process loras from kwargs with support for both old and new formats
+        loras_list = get_loras_list(kwargs)
+        for lora in loras_list:
+            if not lora.get('active', False):
+                continue
+                
+            lora_name = lora['name']
+            model_strength = float(lora['strength'])
+            # Get clip strength - use model strength as default if not specified
+            clip_strength = float(lora.get('clipStrength', model_strength))
+            
+            # Get lora path and trigger words
+            lora_path, trigger_words = get_lora_info(lora_name)
+            
+            # Apply the LoRA using the appropriate loader
+            if is_nunchaku_model:
+                # For Nunchaku models, use our custom function
+                model = nunchaku_load_lora(model, lora_path, model_strength)
+                # clip remains unchanged
+            else:
+                # Use default loader for standard models
+                model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+            
+            # Include clip strength in output if different from model strength and not a Nunchaku model
+            if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
+                loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
+            else:
+                loaded_loras.append(f"{lora_name}: {model_strength}")
+            
+            # Add trigger words to collection
+            all_trigger_words.extend(trigger_words)
+        
+        # use ',, ' to separate trigger words for group mode
         trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
-        formatted_loras_text = _format_loaded_loras(loaded_loras)
-        return (model, clip, trigger_words_text, formatted_loras_text)
+        
+        # Format loaded_loras with support for both formats
+        formatted_loras = []
+        for item in loaded_loras:
+            parts = item.split(":")
+            lora_name = parts[0]
+            strength_parts = parts[1].strip().split(",")
+            
+            if len(strength_parts) > 1:
+                # Different model and clip strengths
+                model_str = strength_parts[0].strip()
+                clip_str = strength_parts[1].strip()
+                formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
+            else:
+                # Same strength for both
+                model_str = strength_parts[0].strip()
+                formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
+                
+        formatted_loras_text = " ".join(formatted_loras)
 
+        return (model, clip, trigger_words_text, formatted_loras_text)
 
 class LoraTextLoaderLM:
     NAME = "LoRA Text Loader (LoraManager)"
     CATEGORY = "Lora Manager/loaders"
-
+    
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -175,55 +139,128 @@ class LoraTextLoaderLM:
                 "model": ("MODEL",),
                 "lora_syntax": ("STRING", {
                     "forceInput": True,
-                    "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
+                    "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation"
                 }),
             },
             "optional": {
                 "clip": ("CLIP",),
                 "lora_stack": ("LORA_STACK",),
-            },
+            }
         }
 
     RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING")
     RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
     FUNCTION = "load_loras_from_text"
-
+    
     def parse_lora_syntax(self, text):
         """Parse LoRA syntax from text input."""
-        pattern = r"<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>"
+        # Pattern to match <lora:name:strength> or <lora:name:model_strength:clip_strength>
+        pattern = r'<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>'
         matches = re.findall(pattern, text, re.IGNORECASE)
-
+        
         loras = []
         for match in matches:
+            lora_name = match[0]
             model_strength = float(match[1])
+            clip_strength = float(match[2]) if match[2] else model_strength
+            
             loras.append({
-                "name": match[0],
-                "model_strength": model_strength,
-                "clip_strength": float(match[2]) if match[2] else model_strength,
+                'name': lora_name,
+                'model_strength': model_strength,
+                'clip_strength': clip_strength
             })
+        
         return loras
-
+    
     def load_loras_from_text(self, model, lora_syntax, clip=None, lora_stack=None):
         """Load LoRAs based on text syntax input."""
-        lora_entries = _collect_stack_entries(lora_stack)
-        for lora in self.parse_lora_syntax(lora_syntax):
-            lora_path, trigger_words = get_lora_info_absolute(lora["name"])
-            lora_entries.append({
-                "name": lora["name"],
-                "absolute_path": lora_path,
-                "input_path": lora_path,
-                "model_strength": lora["model_strength"],
-                "clip_strength": lora["clip_strength"],
-                "trigger_words": trigger_words,
-            })
-
-        nunchaku_model_kind = detect_nunchaku_model_kind(model)
-        if nunchaku_model_kind == "flux":
-            logger.info("Detected Nunchaku Flux model")
-        elif nunchaku_model_kind == "qwen_image":
-            logger.info("Detected Nunchaku Qwen-Image model")
-
-        model, clip, loaded_loras, all_trigger_words = _apply_entries(model, clip, lora_entries, nunchaku_model_kind)
+        loaded_loras = []
+        all_trigger_words = []
+        
+        # Check if model is a Nunchaku Flux model - simplified approach
+        is_nunchaku_model = False
+        
+        try:
+            model_wrapper = model.model.diffusion_model
+            # Check if model is a Nunchaku Flux model using only class name
+            if model_wrapper.__class__.__name__ == "ComfyFluxWrapper":
+                is_nunchaku_model = True
+                logger.info("Detected Nunchaku Flux model")
+        except (AttributeError, TypeError):
+            # Not a model with the expected structure
+            pass
+        
+        # First process lora_stack if available
+        if lora_stack:
+            for lora_path, model_strength, clip_strength in lora_stack:
+                # Apply the LoRA using the appropriate loader
+                if is_nunchaku_model:
+                    # Use our custom function for Flux models
+                    model = nunchaku_load_lora(model, lora_path, model_strength)
+                    # clip remains unchanged for Nunchaku models
+                else:
+                    # Use default loader for standard models
+                    model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+                
+                # Extract lora name for trigger words lookup
+                lora_name = extract_lora_name(lora_path)
+                _, trigger_words = get_lora_info(lora_name)
+                
+                all_trigger_words.extend(trigger_words)
+                # Add clip strength to output if different from model strength (except for Nunchaku models)
+                if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
+                    loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
+                else:
+                    loaded_loras.append(f"{lora_name}: {model_strength}")
+        
+        # Parse and process LoRAs from text syntax
+        parsed_loras = self.parse_lora_syntax(lora_syntax)
+        for lora in parsed_loras:
+            lora_name = lora['name']
+            model_strength = lora['model_strength']
+            clip_strength = lora['clip_strength']
+            
+            # Get lora path and trigger words
+            lora_path, trigger_words = get_lora_info(lora_name)
+            
+            # Apply the LoRA using the appropriate loader
+            if is_nunchaku_model:
+                # For Nunchaku models, use our custom function
+                model = nunchaku_load_lora(model, lora_path, model_strength)
+                # clip remains unchanged
+            else:
+                # Use default loader for standard models
+                model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+            
+            # Include clip strength in output if different from model strength and not a Nunchaku model
+            if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
+                loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
+            else:
+                loaded_loras.append(f"{lora_name}: {model_strength}")
+            
+            # Add trigger words to collection
+            all_trigger_words.extend(trigger_words)
+        
+        # use ',, ' to separate trigger words for group mode
         trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
-        formatted_loras_text = _format_loaded_loras(loaded_loras)
-        return (model, clip, trigger_words_text, formatted_loras_text)
+
+        # Format loaded_loras with support for both formats
+        formatted_loras = []
+        for item in loaded_loras:
+            parts = item.split(":")
+            lora_name = parts[0].strip()
+            strength_parts = parts[1].strip().split(",")
+            
+            if len(strength_parts) > 1:
+                # Different model and clip strengths
+                model_str = strength_parts[0].strip()
+                clip_str = strength_parts[1].strip()
+                formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
+            else:
+                # Same strength for both
+                model_str = strength_parts[0].strip()
+                formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
+                
+        formatted_loras_text = " ".join(formatted_loras)
+
+        return (model, clip, trigger_words_text, formatted_loras_text)

py/nodes/lora_pool.py

@@ -82,7 +82,6 @@ class LoraPoolLM:
                 "folders": {"include": [], "exclude": []},
                 "favoritesOnly": False,
                 "license": {"noCreditRequired": False, "allowSelling": False},
-                "namePatterns": {"include": [], "exclude": [], "useRegex": False},
             },
             "preview": {"matchCount": 0, "lastUpdated": 0},
         }

py/nodes/lora_randomizer.py

@@ -7,8 +7,10 @@ and tracks the last used combination for reuse.
 """
 
 import logging
+import random
 import os
 from ..utils.utils import get_lora_info
+from .utils import extract_lora_name
 
 logger = logging.getLogger(__name__)
 

py/nodes/lora_stack_combiner.py

@@ -1,26 +0,0 @@
-class LoraStackCombinerLM:
-    NAME = "Lora Stack Combiner (LoraManager)"
-    CATEGORY = "Lora Manager/stackers"
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "lora_stack_a": ("LORA_STACK",),
-                "lora_stack_b": ("LORA_STACK",),
-            },
-        }
-
-    RETURN_TYPES = ("LORA_STACK",)
-    RETURN_NAMES = ("LORA_STACK",)
-    FUNCTION = "combine_stacks"
-
-    def combine_stacks(self, lora_stack_a, lora_stack_b):
-        combined_stack = []
-
-        if lora_stack_a:
-            combined_stack.extend(lora_stack_a)
-        if lora_stack_b:
-            combined_stack.extend(lora_stack_b)
-
-        return (combined_stack,)

py/nodes/nunchaku_qwen.py

@@ -1,570 +0,0 @@
-from __future__ import annotations
-
-"""Qwen-Image LoRA support for Nunchaku models.
-
-Portions of the LoRA mapping/application logic in this file are adapted from
-ComfyUI-QwenImageLoraLoader by GitHub user ussoewwin:
-https://github.com/ussoewwin/ComfyUI-QwenImageLoraLoader
-
-The upstream project is licensed under Apache License 2.0.
-"""
-
-import copy
-import logging
-import os
-import re
-from collections import defaultdict
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple, Union
-
-import comfy.utils  # type: ignore
-import folder_paths  # type: ignore
-import torch
-import torch.nn as nn
-from safetensors import safe_open
-
-from nunchaku.lora.flux.nunchaku_converter import (
-    pack_lowrank_weight,
-    unpack_lowrank_weight,
-)
-
-logger = logging.getLogger(__name__)
-
-KEY_MAPPING = [
-    (re.compile(r"^(layers)[._](\d+)[._]attention[._]to[._]([qkv])$"), r"\1.\2.attention.to_qkv", "qkv", lambda m: m.group(3).upper()),
-    (re.compile(r"^(layers)[._](\d+)[._]feed_forward[._](w1|w3)$"), r"\1.\2.feed_forward.net.0.proj", "glu", lambda m: m.group(3)),
-    (re.compile(r"^(layers)[._](\d+)[._]feed_forward[._]w2$"), r"\1.\2.feed_forward.net.2", "regular", None),
-    (re.compile(r"^(layers)[._](\d+)[._](.*)$"), r"\1.\2.\3", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._]to[._]([qkv])$"), r"\1.\2.attn.to_qkv", "qkv", lambda m: m.group(3).upper()),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._](q|k|v)[._]proj$"), r"\1.\2.attn.to_qkv", "qkv", lambda m: m.group(3).upper()),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._]add[._](q|k|v)[._]proj$"), r"\1.\2.attn.add_qkv_proj", "add_qkv", lambda m: m.group(3).upper()),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._]out[._]proj[._]context$"), r"\1.\2.attn.to_add_out", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._]out[._]proj$"), r"\1.\2.attn.to_out.0", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._]to[._]out$"), r"\1.\2.attn.to_out.0", "regular", None),
-    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]attn[._]to[._]([qkv])$"), r"\1.\2.attn.to_qkv", "qkv", lambda m: m.group(3).upper()),
-    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]attn[._]to[._]out$"), r"\1.\2.attn.to_out", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._]ff[._]net[._]0(?:[._]proj)?$"), r"\1.\2.mlp_fc1", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._]ff[._]net[._]2$"), r"\1.\2.mlp_fc2", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._]ff_context[._]net[._]0(?:[._]proj)?$"), r"\1.\2.mlp_context_fc1", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._]ff_context[._]net[._]2$"), r"\1.\2.mlp_context_fc2", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._](img_mlp)[._](net)[._](0)[._](proj)$"), r"\1.\2.\3.\4.\5.\6", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._](img_mlp)[._](net)[._](2)$"), r"\1.\2.\3.\4.\5", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._](txt_mlp)[._](net)[._](0)[._](proj)$"), r"\1.\2.\3.\4.\5.\6", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._](txt_mlp)[._](net)[._](2)$"), r"\1.\2.\3.\4.\5", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._](img_mod)[._](1)$"), r"\1.\2.\3.\4", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._](txt_mod)[._](1)$"), r"\1.\2.\3.\4", "regular", None),
-    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]proj[._]out$"), r"\1.\2.proj_out", "single_proj_out", None),
-    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]proj[._]mlp$"), r"\1.\2.mlp_fc1", "regular", None),
-    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]norm[._]linear$"), r"\1.\2.norm.linear", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._]norm1[._]linear$"), r"\1.\2.norm1.linear", "regular", None),
-    (re.compile(r"^(transformer_blocks)[._](\d+)[._]norm1_context[._]linear$"), r"\1.\2.norm1_context.linear", "regular", None),
-    (re.compile(r"^(img_in)$"), r"\1", "regular", None),
-    (re.compile(r"^(txt_in)$"), r"\1", "regular", None),
-    (re.compile(r"^(proj_out)$"), r"\1", "regular", None),
-    (re.compile(r"^(norm_out)[._](linear)$"), r"\1.\2", "regular", None),
-    (re.compile(r"^(time_text_embed)[._](timestep_embedder)[._](linear_1)$"), r"\1.\2.\3", "regular", None),
-    (re.compile(r"^(time_text_embed)[._](timestep_embedder)[._](linear_2)$"), r"\1.\2.\3", "regular", None),
-]
-
-_RE_LORA_SUFFIX = re.compile(r"\.(?P<tag>lora(?:[._](?:A|B|down|up)))(?:\.[^.]+)*\.weight$")
-_RE_ALPHA_SUFFIX = re.compile(r"\.(?:alpha|lora_alpha)(?:\.[^.]+)*$")
-
-
-def _rename_layer_underscore_layer_name(old_name: str) -> str:
-    rules = [
-        (r"_(\d+)_attn_to_out_(\d+)", r".\1.attn.to_out.\2"),
-        (r"_(\d+)_img_mlp_net_(\d+)_proj", r".\1.img_mlp.net.\2.proj"),
-        (r"_(\d+)_txt_mlp_net_(\d+)_proj", r".\1.txt_mlp.net.\2.proj"),
-        (r"_(\d+)_img_mlp_net_(\d+)", r".\1.img_mlp.net.\2"),
-        (r"_(\d+)_txt_mlp_net_(\d+)", r".\1.txt_mlp.net.\2"),
-        (r"_(\d+)_img_mod_(\d+)", r".\1.img_mod.\2"),
-        (r"_(\d+)_txt_mod_(\d+)", r".\1.txt_mod.\2"),
-        (r"_(\d+)_attn_", r".\1.attn."),
-    ]
-    new_name = old_name
-    for pattern, replacement in rules:
-        new_name = re.sub(pattern, replacement, new_name)
-    return new_name
-
-
-def _is_indexable_module(module):
-    return isinstance(module, (nn.ModuleList, nn.Sequential, list, tuple))
-
-
-def _get_module_by_name(model: nn.Module, name: str) -> Optional[nn.Module]:
-    if not name:
-        return model
-    module = model
-    for part in name.split("."):
-        if not part:
-            continue
-        if hasattr(module, part):
-            module = getattr(module, part)
-        elif part.isdigit() and _is_indexable_module(module):
-            try:
-                module = module[int(part)]
-            except (IndexError, TypeError):
-                return None
-        else:
-            return None
-    return module
-
-
-def _resolve_module_name(model: nn.Module, name: str) -> Tuple[str, Optional[nn.Module]]:
-    module = _get_module_by_name(model, name)
-    if module is not None:
-        return name, module
-
-    replacements = [
-        (".attn.to_out.0", ".attn.to_out"),
-        (".attention.to_qkv", ".attention.qkv"),
-        (".attention.to_out.0", ".attention.out"),
-        (".feed_forward.net.0.proj", ".feed_forward.w13"),
-        (".feed_forward.net.2", ".feed_forward.w2"),
-        (".ff.net.0.proj", ".mlp_fc1"),
-        (".ff.net.2", ".mlp_fc2"),
-        (".ff_context.net.0.proj", ".mlp_context_fc1"),
-        (".ff_context.net.2", ".mlp_context_fc2"),
-    ]
-    for src, dst in replacements:
-        if src in name:
-            alt = name.replace(src, dst)
-            module = _get_module_by_name(model, alt)
-            if module is not None:
-                return alt, module
-    return name, None
-
-
-def _classify_and_map_key(key: str) -> Optional[Tuple[str, str, Optional[str], str]]:
-    normalized = key
-    if normalized.startswith("transformer."):
-        normalized = normalized[len("transformer."):]
-    if normalized.startswith("diffusion_model."):
-        normalized = normalized[len("diffusion_model."):]
-    if normalized.startswith("lora_unet_"):
-        normalized = _rename_layer_underscore_layer_name(normalized[len("lora_unet_"):])
-
-    match = _RE_LORA_SUFFIX.search(normalized)
-    if match:
-        tag = match.group("tag")
-        base = normalized[:match.start()]
-        ab = "A" if ("lora_A" in tag or tag.endswith(".A") or "down" in tag) else "B"
-    else:
-        match = _RE_ALPHA_SUFFIX.search(normalized)
-        if not match:
-            return None
-        base = normalized[:match.start()]
-        ab = "alpha"
-
-    for pattern, template, group, comp_fn in KEY_MAPPING:
-        key_match = pattern.match(base)
-        if key_match:
-            return group, key_match.expand(template), comp_fn(key_match) if comp_fn else None, ab
-    return None
-
-
-def _detect_lora_format(lora_state_dict: Dict[str, torch.Tensor]) -> bool:
-    standard_patterns = (
-        ".lora_up.",
-        ".lora_down.",
-        ".lora_A.",
-        ".lora_B.",
-        ".lora.up.",
-        ".lora.down.",
-        ".lora.A.",
-        ".lora.B.",
-    )
-    return any(pattern in key for key in lora_state_dict for pattern in standard_patterns)
-
-
-def _load_lora_state_dict(path_or_dict: Union[str, Path, Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
-    if isinstance(path_or_dict, dict):
-        return path_or_dict
-    path = Path(path_or_dict)
-    if path.suffix == ".safetensors":
-        state_dict: Dict[str, torch.Tensor] = {}
-        with safe_open(path, framework="pt", device="cpu") as handle:
-            for key in handle.keys():
-                state_dict[key] = handle.get_tensor(key)
-        return state_dict
-    return comfy.utils.load_torch_file(str(path), safe_load=True)
-
-
-def _fuse_glu_lora(glu_weights: Dict[str, torch.Tensor]) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
-    if "w1_A" not in glu_weights or "w3_A" not in glu_weights:
-        return None, None, None
-    a_w1, b_w1 = glu_weights["w1_A"], glu_weights["w1_B"]
-    a_w3, b_w3 = glu_weights["w3_A"], glu_weights["w3_B"]
-    if a_w1.shape[1] != a_w3.shape[1]:
-        return None, None, None
-    a_fused = torch.cat([a_w1, a_w3], dim=0)
-    out1, out3 = b_w1.shape[0], b_w3.shape[0]
-    rank1, rank3 = b_w1.shape[1], b_w3.shape[1]
-    b_fused = torch.zeros(out1 + out3, rank1 + rank3, dtype=b_w1.dtype, device=b_w1.device)
-    b_fused[:out1, :rank1] = b_w1
-    b_fused[out1:, rank1:] = b_w3
-    return a_fused, b_fused, glu_weights.get("w1_alpha")
-
-
-def _fuse_qkv_lora(qkv_weights: Dict[str, torch.Tensor], model: Optional[nn.Module] = None, base_key: Optional[str] = None) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
-    required_keys = ["Q_A", "Q_B", "K_A", "K_B", "V_A", "V_B"]
-    if not all(key in qkv_weights for key in required_keys):
-        return None, None, None
-    a_q, a_k, a_v = qkv_weights["Q_A"], qkv_weights["K_A"], qkv_weights["V_A"]
-    b_q, b_k, b_v = qkv_weights["Q_B"], qkv_weights["K_B"], qkv_weights["V_B"]
-    if not (a_q.shape == a_k.shape == a_v.shape):
-        return None, None, None
-    if not (b_q.shape[1] == b_k.shape[1] == b_v.shape[1]):
-        return None, None, None
-
-    out_features = None
-    if model is not None and base_key is not None:
-        _, module = _resolve_module_name(model, base_key)
-        out_features = getattr(module, "out_features", None) if module is not None else None
-
-    alpha_fused = None
-    alpha_q = qkv_weights.get("Q_alpha")
-    alpha_k = qkv_weights.get("K_alpha")
-    alpha_v = qkv_weights.get("V_alpha")
-    if alpha_q is not None and alpha_k is not None and alpha_v is not None and alpha_q.item() == alpha_k.item() == alpha_v.item():
-        alpha_fused = alpha_q
-
-    a_fused = torch.cat([a_q, a_k, a_v], dim=0)
-    rank = b_q.shape[1]
-    out_q, out_k, out_v = b_q.shape[0], b_k.shape[0], b_v.shape[0]
-    total_out = out_features if out_features is not None else out_q + out_k + out_v
-    b_fused = torch.zeros(total_out, 3 * rank, dtype=b_q.dtype, device=b_q.device)
-    b_fused[:out_q, :rank] = b_q
-    b_fused[out_q:out_q + out_k, rank:2 * rank] = b_k
-    b_fused[out_q + out_k:out_q + out_k + out_v, 2 * rank:] = b_v
-    return a_fused, b_fused, alpha_fused
-
-
-def _handle_proj_out_split(lora_dict: Dict[str, Dict[str, torch.Tensor]], base_key: str, model: nn.Module) -> Tuple[Dict[str, Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]], List[str]]:
-    result: Dict[str, Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]] = {}
-    consumed: List[str] = []
-    match = re.search(r"single_transformer_blocks\.(\d+)", base_key)
-    if not match or base_key not in lora_dict:
-        return result, consumed
-    block_idx = match.group(1)
-    block = _get_module_by_name(model, f"single_transformer_blocks.{block_idx}")
-    if block is None:
-        return result, consumed
-    a_full = lora_dict[base_key].get("A")
-    b_full = lora_dict[base_key].get("B")
-    alpha = lora_dict[base_key].get("alpha")
-    attn_to_out = getattr(getattr(block, "attn", None), "to_out", None)
-    mlp_fc2 = getattr(block, "mlp_fc2", None)
-    if a_full is None or b_full is None or attn_to_out is None or mlp_fc2 is None:
-        return result, consumed
-    attn_in = getattr(attn_to_out, "in_features", None)
-    mlp_in = getattr(mlp_fc2, "in_features", None)
-    if attn_in is None or mlp_in is None or a_full.shape[1] != attn_in + mlp_in:
-        return result, consumed
-    result[f"single_transformer_blocks.{block_idx}.attn.to_out"] = (a_full[:, :attn_in], b_full.clone(), alpha)
-    result[f"single_transformer_blocks.{block_idx}.mlp_fc2"] = (a_full[:, attn_in:], b_full.clone(), alpha)
-    consumed.append(base_key)
-    return result, consumed
-
-
-def _apply_lora_to_module(module: nn.Module, a_tensor: torch.Tensor, b_tensor: torch.Tensor, module_name: str, model: nn.Module) -> None:
-    if not hasattr(module, "in_features") or not hasattr(module, "out_features"):
-        raise ValueError(f"{module_name}: unsupported module without in/out features")
-    if a_tensor.shape[1] != module.in_features or b_tensor.shape[0] != module.out_features:
-        raise ValueError(f"{module_name}: LoRA shape mismatch")
-
-    if module.__class__.__name__ == "AWQW4A16Linear" and hasattr(module, "qweight"):
-        if not hasattr(module, "_lora_original_forward"):
-            module._lora_original_forward = module.forward
-        if not hasattr(module, "_nunchaku_lora_bundle"):
-            module._nunchaku_lora_bundle = []
-        module._nunchaku_lora_bundle.append((a_tensor, b_tensor))
-
-        def _awq_lora_forward(x, *args, **kwargs):
-            out = module._lora_original_forward(x, *args, **kwargs)
-            x_flat = x.reshape(-1, module.in_features)
-            for local_a, local_b in module._nunchaku_lora_bundle:
-                local_a = local_a.to(device=out.device, dtype=out.dtype)
-                local_b = local_b.to(device=out.device, dtype=out.dtype)
-                lora_term = (x_flat @ local_a.transpose(0, 1)) @ local_b.transpose(0, 1)
-                try:
-                    out = out + lora_term.reshape(out.shape)
-                except Exception:
-                    pass
-            return out
-
-        module.forward = _awq_lora_forward
-        if not hasattr(model, "_lora_slots"):
-            model._lora_slots = {}
-        model._lora_slots[module_name] = {"type": "awq_w4a16"}
-        return
-
-    if hasattr(module, "proj_down") and hasattr(module, "proj_up"):
-        proj_down = unpack_lowrank_weight(module.proj_down.data, down=True)
-        proj_up = unpack_lowrank_weight(module.proj_up.data, down=False)
-        base_rank = proj_down.shape[0] if proj_down.shape[1] == module.in_features else proj_down.shape[1]
-        if proj_down.shape[1] == module.in_features:
-            updated_down = torch.cat([proj_down, a_tensor], dim=0)
-            axis_down = 0
-        else:
-            updated_down = torch.cat([proj_down, a_tensor.T], dim=1)
-            axis_down = 1
-        updated_up = torch.cat([proj_up, b_tensor], dim=1)
-        module.proj_down.data = pack_lowrank_weight(updated_down, down=True)
-        module.proj_up.data = pack_lowrank_weight(updated_up, down=False)
-        module.rank = base_rank + a_tensor.shape[0]
-        if not hasattr(model, "_lora_slots"):
-            model._lora_slots = {}
-        model._lora_slots[module_name] = {
-            "type": "nunchaku",
-            "base_rank": base_rank,
-            "axis_down": axis_down,
-        }
-        return
-
-    if isinstance(module, nn.Linear):
-        if not hasattr(model, "_lora_slots"):
-            model._lora_slots = {}
-        if module_name not in model._lora_slots:
-            model._lora_slots[module_name] = {
-                "type": "linear",
-                "original_weight": module.weight.detach().cpu().clone(),
-            }
-        module.weight.data.add_((b_tensor @ a_tensor).to(dtype=module.weight.dtype, device=module.weight.device))
-        return
-
-    raise ValueError(f"{module_name}: unsupported module type {type(module)}")
-
-
-def reset_lora_v2(model: nn.Module) -> None:
-    slots = getattr(model, "_lora_slots", None)
-    if not slots:
-        return
-    for name, info in list(slots.items()):
-        module = _get_module_by_name(model, name)
-        if module is None:
-            continue
-        module_type = info.get("type", "nunchaku")
-        if module_type == "nunchaku":
-            base_rank = info["base_rank"]
-            proj_down = unpack_lowrank_weight(module.proj_down.data, down=True)
-            proj_up = unpack_lowrank_weight(module.proj_up.data, down=False)
-            if info.get("axis_down", 0) == 0:
-                proj_down = proj_down[:base_rank, :].clone()
-            else:
-                proj_down = proj_down[:, :base_rank].clone()
-            proj_up = proj_up[:, :base_rank].clone()
-            module.proj_down.data = pack_lowrank_weight(proj_down, down=True)
-            module.proj_up.data = pack_lowrank_weight(proj_up, down=False)
-            module.rank = base_rank
-        elif module_type == "linear" and "original_weight" in info:
-            module.weight.data.copy_(info["original_weight"].to(device=module.weight.device, dtype=module.weight.dtype))
-        elif module_type == "awq_w4a16":
-            if hasattr(module, "_lora_original_forward"):
-                module.forward = module._lora_original_forward
-            for attr in ("_lora_original_forward", "_nunchaku_lora_bundle"):
-                if hasattr(module, attr):
-                    delattr(module, attr)
-    model._lora_slots = {}
-
-
-def compose_loras_v2(model: nn.Module, lora_configs: List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]], apply_awq_mod: bool = True) -> bool:
-    del apply_awq_mod  # retained for interface compatibility
-    reset_lora_v2(model)
-    aggregated_weights: Dict[str, List[Dict[str, object]]] = defaultdict(list)
-    saw_supported_format = False
-    unresolved_targets = 0
-
-    for index, (path_or_dict, strength) in enumerate(lora_configs):
-        if abs(strength) < 1e-5:
-            continue
-        lora_name = str(path_or_dict) if not isinstance(path_or_dict, dict) else f"lora_{index}"
-        lora_state_dict = _load_lora_state_dict(path_or_dict)
-        if not lora_state_dict or not _detect_lora_format(lora_state_dict):
-            logger.warning("Skipping unsupported Qwen LoRA: %s", lora_name)
-            continue
-        saw_supported_format = True
-
-        grouped_weights: Dict[str, Dict[str, torch.Tensor]] = defaultdict(dict)
-        for key, value in lora_state_dict.items():
-            parsed = _classify_and_map_key(key)
-            if parsed is None:
-                continue
-            group, base_key, component, ab = parsed
-            if component and ab:
-                grouped_weights[base_key][f"{component}_{ab}"] = value
-            else:
-                grouped_weights[base_key][ab] = value
-
-        processed_groups: Dict[str, Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]] = {}
-        handled: set[str] = set()
-        for base_key, weights in grouped_weights.items():
-            if base_key in handled:
-                continue
-            a_tensor = b_tensor = alpha = None
-            if "qkv" in base_key or "add_qkv_proj" in base_key:
-                a_tensor, b_tensor, alpha = _fuse_qkv_lora(weights, model=model, base_key=base_key)
-            elif "w1_A" in weights or "w3_A" in weights:
-                a_tensor, b_tensor, alpha = _fuse_glu_lora(weights)
-            elif ".proj_out" in base_key and "single_transformer_blocks" in base_key:
-                split_map, consumed = _handle_proj_out_split(grouped_weights, base_key, model)
-                processed_groups.update(split_map)
-                handled.update(consumed)
-                continue
-            else:
-                a_tensor, b_tensor, alpha = weights.get("A"), weights.get("B"), weights.get("alpha")
-            if a_tensor is not None and b_tensor is not None:
-                processed_groups[base_key] = (a_tensor, b_tensor, alpha)
-
-        for module_name, (a_tensor, b_tensor, alpha) in processed_groups.items():
-            aggregated_weights[module_name].append({
-                "A": a_tensor,
-                "B": b_tensor,
-                "alpha": alpha,
-                "strength": strength,
-            })
-
-    for module_name, weight_list in aggregated_weights.items():
-        resolved_name, module = _resolve_module_name(model, module_name)
-        if module is None:
-            logger.warning("Skipping unresolved Qwen LoRA target: %s", module_name)
-            unresolved_targets += 1
-            continue
-        all_a = []
-        all_b_scaled = []
-        for item in weight_list:
-            a_tensor = item["A"]
-            b_tensor = item["B"]
-            alpha = item["alpha"]
-            strength = float(item["strength"])
-            rank = a_tensor.shape[0]
-            scale = strength * ((alpha / rank) if alpha is not None else 1.0)
-            if module.__class__.__name__ == "AWQW4A16Linear" and hasattr(module, "qweight"):
-                target_dtype = torch.float16
-                target_device = module.qweight.device
-            elif hasattr(module, "proj_down"):
-                target_dtype = module.proj_down.dtype
-                target_device = module.proj_down.device
-            elif hasattr(module, "weight"):
-                target_dtype = module.weight.dtype
-                target_device = module.weight.device
-            else:
-                target_dtype = torch.float16
-                target_device = "cuda" if torch.cuda.is_available() else "cpu"
-            all_a.append(a_tensor.to(dtype=target_dtype, device=target_device))
-            all_b_scaled.append((b_tensor * scale).to(dtype=target_dtype, device=target_device))
-        if not all_a:
-            continue
-        _apply_lora_to_module(module, torch.cat(all_a, dim=0), torch.cat(all_b_scaled, dim=1), resolved_name, model)
-
-    slot_count = len(getattr(model, "_lora_slots", {}) or {})
-    logger.info(
-        "Qwen LoRA composition finished: requested=%d supported=%s applied_targets=%d unresolved=%d",
-        len(lora_configs),
-        saw_supported_format,
-        slot_count,
-        unresolved_targets,
-    )
-    return saw_supported_format
-
-
-class ComfyQwenImageWrapperLM(nn.Module):
-    def __init__(self, model: nn.Module, config=None, apply_awq_mod: bool = True):
-        super().__init__()
-        self.model = model
-        self.config = {} if config is None else config
-        self.dtype = next(model.parameters()).dtype
-        self.loras: List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]] = []
-        self._applied_loras: Optional[List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]]] = None
-        self.apply_awq_mod = apply_awq_mod
-
-    def __getattr__(self, name):
-        try:
-            inner = object.__getattribute__(self, "_modules").get("model")
-        except (AttributeError, KeyError):
-            inner = None
-        if inner is None:
-            raise AttributeError(f"{type(self).__name__!s} has no attribute {name}")
-        if name == "model":
-            return inner
-        return getattr(inner, name)
-
-    def process_img(self, *args, **kwargs):
-        return self.model.process_img(*args, **kwargs)
-
-    def _ensure_composed(self):
-        if self._applied_loras != self.loras or (not self.loras and getattr(self.model, "_lora_slots", None)):
-            is_supported_format = compose_loras_v2(self.model, self.loras, apply_awq_mod=self.apply_awq_mod)
-            self._applied_loras = self.loras.copy()
-            has_slots = bool(getattr(self.model, "_lora_slots", None))
-            if self.loras and is_supported_format and not has_slots:
-                logger.warning("Qwen LoRA compose produced 0 target modules. Resetting and retrying once.")
-                reset_lora_v2(self.model)
-                compose_loras_v2(self.model, self.loras, apply_awq_mod=self.apply_awq_mod)
-                has_slots = bool(getattr(self.model, "_lora_slots", None))
-                logger.info("Qwen LoRA retry result: applied_targets=%d", len(getattr(self.model, "_lora_slots", {}) or {}))
-
-            offload_manager = getattr(self.model, "offload_manager", None)
-            if offload_manager is not None:
-                offload_settings = {
-                    "num_blocks_on_gpu": getattr(offload_manager, "num_blocks_on_gpu", 1),
-                    "use_pin_memory": getattr(offload_manager, "use_pin_memory", False),
-                }
-                logger.info(
-                    "Rebuilding Qwen offload manager after LoRA compose: num_blocks_on_gpu=%s use_pin_memory=%s",
-                    offload_settings["num_blocks_on_gpu"],
-                    offload_settings["use_pin_memory"],
-                )
-                self.model.set_offload(False)
-                self.model.set_offload(True, **offload_settings)
-
-    def forward(self, *args, **kwargs):
-        self._ensure_composed()
-        return self.model(*args, **kwargs)
-
-
-def _get_qwen_wrapper_and_transformer(model):
-    model_wrapper = model.model.diffusion_model
-    if hasattr(model_wrapper, "model") and hasattr(model_wrapper, "loras"):
-        transformer = model_wrapper.model
-        if transformer.__class__.__name__.endswith("NunchakuQwenImageTransformer2DModel"):
-            return model_wrapper, transformer
-    if model_wrapper.__class__.__name__.endswith("NunchakuQwenImageTransformer2DModel"):
-        wrapped_model = ComfyQwenImageWrapperLM(model_wrapper, getattr(model_wrapper, "config", {}))
-        model.model.diffusion_model = wrapped_model
-        return wrapped_model, wrapped_model.model
-    raise TypeError(f"This LoRA loader only works with Nunchaku Qwen Image models, but got {type(model_wrapper).__name__}.")
-
-
-def nunchaku_load_qwen_loras(model, lora_configs: List[Tuple[str, float]], apply_awq_mod: bool = True):
-    model_wrapper, transformer = _get_qwen_wrapper_and_transformer(model)
-    model_wrapper.apply_awq_mod = apply_awq_mod
-
-    saved_config = None
-    if hasattr(model, "model") and hasattr(model.model, "model_config"):
-        saved_config = model.model.model_config
-        model.model.model_config = None
-
-    model_wrapper.model = None
-    try:
-        ret_model = copy.deepcopy(model)
-    finally:
-        if saved_config is not None:
-            model.model.model_config = saved_config
-        model_wrapper.model = transformer
-
-    ret_model_wrapper = ret_model.model.diffusion_model
-    if saved_config is not None:
-        ret_model.model.model_config = saved_config
-    ret_model_wrapper.model = transformer
-    ret_model_wrapper.apply_awq_mod = apply_awq_mod
-    ret_model_wrapper.loras = list(getattr(model_wrapper, "loras", []))
-
-    for lora_name, lora_strength in lora_configs:
-        lora_path = lora_name if os.path.isfile(lora_name) else folder_paths.get_full_path("loras", lora_name)
-        if not lora_path or not os.path.isfile(lora_path):
-            logger.warning("Skipping Qwen LoRA '%s' because it could not be found", lora_name)
-            continue
-        ret_model_wrapper.loras.append((lora_path, lora_strength))
-
-    return ret_model

py/nodes/prompt.py

@@ -1,39 +1,4 @@
-from __future__ import annotations
-
-from typing import Any
-import inspect
-
-from ..services.wildcard_service import (
-    contains_dynamic_syntax,
-    get_wildcard_service,
-    is_trigger_words_input,
-)
-
-
-class _PromptOptionalInputs:
-    """Lookup that preserves explicit optional inputs and dynamic trigger slots."""
-
-    def __init__(self, explicit_inputs: dict[str, tuple[str, dict[str, Any]]]) -> None:
-        self._explicit_inputs = explicit_inputs
-
-    def __contains__(self, item: object) -> bool:
-        if not isinstance(item, str):
-            return False
-        return item in self._explicit_inputs or is_trigger_words_input(item)
-
-    def __getitem__(self, key: str) -> tuple[str, dict[str, Any]]:
-        if key in self._explicit_inputs:
-            return self._explicit_inputs[key]
-        if is_trigger_words_input(key):
-            return (
-                "STRING",
-                {
-                    "forceInput": True,
-                    "tooltip": "Trigger words to prepend. Connect to add more inputs.",
-                },
-            )
-        raise KeyError(key)
-
+from typing import Any, Optional
 
 class PromptLM:
     """Encodes text (and optional trigger words) into CLIP conditioning."""
@@ -42,91 +7,52 @@ class PromptLM:
     CATEGORY = "Lora Manager/conditioning"
     DESCRIPTION = (
         "Encodes a text prompt using a CLIP model into an embedding that can be used "
-        "to guide the diffusion model towards generating specific images. "
-        "Supports dynamic trigger words inputs and runtime wildcard expansion."
+        "to guide the diffusion model towards generating specific images."
     )
 
     @classmethod
     def INPUT_TYPES(cls):
-        optional_inputs: dict[str, tuple[str, dict[str, Any]]] = {
-            "seed": (
-                "INT",
-                {
-                    "forceInput": True,
-                    "tooltip": "Optional seed for wildcard generation. Leave unconnected for non-deterministic wildcard expansion.",
-                },
-            ),
-            "trigger_words1": (
-                "STRING",
-                {
-                    "forceInput": True,
-                    "tooltip": "Trigger words to prepend. Connect to add more inputs.",
-                },
-            ),
-        }
-
-        stack = inspect.stack()
-        if len(stack) > 2 and stack[2].function == "get_input_info":
-            optional_inputs = _PromptOptionalInputs(optional_inputs)  # type: ignore[assignment]
-
         return {
             "required": {
                 "text": (
                     "AUTOCOMPLETE_TEXT_PROMPT,STRING",
                     {
                         "widgetType": "AUTOCOMPLETE_TEXT_PROMPT",
-                        "placeholder": "Enter prompt... /character, /artist, /wildcard for quick search",
-                        "tooltip": "The text to be encoded. Wildcard references inserted with /wildcard are expanded at runtime.",
+                        "placeholder": "Enter prompt... /char, /artist for quick tag search",
+                        "tooltip": "The text to be encoded.",
                     },
                 ),
                 "clip": (
-                    "CLIP",
+                    'CLIP',
                     {"tooltip": "The CLIP model used for encoding the text."},
                 ),
             },
-            "optional": optional_inputs,
+            "optional": {
+                "trigger_words": (
+                    'STRING',
+                    {
+                        "forceInput": True,
+                        "tooltip": (
+                            "Optional trigger words to prepend to the text before "
+                            "encoding."
+                        )
+                    },
+                )
+            },
         }
 
-    RETURN_TYPES = ("CONDITIONING", "STRING")
-    RETURN_NAMES = ("CONDITIONING", "PROMPT")
+    RETURN_TYPES = ('CONDITIONING', 'STRING',)
+    RETURN_NAMES = ('CONDITIONING', 'PROMPT',)
     OUTPUT_TOOLTIPS = (
         "A conditioning containing the embedded text used to guide the diffusion model.",
     )
     FUNCTION = "encode"
 
