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())

.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

@@ -19,6 +19,3 @@ model_cache/
 vue-widgets/node_modules/
 vue-widgets/.vite/
 vue-widgets/dist/
-
-# Hypothesis test cache
-.hypothesis/

AGENTS.md

@@ -25,127 +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
+- 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

data/supporters.json

@@ -1,627 +0,0 @@
-{
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-    "EbonEagle",
-    "DanielMagPizza",
-    "Scott R"
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-    "megakirbs",
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-    "Takkan",
-    "stone9k",
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-    "itismyelement",
-    "onesecondinosaur",
-    "Carl G.",
-    "Rosenthal",
-    "Francisco Tatis",
-    "Tobi_Swagg",
-    "Andrew Wilson",
-    "Greybush",
-    "Gooohokrbe",
-    "Ricky Carter",
-    "JongWon Han",
-    "OldBones",
-    "VantAI",
-    "runte3221",
-    "FreelancerZ",
-    "Julian V",
-    "Edgar Tejeda",
-    "Birdy",
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-    "Marc Whiffen",
-    "Kiba",
-    "Jorge Hussni",
-    "Reno Lam",
-    "Skalabananen",
-    "esthe",
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-    "Estragon",
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-    "carozzz",
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-    "Karl P.",
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-    "N/A",
-    "The Spawn",
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-    "奚明 刘",
-    "Dan",
-    "Seth Christensen",
-    "Jwk0205",
-    "Bro Xie",
-    "Draven T",
-    "yer fey",
-    "batblue",
-    "carey6409",
-    "Olive",
-    "太郎 ゲーム",
-    "Some Guy Named Barry",
-    "jinxedx",
-    "Aquatic Coffee",
-    "Max Marklund",
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-    "Dankin",
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-    "Matt Wenzel",
-    "Mattssn",
-    "Frank Nitty",
-    "John Saveas",
-    "Focuschannel",
-    "Christopher Michel",
-    "Serge Bekenkamp",
-    "LeoZero",
-    "Antonio Pontes",
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-    "nahinahi9",
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-    "Dustin Chen",
-    "dan",
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-    "Mouthlessman",
-    "Steam Steam",
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-    "otaku fra",
-    "semicolon drainpipe",
-    "Thesharingbrother",
-    "Fotek Design",
-    "Bas Imagineer",
-    "Pat Hen",
-    "ResidentDeviant",
-    "Adam Taylor",
-    "JC",
-    "Weird_With_A_Beard",
-    "Prompt Pirate",
-    "Pozadine1",
-    "uwutismxd",
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-    "elu3199",
-    "Nick “Loadstone” D",
-    "Hasturkun",
-    "Jon Sandman",
-    "Ubivis",
-    "CloudValley",
-    "thesoftwaredruid",
-    "wundershark",
-    "mr_dinosaur",
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-    "linnfrey",
-    "zenobeus",
-    "Jackthemind",
-    "Stryker",
-    "Pkrsky",
-    "raf8osz",
-    "blikkies",
-    "Josef Lanzl",
-    "Griffin Dahlberg",
-    "준희 김",
-    "Error_Rule34_Not_found",
-    "Gerald Welly",
-    "Shock Shockor",
-    "Roslynd",
-    "Geolog",
-    "Goldwaters",
-    "Neco28",
-    "Zude",
-    "Cristian Vazquez",
-    "Kyler",