-    @classmethod
-    def IS_CHANGED(
-        cls,
-        text: str,
-        clip: Any | None = None,
-        seed: int | None = None,
-        **kwargs: Any,
-    ):
-        del clip, kwargs
-        if contains_dynamic_syntax(text) and seed is None:
-            return float("NaN")
-        return False
-
-    def encode(
-        self,
-        text: str,
-        clip: Any,
-        seed: int | None = None,
-        **kwargs: Any,
-    ):
-        expanded_text = get_wildcard_service().expand_text(text, seed=seed)
-
-        trigger_words = []
-        for key, value in kwargs.items():
-            if is_trigger_words_input(key) and value:
-                trigger_words.append(value)
-
+    def encode(self, text: str, clip: Any, trigger_words: Optional[str] = None):
+        prompt = text
         if trigger_words:
-            prompt = ", ".join(trigger_words + [expanded_text])
-        else:
-            prompt = expanded_text
+            prompt = ", ".join([trigger_words, text])
 
         from nodes import CLIPTextEncode  # type: ignore
-
         conditioning = CLIPTextEncode().encode(clip, prompt)[0]
-        return (conditioning, prompt)
+        return (conditioning, prompt,)

py/nodes/save_image.py

@@ -1,23 +1,13 @@
 import json
 import os
 import re
-import time
-import uuid
-from typing import Any, Dict, Optional
 import numpy as np
-import folder_paths  # type: ignore
+import folder_paths # type: ignore
 from ..services.service_registry import ServiceRegistry
 from ..metadata_collector.metadata_processor import MetadataProcessor
 from ..metadata_collector import get_metadata
-from ..utils.constants import CARD_PREVIEW_WIDTH
-from ..utils.exif_utils import ExifUtils
-from ..utils.utils import calculate_recipe_fingerprint
 from PIL import Image, PngImagePlugin
 import piexif
-import logging
-
-logger = logging.getLogger(__name__)
-
 
 class SaveImageLM:
     NAME = "Save Image (LoraManager)"
@@ -30,74 +20,42 @@ class SaveImageLM:
         self.prefix_append = ""
         self.compress_level = 4
         self.counter = 0
-
+    
     # Add pattern format regex for filename substitution
     pattern_format = re.compile(r"(%[^%]+%)")
-
+    
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
                 "images": ("IMAGE",),
-                "filename_prefix": (
-                    "STRING",
-                    {
-                        "default": "ComfyUI",
-                        "tooltip": "Base filename for saved images. Supports format patterns like %seed%, %width%, %height%, %model%, etc.",
-                    },
-                ),
-                "file_format": (
-                    ["png", "jpeg", "webp"],
-                    {
-                        "tooltip": "Image format to save as. PNG preserves quality, JPEG is smaller, WebP balances size and quality."
-                    },
-                ),
+                "filename_prefix": ("STRING", {
+                    "default": "ComfyUI", 
+                    "tooltip": "Base filename for saved images. Supports format patterns like %seed%, %width%, %height%, %model%, etc."
+                }),
+                "file_format": (["png", "jpeg", "webp"], {
+                    "tooltip": "Image format to save as. PNG preserves quality, JPEG is smaller, WebP balances size and quality."
+                }),
             },
             "optional": {
-                "lossless_webp": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": "When enabled, saves WebP images with lossless compression. Results in larger files but no quality loss.",
-                    },
-                ),
-                "quality": (
-                    "INT",
-                    {
-                        "default": 100,
-                        "min": 1,
-                        "max": 100,
-                        "tooltip": "Compression quality for JPEG and lossy WebP formats (1-100). Higher values mean better quality but larger files.",
-                    },
-                ),
-                "embed_workflow": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": "Embeds the complete workflow data into the image metadata. Only works with PNG and WebP formats.",
-                    },
-                ),
-                "save_with_metadata": (
-                    "BOOLEAN",
-                    {
-                        "default": True,
-                        "tooltip": "When enabled, embeds generation parameters into the saved image metadata. Disable to skip writing generation metadata.",
-                    },
-                ),
-                "add_counter_to_filename": (
-                    "BOOLEAN",
-                    {
-                        "default": True,
-                        "tooltip": "Adds an incremental counter to filenames to prevent overwriting previous images.",
-                    },
-                ),
-                "save_as_recipe": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": "Also saves each generated image as a LoRA Manager recipe.",
-                    },
-                ),
+                "lossless_webp": ("BOOLEAN", {
+                    "default": False,
+                    "tooltip": "When enabled, saves WebP images with lossless compression. Results in larger files but no quality loss."
+                }),
+                "quality": ("INT", {
+                    "default": 100, 
+                    "min": 1, 
+                    "max": 100,
+                    "tooltip": "Compression quality for JPEG and lossy WebP formats (1-100). Higher values mean better quality but larger files."
+                }),
+                "embed_workflow": ("BOOLEAN", {
+                    "default": False,
+                    "tooltip": "Embeds the complete workflow data into the image metadata. Only works with PNG and WebP formats."
+                }),
+                "add_counter_to_filename": ("BOOLEAN", {
+                    "default": True,
+                    "tooltip": "Adds an incremental counter to filenames to prevent overwriting previous images."
+                }),
             },
             "hidden": {
                 "id": "UNIQUE_ID",
@@ -114,59 +72,57 @@ class SaveImageLM:
     def get_lora_hash(self, lora_name):
         """Get the lora hash from cache"""
         scanner = ServiceRegistry.get_service_sync("lora_scanner")
-
+        
         # Use the new direct filename lookup method
-        if scanner is not None:
-            hash_value = scanner.get_hash_by_filename(lora_name)
-            if hash_value:
-                return hash_value
-
+        hash_value = scanner.get_hash_by_filename(lora_name)
+        if hash_value:
+            return hash_value
+            
         return None
 
     def get_checkpoint_hash(self, checkpoint_path):
         """Get the checkpoint hash from cache"""
         scanner = ServiceRegistry.get_service_sync("checkpoint_scanner")
-
+        
         if not checkpoint_path:
             return None
-
+            
         # Extract basename without extension
         checkpoint_name = os.path.basename(checkpoint_path)
         checkpoint_name = os.path.splitext(checkpoint_name)[0]
-
+        
         # Try direct filename lookup first
-        if scanner is not None:
-            hash_value = scanner.get_hash_by_filename(checkpoint_name)
-            if hash_value:
-                return hash_value
-
+        hash_value = scanner.get_hash_by_filename(checkpoint_name)
+        if hash_value:
+            return hash_value
+                
         return None
 
     def format_metadata(self, metadata_dict):
         """Format metadata in the requested format similar to userComment example"""
         if not metadata_dict:
             return ""
-
+        
         # Helper function to only add parameter if value is not None
         def add_param_if_not_none(param_list, label, value):
             if value is not None:
                 param_list.append(f"{label}: {value}")
-
+        
         # Extract the prompt and negative prompt
-        prompt = metadata_dict.get("prompt", "")
-        negative_prompt = metadata_dict.get("negative_prompt", "")
-
+        prompt = metadata_dict.get('prompt', '')
+        negative_prompt = metadata_dict.get('negative_prompt', '')
+        
         # Extract loras from the prompt if present
-        loras_text = metadata_dict.get("loras", "")
+        loras_text = metadata_dict.get('loras', '')
         lora_hashes = {}
-
+        
         # If loras are found, add them on a new line after the prompt
         if loras_text:
             prompt_with_loras = f"{prompt}\n{loras_text}"
-
+            
             # Extract lora names from the format <lora:name:strength>
-            lora_matches = re.findall(r"<lora:([^:]+):([^>]+)>", loras_text)
-
+            lora_matches = re.findall(r'<lora:([^:]+):([^>]+)>', loras_text)
+            
             # Get hash for each lora
             for lora_name, strength in lora_matches:
                 hash_value = self.get_lora_hash(lora_name)
@@ -174,114 +130,112 @@ class SaveImageLM:
                     lora_hashes[lora_name] = hash_value
         else:
             prompt_with_loras = prompt
-
+        
         # Format the first part (prompt and loras)
         metadata_parts = [prompt_with_loras]
-
+        
         # Add negative prompt
         if negative_prompt:
             metadata_parts.append(f"Negative prompt: {negative_prompt}")
-
+        
         # Format the second part (generation parameters)
         params = []
-
+        
         # Add standard parameters in the correct order
-        if "steps" in metadata_dict:
-            add_param_if_not_none(params, "Steps", metadata_dict.get("steps"))
-
+        if 'steps' in metadata_dict:
+            add_param_if_not_none(params, "Steps", metadata_dict.get('steps'))
+        
         # Combine sampler and scheduler information
         sampler_name = None
         scheduler_name = None
-
-        if "sampler" in metadata_dict:
-            sampler = metadata_dict.get("sampler")
+        
+        if 'sampler' in metadata_dict:
+            sampler = metadata_dict.get('sampler')
             # Convert ComfyUI sampler names to user-friendly names
             sampler_mapping = {
-                "euler": "Euler",
-                "euler_ancestral": "Euler a",
-                "dpm_2": "DPM2",
-                "dpm_2_ancestral": "DPM2 a",
-                "heun": "Heun",
-                "dpm_fast": "DPM fast",
-                "dpm_adaptive": "DPM adaptive",
-                "lms": "LMS",
-                "dpmpp_2s_ancestral": "DPM++ 2S a",
-                "dpmpp_sde": "DPM++ SDE",
-                "dpmpp_sde_gpu": "DPM++ SDE",
-                "dpmpp_2m": "DPM++ 2M",
-                "dpmpp_2m_sde": "DPM++ 2M SDE",
-                "dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
-                "ddim": "DDIM",
+                'euler': 'Euler',
+                'euler_ancestral': 'Euler a',
+                'dpm_2': 'DPM2',
+                'dpm_2_ancestral': 'DPM2 a',
+                'heun': 'Heun',
+                'dpm_fast': 'DPM fast',
+                'dpm_adaptive': 'DPM adaptive',
+                'lms': 'LMS',
+                'dpmpp_2s_ancestral': 'DPM++ 2S a',
+                'dpmpp_sde': 'DPM++ SDE',
+                'dpmpp_sde_gpu': 'DPM++ SDE',
+                'dpmpp_2m': 'DPM++ 2M',
+                'dpmpp_2m_sde': 'DPM++ 2M SDE',
+                'dpmpp_2m_sde_gpu': 'DPM++ 2M SDE',
+                'ddim': 'DDIM'
             }
             sampler_name = sampler_mapping.get(sampler, sampler)
-
-        if "scheduler" in metadata_dict:
-            scheduler = metadata_dict.get("scheduler")
+        
+        if 'scheduler' in metadata_dict:
+            scheduler = metadata_dict.get('scheduler')
             scheduler_mapping = {
-                "normal": "Simple",
-                "karras": "Karras",
-                "exponential": "Exponential",
-                "sgm_uniform": "SGM Uniform",
-                "sgm_quadratic": "SGM Quadratic",
+                'normal': 'Simple',
+                'karras': 'Karras',
+                'exponential': 'Exponential',
+                'sgm_uniform': 'SGM Uniform',
+                'sgm_quadratic': 'SGM Quadratic'
             }
             scheduler_name = scheduler_mapping.get(scheduler, scheduler)
-
+        
         # Add combined sampler and scheduler information
         if sampler_name:
             if scheduler_name:
                 params.append(f"Sampler: {sampler_name} {scheduler_name}")
             else:
                 params.append(f"Sampler: {sampler_name}")
-
+        
         # CFG scale (Use guidance if available, otherwise fall back to cfg_scale or cfg)
-        if "guidance" in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get("guidance"))
-        elif "cfg_scale" in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get("cfg_scale"))
-        elif "cfg" in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get("cfg"))
-
+        if 'guidance' in metadata_dict:
+            add_param_if_not_none(params, "CFG scale", metadata_dict.get('guidance'))
+        elif 'cfg_scale' in metadata_dict:
+            add_param_if_not_none(params, "CFG scale", metadata_dict.get('cfg_scale'))
+        elif 'cfg' in metadata_dict:
+            add_param_if_not_none(params, "CFG scale", metadata_dict.get('cfg'))
+        
         # Seed
-        if "seed" in metadata_dict:
-            add_param_if_not_none(params, "Seed", metadata_dict.get("seed"))
-
+        if 'seed' in metadata_dict:
+            add_param_if_not_none(params, "Seed", metadata_dict.get('seed'))
+        
         # Size
-        if "size" in metadata_dict:
-            add_param_if_not_none(params, "Size", metadata_dict.get("size"))
-
+        if 'size' in metadata_dict:
+            add_param_if_not_none(params, "Size", metadata_dict.get('size'))
+        
         # Model info
-        if "checkpoint" in metadata_dict:
+        if 'checkpoint' in metadata_dict:
             # Ensure checkpoint is a string before processing
-            checkpoint = metadata_dict.get("checkpoint")
+            checkpoint = metadata_dict.get('checkpoint')
             if checkpoint is not None:
                 # Get model hash
                 model_hash = self.get_checkpoint_hash(checkpoint)
-
+                
                 # Extract basename without path
                 checkpoint_name = os.path.basename(checkpoint)
                 # Remove extension if present
                 checkpoint_name = os.path.splitext(checkpoint_name)[0]
-
+                
                 # Add model hash if available
                 if model_hash:
-                    params.append(
-                        f"Model hash: {model_hash[:10]}, Model: {checkpoint_name}"
-                    )
+                    params.append(f"Model hash: {model_hash[:10]}, Model: {checkpoint_name}")
                 else:
                     params.append(f"Model: {checkpoint_name}")
-
+        
         # Add LoRA hashes if available
         if lora_hashes:
             lora_hash_parts = []
             for lora_name, hash_value in lora_hashes.items():
                 lora_hash_parts.append(f"{lora_name}: {hash_value[:10]}")
-
+            
             if lora_hash_parts:
-                params.append(f'Lora hashes: "{", ".join(lora_hash_parts)}"')
-
+                params.append(f"Lora hashes: \"{', '.join(lora_hash_parts)}\"")
+        
         # Combine all parameters with commas
         metadata_parts.append(", ".join(params))
-
+        
         # Join all parts with a new line
         return "\n".join(metadata_parts)
 
@@ -291,36 +245,36 @@ class SaveImageLM:
         """Format filename with metadata values"""
         if not metadata_dict:
             return filename
-
+            
         result = re.findall(self.pattern_format, filename)
         for segment in result:
             parts = segment.replace("%", "").split(":")
             key = parts[0]
-
-            if key == "seed" and "seed" in metadata_dict:
-                filename = filename.replace(segment, str(metadata_dict.get("seed", "")))
-            elif key == "width" and "size" in metadata_dict:
-                size = metadata_dict.get("size", "x")
-                w = size.split("x")[0] if isinstance(size, str) else size[0]
+            
+            if key == "seed" and 'seed' in metadata_dict:
+                filename = filename.replace(segment, str(metadata_dict.get('seed', '')))
+            elif key == "width" and 'size' in metadata_dict:
+                size = metadata_dict.get('size', 'x')
+                w = size.split('x')[0] if isinstance(size, str) else size[0]
                 filename = filename.replace(segment, str(w))
-            elif key == "height" and "size" in metadata_dict:
-                size = metadata_dict.get("size", "x")
-                h = size.split("x")[1] if isinstance(size, str) else size[1]
+            elif key == "height" and 'size' in metadata_dict:
+                size = metadata_dict.get('size', 'x')
+                h = size.split('x')[1] if isinstance(size, str) else size[1]
                 filename = filename.replace(segment, str(h))
-            elif key == "pprompt" and "prompt" in metadata_dict:
-                prompt = metadata_dict.get("prompt", "").replace("\n", " ")
+            elif key == "pprompt" and 'prompt' in metadata_dict:
+                prompt = metadata_dict.get('prompt', '').replace("\n", " ")
                 if len(parts) >= 2:
                     length = int(parts[1])
                     prompt = prompt[:length]
                 filename = filename.replace(segment, prompt.strip())
-            elif key == "nprompt" and "negative_prompt" in metadata_dict:
-                prompt = metadata_dict.get("negative_prompt", "").replace("\n", " ")
+            elif key == "nprompt" and 'negative_prompt' in metadata_dict:
+                prompt = metadata_dict.get('negative_prompt', '').replace("\n", " ")
                 if len(parts) >= 2:
                     length = int(parts[1])
                     prompt = prompt[:length]
                 filename = filename.replace(segment, prompt.strip())
             elif key == "model":
-                model_value = metadata_dict.get("checkpoint")
+                model_value = metadata_dict.get('checkpoint')
                 if isinstance(model_value, (bytes, os.PathLike)):
                     model_value = str(model_value)
 
@@ -334,7 +288,6 @@ class SaveImageLM:
                 filename = filename.replace(segment, model)
             elif key == "date":
                 from datetime import datetime
-
                 now = datetime.now()
                 date_table = {
                     "yyyy": f"{now.year:04d}",
@@ -355,261 +308,46 @@ class SaveImageLM:
                     for k, v in date_table.items():
                         date_format = date_format.replace(k, v)
                     filename = filename.replace(segment, date_format)
-
+                    
         return filename
 
-    @staticmethod
-    def _get_cached_model_by_name(scanner, name):
-        cache = getattr(scanner, "_cache", None)
-        if cache is None or not name:
-            return None
-
-        candidates = [
-            name,
-            os.path.basename(name),
-            os.path.splitext(os.path.basename(name))[0],
-        ]
-        for model in getattr(cache, "raw_data", []):
-            file_name = model.get("file_name")
-            if file_name in candidates:
-                return model
-        return None
-
-    def _build_recipe_loras(self, recipe_scanner, lora_stack):
-        lora_matches = re.findall(r"<lora:([^:]+):([^>]+)>", lora_stack or "")
-        lora_scanner = getattr(recipe_scanner, "_lora_scanner", None)
-        loras_data = []
-        base_model_counts = {}
-
-        for name, strength in lora_matches:
-            lora_info = self._get_cached_model_by_name(lora_scanner, name)
-            civitai = (lora_info or {}).get("civitai") or {}
-            civitai_model = civitai.get("model") or {}
-            try:
-                parsed_strength = float(strength)
-            except (TypeError, ValueError):
-                parsed_strength = 1.0
-
-            loras_data.append(
-                {
-                    "file_name": name,
-                    "strength": parsed_strength,
-                    "hash": ((lora_info or {}).get("sha256") or "").lower(),
-                    "modelVersionId": civitai.get("id", 0),
-                    "modelName": civitai_model.get("name", name) if lora_info else "",
-                    "modelVersionName": civitai.get("name", "") if lora_info else "",
-                    "isDeleted": False,
-                    "exclude": False,
-                }
-            )
-
-            base_model = (lora_info or {}).get("base_model")
-            if base_model:
-                base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
-
-        return lora_matches, loras_data, base_model_counts
-
-    def _build_recipe_checkpoint(self, recipe_scanner, checkpoint_raw):
-        if not isinstance(checkpoint_raw, str) or not checkpoint_raw.strip():
-            return None
-
-        checkpoint_name = checkpoint_raw.strip()
-        file_name = os.path.splitext(os.path.basename(checkpoint_name))[0]
-        checkpoint_scanner = getattr(recipe_scanner, "_checkpoint_scanner", None)
-        checkpoint_info = self._get_cached_model_by_name(
-            checkpoint_scanner, checkpoint_name
-        )
-
-        if not checkpoint_info:
-            return {
-                "type": "checkpoint",
-                "name": checkpoint_name,
-                "file_name": file_name,
-                "hash": self.get_checkpoint_hash(checkpoint_name) or "",
-            }
-
-        civitai = checkpoint_info.get("civitai") or {}
-        civitai_model = civitai.get("model") or {}
-        file_path = checkpoint_info.get("file_path") or checkpoint_info.get("path") or ""
-        cached_file_name = (
-            checkpoint_info.get("file_name")
-            or (os.path.splitext(os.path.basename(file_path))[0] if file_path else "")
-            or file_name
-        )
-
-        return {
-            "type": "checkpoint",
-            "modelId": civitai_model.get("id", 0),
-            "modelVersionId": civitai.get("id", 0),
-            "name": civitai_model.get("name")
-            or checkpoint_info.get("model_name")
-            or checkpoint_name,
-            "version": civitai.get("name", ""),
-            "hash": (
-                checkpoint_info.get("sha256") or checkpoint_info.get("hash") or ""
-            ).lower(),
-            "file_name": cached_file_name,
-            "modelName": civitai_model.get("name", ""),
-            "modelVersionName": civitai.get("name", ""),
-            "baseModel": checkpoint_info.get("base_model")
-            or civitai.get("baseModel", ""),
-        }
-
-    @staticmethod
-    def _derive_recipe_name(lora_matches):
-        recipe_name_parts = [
-            f"{name.strip()}-{float(strength):.2f}" for name, strength in lora_matches[:3]
-        ]
-        return "_".join(recipe_name_parts) or "recipe"
-
-    @staticmethod
-    def _sync_recipe_cache(recipe_scanner, recipe_data, json_path):
-        cache = getattr(recipe_scanner, "_cache", None)
-        if cache is not None:
-            cache.raw_data.append(recipe_data)
-            cache.sorted_by_name = sorted(
-                cache.raw_data, key=lambda item: item.get("title", "").lower()
-            )
-            cache.sorted_by_date = sorted(
-                cache.raw_data,
-                key=lambda item: (
-                    item.get("modified", item.get("created_date", 0)),
-                    item.get("file_path", ""),
-                ),
-                reverse=True,
-            )
-            recipe_scanner._update_folder_metadata(cache)
-            recipe_scanner._update_fts_index_for_recipe(recipe_data, "add")
-
-        recipe_id = str(recipe_data.get("id", ""))
-        if recipe_id:
-            recipe_scanner._json_path_map[recipe_id] = json_path
-        persistent_cache = getattr(recipe_scanner, "_persistent_cache", None)
-        if persistent_cache:
-            persistent_cache.update_recipe(recipe_data, json_path)
-
-    def _save_image_as_recipe(self, file_path, metadata_dict):
-        if not metadata_dict:
-            raise ValueError("No generation metadata found")
-
-        recipe_scanner = ServiceRegistry.get_service_sync("recipe_scanner")
-        if recipe_scanner is None:
-            raise RuntimeError("Recipe scanner unavailable")
-
-        recipes_dir = recipe_scanner.recipes_dir
-        if not recipes_dir:
-            raise RuntimeError("Recipes directory unavailable")
-        os.makedirs(recipes_dir, exist_ok=True)
-
-        recipe_id = str(uuid.uuid4())
-        optimized_image, extension = ExifUtils.optimize_image(
-            image_data=file_path,
-            target_width=CARD_PREVIEW_WIDTH,
-            format="webp",
-            quality=85,
-            preserve_metadata=True,
-        )
-        image_path = os.path.normpath(os.path.join(recipes_dir, f"{recipe_id}{extension}"))
-        with open(image_path, "wb") as file_obj:
-            file_obj.write(optimized_image)
-
-        lora_stack = metadata_dict.get("loras", "")
-        lora_matches, loras_data, base_model_counts = self._build_recipe_loras(
-            recipe_scanner, lora_stack
-        )
-        checkpoint_entry = self._build_recipe_checkpoint(
-            recipe_scanner, metadata_dict.get("checkpoint")
-        )
-        most_common_base_model = (
-            max(base_model_counts.items(), key=lambda item: item[1])[0]
-            if base_model_counts
-            else ""
-        )
-        current_time = time.time()
-        recipe_data = {
-            "id": recipe_id,
-            "file_path": image_path,
-            "title": self._derive_recipe_name(lora_matches),
-            "modified": current_time,
-            "created_date": current_time,
-            "base_model": most_common_base_model
-            or (checkpoint_entry or {}).get("baseModel", ""),
-            "loras": loras_data,
-            "gen_params": {
-                key: value
-                for key, value in metadata_dict.items()
-                if key not in ["checkpoint", "loras"]
-            },
-            "loras_stack": lora_stack,
-            "fingerprint": calculate_recipe_fingerprint(loras_data),
-        }
-        if checkpoint_entry:
-            recipe_data["checkpoint"] = checkpoint_entry
-
-        json_path = os.path.normpath(
-            os.path.join(recipes_dir, f"{recipe_id}.recipe.json")
-        )
-        with open(json_path, "w", encoding="utf-8") as file_obj:
-            json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
-
-        ExifUtils.append_recipe_metadata(image_path, recipe_data)
-        self._sync_recipe_cache(recipe_scanner, recipe_data, json_path)
-
-    def save_images(
-        self,
-        images,
-        filename_prefix,
-        file_format,
-        id,
-        prompt=None,
-        extra_pnginfo=None,
-        lossless_webp=True,
-        quality=100,
-        embed_workflow=False,
-        save_with_metadata=True,
-        add_counter_to_filename=True,
-        save_as_recipe=False,
-    ):
+    def save_images(self, images, filename_prefix, file_format, id, prompt=None, extra_pnginfo=None, 
+                   lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
         """Save images with metadata"""
         results = []
 
         # Get metadata using the metadata collector
         raw_metadata = get_metadata()
         metadata_dict = MetadataProcessor.to_dict(raw_metadata, id)
-
+            
         metadata = self.format_metadata(metadata_dict)
-
+        
         # Process filename_prefix with pattern substitution
         filename_prefix = self.format_filename(filename_prefix, metadata_dict)
-
+        
         # Get initial save path info once for the batch
-        full_output_folder, filename, counter, subfolder, processed_prefix = (
-            folder_paths.get_save_image_path(
-                filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]
-            )
+        full_output_folder, filename, counter, subfolder, processed_prefix = folder_paths.get_save_image_path(
+            filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]
         )
-
+        
         # Create directory if it doesn't exist
         if not os.path.exists(full_output_folder):
             os.makedirs(full_output_folder, exist_ok=True)
-
+        
         # Process each image with incrementing counter
         for i, image in enumerate(images):
             # Convert the tensor image to numpy array
-            img = 255.0 * image.cpu().numpy()
+            img = 255. * image.cpu().numpy()
             img = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
-
+            
             # Generate filename with counter if needed
             base_filename = filename
             if add_counter_to_filename:
                 # Use counter + i to ensure unique filenames for all images in batch
                 current_counter = counter + i
                 base_filename += f"_{current_counter:05}_"
-
+                
             # Set file extension and prepare saving parameters
-            file: str
-            save_kwargs: Dict[str, Any]
-            pnginfo: Optional[PngImagePlugin.PngInfo] = None
             if file_format == "png":
                 file = base_filename + ".png"
                 file_extension = ".png"
@@ -621,25 +359,18 @@ class SaveImageLM:
                 file_extension = ".jpg"
                 save_kwargs = {"quality": quality, "optimize": True}
             elif file_format == "webp":
-                file = base_filename + ".webp"
+                file = base_filename + ".webp" 
                 file_extension = ".webp"
                 # Add optimization param to control performance
-                save_kwargs = {
-                    "quality": quality,
-                    "lossless": lossless_webp,
-                    "method": 0,
-                }
-            else:
-                raise ValueError(f"Unsupported file format: {file_format}")
-
+                save_kwargs = {"quality": quality, "lossless": lossless_webp, "method": 0}
+            
             # Full save path
             file_path = os.path.join(full_output_folder, file)
-
+            
             # Save the image with metadata
             try:
                 if file_format == "png":
-                    assert pnginfo is not None
-                    if save_with_metadata and metadata:
+                    if metadata:
                         pnginfo.add_text("parameters", metadata)
                     if embed_workflow and extra_pnginfo is not None:
                         workflow_json = json.dumps(extra_pnginfo["workflow"])
@@ -648,81 +379,51 @@ class SaveImageLM:
                     img.save(file_path, format="PNG", **save_kwargs)
                 elif file_format == "jpeg":
                     # For JPEG, use piexif
-                    if save_with_metadata and metadata:
+                    if metadata:
                         try:
-                            exif_dict = {
-                                "Exif": {
-                                    piexif.ExifIFD.UserComment: b"UNICODE\0"
-                                    + metadata.encode("utf-16be")
-                                }
-                            }
+                            exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
                             exif_bytes = piexif.dump(exif_dict)
                             save_kwargs["exif"] = exif_bytes
                         except Exception as e:
-                            logger.error(f"Error adding EXIF data: {e}")
+                            print(f"Error adding EXIF data: {e}")
                     img.save(file_path, format="JPEG", **save_kwargs)
                 elif file_format == "webp":
                     try:
                         # For WebP, use piexif for metadata
                         exif_dict = {}
 
-                        if save_with_metadata and metadata:
-                            exif_dict["Exif"] = {
-                                piexif.ExifIFD.UserComment: b"UNICODE\0"
-                                + metadata.encode("utf-16be")
-                            }
-
+                        if metadata:
+                            exif_dict['Exif'] = {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}
+                        
                         # Add workflow if needed
                         if embed_workflow and extra_pnginfo is not None:
-                            workflow_json = json.dumps(extra_pnginfo["workflow"])
-                            exif_dict["0th"] = {
-                                piexif.ImageIFD.ImageDescription: "Workflow:"
-                                + workflow_json
-                            }
-
+                            workflow_json = json.dumps(extra_pnginfo["workflow"]) 
+                            exif_dict['0th'] = {piexif.ImageIFD.ImageDescription: "Workflow:" + workflow_json}
+                            
                         exif_bytes = piexif.dump(exif_dict)
                         save_kwargs["exif"] = exif_bytes
                     except Exception as e:
-                        logger.error(f"Error adding EXIF data: {e}")
-
+                        print(f"Error adding EXIF data: {e}")
+                    
                     img.save(file_path, format="WEBP", **save_kwargs)
-
-                if save_as_recipe:
-                    try:
-                        self._save_image_as_recipe(file_path, metadata_dict)
-                    except Exception as e:
-                        logger.warning(
-                            "Failed to save image as recipe: %s", e, exc_info=True
-                        )
-
-                results.append(
-                    {"filename": file, "subfolder": subfolder, "type": self.type}
-                )
-
+                
+                results.append({
+                    "filename": file,
+                    "subfolder": subfolder,
+                    "type": self.type
+                })
+                
             except Exception as e:
-                logger.error(f"Error saving image: {e}")
-
+                print(f"Error saving image: {e}")
+        
         return results
 
-    def process_image(
-        self,
-        images,
-        id,
-        filename_prefix="ComfyUI",
-        file_format="png",
-        prompt=None,
-        extra_pnginfo=None,
-        lossless_webp=True,
-        quality=100,
-        embed_workflow=False,
-        save_with_metadata=True,
-        add_counter_to_filename=True,
-        save_as_recipe=False,
-    ):
+    def process_image(self, images, id, filename_prefix="ComfyUI", file_format="png", prompt=None, extra_pnginfo=None,
+                     lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
         """Process and save image with metadata"""
         # Make sure the output directory exists
         os.makedirs(self.output_dir, exist_ok=True)
-
+        
         # If images is already a list or array of images, do nothing; otherwise, convert to list
         if isinstance(images, (list, np.ndarray)):
             pass
@@ -732,24 +433,19 @@ class SaveImageLM:
                 images = [images]
             else:  # Multiple images (batch, height, width, channels)
                 images = [img for img in images]
-
+        
         # Save all images
         results = self.save_images(
-            images,
-            filename_prefix,
-            file_format,
+            images, 
+            filename_prefix, 
+            file_format, 
             id,
-            prompt,
+            prompt, 
             extra_pnginfo,
             lossless_webp,
             quality,
             embed_workflow,
-            save_with_metadata,
-            add_counter_to_filename,
-            save_as_recipe,
+            add_counter_to_filename
         )
-
-        return {
-            "result": (images,),
-            "ui": {"images": results},
-        }
+        
+        return (images,)

py/nodes/text.py

@@ -1,15 +1,10 @@
-from __future__ import annotations
-
-from ..services.wildcard_service import contains_dynamic_syntax, get_wildcard_service
-
-
 class TextLM:
     """A simple text node with autocomplete support."""
 
     NAME = "Text (LoraManager)"
     CATEGORY = "Lora Manager/utils"
     DESCRIPTION = (
-        "A simple text input node with autocomplete support for tags, styles, and wildcard expansion."
+        "A simple text input node with autocomplete support for tags and styles."
     )
 
     @classmethod
@@ -20,17 +15,8 @@ class TextLM:
                     "AUTOCOMPLETE_TEXT_PROMPT,STRING",
                     {
                         "widgetType": "AUTOCOMPLETE_TEXT_PROMPT",
-                        "placeholder": "Enter text... /character, /artist, /wildcard for quick search",
-                        "tooltip": "The text output. Wildcard references inserted with /wildcard are expanded at runtime.",
-                    },
-                ),
-            },
-            "optional": {
-                "seed": (
-                    "INT",
-                    {
-                        "forceInput": True,
-                        "tooltip": "Optional seed for wildcard generation. Leave unconnected for non-deterministic wildcard expansion.",
+                        "placeholder": "Enter text... /char, /artist for quick tag search",
+                        "tooltip": "The text output.",
                     },
                 ),
             },
@@ -38,14 +24,10 @@ class TextLM:
 
     RETURN_TYPES = ("STRING",)
     RETURN_NAMES = ("STRING",)
-    OUTPUT_TOOLTIPS = ("The text output.",)
+    OUTPUT_TOOLTIPS = (
+        "The text output.",
+    )
     FUNCTION = "process"
 
-    @classmethod
-    def IS_CHANGED(cls, text: str, seed: int | None = None):
-        if contains_dynamic_syntax(text) and seed is None:
-            return float("NaN")
-        return False
-
-    def process(self, text: str, seed: int | None = None):
-        return (get_wildcard_service().expand_text(text, seed=seed),)
+    def process(self, text: str):
+        return (text,)

py/nodes/trigger_word_toggle.py

@@ -60,25 +60,6 @@ class TriggerWordToggleLM:
         else:
             return data
 
-    def _normalize_trigger_words(self, trigger_words):
-        """Normalize trigger words by splitting by both single and double commas, stripping whitespace, and filtering empty strings"""
-        if not trigger_words or not isinstance(trigger_words, str):
-            return set()
-        
-        # Split by double commas first to preserve groups, then by single commas
-        groups = re.split(r",{2,}", trigger_words)
-        words = []
-        for group in groups:
-            # Split each group by single comma
-            group_words = [word.strip() for word in group.split(",")]
-            words.extend(group_words)
-        
-        # Filter out empty strings and return as set
-        return set(word for word in words if word)
-
-    def _group_has_child_items(self, item):
-        return isinstance(item, dict) and isinstance(item.get("items"), list)
-
     def process_trigger_words(
         self,
         id,
@@ -100,7 +81,7 @@ class TriggerWordToggleLM:
         if (
             trigger_words_override
             and isinstance(trigger_words_override, str)
-            and self._normalize_trigger_words(trigger_words_override) != self._normalize_trigger_words(trigger_words)
+            and trigger_words_override != trigger_words
         ):
             filtered_triggers = trigger_words_override
             return (filtered_triggers,)
@@ -115,11 +96,7 @@ class TriggerWordToggleLM:
 
                 if isinstance(trigger_data, list):
                     if group_mode:
-                        if any(self._group_has_child_items(item) for item in trigger_data):
-                            filtered_groups = self._process_group_items(
-                                trigger_data, allow_strength_adjustment
-                            )
-                        elif allow_strength_adjustment:
+                        if allow_strength_adjustment:
                             parsed_items = [
                                 self._parse_trigger_item(
                                     item, allow_strength_adjustment
@@ -181,41 +158,6 @@ class TriggerWordToggleLM:
 
         return (filtered_triggers,)
 
-    def _process_group_items(self, trigger_data, allow_strength_adjustment):
-        filtered_groups = []
-
-        for item in trigger_data:
-            group = self._parse_trigger_item(item, allow_strength_adjustment)
-            if not group["text"] or not group["active"]:
-                continue
-
-            raw_items = item.get("items") if isinstance(item, dict) else None
-            if isinstance(raw_items, list):
-                active_items = []
-                for raw_item in raw_items:
-                    child = self._parse_trigger_item(
-                        raw_item, allow_strength_adjustment=False
-                    )
-                    if child["text"] and child["active"]:
-                        active_items.append(child["text"])
-
-                if not active_items:
-                    continue
-
-                group_text = ", ".join(active_items)
-            else:
-                group_text = group["text"]
-
-            filtered_groups.append(
-                self._format_word_output(
-                    group_text,
-                    group["strength"],
-                    allow_strength_adjustment,
-                )
-            )
-
-        return filtered_groups
-
     def _parse_trigger_item(self, item, allow_strength_adjustment):
         text = (item.get("text") or "").strip()
         active = bool(item.get("active", False))

py/nodes/unet_loader.py

@@ -1,205 +0,0 @@
-import logging
-import os
-from typing import List, Tuple
-import comfy.sd # type: ignore
-from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_comfyui
-
-logger = logging.getLogger(__name__)
-
-
-class UNETLoaderLM:
-    """UNET Loader with support for extra folder paths
-
-    Loads diffusion models/UNets from both standard ComfyUI folders and LoRA Manager's
-    extra folder paths, providing a unified interface for UNET loading.
-    Supports both regular diffusion models and GGUF format models.
-    """
-
-    NAME = "Unet Loader (LoraManager)"
-    CATEGORY = "Lora Manager/loaders"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        # Get list of unet names from scanner (includes extra folder paths)
-        unet_names = s._get_unet_names()
-        return {
-            "required": {
-                "unet_name": (
-                    unet_names,
-                    {"tooltip": "The name of the diffusion model to load."},
-                ),
-                "weight_dtype": (
-                    ["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],
-                    {"tooltip": "The dtype to use for the model weights."},
-                ),
-            }
-        }
-
-    RETURN_TYPES = ("MODEL",)
-    RETURN_NAMES = ("MODEL",)
-    OUTPUT_TOOLTIPS = ("The model used for denoising latents.",)
-    FUNCTION = "load_unet"
-
-    @classmethod
-    def _get_unet_names(cls) -> List[str]:
-        """Get list of diffusion model names from scanner cache in ComfyUI format (relative path with extension)"""
-        try:
-            from ..services.service_registry import ServiceRegistry
-            import asyncio
-
-            async def _get_names():
-                scanner = await ServiceRegistry.get_checkpoint_scanner()
-                cache = await scanner.get_cached_data()
-
-                # Get all model roots for calculating relative paths
-                model_roots = scanner.get_model_roots()
-
-                # Filter only diffusion_model type and format names
-                names = []
-                for item in cache.raw_data:
-                    if item.get("sub_type") == "diffusion_model":
-                        file_path = item.get("file_path", "")
-                        if file_path:
-                            # Format using relative path with OS-native separator
-                            formatted_name = _format_model_name_for_comfyui(
-                                file_path, model_roots
-                            )
-                            if formatted_name:
-                                names.append(formatted_name)
-
-                return sorted(names)
-
-            try:
-                loop = asyncio.get_running_loop()
-                import concurrent.futures
-
-                def run_in_thread():
-                    new_loop = asyncio.new_event_loop()
-                    asyncio.set_event_loop(new_loop)
-                    try:
-                        return new_loop.run_until_complete(_get_names())
-                    finally:
-                        new_loop.close()
-
-                with concurrent.futures.ThreadPoolExecutor() as executor:
-                    future = executor.submit(run_in_thread)
-                    return future.result()
-            except RuntimeError:
-                return asyncio.run(_get_names())
-        except Exception as e:
-            logger.error(f"Error getting unet names: {e}")
-            return []
-
-    def load_unet(self, unet_name: str, weight_dtype: str) -> Tuple:
-        """Load a diffusion model by name, supporting extra folder paths
-
-        Args:
-            unet_name: The name of the diffusion model to load (relative path with extension)
-            weight_dtype: The dtype to use for model weights
-
-        Returns:
-            Tuple of (MODEL,)
-        """
-        import torch
-
-        # Get absolute path from cache using ComfyUI-style name
-        unet_path, metadata = get_checkpoint_info_absolute(unet_name)
-
-        if metadata is None:
-            raise FileNotFoundError(
-                f"Diffusion model '{unet_name}' not found in LoRA Manager cache. "
-                "Make sure the model is indexed and try again."
-            )
-
-        # Check if it's a GGUF model
-        if unet_path.endswith(".gguf"):
-            return self._load_gguf_unet(unet_path, unet_name, weight_dtype)
-
-        # Load regular diffusion model using ComfyUI's API
-        logger.info(f"Loading diffusion model from: {unet_path}")
-
-        # Build model options based on weight_dtype
-        model_options = {}
-        if weight_dtype == "fp8_e4m3fn":
-            model_options["dtype"] = torch.float8_e4m3fn
-        elif weight_dtype == "fp8_e4m3fn_fast":
-            model_options["dtype"] = torch.float8_e4m3fn
-            model_options["fp8_optimizations"] = True
-        elif weight_dtype == "fp8_e5m2":
-            model_options["dtype"] = torch.float8_e5m2
-
-        model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
-        return (model,)
-
-    def _load_gguf_unet(
-        self, unet_path: str, unet_name: str, weight_dtype: str
-    ) -> Tuple:
-        """Load a GGUF format diffusion model
-
-        Args:
-            unet_path: Absolute path to the GGUF file
-            unet_name: Name of the model for error messages
-            weight_dtype: The dtype to use for model weights
-
-        Returns:
-            Tuple of (MODEL,)
-        """
-        import torch
-        from .gguf_import_helper import get_gguf_modules
-
-        # Get ComfyUI-GGUF modules using helper (handles various import scenarios)
-        try:
-            loader_module, ops_module, nodes_module = get_gguf_modules()
-            gguf_sd_loader = getattr(loader_module, "gguf_sd_loader")
-            GGMLOps = getattr(ops_module, "GGMLOps")
-            GGUFModelPatcher = getattr(nodes_module, "GGUFModelPatcher")
-        except RuntimeError as e:
-            raise RuntimeError(f"Cannot load GGUF model '{unet_name}'. {str(e)}")
-
-        logger.info(f"Loading GGUF diffusion model from: {unet_path}")
-
-        try:
-            # Load GGUF state dict
-            sd, extra = gguf_sd_loader(unet_path)
-
-            # Prepare kwargs for metadata if supported
-            kwargs = {}
-            import inspect
-
-            valid_params = inspect.signature(
-                comfy.sd.load_diffusion_model_state_dict
-            ).parameters
-            if "metadata" in valid_params:
-                kwargs["metadata"] = extra.get("metadata", {})
-
-            # Setup custom operations with GGUF support
-            ops = GGMLOps()
-
-            # Handle weight_dtype for GGUF models
-            if weight_dtype in ("default", None):
-                ops.Linear.dequant_dtype = None
-            elif weight_dtype in ["target"]:
-                ops.Linear.dequant_dtype = weight_dtype
-            else:
-                ops.Linear.dequant_dtype = getattr(torch, weight_dtype, None)
-
-            # Load the model
-            model = comfy.sd.load_diffusion_model_state_dict(
-                sd, model_options={"custom_operations": ops}, **kwargs
-            )
-
-            if model is None:
-                raise RuntimeError(
-                    f"Could not detect model type for GGUF diffusion model: {unet_path}"
-                )
-
-            # Wrap with GGUFModelPatcher
-            model = GGUFModelPatcher.clone(model)
-
-            return (model,)
-
-        except Exception as e:
-            logger.error(f"Error loading GGUF diffusion model '{unet_name}': {e}")
-            raise RuntimeError(
-                f"Failed to load GGUF diffusion model '{unet_name}': {str(e)}"
-            )

py/nodes/utils.py

@@ -1,35 +1,33 @@
 class AnyType(str):
-    """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
-
-    def __ne__(self, __value: object) -> bool:
-        return False
+  """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
 
+  def __ne__(self, __value: object) -> bool:
+    return False
 
 # Credit to Regis Gaughan, III (rgthree)
 class FlexibleOptionalInputType(dict):
-    """A special class to make flexible nodes that pass data to our python handlers.
+  """A special class to make flexible nodes that pass data to our python handlers.
 
-    Enables both flexible/dynamic input types (like for Any Switch) or a dynamic number of inputs
-    (like for Any Switch, Context Switch, Context Merge, Power Lora Loader, etc).
+  Enables both flexible/dynamic input types (like for Any Switch) or a dynamic number of inputs
+  (like for Any Switch, Context Switch, Context Merge, Power Lora Loader, etc).
 
-    Note, for ComfyUI, all that's needed is the `__contains__` override below, which tells ComfyUI
-    that our node will handle the input, regardless of what it is.
+  Note, for ComfyUI, all that's needed is the `__contains__` override below, which tells ComfyUI
+  that our node will handle the input, regardless of what it is.
 
-    However, with https://github.com/comfyanonymous/ComfyUI/pull/2666 a large change would occur
-    requiring more details on the input itself. There, we need to return a list/tuple where the first
-    item is the type. This can be a real type, or use the AnyType for additional flexibility.
+  However, with https://github.com/comfyanonymous/ComfyUI/pull/2666 a large change would occur
+  requiring more details on the input itself. There, we need to return a list/tuple where the first
+  item is the type. This can be a real type, or use the AnyType for additional flexibility.
 
-    This should be forwards compatible unless more changes occur in the PR.
-    """
+  This should be forwards compatible unless more changes occur in the PR.
+  """
+  def __init__(self, type):
+    self.type = type
 
-    def __init__(self, type):
-        self.type = type
+  def __getitem__(self, key):
+    return (self.type, )
 
-    def __getitem__(self, key):
-        return (self.type,)
-
-    def __contains__(self, key):
-        return True
+  def __contains__(self, key):
+    return True
 
 
 any_type = AnyType("*")
@@ -39,27 +37,25 @@ import os
 import logging
 import copy
 import sys
-import folder_paths  # type: ignore
+import folder_paths
 
 logger = logging.getLogger(__name__)
 
-
 def extract_lora_name(lora_path):
     """Extract the lora name from a lora path (e.g., 'IL\\aorunIllstrious.safetensors' -> 'aorunIllstrious')"""
     # Get the basename without extension
     basename = os.path.basename(lora_path)
     return os.path.splitext(basename)[0]
 
-
 def get_loras_list(kwargs):
     """Helper to extract loras list from either old or new kwargs format"""
-    if "loras" not in kwargs:
+    if 'loras' not in kwargs:
         return []
-
-    loras_data = kwargs["loras"]
+        
+    loras_data = kwargs['loras']
     # Handle new format: {'loras': {'__value__': [...]}}
-    if isinstance(loras_data, dict) and "__value__" in loras_data:
-        return loras_data["__value__"]
+    if isinstance(loras_data, dict) and '__value__' in loras_data:
+        return loras_data['__value__']
     # Handle old format: {'loras': [...]}
     elif isinstance(loras_data, list):
         return loras_data
@@ -68,26 +64,24 @@ def get_loras_list(kwargs):
         logger.warning(f"Unexpected loras format: {type(loras_data)}")
         return []
 
-
 def load_state_dict_in_safetensors(path, device="cpu", filter_prefix=""):
-    """Simplified version of load_state_dict_in_safetensors that just loads from a local path"""
+    """Simplified version of load_state_dict_in_safetensors that just loads from a local path"""  
     import safetensors.torch
-
+    
     state_dict = {}
-    with safetensors.torch.safe_open(path, framework="pt", device=device) as f:  # type: ignore[attr-defined]
+    with safetensors.torch.safe_open(path, framework="pt", device=device) as f:
         for k in f.keys():
             if filter_prefix and not k.startswith(filter_prefix):
                 continue
             state_dict[k.removeprefix(filter_prefix)] = f.get_tensor(k)
     return state_dict
 
-
 def to_diffusers(input_lora):
     """Simplified version of to_diffusers for Flux LoRA conversion"""
     import torch
     from diffusers.utils.state_dict_utils import convert_unet_state_dict_to_peft
-    from diffusers.loaders import FluxLoraLoaderMixin  # type: ignore[attr-defined]
-
+    from diffusers.loaders import FluxLoraLoaderMixin
+    
     if isinstance(input_lora, str):
         tensors = load_state_dict_in_safetensors(input_lora, device="cpu")
     else:
@@ -97,27 +91,22 @@ def to_diffusers(input_lora):
     for k, v in tensors.items():
         if v.dtype not in [torch.float64, torch.float32, torch.bfloat16, torch.float16]:
             tensors[k] = v.to(torch.bfloat16)
-
+    
     new_tensors = FluxLoraLoaderMixin.lora_state_dict(tensors)
     new_tensors = convert_unet_state_dict_to_peft(new_tensors)
 
     return new_tensors
 
-
 def nunchaku_load_lora(model, lora_name, lora_strength):
-    """Load a Flux LoRA for Nunchaku model"""
+    """Load a Flux LoRA for Nunchaku model"""   
     # Get full path to the LoRA file. Allow both direct paths and registered LoRA names.
-    lora_path = (
-        lora_name
-        if os.path.isfile(lora_name)
-        else folder_paths.get_full_path("loras", lora_name)
-    )
+    lora_path = lora_name if os.path.isfile(lora_name) else folder_paths.get_full_path("loras", lora_name)
     if not lora_path or not os.path.isfile(lora_path):
         logger.warning("Skipping LoRA '%s' because it could not be found", lora_name)
         return model
 
     model_wrapper = model.model.diffusion_model
-
+    
     # Try to find copy_with_ctx in the same module as ComfyFluxWrapper
     module_name = model_wrapper.__class__.__module__
     module = sys.modules.get(module_name)
@@ -129,16 +118,14 @@ def nunchaku_load_lora(model, lora_name, lora_strength):
         ret_model_wrapper.loras = [*model_wrapper.loras, (lora_path, lora_strength)]
     else:
         # Fallback to legacy logic
-        logger.warning(
-            "Please upgrade ComfyUI-nunchaku to 1.1.0 or above for better LoRA support. Falling back to legacy loading logic."
-        )
+        logger.warning("Please upgrade ComfyUI-nunchaku to 1.1.0 or above for better LoRA support. Falling back to legacy loading logic.")
         transformer = model_wrapper.model
-
+        
         # Save the transformer temporarily
         model_wrapper.model = None
         ret_model = copy.deepcopy(model)  # copy everything except the model
         ret_model_wrapper = ret_model.model.diffusion_model
-
+        
         # Restore the model and set it for the copy
         model_wrapper.model = transformer
         ret_model_wrapper.model = transformer
@@ -146,36 +133,15 @@ def nunchaku_load_lora(model, lora_name, lora_strength):
 
     # Convert the LoRA to diffusers format
     sd = to_diffusers(lora_path)
-
+    
     # Handle embedding adjustment if needed
     if "transformer.x_embedder.lora_A.weight" in sd:
         new_in_channels = sd["transformer.x_embedder.lora_A.weight"].shape[1]
         assert new_in_channels % 4 == 0
         new_in_channels = new_in_channels // 4
-
+        
         old_in_channels = ret_model.model.model_config.unet_config["in_channels"]
         if old_in_channels < new_in_channels:
             ret_model.model.model_config.unet_config["in_channels"] = new_in_channels
-
-    return ret_model
-
-
-def detect_nunchaku_model_kind(model):
-    """Return the supported Nunchaku model kind for a Comfy model, if any."""
-    try:
-        model_wrapper = model.model.diffusion_model
-    except (AttributeError, TypeError):
-        return None
-
-    wrapper_name = model_wrapper.__class__.__name__
-    if wrapper_name == "ComfyFluxWrapper":
-        return "flux"
-
-    inner_model = getattr(model_wrapper, "model", None)
-    inner_name = inner_model.__class__.__name__ if inner_model is not None else ""
-    if wrapper_name.endswith("NunchakuQwenImageTransformer2DModel"):
-        return "qwen_image"
-    if inner_name.endswith("NunchakuQwenImageTransformer2DModel"):
-        return "qwen_image"
-
-    return None
+    
+    return ret_model

py/nodes/wanvideo_lora_select.py

@@ -1,22 +1,10 @@
-import os
-from ..utils.utils import get_lora_info_absolute
-from ..config import config
+import folder_paths # type: ignore
+from ..utils.utils import get_lora_info
 from .utils import FlexibleOptionalInputType, any_type, get_loras_list
 import logging
 
 logger = logging.getLogger(__name__)
 
-
-def _relpath_within_loras(abs_path):
-    """Return abs_path relative to the first matching lora root, or basename as fallback."""
-    all_roots = list(config.loras_roots or []) + list(config.extra_loras_roots or [])
-    for root in all_roots:
-        try:
-            return os.path.relpath(abs_path, root)
-        except ValueError:
-            continue
-    return os.path.basename(abs_path)
-
 class WanVideoLoraSelectLM:
     NAME = "WanVideo Lora Select (LoraManager)"
     CATEGORY = "Lora Manager/stackers"
@@ -68,13 +56,13 @@ class WanVideoLoraSelectLM:
             clip_strength = float(lora.get('clipStrength', model_strength))
             
             # Get lora path and trigger words
-            lora_path, trigger_words = get_lora_info_absolute(lora_name)
+            lora_path, trigger_words = get_lora_info(lora_name)
             
             # Create lora item for WanVideo format
             lora_item = {
-                "path": lora_path,
+                "path": folder_paths.get_full_path("loras", lora_path),
                 "strength": model_strength,
-                "name": os.path.splitext(_relpath_within_loras(lora_path))[0],
+                "name": lora_path.split(".")[0],
                 "blocks": selected_blocks,
                 "layer_filter": layer_filter,
                 "low_mem_load": low_mem_load,

py/nodes/wanvideo_lora_select_from_text.py

@@ -1,23 +1,11 @@
-import os
-from ..utils.utils import get_lora_info_absolute
-from ..config import config
+import folder_paths # type: ignore
+from ..utils.utils import get_lora_info
 from .utils import any_type 
 import logging
 
 # 初始化日志记录器
 logger = logging.getLogger(__name__)
 
-
-def _relpath_within_loras(abs_path):
-    """Return abs_path relative to the first matching lora root, or basename as fallback."""
-    all_roots = list(config.loras_roots or []) + list(config.extra_loras_roots or [])
-    for root in all_roots:
-        try:
-            return os.path.relpath(abs_path, root)
-        except ValueError:
-            continue
-    return os.path.basename(abs_path)
-
 # 定义新节点的类
 class WanVideoLoraTextSelectLM:
     # 节点在UI中显示的名称
@@ -99,12 +87,12 @@ class WanVideoLoraTextSelectLM:
             else:
                 continue
 
-            lora_path, trigger_words = get_lora_info_absolute(lora_name_raw)
+            lora_path, trigger_words = get_lora_info(lora_name_raw)
 
             lora_item = {
-                "path": lora_path,
+                "path": folder_paths.get_full_path("loras", lora_path),
                 "strength": model_strength,
-                "name": os.path.splitext(_relpath_within_loras(lora_path))[0],
+                "name": lora_path.split(".")[0],
                 "blocks": selected_blocks,
                 "layer_filter": layer_filter,
                 "low_mem_load": low_mem_load,

py/recipes/constants.py

@@ -13,5 +13,4 @@ GEN_PARAM_KEYS = [
     'seed',
     'size',
     'clip_skip',
-    'denoising_strength',
 ]

py/recipes/enrichment.py

@@ -1,11 +1,11 @@
 import logging
 import json
+import re
 import os
 from typing import Any, Dict, Optional
 from .merger import GenParamsMerger
 from .base import RecipeMetadataParser
 from ..services.metadata_service import get_default_metadata_provider
-from ..utils.civitai_utils import extract_civitai_image_id
 
 logger = logging.getLogger(__name__)
 
@@ -39,12 +39,11 @@ class RecipeEnricher:
         source_url = recipe.get("source_url") or recipe.get("source_path", "")
         
         # Check if it's a Civitai image URL
-        image_id = extract_civitai_image_id(str(source_url))
-        if image_id:
+        image_id_match = re.search(r'civitai\.com/images/(\d+)', str(source_url))
+        if image_id_match:
+            image_id = image_id_match.group(1)
             try:
-                image_info = await civitai_client.get_image_info(
-                    image_id, source_url=str(source_url)
-                )
+                image_info = await civitai_client.get_image_info(image_id)
                 if image_info:
                     # Handle nested meta often found in Civitai API responses
                     raw_meta = image_info.get("meta")

py/recipes/factory.py

@@ -6,25 +6,23 @@ from .parsers import (
     ComfyMetadataParser,
     MetaFormatParser,
     AutomaticMetadataParser,
-    CivitaiApiMetadataParser,
-    SuiImageParamsParser,
+    CivitaiApiMetadataParser
 )
 from .base import RecipeMetadataParser
 
 logger = logging.getLogger(__name__)
 
-
 class RecipeParserFactory:
     """Factory for creating recipe metadata parsers"""
-
+    
     @staticmethod
-    def create_parser(metadata) -> RecipeMetadataParser | None:
+    def create_parser(metadata) -> RecipeMetadataParser:
         """
         Create appropriate parser based on the metadata content
-
+        
         Args:
             metadata: The metadata from the image (dict or str)
-
+            
         Returns:
             Appropriate RecipeMetadataParser implementation
         """
@@ -36,18 +34,17 @@ class RecipeParserFactory:
             except Exception as e:
                 logger.debug(f"CivitaiApiMetadataParser check failed: {e}")
                 pass
-
+            
             # Convert dict to string for other parsers that expect string input
             try:
                 import json
-
                 metadata_str = json.dumps(metadata)
             except Exception as e:
                 logger.debug(f"Failed to convert dict to JSON string: {e}")
                 return None
         else:
             metadata_str = metadata
-
+        
         # Try ComfyMetadataParser which requires valid JSON
         try:
             if ComfyMetadataParser().is_metadata_matching(metadata_str):
@@ -55,14 +52,7 @@ class RecipeParserFactory:
         except Exception:
             # If JSON parsing fails, move on to other parsers
             pass
-
-        # Try SuiImageParamsParser for SuiImage metadata format
-        try:
-            if SuiImageParamsParser().is_metadata_matching(metadata_str):
-                return SuiImageParamsParser()
-        except Exception:
-            pass
-
+        
         # Check other parsers that expect string input
         if RecipeFormatParser().is_metadata_matching(metadata_str):
             return RecipeFormatParser()

py/recipes/merger.py

@@ -1,33 +1,27 @@
 from typing import Any, Dict, Optional
 import logging
 
-from .constants import GEN_PARAM_KEYS
-
 logger = logging.getLogger(__name__)
 
-
 class GenParamsMerger:
     """Utility to merge generation parameters from multiple sources with priority."""
 
-    ALLOWED_KEYS = set(GEN_PARAM_KEYS)
-
     BLACKLISTED_KEYS = {
         "id", "url", "userId", "username", "createdAt", "updatedAt", "hash", "meta",
         "draft", "extra", "width", "height", "process", "quantity", "workflow",
         "baseModel", "resources", "disablePoi", "aspectRatio", "Created Date",
         "experimental", "civitaiResources", "civitai_resources", "Civitai resources",
         "modelVersionId", "modelId", "hashes", "Model", "Model hash", "checkpoint_hash",
-        "checkpoint", "checksum", "model_checksum", "raw_metadata",
+        "checkpoint", "checksum", "model_checksum"
     }
-
+    
     NORMALIZATION_MAPPING = {
-        "cfg": "cfg_scale",
+        # Civitai specific
         "cfgScale": "cfg_scale",
         "clipSkip": "clip_skip",
         "negativePrompt": "negative_prompt",
+        # Case variations
         "Sampler": "sampler",
-        "sampler_name": "sampler",
-        "scheduler": "sampler",
         "Steps": "steps",
         "Seed": "seed",
         "Size": "size",
@@ -42,40 +36,63 @@ class GenParamsMerger:
     def merge(
         request_params: Optional[Dict[str, Any]] = None,
         civitai_meta: Optional[Dict[str, Any]] = None,
-        embedded_metadata: Optional[Dict[str, Any]] = None,
+        embedded_metadata: Optional[Dict[str, Any]] = None
     ) -> Dict[str, Any]:
         """
         Merge generation parameters from three sources.
-
+        
         Priority: request_params > civitai_meta > embedded_metadata
+        
+        Args:
+            request_params: Params provided directly in the import request
+            civitai_meta: Params from Civitai Image API 'meta' field
+            embedded_metadata: Params extracted from image EXIF/embedded metadata
+            
+        Returns:
+            Merged parameters dictionary
         """
-        result: Dict[str, Any] = {}
+        result = {}
 
+        # 1. Start with embedded metadata (lowest priority)
         if embedded_metadata:
-            if "gen_params" in embedded_metadata and isinstance(
-                embedded_metadata["gen_params"], dict
-            ):
+            # If it's a full recipe metadata, we use its gen_params
+            if "gen_params" in embedded_metadata and isinstance(embedded_metadata["gen_params"], dict):
                 GenParamsMerger._update_normalized(result, embedded_metadata["gen_params"])
             else:
+                # Otherwise assume the dict itself contains gen_params
                 GenParamsMerger._update_normalized(result, embedded_metadata)
 
+        # 2. Layer Civitai meta (medium priority)
         if civitai_meta:
             GenParamsMerger._update_normalized(result, civitai_meta)
 
+        # 3. Layer request params (highest priority)
         if request_params:
             GenParamsMerger._update_normalized(result, request_params)
 
-        return result
+        # Filter out blacklisted keys and also the original camelCase keys if they were normalized
+        final_result = {}
+        for k, v in result.items():
+            if k in GenParamsMerger.BLACKLISTED_KEYS:
+                continue
+            if k in GenParamsMerger.NORMALIZATION_MAPPING:
+                continue
+            final_result[k] = v
+            
+        return final_result
 
     @staticmethod
     def _update_normalized(target: Dict[str, Any], source: Dict[str, Any]) -> None:
-        """Update target dict with normalized, persistence-safe keys from source."""
-        for key, value in source.items():
-            if key in GenParamsMerger.BLACKLISTED_KEYS:
-                continue
-
-            normalized_key = GenParamsMerger.NORMALIZATION_MAPPING.get(key, key)
-            if normalized_key not in GenParamsMerger.ALLOWED_KEYS:
-                continue
-
-            target[normalized_key] = value
+        """Update target dict with normalized keys from source."""
+        for k, v in source.items():
+            normalized_key = GenParamsMerger.NORMALIZATION_MAPPING.get(k, k)
+            target[normalized_key] = v
+            # Also keep the original key for now if it's not the same, 
+            # so we can filter at the end or avoid losing it if it wasn't supposed to be renamed?
+            # Actually, if we rename it, we should probably NOT keep both in 'target' 
+            # because we want to filter them out at the end anyway.
+            if normalized_key != k:
+                # If we are overwriting an existing snake_case key with a camelCase one's value,
+                # that's fine because of the priority order of calls to _update_normalized.
+                pass
+            target[k] = v

py/recipes/parsers/__init__.py

@@ -5,7 +5,6 @@ from .comfy import ComfyMetadataParser
 from .meta_format import MetaFormatParser
 from .automatic import AutomaticMetadataParser
 from .civitai_image import CivitaiApiMetadataParser
-from .sui_image_params import SuiImageParamsParser
 
 __all__ = [
     'RecipeFormatParser',
@@ -13,5 +12,4 @@ __all__ = [
     'MetaFormatParser',
     'AutomaticMetadataParser',
     'CivitaiApiMetadataParser',
-    'SuiImageParamsParser',
 ]

py/recipes/parsers/civitai_image.py

@@ -9,16 +9,15 @@ from ...services.metadata_service import get_default_metadata_provider
 
 logger = logging.getLogger(__name__)
 
-
 class CivitaiApiMetadataParser(RecipeMetadataParser):
     """Parser for Civitai image metadata format"""
-
+    
     def is_metadata_matching(self, metadata) -> bool:
         """Check if the metadata matches the Civitai image metadata format
-
+        
         Args:
             metadata: The metadata from the image (dict)
-
+            
         Returns:
             bool: True if this parser can handle the metadata
         """
@@ -29,7 +28,7 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             # Check for common CivitAI image metadata fields
             civitai_image_fields = (
                 "resources",
-                "civitaiResources",
+                "civitaiResources", 
                 "additionalResources",
                 "hashes",
                 "prompt",
@@ -41,8 +40,7 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                 "width",
                 "height",
                 "Model",
-                "Model hash",
-                "modelVersionIds",
+                "Model hash"
             )
             return any(key in payload for key in civitai_image_fields)
 
@@ -52,9 +50,7 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
         # Check for LoRA hash patterns
         hashes = metadata.get("hashes")
-        if isinstance(hashes, dict) and any(
-            str(key).lower().startswith("lora:") for key in hashes
-        ):
+        if isinstance(hashes, dict) and any(str(key).lower().startswith("lora:") for key in hashes):
             return True
 
         # Check nested meta object (common in CivitAI image responses)
@@ -65,28 +61,22 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
             # Also check for LoRA hash patterns in nested meta
             hashes = nested_meta.get("hashes")
-            if isinstance(hashes, dict) and any(
-                str(key).lower().startswith("lora:") for key in hashes
-            ):
+            if isinstance(hashes, dict) and any(str(key).lower().startswith("lora:") for key in hashes):
                 return True
 
         return False
-
-    async def parse_metadata(  # type: ignore[override]
-        self, user_comment, recipe_scanner=None, civitai_client=None
-    ) -> Dict[str, Any]:
+    
+    async def parse_metadata(self, metadata, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
         """Parse metadata from Civitai image format
-
+        
         Args:
-            user_comment: The metadata from the image (dict)
+            metadata: The metadata from the image (dict)
             recipe_scanner: Optional recipe scanner service
             civitai_client: Optional Civitai API client (deprecated, use metadata_provider instead)
-
+            
         Returns:
             Dict containing parsed recipe data
         """
-        metadata: Dict[str, Any] = user_comment  # type: ignore[assignment]
-        metadata = user_comment
         try:
             # Get metadata provider instead of using civitai_client directly
             metadata_provider = await get_default_metadata_provider()
@@ -110,19 +100,19 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     )
                 ):
                     metadata = inner_meta
-
+            
             # Initialize result structure
             result = {
-                "base_model": None,
-                "loras": [],
-                "model": None,
-                "gen_params": {},
-                "from_civitai_image": True,
+                'base_model': None,
+                'loras': [],
+                'model': None,
+                'gen_params': {},
+                'from_civitai_image': True
             }
-
+            
             # Track already added LoRAs to prevent duplicates
             added_loras = {}  # key: model_version_id or hash, value: index in result["loras"]
-
+            
             # Extract hash information from hashes field for LoRA matching
             lora_hashes = {}
             if "hashes" in metadata and isinstance(metadata["hashes"], dict):
@@ -131,14 +121,14 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     if key_str.lower().startswith("lora:"):
                         lora_name = key_str.split(":", 1)[1]
                         lora_hashes[lora_name] = hash_value
-
+            
             # Extract prompt and negative prompt
             if "prompt" in metadata:
                 result["gen_params"]["prompt"] = metadata["prompt"]
-
+            
             if "negativePrompt" in metadata:
                 result["gen_params"]["negative_prompt"] = metadata["negativePrompt"]
-
+            
             # Extract other generation parameters
             param_mapping = {
                 "steps": "steps",
@@ -148,117 +138,98 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                 "Size": "size",
                 "clipSkip": "clip_skip",
             }
-
+            
             for civitai_key, our_key in param_mapping.items():
                 if civitai_key in metadata and our_key in GEN_PARAM_KEYS:
                     result["gen_params"][our_key] = metadata[civitai_key]
-
+            
             # Extract base model information - directly if available
             if "baseModel" in metadata:
                 result["base_model"] = metadata["baseModel"]
             elif "Model hash" in metadata and metadata_provider:
                 model_hash = metadata["Model hash"]
-                model_info, error = await metadata_provider.get_model_by_hash(
-                    model_hash
-                )
+                model_info, error = await metadata_provider.get_model_by_hash(model_hash)
                 if model_info:
                     result["base_model"] = model_info.get("baseModel", "")
             elif "Model" in metadata and isinstance(metadata.get("resources"), list):
                 # Try to find base model in resources
                 for resource in metadata.get("resources", []):
-                    if resource.get("type") == "model" and resource.get(
-                        "name"
-                    ) == metadata.get("Model"):
+                    if resource.get("type") == "model" and resource.get("name") == metadata.get("Model"):
                         # This is likely the checkpoint model
                         if metadata_provider and resource.get("hash"):
-                            (
-                                model_info,
-                                error,
-                            ) = await metadata_provider.get_model_by_hash(
-                                resource.get("hash")
-                            )
+                            model_info, error = await metadata_provider.get_model_by_hash(resource.get("hash"))
                             if model_info:
                                 result["base_model"] = model_info.get("baseModel", "")
-
+            
             base_model_counts = {}
-
+            
             # Process standard resources array
             if "resources" in metadata and isinstance(metadata["resources"], list):
                 for resource in metadata["resources"]:
                     # Modified to process resources without a type field as potential LoRAs
                     if resource.get("type", "lora") == "lora":
                         lora_hash = resource.get("hash", "")
-
+                        
                         # Try to get hash from the hashes field if not present in resource
                         if not lora_hash and resource.get("name"):
                             lora_hash = lora_hashes.get(resource["name"], "")
-
+                        
                         # Skip LoRAs without proper identification (hash or modelVersionId)
                         if not lora_hash and not resource.get("modelVersionId"):
-                            logger.debug(
-                                f"Skipping LoRA resource '{resource.get('name', 'Unknown')}' - no hash or modelVersionId"
-                            )
+                            logger.debug(f"Skipping LoRA resource '{resource.get('name', 'Unknown')}' - no hash or modelVersionId")
                             continue
-
+                        
                         # Skip if we've already added this LoRA by hash
                         if lora_hash and lora_hash in added_loras:
                             continue
-
+                        
                         lora_entry = {
-                            "name": resource.get("name", "Unknown LoRA"),
-                            "type": "lora",
-                            "weight": float(resource.get("weight", 1.0)),
-                            "hash": lora_hash,
-                            "existsLocally": False,
-                            "localPath": None,
-                            "file_name": resource.get("name", "Unknown"),
-                            "thumbnailUrl": "/loras_static/images/no-preview.png",
-                            "baseModel": "",
-                            "size": 0,
-                            "downloadUrl": "",
-                            "isDeleted": False,
+                            'name': resource.get("name", "Unknown LoRA"),
+                            'type': "lora",
+                            'weight': float(resource.get("weight", 1.0)),
+                            'hash': lora_hash,
+                            'existsLocally': False,
+                            'localPath': None,
+                            'file_name': resource.get("name", "Unknown"),
+                            'thumbnailUrl': '/loras_static/images/no-preview.png',
+                            'baseModel': '',
+                            'size': 0,
+                            'downloadUrl': '',
+                            'isDeleted': False
                         }
-
+                        
                         # Try to get info from Civitai if hash is available
-                        if lora_entry["hash"] and metadata_provider:
+                        if lora_entry['hash'] and metadata_provider:
                             try:
-                                civitai_info = (
-                                    await metadata_provider.get_model_by_hash(lora_hash)
-                                )
-
+                                civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
+                                
                                 populated_entry = await self.populate_lora_from_civitai(
                                     lora_entry,
                                     civitai_info,
                                     recipe_scanner,
                                     base_model_counts,
-                                    lora_hash,
+                                    lora_hash
                                 )
-
+                                
                                 if populated_entry is None:
                                     continue  # Skip invalid LoRA types
-
+                                    
                                 lora_entry = populated_entry
-
+                                
                                 # If we have a version ID from Civitai, track it for deduplication
-                                if "id" in lora_entry and lora_entry["id"]:
-                                    added_loras[str(lora_entry["id"])] = len(
-                                        result["loras"]
-                                    )
+                                if 'id' in lora_entry and lora_entry['id']:
+                                    added_loras[str(lora_entry['id'])] = len(result["loras"])
                             except Exception as e:
-                                logger.error(
-                                    f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}"
-                                )
-
+                                logger.error(f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}")
+                        
                         # Track by hash if we have it
                         if lora_hash:
                             added_loras[lora_hash] = len(result["loras"])
-
+                            
                         result["loras"].append(lora_entry)
-
+            
             # Process civitaiResources array
-            if "civitaiResources" in metadata and isinstance(
-                metadata["civitaiResources"], list
-            ):
+            if "civitaiResources" in metadata and isinstance(metadata["civitaiResources"], list):
                 for resource in metadata["civitaiResources"]:
                     # Get resource type and identifier
                     resource_type = str(resource.get("type") or "").lower()
@@ -266,39 +237,32 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
                     if resource_type == "checkpoint":
                         checkpoint_entry = {
-                            "id": resource.get("modelVersionId", 0),
-                            "modelId": resource.get("modelId", 0),
-                            "name": resource.get("modelName", "Unknown Checkpoint"),
-                            "version": resource.get("modelVersionName", ""),
-                            "type": resource.get("type", "checkpoint"),
-                            "existsLocally": False,
-                            "localPath": None,
-                            "file_name": resource.get("modelName", ""),
-                            "hash": resource.get("hash", "") or "",
-                            "thumbnailUrl": "/loras_static/images/no-preview.png",
-                            "baseModel": "",
-                            "size": 0,
-                            "downloadUrl": "",
-                            "isDeleted": False,
+                            'id': resource.get("modelVersionId", 0),
+                            'modelId': resource.get("modelId", 0),
+                            'name': resource.get("modelName", "Unknown Checkpoint"),
+                            'version': resource.get("modelVersionName", ""),
+                            'type': resource.get("type", "checkpoint"),
+                            'existsLocally': False,
+                            'localPath': None,
+                            'file_name': resource.get("modelName", ""),
+                            'hash': resource.get("hash", "") or "",
+                            'thumbnailUrl': '/loras_static/images/no-preview.png',
+                            'baseModel': '',
+                            'size': 0,
+                            'downloadUrl': '',
+                            'isDeleted': False
                         }
 
                         if version_id and metadata_provider:
                             try:
-                                civitai_info = (
-                                    await metadata_provider.get_model_version_info(
-                                        version_id
-                                    )
-                                )
+                                civitai_info = await metadata_provider.get_model_version_info(version_id)
 
-                                checkpoint_entry = (
-                                    await self.populate_checkpoint_from_civitai(
-                                        checkpoint_entry, civitai_info
-                                    )
+                                checkpoint_entry = await self.populate_checkpoint_from_civitai(
+                                    checkpoint_entry,
+                                    civitai_info
                                 )
                             except Exception as e:
-                                logger.error(
-                                    f"Error fetching Civitai info for checkpoint version {version_id}: {e}"
-                                )
+                                logger.error(f"Error fetching Civitai info for checkpoint version {version_id}: {e}")
 
                         if result["model"] is None:
                             result["model"] = checkpoint_entry
@@ -311,35 +275,31 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
                     # Initialize lora entry
                     lora_entry = {
-                        "id": resource.get("modelVersionId", 0),
-                        "modelId": resource.get("modelId", 0),
-                        "name": resource.get("modelName", "Unknown LoRA"),
-                        "version": resource.get("modelVersionName", ""),
-                        "type": resource.get("type", "lora"),
-                        "weight": round(float(resource.get("weight", 1.0)), 2),
-                        "existsLocally": False,
-                        "thumbnailUrl": "/loras_static/images/no-preview.png",
-                        "baseModel": "",
-                        "size": 0,
-                        "downloadUrl": "",
-                        "isDeleted": False,
+                        'id': resource.get("modelVersionId", 0),
+                        'modelId': resource.get("modelId", 0),
+                        'name': resource.get("modelName", "Unknown LoRA"),
+                        'version': resource.get("modelVersionName", ""),
+                        'type': resource.get("type", "lora"),
+                        'weight': round(float(resource.get("weight", 1.0)), 2),
+                        'existsLocally': False,
+                        'thumbnailUrl': '/loras_static/images/no-preview.png',
+                        'baseModel': '',
+                        'size': 0,
+                        'downloadUrl': '',
+                        'isDeleted': False
                     }
 
                     # Try to get info from Civitai if modelVersionId is available
                     if version_id and metadata_provider:
                         try:
                             # Use get_model_version_info instead of get_model_version
-                            civitai_info = (
-                                await metadata_provider.get_model_version_info(
-                                    version_id
-                                )
-                            )
+                            civitai_info = await metadata_provider.get_model_version_info(version_id)
 
                             populated_entry = await self.populate_lora_from_civitai(
                                 lora_entry,
                                 civitai_info,
                                 recipe_scanner,
-                                base_model_counts,
+                                base_model_counts
                             )
 
                             if populated_entry is None:
@@ -347,148 +307,76 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
                             lora_entry = populated_entry
                         except Exception as e:
-                            logger.error(
-                                f"Error fetching Civitai info for model version {version_id}: {e}"
-                            )
+                            logger.error(f"Error fetching Civitai info for model version {version_id}: {e}")
 
                     # Track this LoRA in our deduplication dict
                     if version_id:
                         added_loras[version_id] = len(result["loras"])
 
                     result["loras"].append(lora_entry)
-
+            
             # Process additionalResources array
-            if "additionalResources" in metadata and isinstance(
-                metadata["additionalResources"], list
-            ):
+            if "additionalResources" in metadata and isinstance(metadata["additionalResources"], list):
                 for resource in metadata["additionalResources"]:
                     # Skip resources that aren't LoRAs or LyCORIS
-                    if (
-                        resource.get("type") not in ["lora", "lycoris"]
-                        and "type" not in resource
-                    ):
+                    if resource.get("type") not in ["lora", "lycoris"] and "type" not in resource:
                         continue
-
+                        
                     lora_type = resource.get("type", "lora")
                     name = resource.get("name", "")
-
+                    
                     # Extract ID from URN format if available
                     version_id = None
                     if name and "civitai:" in name:
                         parts = name.split("@")
                         if len(parts) > 1:
                             version_id = parts[1]
-
+                            
                             # Skip if we've already added this LoRA
                             if version_id in added_loras:
                                 continue
-
+                    
                     lora_entry = {
-                        "name": name,
-                        "type": lora_type,
-                        "weight": float(resource.get("strength", 1.0)),
-                        "hash": "",
-                        "existsLocally": False,
-                        "localPath": None,
-                        "file_name": name,
-                        "thumbnailUrl": "/loras_static/images/no-preview.png",
-                        "baseModel": "",
-                        "size": 0,
-                        "downloadUrl": "",
-                        "isDeleted": False,
+                        'name': name,
+                        'type': lora_type,
+                        'weight': float(resource.get("strength", 1.0)),
+                        'hash': "",
+                        'existsLocally': False,
+                        'localPath': None,
+                        'file_name': name,
+                        'thumbnailUrl': '/loras_static/images/no-preview.png',
+                        'baseModel': '',
+                        'size': 0,
+                        'downloadUrl': '',
+                        'isDeleted': False
                     }
-
+                    
                     # If we have a version ID and metadata provider, try to get more info
                     if version_id and metadata_provider:
                         try:
                             # Use get_model_version_info with the version ID
-                            civitai_info = (
-                                await metadata_provider.get_model_version_info(
-                                    version_id
-                                )
-                            )
-
+                            civitai_info = await metadata_provider.get_model_version_info(version_id)
+                            
                             populated_entry = await self.populate_lora_from_civitai(
                                 lora_entry,
                                 civitai_info,
                                 recipe_scanner,
-                                base_model_counts,
+                                base_model_counts
                             )
-
+                            
                             if populated_entry is None:
                                 continue  # Skip invalid LoRA types
-
+                                
                             lora_entry = populated_entry
-
+                            
                             # Track this LoRA for deduplication
                             if version_id:
                                 added_loras[version_id] = len(result["loras"])
                         except Exception as e:
-                            logger.error(
-                                f"Error fetching Civitai info for model ID {version_id}: {e}"
-                            )
-
+                            logger.error(f"Error fetching Civitai info for model ID {version_id}: {e}")
+                    
                         result["loras"].append(lora_entry)
 
-            # Process modelVersionIds from Civitai image API
-            # These are model version IDs returned at root level when meta doesn't contain resources
-            if "modelVersionIds" in metadata and isinstance(
-                metadata["modelVersionIds"], list
-            ):
-                for version_id in metadata["modelVersionIds"]:
-                    version_id_str = str(version_id)
-
-                    # Skip if we've already added this LoRA by version ID
-                    if version_id_str in added_loras:
-                        continue
-
-                    # Initialize lora entry with version ID
-                    lora_entry = {
-                        "id": version_id,
-                        "modelId": 0,
-                        "name": "Unknown LoRA",
-                        "version": "",
-                        "type": "lora",
-                        "weight": 1.0,
-                        "existsLocally": False,
-                        "thumbnailUrl": "/loras_static/images/no-preview.png",
-                        "baseModel": "",
-                        "size": 0,
-                        "downloadUrl": "",
-                        "isDeleted": False,
-                    }
-
-                    # Fetch model info from Civitai
-                    if metadata_provider and version_id_str:
-                        try:
-                            civitai_info = (
-                                await metadata_provider.get_model_version_info(
-                                    version_id_str
-                                )
-                            )
-
-                            populated_entry = await self.populate_lora_from_civitai(
-                                lora_entry,
-                                civitai_info,
-                                recipe_scanner,
-                                base_model_counts,
-                            )
-
-                            if populated_entry is None:
-                                continue  # Skip invalid LoRA types
-
-                            lora_entry = populated_entry
-                        except Exception as e:
-                            logger.error(
-                                f"Error fetching Civitai info for model version {version_id}: {e}"
-                            )
-
-                    # Track this LoRA for deduplication
-                    if version_id_str:
-                        added_loras[version_id_str] = len(result["loras"])
-
-                    result["loras"].append(lora_entry)
-
             # If we found LoRA hashes in the metadata but haven't already
             # populated entries for them, fall back to creating LoRAs from
             # the hashes section. Some Civitai image responses only include
@@ -502,32 +390,30 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     continue
 
                 lora_entry = {
-                    "name": lora_name,
-                    "type": "lora",
-                    "weight": 1.0,
-                    "hash": lora_hash,
-                    "existsLocally": False,
-                    "localPath": None,
-                    "file_name": lora_name,
-                    "thumbnailUrl": "/loras_static/images/no-preview.png",
-                    "baseModel": "",
-                    "size": 0,
-                    "downloadUrl": "",
-                    "isDeleted": False,
+                    'name': lora_name,
+                    'type': "lora",
+                    'weight': 1.0,
+                    'hash': lora_hash,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': lora_name,
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
                 }
 
                 if metadata_provider:
                     try:
-                        civitai_info = await metadata_provider.get_model_by_hash(
-                            lora_hash
-                        )
+                        civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
 
                         populated_entry = await self.populate_lora_from_civitai(
                             lora_entry,
                             civitai_info,
                             recipe_scanner,
                             base_model_counts,
-                            lora_hash,
+                            lora_hash
                         )
 
                         if populated_entry is None:
@@ -535,93 +421,80 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
                         lora_entry = populated_entry
 
-                        if "id" in lora_entry and lora_entry["id"]:
-                            added_loras[str(lora_entry["id"])] = len(result["loras"])
+                        if 'id' in lora_entry and lora_entry['id']:
+                            added_loras[str(lora_entry['id'])] = len(result["loras"])
                     except Exception as e:
-                        logger.error(
-                            f"Error fetching Civitai info for LoRA hash {lora_hash}: {e}"
-                        )
+                        logger.error(f"Error fetching Civitai info for LoRA hash {lora_hash}: {e}")
 
                 added_loras[lora_hash] = len(result["loras"])
                 result["loras"].append(lora_entry)
 