-    "Magic Noob",
-    "aRtFuL_DodGeR",
-    "X",
-    "DougPeterson",
-    "Jeff",
-    "Bruce",
-    "CrimsonDX",
-    "Kevin John Duck",
-    "Kevin Christopher",
-    "Ouro Boros",
-    "DarkSunset",
-    "dd",
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-    "Probis",
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-    "Ray Wing",
-    "Ranzitho",
-    "Gus",
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-    "MJG",
-    "David LaVallee",
-    "ae",
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-    "Chris",
-    "Brian M",
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-    "Haru Yotu",
-    "Eris3D",
-    "m",
-    "Pierce McBride",
-    "Joshua Gray",
-    "Mikko Hemilä",
-    "Matura Arbeit",
-    "Jamie Ogletree",
-    "TBitz33",
-    "Emil Bernhoff",
-    "a _",
-    "SendingRavens",
-    "James Coleman",
-    "Martial",
-    "battu",
-    "Emil Andersson",
-    "Chad Idk",
-    "Michael Docherty",
-    "Yuji Kaneko",
-    "elitassj",
-    "Jacob Winter",
-    "Jordan Shaw",
-    "Sam",
-    "Rops Alot",
-    "SRDB",
-    "g unit",
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-    "David",
-    "Meilo",
-    "Pen Bouryoung",
-    "shinonomeiro",
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-    "kudari",
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-    "dc7431",
-    "ken",
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-    "Wind",
-    "Nexus",
-    "Mitchell Robson",
-    "Ramneek“Guy”Ashok",
-    "squid_actually",
-    "Nat_20",
-    "Kiyoe",
-    "Edward Weeks",
-    "kyoumei",
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-    "JohnDoe42054",
-    "BillyHill",
-    "humptynutz",
-    "emyth",
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-    "KitKatM",
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-    "ResidentDeviant",
-    "Scott",
-    "gzmzmvp",
-    "Welkor",
-    "hayden",
-    "Richard",
-    "ahoystan",
-    "Leland Saunders",
-    "Andrew",
-    "Bob Barker",
-    "Robert Wegemund",
-    "Littlehuggy",
-    "Gregory Kozhemiak",
-    "mrjuan",
-    "Aeternyx",
-    "Brian Buie",
-    "YOU SINWOO",
-    "Sadlip",
-    "ja s",
-    "Eric Whitney",
-    "Doug Mason",
-    "Joey Callahan",
-    "Ivan Tadic",
-    "y2Rxy7FdXzWo",
-    "Jeremy Townsend",
-    "Mike Simone",
-    "Sean voets",
-    "Owen Gwosdz",
-    "Morgandel",
-    "Thomas Wanner",
-    "Kyron Mahan",
-    "Theerat Jiramate",
-    "Noah",
-    "Jacob McDaniel",
-    "kevin stoddard",
-    "Sloan Steddy",
-    "Jack Dole",
-    "Ezokewn",
-    "Temikus",
-    "Artokun",
-    "Michael Taylor",
-    "Derek Baker",
-    "Michael Anthony Scott",
-    "Atilla Berke Pekduyar",
-    "Maso",
-    "Nathan",
-    "Decx _",
-    "Kevin Wallace",
-    "Matheus Couto",
-    "Paul Hartsuyker",
-    "ChicRic",
-    "mercur",
-    "J C",
-    "Distortik",
-    "Yves Poezevara",
-    "Teriak47",
-    "Just me",
-    "Raf Stahelin",
-    "Вячеслав Маринин",
-    "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",
-    "Tree Tagger",
-    "gwyar",
-    "D",
-    "edgecase",
-    "Neoxena",
-    "mrmhalo",
-    "dg",
-    "Whitepinetrader",
-    "Maarten Harms",
-    "OrganicArtifact",
-    "四糸凜音",
-    "MudkipMedkitz",
-    "Israel",
-    "deanbrian",
-    "POPPIN",
-    "Muratoraccio",
-    "SelfishMedic",
-    "Ginnie",
-    "Alex Wortman",
-    "Cody",
-    "adderleighn",
-    "Raku",