             # Check for LoRA info in the format "Lora_0 Model hash", "Lora_0 Model name", etc.
             lora_index = 0
-            while (
-                f"Lora_{lora_index} Model hash" in metadata
-                and f"Lora_{lora_index} Model name" in metadata
-            ):
+            while f"Lora_{lora_index} Model hash" in metadata and f"Lora_{lora_index} Model name" in metadata:
                 lora_hash = metadata[f"Lora_{lora_index} Model hash"]
                 lora_name = metadata[f"Lora_{lora_index} Model name"]
-                lora_strength_model = float(
-                    metadata.get(f"Lora_{lora_index} Strength model", 1.0)
-                )
-
+                lora_strength_model = float(metadata.get(f"Lora_{lora_index} Strength model", 1.0))
+                
                 # Skip if we've already added this LoRA by hash
                 if lora_hash and lora_hash in added_loras:
                     lora_index += 1
                     continue
-
+                
                 lora_entry = {
-                    "name": lora_name,
-                    "type": "lora",
-                    "weight": lora_strength_model,
-                    "hash": lora_hash,
-                    "existsLocally": False,
-                    "localPath": None,
-                    "file_name": lora_name,
-                    "thumbnailUrl": "/loras_static/images/no-preview.png",
-                    "baseModel": "",
-                    "size": 0,
-                    "downloadUrl": "",
-                    "isDeleted": False,
+                    'name': lora_name,
+                    'type': "lora",
+                    'weight': lora_strength_model,
+                    'hash': lora_hash,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': lora_name,
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
                 }
-
+                
                 # Try to get info from Civitai if hash is available
-                if lora_entry["hash"] and metadata_provider:
+                if lora_entry['hash'] and metadata_provider:
                     try:
-                        civitai_info = await metadata_provider.get_model_by_hash(
-                            lora_hash
-                        )
-
+                        civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
+                        
                         populated_entry = await self.populate_lora_from_civitai(
                             lora_entry,
                             civitai_info,
                             recipe_scanner,
                             base_model_counts,
-                            lora_hash,
+                            lora_hash
                         )
-
+                        
                         if populated_entry is None:
                             lora_index += 1
                             continue  # Skip invalid LoRA types
-
+                            
                         lora_entry = populated_entry
-
+                        
                         # If we have a version ID from Civitai, track it for deduplication
-                        if "id" in lora_entry and lora_entry["id"]:
-                            added_loras[str(lora_entry["id"])] = len(result["loras"])
+                        if 'id' in lora_entry and lora_entry['id']:
+                            added_loras[str(lora_entry['id'])] = len(result["loras"])
                     except Exception as e:
-                        logger.error(
-                            f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}"
-                        )
-
+                        logger.error(f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}")
+                
                 # Track by hash if we have it
                 if lora_hash:
                     added_loras[lora_hash] = len(result["loras"])
-
+                    
                 result["loras"].append(lora_entry)
-
+                
                 lora_index += 1
-
+            
             # If base model wasn't found earlier, use the most common one from LoRAs
             if not result["base_model"] and base_model_counts:
-                result["base_model"] = max(
-                    base_model_counts.items(), key=lambda x: x[1]
-                )[0]
-
+                result["base_model"] = max(base_model_counts.items(), key=lambda x: x[1])[0]
+            
             return result
-
+            
         except Exception as e:
             logger.error(f"Error parsing Civitai image metadata: {e}", exc_info=True)
             return {"error": str(e), "loras": []}

py/recipes/parsers/sui_image_params.py

@@ -1,188 +0,0 @@
-"""Parser for SuiImage (Stable Diffusion WebUI) metadata format."""
-
-import json
-import logging
-from typing import Dict, Any, Optional, List
-from ..base import RecipeMetadataParser
-from ...services.metadata_service import get_default_metadata_provider
-
-logger = logging.getLogger(__name__)
-
-
-class SuiImageParamsParser(RecipeMetadataParser):
-    """Parser for SuiImage metadata JSON format.
-
-    This format is used by some Stable Diffusion WebUI variants.
-    Structure:
-    {
-        "sui_image_params": {
-            "prompt": "...",
-            "negativeprompt": "...",
-            "model": "...",
-            "seed": ...,
-            "steps": ...,
-            ...
-        },
-        "sui_models": [
-            {"name": "...", "param": "model", "hash": "..."},
-            ...
-        ],
-        "sui_extra_data": {...}
-    }
-    """
-
-    def is_metadata_matching(self, user_comment: str) -> bool:
-        """Check if the user comment matches the SuiImage metadata format"""
-        try:
-            data = json.loads(user_comment)
-            return isinstance(data, dict) and 'sui_image_params' in data
-        except (json.JSONDecodeError, TypeError):
-            return False
-
-    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
-        """Parse metadata from SuiImage metadata format"""
-        try:
-            metadata_provider = await get_default_metadata_provider()
-
-            data = json.loads(user_comment)
-            params = data.get('sui_image_params', {})
-            models = data.get('sui_models', [])
-
-            # Extract prompt and negative prompt
-            prompt = params.get('prompt', '')
-            negative_prompt = params.get('negativeprompt', '') or params.get('negative_prompt', '')
-
-            # Extract generation parameters
-            gen_params = {}
-            if prompt:
-                gen_params['prompt'] = prompt
-            if negative_prompt:
-                gen_params['negative_prompt'] = negative_prompt
-
-            # Map standard parameters
-            param_mapping = {
-                'steps': 'steps',
-                'seed': 'seed',
-                'cfgscale': 'cfg_scale',
-                'cfg_scale': 'cfg_scale',
-                'width': 'width',
-                'height': 'height',
-                'sampler': 'sampler',
-                'scheduler': 'scheduler',
-                'model': 'model',
-                'vae': 'vae',
-            }
-
-            for src_key, dest_key in param_mapping.items():
-                if src_key in params and params[src_key] is not None:
-                    gen_params[dest_key] = params[src_key]
-
-            # Add size info if available
-            if 'width' in gen_params and 'height' in gen_params:
-                gen_params['size'] = f"{gen_params['width']}x{gen_params['height']}"
-
-            # Process models - extract checkpoint and loras
-            loras: List[Dict[str, Any]] = []
-            checkpoint: Optional[Dict[str, Any]] = None
-
-            for model in models:
-                model_name = model.get('name', '')
-                param_type = model.get('param', '')
-                model_hash = model.get('hash', '')
-
-                # Remove .safetensors extension for cleaner name
-                clean_name = model_name.replace('.safetensors', '') if model_name else ''
-
-                # Check if this is a LoRA by looking at the name or param type
-                is_lora = 'lora' in model_name.lower() or param_type.lower().startswith('lora')
-
-                if is_lora:
-                    lora_entry = {
-                        'id': 0,
-                        'modelId': 0,
-                        'name': clean_name,
-                        'version': '',
-                        'type': 'lora',
-                        'weight': 1.0,
-                        'existsLocally': False,
-                        'localPath': None,
-                        'file_name': model_name,
-                        'hash': model_hash.replace('0x', '') if model_hash.startswith('0x') else model_hash,
-                        'thumbnailUrl': '/loras_static/images/no-preview.png',
-                        'baseModel': '',
-                        'size': 0,
-                        'downloadUrl': '',
-                        'isDeleted': False
-                    }
-
-                    # Try to get additional info from metadata provider
-                    if metadata_provider and model_hash:
-                        try:
-                            civitai_info = await metadata_provider.get_model_by_hash(
-                                model_hash.replace('0x', '') if model_hash.startswith('0x') else model_hash
-                            )
-                            if civitai_info:
-                                lora_entry = await self.populate_lora_from_civitai(
-                                    lora_entry, civitai_info, recipe_scanner
-                                )
-                        except Exception as e:
-                            logger.debug(f"Error fetching info for LoRA {clean_name}: {e}")
-
-                    if lora_entry:
-                        loras.append(lora_entry)
-                elif param_type == 'model' or 'lora' not in model_name.lower():
-                    # This is likely a checkpoint
-                    checkpoint_entry = {
-                        'id': 0,
-                        'modelId': 0,
-                        'name': clean_name,
-                        'version': '',
-                        'type': 'checkpoint',
-                        'hash': model_hash.replace('0x', '') if model_hash.startswith('0x') else model_hash,
-                        'existsLocally': False,
-                        'localPath': None,
-                        'file_name': model_name,
-                        'thumbnailUrl': '/loras_static/images/no-preview.png',
-                        'baseModel': '',
-                        'size': 0,
-                        'downloadUrl': '',
-                        'isDeleted': False
-                    }
-
-                    # Try to get additional info from metadata provider
-                    if metadata_provider and model_hash:
-                        try:
-                            civitai_info = await metadata_provider.get_model_by_hash(
-                                model_hash.replace('0x', '') if model_hash.startswith('0x') else model_hash
-                            )
-                            if civitai_info:
-                                checkpoint_entry = await self.populate_checkpoint_from_civitai(
-                                    checkpoint_entry, civitai_info
-                                )
-                        except Exception as e:
-                            logger.debug(f"Error fetching info for checkpoint {clean_name}: {e}")
-
-                    checkpoint = checkpoint_entry
-
-            # Determine base model from loras or checkpoint
-            base_model = None
-            if loras:
-                base_models = [lora.get('baseModel') for lora in loras if lora.get('baseModel')]
-                if base_models:
-                    from collections import Counter
-                    base_model_counts = Counter(base_models)
-                    base_model = base_model_counts.most_common(1)[0][0]
-            elif checkpoint and checkpoint.get('baseModel'):
-                base_model = checkpoint['baseModel']
-
-            return {
-                'base_model': base_model,
-                'loras': loras,
-                'checkpoint': checkpoint,
-                'gen_params': gen_params,
-                'from_sui_image_params': True
-            }
-
-        except Exception as e:
-            logger.error(f"Error parsing SuiImage metadata: {e}", exc_info=True)
-            return {"error": str(e), "loras": []}

py/routes/base_model_routes.py

@@ -204,7 +204,6 @@ class BaseModelRoutes(ABC):
             service=service,
             update_service=update_service,
             metadata_provider_selector=get_metadata_provider,
-            settings_service=self._settings,
             logger=logger,
         )
         return ModelHandlerSet(
@@ -251,7 +250,7 @@ class BaseModelRoutes(ABC):
 
     def _find_model_file(self, files):
         """Find the appropriate model file from the files list - can be overridden by subclasses."""
-        return next((file for file in files if file.get("type") in ("Model", "Diffusion Model") and file.get("primary") is True), None)
+        return next((file for file in files if file.get("type") == "Model" and file.get("primary") is True), None)
 
     def get_handler(self, name: str) -> Callable[[web.Request], web.StreamResponse]:
         """Expose handlers for subclasses or tests."""

py/routes/base_recipe_routes.py

@@ -1,5 +1,4 @@
 """Base infrastructure shared across recipe routes."""
-
 from __future__ import annotations
 
 import logging
@@ -17,14 +16,12 @@ from ..services.recipes import (
     RecipePersistenceService,
     RecipeSharingService,
 )
-from ..services.batch_import_service import BatchImportService
 from ..services.server_i18n import server_i18n
 from ..services.service_registry import ServiceRegistry
 from ..services.settings_manager import get_settings_manager
 from ..utils.constants import CARD_PREVIEW_WIDTH
 from ..utils.exif_utils import ExifUtils
 from .handlers.recipe_handlers import (
-    BatchImportHandler,
     RecipeAnalysisHandler,
     RecipeHandlerSet,
     RecipeListingHandler,
@@ -119,10 +116,7 @@ class BaseRecipeRoutes:
         recipe_scanner_getter = lambda: self.recipe_scanner
         civitai_client_getter = lambda: self.civitai_client
 
-        standalone_mode = (
-            os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"
-            or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
-        )
+        standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
         if not standalone_mode:
             from ..metadata_collector import get_metadata  # type: ignore[import-not-found]
             from ..metadata_collector.metadata_processor import (  # type: ignore[import-not-found]
@@ -196,22 +190,6 @@ class BaseRecipeRoutes:
             sharing_service=sharing_service,
         )
 
-        from ..services.websocket_manager import ws_manager
-
-        batch_import_service = BatchImportService(
-            analysis_service=analysis_service,
-            persistence_service=persistence_service,
-            ws_manager=ws_manager,
-            logger=logger,
-        )
-        batch_import = BatchImportHandler(
-            ensure_dependencies_ready=self.ensure_dependencies_ready,
-            recipe_scanner_getter=recipe_scanner_getter,
-            civitai_client_getter=civitai_client_getter,
-            logger=logger,
-            batch_import_service=batch_import_service,
-        )
-
         return RecipeHandlerSet(
             page_view=page_view,
             listing=listing,
@@ -219,5 +197,4 @@ class BaseRecipeRoutes:
             management=management,
             analysis=analysis,
             sharing=sharing,
-            batch_import=batch_import,
         )

py/routes/checkpoint_routes.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Dict, List, Set
+from typing import Dict
 from aiohttp import web
 
 from .base_model_routes import BaseModelRoutes
@@ -82,22 +82,12 @@ class CheckpointRoutes(BaseModelRoutes):
             return web.json_response({"error": str(e)}, status=500)
     
     async def get_checkpoints_roots(self, request: web.Request) -> web.Response:
-        """Return the list of checkpoint roots from config (including extra paths)"""
+        """Return the list of checkpoint roots from config"""
         try:
-            # Merge checkpoints_roots with extra_checkpoints_roots, preserving order and removing duplicates
-            roots: List[str] = []
-            roots.extend(config.checkpoints_roots or [])
-            roots.extend(config.extra_checkpoints_roots or [])
-            # Remove duplicates while preserving order
-            seen: set = set()
-            unique_roots: List[str] = []
-            for root in roots:
-                if root and root not in seen:
-                    seen.add(root)
-                    unique_roots.append(root)
+            roots = config.checkpoints_roots
             return web.json_response({
                 "success": True,
-                "roots": unique_roots
+                "roots": roots
             })
         except Exception as e:
             logger.error(f"Error getting checkpoint roots: {e}", exc_info=True)
@@ -107,22 +97,12 @@ class CheckpointRoutes(BaseModelRoutes):
             }, status=500)
 
     async def get_unet_roots(self, request: web.Request) -> web.Response:
-        """Return the list of unet roots from config (including extra paths)"""
+        """Return the list of unet roots from config"""
         try:
-            # Merge unet_roots with extra_unet_roots, preserving order and removing duplicates
-            roots: List[str] = []
-            roots.extend(config.unet_roots or [])
-            roots.extend(config.extra_unet_roots or [])
-            # Remove duplicates while preserving order
-            seen: set = set()
-            unique_roots: List[str] = []
-            for root in roots:
-                if root and root not in seen:
-                    seen.add(root)
-                    unique_roots.append(root)
+            roots = config.unet_roots
             return web.json_response({
                 "success": True,
-                "roots": unique_roots
+                "roots": roots
             })
         except Exception as e:
             logger.error(f"Error getting unet roots: {e}", exc_info=True)

py/routes/example_images_route_registrar.py

@@ -30,7 +30,6 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/force-download-example-images", "force_download_example_images"),
     RouteDefinition("POST", "/api/lm/cleanup-example-image-folders", "cleanup_example_image_folders"),
     RouteDefinition("POST", "/api/lm/example-images/set-nsfw-level", "set_example_image_nsfw_level"),
-    RouteDefinition("POST", "/api/lm/check-example-images-needed", "check_example_images_needed"),
 )
 
 

py/routes/handlers/base_model_handlers.py

@@ -1,141 +0,0 @@
-"""Handlers for base model related endpoints."""
-
-from __future__ import annotations
-
-import logging
-from typing import Any, Awaitable, Callable, Dict
-
-from aiohttp import web
-
-from ...services.civitai_base_model_service import get_civitai_base_model_service
-
-logger = logging.getLogger(__name__)
-
-
-class BaseModelHandlerSet:
-    """Collection of handlers for base model operations."""
-
-    def __init__(
-        self,
-        base_model_service_factory: Callable[[], Any] = get_civitai_base_model_service,
-    ) -> None:
-        self._base_model_service_factory = base_model_service_factory
-
-    def to_route_mapping(
-        self,
-    ) -> Dict[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
-        """Return mapping of route names to handler methods."""
-        return {
-            "get_base_models": self.get_base_models,
-            "refresh_base_models": self.refresh_base_models,
-            "get_base_model_categories": self.get_base_model_categories,
-            "get_base_model_cache_status": self.get_base_model_cache_status,
-        }
-
-    async def get_base_models(self, request: web.Request) -> web.Response:
-        """Get merged base models (hardcoded + remote from Civitai).
-
-        Query Parameters:
-            refresh: If 'true', force refresh from API
-
-        Returns:
-            JSON response with:
-            - models: List of base model names
-            - source: 'cache', 'api', or 'fallback'
-            - last_updated: ISO timestamp
-            - counts: hardcoded_count, remote_count, merged_count
-        """
-        try:
-            service = await self._base_model_service_factory()
-
-            # Check for refresh parameter
-            force_refresh = request.query.get("refresh", "").lower() == "true"
-
-            result = await service.get_base_models(force_refresh=force_refresh)
-
-            return web.json_response(
-                {
-                    "success": True,
-                    "data": result,
-                }
-            )
-
-        except Exception as e:
-            logger.error(f"Error in get_base_models: {e}")
-            return web.json_response(
-                {"success": False, "error": str(e)},
-                status=500,
-            )
-
-    async def refresh_base_models(self, request: web.Request) -> web.Response:
-        """Force refresh base models from Civitai API.
-
-        Returns:
-            JSON response with refreshed data
-        """
-        try:
-            service = await self._base_model_service_factory()
-            result = await service.refresh_cache()
-
-            return web.json_response(
-                {
-                    "success": True,
-                    "data": result,
-                    "message": "Base models cache refreshed successfully",
-                }
-            )
-
-        except Exception as e:
-            logger.error(f"Error in refresh_base_models: {e}")
-            return web.json_response(
-                {"success": False, "error": str(e)},
-                status=500,
-            )
-
-    async def get_base_model_categories(self, request: web.Request) -> web.Response:
-        """Get categorized base models.
-
-        Returns:
-            JSON response with categorized models
-        """
-        try:
-            service = await self._base_model_service_factory()
-            categories = service.get_model_categories()
-
-            return web.json_response(
-                {
-                    "success": True,
-                    "data": categories,
-                }
-            )
-
-        except Exception as e:
-            logger.error(f"Error in get_base_model_categories: {e}")
-            return web.json_response(
-                {"success": False, "error": str(e)},
-                status=500,
-            )
-
-    async def get_base_model_cache_status(self, request: web.Request) -> web.Response:
-        """Get cache status for base models.
-
-        Returns:
-            JSON response with cache status
-        """
-        try:
-            service = await self._base_model_service_factory()
-            status = service.get_cache_status()
-
-            return web.json_response(
-                {
-                    "success": True,
-                    "data": status,
-                }
-            )
-
-        except Exception as e:
-            logger.error(f"Error in get_base_model_cache_status: {e}")
-            return web.json_response(
-                {"success": False, "error": str(e)},
-                status=500,
-            )

py/routes/handlers/example_images_handlers.py

@@ -1,14 +1,11 @@
 """Handler set for example image routes."""
 from __future__ import annotations
 
-import logging
 from dataclasses import dataclass
 from typing import Callable, Mapping
 
 from aiohttp import web
 
-logger = logging.getLogger(__name__)
-
 from ...services.use_cases.example_images import (
     DownloadExampleImagesConfigurationError,
     DownloadExampleImagesInProgressError,
@@ -95,19 +92,6 @@ class ExampleImagesDownloadHandler:
         except ExampleImagesDownloadError as exc:
             return web.json_response({'success': False, 'error': str(exc)}, status=500)
 
-    async def check_example_images_needed(self, request: web.Request) -> web.StreamResponse:
-        """Lightweight check to see if any models need example images downloaded."""
-        try:
-            payload = await request.json()
-            model_types = payload.get('model_types', ['lora', 'checkpoint', 'embedding'])
-            result = await self._download_manager.check_pending_models(model_types)
-            return web.json_response(result)
-        except Exception as exc:
-            return web.json_response(
-                {'success': False, 'error': str(exc)},
-                status=500
-            )
-
 
 class ExampleImagesManagementHandler:
     """HTTP adapters for import/delete endpoints."""
@@ -125,9 +109,6 @@ class ExampleImagesManagementHandler:
             return web.json_response({'success': False, 'error': str(exc)}, status=400)
         except ExampleImagesImportError as exc:
             return web.json_response({'success': False, 'error': str(exc)}, status=500)
-        except Exception as exc:
-            logger.exception("Unexpected error importing example images")
-            return web.json_response({'success': False, 'error': str(exc)}, status=500)
 
     async def delete_example_image(self, request: web.Request) -> web.StreamResponse:
         return await self._processor.delete_custom_image(request)
@@ -180,7 +161,6 @@ class ExampleImagesHandlerSet:
             "resume_example_images": self.download.resume_example_images,
             "stop_example_images": self.download.stop_example_images,
             "force_download_example_images": self.download.force_download_example_images,
-            "check_example_images_needed": self.download.check_example_images_needed,
             "import_example_images": self.management.import_example_images,
             "delete_example_image": self.management.delete_example_image,
             "set_example_image_nsfw_level": self.management.set_example_image_nsfw_level,

py/routes/handlers/model_handlers.py

@@ -6,7 +6,6 @@ import asyncio
 import json
 import logging
 import os
-import re
 import time
 from dataclasses import dataclass
 from typing import Any, Awaitable, Callable, Dict, Iterable, List, Mapping, Optional
@@ -16,14 +15,9 @@ import jinja2
 
 from ...config import config
 from ...services.download_coordinator import DownloadCoordinator
-from ...services.connectivity_guard import (
-    OFFLINE_FRIENDLY_MESSAGE,
-    is_expected_offline_error,
-)
 from ...services.metadata_sync_service import MetadataSyncService
 from ...services.model_file_service import ModelMoveService
 from ...services.preview_asset_service import PreviewAssetService
-from ...services.service_registry import ServiceRegistry
 from ...services.settings_manager import SettingsManager, get_settings_manager
 from ...services.tag_update_service import TagUpdateService
 from ...services.use_cases import (
@@ -69,23 +63,7 @@ class ModelPageView:
         self._settings = settings_service
         self._server_i18n = server_i18n
         self._logger = logger
-
-    def _load_supporters(self) -> dict:
-        """Load supporters data from JSON file."""
-        try:
-            current_file = os.path.abspath(__file__)
-            root_dir = os.path.dirname(
-                os.path.dirname(os.path.dirname(os.path.dirname(current_file)))
-            )
-            supporters_path = os.path.join(root_dir, "data", "supporters.json")
-
-            if os.path.exists(supporters_path):
-                with open(supporters_path, "r", encoding="utf-8") as f:
-                    return json.load(f)
-        except Exception as e:
-            self._logger.debug(f"Failed to load supporters data: {e}")
-
-        return {"specialThanks": [], "allSupporters": [], "totalCount": 0}
+        self._app_version = self._get_app_version()
 
     def _get_app_version(self) -> str:
         version = "1.0.0"
@@ -159,7 +137,7 @@ class ModelPageView:
                 "request": request,
                 "folders": [],
                 "t": self._server_i18n.get_translation,
-                "version": self._get_app_version(),
+                "version": self._app_version,
             }
 
             if not is_initializing:
@@ -228,42 +206,6 @@ class ModelListingHandler:
             )
             return web.json_response({"error": str(exc)}, status=500)
 
-    async def get_excluded_models(self, request: web.Request) -> web.Response:
-        start_time = time.perf_counter()
-        try:
-            params = self._parse_common_params(request)
-            result = await self._service.get_excluded_paginated_data(**params)
-
-            format_start = time.perf_counter()
-            formatted_result = {
-                "items": [
-                    await self._service.format_response(item)
-                    for item in result["items"]
-                ],
-                "total": result["total"],
-                "page": result["page"],
-                "page_size": result["page_size"],
-                "total_pages": result["total_pages"],
-            }
-            format_duration = time.perf_counter() - format_start
-
-            duration = time.perf_counter() - start_time
-            self._logger.debug(
-                "Request for %s/excluded took %.3fs (formatting: %.3fs)",
-                self._service.model_type,
-                duration,
-                format_duration,
-            )
-            return web.json_response(formatted_result)
-        except Exception as exc:
-            self._logger.error(
-                "Error retrieving excluded %ss: %s",
-                self._service.model_type,
-                exc,
-                exc_info=True,
-            )
-            return web.json_response({"error": str(exc)}, status=500)
-
     def _parse_common_params(self, request: web.Request) -> Dict:
         page = int(request.query.get("page", "1"))
         page_size = min(int(request.query.get("page_size", "20")), 100)
@@ -327,11 +269,6 @@ class ModelListingHandler:
             request.query.get("update_available_only", "false").lower() == "true"
         )
 
-        # Tag logic: "any" (OR) or "all" (AND) for include tags
-        tag_logic = request.query.get("tag_logic", "any").lower()
-        if tag_logic not in ("any", "all"):
-            tag_logic = "any"
-
         # New license-based query filters
         credit_required = request.query.get("credit_required")
         if credit_required is not None:
@@ -349,13 +286,6 @@ class ModelListingHandler:
         else:
             allow_selling_generated_content = None  # None means no filter applied
 
-        # Name pattern filters for LoRA Pool
-        name_pattern_include = request.query.getall("name_pattern_include", [])
-        name_pattern_exclude = request.query.getall("name_pattern_exclude", [])
-        name_pattern_use_regex = (
-            request.query.get("name_pattern_use_regex", "false").lower() == "true"
-        )
-
         return {
             "page": page,
             "page_size": page_size,
@@ -367,7 +297,6 @@ class ModelListingHandler:
             "fuzzy_search": fuzzy_search,
             "base_models": base_models,
             "tags": tag_filters,
-            "tag_logic": tag_logic,
             "search_options": search_options,
             "hash_filters": hash_filters,
             "favorites_only": favorites_only,
@@ -375,9 +304,6 @@ class ModelListingHandler:
             "credit_required": credit_required,
             "allow_selling_generated_content": allow_selling_generated_content,
             "model_types": model_types,
-            "name_pattern_include": name_pattern_include,
-            "name_pattern_exclude": name_pattern_exclude,
-            "name_pattern_use_regex": name_pattern_use_regex,
             **self._parse_specific_params(request),
         }
 
@@ -432,21 +358,6 @@ class ModelManagementHandler:
             self._logger.error("Error excluding model: %s", exc, exc_info=True)
             return web.Response(text=str(exc), status=500)
 
-    async def unexclude_model(self, request: web.Request) -> web.Response:
-        try:
-            data = await request.json()
-            file_path = data.get("file_path")
-            if not file_path:
-                return web.Response(text="Model path is required", status=400)
-
-            result = await self._lifecycle_service.unexclude_model(file_path)
-            return web.json_response(result)
-        except ValueError as exc:
-            return web.json_response({"success": False, "error": str(exc)}, status=400)
-        except Exception as exc:
-            self._logger.error("Error restoring model: %s", exc, exc_info=True)
-            return web.Response(text=str(exc), status=500)
-
     async def fetch_civitai(self, request: web.Request) -> web.Response:
         try:
             data = await request.json()
@@ -465,34 +376,10 @@ class ModelManagementHandler:
                 return web.json_response(
                     {"success": False, "error": "Model not found in cache"}, status=404
                 )
-
-            # Check if hash needs to be calculated (lazy hash for checkpoints)
-            sha256 = model_data.get("sha256")
-            hash_status = model_data.get("hash_status", "completed")
-
-            if not sha256 or hash_status != "completed":
-                # For checkpoints, calculate hash on-demand
-                scanner = self._service.scanner
-                if hasattr(scanner, "calculate_hash_for_model"):
-                    self._logger.info(
-                        f"Lazy hash calculation triggered for {file_path}"
-                    )
-                    sha256 = await scanner.calculate_hash_for_model(file_path)
-                    if not sha256:
-                        return web.json_response(
-                            {
-                                "success": False,
-                                "error": "Failed to calculate SHA256 hash",
-                            },
-                            status=500,
-                        )
-                    # Update model_data with new hash
-                    model_data["sha256"] = sha256
-                    model_data["hash_status"] = "completed"
-                else:
-                    return web.json_response(
-                        {"success": False, "error": "No SHA256 hash found"}, status=400
-                    )
+            if not model_data.get("sha256"):
+                return web.json_response(
+                    {"success": False, "error": "No SHA256 hash found"}, status=400
+                )
 
             await MetadataManager.hydrate_model_data(model_data)
 
@@ -508,11 +395,6 @@ class ModelManagementHandler:
             formatted_metadata = await self._service.format_response(model_data)
             return web.json_response({"success": True, "metadata": formatted_metadata})
         except Exception as exc:
-            if is_expected_offline_error(str(exc)):
-                return web.json_response(
-                    {"success": False, "error": OFFLINE_FRIENDLY_MESSAGE},
-                    status=503,
-                )
             self._logger.error("Error fetching from CivitAI: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
@@ -559,11 +441,6 @@ class ModelManagementHandler:
                 }
             )
         except Exception as exc:
-            if is_expected_offline_error(str(exc)):
-                return web.json_response(
-                    {"success": False, "error": OFFLINE_FRIENDLY_MESSAGE},
-                    status=503,
-                )
             self._logger.error("Error re-linking to CivitAI: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
@@ -622,153 +499,6 @@ class ModelManagementHandler:
             self._logger.error("Error replacing preview: %s", exc, exc_info=True)
             return web.Response(text=str(exc), status=500)
 
-    async def set_preview_from_url(self, request: web.Request) -> web.Response:
-        """Set a preview image from a remote URL (e.g., CivitAI)."""
-        try:
-            from ...utils.civitai_utils import rewrite_preview_url
-            from ...services.downloader import get_downloader
-
-            data = await request.json()
-            model_path = data.get("model_path")
-            image_url = data.get("image_url")
-            nsfw_level = data.get("nsfw_level", 0)
-
-            if not model_path:
-                return web.json_response(
-                    {"success": False, "error": "Model path is required"}, status=400
-                )
-
-            if not image_url:
-                return web.json_response(
-                    {"success": False, "error": "Image URL is required"}, status=400
-                )
-
-            # Rewrite URL to use optimized rendition if it's a Civitai URL
-            optimized_url, was_rewritten = rewrite_preview_url(
-                image_url, media_type="image"
-            )
-            if was_rewritten and optimized_url:
-                self._logger.info(
-                    f"Rewritten preview URL to optimized version: {optimized_url}"
-                )
-            else:
-                optimized_url = image_url
-
-            # Download the image using the Downloader service
-            self._logger.info(
-                f"Downloading preview from {optimized_url} for {model_path}"
-            )
-            downloader = await get_downloader()
-            success, preview_data, headers = await downloader.download_to_memory(
-                optimized_url, use_auth=False, return_headers=True
-            )
-
-            if not success:
-                return web.json_response(
-                    {
-                        "success": False,
-                        "error": f"Failed to download image: {preview_data}",
-                    },
-                    status=502,
-                )
-
-            # preview_data is bytes when success is True
-            preview_bytes = (
-                preview_data
-                if isinstance(preview_data, bytes)
-                else preview_data.encode("utf-8")
-            )
-
-            # Determine content type from response headers
-            content_type = (
-                headers.get("Content-Type", "image/jpeg") if headers else "image/jpeg"
-            )
-
-            # Extract original filename from URL
-            original_filename = None
-            if "?" in image_url:
-                url_path = image_url.split("?")[0]
-            else:
-                url_path = image_url
-            original_filename = url_path.split("/")[-1] if "/" in url_path else None
-
-            result = await self._preview_service.replace_preview(
-                model_path=model_path,
-                preview_data=preview_data,
-                content_type=content_type,
-                original_filename=original_filename,
-                nsfw_level=nsfw_level,
-                update_preview_in_cache=self._service.scanner.update_preview_in_cache,
-                metadata_loader=self._metadata_sync.load_local_metadata,
-            )
-
-            return web.json_response(
-                {
-                    "success": True,
-                    "preview_url": config.get_preview_static_url(
-                        result["preview_path"]
-                    ),
-                    "preview_nsfw_level": result["preview_nsfw_level"],
-                }
-            )
-        except Exception as exc:
-            self._logger.error("Error setting preview from URL: %s", exc, exc_info=True)
-            return web.json_response({"success": False, "error": str(exc)}, status=500)
-
-            if not image_url:
-                return web.json_response(
-                    {"success": False, "error": "Image URL is required"}, status=400
-                )
-
-            # Download the image from the remote URL
-            self._logger.info(f"Downloading preview from {image_url} for {model_path}")
-            async with aiohttp.ClientSession() as session:
-                async with session.get(image_url) as response:
-                    if response.status != 200:
-                        return web.json_response(
-                            {
-                                "success": False,
-                                "error": f"Failed to download image: HTTP {response.status}",
-                            },
-                            status=502,
-                        )
-
-                    content_type = response.headers.get("Content-Type", "image/jpeg")
-                    preview_data = await response.read()
-
-                    # Extract original filename from URL
-                    original_filename = None
-                    if "?" in image_url:
-                        url_path = image_url.split("?")[0]
-                    else:
-                        url_path = image_url
-                    original_filename = (
-                        url_path.split("/")[-1] if "/" in url_path else None
-                    )
-
-            result = await self._preview_service.replace_preview(
-                model_path=model_path,
-                preview_data=preview_bytes,
-                content_type=content_type,
-                original_filename=original_filename,
-                nsfw_level=nsfw_level,
-                update_preview_in_cache=self._service.scanner.update_preview_in_cache,
-                metadata_loader=self._metadata_sync.load_local_metadata,
-            )
-
-            return web.json_response(
-                {
-                    "success": True,
-                    "preview_url": config.get_preview_static_url(
-                        result["preview_path"]
-                    ),
-                    "preview_nsfw_level": result["preview_nsfw_level"],
-                }
-            )
-        except Exception as exc:
-            self._logger.error("Error setting preview from URL: %s", exc, exc_info=True)
-            return web.json_response({"success": False, "error": str(exc)}, status=500)
-
     async def save_metadata(self, request: web.Request) -> web.Response:
         try:
             data = await request.json()
@@ -911,7 +641,7 @@ class ModelQueryHandler:
     async def get_top_tags(self, request: web.Request) -> web.Response:
         try:
             limit = int(request.query.get("limit", "20"))
-            if limit < 0:
+            if limit < 1 or limit > 100:
                 limit = 20
             top_tags = await self._service.get_top_tags(limit)
             return web.json_response({"success": True, "tags": top_tags})
@@ -924,7 +654,7 @@ class ModelQueryHandler:
     async def get_base_models(self, request: web.Request) -> web.Response:
         try:
             limit = int(request.query.get("limit", "20"))
-            if limit < 0 or limit > 100:
+            if limit < 1 or limit > 100:
                 limit = 20
             base_models = await self._service.get_base_models(limit)
             return web.json_response({"success": True, "base_models": base_models})
@@ -1025,22 +755,19 @@ class ModelQueryHandler:
 
     async def find_duplicate_models(self, request: web.Request) -> web.Response:
         try:
-            filters = self._parse_duplicate_filters(request)
             duplicates = self._service.find_duplicate_hashes()
             result = []
             cache = await self._service.scanner.get_cached_data()
-
             for sha256, paths in duplicates.items():
-                # Collect all models in this group
-                all_models = []
+                group = {"hash": sha256, "models": []}
                 for path in paths:
                     model = next(
                         (m for m in cache.raw_data if m["file_path"] == path), None
                     )
                     if model:
-                        all_models.append(model)
-
-                # Include primary if not already in paths
+                        group["models"].append(
+                            await self._service.format_response(model)
+                        )
                 primary_path = self._service.get_path_by_hash(sha256)
                 if primary_path and primary_path not in paths:
                     primary_model = next(
@@ -1048,23 +775,11 @@ class ModelQueryHandler:
                         None,
                     )
                     if primary_model:
-                        all_models.insert(0, primary_model)
-
-                # Apply filters
-                filtered = self._apply_duplicate_filters(all_models, filters)
-
-                # Sort: originals first, copies last
-                sorted_models = self._sort_duplicate_group(filtered)
-
-                # Format response
-                group = {"hash": sha256, "models": []}
-                for model in sorted_models:
-                    group["models"].append(await self._service.format_response(model))
-
-                # Only include groups with 2+ models after filtering
+                        group["models"].insert(
+                            0, await self._service.format_response(primary_model)
+                        )
                 if len(group["models"]) > 1:
                     result.append(group)
-
             return web.json_response(
                 {"success": True, "duplicates": result, "count": len(result)}
             )
@@ -1077,87 +792,6 @@ class ModelQueryHandler:
             )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
-    def _parse_duplicate_filters(self, request: web.Request) -> Dict[str, Any]:
-        """Parse filter parameters from the request for duplicate finding."""
-        return {
-            "base_models": request.query.getall("base_model", []),
-            "tag_include": request.query.getall("tag_include", []),
-            "tag_exclude": request.query.getall("tag_exclude", []),
-            "model_types": request.query.getall("model_type", []),
-            "folder": request.query.get("folder"),
-            "favorites_only": request.query.get("favorites_only", "").lower() == "true",
-        }
-
-    def _apply_duplicate_filters(
-        self, models: List[Dict[str, Any]], filters: Dict[str, Any]
-    ) -> List[Dict[str, Any]]:
-        """Apply filters to a list of models within a duplicate group."""
-        result = models
-
-        # Apply base model filter
-        if filters.get("base_models"):
-            base_set = set(filters["base_models"])
-            result = [m for m in result if m.get("base_model") in base_set]
-
-        # Apply tag filters (include)
-        for tag in filters.get("tag_include", []):
-            if tag == "__no_tags__":
-                result = [m for m in result if not m.get("tags")]
-            else:
-                result = [m for m in result if tag in (m.get("tags") or [])]
-
-        # Apply tag filters (exclude)
-        for tag in filters.get("tag_exclude", []):
-            if tag == "__no_tags__":
-                result = [m for m in result if m.get("tags")]
-            else:
-                result = [m for m in result if tag not in (m.get("tags") or [])]
-
-        # Apply model type filter
-        if filters.get("model_types"):
-            type_set = {t.lower() for t in filters["model_types"]}
-            result = [
-                m for m in result if (m.get("model_type") or "").lower() in type_set
-            ]
-
-        # Apply folder filter
-        if filters.get("folder"):
-            folder = filters["folder"]
-            result = [m for m in result if m.get("folder", "").startswith(folder)]
-
-        # Apply favorites filter
-        if filters.get("favorites_only"):
-            result = [m for m in result if m.get("favorite", False)]
-
-        return result
-
-    def _sort_duplicate_group(
-        self, models: List[Dict[str, Any]]
-    ) -> List[Dict[str, Any]]:
-        """Sort models: originals first (left), copies (with -????. pattern) last (right)."""
-        if len(models) <= 1:
-            return models
-
-        min_len = min(len(m.get("file_name", "")) for m in models)
-
-        def copy_score(m):
-            fn = m.get("file_name", "")
-            score = 0
-            # Match -0001.safetensors, -1234.safetensors etc.
-            if re.search(r"-\d{4}\.", fn):
-                score += 100
-            # Match (1), (2) etc.
-            if re.search(r"\(\d+\)", fn):
-                score += 50
-            # Match 'copy' in filename
-            if "copy" in fn.lower():
-                score += 50
-            # Longer filenames are more likely copies
-            score += len(fn) - min_len
-            return (score, fn.lower())
-
-        return sorted(models, key=copy_score)
-
     async def find_filename_conflicts(self, request: web.Request) -> web.Response:
         try:
             duplicates = self._service.find_duplicate_filenames()
@@ -1343,11 +977,8 @@ class ModelQueryHandler:
     async def get_relative_paths(self, request: web.Request) -> web.Response:
         try:
             search = request.query.get("search", "").strip()
-            limit = min(int(request.query.get("limit", "15")), 100)
-            offset = max(0, int(request.query.get("offset", "0")))
-            matching_paths = await self._service.search_relative_paths(
-                search, limit, offset
-            )
+            limit = min(int(request.query.get("limit", "15")), 50)
+            matching_paths = await self._service.search_relative_paths(search, limit)
             return web.json_response(
                 {"success": True, "relative_paths": matching_paths}
             )
@@ -1410,7 +1041,6 @@ class ModelDownloadHandler:
                 request.query.get("use_default_paths", "false").lower() == "true"
             )
             source = request.query.get("source")
-            file_params_json = request.query.get("file_params")
 
             data = {"model_id": model_id, "use_default_paths": use_default_paths}
             if model_version_id:
@@ -1419,15 +1049,6 @@ class ModelDownloadHandler:
                 data["download_id"] = download_id
             if source:
                 data["source"] = source
-            if file_params_json:
-                import json
-
-                try:
-                    data["file_params"] = json.loads(file_params_json)
-                except json.JSONDecodeError:
-                    self._logger.warning(
-                        "Invalid file_params JSON: %s", file_params_json
-                    )
 
             loop = asyncio.get_event_loop()
             future = loop.create_future()
@@ -1597,20 +1218,6 @@ class ModelCivitaiHandler:
 
             cache = await self._service.scanner.get_cached_data()
             version_index = cache.version_index
-            downloaded_version_ids: set[int] = set()
-            try:
-                history_service = await ServiceRegistry.get_downloaded_version_history_service()
-                downloaded_version_ids = set(
-                    await history_service.get_downloaded_version_ids(
-                        self._service.model_type,
-                        model_id,
-                    )
-                )
-            except Exception as exc:  # pragma: no cover - defensive logging
-                self._logger.debug(
-                    "Failed to load download history for CivitAI versions: %s",
-                    exc,
-                )
 
             for version in versions:
                 version_id = None
@@ -1627,9 +1234,6 @@ class ModelCivitaiHandler:
                     else None
                 )
                 version["existsLocally"] = cache_entry is not None
-                version["hasBeenDownloaded"] = (
-                    version_id in downloaded_version_ids if version_id is not None else False
-                )
                 if cache_entry and isinstance(cache_entry, Mapping):
                     local_path = cache_entry.get("file_path")
                     if local_path:
@@ -1828,13 +1432,11 @@ class ModelUpdateHandler:
         service,
         update_service,
         metadata_provider_selector,
-        settings_service,
         logger: logging.Logger,
     ) -> None:
         self._service = service
         self._update_service = update_service
         self._metadata_provider_selector = metadata_provider_selector
-        self._settings = settings_service
         self._logger = logger
 
     async def fetch_missing_civitai_license_data(
@@ -1872,11 +1474,6 @@ class ModelUpdateHandler:
                 status=429,
             )
         except Exception as exc:  # pragma: no cover - defensive log
-            if is_expected_offline_error(str(exc)):
-                return web.json_response(
-                    {"success": False, "error": OFFLINE_FRIENDLY_MESSAGE},
-                    status=503,
-                )
             self._logger.error("Failed to fetch license info: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
@@ -1965,12 +1562,9 @@ class ModelUpdateHandler:
                 {"success": False, "error": str(exc) or "Rate limited"}, status=429
             )
         except Exception as exc:  # pragma: no cover - defensive logging
-            if is_expected_offline_error(str(exc)):
-                return web.json_response(
-                    {"success": False, "error": OFFLINE_FRIENDLY_MESSAGE},
-                    status=503,
-                )
-            self._logger.error("Failed to refresh model updates: %s", exc, exc_info=True)
+            self._logger.error(
+                "Failed to refresh model updates: %s", exc, exc_info=True
+            )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
         serialized_records = []
@@ -2079,9 +1673,6 @@ class ModelUpdateHandler:
                 {"success": False, "error": "Model not tracked"}, status=404
             )
 
-        # Enrich EA versions with detailed info if needed
-        record = await self._enrich_early_access_details(record)
-
         overrides = await self._build_version_context(record)
         return web.json_response(
             {
@@ -2120,79 +1711,6 @@ class ModelUpdateHandler:
             )
             return None
 
-    async def _enrich_early_access_details(self, record):
-        """Fetch detailed EA info for versions missing exact end time.
-
-        Identifies versions with is_early_access=True but no early_access_ends_at,
-        then fetches detailed info from CivitAI to get the exact end time.
-        """
-        if not record or not record.versions:
-            return record
-
-        # Find versions that need enrichment
-        versions_needing_update = []
-        for version in record.versions:
-            if version.is_early_access and not version.early_access_ends_at:
-                versions_needing_update.append(version)
-
-        if not versions_needing_update:
-            return record
-
-        provider = await self._get_civitai_provider()
-        if not provider:
-            return record
-
-        # Fetch detailed info for each version needing update
-        updated_versions = []
-        for version in versions_needing_update:
-            try:
-                version_info, error = await provider.get_model_version_info(
-                    str(version.version_id)
-                )
-                if version_info and not error:
-                    ea_ends_at = version_info.get("earlyAccessEndsAt")
-                    if ea_ends_at:
-                        # Create updated version with EA end time
-                        from dataclasses import replace
-
-                        updated_version = replace(
-                            version, early_access_ends_at=ea_ends_at
-                        )
-                        updated_versions.append(updated_version)
-                        self._logger.debug(
-                            "Enriched EA info for version %s: %s",
-                            version.version_id,
-                            ea_ends_at,
-                        )
-            except Exception as exc:
-                self._logger.debug(
-                    "Failed to fetch EA details for version %s: %s",
-                    version.version_id,
-                    exc,
-                )
-
-        if not updated_versions:
-            return record
-
-        # Update record with enriched versions
-        version_map = {v.version_id: v for v in record.versions}
-        for updated in updated_versions:
-            version_map[updated.version_id] = updated
-
-        # Create new record with updated versions
-        from dataclasses import replace
-
-        new_record = replace(
-            record,
-            versions=list(version_map.values()),
-        )
-
-        # Optionally persist to database for caching
-        # Note: We don't persist here to avoid side effects; the data will be
-        # refreshed on next bulk update if still needed
-
-        return new_record
-
     async def _collect_models_missing_license(
         self,
         cache,
@@ -2356,18 +1874,9 @@ class ModelUpdateHandler:
         self,
         record,
         *,
-        version_context: Optional[Dict[int, Dict[str, Any]]] = None,
+        version_context: Optional[Dict[int, Dict[str, Optional[str]]]] = None,
     ) -> Dict:
         context = version_context or {}
-        # Check user setting for hiding early access versions
-        hide_early_access = False
-        if self._settings is not None:
-            try:
-                hide_early_access = bool(
-                    self._settings.get("hide_early_access_updates", False)
-                )
-            except Exception:
-                pass
         return {
             "modelType": record.model_type,
             "modelId": record.model_id,
@@ -2376,7 +1885,7 @@ class ModelUpdateHandler:
             "inLibraryVersionIds": record.in_library_version_ids,
             "lastCheckedAt": record.last_checked_at,
             "shouldIgnore": record.should_ignore_model,
-            "hasUpdate": record.has_update(hide_early_access=hide_early_access),
+            "hasUpdate": record.has_update(),
             "versions": [
                 self._serialize_version(version, context.get(version.version_id))
                 for version in record.versions
@@ -2385,32 +1894,13 @@ class ModelUpdateHandler:
 