-    "smart.edge5178",
-    "emadsultan",
-    "InformedViewz",
-    "CHKeeho80",
-    "Bubbafett",
-    "leaf",
-    "Menard",
-    "Skyfire83",
-    "Adam Rinehart",
-    "D",
-    "Pitpe11",
-    "TheD1rtyD03",
-    "EnragedAntelope",
-    "moonpetal",
-    "SomeDude",
-    "g9p0o",
-    "nanana",
-    "TheHolySheep",
-    "Monte Won",
-    "SpringBootisTrash",
-    "carsten",
-    "ikok",
-    "Buecyb99",
-    "4IXplr0r3r",
-    "Coeur+de+cochon",
-    "David Schenck",
-    "han b",
-    "Nico",
-    "Wolfe7D1",
-    "Banana Joe",
-    "_ G3n",
-    "Donovan Jenkins",
-    "Ink Temptation",
-    "edk",
-    "Michael Eid",
-    "beersandbacon",
-    "Maximilian Pyko",
-    "Invis",
-    "Kalli Core",
-    "Justin Houston",
-    "james",
-    "elleshar666",
-    "OrochiNights",
-    "Michael Zhu",
-    "ACTUALLY_the_Real_Willem_Dafoe",
-    "gonzalo",
-    "Seraphy",
-    "雨の心 落",
-    "AllTimeNoobie",
-    "jumpd",
-    "John C",
-    "Kauffy",
-    "Rim",
-    "Dismem",
-    "EpicElric",
-    "John J Linehan",
-    "Xan Dionysus",
-    "Nathan lee",
-    "Mewtora",
-    "Elliot E",
-    "Middo",
-    "Forbidden Atelier",
-    "Edward Kennedy",
-    "Justin Blaylock",
-    "Adictedtohumping",
-    "Devil Lude",
-    "Nick Kage",
-    "Towelie",
-    "Vane Holzer",
-    "psytrax",
-    "Cyrus Fett",
-    "Jean-françois SEMA",
-    "Kurt",
-    "hexxish",
-    "giani kidd",
-    "CptNeo",
-    "notedfakes",
-    "Chase Kwon",
-    "Goober719",
-    "Eric Ketchum",
-    "Chad Barnes",
-    "NICHOLAS BAXLEY",
-    "Michael Scott",
-    "James Ming",
-    "vanditking",
-    "kripitonga",
-    "Rizzi",
-    "nimin",
-    "OMAR LUCIANO",
-    "Jo+Example",
-    "BrentBertram",
-    "eumelzocker",
-    "dxjaymz",
-    "L C",
-    "Dude"
-  ],
-  "totalCount": 620
-}

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/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",
@@ -134,8 +131,7 @@
         },
         "badges": {
             "update": "Update",
-            "updateAvailable": "Update verfügbar",
-            "skipRefresh": "Metadaten-Aktualisierung übersprungen"
+            "updateAvailable": "Update verfügbar"
         },
         "usage": {
             "timesUsed": "Verwendungsanzahl"
@@ -183,6 +179,7 @@
             "recipes": "Rezepte",
             "checkpoints": "Checkpoints",
             "embeddings": "Embeddings",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "Statistiken"
         },
         "search": {
@@ -191,7 +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:",
@@ -222,16 +220,12 @@
             "presetNamePlaceholder": "Voreinstellungsname...",
             "baseModel": "Basis-Modell",
             "modelTags": "Tags (Top 20)",
-            "modelTypes": "Modelltypen",
+            "modelTypes": "Model Types",
             "license": "Lizenz",
             "noCreditRequired": "Kein Credit erforderlich",
             "allowSellingGeneratedContent": "Verkauf erlaubt",
             "noTags": "Keine Tags",
-            "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",
@@ -261,27 +255,17 @@
             "contentFiltering": "Inhaltsfilterung",
             "videoSettings": "Video-Einstellungen",
             "layoutSettings": "Layout-Einstellungen",
-            "misc": "Verschiedenes",
-            "folderSettings": "Standard-Roots",
-            "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."
@@ -305,15 +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}"
-            }
-        },
         "layoutSettings": {
             "displayDensity": "Anzeige-Dichte",
             "displayDensityOptions": {
@@ -354,33 +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",
             "noDefault": "Kein Standard"
         },
-        "extraFolderPaths": {
-            "title": "Zusätzliche Ordnerpfade",
-            "help": "Fügen Sie zusätzliche Modellordner außerhalb der Standardpfade von ComfyUI hinzu. Diese Pfade werden separat gespeichert und zusammen mit den Standardordnern gescannt.",
-            "description": "Konfigurieren Sie zusätzliche Ordner zum Scannen von Modellen. Diese Pfade sind spezifisch für LoRA Manager und werden mit den Standardpfaden von ComfyUI zusammengeführt.",
-            "modelTypes": {
-                "lora": "LoRA-Pfade",
-                "checkpoint": "Checkpoint-Pfade",
-                "unet": "Diffusionsmodell-Pfade",
-                "embedding": "Embedding-Pfade"
-            },
-            "pathPlaceholder": "/pfad/zu/extra/modellen",
-            "saveSuccess": "Zusätzliche Ordnerpfade aktualisiert.",
-            "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))",
@@ -456,10 +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"
@@ -571,12 +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": "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...",
@@ -685,11 +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": {
@@ -744,6 +690,16 @@
     "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",
@@ -757,17 +713,7 @@
         "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": {
@@ -1079,19 +1025,12 @@
                 },
                 "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": "Aktuelle Version",
                     "inLibrary": "In der Bibliothek",
                     "newer": "Neuere Version",
-                    "earlyAccess": "Früher Zugriff",
                     "ignored": "Ignoriert"
                 },
                 "actions": {
@@ -1099,7 +1038,6 @@
                     "delete": "Löschen",
                     "ignore": "Ignorieren",
                     "unignore": "Ignorierung aufheben",
-                    "earlyAccessTooltip": "Erfordert Early-Access-Kauf",
                     "resumeModelUpdates": "Aktualisierungen für dieses Modell fortsetzen",
                     "ignoreModelUpdates": "Aktualisierungen für dieses Modell ignorieren",
                     "viewLocalVersions": "Alle lokalen Versionen anzeigen",
@@ -1178,6 +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": {
@@ -1237,12 +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"
         },
         "nodeSelector": {
             "recipe": "Rezept",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "Ersetzen",
             "append": "Anhängen",
             "selectTargetNode": "Zielknoten auswählen",
@@ -1359,14 +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": {
@@ -1400,8 +1341,6 @@
             "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",
@@ -1458,11 +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",
@@ -1550,7 +1484,6 @@
             "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": {
@@ -1661,20 +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",
@@ -134,8 +131,7 @@
         },
         "badges": {
             "update": "Update",
-            "updateAvailable": "Update available",
-            "skipRefresh": "Metadata refresh skipped"
+            "updateAvailable": "Update available"
         },
         "usage": {
             "timesUsed": "Times used"
@@ -183,6 +179,7 @@
             "recipes": "Recipes",
             "checkpoints": "Checkpoints",
             "embeddings": "Embeddings",
+            "misc": "Misc",
             "statistics": "Stats"
         },
         "search": {
@@ -191,7 +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:",
@@ -227,11 +225,7 @@
             "noCreditRequired": "No Credit Required",
             "allowSellingGeneratedContent": "Allow Selling",
             "noTags": "No tags",
-            "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",
@@ -261,27 +255,17 @@
             "contentFiltering": "Content Filtering",
             "videoSettings": "Video Settings",
             "layoutSettings": "Layout Settings",
-            "misc": "Miscellaneous",
-            "folderSettings": "Default Roots",
-            "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."