     @staticmethod
     def _serialize_version(
-        version, context: Optional[Dict[str, Any]]
+        version, context: Optional[Dict[str, Optional[str]]]
     ) -> Dict:
         context = context or {}
         preview_override = context.get("preview_override")
         preview_url = (
             preview_override if preview_override is not None else version.preview_url
         )
-
-        # Determine if version is currently in early access
-        # Two-phase detection: use exact end time if available, otherwise fallback to basic flag
-        is_early_access = False
-        if version.early_access_ends_at:
-            try:
-                from datetime import datetime, timezone
-
-                ea_date = datetime.fromisoformat(
-                    version.early_access_ends_at.replace("Z", "+00:00")
-                )
-                is_early_access = ea_date > datetime.now(timezone.utc)
-            except (ValueError, AttributeError):
-                # If date parsing fails, treat as active EA (conservative)
-                is_early_access = True
-        elif getattr(version, "is_early_access", False):
-            # Fallback to basic EA flag from bulk API
-            is_early_access = True
-
         return {
             "versionId": version.version_id,
             "name": version.name,
@@ -2419,42 +1909,15 @@ class ModelUpdateHandler:
             "sizeBytes": version.size_bytes,
             "previewUrl": preview_url,
             "isInLibrary": version.is_in_library,
-            "hasBeenDownloaded": bool(context.get("has_been_downloaded", False)),
             "shouldIgnore": version.should_ignore,
-            "earlyAccessEndsAt": version.early_access_ends_at,
-            "isEarlyAccess": is_early_access,
-            "usageControl": version.usage_control,
             "filePath": context.get("file_path"),
             "fileName": context.get("file_name"),
         }
 
     async def _build_version_context(
         self, record
-    ) -> Dict[int, Dict[str, Any]]:
-        context: Dict[int, Dict[str, Any]] = {}
-        downloaded_version_ids: set[int] = set()
-        try:
-            history_service = await ServiceRegistry.get_downloaded_version_history_service()
-            downloaded_version_ids = set(
-                await history_service.get_downloaded_version_ids(
-                    record.model_type,
-                    record.model_id,
-                )
-            )
-        except Exception as exc:  # pragma: no cover - defensive logging
-            self._logger.debug(
-                "Failed to load download history while building version context: %s",
-                exc,
-            )
-
-        for version in record.versions:
-            context[version.version_id] = {
-                "file_path": None,
-                "file_name": None,
-                "preview_override": None,
-                "has_been_downloaded": version.version_id in downloaded_version_ids,
-            }
-
+    ) -> Dict[int, Dict[str, Optional[str]]]:
+        context: Dict[int, Dict[str, Optional[str]]] = {}
         try:
             cache = await self._service.scanner.get_cached_data()
         except Exception as exc:  # pragma: no cover - defensive logging
@@ -2473,21 +1936,16 @@ class ModelUpdateHandler:
             cache_entry = version_index.get(version.version_id)
             if isinstance(cache_entry, Mapping):
                 preview = cache_entry.get("preview_url")
-                context_entry = context.setdefault(
-                    version.version_id,
-                    {
-                        "file_path": None,
-                        "file_name": None,
-                        "preview_override": None,
-                        "has_been_downloaded": version.version_id in downloaded_version_ids,
-                    },
-                )
-                context_entry["file_path"] = cache_entry.get("file_path")
-                context_entry["file_name"] = cache_entry.get("file_name")
+                context_entry: Dict[str, Optional[str]] = {
+                    "file_path": cache_entry.get("file_path"),
+                    "file_name": cache_entry.get("file_name"),
+                    "preview_override": None,
+                }
                 if isinstance(preview, str) and preview:
                     context_entry["preview_override"] = config.get_preview_static_url(
                         preview
                     )
+                context[version.version_id] = context_entry
         return context
 
 
@@ -2511,15 +1969,12 @@ class ModelHandlerSet:
         return {
             "handle_models_page": self.page_view.handle,
             "get_models": self.listing.get_models,
-            "get_excluded_models": self.listing.get_excluded_models,
             "delete_model": self.management.delete_model,
             "exclude_model": self.management.exclude_model,
-            "unexclude_model": self.management.unexclude_model,
             "fetch_civitai": self.management.fetch_civitai,
             "fetch_all_civitai": self.civitai.fetch_all_civitai,
             "relink_civitai": self.management.relink_civitai,
             "replace_preview": self.management.replace_preview,
-            "set_preview_from_url": self.management.set_preview_from_url,
             "save_metadata": self.management.save_metadata,
             "add_tags": self.management.add_tags,
             "rename_model": self.management.rename_model,

py/routes/handlers/preview_handlers.py

@@ -33,10 +33,6 @@ class PreviewHandler:
             raise web.HTTPBadRequest(text="Invalid preview path encoding") from exc
 
         normalized = decoded_path.replace("\\", "/")
-
-        if not self._config.is_preview_path_allowed(normalized):
-            raise web.HTTPForbidden(text="Preview path is not within an allowed directory")
-
         candidate = Path(normalized)
         try:
             resolved = candidate.expanduser().resolve(strict=False)
@@ -44,8 +40,12 @@ class PreviewHandler:
             logger.debug("Failed to resolve preview path %s: %s", normalized, exc)
             raise web.HTTPBadRequest(text="Unable to resolve preview path") from exc
 
+        resolved_str = str(resolved)
+        if not self._config.is_preview_path_allowed(resolved_str):
+            raise web.HTTPForbidden(text="Preview path is not within an allowed directory")
+
         if not resolved.is_file():
-            logger.debug("Preview file not found at %s", str(resolved))
+            logger.debug("Preview file not found at %s", resolved_str)
             raise web.HTTPNotFound(text="Preview file not found")
 
         # aiohttp's FileResponse handles range requests and content headers for us.

py/routes/misc_model_routes.py

@@ -0,0 +1,112 @@
+import logging
+from typing import Dict
+from aiohttp import web
+
+from .base_model_routes import BaseModelRoutes
+from .model_route_registrar import ModelRouteRegistrar
+from ..services.misc_service import MiscService
+from ..services.service_registry import ServiceRegistry
+from ..config import config
+
+logger = logging.getLogger(__name__)
+
+class MiscModelRoutes(BaseModelRoutes):
+    """Misc-specific route controller (VAE, Upscaler)"""
+
+    def __init__(self):
+        """Initialize Misc routes with Misc service"""
+        super().__init__()
+        self.template_name = "misc.html"
+
+    async def initialize_services(self):
+        """Initialize services from ServiceRegistry"""
+        misc_scanner = await ServiceRegistry.get_misc_scanner()
+        update_service = await ServiceRegistry.get_model_update_service()
+        self.service = MiscService(misc_scanner, update_service=update_service)
+        self.set_model_update_service(update_service)
+
+        # Attach service dependencies
+        self.attach_service(self.service)
+
+    def setup_routes(self, app: web.Application):
+        """Setup Misc routes"""
+        # Schedule service initialization on app startup
+        app.on_startup.append(lambda _: self.initialize_services())
+
+        # Setup common routes with 'misc' prefix (includes page route)
+        super().setup_routes(app, 'misc')
+
+    def setup_specific_routes(self, registrar: ModelRouteRegistrar, prefix: str):
+        """Setup Misc-specific routes"""
+        # Misc info by name
+        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/info/{name}', prefix, self.get_misc_info)
+
+        # VAE roots and Upscaler roots
+        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/vae_roots', prefix, self.get_vae_roots)
+        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/upscaler_roots', prefix, self.get_upscaler_roots)
+
+    def _validate_civitai_model_type(self, model_type: str) -> bool:
+        """Validate CivitAI model type for Misc (VAE or Upscaler)"""
+        return model_type.lower() in ['vae', 'upscaler']
+
+    def _get_expected_model_types(self) -> str:
+        """Get expected model types string for error messages"""
+        return "VAE or Upscaler"
+
+    def _parse_specific_params(self, request: web.Request) -> Dict:
+        """Parse Misc-specific parameters"""
+        params: Dict = {}
+
+        if 'misc_hash' in request.query:
+            params['hash_filters'] = {'single_hash': request.query['misc_hash'].lower()}
+        elif 'misc_hashes' in request.query:
+            params['hash_filters'] = {
+                'multiple_hashes': [h.lower() for h in request.query['misc_hashes'].split(',')]
+            }
+
+        return params
+
+    async def get_misc_info(self, request: web.Request) -> web.Response:
+        """Get detailed information for a specific misc model by name"""
+        try:
+            name = request.match_info.get('name', '')
+            misc_info = await self.service.get_model_info_by_name(name)
+
+            if misc_info:
+                return web.json_response(misc_info)
+            else:
+                return web.json_response({"error": "Misc model not found"}, status=404)
+
+        except Exception as e:
+            logger.error(f"Error in get_misc_info: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)
+
+    async def get_vae_roots(self, request: web.Request) -> web.Response:
+        """Return the list of VAE roots from config"""
+        try:
+            roots = config.vae_roots
+            return web.json_response({
+                "success": True,
+                "roots": roots
+            })
+        except Exception as e:
+            logger.error(f"Error getting VAE roots: {e}", exc_info=True)
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)
+
+    async def get_upscaler_roots(self, request: web.Request) -> web.Response:
+        """Return the list of upscaler roots from config"""
+        try:
+            roots = config.upscaler_roots
+            return web.json_response({
+                "success": True,
+                "roots": roots
+            })
+        except Exception as e:
+            logger.error(f"Error getting upscaler roots: {e}", exc_info=True)
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)

py/routes/misc_route_registrar.py

@@ -22,17 +22,10 @@ class RouteDefinition:
 MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/settings", "get_settings"),
     RouteDefinition("POST", "/api/lm/settings", "update_settings"),
-    RouteDefinition("GET", "/api/lm/doctor/diagnostics", "get_doctor_diagnostics"),
-    RouteDefinition("POST", "/api/lm/doctor/repair-cache", "repair_doctor_cache"),
-    RouteDefinition("POST", "/api/lm/doctor/resolve-filename-conflicts", "resolve_doctor_filename_conflicts"),
-    RouteDefinition("POST", "/api/lm/doctor/export-bundle", "export_doctor_bundle"),
     RouteDefinition("GET", "/api/lm/priority-tags", "get_priority_tags"),
     RouteDefinition("GET", "/api/lm/settings/libraries", "get_settings_libraries"),
     RouteDefinition("POST", "/api/lm/settings/libraries/activate", "activate_library"),
     RouteDefinition("GET", "/api/lm/health-check", "health_check"),
-    RouteDefinition("GET", "/api/lm/supporters", "get_supporters"),
-    RouteDefinition("GET", "/api/lm/wildcards/search", "search_wildcards"),
-    RouteDefinition("POST", "/api/lm/wildcards/open-location", "open_wildcards_location"),
     RouteDefinition("POST", "/api/lm/open-file-location", "open_file_location"),
     RouteDefinition("POST", "/api/lm/update-usage-stats", "update_usage_stats"),
     RouteDefinition("GET", "/api/lm/get-usage-stats", "get_usage_stats"),
@@ -43,54 +36,13 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/update-node-widget", "update_node_widget"),
     RouteDefinition("GET", "/api/lm/get-registry", "get_registry"),
     RouteDefinition("GET", "/api/lm/check-model-exists", "check_model_exists"),
-    RouteDefinition("GET", "/api/lm/check-models-exist", "check_models_exist"),
-    RouteDefinition(
-        "GET",
-        "/api/lm/model-version-download-status",
-        "get_model_version_download_status",
-    ),
-    RouteDefinition(
-        "POST",
-        "/api/lm/model-version-download-status",
-        "set_model_version_download_status",
-    ),
-    RouteDefinition(
-        "GET",
-        "/api/lm/set-model-version-download-status",
-        "set_model_version_download_status",
-    ),
     RouteDefinition("GET", "/api/lm/civitai/user-models", "get_civitai_user_models"),
-    RouteDefinition(
-        "POST", "/api/lm/download-metadata-archive", "download_metadata_archive"
-    ),
-    RouteDefinition(
-        "POST", "/api/lm/remove-metadata-archive", "remove_metadata_archive"
-    ),
-    RouteDefinition(
-        "GET", "/api/lm/metadata-archive-status", "get_metadata_archive_status"
-    ),
-    RouteDefinition("GET", "/api/lm/backup/status", "get_backup_status"),
-    RouteDefinition("POST", "/api/lm/backup/export", "export_backup"),
-    RouteDefinition("POST", "/api/lm/backup/import", "import_backup"),
-    RouteDefinition("POST", "/api/lm/backup/open-location", "open_backup_location"),
-    RouteDefinition(
-        "GET", "/api/lm/model-versions-status", "get_model_versions_status"
-    ),
+    RouteDefinition("POST", "/api/lm/download-metadata-archive", "download_metadata_archive"),
+    RouteDefinition("POST", "/api/lm/remove-metadata-archive", "remove_metadata_archive"),
+    RouteDefinition("GET", "/api/lm/metadata-archive-status", "get_metadata_archive_status"),
+    RouteDefinition("GET", "/api/lm/model-versions-status", "get_model_versions_status"),
     RouteDefinition("POST", "/api/lm/settings/open-location", "open_settings_location"),
     RouteDefinition("GET", "/api/lm/custom-words/search", "search_custom_words"),
-    RouteDefinition("GET", "/api/lm/example-workflows", "get_example_workflows"),
-    RouteDefinition(
-        "GET", "/api/lm/example-workflows/{filename}", "get_example_workflow"
-    ),
-    # Base model management routes
-    RouteDefinition("GET", "/api/lm/base-models", "get_base_models"),
-    RouteDefinition("POST", "/api/lm/base-models/refresh", "refresh_base_models"),
-    RouteDefinition(
-        "GET", "/api/lm/base-models/categories", "get_base_model_categories"
-    ),
-    RouteDefinition(
-        "GET", "/api/lm/base-models/cache-status", "get_base_model_cache_status"
-    ),
 )
 
 
@@ -114,11 +66,7 @@ class MiscRouteRegistrar:
         definitions: Iterable[RouteDefinition] = MISC_ROUTE_DEFINITIONS,
     ) -> None:
         for definition in definitions:
-            self._bind(
-                definition.method,
-                definition.path,
-                handler_lookup[definition.handler_name],
-            )
+            self._bind(definition.method, definition.path, handler_lookup[definition.handler_name])
 
     def _bind(self, method: str, path: str, handler: Callable) -> None:
         add_method_name = self._METHOD_MAP[method.upper()]

py/routes/misc_routes.py

@@ -19,12 +19,9 @@ from ..services.downloader import get_downloader
 from ..utils.usage_stats import UsageStats
 from .handlers.misc_handlers import (
     CustomWordsHandler,
-    DoctorHandler,
-    ExampleWorkflowsHandler,
     FileSystemHandler,
     HealthCheckHandler,
     LoraCodeHandler,
-    BackupHandler,
     MetadataArchiveHandler,
     MiscHandlerSet,
     ModelExampleFilesHandler,
@@ -32,21 +29,17 @@ from .handlers.misc_handlers import (
     NodeRegistry,
     NodeRegistryHandler,
     SettingsHandler,
-    SupportersHandler,
     TrainedWordsHandler,
     UsageStatsHandler,
-    WildcardsHandler,
     build_service_registry_adapter,
 )
-from .handlers.base_model_handlers import BaseModelHandlerSet
 from .misc_route_registrar import MiscRouteRegistrar
 
 logger = logging.getLogger(__name__)
 
-standalone_mode = (
-    os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"
-    or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
-)
+standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get(
+    "HF_HUB_DISABLE_TELEMETRY", "0"
+) == "0"
 
 
 class MiscRoutes:
@@ -81,9 +74,7 @@ class MiscRoutes:
         self._node_registry = node_registry or NodeRegistry()
         self._standalone_mode = standalone_mode_flag
 
-        self._handler_mapping: (
-            Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]] | None
-        ) = None
+        self._handler_mapping: Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]] | None = None
 
     @staticmethod
     def setup_routes(app: web.Application) -> None:
@@ -95,9 +86,7 @@ class MiscRoutes:
         registrar = self._registrar_factory(app)
         registrar.register_routes(self._ensure_handler_mapping())
 
-    def _ensure_handler_mapping(
-        self,
-    ) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
+    def _ensure_handler_mapping(self) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
         if self._handler_mapping is None:
             handler_set = self._create_handler_set()
             self._handler_mapping = handler_set.to_route_mapping()
@@ -119,7 +108,6 @@ class MiscRoutes:
             settings_service=self._settings,
             metadata_provider_updater=self._metadata_provider_updater,
         )
-        backup = BackupHandler()
         filesystem = FileSystemHandler(settings_service=self._settings)
         node_registry_handler = NodeRegistryHandler(
             node_registry=self._node_registry,
@@ -131,11 +119,6 @@ class MiscRoutes:
             metadata_provider_factory=self._metadata_provider_factory,
         )
         custom_words = CustomWordsHandler()
-        wildcards = WildcardsHandler()
-        supporters = SupportersHandler()
-        doctor = DoctorHandler(settings_service=self._settings)
-        example_workflows = ExampleWorkflowsHandler()
-        base_model = BaseModelHandlerSet()
 
         return self._handler_set_factory(
             health=health,
@@ -147,14 +130,8 @@ class MiscRoutes:
             node_registry=node_registry_handler,
             model_library=model_library,
             metadata_archive=metadata_archive,
-            backup=backup,
             filesystem=filesystem,
             custom_words=custom_words,
-            wildcards=wildcards,
-            supporters=supporters,
-            doctor=doctor,
-            example_workflows=example_workflows,
-            base_model=base_model,
         )
 
 

py/routes/model_route_registrar.py

@@ -1,5 +1,4 @@
 """Route registrar for model endpoints."""
-
 from __future__ import annotations
 
 from dataclasses import dataclass
@@ -22,17 +21,12 @@ class RouteDefinition:
 
 COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/{prefix}/list", "get_models"),
-    RouteDefinition("GET", "/api/lm/{prefix}/excluded", "get_excluded_models"),
     RouteDefinition("POST", "/api/lm/{prefix}/delete", "delete_model"),
     RouteDefinition("POST", "/api/lm/{prefix}/exclude", "exclude_model"),
-    RouteDefinition("POST", "/api/lm/{prefix}/unexclude", "unexclude_model"),
     RouteDefinition("POST", "/api/lm/{prefix}/fetch-civitai", "fetch_civitai"),
     RouteDefinition("POST", "/api/lm/{prefix}/fetch-all-civitai", "fetch_all_civitai"),
     RouteDefinition("POST", "/api/lm/{prefix}/relink-civitai", "relink_civitai"),
     RouteDefinition("POST", "/api/lm/{prefix}/replace-preview", "replace_preview"),
-    RouteDefinition(
-        "POST", "/api/lm/{prefix}/set-preview-from-url", "set_preview_from_url"
-    ),
     RouteDefinition("POST", "/api/lm/{prefix}/save-metadata", "save_metadata"),
     RouteDefinition("POST", "/api/lm/{prefix}/add-tags", "add_tags"),
     RouteDefinition("POST", "/api/lm/{prefix}/rename", "rename_model"),
@@ -42,9 +36,7 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/{prefix}/move_models_bulk", "move_models_bulk"),
     RouteDefinition("GET", "/api/lm/{prefix}/auto-organize", "auto_organize_models"),
     RouteDefinition("POST", "/api/lm/{prefix}/auto-organize", "auto_organize_models"),
-    RouteDefinition(
-        "GET", "/api/lm/{prefix}/auto-organize-progress", "get_auto_organize_progress"
-    ),
+    RouteDefinition("GET", "/api/lm/{prefix}/auto-organize-progress", "get_auto_organize_progress"),
     RouteDefinition("GET", "/api/lm/{prefix}/top-tags", "get_top_tags"),
     RouteDefinition("GET", "/api/lm/{prefix}/base-models", "get_base_models"),
     RouteDefinition("GET", "/api/lm/{prefix}/model-types", "get_model_types"),
@@ -52,60 +44,30 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/{prefix}/roots", "get_model_roots"),
     RouteDefinition("GET", "/api/lm/{prefix}/folders", "get_folders"),
     RouteDefinition("GET", "/api/lm/{prefix}/folder-tree", "get_folder_tree"),
-    RouteDefinition(
-        "GET", "/api/lm/{prefix}/unified-folder-tree", "get_unified_folder_tree"
-    ),
+    RouteDefinition("GET", "/api/lm/{prefix}/unified-folder-tree", "get_unified_folder_tree"),
     RouteDefinition("GET", "/api/lm/{prefix}/find-duplicates", "find_duplicate_models"),
-    RouteDefinition(
-        "GET", "/api/lm/{prefix}/find-filename-conflicts", "find_filename_conflicts"
-    ),
+    RouteDefinition("GET", "/api/lm/{prefix}/find-filename-conflicts", "find_filename_conflicts"),
     RouteDefinition("GET", "/api/lm/{prefix}/get-notes", "get_model_notes"),
     RouteDefinition("GET", "/api/lm/{prefix}/preview-url", "get_model_preview_url"),
     RouteDefinition("GET", "/api/lm/{prefix}/civitai-url", "get_model_civitai_url"),
     RouteDefinition("GET", "/api/lm/{prefix}/metadata", "get_model_metadata"),
-    RouteDefinition(
-        "GET", "/api/lm/{prefix}/model-description", "get_model_description"
-    ),
+    RouteDefinition("GET", "/api/lm/{prefix}/model-description", "get_model_description"),
     RouteDefinition("GET", "/api/lm/{prefix}/relative-paths", "get_relative_paths"),
-    RouteDefinition(
-        "GET", "/api/lm/{prefix}/civitai/versions/{model_id}", "get_civitai_versions"
-    ),
-    RouteDefinition(
-        "GET",
-        "/api/lm/{prefix}/civitai/model/version/{modelVersionId}",
-        "get_civitai_model_by_version",
-    ),
-    RouteDefinition(
-        "GET", "/api/lm/{prefix}/civitai/model/hash/{hash}", "get_civitai_model_by_hash"
-    ),
-    RouteDefinition(
-        "POST", "/api/lm/{prefix}/updates/refresh", "refresh_model_updates"
-    ),
-    RouteDefinition(
-        "POST",
-        "/api/lm/{prefix}/updates/fetch-missing-license",
-        "fetch_missing_civitai_license_data",
-    ),
-    RouteDefinition(
-        "POST", "/api/lm/{prefix}/updates/ignore", "set_model_update_ignore"
-    ),
-    RouteDefinition(
-        "POST", "/api/lm/{prefix}/updates/ignore-version", "set_version_update_ignore"
-    ),
-    RouteDefinition(
-        "GET", "/api/lm/{prefix}/updates/status/{model_id}", "get_model_update_status"
-    ),
-    RouteDefinition(
-        "GET", "/api/lm/{prefix}/updates/versions/{model_id}", "get_model_versions"
-    ),
+    RouteDefinition("GET", "/api/lm/{prefix}/civitai/versions/{model_id}", "get_civitai_versions"),
+    RouteDefinition("GET", "/api/lm/{prefix}/civitai/model/version/{modelVersionId}", "get_civitai_model_by_version"),
+    RouteDefinition("GET", "/api/lm/{prefix}/civitai/model/hash/{hash}", "get_civitai_model_by_hash"),
+    RouteDefinition("POST", "/api/lm/{prefix}/updates/refresh", "refresh_model_updates"),
+    RouteDefinition("POST", "/api/lm/{prefix}/updates/fetch-missing-license", "fetch_missing_civitai_license_data"),
+    RouteDefinition("POST", "/api/lm/{prefix}/updates/ignore", "set_model_update_ignore"),
+    RouteDefinition("POST", "/api/lm/{prefix}/updates/ignore-version", "set_version_update_ignore"),
+    RouteDefinition("GET", "/api/lm/{prefix}/updates/status/{model_id}", "get_model_update_status"),
+    RouteDefinition("GET", "/api/lm/{prefix}/updates/versions/{model_id}", "get_model_versions"),
     RouteDefinition("POST", "/api/lm/download-model", "download_model"),
     RouteDefinition("GET", "/api/lm/download-model-get", "download_model_get"),
     RouteDefinition("GET", "/api/lm/cancel-download-get", "cancel_download_get"),
     RouteDefinition("GET", "/api/lm/pause-download", "pause_download_get"),
     RouteDefinition("GET", "/api/lm/resume-download", "resume_download_get"),
-    RouteDefinition(
-        "GET", "/api/lm/download-progress/{download_id}", "get_download_progress"
-    ),
+    RouteDefinition("GET", "/api/lm/download-progress/{download_id}", "get_download_progress"),
     RouteDefinition("POST", "/api/lm/{prefix}/cancel-task", "cancel_task"),
     RouteDefinition("GET", "/{prefix}", "handle_models_page"),
 )
@@ -132,18 +94,12 @@ class ModelRouteRegistrar:
         definitions: Iterable[RouteDefinition] = COMMON_ROUTE_DEFINITIONS,
     ) -> None:
         for definition in definitions:
-            self._bind_route(
-                definition.method,
-                definition.build_path(prefix),
-                handler_lookup[definition.handler_name],
-            )
+            self._bind_route(definition.method, definition.build_path(prefix), handler_lookup[definition.handler_name])
 
     def add_route(self, method: str, path: str, handler: Callable) -> None:
         self._bind_route(method, path, handler)
 
-    def add_prefixed_route(
-        self, method: str, path_template: str, prefix: str, handler: Callable
-    ) -> None:
+    def add_prefixed_route(self, method: str, path_template: str, prefix: str, handler: Callable) -> None:
         self._bind_route(method, path_template.replace("{prefix}", prefix), handler)
 
     def _bind_route(self, method: str, path: str, handler: Callable) -> None:

py/routes/recipe_route_registrar.py

@@ -1,5 +1,4 @@
 """Route registrar for recipe endpoints."""
-
 from __future__ import annotations
 
 from dataclasses import dataclass
@@ -23,9 +22,7 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/recipe/{recipe_id}", "get_recipe"),
     RouteDefinition("GET", "/api/lm/recipes/import-remote", "import_remote_recipe"),
     RouteDefinition("POST", "/api/lm/recipes/analyze-image", "analyze_uploaded_image"),
-    RouteDefinition(
-        "POST", "/api/lm/recipes/analyze-local-image", "analyze_local_image"
-    ),
+    RouteDefinition("POST", "/api/lm/recipes/analyze-local-image", "analyze_local_image"),
     RouteDefinition("POST", "/api/lm/recipes/save", "save_recipe"),
     RouteDefinition("DELETE", "/api/lm/recipe/{recipe_id}", "delete_recipe"),
     RouteDefinition("GET", "/api/lm/recipes/top-tags", "get_top_tags"),
@@ -33,13 +30,9 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/recipes/roots", "get_roots"),
     RouteDefinition("GET", "/api/lm/recipes/folders", "get_folders"),
     RouteDefinition("GET", "/api/lm/recipes/folder-tree", "get_folder_tree"),
-    RouteDefinition(
-        "GET", "/api/lm/recipes/unified-folder-tree", "get_unified_folder_tree"
-    ),
+    RouteDefinition("GET", "/api/lm/recipes/unified-folder-tree", "get_unified_folder_tree"),
     RouteDefinition("GET", "/api/lm/recipe/{recipe_id}/share", "share_recipe"),
-    RouteDefinition(
-        "GET", "/api/lm/recipe/{recipe_id}/share/download", "download_shared_recipe"
-    ),
+    RouteDefinition("GET", "/api/lm/recipe/{recipe_id}/share/download", "download_shared_recipe"),
     RouteDefinition("GET", "/api/lm/recipe/{recipe_id}/syntax", "get_recipe_syntax"),
     RouteDefinition("PUT", "/api/lm/recipe/{recipe_id}/update", "update_recipe"),
     RouteDefinition("POST", "/api/lm/recipe/move", "move_recipe"),
@@ -47,29 +40,13 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/recipe/lora/reconnect", "reconnect_lora"),
     RouteDefinition("GET", "/api/lm/recipes/find-duplicates", "find_duplicates"),
     RouteDefinition("POST", "/api/lm/recipes/bulk-delete", "bulk_delete"),
-    RouteDefinition(
-        "POST", "/api/lm/recipes/save-from-widget", "save_recipe_from_widget"
-    ),
+    RouteDefinition("POST", "/api/lm/recipes/save-from-widget", "save_recipe_from_widget"),
     RouteDefinition("GET", "/api/lm/recipes/for-lora", "get_recipes_for_lora"),
-    RouteDefinition(
-        "GET", "/api/lm/recipes/for-checkpoint", "get_recipes_for_checkpoint"
-    ),
     RouteDefinition("GET", "/api/lm/recipes/scan", "scan_recipes"),
     RouteDefinition("POST", "/api/lm/recipes/repair", "repair_recipes"),
     RouteDefinition("POST", "/api/lm/recipes/cancel-repair", "cancel_repair"),
     RouteDefinition("POST", "/api/lm/recipe/{recipe_id}/repair", "repair_recipe"),
     RouteDefinition("GET", "/api/lm/recipes/repair-progress", "get_repair_progress"),
-    RouteDefinition("POST", "/api/lm/recipes/batch-import/start", "start_batch_import"),
-    RouteDefinition(
-        "GET", "/api/lm/recipes/batch-import/progress", "get_batch_import_progress"
-    ),
-    RouteDefinition(
-        "POST", "/api/lm/recipes/batch-import/cancel", "cancel_batch_import"
-    ),
-    RouteDefinition(
-        "POST", "/api/lm/recipes/batch-import/directory", "start_directory_import"
-    ),
-    RouteDefinition("POST", "/api/lm/recipes/browse-directory", "browse_directory"),
 )
 
 
@@ -86,9 +63,7 @@ class RecipeRouteRegistrar:
     def __init__(self, app: web.Application) -> None:
         self._app = app
 
-    def register_routes(
-        self, handler_lookup: Mapping[str, Callable[[web.Request], object]]
-    ) -> None:
+    def register_routes(self, handler_lookup: Mapping[str, Callable[[web.Request], object]]) -> None:
         for definition in ROUTE_DEFINITIONS:
             handler = handler_lookup[definition.handler_name]
             self._bind_route(definition.method, definition.path, handler)

py/routes/stats_routes.py

@@ -209,80 +209,6 @@ class StatsRoutes:
                 'error': str(e)
             }, status=500)
 
-    async def get_model_usage_list(self, request: web.Request) -> web.Response:
-        """Get paginated model usage list for infinite scrolling"""
-        try:
-            await self.init_services()
-            
-            model_type = request.query.get('type', 'lora')
-            sort_order = request.query.get('sort', 'desc')
-            
-            try:
-                limit = int(request.query.get('limit', '50'))
-                offset = int(request.query.get('offset', '0'))
-            except ValueError:
-                limit = 50
-                offset = 0
-
-            # Get usage statistics
-            usage_data = await self.usage_stats.get_stats()
-            
-            # Select proper cache and usage dict based on type
-            if model_type == 'lora':
-                cache = await self.lora_scanner.get_cached_data()
-                type_usage_data = usage_data.get('loras', {})
-            elif model_type == 'checkpoint':
-                cache = await self.checkpoint_scanner.get_cached_data()
-                type_usage_data = usage_data.get('checkpoints', {})
-            elif model_type == 'embedding':
-                cache = await self.embedding_scanner.get_cached_data()
-                type_usage_data = usage_data.get('embeddings', {})
-            else:
-                return web.json_response({'success': False, 'error': f"Invalid model type: {model_type}"}, status=400)
-
-            # Create list of all models
-            all_models = []
-            for item in cache.raw_data:
-                sha256 = item.get('sha256')
-                usage_info = type_usage_data.get(sha256, {}) if sha256 else {}
-                usage_count = usage_info.get('total', 0) if isinstance(usage_info, dict) else 0
-                
-                all_models.append({
-                    'name': item.get('model_name', 'Unknown'),
-                    'usage_count': usage_count,
-                    'base_model': item.get('base_model', 'Unknown'),
-                    'preview_url': config.get_preview_static_url(item.get('preview_url', '')),
-                    'folder': item.get('folder', '')
-                })
-
-            # Sort the models
-            reverse = (sort_order == 'desc')
-            all_models.sort(key=lambda x: (x['usage_count'], x['name'].lower()), reverse=reverse)
-            if not reverse:
-                # If asc, sort by usage_count ascending, but keep name ascending
-                all_models.sort(key=lambda x: (x['usage_count'], x['name'].lower()))
-            else:
-                all_models.sort(key=lambda x: (-x['usage_count'], x['name'].lower()))
-
-            # Slice for pagination
-            paginated_models = all_models[offset:offset + limit]
-
-            return web.json_response({
-                'success': True,
-                'data': {
-                    'items': paginated_models,
-                    'total': len(all_models),
-                    'type': model_type
-                }
-            })
-
-        except Exception as e:
-            logger.error(f"Error getting model usage list: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-
     async def get_base_model_distribution(self, request: web.Request) -> web.Response:
         """Get base model distribution statistics"""
         try:
@@ -604,7 +530,6 @@ class StatsRoutes:
         # Register API routes
         app.router.add_get('/api/lm/stats/collection-overview', self.get_collection_overview)
         app.router.add_get('/api/lm/stats/usage-analytics', self.get_usage_analytics)
-        app.router.add_get('/api/lm/stats/model-usage-list', self.get_model_usage_list)
         app.router.add_get('/api/lm/stats/base-model-distribution', self.get_base_model_distribution)
         app.router.add_get('/api/lm/stats/tag-analytics', self.get_tag_analytics)
         app.router.add_get('/api/lm/stats/storage-analytics', self.get_storage_analytics)

py/services/aria2_downloader.py

@@ -1,570 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import json
-import logging
-import os
-import secrets
-import shutil
-import socket
-from dataclasses import dataclass
-from datetime import datetime
-from pathlib import Path
-from typing import Any, Dict, Optional, Tuple
-
-import aiohttp
-
-from .downloader import DownloadProgress, get_downloader
-from .aria2_transfer_state import Aria2TransferStateStore
-from .settings_manager import get_settings_manager
-
-logger = logging.getLogger(__name__)
-
-CIVITAI_DOWNLOAD_URL_PREFIXES = (
-    "https://civitai.com/api/download/",
-    "https://civitai.red/api/download/",
-)
-
-
-class Aria2Error(RuntimeError):
-    """Raised when aria2 integration fails."""
-
-
-@dataclass
-class Aria2Transfer:
-    """Track an aria2 download registered by the Python coordinator."""
-
-    gid: str
-    save_path: str
-
-
-class Aria2Downloader:
-    """Manage an aria2 RPC daemon for experimental model downloads."""
-
-    _instance = None
-    _lock = asyncio.Lock()
-
-    @classmethod
-    async def get_instance(cls) -> "Aria2Downloader":
-        async with cls._lock:
-            if cls._instance is None:
-                cls._instance = cls()
-            return cls._instance
-
-    def __init__(self) -> None:
-        if hasattr(self, "_initialized"):
-            return
-
-        self._initialized = True
-        self._process: Optional[asyncio.subprocess.Process] = None
-        self._rpc_port: Optional[int] = None
-        self._rpc_secret = ""
-        self._rpc_url = ""
-        self._rpc_session: Optional[aiohttp.ClientSession] = None
-        self._rpc_session_lock = asyncio.Lock()
-        self._process_lock = asyncio.Lock()
-        self._transfers: Dict[str, Aria2Transfer] = {}
-        self._poll_interval = 0.5
-        self._state_store = Aria2TransferStateStore()
-
-    @property
-    def is_running(self) -> bool:
-        return self._process is not None and self._process.returncode is None
-
-    async def download_file(
-        self,
-        url: str,
-        save_path: str,
-        *,
-        download_id: str,
-        progress_callback=None,
-        headers: Optional[Dict[str, str]] = None,
-    ) -> Tuple[bool, str]:
-        """Download a file using aria2 RPC and wait for completion."""
-
-        await self._ensure_process()
-        save_path = os.path.abspath(save_path)
-        transfer = self._transfers.get(download_id)
-        if transfer is None or os.path.abspath(transfer.save_path) != save_path:
-            gid = await self._schedule_download(
-                url,
-                save_path,
-                download_id=download_id,
-                headers=headers,
-            )
-            transfer = Aria2Transfer(gid=gid, save_path=save_path)
-            self._transfers[download_id] = transfer
-
-        try:
-            while True:
-                status = await self.get_status(download_id)
-                if status is None:
-                    return False, "aria2 download not found"
-
-                snapshot = self._build_progress_snapshot(status)
-                if progress_callback is not None:
-                    await self._dispatch_progress(progress_callback, snapshot)
-
-                state = status.get("status", "")
-                if state == "complete":
-                    completed_path = self._resolve_completed_path(status, save_path)
-                    return True, completed_path
-                if state == "error":
-                    return False, status.get("errorMessage") or "aria2 download failed"
-                if state == "removed":
-                    return False, "Download was cancelled"
-
-                await asyncio.sleep(self._poll_interval)
-        finally:
-            self._transfers.pop(download_id, None)
-
-    async def _schedule_download(
-        self,
-        url: str,
-        save_path: str,
-        *,
-        download_id: str,
-        headers: Optional[Dict[str, str]] = None,
-    ) -> str:
-        save_dir = os.path.dirname(save_path)
-        out_name = os.path.basename(save_path)
-
-        Path(save_dir).mkdir(parents=True, exist_ok=True)
-
-        resolved_url = url
-        request_headers = headers
-        if headers and url.startswith(CIVITAI_DOWNLOAD_URL_PREFIXES):
-            resolved_url = await self._resolve_authenticated_redirect_url(url, headers)
-            if resolved_url != url:
-                request_headers = None
-                logger.debug(
-                    "Resolved Civitai download %s to signed URL for aria2",
-                    download_id,
-                )
-
-        options: Dict[str, str] = {
-            "dir": save_dir,
-            "out": out_name,
-            "continue": "true",
-            "max-connection-per-server": "4",
-            "split": "4",
-            "min-split-size": "1M",
-            "allow-overwrite": "true",
-            "auto-file-renaming": "false",
-            "file-allocation": "none",
-        }
-        if request_headers:
-            options["header"] = [
-                f"{key}: {value}" for key, value in request_headers.items()
-            ]
-
-        logger.debug(
-            "Submitting aria2 download %s -> %s (auth=%s, civitai_signed=%s)",
-            download_id,
-            save_path,
-            bool(request_headers),
-            resolved_url != url,
-        )
-
-        try:
-            gid = await self._rpc_call("aria2.addUri", [[resolved_url], options])
-        except Exception as exc:
-            raise Aria2Error(f"Failed to schedule aria2 download: {exc}") from exc
-
-        logger.debug("aria2 accepted download %s with gid %s", download_id, gid)
-        await self._state_store.upsert(
-            download_id,
-            {
-                "gid": gid,
-                "save_path": save_path,
-                "status": "downloading",
-                "url": url,
-            },
-        )
-        return gid
-
-    async def get_status(self, download_id: str) -> Optional[Dict[str, Any]]:
-        """Return the raw aria2 status payload for a known download."""
-
-        transfer = self._transfers.get(download_id)
-        if transfer is None:
-            return None
-
-        keys = [
-            "gid",
-            "status",
-            "totalLength",
-            "completedLength",
-            "downloadSpeed",
-            "errorMessage",
-            "files",
-        ]
-        try:
-            status = await self._rpc_call("aria2.tellStatus", [transfer.gid, keys])
-        except Exception as exc:
-            raise Aria2Error(f"Failed to query aria2 download status: {exc}") from exc
-
-        if isinstance(status, dict):
-            return status
-        return None
-
-    async def get_status_by_gid(self, gid: str) -> Optional[Dict[str, Any]]:
-        keys = [
-            "gid",
-            "status",
-            "totalLength",
-            "completedLength",
-            "downloadSpeed",
-            "errorMessage",
-            "files",
-        ]
-        try:
-            status = await self._rpc_call("aria2.tellStatus", [gid, keys])
-        except Exception as exc:
-            message = str(exc)
-            if "cannot be found" in message.lower() or "not found" in message.lower():
-                return None
-            raise Aria2Error(f"Failed to query aria2 download status: {exc}") from exc
-
-        if isinstance(status, dict):
-            return status
-        return None
-
-    async def restore_transfer(self, download_id: str, gid: str, save_path: str) -> None:
-        await self._ensure_process()
-        self._transfers[download_id] = Aria2Transfer(
-            gid=gid,
-            save_path=os.path.abspath(save_path),
-        )
-
-    async def reassign_transfer(
-        self, from_download_id: str, to_download_id: str
-    ) -> Optional[Aria2Transfer]:
-        transfer = self._transfers.get(from_download_id)
-        if transfer is None:
-            return None
-
-        self._transfers[to_download_id] = transfer
-        if from_download_id != to_download_id:
-            self._transfers.pop(from_download_id, None)
-        return transfer
-
-    async def has_transfer(self, download_id: str) -> bool:
-        return download_id in self._transfers
-
-    async def pause_download(self, download_id: str) -> Dict[str, Any]:
-        transfer = self._transfers.get(download_id)
-        if transfer is None:
-            return {"success": False, "error": "Download task not found"}
-
-        try:
-            await self._rpc_call("aria2.forcePause", [transfer.gid])
-        except Exception as exc:
-            return {"success": False, "error": str(exc)}
-
-        await self._state_store.upsert(download_id, {"status": "paused"})
-        return {"success": True, "message": "Download paused successfully"}
-
-    async def resume_download(self, download_id: str) -> Dict[str, Any]:
-        transfer = self._transfers.get(download_id)
-        if transfer is None:
-            return {"success": False, "error": "Download task not found"}
-
-        try:
-            await self._rpc_call("aria2.unpause", [transfer.gid])
-        except Exception as exc:
-            return {"success": False, "error": str(exc)}
-
-        await self._state_store.upsert(download_id, {"status": "downloading"})
-        return {"success": True, "message": "Download resumed successfully"}
-
-    async def cancel_download(self, download_id: str) -> Dict[str, Any]:
-        transfer = self._transfers.get(download_id)
-        if transfer is None:
-            return {"success": False, "error": "Download task not found"}
-
-        try:
-            await self._rpc_call("aria2.forceRemove", [transfer.gid])
-        except Exception as exc:
-            return {"success": False, "error": str(exc)}
-
-        await self._state_store.remove(download_id)
-        return {"success": True, "message": "Download cancelled successfully"}
-
-    async def close(self) -> None:
-        """Shut down the RPC process and session."""
-
-        if self._rpc_session is not None:
-            await self._rpc_session.close()
-            self._rpc_session = None
-
-        process = self._process
-        self._process = None
-        self._transfers.clear()
-
-        if process is None:
-            return
-
-        if process.returncode is None:
-            process.terminate()
-            try:
-                await asyncio.wait_for(process.wait(), timeout=5.0)
-            except asyncio.TimeoutError:
-                process.kill()
-                await process.wait()
-
-    async def _dispatch_progress(self, callback, snapshot: DownloadProgress) -> None:
-        try:
-            result = callback(snapshot, snapshot)
-        except TypeError:
-            result = callback(snapshot.percent_complete)
-
-        if asyncio.iscoroutine(result):
-            await result
-        elif hasattr(result, "__await__"):
-            await result
-
-    def _build_progress_snapshot(self, status: Dict[str, Any]) -> DownloadProgress:
-        completed = self._parse_int(status.get("completedLength"))
-        total = self._parse_int(status.get("totalLength"))
-        speed = float(self._parse_int(status.get("downloadSpeed")))
-        percent = 0.0
-        if total > 0:
-            percent = (completed / total) * 100.0
-
-        return DownloadProgress(
-            percent_complete=max(0.0, min(percent, 100.0)),
-            bytes_downloaded=completed,
-            total_bytes=total or None,
-            bytes_per_second=speed,
-            timestamp=datetime.now().timestamp(),
-        )
-
-    def _resolve_completed_path(self, status: Dict[str, Any], default_path: str) -> str:
-        files = status.get("files")
-        if isinstance(files, list) and files:
-            first = files[0]
-            if isinstance(first, dict):
-                candidate = first.get("path")
-                if isinstance(candidate, str) and candidate:
-                    return candidate
-        return default_path
-
-    @staticmethod
-    def _parse_int(value: Any) -> int:
-        try:
-            return int(value)
-        except (TypeError, ValueError):
-            return 0
-
-    async def _resolve_authenticated_redirect_url(
-        self,
-        url: str,
-        headers: Dict[str, str],
-    ) -> str:
-        downloader = await get_downloader()
-        session = await downloader.session
-        request_headers = dict(downloader.default_headers)
-        request_headers.update(headers)
-        request_headers["Accept-Encoding"] = "identity"
-
-        try:
-            async with session.get(
-                url,
-                headers=request_headers,
-                allow_redirects=False,
-                proxy=downloader.proxy_url,
-            ) as response:
-                if response.status in {301, 302, 303, 307, 308}:
-                    location = response.headers.get("Location")
-                    if location:
-                        return location
-                    raise Aria2Error(
-                        "Authenticated Civitai redirect did not include a Location header"
-                    )
-
-                if response.status == 200:
-                    return url
-
-                body = await response.text()
-                raise Aria2Error(
-                    f"Failed to resolve authenticated Civitai redirect: status={response.status} body={body[:300]}"
-                )
-        except aiohttp.ClientError as exc:
-            raise Aria2Error(
-                f"Failed to resolve authenticated Civitai redirect: {exc}"
-            ) from exc
-
-    async def _ensure_process(self) -> None:
-        async with self._process_lock:
-            if self.is_running and await self._ping():
-                return
-
-            await self.close()
-
-            executable = self._resolve_executable()
-            self._rpc_port = self._find_free_port()
-            self._rpc_secret = secrets.token_hex(16)
-            self._rpc_url = f"http://127.0.0.1:{self._rpc_port}/jsonrpc"
-
-            command = [
-                executable,
-                "--enable-rpc=true",
-                "--rpc-listen-all=false",
-                f"--rpc-listen-port={self._rpc_port}",
-                f"--rpc-secret={self._rpc_secret}",
-                "--check-certificate=true",
-                "--allow-overwrite=true",
-                "--auto-file-renaming=false",
-                "--file-allocation=none",
-                "--max-concurrent-downloads=5",
-                "--continue=true",
-                "--daemon=false",
-                "--quiet=true",
-                f"--stop-with-process={os.getpid()}",
-            ]
-
-            logger.info("Starting aria2 RPC daemon from %s", executable)
-            self._process = await asyncio.create_subprocess_exec(
-                *command,
-                stdout=asyncio.subprocess.DEVNULL,
-                stderr=asyncio.subprocess.PIPE,
-            )
-
-            await self._wait_until_ready()
-
-    def _resolve_executable(self) -> str:
-        settings = get_settings_manager()
-        configured_path = (settings.get("aria2c_path") or "").strip()
-        candidate = configured_path or "aria2c"
-
-        resolved = shutil.which(candidate)
-        if resolved:
-            return resolved
-
-        if configured_path and os.path.isfile(configured_path) and os.access(
-            configured_path, os.X_OK
-        ):
-            return configured_path
-
-        raise Aria2Error(
-            "aria2c executable was not found. Install aria2 or configure aria2c_path."
-        )
-
-    async def _wait_until_ready(self) -> None:
-        assert self._process is not None
-
-        start_time = asyncio.get_running_loop().time()
-        last_error = ""
-        while asyncio.get_running_loop().time() - start_time < 10.0:
-            if self._process.returncode is not None:
-                stderr_output = ""
-                if self._process.stderr is not None:
-                    try:
-                        stderr_output = (
-                            await asyncio.wait_for(self._process.stderr.read(), timeout=0.2)
-                        ).decode("utf-8", errors="replace")
-                    except Exception:
-                        stderr_output = ""
-                raise Aria2Error(
-                    f"aria2 RPC process exited early with code {self._process.returncode}: {stderr_output.strip()}"
-                )
-
-            try:
-                if await self._ping():
-                    return
-            except Exception as exc:  # pragma: no cover - startup race
-                last_error = str(exc)
-
-            await asyncio.sleep(0.2)
-
-        raise Aria2Error(
-            f"Timed out waiting for aria2 RPC to become ready{': ' + last_error if last_error else ''}"
-        )
-
-    async def _ping(self) -> bool:
-        try:
-            result = await self._rpc_call("aria2.getVersion", [])
-        except Exception:
-            return False
-
-        return isinstance(result, dict)
-
-    async def _rpc_call(self, method: str, params: list[Any]) -> Any:
-        if not self._rpc_url:
-            raise Aria2Error("aria2 RPC endpoint is not initialized")
-
-        session = await self._get_rpc_session()
-        payload = {
-            "jsonrpc": "2.0",
-            "id": secrets.token_hex(8),
-            "method": method,
-            "params": [f"token:{self._rpc_secret}", *params],
-        }
-
-        async with session.post(self._rpc_url, json=payload) as response:
-            text = await response.text()
-
-        try:
-            body = json.loads(text)
-        except json.JSONDecodeError:
-            body = None
-
-        if body is None:
-            if response.status != 200:
-                raise Aria2Error(
-                    f"aria2 RPC returned status {response.status} with non-JSON body: {text}"
-                )
-            raise Aria2Error(f"Invalid aria2 RPC response: {text}")
-
-        if "error" in body:
-            error = body["error"] or {}
-            code = error.get("code") if isinstance(error, dict) else None
-            message = error.get("message") if isinstance(error, dict) else str(error)
-            logger.error(
-                "aria2 RPC %s failed with HTTP %s, code=%s, message=%s",
-                method,
-                response.status,
-                code,
-                message,
-            )
-            status_message = (
-                f"aria2 RPC {method} failed with status {response.status}: {message}"
-                if response.status != 200
-                else message
-            )
-            raise Aria2Error(status_message or "Unknown aria2 RPC error")
-
-        if response.status != 200:
-            logger.error(
-                "aria2 RPC %s returned unexpected HTTP status %s without error payload: %s",
-                method,
-                response.status,
-                body,
-            )
-            raise Aria2Error(
-                f"aria2 RPC {method} returned unexpected status {response.status}"
-            )
-
-        return body.get("result")
-
-    async def _get_rpc_session(self) -> aiohttp.ClientSession:
-        if self._rpc_session is None or self._rpc_session.closed:
-            async with self._rpc_session_lock:
-                if self._rpc_session is None or self._rpc_session.closed:
-                    timeout = aiohttp.ClientTimeout(total=30)
-                    self._rpc_session = aiohttp.ClientSession(timeout=timeout)
-        return self._rpc_session
-
-    @staticmethod
-    def _find_free_port() -> int:
-        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
-            sock.bind(("127.0.0.1", 0))
-            sock.listen(1)
-            return int(sock.getsockname()[1])
-
-
-async def get_aria2_downloader() -> Aria2Downloader:
-    """Get the singleton aria2 downloader."""
-
-    return await Aria2Downloader.get_instance()

py/services/aria2_transfer_state.py

@@ -1,108 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import json
-import os
-from copy import deepcopy
-from typing import Any, Dict, Optional
-
-from ..utils.cache_paths import get_cache_base_dir
-
-
-def get_aria2_state_path() -> str:
-    base_dir = get_cache_base_dir(create=True)
-    state_dir = os.path.join(base_dir, "aria2")
-    os.makedirs(state_dir, exist_ok=True)
-    return os.path.join(state_dir, "downloads.json")
-
-
-class Aria2TransferStateStore:
-    """Persist aria2 transfer metadata needed for restart recovery."""
-
-    _locks_by_path: Dict[str, asyncio.Lock] = {}
-
-    def __init__(self, state_path: Optional[str] = None) -> None:
-        self._state_path = os.path.abspath(state_path or get_aria2_state_path())
-        self._lock = self._locks_by_path.setdefault(self._state_path, asyncio.Lock())
-
-    def _read_all_unlocked(self) -> Dict[str, Dict[str, Any]]:
-        try:
-            with open(self._state_path, "r", encoding="utf-8") as handle:
-                data = json.load(handle)
-        except FileNotFoundError:
-            return {}
-        except json.JSONDecodeError:
-            return {}
-
-        if not isinstance(data, dict):
-            return {}
-
-        normalized: Dict[str, Dict[str, Any]] = {}
-        for download_id, entry in data.items():
-            if isinstance(download_id, str) and isinstance(entry, dict):
-                normalized[download_id] = entry
-        return normalized
-
-    def _write_all_unlocked(self, data: Dict[str, Dict[str, Any]]) -> None:
-        directory = os.path.dirname(self._state_path)
-        if directory:
-            os.makedirs(directory, exist_ok=True)
-
-        temp_path = f"{self._state_path}.tmp"
-        with open(temp_path, "w", encoding="utf-8") as handle:
-            json.dump(data, handle, ensure_ascii=True, indent=2, sort_keys=True)
-        os.replace(temp_path, self._state_path)
-
-    async def load_all(self) -> Dict[str, Dict[str, Any]]:
-        async with self._lock:
-            return deepcopy(self._read_all_unlocked())
-
-    async def get(self, download_id: str) -> Optional[Dict[str, Any]]:
-        async with self._lock:
-            return deepcopy(self._read_all_unlocked().get(download_id))
-
-    async def upsert(self, download_id: str, payload: Dict[str, Any]) -> Dict[str, Any]:
-        async with self._lock:
-            data = self._read_all_unlocked()
-            current = data.get(download_id, {})
-            current.update(payload)
-            data[download_id] = current
-            self._write_all_unlocked(data)
-            return deepcopy(current)
-
-    async def remove(self, download_id: str) -> None:
-        async with self._lock:
-            data = self._read_all_unlocked()
-            if download_id in data:
-                del data[download_id]
-                self._write_all_unlocked(data)
-
-    async def find_by_save_path(
-        self, save_path: str, *, exclude_download_id: Optional[str] = None
-    ) -> Optional[Dict[str, Any]]:
-        normalized_target = os.path.abspath(save_path)
-        async with self._lock:
-            data = self._read_all_unlocked()
-            for download_id, entry in data.items():
-                if exclude_download_id and download_id == exclude_download_id:
-                    continue
-                candidate = entry.get("save_path")
-                if isinstance(candidate, str) and os.path.abspath(candidate) == normalized_target:
-                    result = dict(entry)
-                    result["download_id"] = download_id
-                    return result
-        return None
-
-    async def reassign(self, from_download_id: str, to_download_id: str) -> Optional[Dict[str, Any]]:
-        async with self._lock:
-            data = self._read_all_unlocked()
-            existing = data.get(from_download_id)
-            if existing is None:
-                return None
-            updated = dict(existing)
-            updated["download_id"] = to_download_id
-            data[to_download_id] = updated
-            if from_download_id != to_download_id:
-                data.pop(from_download_id, None)
-            self._write_all_unlocked(data)
-            return deepcopy(updated)

py/services/backup_service.py

@@ -1,411 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import contextlib
-import hashlib
-import json
-import logging
-import os
-import shutil
-import tempfile
-import time
-import zipfile
-from dataclasses import dataclass
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Iterable, Optional
-
-from ..utils.cache_paths import CacheType, get_cache_base_dir, get_cache_file_path
-from ..utils.settings_paths import get_settings_dir
-from .settings_manager import get_settings_manager
-
-logger = logging.getLogger(__name__)
-
-
-BACKUP_MANIFEST_VERSION = 1
-DEFAULT_BACKUP_RETENTION_COUNT = 5
-DEFAULT_BACKUP_INTERVAL_SECONDS = 24 * 60 * 60
-
-
-@dataclass(frozen=True)
-class BackupEntry:
-    kind: str
-    archive_path: str
-    target_path: str
-    sha256: str
-    size: int
-    mtime: float
-
-
-class BackupService:
-    """Create and restore user-state backup archives."""
-
-    _instance: "BackupService | None" = None
-    _instance_lock = asyncio.Lock()
-
-    def __init__(self, *, settings_manager=None, backup_dir: str | None = None) -> None:
-        self._settings = settings_manager or get_settings_manager()
-        self._backup_dir = Path(backup_dir or self._resolve_backup_dir())
-        self._backup_dir.mkdir(parents=True, exist_ok=True)
-        self._lock = asyncio.Lock()
-        self._auto_task: asyncio.Task[None] | None = None
-
-    @classmethod
-    async def get_instance(cls) -> "BackupService":
-        async with cls._instance_lock:
-            if cls._instance is None:
-                cls._instance = cls()
-            cls._instance._ensure_auto_snapshot_task()
-            return cls._instance
-
-    @staticmethod
-    def _resolve_backup_dir() -> str:
-        return os.path.join(get_settings_dir(create=True), "backups")
-
-    def get_backup_dir(self) -> str:
-        return str(self._backup_dir)
-
-    def _ensure_auto_snapshot_task(self) -> None:
-        if self._auto_task is not None and not self._auto_task.done():
-            return
-
-        try:
-            loop = asyncio.get_running_loop()
-        except RuntimeError:
-            return
-
-        self._auto_task = loop.create_task(self._auto_backup_loop())
-
-    def _get_setting_bool(self, key: str, default: bool) -> bool:
-        try:
-            return bool(self._settings.get(key, default))
-        except Exception:
-            return default
-
-    def _get_setting_int(self, key: str, default: int) -> int:
-        try:
-            value = self._settings.get(key, default)
-            return max(1, int(value))
-        except Exception:
-            return default
-
-    def _settings_file_path(self) -> str:
-        settings_file = getattr(self._settings, "settings_file", None)
-        if settings_file:
-            return str(settings_file)
-        return os.path.join(get_settings_dir(create=True), "settings.json")
-
-    def _download_history_path(self) -> str:
-        base_dir = get_cache_base_dir(create=True)
-        history_dir = os.path.join(base_dir, "download_history")
-        os.makedirs(history_dir, exist_ok=True)
-        return os.path.join(history_dir, "downloaded_versions.sqlite")
-
-    def _model_update_dir(self) -> str:
-        return str(Path(get_cache_file_path(CacheType.MODEL_UPDATE, create_dir=True)).parent)
-
-    def _model_update_targets(self) -> list[tuple[str, str, str]]:
-        """Return (kind, archive_path, target_path) tuples for backup."""
-
-        targets: list[tuple[str, str, str]] = []
-
-        settings_path = self._settings_file_path()
-        targets.append(("settings", "settings/settings.json", settings_path))
-
-        history_path = self._download_history_path()
-        targets.append(
-            (
-                "download_history",
-                "cache/download_history/downloaded_versions.sqlite",
-                history_path,
-            )
-        )
-
-        symlink_path = get_cache_file_path(CacheType.SYMLINK, create_dir=True)
-        targets.append(
-            (
-                "symlink_map",
-                "cache/symlink/symlink_map.json",
-                symlink_path,
-            )
-        )
-
-        model_update_dir = Path(self._model_update_dir())
-        if model_update_dir.exists():
-            for sqlite_file in sorted(model_update_dir.glob("*.sqlite")):
-                targets.append(
-                    (
-                        "model_update",
-                        f"cache/model_update/{sqlite_file.name}",
-                        str(sqlite_file),
-                    )
-                )
-
-        return targets
-
-    @staticmethod
-    def _hash_file(path: str) -> tuple[str, int, float]:
-        digest = hashlib.sha256()
-        total = 0
-        with open(path, "rb") as handle:
-            for chunk in iter(lambda: handle.read(1024 * 1024), b""):
-                total += len(chunk)
-                digest.update(chunk)
-        mtime = os.path.getmtime(path)
-        return digest.hexdigest(), total, mtime
-
-    def _build_manifest(self, entries: Iterable[BackupEntry], *, snapshot_type: str) -> dict[str, Any]:
-        created_at = datetime.now(timezone.utc).isoformat()
-        active_library = None
-        try:
-            active_library = self._settings.get_active_library_name()
-        except Exception:
-            active_library = None
-
-        return {
-            "manifest_version": BACKUP_MANIFEST_VERSION,
-            "created_at": created_at,
-            "snapshot_type": snapshot_type,
-            "active_library": active_library,
-            "files": [
-                {
-                    "kind": entry.kind,
-                    "archive_path": entry.archive_path,
-                    "target_path": entry.target_path,
-                    "sha256": entry.sha256,
-                    "size": entry.size,
-                    "mtime": entry.mtime,
-                }
-                for entry in entries
-            ],
-        }
-
-    def _write_archive(self, archive_path: str, entries: list[BackupEntry], manifest: dict[str, Any]) -> None:
-        with zipfile.ZipFile(
-            archive_path,
-            mode="w",
-            compression=zipfile.ZIP_DEFLATED,
-            compresslevel=6,
-        ) as zf:
-            zf.writestr(
-                "manifest.json",
-                json.dumps(manifest, indent=2, ensure_ascii=False).encode("utf-8"),
-            )
-            for entry in entries:
-                zf.write(entry.target_path, arcname=entry.archive_path)
-
-    async def create_snapshot(self, *, snapshot_type: str = "manual", persist: bool = False) -> dict[str, Any]:
-        """Create a backup archive.
-
-        If ``persist`` is true, the archive is stored in the backup directory
-        and retained according to the configured retention policy.
-        """
-
-        async with self._lock:
-            raw_targets = self._model_update_targets()
-            entries: list[BackupEntry] = []
-            for kind, archive_path, target_path in raw_targets:
-                if not os.path.exists(target_path):
-                    continue
-                sha256, size, mtime = self._hash_file(target_path)
-                entries.append(
-                    BackupEntry(
-                        kind=kind,
-                        archive_path=archive_path,
-                        target_path=target_path,
-                        sha256=sha256,
-                        size=size,
-                        mtime=mtime,
-                    )
-                )
-
-            if not entries:
-                raise FileNotFoundError("No backupable files were found")
-
-            manifest = self._build_manifest(entries, snapshot_type=snapshot_type)
-            archive_name = self._build_archive_name(snapshot_type=snapshot_type)
-            fd, temp_path = tempfile.mkstemp(suffix=".zip", dir=str(self._backup_dir))
-            os.close(fd)
-
-            try:
-                self._write_archive(temp_path, entries, manifest)
-                if persist:
-                    final_path = self._backup_dir / archive_name
-                    os.replace(temp_path, final_path)
-                    self._prune_snapshots()
-                    return {
-                        "archive_path": str(final_path),
-                        "archive_name": final_path.name,
-                        "manifest": manifest,
-                    }
-
-                with open(temp_path, "rb") as handle:
-                    data = handle.read()
-                return {
-                    "archive_name": archive_name,
-                    "archive_bytes": data,
-                    "manifest": manifest,
-                }
-            finally:
-                with contextlib.suppress(FileNotFoundError):
-                    os.remove(temp_path)
-
-    def _build_archive_name(self, *, snapshot_type: str) -> str:
-        timestamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
-        return f"lora-manager-backup-{timestamp}-{snapshot_type}.zip"
-
-    def _prune_snapshots(self) -> None:
-        retention = self._get_setting_int(
-            "backup_retention_count", DEFAULT_BACKUP_RETENTION_COUNT
-        )
-        archives = sorted(
-            self._backup_dir.glob("lora-manager-backup-*-auto.zip"),
-            key=lambda path: path.stat().st_mtime,
-            reverse=True,
-        )
-        for path in archives[retention:]:
-            with contextlib.suppress(OSError):
-                path.unlink()
-
-    async def restore_snapshot(self, archive_path: str) -> dict[str, Any]:
-        """Restore backup contents from a ZIP archive."""
-
-        async with self._lock:
-            try:
-                zf = zipfile.ZipFile(archive_path, mode="r")
-            except zipfile.BadZipFile as exc:
-                raise ValueError("Backup archive is not a valid ZIP file") from exc
-
-            with zf:
-                try:
-                    manifest = json.loads(zf.read("manifest.json").decode("utf-8"))
-                except KeyError as exc:
-                    raise ValueError("Backup archive is missing manifest.json") from exc
-
-                if not isinstance(manifest, dict):
-                    raise ValueError("Backup manifest is invalid")
-                if manifest.get("manifest_version") != BACKUP_MANIFEST_VERSION:
-                    raise ValueError("Backup manifest version is not supported")
-
-                files = manifest.get("files", [])
-                if not isinstance(files, list):
-                    raise ValueError("Backup manifest file list is invalid")
-
-                extracted_paths: list[tuple[str, str]] = []
-                temp_dir = Path(tempfile.mkdtemp(prefix="lora-manager-restore-"))
-                try:
-                    for item in files:
-                        if not isinstance(item, dict):
-                            continue
-                        archive_member = item.get("archive_path")
-                        if not isinstance(archive_member, str) or not archive_member:
-                            continue
-                        archive_member_path = Path(archive_member)
-                        if archive_member_path.is_absolute() or ".." in archive_member_path.parts:
-                            raise ValueError(f"Invalid archive member path: {archive_member}")
-
-                        kind = item.get("kind")
-                        target_path = self._resolve_restore_target(kind, archive_member)
-                        if target_path is None:
-                            continue
-
-                        extracted_path = temp_dir / archive_member_path
-                        extracted_path.parent.mkdir(parents=True, exist_ok=True)
-                        with zf.open(archive_member) as source, open(
-                            extracted_path, "wb"
-                        ) as destination:
-                            shutil.copyfileobj(source, destination)
-
-                        expected_hash = item.get("sha256")
-                        if isinstance(expected_hash, str) and expected_hash:
-                            actual_hash, _, _ = self._hash_file(str(extracted_path))
-                            if actual_hash != expected_hash:
-                                raise ValueError(
-                                    f"Checksum mismatch for {archive_member}"
-                                )
-
-                        extracted_paths.append((str(extracted_path), target_path))
-
-                    for extracted_path, target_path in extracted_paths:
-                        os.makedirs(os.path.dirname(target_path), exist_ok=True)
-                        os.replace(extracted_path, target_path)
-                finally:
-                    shutil.rmtree(temp_dir, ignore_errors=True)
-
-            return {
-                "success": True,
-                "restored_files": len(extracted_paths),
-                "snapshot_type": manifest.get("snapshot_type"),
-            }
-
-    def _resolve_restore_target(self, kind: Any, archive_member: str) -> str | None:
-        if kind == "settings":
-            return self._settings_file_path()
-        if kind == "download_history":
-            return self._download_history_path()
-        if kind == "symlink_map":
-            return get_cache_file_path(CacheType.SYMLINK, create_dir=True)
-        if kind == "model_update":
-            filename = os.path.basename(archive_member)
-            return str(Path(get_cache_file_path(CacheType.MODEL_UPDATE, create_dir=True)).parent / filename)
-        return None
-
-    async def create_auto_snapshot_if_due(self) -> Optional[dict[str, Any]]:
-        if not self._get_setting_bool("backup_auto_enabled", True):
-            return None
-
-        latest = self.get_latest_auto_snapshot()
-        now = time.time()
-        if latest and now - latest["mtime"] < DEFAULT_BACKUP_INTERVAL_SECONDS:
-            return None
-
-        return await self.create_snapshot(snapshot_type="auto", persist=True)
-
-    async def _auto_backup_loop(self) -> None:
-        while True:
-            try:
-                await self.create_auto_snapshot_if_due()
-                await asyncio.sleep(DEFAULT_BACKUP_INTERVAL_SECONDS)
-            except asyncio.CancelledError:
-                raise
-            except Exception as exc:  # pragma: no cover - defensive guard
-                logger.warning("Automatic backup snapshot failed: %s", exc, exc_info=True)
-                await asyncio.sleep(60)
-
-    def get_available_snapshots(self) -> list[dict[str, Any]]:
-        snapshots: list[dict[str, Any]] = []
-        for path in sorted(self._backup_dir.glob("lora-manager-backup-*.zip")):
-            try:
-                stat = path.stat()
-            except OSError:
-                continue
-            snapshots.append(
-                {
-                    "name": path.name,
-                    "path": str(path),
-                    "size": stat.st_size,
-                    "mtime": stat.st_mtime,
-                    "is_auto": path.name.endswith("-auto.zip"),
-                }
-            )
-        snapshots.sort(key=lambda item: item["mtime"], reverse=True)
-        return snapshots
-
-    def get_latest_auto_snapshot(self) -> Optional[dict[str, Any]]:
-        autos = [snapshot for snapshot in self.get_available_snapshots() if snapshot["is_auto"]]
-        if not autos:
-            return None
-        return autos[0]
-
-    def get_status(self) -> dict[str, Any]:
-        snapshots = self.get_available_snapshots()
-        return {
-            "backupDir": self.get_backup_dir(),
-            "enabled": self._get_setting_bool("backup_auto_enabled", True),
-            "retentionCount": self._get_setting_int(
-                "backup_retention_count", DEFAULT_BACKUP_RETENTION_COUNT
-            ),
-            "snapshotCount": len(snapshots),
-            "latestSnapshot": snapshots[0] if snapshots else None,
-            "latestAutoSnapshot": self.get_latest_auto_snapshot(),
-        }

py/services/base_model_service.py

@@ -1,6 +1,5 @@
 from abc import ABC, abstractmethod
 import asyncio
-import re
 from typing import Any, Dict, List, Optional, Type, TYPE_CHECKING
 import logging
 import os
@@ -20,7 +19,6 @@ from .model_query import (
     resolve_sub_type,
 )
 from .settings_manager import get_settings_manager
-from ..utils.civitai_utils import build_civitai_model_page_url
 
 logger = logging.getLogger(__name__)
 
@@ -83,7 +81,6 @@ class BaseModelService(ABC):
         update_available_only: bool = False,
         credit_required: Optional[bool] = None,
         allow_selling_generated_content: Optional[bool] = None,
-        tag_logic: str = "any",
         **kwargs,
     ) -> Dict:
         """Get paginated and filtered model data"""
@@ -112,7 +109,6 @@ class BaseModelService(ABC):
                 tags=tags,
                 favorites_only=favorites_only,
                 search_options=search_options,
-                tag_logic=tag_logic,
             )
 
             if search:
@@ -179,57 +175,6 @@ class BaseModelService(ABC):
         )
         return paginated
 
-    async def get_excluded_paginated_data(
-        self,
-        page: int,
-        page_size: int,
-        sort_by: str = "name",
-        search: str = None,
-        fuzzy_search: bool = False,
-        search_options: dict = None,
-        **kwargs,
-    ) -> Dict:
-        """Get paginated excluded model data."""
-        excluded_paths = list(self.scanner.get_excluded_models())
-        excluded_entries: List[Dict[str, Any]] = []
-        stale_paths: List[str] = []
-
-        for file_path in excluded_paths:
-            if not file_path or not os.path.exists(file_path):
-                stale_paths.append(file_path)
-                continue
-
-            entry = await self._build_excluded_entry(file_path)
-            if entry:
-                excluded_entries.append(entry)
-            else:
-                stale_paths.append(file_path)
-
-        if stale_paths:
-            current_excluded = getattr(self.scanner, "_excluded_models", None)
-            if isinstance(current_excluded, list):
-                stale_set = set(stale_paths)
-                self.scanner._excluded_models = [
-                    path for path in current_excluded if path not in stale_set
-                ]
-                persist_current_cache = getattr(self.scanner, "_persist_current_cache", None)
-                if callable(persist_current_cache):
-                    await persist_current_cache()
-
-        excluded_entries = self._sort_entries(excluded_entries, sort_by)
-
-        if search:
-            excluded_entries = await self._apply_search_filters(
-                excluded_entries,
-                search,
-                fuzzy_search,
-                search_options,
-            )
-
-        paginated = self._paginate(excluded_entries, page, page_size)
-        paginated["items"] = await self._annotate_update_flags(paginated["items"])
-        return paginated
-
     async def _fetch_with_usage_sort(self, sort_params):
         """Fetch data sorted by usage count (desc/asc)."""
         cache = await self.cache_repository.get_cache()
@@ -260,71 +205,11 @@ class BaseModelService(ABC):
 
         reverse = sort_params.order == "desc"
         annotated.sort(
-            key=lambda x: (
-                x.get("usage_count", 0),
-                x.get("model_name", "").lower(),
-                x.get("file_path", "").lower()
-            ),
+            key=lambda x: (x.get("usage_count", 0), x.get("model_name", "").lower()),
             reverse=reverse,
         )
         return annotated
 
-    def _sort_entries(self, data: List[Dict[str, Any]], sort_by: str) -> List[Dict[str, Any]]:
-        sort_params = self.cache_repository.parse_sort(sort_by)
-        key_name = sort_params.key
-
-        if key_name == "date":
-            key_fn = lambda item: (
-                float(item.get("modified", 0.0) or 0.0),
-                (item.get("model_name") or item.get("file_name") or "").lower(),
-                item.get("file_path", "").lower(),
-            )
-        elif key_name == "size":
-            key_fn = lambda item: (
-                int(item.get("size", 0) or 0),
-                (item.get("model_name") or item.get("file_name") or "").lower(),
-                item.get("file_path", "").lower(),
-            )
-        elif key_name == "usage":
-            key_fn = lambda item: (
-                int(item.get("usage_count", 0) or 0),
-                (item.get("model_name") or item.get("file_name") or "").lower(),
-                item.get("file_path", "").lower(),
-            )
-        else:
-            key_fn = lambda item: (
-                (item.get("model_name") or item.get("file_name") or "").lower(),
-                item.get("file_path", "").lower(),
-            )
-
-        return sorted(data, key=key_fn, reverse=sort_params.order == "desc")
-
-    async def _build_excluded_entry(self, file_path: str) -> Optional[Dict[str, Any]]:
-        root_path = self.scanner._find_root_for_file(file_path)
-        if not root_path:
-            return None
-
-        metadata, should_skip = await MetadataManager.load_metadata(
-            file_path,
-            self.metadata_class,
-        )
-        if should_skip:
-            return None
-
-        if metadata is None:
-            metadata = await self.scanner._create_default_metadata(file_path)
-            if metadata is None:
-                return None
-
-        metadata = self.scanner.adjust_metadata(metadata, file_path, root_path)
-        folder = os.path.dirname(os.path.relpath(file_path, root_path)).replace(
-            os.path.sep, "/"
-        )
-        entry = self.scanner._build_cache_entry(metadata, folder=folder)
-        entry = self.scanner.adjust_cached_entry(entry)
-        entry["exclude"] = True
-        return entry
-
     async def _apply_hash_filters(
         self, data: List[Dict], hash_filters: Dict
     ) -> List[Dict]:
@@ -356,7 +241,6 @@ class BaseModelService(ABC):
         tags: Optional[Dict[str, str]] = None,
         favorites_only: bool = False,
         search_options: dict = None,
-        tag_logic: str = "any",
     ) -> List[Dict]:
         """Apply common filters that work across all model types"""
         normalized_options = self.search_strategy.normalize_options(search_options)
@@ -369,7 +253,6 @@ class BaseModelService(ABC):
             tags=tags,
             favorites_only=favorites_only,
             search_options=normalized_options,
-            tag_logic=tag_logic,
         )
         return self.filter_set.apply(data, criteria)
 
@@ -493,15 +376,6 @@ class BaseModelService(ABC):
             strategy = "same_base"
         same_base_mode = strategy == "same_base"
 
-        # Check user setting for hiding early access updates
-        hide_early_access = False
-        try:
-            hide_early_access = bool(
-                self.settings.get("hide_early_access_updates", False)
-            )
-        except Exception:
-            hide_early_access = False
-
         records = None
         resolved: Optional[Dict[int, bool]] = None
         if same_base_mode:
@@ -510,7 +384,7 @@ class BaseModelService(ABC):
                 try:
                     records = await record_method(self.model_type, ordered_ids)
                     resolved = {
-                        model_id: record.has_update(hide_early_access=hide_early_access)
+                        model_id: record.has_update()
                         for model_id, record in records.items()
                     }
                 except Exception as exc:
@@ -528,11 +402,7 @@ class BaseModelService(ABC):
             bulk_method = getattr(self.update_service, "has_updates_bulk", None)
             if callable(bulk_method):
                 try:
-                    resolved = await bulk_method(
-                        self.model_type,
-                        ordered_ids,
-                        hide_early_access=hide_early_access,
-                    )
+                    resolved = await bulk_method(self.model_type, ordered_ids)
                 except Exception as exc:
                     logger.error(
                         "Failed to resolve update status in bulk for %s models (%s): %s",
@@ -545,9 +415,7 @@ class BaseModelService(ABC):
 
         if resolved is None:
             tasks = [
-                self.update_service.has_update(
-                    self.model_type, model_id, hide_early_access=hide_early_access
-                )
+                self.update_service.has_update(self.model_type, model_id)
                 for model_id in ordered_ids
             ]
             results = await asyncio.gather(*tasks, return_exceptions=True)
@@ -585,7 +453,6 @@ class BaseModelService(ABC):
                     flag = record.has_update_for_base(
                         threshold_version,
                         base_model,
-                        hide_early_access=hide_early_access,
                     )
                 else:
                     flag = default_flag
@@ -709,19 +576,13 @@ class BaseModelService(ABC):
             normalized_type = normalize_sub_type(resolve_sub_type(entry))
             if not normalized_type:
                 continue
-
+            
             # Filter by valid sub-types based on scanner type
-            if (
-                self.model_type == "lora"
-                and normalized_type not in VALID_LORA_SUB_TYPES
-            ):
+            if self.model_type == "lora" and normalized_type not in VALID_LORA_SUB_TYPES:
                 continue
-            if (
-                self.model_type == "checkpoint"
-                and normalized_type not in VALID_CHECKPOINT_SUB_TYPES
-            ):
+            if self.model_type == "checkpoint" and normalized_type not in VALID_CHECKPOINT_SUB_TYPES:
                 continue
-
+            
             type_counts[normalized_type] = type_counts.get(normalized_type, 0) + 1
 
         sorted_types = sorted(
@@ -882,12 +743,9 @@ class BaseModelService(ABC):
                 version_id = civitai_data.get("id")
 
                 if model_id:
-                    civitai_host = self.settings.get("civitai_host", "civitai.com")
-                    civitai_url = build_civitai_model_page_url(
-                        model_id,
-                        version_id,
-                        host=civitai_host,
-                    )
+                    civitai_url = f"https://civitai.com/models/{model_id}"
+                    if version_id:
+                        civitai_url += f"?modelVersionId={version_id}"
 
                     return {
                         "civitai_url": civitai_url,
@@ -937,61 +795,38 @@ class BaseModelService(ABC):
 
         return include_terms, exclude_terms
 
-    @staticmethod
-    def _remove_model_extension(path: str) -> str:
-        """Remove model file extension (.safetensors, .ckpt, .pt, .bin) for cleaner matching."""
-        return re.sub(r"\.(safetensors|ckpt|pt|bin)$", "", path, flags=re.IGNORECASE)
-
     @staticmethod
     def _relative_path_matches_tokens(
         path_lower: str, include_terms: List[str], exclude_terms: List[str]
     ) -> bool:
-        """Determine whether a relative path string satisfies include/exclude tokens.
-
-        Matches against the path without extension to avoid matching .safetensors
-        when searching for 's'.
-        """
-        # Use path without extension for matching
-        path_for_matching = BaseModelService._remove_model_extension(path_lower)
-
-        if any(term and term in path_for_matching for term in exclude_terms):
+        """Determine whether a relative path string satisfies include/exclude tokens."""
+        if any(term and term in path_lower for term in exclude_terms):
             return False
 
         for term in include_terms:
-            if term and term not in path_for_matching:
+            if term and term not in path_lower:
                 return False
 
         return True
 
     @staticmethod
     def _relative_path_sort_key(relative_path: str, include_terms: List[str]) -> tuple:
-        """Sort paths by how well they satisfy the include tokens.
-
-        Sorts based on path without extension for consistent ordering.
-        """
-        # Use path without extension for sorting
-        path_for_sorting = BaseModelService._remove_model_extension(
-            relative_path.lower()
-        )
+        """Sort paths by how well they satisfy the include tokens."""
+        path_lower = relative_path.lower()
         prefix_hits = sum(
-            1 for term in include_terms if term and path_for_sorting.startswith(term)
+            1 for term in include_terms if term and path_lower.startswith(term)
         )
         match_positions = [
-            path_for_sorting.find(term)
+            path_lower.find(term)
             for term in include_terms
-            if term and term in path_for_sorting
+            if term and term in path_lower
         ]
         first_match_index = min(match_positions) if match_positions else 0
 
-        return (
-            -prefix_hits,
-            first_match_index,
-            len(path_for_sorting),
-            path_for_sorting,
-        )
+        return (-prefix_hits, first_match_index, len(relative_path), path_lower)
 
     async def search_relative_paths(
-        self, search_term: str, limit: int = 15, offset: int = 0
+        self, search_term: str, limit: int = 15
     ) -> List[str]:
         """Search model relative file paths for autocomplete functionality"""
         cache = await self.scanner.get_cached_data()
@@ -1002,7 +837,6 @@ class BaseModelService(ABC):
         # Get model roots for path calculation
         model_roots = self.scanner.get_model_roots()
 
-        # Collect all matching paths first (needed for proper sorting and offset)
         for model in cache.raw_data:
             file_path = model.get("file_path", "")
             if not file_path:
@@ -1031,12 +865,12 @@ class BaseModelService(ABC):
             ):
                 matching_paths.append(relative_path)
 
+                if len(matching_paths) >= limit * 2:  # Get more for better sorting
+                    break
+
         # Sort by relevance (prefix and earliest hits first, then by length and alphabetically)
         matching_paths.sort(
             key=lambda relative: self._relative_path_sort_key(relative, include_terms)
         )
 