@@ -305,15 +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}"
-            }
-        },
         "layoutSettings": {
             "displayDensity": "Display Density",
             "displayDensityOptions": {
@@ -354,33 +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",
             "noDefault": "No Default"
         },
-        "extraFolderPaths": {
-            "title": "Extra Folder Paths",
-            "help": "Add additional model folders outside of ComfyUI's standard paths. These paths are stored separately and scanned alongside the default folders.",
-            "description": "Configure additional folders to scan for models. These paths are specific to LoRA Manager and will be merged with ComfyUI's default paths.",
-            "modelTypes": {
-                "lora": "LoRA Paths",
-                "checkpoint": "Checkpoint Paths",
-                "unet": "Diffusion Model Paths",
-                "embedding": "Embedding Paths"
-            },
-            "pathPlaceholder": "/path/to/extra/models",
-            "saveSuccess": "Extra folder paths updated.",
-            "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))",
@@ -456,10 +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"
@@ -571,12 +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 Models",
             "clear": "Clear Selection",
-            "skipMetadataRefreshCount": "Skip ({count} models)",
-            "resumeMetadataRefreshCount": "Resume ({count} models)",
             "autoOrganizeProgress": {
                 "initializing": "Initializing auto-organize...",
                 "starting": "Starting auto-organize for {type}...",
@@ -685,11 +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": {
@@ -744,6 +690,16 @@
     "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",
@@ -757,17 +713,7 @@
         "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": {
@@ -1079,19 +1025,12 @@
                 },
                 "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": "Current Version",
                     "inLibrary": "In Library",
                     "newer": "Newer Version",
-                    "earlyAccess": "Early Access",
                     "ignored": "Ignored"
                 },
                 "actions": {
@@ -1099,7 +1038,6 @@
                     "delete": "Delete",
                     "ignore": "Ignore",
                     "unignore": "Unignore",
-                    "earlyAccessTooltip": "Requires early access purchase",
                     "resumeModelUpdates": "Resume updates for this model",
                     "ignoreModelUpdates": "Ignore updates for this model",
                     "viewLocalVersions": "View all local versions",
@@ -1178,6 +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": {
@@ -1237,12 +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"
         },
         "nodeSelector": {
             "recipe": "Recipe",
             "lora": "LoRA",
+            "vae": "VAE",
+            "upscaler": "Upscaler",
             "replace": "Replace",
             "append": "Append",
             "selectTargetNode": "Select target node",
@@ -1359,14 +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": {
@@ -1400,8 +1341,6 @@
             "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",
@@ -1458,11 +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",
@@ -1550,7 +1484,6 @@
             "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": {
@@ -1661,20 +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",
@@ -134,8 +131,7 @@
         },
         "badges": {
             "update": "Actualización",
-            "updateAvailable": "Actualización disponible",
-            "skipRefresh": "Actualización de metadatos omitida"
+            "updateAvailable": "Actualización disponible"
         },
         "usage": {
             "timesUsed": "Veces usado"
@@ -183,6 +179,7 @@
             "recipes": "Recetas",
             "checkpoints": "Checkpoints",
             "embeddings": "Embeddings",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "Estadísticas"
         },
         "search": {
@@ -191,7 +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:",
@@ -222,16 +220,12 @@
             "presetNamePlaceholder": "Nombre del preajuste...",
             "baseModel": "Modelo base",
             "modelTags": "Etiquetas (Top 20)",
-            "modelTypes": "Tipos de modelos",
+            "modelTypes": "Model Types",
             "license": "Licencia",
             "noCreditRequired": "Sin crédito requerido",
             "allowSellingGeneratedContent": "Venta permitida",
             "noTags": "Sin etiquetas",
-            "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",
@@ -261,27 +255,17 @@
             "contentFiltering": "Filtrado de contenido",
             "videoSettings": "Configuración de video",
             "layoutSettings": "Configuración de diseño",
-            "misc": "Varios",
-            "folderSettings": "Raíces predeterminadas",
-            "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."
@@ -305,15 +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}"
-            }
-        },
         "layoutSettings": {
             "displayDensity": "Densidad de visualización",
             "displayDensityOptions": {
@@ -354,33 +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",
             "noDefault": "Sin predeterminado"
         },
-        "extraFolderPaths": {
-            "title": "Rutas de carpetas adicionales",
-            "help": "Agregue carpetas de modelos adicionales fuera de las rutas estándar de ComfyUI. Estas rutas se almacenan por separado y se escanean junto con las carpetas predeterminadas.",
-            "description": "Configure carpetas adicionales para escanear modelos. Estas rutas son específicas de LoRA Manager y se fusionarán con las rutas predeterminadas de ComfyUI.",
-            "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.",
-            "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))",
@@ -456,10 +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"
@@ -571,12 +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 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}...",