-        # Apply offset and limit
-        start = min(offset, len(matching_paths))
-        end = min(start + limit, len(matching_paths))
-        return matching_paths[start:end]
+        return matching_paths[:limit]

py/services/batch_import_service.py

@@ -1,593 +0,0 @@
-"""Batch import service for importing multiple images as recipes."""
-
-from __future__ import annotations
-
-import asyncio
-import logging
-import os
-import time
-import uuid
-from dataclasses import dataclass, field
-from enum import Enum
-from typing import Any, Callable, Dict, List, Optional, Set
-
-from aiohttp import web
-
-from .recipes import (
-    RecipeAnalysisService,
-    RecipePersistenceService,
-    RecipeValidationError,
-    RecipeDownloadError,
-    RecipeNotFoundError,
-)
-
-
-class ImportItemType(Enum):
-    """Type of import item."""
-
-    URL = "url"
-    LOCAL_PATH = "local_path"
-
-
-class ImportStatus(Enum):
-    """Status of an individual import item."""
-
-    PENDING = "pending"
-    PROCESSING = "processing"
-    SUCCESS = "success"
-    FAILED = "failed"
-    SKIPPED = "skipped"
-
-
-@dataclass
-class BatchImportItem:
-    """Represents a single item to import."""
-
-    id: str
-    source: str
-    item_type: ImportItemType
-    status: ImportStatus = ImportStatus.PENDING
-    error_message: Optional[str] = None
-    recipe_name: Optional[str] = None
-    recipe_id: Optional[str] = None
-    duration: float = 0.0
-
-
-@dataclass
-class BatchImportProgress:
-    """Tracks progress of a batch import operation."""
-
-    operation_id: str
-    total: int
-    completed: int = 0
-    success: int = 0
-    failed: int = 0
-    skipped: int = 0
-    current_item: str = ""
-    status: str = "pending"
-    started_at: float = field(default_factory=time.time)
-    finished_at: Optional[float] = None
-    items: List[BatchImportItem] = field(default_factory=list)
-    tags: List[str] = field(default_factory=list)
-    skip_no_metadata: bool = False
-    skip_duplicates: bool = False
-
-    def to_dict(self) -> Dict[str, Any]:
-        return {
-            "operation_id": self.operation_id,
-            "total": self.total,
-            "completed": self.completed,
-            "success": self.success,
-            "failed": self.failed,
-            "skipped": self.skipped,
-            "current_item": self.current_item,
-            "status": self.status,
-            "started_at": self.started_at,
-            "finished_at": self.finished_at,
-            "progress_percent": round((self.completed / self.total) * 100, 1)
-            if self.total > 0
-            else 0,
-            "items": [
-                {
-                    "id": item.id,
-                    "source": item.source,
-                    "item_type": item.item_type.value,
-                    "status": item.status.value,
-                    "error_message": item.error_message,
-                    "recipe_name": item.recipe_name,
-                    "recipe_id": item.recipe_id,
-                    "duration": item.duration,
-                }
-                for item in self.items
-            ],
-        }
-
-
-class AdaptiveConcurrencyController:
-    """Adjusts concurrency based on task performance."""
-
-    def __init__(
-        self,
-        min_concurrency: int = 1,
-        max_concurrency: int = 5,
-        initial_concurrency: int = 3,
-    ) -> None:
-        self.min_concurrency = min_concurrency
-        self.max_concurrency = max_concurrency
-        self.current_concurrency = initial_concurrency
-        self._task_durations: List[float] = []
-        self._recent_errors = 0
-        self._recent_successes = 0
-
-    def record_result(self, duration: float, success: bool) -> None:
-        self._task_durations.append(duration)
-        if len(self._task_durations) > 10:
-            self._task_durations.pop(0)
-
-        if success:
-            self._recent_successes += 1
-            if duration < 1.0 and self.current_concurrency < self.max_concurrency:
-                self.current_concurrency = min(
-                    self.current_concurrency + 1, self.max_concurrency
-                )
-            elif duration > 10.0 and self.current_concurrency > self.min_concurrency:
-                self.current_concurrency = max(
-                    self.current_concurrency - 1, self.min_concurrency
-                )
-        else:
-            self._recent_errors += 1
-            if self.current_concurrency > self.min_concurrency:
-                self.current_concurrency = max(
-                    self.current_concurrency - 1, self.min_concurrency
-                )
-
-    def reset_counters(self) -> None:
-        self._recent_errors = 0
-        self._recent_successes = 0
-
-    def get_semaphore(self) -> asyncio.Semaphore:
-        return asyncio.Semaphore(self.current_concurrency)
-
-
-class BatchImportService:
-    """Service for batch importing images as recipes."""
-
-    SUPPORTED_EXTENSIONS: Set[str] = {".jpg", ".jpeg", ".png", ".webp", ".gif", ".bmp"}
-
-    def __init__(
-        self,
-        *,
-        analysis_service: RecipeAnalysisService,
-        persistence_service: RecipePersistenceService,
-        ws_manager: Any,
-        logger: logging.Logger,
-    ) -> None:
-        self._analysis_service = analysis_service
-        self._persistence_service = persistence_service
-        self._ws_manager = ws_manager
-        self._logger = logger
-        self._active_operations: Dict[str, BatchImportProgress] = {}
-        self._cancellation_flags: Dict[str, bool] = {}
-        self._concurrency_controller = AdaptiveConcurrencyController()
-
-    def is_import_running(self, operation_id: Optional[str] = None) -> bool:
-        if operation_id:
-            progress = self._active_operations.get(operation_id)
-            return progress is not None and progress.status in ("pending", "running")
-        return any(
-            p.status in ("pending", "running") for p in self._active_operations.values()
-        )
-
-    def get_progress(self, operation_id: str) -> Optional[BatchImportProgress]:
-        return self._active_operations.get(operation_id)
-
-    def cancel_import(self, operation_id: str) -> bool:
-        if operation_id in self._active_operations:
-            self._cancellation_flags[operation_id] = True
-            return True
-        return False
-
-    def _validate_url(self, url: str) -> bool:
-        import re
-
-        url_pattern = re.compile(
-            r"^https?://"
-            r"(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+[A-Z]{2,6}\.?|"
-            r"localhost|"
-            r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})"
-            r"(?::\d+)?"
-            r"(?:/?|[/?]\S+)$",
-            re.IGNORECASE,
-        )
-        return url_pattern.match(url) is not None
-
-    def _validate_local_path(self, path: str) -> bool:
-        try:
-            normalized = os.path.normpath(path)
-            if not os.path.isabs(normalized):
-                return False
-            if ".." in normalized:
-                return False
-            return True
-        except Exception:
-            return False
-
-    def _is_duplicate_source(
-        self,
-        source: str,
-        item_type: ImportItemType,
-        recipe_scanner: Any,
-    ) -> bool:
-        try:
-            cache = recipe_scanner.get_cached_data_sync()
-            if not cache:
-                return False
-
-            for recipe in getattr(cache, "raw_data", []):
-                source_path = recipe.get("source_path") or recipe.get("source_url")
-                if source_path and source_path == source:
-                    return True
-            return False
-        except Exception:
-            self._logger.warning("Failed to check for duplicates", exc_info=True)
-            return False
-
-    async def start_batch_import(
-        self,
-        *,
-        recipe_scanner_getter: Callable[[], Any],
-        civitai_client_getter: Callable[[], Any],
-        items: List[Dict[str, str]],
-        tags: Optional[List[str]] = None,
-        skip_no_metadata: bool = False,
-        skip_duplicates: bool = False,
-    ) -> str:
-        operation_id = str(uuid.uuid4())
-
-        import_items = []
-        for idx, item in enumerate(items):
-            source = item.get("source", "")
-            item_type_str = item.get("type", "url")
-
-            if item_type_str == "url" or source.startswith(("http://", "https://")):
-                item_type = ImportItemType.URL
-            else:
-                item_type = ImportItemType.LOCAL_PATH
-
-            batch_import_item = BatchImportItem(
-                id=f"{operation_id}_{idx}",
-                source=source,
-                item_type=item_type,
-            )
-            import_items.append(batch_import_item)
-
-        progress = BatchImportProgress(
-            operation_id=operation_id,
-            total=len(import_items),
-            items=import_items,
-            tags=tags or [],
-            skip_no_metadata=skip_no_metadata,
-            skip_duplicates=skip_duplicates,
-        )
-
-        self._active_operations[operation_id] = progress
-        self._cancellation_flags[operation_id] = False
-
-        asyncio.create_task(
-            self._run_batch_import(
-                operation_id=operation_id,
-                recipe_scanner_getter=recipe_scanner_getter,
-                civitai_client_getter=civitai_client_getter,
-            )
-        )
-
-        return operation_id
-
-    async def start_directory_import(
-        self,
-        *,
-        recipe_scanner_getter: Callable[[], Any],
-        civitai_client_getter: Callable[[], Any],
-        directory: str,
-        recursive: bool = True,
-        tags: Optional[List[str]] = None,
-        skip_no_metadata: bool = False,
-        skip_duplicates: bool = False,
-    ) -> str:
-        image_paths = await self._discover_images(directory, recursive)
-
-        items = [{"source": path, "type": "local_path"} for path in image_paths]
-
-        return await self.start_batch_import(
-            recipe_scanner_getter=recipe_scanner_getter,
-            civitai_client_getter=civitai_client_getter,
-            items=items,
-            tags=tags,
-            skip_no_metadata=skip_no_metadata,
-            skip_duplicates=skip_duplicates,
-        )
-
-    async def _discover_images(
-        self,
-        directory: str,
-        recursive: bool = True,
-    ) -> List[str]:
-        if not os.path.isdir(directory):
-            raise RecipeValidationError(f"Directory not found: {directory}")
-
-        image_paths: List[str] = []
-
-        if recursive:
-            for root, _, files in os.walk(directory):
-                for filename in files:
-                    if self._is_supported_image(filename):
-                        image_paths.append(os.path.join(root, filename))
-        else:
-            for filename in os.listdir(directory):
-                filepath = os.path.join(directory, filename)
-                if os.path.isfile(filepath) and self._is_supported_image(filename):
-                    image_paths.append(filepath)
-
-        return sorted(image_paths)
-
-    def _is_supported_image(self, filename: str) -> bool:
-        ext = os.path.splitext(filename)[1].lower()
-        return ext in self.SUPPORTED_EXTENSIONS
-
-    async def _run_batch_import(
-        self,
-        *,
-        operation_id: str,
-        recipe_scanner_getter: Callable[[], Any],
-        civitai_client_getter: Callable[[], Any],
-    ) -> None:
-        progress = self._active_operations.get(operation_id)
-        if not progress:
-            return
-
-        progress.status = "running"
-        await self._broadcast_progress(progress)
-
-        self._concurrency_controller = AdaptiveConcurrencyController()
-
-        async def process_item(item: BatchImportItem) -> None:
-            if self._cancellation_flags.get(operation_id, False):
-                return
-
-            progress.current_item = (
-                os.path.basename(item.source)
-                if item.item_type == ImportItemType.LOCAL_PATH
-                else item.source[:50]
-            )
-            item.status = ImportStatus.PROCESSING
-            await self._broadcast_progress(progress)
-
-            start_time = time.time()
-            try:
-                result = await self._import_single_item(
-                    item=item,
-                    recipe_scanner_getter=recipe_scanner_getter,
-                    civitai_client_getter=civitai_client_getter,
-                    tags=progress.tags,
-                    skip_no_metadata=progress.skip_no_metadata,
-                    skip_duplicates=progress.skip_duplicates,
-                    semaphore=self._concurrency_controller.get_semaphore(),
-                )
-
-                duration = time.time() - start_time
-                item.duration = duration
-                self._concurrency_controller.record_result(
-                    duration, result.get("success", False)
-                )
-
-                if result.get("success"):
-                    item.status = ImportStatus.SUCCESS
-                    item.recipe_name = result.get("recipe_name")
-                    item.recipe_id = result.get("recipe_id")
-                    progress.success += 1
-                elif result.get("skipped"):
-                    item.status = ImportStatus.SKIPPED
-                    item.error_message = result.get("error")
-                    progress.skipped += 1
-                else:
-                    item.status = ImportStatus.FAILED
-                    item.error_message = result.get("error")
-                    progress.failed += 1
-
-            except Exception as e:
-                self._logger.error(f"Error importing {item.source}: {e}")
-                item.status = ImportStatus.FAILED
-                item.error_message = str(e)
-                item.duration = time.time() - start_time
-                progress.failed += 1
-                self._concurrency_controller.record_result(item.duration, False)
-
-            progress.completed += 1
-            await self._broadcast_progress(progress)
-
-        tasks = [process_item(item) for item in progress.items]
-        await asyncio.gather(*tasks, return_exceptions=True)
-
-        if self._cancellation_flags.get(operation_id, False):
-            progress.status = "cancelled"
-        else:
-            progress.status = "completed"
-
-        progress.finished_at = time.time()
-        progress.current_item = ""
-        await self._broadcast_progress(progress)
-
-        await asyncio.sleep(5)
-        self._cleanup_operation(operation_id)
-
-    async def _import_single_item(
-        self,
-        *,
-        item: BatchImportItem,
-        recipe_scanner_getter: Callable[[], Any],
-        civitai_client_getter: Callable[[], Any],
-        tags: List[str],
-        skip_no_metadata: bool,
-        skip_duplicates: bool,
-        semaphore: asyncio.Semaphore,
-    ) -> Dict[str, Any]:
-        async with semaphore:
-            recipe_scanner = recipe_scanner_getter()
-            if recipe_scanner is None:
-                return {"success": False, "error": "Recipe scanner unavailable"}
-
-            try:
-                if item.item_type == ImportItemType.URL:
-                    if not self._validate_url(item.source):
-                        return {
-                            "success": False,
-                            "error": f"Invalid URL format: {item.source}",
-                        }
-
-                    if skip_duplicates:
-                        if self._is_duplicate_source(
-                            item.source, item.item_type, recipe_scanner
-                        ):
-                            return {
-                                "success": False,
-                                "skipped": True,
-                                "error": "Duplicate source URL",
-                            }
-
-                    civitai_client = civitai_client_getter()
-                    analysis_result = await self._analysis_service.analyze_remote_image(
-                        url=item.source,
-                        recipe_scanner=recipe_scanner,
-                        civitai_client=civitai_client,
-                    )
-                else:
-                    if not self._validate_local_path(item.source):
-                        return {
-                            "success": False,
-                            "error": f"Invalid or unsafe path: {item.source}",
-                        }
-
-                    if not os.path.exists(item.source):
-                        return {
-                            "success": False,
-                            "error": f"File not found: {item.source}",
-                        }
-
-                    if skip_duplicates:
-                        if self._is_duplicate_source(
-                            item.source, item.item_type, recipe_scanner
-                        ):
-                            return {
-                                "success": False,
-                                "skipped": True,
-                                "error": "Duplicate source path",
-                            }
-
-                    analysis_result = await self._analysis_service.analyze_local_image(
-                        file_path=item.source,
-                        recipe_scanner=recipe_scanner,
-                    )
-
-                payload = analysis_result.payload
-
-                if payload.get("error"):
-                    if skip_no_metadata and "No metadata" in payload.get("error", ""):
-                        return {
-                            "success": False,
-                            "skipped": True,
-                            "error": payload["error"],
-                        }
-                    return {"success": False, "error": payload["error"]}
-
-                loras = payload.get("loras", [])
-                if not loras:
-                    if skip_no_metadata:
-                        return {
-                            "success": False,
-                            "skipped": True,
-                            "error": "No LoRAs found in image",
-                        }
-                    # When skip_no_metadata is False, allow importing images without LoRAs
-                    # Continue with empty loras list
-
-                recipe_name = self._generate_recipe_name(item, payload)
-                all_tags = list(set(tags + (payload.get("tags", []) or [])))
-
-                metadata = {
-                    "base_model": payload.get("base_model", ""),
-                    "loras": loras,
-                    "gen_params": payload.get("gen_params", {}),
-                    "source_path": item.source,
-                }
-
-                if payload.get("checkpoint"):
-                    metadata["checkpoint"] = payload["checkpoint"]
-
-                image_bytes = None
-                image_base64 = payload.get("image_base64")
-
-                if item.item_type == ImportItemType.LOCAL_PATH:
-                    with open(item.source, "rb") as f:
-                        image_bytes = f.read()
-                    image_base64 = None
-
-                save_result = await self._persistence_service.save_recipe(
-                    recipe_scanner=recipe_scanner,
-                    image_bytes=image_bytes,
-                    image_base64=image_base64,
-                    name=recipe_name,
-                    tags=all_tags,
-                    metadata=metadata,
-                    extension=payload.get("extension"),
-                )
-
-                if save_result.status == 200:
-                    return {
-                        "success": True,
-                        "recipe_name": recipe_name,
-                        "recipe_id": save_result.payload.get("id"),
-                    }
-                else:
-                    return {
-                        "success": False,
-                        "error": save_result.payload.get(
-                            "error", "Failed to save recipe"
-                        ),
-                    }
-
-            except RecipeValidationError as e:
-                return {"success": False, "error": str(e)}
-            except RecipeDownloadError as e:
-                return {"success": False, "error": str(e)}
-            except RecipeNotFoundError as e:
-                return {"success": False, "skipped": True, "error": str(e)}
-            except Exception as e:
-                self._logger.error(
-                    f"Unexpected error importing {item.source}: {e}", exc_info=True
-                )
-                return {"success": False, "error": str(e)}
-
-    def _generate_recipe_name(
-        self, item: BatchImportItem, payload: Dict[str, Any]
-    ) -> str:
-        if item.item_type == ImportItemType.LOCAL_PATH:
-            base_name = os.path.splitext(os.path.basename(item.source))[0]
-            return base_name[:100]
-        else:
-            loras = payload.get("loras", [])
-            if loras:
-                first_lora = loras[0].get("name", "Recipe")
-                return f"Import - {first_lora}"[:100]
-            return f"Imported Recipe {item.id[:8]}"
-
-    async def _broadcast_progress(self, progress: BatchImportProgress) -> None:
-        await self._ws_manager.broadcast(
-            {
-                "type": "batch_import_progress",
-                **progress.to_dict(),
-            }
-        )
-
-    def _cleanup_operation(self, operation_id: str) -> None:
-        if operation_id in self._cancellation_flags:
-            del self._cancellation_flags[operation_id]

py/services/cache_entry_validator.py

@@ -1,291 +0,0 @@
-"""
-Cache Entry Validator
-
-Validates and repairs cache entries to prevent runtime errors from
-missing or invalid critical fields.
-"""
-
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Tuple
-import logging
-import os
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ValidationResult:
-    """Result of validating a single cache entry."""
-    is_valid: bool
-    repaired: bool
-    errors: List[str] = field(default_factory=list)
-    entry: Optional[Dict[str, Any]] = None
-
-
-class CacheEntryValidator:
-    """
-    Validates and repairs cache entry core fields.
-
-    Critical fields that cause runtime errors when missing:
-    - file_path: KeyError in multiple locations
-    - sha256: KeyError/AttributeError in hash operations
-
-    Medium severity fields that may cause sorting/display issues:
-    - size: KeyError during sorting
-    - modified: KeyError during sorting
-    - model_name: AttributeError on .lower() calls
-
-    Low severity fields:
-    - tags: KeyError/TypeError in recipe operations
-    """
-
-    # Field definitions: (default_value, is_required)
-    CORE_FIELDS: Dict[str, Tuple[Any, bool]] = {
-        'file_path': ('', True),
-        'sha256': ('', True),
-        'file_name': ('', False),
-        'model_name': ('', False),
-        'folder': ('', False),
-        'size': (0, False),
-        'modified': (0.0, False),
-        'tags': ([], False),
-        'preview_url': ('', False),
-        'base_model': ('', False),
-        'from_civitai': (True, False),
-        'favorite': (False, False),
-        'exclude': (False, False),
-        'db_checked': (False, False),
-        'preview_nsfw_level': (0, False),
-        'notes': ('', False),
-        'usage_tips': ('', False),
-        'hash_status': ('completed', False),
-    }
-
-    @classmethod
-    def validate(cls, entry: Dict[str, Any], *, auto_repair: bool = True) -> ValidationResult:
-        """
-        Validate a single cache entry.
-
-        Args:
-            entry: The cache entry dictionary to validate
-            auto_repair: If True, attempt to repair missing/invalid fields
-
-        Returns:
-            ValidationResult with validation status and optionally repaired entry
-        """
-        if entry is None:
-            return ValidationResult(
-                is_valid=False,
-                repaired=False,
-                errors=['Entry is None'],
-                entry=None
-            )
-
-        if not isinstance(entry, dict):
-            return ValidationResult(
-                is_valid=False,
-                repaired=False,
-                errors=[f'Entry is not a dict: {type(entry).__name__}'],
-                entry=None
-            )
-
-        errors: List[str] = []
-        repaired = False
-        
-        # If auto_repair is on, we work on a copy. If not, we still need a safe way to check fields.
-        working_entry = dict(entry) if auto_repair else entry
-
-        # Determine effective hash_status for validation logic
-        hash_status = entry.get('hash_status')
-        if hash_status is None:
-            if auto_repair:
-                working_entry['hash_status'] = 'completed'
-                repaired = True
-            hash_status = 'completed'
-
-        for field_name, (default_value, is_required) in cls.CORE_FIELDS.items():
-            # Get current value from the original entry to avoid side effects during validation
-            value = entry.get(field_name)
-
-            # Check if field is missing or None
-            if value is None:
-                # Special case: sha256 can be None/empty if hash_status is pending
-                if field_name == 'sha256' and hash_status == 'pending':
-                    if auto_repair:
-                        working_entry[field_name] = ''
-                        repaired = True
-                    continue
-
-                if is_required:
-                    errors.append(f"Required field '{field_name}' is missing or None")
-                if auto_repair:
-                    working_entry[field_name] = cls._get_default_copy(default_value)
-                    repaired = True
-                continue
-
-            # Validate field type and value
-            field_error = cls._validate_field(field_name, value, default_value)
-            if field_error:
-                # Special case: allow empty string for sha256 if pending
-                if field_name == 'sha256' and hash_status == 'pending' and value == '':
-                    continue
-
-                errors.append(field_error)
-                if auto_repair:
-                    working_entry[field_name] = cls._get_default_copy(default_value)
-                    repaired = True
-
-        # Special validation: file_path must not be empty for required field
-        file_path = working_entry.get('file_path', '')
-        if not file_path or (isinstance(file_path, str) and not file_path.strip()):
-            errors.append("Required field 'file_path' is empty")
-            # Cannot repair empty file_path - entry is invalid
-            return ValidationResult(
-                is_valid=False,
-                repaired=repaired,
-                errors=errors,
-                entry=working_entry if auto_repair else None
-            )
-
-        # Special validation: sha256 must not be empty for required field
-        # BUT allow empty sha256 when hash_status is pending (lazy hash calculation)
-        sha256 = working_entry.get('sha256', '')
-        # Use the effective hash_status we determined earlier
-        if not sha256 or (isinstance(sha256, str) and not sha256.strip()):
-            # Allow empty sha256 for lazy hash calculation (checkpoints)
-            if hash_status != 'pending':
-                errors.append("Required field 'sha256' is empty")
-                # Cannot repair empty sha256 - entry is invalid
-                return ValidationResult(
-                    is_valid=False,
-                    repaired=repaired,
-                    errors=errors,
-                    entry=working_entry if auto_repair else None
-                )
-
-        # Normalize sha256 to lowercase if needed
-        if isinstance(sha256, str):
-            normalized_sha = sha256.lower().strip()
-            if normalized_sha != sha256:
-                if auto_repair:
-                    working_entry['sha256'] = normalized_sha
-                    repaired = True
-                else:
-                    # If not auto-repairing, we don't consider case difference as a "critical error" 
-                    # that invalidates the entry, but we also don't mark it repaired.
-                    pass
-
-        # Determine if entry is valid
-        # Entry is valid if no critical required field errors remain after repair
-        # Critical fields are file_path and sha256
-        CRITICAL_REQUIRED_FIELDS = {'file_path', 'sha256'}
-        has_critical_errors = any(
-            "Required field" in error and
-            any(f"'{field}'" in error for field in CRITICAL_REQUIRED_FIELDS)
-            for error in errors
-        )
-
-        is_valid = not has_critical_errors
-
-        return ValidationResult(
-            is_valid=is_valid,
-            repaired=repaired,
-            errors=errors,
-            entry=working_entry if auto_repair else entry
-        )
-
-    @classmethod
-    def validate_batch(
-        cls,
-        entries: List[Dict[str, Any]],
-        *,
-        auto_repair: bool = True
-    ) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
-        """
-        Validate a batch of cache entries.
-
-        Args:
-            entries: List of cache entry dictionaries to validate
-            auto_repair: If True, attempt to repair missing/invalid fields
-
-        Returns:
-            Tuple of (valid_entries, invalid_entries)
-        """
-        if not entries:
-            return [], []
-
-        valid_entries: List[Dict[str, Any]] = []
-        invalid_entries: List[Dict[str, Any]] = []
-
-        for entry in entries:
-            result = cls.validate(entry, auto_repair=auto_repair)
-
-            if result.is_valid:
-                # Use repaired entry if available, otherwise original
-                valid_entries.append(result.entry if result.entry else entry)
-            else:
-                invalid_entries.append(entry)
-                # Log invalid entries for debugging
-                file_path = entry.get('file_path', '<unknown>') if isinstance(entry, dict) else '<not a dict>'
-                logger.warning(
-                    f"Invalid cache entry for '{file_path}': {', '.join(result.errors)}"
-                )
-
-        return valid_entries, invalid_entries
-
-    @classmethod
-    def _validate_field(cls, field_name: str, value: Any, default_value: Any) -> Optional[str]:
-        """
-        Validate a specific field value.
-
-        Returns an error message if invalid, None if valid.
-        """
-        expected_type = type(default_value)
-
-        # Special handling for numeric types
-        if expected_type == int:
-            if not isinstance(value, (int, float)):
-                return f"Field '{field_name}' should be numeric, got {type(value).__name__}"
-        elif expected_type == float:
-            if not isinstance(value, (int, float)):
-                return f"Field '{field_name}' should be numeric, got {type(value).__name__}"
-        elif expected_type == bool:
-            # Be lenient with boolean fields - accept truthy/falsy values
-            pass
-        elif expected_type == str:
-            if not isinstance(value, str):
-                return f"Field '{field_name}' should be string, got {type(value).__name__}"
-        elif expected_type == list:
-            if not isinstance(value, (list, tuple)):
-                return f"Field '{field_name}' should be list, got {type(value).__name__}"
-
-        return None
-
-    @classmethod
-    def _get_default_copy(cls, default_value: Any) -> Any:
-        """Get a copy of the default value to avoid shared mutable state."""
-        if isinstance(default_value, list):
-            return list(default_value)
-        if isinstance(default_value, dict):
-            return dict(default_value)
-        return default_value
-
-    @classmethod
-    def get_file_path_safe(cls, entry: Dict[str, Any], default: str = '') -> str:
-        """Safely get file_path from an entry."""
-        if not isinstance(entry, dict):
-            return default
-        value = entry.get('file_path')
-        if isinstance(value, str):
-            return value
-        return default
-
-    @classmethod
-    def get_sha256_safe(cls, entry: Dict[str, Any], default: str = '') -> str:
-        """Safely get sha256 from an entry."""
-        if not isinstance(entry, dict):
-            return default
-        value = entry.get('sha256')
-        if isinstance(value, str):
-            return value.lower()
-        return default

py/services/cache_health_monitor.py

@@ -1,201 +0,0 @@
-"""
-Cache Health Monitor
-
-Monitors cache health status and determines when user intervention is needed.
-"""
-
-from dataclasses import dataclass, field
-from enum import Enum
-from typing import Any, Dict, List, Optional
-import logging
-
-from .cache_entry_validator import CacheEntryValidator, ValidationResult
-
-logger = logging.getLogger(__name__)
-
-
-class CacheHealthStatus(Enum):
-    """Health status of the cache."""
-    HEALTHY = "healthy"
-    DEGRADED = "degraded"
-    CORRUPTED = "corrupted"
-
-
-@dataclass
-class HealthReport:
-    """Report of cache health check."""
-    status: CacheHealthStatus
-    total_entries: int
-    valid_entries: int
-    invalid_entries: int
-    repaired_entries: int
-    invalid_paths: List[str] = field(default_factory=list)
-    message: str = ""
-
-    @property
-    def corruption_rate(self) -> float:
-        """Calculate the percentage of invalid entries."""
-        if self.total_entries <= 0:
-            return 0.0
-        return self.invalid_entries / self.total_entries
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Convert to dictionary for JSON serialization."""
-        return {
-            'status': self.status.value,
-            'total_entries': self.total_entries,
-            'valid_entries': self.valid_entries,
-            'invalid_entries': self.invalid_entries,
-            'repaired_entries': self.repaired_entries,
-            'corruption_rate': f"{self.corruption_rate:.1%}",
-            'invalid_paths': self.invalid_paths[:10],  # Limit to first 10
-            'message': self.message,
-        }
-
-
-class CacheHealthMonitor:
-    """
-    Monitors cache health and determines appropriate status.
-
-    Thresholds:
-    - HEALTHY: 0% invalid entries
-    - DEGRADED: 0-5% invalid entries (auto-repaired, user should rebuild)
-    - CORRUPTED: >5% invalid entries (significant data loss likely)
-    """
-
-    # Threshold percentages
-    DEGRADED_THRESHOLD = 0.01   # 1% - show warning
-    CORRUPTED_THRESHOLD = 0.05  # 5% - critical warning
-
-    def __init__(
-        self,
-        *,
-        degraded_threshold: float = DEGRADED_THRESHOLD,
-        corrupted_threshold: float = CORRUPTED_THRESHOLD
-    ):
-        """
-        Initialize the health monitor.
-
-        Args:
-            degraded_threshold: Corruption rate threshold for DEGRADED status
-            corrupted_threshold: Corruption rate threshold for CORRUPTED status
-        """
-        self.degraded_threshold = degraded_threshold
-        self.corrupted_threshold = corrupted_threshold
-
-    def check_health(
-        self,
-        entries: List[Dict[str, Any]],
-        *,
-        auto_repair: bool = True
-    ) -> HealthReport:
-        """
-        Check the health of cache entries.
-
-        Args:
-            entries: List of cache entry dictionaries to check
-            auto_repair: If True, attempt to repair entries during validation
-
-        Returns:
-            HealthReport with status and statistics
-        """
-        if not entries:
-            return HealthReport(
-                status=CacheHealthStatus.HEALTHY,
-                total_entries=0,
-                valid_entries=0,
-                invalid_entries=0,
-                repaired_entries=0,
-                message="Cache is empty"
-            )
-
-        total_entries = len(entries)
-        valid_entries: List[Dict[str, Any]] = []
-        invalid_entries: List[Dict[str, Any]] = []
-        repaired_count = 0
-        invalid_paths: List[str] = []
-
-        for entry in entries:
-            result = CacheEntryValidator.validate(entry, auto_repair=auto_repair)
-
-            if result.is_valid:
-                valid_entries.append(result.entry if result.entry else entry)
-                if result.repaired:
-                    repaired_count += 1
-            else:
-                invalid_entries.append(entry)
-                # Extract file path for reporting
-                file_path = CacheEntryValidator.get_file_path_safe(entry, '<unknown>')
-                invalid_paths.append(file_path)
-
-        invalid_count = len(invalid_entries)
-        valid_count = len(valid_entries)
-
-        # Determine status based on corruption rate
-        corruption_rate = invalid_count / total_entries if total_entries > 0 else 0.0
-
-        if invalid_count == 0:
-            status = CacheHealthStatus.HEALTHY
-            message = "Cache is healthy"
-        elif corruption_rate >= self.corrupted_threshold:
-            status = CacheHealthStatus.CORRUPTED
-            message = (
-                f"Cache is corrupted: {invalid_count} invalid entries "
-                f"({corruption_rate:.1%}). Rebuild recommended."
-            )
-        elif corruption_rate >= self.degraded_threshold or invalid_count > 0:
-            status = CacheHealthStatus.DEGRADED
-            message = (
-                f"Cache has {invalid_count} invalid entries "
-                f"({corruption_rate:.1%}). Consider rebuilding cache."
-            )
-        else:
-            # This shouldn't happen, but handle gracefully
-            status = CacheHealthStatus.HEALTHY
-            message = "Cache is healthy"
-
-        # Log the health check result
-        if status != CacheHealthStatus.HEALTHY:
-            logger.warning(
-                f"Cache health check: {status.value} - "
-                f"{invalid_count}/{total_entries} invalid, "
-                f"{repaired_count} repaired"
-            )
-            if invalid_paths:
-                logger.debug(f"Invalid entry paths: {invalid_paths[:5]}")
-
-        return HealthReport(
-            status=status,
-            total_entries=total_entries,
-            valid_entries=valid_count,
-            invalid_entries=invalid_count,
-            repaired_entries=repaired_count,
-            invalid_paths=invalid_paths,
-            message=message
-        )
-
-    def should_notify_user(self, report: HealthReport) -> bool:
-        """
-        Determine if the user should be notified about cache health.
-
-        Args:
-            report: The health report to evaluate
-
-        Returns:
-            True if user should be notified
-        """
-        return report.status != CacheHealthStatus.HEALTHY
-
-    def get_notification_severity(self, report: HealthReport) -> str:
-        """
-        Get the severity level for user notification.
-
-        Args:
-            report: The health report to evaluate
-
-        Returns:
-            Severity string: 'warning' or 'error'
-        """
-        if report.status == CacheHealthStatus.CORRUPTED:
-            return 'error'
-        return 'warning'

py/services/checkpoint_scanner.py

@@ -1,360 +1,37 @@
-import asyncio
-import json
 import logging
-import os
-from datetime import datetime
 from typing import Any, Dict, List, Optional
 
 from ..utils.models import CheckpointMetadata
-from ..utils.file_utils import find_preview_file, normalize_path
-from ..utils.metadata_manager import MetadataManager
 from ..config import config
 from .model_scanner import ModelScanner
 from .model_hash_index import ModelHashIndex
 
 logger = logging.getLogger(__name__)
 
-
 class CheckpointScanner(ModelScanner):
     """Service for scanning and managing checkpoint files"""
-
+    
     def __init__(self):
         # Define supported file extensions
-        file_extensions = {
-            ".ckpt",
-            ".pt",
-            ".pt2",
-            ".bin",
-            ".pth",
-            ".safetensors",
-            ".pkl",
-            ".sft",
-            ".gguf",
-        }
+        file_extensions = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft', '.gguf'}
         super().__init__(
             model_type="checkpoint",
             model_class=CheckpointMetadata,
             file_extensions=file_extensions,
-            hash_index=ModelHashIndex(),
+            hash_index=ModelHashIndex()
         )
-        if not hasattr(self, "_hash_calculation_lock"):
-            self._hash_calculation_lock = asyncio.Lock()
-            self._hash_calculation_tasks: dict[str, asyncio.Task[Optional[str]]] = {}
-
-    async def _create_default_metadata(
-        self, file_path: str
-    ) -> Optional[CheckpointMetadata]:
-        """Create default metadata for checkpoint without calculating hash (lazy hash).
-
-        Checkpoints are typically large (10GB+), so we skip hash calculation during initial
-        scanning to improve startup performance. Hash will be calculated on-demand when
-        fetching metadata from Civitai.
-        """
-        try:
-            real_path = os.path.realpath(file_path)
-            if not os.path.exists(real_path):
-                logger.error(f"File not found: {file_path}")
-                return None
-
-            base_name = os.path.splitext(os.path.basename(file_path))[0]
-            dir_path = os.path.dirname(file_path)
-
-            # Find preview image
-            preview_url = find_preview_file(base_name, dir_path)
-
-            # Create metadata WITHOUT calculating hash
-            metadata = CheckpointMetadata(
-                file_name=base_name,
-                model_name=base_name,
-                file_path=normalize_path(file_path),
-                size=os.path.getsize(real_path),
-                modified=datetime.now().timestamp(),
-                sha256="",  # Empty hash - will be calculated on-demand
-                base_model="Unknown",
-                preview_url=normalize_path(preview_url),
-                tags=[],
-                modelDescription="",
-                sub_type="checkpoint",
-                from_civitai=False,  # Mark as local model since no hash yet
-                hash_status="pending",  # Mark hash as pending
-            )
-
-            # Save the created metadata
-            logger.info(f"Creating checkpoint metadata (hash pending) for {file_path}")
-            await MetadataManager.save_metadata(file_path, metadata)
-
-            return metadata
-
-        except Exception as e:
-            logger.error(
-                f"Error creating default checkpoint metadata for {file_path}: {e}"
-            )
-            return None
-
-    async def calculate_hash_for_model(self, file_path: str) -> Optional[str]:
-        """Calculate hash for a checkpoint on-demand with per-file singleflight.
-
-        Args:
-            file_path: Path to the model file
-
-        Returns:
-            SHA256 hash string, or None if calculation failed
-        """
-        try:
-            real_path = os.path.realpath(file_path)
-            if not os.path.exists(real_path):
-                logger.error(f"File not found for hash calculation: {file_path}")
-                return None
-
-            metadata, _ = await MetadataManager.load_metadata(
-                file_path, self.model_class
-            )
-            if (
-                metadata is not None
-                and metadata.hash_status == "completed"
-                and metadata.sha256
-            ):
-                return metadata.sha256
-
-            async with self._hash_calculation_lock:
-                metadata, _ = await MetadataManager.load_metadata(
-                    file_path, self.model_class
-                )
-                if (
-                    metadata is not None
-                    and metadata.hash_status == "completed"
-                    and metadata.sha256
-                ):
-                    return metadata.sha256
-
-                task = self._hash_calculation_tasks.get(real_path)
-                if task is None:
-                    task = asyncio.create_task(
-                        self._run_hash_calculation_task(file_path, real_path)
-                    )
-                    self._hash_calculation_tasks[real_path] = task
-
-            return await asyncio.shield(task)
-
-        except Exception as e:
-            logger.error(f"Error calculating hash for {file_path}: {e}")
-            return None
-
-    async def _run_hash_calculation_task(
-        self, file_path: str, real_path: str
-    ) -> Optional[str]:
-        """Run a hash calculation task and remove it from the in-flight map."""
-        try:
-            return await self._calculate_hash_for_model_uncached(file_path, real_path)
-        finally:
-            task = asyncio.current_task()
-            async with self._hash_calculation_lock:
-                if self._hash_calculation_tasks.get(real_path) is task:
-                    del self._hash_calculation_tasks[real_path]
-
-    async def _calculate_hash_for_model_uncached(
-        self, file_path: str, real_path: str
-    ) -> Optional[str]:
-        """Calculate hash for a checkpoint without checking in-flight tasks."""
-        from ..utils.file_utils import calculate_sha256
-
-        try:
-            # Load current metadata
-            metadata, should_skip = await MetadataManager.load_metadata(
-                file_path, self.model_class
-            )
-            if metadata is None:
-                if should_skip:
-                    logger.error(f"Invalid metadata found for {file_path}")
-                    return None
-                created_metadata = await self._create_default_metadata(file_path)
-                if created_metadata is None:
-                    logger.error(f"No metadata found for {file_path}")
-                    return None
-                metadata = created_metadata
-
-            # Check if hash is already calculated
-            if metadata.hash_status == "completed" and metadata.sha256:
-                return metadata.sha256
-
-            # Update status to calculating
-            metadata.hash_status = "calculating"
-            await MetadataManager.save_metadata(file_path, metadata)
-
-            # Calculate hash
-            logger.info(f"Calculating hash for checkpoint: {file_path}")
-            sha256 = await calculate_sha256(real_path)
-
-            # Update metadata with hash
-            metadata.sha256 = sha256
-            metadata.hash_status = "completed"
-            await MetadataManager.save_metadata(file_path, metadata)
-
-            # Update hash index
-            self._hash_index.add_entry(sha256.lower(), file_path)
-
-            logger.info(f"Hash calculated for checkpoint: {file_path}")
-            return sha256
-
-        except Exception as e:
-            logger.error(f"Error calculating hash for {file_path}: {e}")
-            # Update status to failed
-            try:
-                metadata, _ = await MetadataManager.load_metadata(
-                    file_path, self.model_class
-                )
-                if metadata:
-                    metadata.hash_status = "failed"
-                    await MetadataManager.save_metadata(file_path, metadata)
-            except Exception:
-                pass
-            return None
-
-    async def calculate_all_pending_hashes(
-        self, progress_callback=None
-    ) -> Dict[str, int]:
-        """Calculate hashes for all checkpoints with pending hash status.
-
-        If cache is not initialized, scans filesystem directly for metadata files
-        with hash_status != 'completed'.
-
-        Args:
-            progress_callback: Optional callback(progress, total, current_file)
-
-        Returns:
-            Dict with 'completed', 'failed', 'total' counts
-        """
-        # Try to get from cache first
-        cache = await self.get_cached_data()
-
-        if cache and cache.raw_data:
-            # Use cache if available
-            pending_models = [
-                item
-                for item in cache.raw_data
-                if item.get("hash_status") != "completed" or not item.get("sha256")
-            ]
-        else:
-            # Cache not initialized, scan filesystem directly
-            pending_models = await self._find_pending_models_from_filesystem()
-
-        if not pending_models:
-            return {"completed": 0, "failed": 0, "total": 0}
-
-        total = len(pending_models)
-        completed = 0
-        failed = 0
-
-        for i, model_data in enumerate(pending_models):
-            file_path = model_data.get("file_path")
-            if not file_path:
-                continue
-
-            try:
-                sha256 = await self.calculate_hash_for_model(file_path)
-                if sha256:
-                    completed += 1
-                else:
-                    failed += 1
-            except Exception as e:
-                logger.error(f"Error calculating hash for {file_path}: {e}")
-                failed += 1
-
-            if progress_callback:
-                try:
-                    await progress_callback(i + 1, total, file_path)
-                except Exception:
-                    pass
-
-        return {"completed": completed, "failed": failed, "total": total}
-
-    async def _find_pending_models_from_filesystem(self) -> List[Dict[str, Any]]:
-        """Scan filesystem for checkpoint metadata files with pending hash status."""
-        pending_models = []
-
-        for root_path in self.get_model_roots():
-            if not os.path.exists(root_path):
-                continue
-
-            for dirpath, _dirnames, filenames in os.walk(root_path):
-                for filename in filenames:
-                    if not filename.endswith(".metadata.json"):
-                        continue
-
-                    metadata_path = os.path.join(dirpath, filename)
-                    try:
-                        with open(metadata_path, "r", encoding="utf-8") as f:
-                            data = json.load(f)
-
-                        # Check if hash is pending
-                        hash_status = data.get("hash_status", "completed")
-                        sha256 = data.get("sha256", "")
-
-                        if hash_status != "completed" or not sha256:
-                            # Find corresponding model file
-                            model_name = filename.replace(".metadata.json", "")
-                            model_path = None
-
-                            # Look for model file with matching name
-                            for ext in self.file_extensions:
-                                potential_path = os.path.join(dirpath, model_name + ext)
-                                if os.path.exists(potential_path):
-                                    model_path = potential_path
-                                    break
-
-                            if model_path:
-                                pending_models.append(
-                                    {
-                                        "file_path": model_path.replace(os.sep, "/"),
-                                        "hash_status": hash_status,
-                                        "sha256": sha256,
-                                        **{
-                                            k: v
-                                            for k, v in data.items()
-                                            if k
-                                            not in [
-                                                "file_path",
-                                                "hash_status",
-                                                "sha256",
-                                            ]
-                                        },
-                                    }
-                                )
-                    except (json.JSONDecodeError, Exception) as e:
-                        logger.debug(
-                            f"Error reading metadata file {metadata_path}: {e}"
-                        )
-                        continue
-
-        return pending_models
 
     def _resolve_sub_type(self, root_path: Optional[str]) -> Optional[str]:
-        """Resolve the sub-type based on the root path.
-
-        Checks both standard ComfyUI paths and LoRA Manager's extra folder paths.
-        """
+        """Resolve the sub-type based on the root path."""
         if not root_path:
             return None
 
-        # Check standard ComfyUI checkpoint paths
         if config.checkpoints_roots and root_path in config.checkpoints_roots:
             return "checkpoint"
 
-        # Check extra checkpoint paths
-        if (
-            config.extra_checkpoints_roots
-            and root_path in config.extra_checkpoints_roots
-        ):
-            return "checkpoint"
-
-        # Check standard ComfyUI unet paths
         if config.unet_roots and root_path in config.unet_roots:
             return "diffusion_model"
 
-        # Check extra unet paths
-        if config.extra_unet_roots and root_path in config.extra_unet_roots:
-            return "diffusion_model"
-
         return None
 
     def adjust_metadata(self, metadata, file_path, root_path):
@@ -374,16 +51,5 @@ class CheckpointScanner(ModelScanner):
         return entry
 
     def get_model_roots(self) -> List[str]:
-        """Get checkpoint root directories (including extra paths)"""
-        roots: List[str] = []
-        roots.extend(config.base_models_roots or [])
-        roots.extend(config.extra_checkpoints_roots or [])
-        roots.extend(config.extra_unet_roots or [])
-        # Remove duplicates while preserving order
-        seen: set = set()
-        unique_roots: List[str] = []
-        for root in roots:
-            if root not in seen:
-                seen.add(root)
-                unique_roots.append(root)
-        return unique_roots
+        """Get checkpoint root directories"""
+        return config.base_models_roots

py/services/checkpoint_service.py

@@ -42,9 +42,7 @@ class CheckpointService(BaseModelService):
             "notes": checkpoint_data.get("notes", ""),
             "sub_type": sub_type,
             "favorite": checkpoint_data.get("favorite", False),
-            "exclude": bool(checkpoint_data.get("exclude", False)),
             "update_available": bool(checkpoint_data.get("update_available", False)),
-            "skip_metadata_refresh": bool(checkpoint_data.get("skip_metadata_refresh", False)),
             "civitai": self.filter_civitai_data(checkpoint_data.get("civitai", {}), minimal=True)
         }
     

py/services/civitai_base_model_service.py

@@ -1,430 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import json
-import logging
-import re
-from datetime import datetime, timezone
-from typing import Any, Dict, List, Optional, Set, Tuple
-
-from ..utils.constants import SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS
-from .downloader import get_downloader
-
-logger = logging.getLogger(__name__)
-
-
-class CivitaiBaseModelService:
-    """Service for fetching and managing Civitai base models.
-
-    This service provides:
-    - Fetching base models from Civitai API
-    - Caching with TTL (7 days default)
-    - Merging hardcoded and remote base models
-    - Generating abbreviations for new/unknown models
-    """
-
-    _instance: Optional[CivitaiBaseModelService] = None
-    _lock = asyncio.Lock()
-
-    # Default TTL for cache in seconds (7 days)
-    DEFAULT_CACHE_TTL = 7 * 24 * 60 * 60
-
-    # Civitai API endpoint for enums
-    CIVITAI_ENUMS_URL = "https://civitai.red/api/v1/enums"
-
-    @classmethod
-    async def get_instance(cls) -> CivitaiBaseModelService:
-        """Get singleton instance of the service."""
-        async with cls._lock:
-            if cls._instance is None:
-                cls._instance = cls()
-            return cls._instance
-
-    def __init__(self):
-        """Initialize the service."""
-        if hasattr(self, "_initialized"):
-            return
-        self._initialized = True
-
-        # Cache storage
-        self._cache: Optional[Dict[str, Any]] = None
-        self._cache_timestamp: Optional[datetime] = None
-        self._cache_ttl = self.DEFAULT_CACHE_TTL
-
-        # Hardcoded models for fallback
-        self._hardcoded_models = set(SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS)
-
-        logger.info("CivitaiBaseModelService initialized")
-
-    async def get_base_models(self, force_refresh: bool = False) -> Dict[str, Any]:
-        """Get merged base models (hardcoded + remote).
-
-        Args:
-            force_refresh: If True, fetch from API regardless of cache state.
-
-        Returns:
-            Dictionary containing:
-            - models: List of merged base model names
-            - source: 'cache', 'api', or 'fallback'
-            - last_updated: ISO timestamp of last successful API fetch
-            - hardcoded_count: Number of hardcoded models
-            - remote_count: Number of remote models
-            - merged_count: Total unique models
-        """
-        # Check if cache is valid
-        if not force_refresh and self._is_cache_valid():
-            logger.debug("Returning cached base models")
-            return self._build_response("cache")
-
-        # Try to fetch from API
-        try:
-            remote_models = await self._fetch_from_civitai()
-            if remote_models:
-                self._update_cache(remote_models)
-                return self._build_response("api")
-        except Exception as e:
-            logger.error(f"Failed to fetch base models from Civitai: {e}")
-
-        # Fallback to hardcoded models
-        return self._build_response("fallback")
-
-    async def refresh_cache(self) -> Dict[str, Any]:
-        """Force refresh the cache from Civitai API.
-
-        Returns:
-            Response dict same as get_base_models()
-        """
-        return await self.get_base_models(force_refresh=True)
-
-    def get_cache_status(self) -> Dict[str, Any]:
-        """Get current cache status.
-
-        Returns:
-            Dictionary containing:
-            - has_cache: Whether cache exists
-            - last_updated: ISO timestamp or None
-            - is_expired: Whether cache is expired
-            - ttl_seconds: TTL in seconds
-            - age_seconds: Age of cache in seconds (if exists)
-        """
-        if self._cache is None or self._cache_timestamp is None:
-            return {
-                "has_cache": False,
-                "last_updated": None,
-                "is_expired": True,
-                "ttl_seconds": self._cache_ttl,
-                "age_seconds": None,
-            }
-
-        age = (datetime.now(timezone.utc) - self._cache_timestamp).total_seconds()
-        return {
-            "has_cache": True,
-            "last_updated": self._cache_timestamp.isoformat(),
-            "is_expired": age > self._cache_ttl,
-            "ttl_seconds": self._cache_ttl,
-            "age_seconds": int(age),
-        }
-
-    def generate_abbreviation(self, model_name: str) -> str:
-        """Generate abbreviation for a base model name.
-
-        Algorithm:
-        1. Extract version patterns (e.g., "2.5" from "Wan Video 2.5")
-        2. Extract main acronym (e.g., "SD" from "SD 1.5")
-        3. Handle special cases (Flux, Wan, etc.)
-        4. Fallback to first letters of words (max 4 chars)
-
-        Args:
-            model_name: Full base model name
-
-        Returns:
-            Generated abbreviation (max 4 characters)
-        """
-        if not model_name or not isinstance(model_name, str):
-            return "OTH"
-
-        name = model_name.strip()
-        if not name:
-            return "OTH"
-
-        # Check if it's already in hardcoded abbreviations
-        # This is a simplified check - in practice you'd have a mapping
-        lower_name = name.lower()
-
-        # Special cases
-        special_cases = {
-            "sd 1.4": "SD1",
-            "sd 1.5": "SD1",
-            "sd 1.5 lcm": "SD1",
-            "sd 1.5 hyper": "SD1",
-            "sd 2.0": "SD2",
-            "sd 2.1": "SD2",
-            "sd 3": "SD3",
-            "sd 3.5": "SD3",
-            "sd 3.5 medium": "SD3",
-            "sd 3.5 large": "SD3",
-            "sd 3.5 large turbo": "SD3",
-            "sdxl 1.0": "XL",
-            "sdxl lightning": "XL",
-            "sdxl hyper": "XL",
-            "flux.1 d": "F1D",
-            "flux.1 s": "F1S",
-            "flux.1 krea": "F1KR",
-            "flux.1 kontext": "F1KX",
-            "flux.2 d": "F2D",
-            "flux.2 klein 9b": "FK9",
-            "flux.2 klein 9b-base": "FK9B",
-            "flux.2 klein 4b": "FK4",
-            "flux.2 klein 4b-base": "FK4B",
-            "auraflow": "AF",
-            "chroma": "CHR",
-            "pixart a": "PXA",
-            "pixart e": "PXE",
-            "hunyuan 1": "HY",
-            "hunyuan video": "HYV",
-            "lumina": "L",
-            "kolors": "KLR",
-            "noobai": "NAI",
-            "illustrious": "IL",
-            "pony": "PONY",
-            "pony v7": "PNY7",
-            "hidream": "HID",
-            "qwen": "QWEN",
-            "zimageturbo": "ZIT",
-            "zimagebase": "ZIB",
-            "anima": "ANI",
-            "svd": "SVD",
-            "ltxv": "LTXV",
-            "ltxv2": "LTV2",
-            "ltxv 2.3": "LTX",
-            "cogvideox": "CVX",
-            "mochi": "MCHI",
-            "wan video": "WAN",
-            "wan video 1.3b t2v": "WAN",
-            "wan video 14b t2v": "WAN",
-            "wan video 14b i2v 480p": "WAN",
-            "wan video 14b i2v 720p": "WAN",
-            "wan video 2.2 ti2v-5b": "WAN",
-            "wan video 2.2 t2v-a14b": "WAN",
-            "wan video 2.2 i2v-a14b": "WAN",
-            "wan video 2.5 t2v": "WAN",
-            "wan video 2.5 i2v": "WAN",
-        }
-
-        if lower_name in special_cases:
-            return special_cases[lower_name]
-
-        # Try to extract acronym from version pattern
-        # e.g., "Model Name 2.5" -> "MN25"
-        version_match = re.search(r"(\d+(?:\.\d+)?)", name)
-        version = version_match.group(1) if version_match else ""
-
-        # Remove version and common words
-        words = re.sub(r"\d+(?:\.\d+)?", "", name)
-        words = re.sub(
-            r"\b(model|video|diffusion|checkpoint|textualinversion)\b",
-            "",
-            words,
-            flags=re.I,
-        )
-        words = words.strip()
-
-        # Get first letters of remaining words
-        tokens = re.findall(r"[A-Za-z]+", words)
-        if tokens:
-            # Build abbreviation from first letters
-            abbrev = "".join(token[0].upper() for token in tokens)
-            # Add version if present
-            if version:
-                # Clean version (remove dots for abbreviation)
-                version_clean = version.replace(".", "")
-                abbrev = abbrev[: 4 - len(version_clean)] + version_clean
-            return abbrev[:4]
-
-        # Final fallback: just take first 4 alphanumeric chars
-        alphanumeric = re.sub(r"[^A-Za-z0-9]", "", name)
-        if alphanumeric:
-            return alphanumeric[:4].upper()
-
-        return "OTH"
-
-    async def _fetch_from_civitai(self) -> Optional[Set[str]]:
-        """Fetch base models from Civitai API.
-
-        Returns:
-            Set of base model names, or None if failed
-        """
-        try:
-            downloader = await get_downloader()
-            success, result = await downloader.make_request(
-                "GET",
-                self.CIVITAI_ENUMS_URL,
-                use_auth=False,  # enums endpoint doesn't require auth
-            )
-
-            if not success:
-                logger.warning(f"Failed to fetch enums from Civitai: {result}")
-                return None
-
-            if isinstance(result, str):
-                data = json.loads(result)
-            else:
-                data = result
-
-            # Extract base models from response
-            base_models = set()
-
-            # Use ActiveBaseModel if available (recommended active models)
-            if "ActiveBaseModel" in data:
-                base_models.update(data["ActiveBaseModel"])
-                logger.info(f"Fetched {len(base_models)} models from ActiveBaseModel")
-            # Fallback to full BaseModel list
-            elif "BaseModel" in data:
-                base_models.update(data["BaseModel"])
-                logger.info(f"Fetched {len(base_models)} models from BaseModel")
-            else:
-                logger.warning("No base model data found in Civitai response")
-                return None
-
-            return base_models
-
-        except Exception as e:
-            logger.error(f"Error fetching from Civitai: {e}")
-            return None
-
-    def _update_cache(self, remote_models: Set[str]) -> None:
-        """Update internal cache with fetched models.
-
-        Args:
-            remote_models: Set of base model names from API
-        """
-        self._cache = {
-            "remote_models": sorted(remote_models),
-            "hardcoded_models": sorted(self._hardcoded_models),
-        }
-        self._cache_timestamp = datetime.now(timezone.utc)
-        logger.info(f"Cache updated with {len(remote_models)} remote models")
-
-    def _is_cache_valid(self) -> bool:
-        """Check if current cache is valid (not expired).
-
-        Returns:
-            True if cache exists and is not expired
-        """
-        if self._cache is None or self._cache_timestamp is None:
-            return False
-
-        age = (datetime.now(timezone.utc) - self._cache_timestamp).total_seconds()
-        return age <= self._cache_ttl
-
-    def _build_response(self, source: str) -> Dict[str, Any]:
-        """Build response dictionary.
-
-        Args:
-            source: 'cache', 'api', or 'fallback'
-
-        Returns:
-            Response dictionary
-        """
-        if source == "fallback" or self._cache is None:
-            # Use only hardcoded models
-            merged = sorted(self._hardcoded_models)
-            return {
-                "models": merged,
-                "source": source,
-                "last_updated": None,
-                "hardcoded_count": len(self._hardcoded_models),
-                "remote_count": 0,
-                "merged_count": len(merged),
-            }
-
-        # Merge hardcoded and remote models
-        remote_set = set(self._cache.get("remote_models", []))
-        merged = sorted(self._hardcoded_models | remote_set)
-
-        return {
-            "models": merged,
-            "source": source,
-            "last_updated": self._cache_timestamp.isoformat()
-            if self._cache_timestamp
-            else None,
-            "hardcoded_count": len(self._hardcoded_models),
-            "remote_count": len(remote_set),
-            "merged_count": len(merged),
-        }
-
-    def get_model_categories(self) -> Dict[str, List[str]]:
-        """Get categorized base models.
-
-        Returns:
-            Dictionary mapping category names to lists of model names
-        """
-        # Define category patterns
-        categories = {
-            "Stable Diffusion 1.x": ["SD 1.4", "SD 1.5", "SD 1.5 LCM", "SD 1.5 Hyper"],
-            "Stable Diffusion 2.x": ["SD 2.0", "SD 2.1"],
-            "Stable Diffusion 3.x": [
-                "SD 3",
-                "SD 3.5",
-                "SD 3.5 Medium",
-                "SD 3.5 Large",
-                "SD 3.5 Large Turbo",
-            ],
-            "SDXL": ["SDXL 1.0", "SDXL Lightning", "SDXL Hyper"],
-            "Flux Models": [
-                "Flux.1 D",
-                "Flux.1 S",
-                "Flux.1 Krea",
-                "Flux.1 Kontext",
-                "Flux.2 D",
-                "Flux.2 Klein 9B",
-                "Flux.2 Klein 9B-base",
-                "Flux.2 Klein 4B",
-                "Flux.2 Klein 4B-base",
-            ],
-            "Video Models": [
-                "SVD",
-                "LTXV",
-                "LTXV2",
-                "LTXV 2.3",
-                "CogVideoX",
-                "Mochi",
-                "Hunyuan Video",
-                "Wan Video",
-                "Wan Video 1.3B t2v",
-                "Wan Video 14B t2v",
-                "Wan Video 14B i2v 480p",
-                "Wan Video 14B i2v 720p",
-                "Wan Video 2.2 TI2V-5B",
-                "Wan Video 2.2 T2V-A14B",
-                "Wan Video 2.2 I2V-A14B",
-                "Wan Video 2.5 T2V",
-                "Wan Video 2.5 I2V",
-            ],
-            "Other Models": [
-                "Illustrious",
-                "Pony",
-                "Pony V7",
-                "HiDream",
-                "Qwen",
-                "AuraFlow",
-                "Chroma",
-                "ZImageTurbo",
-                "ZImageBase",
-                "PixArt a",
-                "PixArt E",
-                "Hunyuan 1",
-                "Lumina",
-                "Kolors",
-                "NoobAI",
-                "Anima",
-            ],
-        }
-
-        return categories
-
-
-# Convenience function for getting the singleton instance
-async def get_civitai_base_model_service() -> CivitaiBaseModelService:
-    """Get the singleton instance of CivitaiBaseModelService."""
-    return await CivitaiBaseModelService.get_instance()

py/services/civitai_client.py

@@ -3,51 +3,37 @@ import copy
 import logging
 import os
 from typing import Any, Optional, Dict, Tuple, List, Sequence
-from .connectivity_guard import (
-    OFFLINE_FRIENDLY_MESSAGE,
-    is_expected_offline_error,
-    is_offline_cooldown_error,
-)
-from .model_metadata_provider import (
-    CivitaiModelMetadataProvider,
-    ModelMetadataProviderManager,
-)
+from .model_metadata_provider import CivitaiModelMetadataProvider, ModelMetadataProviderManager
 from .downloader import get_downloader
 from .errors import RateLimitError, ResourceNotFoundError
 from ..utils.civitai_utils import resolve_license_payload
 
 logger = logging.getLogger(__name__)
 
-
 class CivitaiClient:
     _instance = None
     _lock = asyncio.Lock()
-
+    
     @classmethod
     async def get_instance(cls):
         """Get singleton instance of CivitaiClient"""
         async with cls._lock:
             if cls._instance is None:
                 cls._instance = cls()
-
+                
                 # Register this client as a metadata provider
                 provider_manager = await ModelMetadataProviderManager.get_instance()
-                provider_manager.register_provider(
-                    "civitai", CivitaiModelMetadataProvider(cls._instance), True
-                )
-
+                provider_manager.register_provider('civitai', CivitaiModelMetadataProvider(cls._instance), True)
+                
             return cls._instance
 
     def __init__(self):
         # Check if already initialized for singleton pattern
-        if hasattr(self, "_initialized"):
+        if hasattr(self, '_initialized'):
             return
         self._initialized = True
-
-        self.base_url = "https://civitai.red/api/v1"
-
-    def _build_image_info_url(self, image_id: str) -> str:
-        return f"{self.base_url}/images?imageId={image_id}&nsfw=X"
+        
+        self.base_url = "https://civitai.com/api/v1"
 
     async def _make_request(
         self,
@@ -70,8 +56,6 @@ class CivitaiClient:
             if result.provider is None:
                 result.provider = "civitai_api"
             raise result
-        if not success and is_offline_cooldown_error(result):
-            return False, OFFLINE_FRIENDLY_MESSAGE
         return success, result
 
     @staticmethod
@@ -91,10 +75,8 @@ class CivitaiClient:
             meta = image.get("meta")
             if isinstance(meta, dict) and "comfy" in meta:
                 meta.pop("comfy", None)
-
-    async def download_file(
-        self, url: str, save_dir: str, default_filename: str, progress_callback=None
-    ) -> Tuple[bool, str]:
+    
+    async def download_file(self, url: str, save_dir: str, default_filename: str, progress_callback=None) -> Tuple[bool, str]:
         """Download file with resumable downloads and retry mechanism
 
         Args:
@@ -108,50 +90,41 @@ class CivitaiClient:
         """
         downloader = await get_downloader()
         save_path = os.path.join(save_dir, default_filename)
-
+        
         # Use unified downloader with CivitAI authentication
         success, result = await downloader.download_file(
             url=url,
             save_path=save_path,
             progress_callback=progress_callback,
             use_auth=True,  # Enable CivitAI authentication
-            allow_resume=True,
+            allow_resume=True
         )
-
+        
         return success, result
 
-    async def get_model_by_hash(
-        self, model_hash: str
-    ) -> Tuple[Optional[Dict], Optional[str]]:
+    async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict], Optional[str]]:
         try:
             success, version = await self._make_request(
-                "GET",
+                'GET',
                 f"{self.base_url}/model-versions/by-hash/{model_hash}",
-                use_auth=True,
+                use_auth=True
             )
             if not success:
                 message = str(version)
-                if is_expected_offline_error(message):
-                    return None, OFFLINE_FRIENDLY_MESSAGE
                 if "not found" in message.lower():
                     return None, "Model not found"
 
-                logger.error(
-                    "Failed to fetch model info for %s: %s", model_hash[:10], message
-                )
+                logger.error("Failed to fetch model info for %s: %s", model_hash[:10], message)
                 return None, message
 
-            if isinstance(version, dict):
-                model_id = version.get("modelId")
-                if model_id:
-                    model_data = await self._fetch_model_data(model_id)
-                    if model_data:
-                        self._enrich_version_with_model_data(version, model_data)
+            model_id = version.get('modelId')
+            if model_id:
+                model_data = await self._fetch_model_data(model_id)
+                if model_data:
+                    self._enrich_version_with_model_data(version, model_data)
 
-                self._remove_comfy_metadata(version)
-                return version, None
-            else:
-                return None, "Invalid response format"
+            self._remove_comfy_metadata(version)
+            return version, None
         except RateLimitError:
             raise
         except Exception as exc:
@@ -163,22 +136,19 @@ class CivitaiClient:
             downloader = await get_downloader()
             success, content, headers = await downloader.download_to_memory(
                 image_url,
-                use_auth=False,  # Preview images don't need auth
+                use_auth=False  # Preview images don't need auth
             )
             if success:
                 # Ensure directory exists
                 os.makedirs(os.path.dirname(save_path), exist_ok=True)
-                with open(save_path, "wb") as f:
+                with open(save_path, 'wb') as f:
                     f.write(content)
                 return True
             return False
         except Exception as e:
-            if is_expected_offline_error(str(e)):
-                logger.debug("Preview download skipped due to offline state.")
-                return False
             logger.error(f"Download Error: {str(e)}")
             return False
-
+            
     @staticmethod
     def _extract_error_message(payload: Any) -> str:
         """Return a human-readable error message from an API payload."""
@@ -205,23 +175,20 @@ class CivitaiClient:
         """Get all versions of a model with local availability info"""
         try:
             success, result = await self._make_request(
-                "GET",
+                'GET',
                 f"{self.base_url}/models/{model_id}",
-                use_auth=True,
+                use_auth=True
             )
             if success:
                 # Also return model type along with versions
                 return {
-                    "modelVersions": result.get("modelVersions", []),
-                    "type": result.get("type", ""),
-                    "name": result.get("name", ""),
+                    'modelVersions': result.get('modelVersions', []),
+                    'type': result.get('type', ''),
+                    'name': result.get('name', '')
                 }
             message = self._extract_error_message(result)
-            if message and "not found" in message.lower():
+            if message and 'not found' in message.lower():
                 raise ResourceNotFoundError(f"Resource not found for model {model_id}")
-            if is_expected_offline_error(message):
-                logger.info("Civitai request skipped: %s", OFFLINE_FRIENDLY_MESSAGE)
-                return None
             if message:
                 raise RuntimeError(message)
             return None
@@ -254,15 +221,15 @@ class CivitaiClient:
         try:
             query = ",".join(normalized_ids)
             success, result = await self._make_request(
-                "GET",
+                'GET',
                 f"{self.base_url}/models",
                 use_auth=True,
-                params={"ids": query},
+                params={'ids': query},
             )
             if not success:
                 return None
 
-            items = result.get("items") if isinstance(result, dict) else None
+            items = result.get('items') if isinstance(result, dict) else None
             if not isinstance(items, list):
                 return {}
 
@@ -270,19 +237,19 @@ class CivitaiClient:
             for item in items:
                 if not isinstance(item, dict):
                     continue
-                model_id = item.get("id")
+                model_id = item.get('id')
                 try:
                     normalized_id = int(model_id)
                 except (TypeError, ValueError):
                     continue
                 payload[normalized_id] = {
-                    "modelVersions": item.get("modelVersions", []),
-                    "type": item.get("type", ""),
-                    "name": item.get("name", ""),
-                    "allowNoCredit": item.get("allowNoCredit"),
-                    "allowCommercialUse": item.get("allowCommercialUse"),
-                    "allowDerivatives": item.get("allowDerivatives"),
-                    "allowDifferentLicense": item.get("allowDifferentLicense"),
+                    'modelVersions': item.get('modelVersions', []),
+                    'type': item.get('type', ''),
+                    'name': item.get('name', ''),
+                    'allowNoCredit': item.get('allowNoCredit'),
+                    'allowCommercialUse': item.get('allowCommercialUse'),
+                    'allowDerivatives': item.get('allowDerivatives'),
+                    'allowDifferentLicense': item.get('allowDifferentLicense'),
                 }
             return payload
         except RateLimitError:
@@ -290,10 +257,8 @@ class CivitaiClient:
         except Exception as exc:
             logger.error(f"Error fetching model versions in bulk: {exc}")
             return None
-
-    async def get_model_version(
-        self, model_id: int = None, version_id: int = None
-    ) -> Optional[Dict]:
+            
+    async def get_model_version(self, model_id: int = None, version_id: int = None) -> Optional[Dict]:
         """Get specific model version with additional metadata."""
         try:
             if model_id is None and version_id is not None:
@@ -316,7 +281,7 @@ class CivitaiClient:
         if version is None:
             return None
 
-        model_id = version.get("modelId")
+        model_id = version.get('modelId')
         if not model_id:
             logger.error(f"No modelId found in version {version_id}")
             return None
@@ -328,9 +293,7 @@ class CivitaiClient:
         self._remove_comfy_metadata(version)
         return version
 
-    async def _get_version_with_model_id(
-        self, model_id: int, version_id: Optional[int]
-    ) -> Optional[Dict]:
+    async def _get_version_with_model_id(self, model_id: int, version_id: Optional[int]) -> Optional[Dict]:
         model_data = await self._fetch_model_data(model_id)
         if not model_data:
             return None
@@ -339,12 +302,8 @@ class CivitaiClient:
         if target_version is None:
             return None
 
-        target_version_id = target_version.get("id")
-        version = (
-            await self._fetch_version_by_id(target_version_id)
-            if target_version_id
-            else None
-        )
+        target_version_id = target_version.get('id')
+        version = await self._fetch_version_by_id(target_version_id) if target_version_id else None
 
         if version is None:
             model_hash = self._extract_primary_model_hash(target_version)
@@ -356,9 +315,7 @@ class CivitaiClient:
                 )
 
         if version is None:
-            version = self._build_version_from_model_data(
-                target_version, model_id, model_data
-            )
+            version = self._build_version_from_model_data(target_version, model_id, model_data)
 
         self._enrich_version_with_model_data(version, model_data)
         self._remove_comfy_metadata(version)
@@ -366,14 +323,12 @@ class CivitaiClient:
 
     async def _fetch_model_data(self, model_id: int) -> Optional[Dict]:
         success, data = await self._make_request(
-            "GET",
+            'GET',
             f"{self.base_url}/models/{model_id}",
-            use_auth=True,
+            use_auth=True
         )
         if success:
             return data
-        if is_expected_offline_error(data):
-            return None
         logger.warning(f"Failed to fetch model data for model {model_id}")
         return None
 
@@ -382,14 +337,12 @@ class CivitaiClient:
             return None
 
         success, version = await self._make_request(
-            "GET",
+            'GET',
             f"{self.base_url}/model-versions/{version_id}",
-            use_auth=True,
+            use_auth=True
         )
         if success:
             return version
-        if is_expected_offline_error(version):
-            return None
 
         logger.warning(f"Failed to fetch version by id {version_id}")
         return None
@@ -399,29 +352,26 @@ class CivitaiClient:
             return None
 
         success, version = await self._make_request(
-            "GET",
+            'GET',
             f"{self.base_url}/model-versions/by-hash/{model_hash}",
-            use_auth=True,
+            use_auth=True
         )
         if success:
             return version
-        if is_expected_offline_error(version):
-            return None
 
         logger.warning(f"Failed to fetch version by hash {model_hash}")
         return None
 
-    def _select_target_version(
-        self, model_data: Dict, model_id: int, version_id: Optional[int]
-    ) -> Optional[Dict]:
-        model_versions = model_data.get("modelVersions", [])
+    def _select_target_version(self, model_data: Dict, model_id: int, version_id: Optional[int]) -> Optional[Dict]:
+        model_versions = model_data.get('modelVersions', [])
         if not model_versions:
             logger.warning(f"No model versions found for model {model_id}")
             return None
 
         if version_id is not None:
             target_version = next(
-                (item for item in model_versions if item.get("id") == version_id), None
+                (item for item in model_versions if item.get('id') == version_id),
+                None
             )
             if target_version is None:
                 logger.warning(
@@ -433,50 +383,46 @@ class CivitaiClient:
         return model_versions[0]
 
     def _extract_primary_model_hash(self, version_entry: Dict) -> Optional[str]:
-        for file_info in version_entry.get("files", []):
-            if file_info.get("type") == "Model" and file_info.get("primary"):
-                hashes = file_info.get("hashes", {})
-                model_hash = hashes.get("SHA256")
+        for file_info in version_entry.get('files', []):
+            if file_info.get('type') == 'Model' and file_info.get('primary'):
+                hashes = file_info.get('hashes', {})
+                model_hash = hashes.get('SHA256')
                 if model_hash:
                     return model_hash
         return None
 
-    def _build_version_from_model_data(
-        self, version_entry: Dict, model_id: int, model_data: Dict
-    ) -> Dict:
+    def _build_version_from_model_data(self, version_entry: Dict, model_id: int, model_data: Dict) -> Dict:
         version = copy.deepcopy(version_entry)
-        version.pop("index", None)
-        version["modelId"] = model_id
-        version["model"] = {
-            "name": model_data.get("name"),
-            "type": model_data.get("type"),
-            "nsfw": model_data.get("nsfw"),
-            "poi": model_data.get("poi"),
+        version.pop('index', None)
+        version['modelId'] = model_id
+        version['model'] = {
+            'name': model_data.get('name'),
+            'type': model_data.get('type'),
+            'nsfw': model_data.get('nsfw'),
+            'poi': model_data.get('poi')
         }
         return version
 
     def _enrich_version_with_model_data(self, version: Dict, model_data: Dict) -> None:
-        model_info = version.get("model")
+        model_info = version.get('model')
         if not isinstance(model_info, dict):
             model_info = {}
-            version["model"] = model_info
+            version['model'] = model_info
 
-        model_info["description"] = model_data.get("description")
-        model_info["tags"] = model_data.get("tags", [])
-        version["creator"] = model_data.get("creator")
+        model_info['description'] = model_data.get("description")
+        model_info['tags'] = model_data.get("tags", [])
+        version['creator'] = model_data.get("creator")
 
         license_payload = resolve_license_payload(model_data)
         for field, value in license_payload.items():
             model_info[field] = value
 
-    async def get_model_version_info(
-        self, version_id: str
-    ) -> Tuple[Optional[Dict], Optional[str]]:
+    async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict], Optional[str]]:
         """Fetch model version metadata from Civitai
-
+        
         Args:
             version_id: The Civitai model version ID
-
+            
         Returns:
             Tuple[Optional[Dict], Optional[str]]: A tuple containing:
                 - The model version data or None if not found
@@ -484,23 +430,25 @@ class CivitaiClient:
         """
         try:
             url = f"{self.base_url}/model-versions/{version_id}"
-
-            logger.debug("Resolving Civitai model version info: %s", url)
-            success, result = await self._make_request("GET", url, use_auth=True)
-
+            
+            logger.debug(f"Resolving DNS for model version info: {url}")
+            success, result = await self._make_request(
+                'GET',
+                url,
+                use_auth=True
+            )
+            
             if success:
-                logger.debug("Successfully fetched model version info for: %s", version_id)
+                logger.debug(f"Successfully fetched model version info for: {version_id}")
                 self._remove_comfy_metadata(result)
                 return result, None
-
+            
             # Handle specific error cases
-            if is_expected_offline_error(result):
-                return None, OFFLINE_FRIENDLY_MESSAGE
             if "not found" in str(result):
                 error_msg = f"Model not found"
                 logger.warning(f"Model version not found: {version_id} - {error_msg}")
                 return None, error_msg
-
+            
             # Other error cases
             logger.error(f"Failed to fetch model info for {version_id}: {result}")
             return None, str(result)
@@ -511,67 +459,36 @@ class CivitaiClient:
             logger.error(error_msg)
             return None, error_msg
 
-    async def get_image_info(
-        self, image_id: str, source_url: str | None = None
-    ) -> Optional[Dict]:
+    async def get_image_info(self, image_id: str) -> Optional[Dict]:
         """Fetch image information from Civitai API
 
         Args:
             image_id: The Civitai image ID
-            source_url: Original image page URL. Accepted for caller compatibility;
-                API requests always target ``civitai.red``.
-
+            
         Returns:
             Optional[Dict]: The image data or None if not found
         """
         try:
-            requested_id = int(image_id)
-            url = self._build_image_info_url(image_id)
-            success, result = await self._make_request("GET", url, use_auth=True)
-
-            if not success:
-                if is_expected_offline_error(result):
-                    return None
-                logger.error(
-                    "Failed to fetch image info for ID %s from civitai.red: %s",
-                    image_id,
-                    result,
-                )
+            url = f"{self.base_url}/images?imageId={image_id}&nsfw=X"
+            
+            logger.debug(f"Fetching image info for ID: {image_id}")
+            success, result = await self._make_request(
+                'GET',
+                url,
+                use_auth=True
+            )
+            
+            if success:
+                if result and "items" in result and len(result["items"]) > 0:
+                    logger.debug(f"Successfully fetched image info for ID: {image_id}")
+                    return result["items"][0]
+                logger.warning(f"No image found with ID: {image_id}")
                 return None
-
-            if result and "items" in result and isinstance(result["items"], list):
-                items = result["items"]
-
-                for item in items:
-                    if isinstance(item, dict) and item.get("id") == requested_id:
-                        logger.debug(
-                            "Successfully fetched image info for ID %s from civitai.red",
-                            image_id,
-                        )
-                        return item
-
-                returned_ids = [
-                    item.get("id")
-                    for item in items
-                    if isinstance(item, dict) and "id" in item
-                ]
-
-                logger.warning(
-                    "CivitAI API returned no matching image for requested ID %s from civitai.red. Returned %d item(s) with IDs: %s. This may indicate the image was deleted, hidden, or there is a database lag.",
-                    image_id,
-                    len(items),
-                    returned_ids,
-                )
-                return None
-
-            logger.warning("No image found with ID: %s", image_id)
+            
+            logger.error(f"Failed to fetch image info for ID: {image_id}: {result}")
             return None
         except RateLimitError:
             raise
-        except ValueError as e:
-            error_msg = f"Invalid image ID format: {image_id}"
-            logger.error(error_msg)
-            return None
         except Exception as e:
             error_msg = f"Error fetching image info: {e}"
             logger.error(error_msg)
@@ -583,17 +500,14 @@ class CivitaiClient:
             return None
 
         try:
+            url = f"{self.base_url}/models?username={username}"
             success, result = await self._make_request(
-                "GET",
-                f"{self.base_url}/models",
-                use_auth=True,
-                params={"username": username},
+                'GET',
+                url,
+                use_auth=True
             )
 
             if not success:
-                if is_expected_offline_error(result):
-                    logger.info("User model fetch skipped: %s", OFFLINE_FRIENDLY_MESSAGE)
-                    return None
                 logger.error("Failed to fetch models for %s: %s", username, result)
                 return None
 

py/services/connectivity_guard.py

@@ -1,204 +0,0 @@
-"""In-memory connectivity guard to suppress repeated network retries when offline."""
-
-from __future__ import annotations
-
-import asyncio
-import errno
-import logging
-import socket
-from dataclasses import dataclass
-from datetime import datetime, timedelta
-from typing import Any
-
-import aiohttp
-
-logger = logging.getLogger(__name__)
-
-OFFLINE_COOLDOWN_ERROR = "offline_cooldown"
-OFFLINE_FRIENDLY_MESSAGE = "Network offline, will retry automatically later"
-
-
-def is_offline_cooldown_error(value: Any) -> bool:
-    """Return True when a response payload represents guard short-circuit."""
-    return isinstance(value, str) and value == OFFLINE_COOLDOWN_ERROR
-
-
-def is_expected_offline_error(value: Any) -> bool:
-    """Return True when payload is an expected offline-related result."""
-    if is_offline_cooldown_error(value):
-        return True
-    if not isinstance(value, str):
-        return False
-    normalized = value.lower()
-    return "network offline" in normalized or "offline" in normalized
-
-
-class ConnectivityGuard:
-    """Tracks network failures and gates outbound requests during cooldown."""
-
-    _instance: "ConnectivityGuard | None" = None
-    _instance_lock = asyncio.Lock()
-
-    @classmethod
-    async def get_instance(cls) -> "ConnectivityGuard":
-        async with cls._instance_lock:
-            if cls._instance is None:
-                cls._instance = cls()
-            return cls._instance
-
-    def __init__(self) -> None:
-        if hasattr(self, "_initialized"):
-            return
-        self._initialized = True
-        self._default_destination = "__global__"
-        self._destination_states: dict[str, _DestinationState] = {
-            self._default_destination: _DestinationState()
-        }
-        self.base_backoff_seconds = 30
-        self.max_backoff_seconds = 300
-        self.failure_threshold = 3
-
-    @property
-    def online(self) -> bool:
-        return self._state_for_destination(None).online
-
-    @online.setter
-    def online(self, value: bool) -> None:
-        self._state_for_destination(None).online = value
-
-    @property
-    def failure_count(self) -> int:
-        return self._state_for_destination(None).failure_count
-
-    @failure_count.setter
-    def failure_count(self, value: int) -> None:
-        self._state_for_destination(None).failure_count = value
-
-    @property
-    def cooldown_until(self) -> datetime | None:
-        return self._state_for_destination(None).cooldown_until
-
-    @cooldown_until.setter
-    def cooldown_until(self, value: datetime | None) -> None:
-        self._state_for_destination(None).cooldown_until = value
-
-    def _now(self) -> datetime:
-        return datetime.now()
-
-    def _normalize_destination(self, destination: str | None) -> str:
-        if destination is None or not destination.strip():
-            return self._default_destination
-        return destination.lower().strip()
-
-    def _state_for_destination(self, destination: str | None) -> "_DestinationState":
-        destination_key = self._normalize_destination(destination)
-        if destination_key not in self._destination_states:
-            self._destination_states[destination_key] = _DestinationState()
-        return self._destination_states[destination_key]
-
-    def in_cooldown(self, destination: str | None = None) -> bool:
-        state = self._state_for_destination(destination)
-        if state.cooldown_until is None:
-            return False
-        return self._now() < state.cooldown_until
-
-    def cooldown_remaining_seconds(self, destination: str | None = None) -> float:
-        state = self._state_for_destination(destination)
-        if state.cooldown_until is None:
-            return 0.0
-        return max(0.0, (state.cooldown_until - self._now()).total_seconds())
-
-    def should_block_request(self, destination: str | None = None) -> bool:
-        return self.in_cooldown(destination)
-
-    def register_success(self, destination: str | None = None) -> None:
-        destination_key = self._normalize_destination(destination)
-        state = self._state_for_destination(destination_key)
-        was_offline = (not state.online) or state.cooldown_until is not None
-        state.online = True
-        state.failure_count = 0
-        state.cooldown_until = None
-        if was_offline:
-            logger.info(
-                "Connectivity restored for destination '%s'; requests resumed.",
-                destination_key,
-            )
-
-    def register_network_failure(
-        self, exc: Exception, destination: str | None = None
-    ) -> None:
-        destination_key = self._normalize_destination(destination)
-        state = self._state_for_destination(destination_key)
-        state.online = False
-        state.failure_count += 1
-
-        if state.failure_count < self.failure_threshold:
-            logger.debug(
-                "Network failure tracked for destination '%s' (%d/%d): %s",
-                destination_key,
-                state.failure_count,
-                self.failure_threshold,
-                exc,
-            )
-            return
-
-        retry_step = state.failure_count - self.failure_threshold
-        backoff = min(
-            self.max_backoff_seconds,
-            self.base_backoff_seconds * (2**retry_step),
-        )
-        should_log_warning = not self.in_cooldown(destination_key)
-        state.cooldown_until = self._now() + timedelta(seconds=backoff)
-
-        if should_log_warning:
-            logger.warning(
-                "Connectivity offline for destination '%s'; enter cooldown for %ss after %d network failures.",
-                destination_key,
-                int(backoff),
-                state.failure_count,
-            )
-        else:
-            logger.debug(
-                "Cooldown still active for destination '%s'; failure_count=%d, backoff=%ss.",
-                destination_key,
-                state.failure_count,
-                int(backoff),
-            )
-
-    @staticmethod
-    def is_network_unreachable_error(exc: Exception) -> bool:
-        """Return whether the exception should count as connectivity failure."""
-        if isinstance(exc, asyncio.CancelledError):
-            return False
-
-        if isinstance(
-            exc,
-            (
-                asyncio.TimeoutError,
-                TimeoutError,
-                ConnectionRefusedError,
-                socket.gaierror,
-                aiohttp.ServerTimeoutError,
-                aiohttp.ConnectionTimeoutError,
-                aiohttp.ClientConnectorError,
-                aiohttp.ClientConnectionError,
-            ),
-        ):
-            return True
-
-        if isinstance(exc, OSError) and exc.errno in {
-            errno.ENETUNREACH,
-            errno.EHOSTUNREACH,
-            errno.ETIMEDOUT,
-            errno.ECONNREFUSED,
-        }:
-            return True
-
-        return False
-
-
-@dataclass
-class _DestinationState:
-    online: bool = True
-    failure_count: int = 0
-    cooldown_until: datetime | None = None

py/services/custom_words_service.py

@@ -7,13 +7,11 @@ with category filtering and enriched results including post counts.
 from __future__ import annotations
 
 import logging
-import re
 from typing import List, Dict, Any, Optional
 
 logger = logging.getLogger(__name__)
 
 
-_EMBEDDED_COMMAND_PATTERN = re.compile(r"\s/\w")
 class CustomWordsService:
     """Service for autocomplete via TagFTSIndex.
 
@@ -51,7 +49,6 @@ class CustomWordsService:
         if self._tag_index is None:
             try:
                 from .tag_fts_index import get_tag_fts_index
-
                 self._tag_index = get_tag_fts_index()
             except Exception as e:
                 logger.warning(f"Failed to initialize TagFTSIndex: {e}")
@@ -62,16 +59,14 @@ class CustomWordsService:
         self,
         search_term: str,
         limit: int = 20,
-        offset: int = 0,
         categories: Optional[List[int]] = None,
-        enriched: bool = False,
+        enriched: bool = False
     ) -> List[Dict[str, Any]]:
         """Search tags using TagFTSIndex with category filtering.
 
         Args:
             search_term: The search term to match against.
             limit: Maximum number of results to return.
-            offset: Number of results to skip.
             categories: Optional list of category IDs to filter by.
             enriched: If True, always return enriched results with category
                        and post_count (default behavior now).
@@ -79,28 +74,10 @@ class CustomWordsService:
         Returns:
             List of dicts with tag_name, category, and post_count.
         """
-        normalized_search = search_term.strip()
-        if not normalized_search:
-            return []
-
-        # Prompt widgets should only send the active token, but guard against
-        # accidental full-prompt queries reaching the FTS path.
-        if (
-            "__" in normalized_search
-            or "," in normalized_search
-            or ">" in normalized_search
-            or "\n" in normalized_search
-            or "\r" in normalized_search
-            or _EMBEDDED_COMMAND_PATTERN.search(normalized_search)
-        ):
-            logger.debug("Skipping prompt-like custom words query: %s", normalized_search)
-            return []
-
         tag_index = self._get_tag_index()
         if tag_index is not None:
-            return tag_index.search(
-                normalized_search, categories=categories, limit=limit, offset=offset
-            )
+            results = tag_index.search(search_term, categories=categories, limit=limit)
+            return results
 
         logger.debug("TagFTSIndex not available, returning empty results")
         return []