@@ -685,11 +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": {
@@ -744,6 +690,16 @@
     "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",
@@ -757,17 +713,7 @@
         "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": {
@@ -1079,19 +1025,12 @@
                 },
                 "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 actual",
                     "inLibrary": "En la biblioteca",
                     "newer": "Versión más reciente",
-                    "earlyAccess": "Acceso temprano",
                     "ignored": "Ignorada"
                 },
                 "actions": {
@@ -1099,7 +1038,6 @@
                     "delete": "Eliminar",
                     "ignore": "Ignorar",
                     "unignore": "Dejar de ignorar",
-                    "earlyAccessTooltip": "Requiere compra de acceso temprano",
                     "resumeModelUpdates": "Reanudar actualizaciones para este modelo",
                     "ignoreModelUpdates": "Ignorar actualizaciones para este modelo",
                     "viewLocalVersions": "Ver todas las versiones locales",
@@ -1178,6 +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": {
@@ -1237,12 +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"
         },
         "nodeSelector": {
             "recipe": "Receta",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "Reemplazar",
             "append": "Añadir",
             "selectTargetNode": "Seleccionar nodo de destino",
@@ -1359,14 +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": {
@@ -1400,8 +1341,6 @@
             "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",
@@ -1458,11 +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",
@@ -1550,7 +1484,6 @@
             "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": {
@@ -1661,20 +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",
@@ -134,8 +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"
@@ -183,6 +179,7 @@
             "recipes": "Recipes",
             "checkpoints": "Checkpoints",
             "embeddings": "Embeddings",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "Statistiques"
         },
         "search": {
@@ -191,7 +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 :",
@@ -222,16 +220,12 @@
             "presetNamePlaceholder": "Nom du préréglage...",
             "baseModel": "Modèle 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",
-            "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",
@@ -261,27 +255,17 @@
             "contentFiltering": "Filtrage du contenu",
             "videoSettings": "Paramètres vidéo",
             "layoutSettings": "Paramètres d'affichage",
-            "misc": "Divers",
-            "folderSettings": "Racines par défaut",
-            "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."
@@ -305,15 +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}"
-            }
-        },
         "layoutSettings": {
             "displayDensity": "Densité d'affichage",
             "displayDensityOptions": {
@@ -354,33 +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",
             "noDefault": "Aucun par défaut"
         },
-        "extraFolderPaths": {
-            "title": "Chemins de dossiers supplémentaires",
-            "help": "Ajoutez des dossiers de modèles supplémentaires en dehors des chemins standard de ComfyUI. Ces chemins sont stockés séparément et analysés aux côtés des dossiers par défaut.",
-            "description": "Configurez des dossiers supplémentaires pour l'analyse de modèles. Ces chemins sont spécifiques à LoRA Manager et seront fusionnés avec les chemins par défaut de ComfyUI.",
-            "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.",
-            "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))",
@@ -456,10 +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"
@@ -571,12 +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 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}...",
@@ -685,11 +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": {
@@ -744,6 +690,16 @@
     "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",
@@ -757,17 +713,7 @@
         "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": {
@@ -1079,19 +1025,12 @@
                 },
                 "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 actuelle",
                     "inLibrary": "Dans la bibliothèque",
                     "newer": "Version plus récente",
-                    "earlyAccess": "Accès anticipé",
                     "ignored": "Ignorée"
                 },
                 "actions": {
@@ -1099,7 +1038,6 @@
                     "delete": "Supprimer",
                     "ignore": "Ignorer",
                     "unignore": "Ne plus ignorer",
-                    "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",
@@ -1178,6 +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": {
@@ -1237,12 +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"
         },
         "nodeSelector": {
             "recipe": "Recipe",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "Remplacer",
             "append": "Ajouter",
             "selectTargetNode": "Sélectionner le nœud cible",
@@ -1359,14 +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": {
@@ -1400,8 +1341,6 @@
             "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",
@@ -1458,11 +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)",
@@ -1550,7 +1484,6 @@
             "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": {
@@ -1661,20 +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

@@ -1,20 +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"
+            "add": "הוסף"
         },
         "status": {
             "loading": "טוען...",
@@ -134,8 +131,7 @@
         },
         "badges": {
             "update": "עדכון",
-            "updateAvailable": "עדכון זמין",
-            "skipRefresh": "רענון המטא-נתונים דולג"
+            "updateAvailable": "עדכון זמין"
         },
         "usage": {
             "timesUsed": "מספר שימושים"
@@ -183,6 +179,7 @@
             "recipes": "מתכונים",
             "checkpoints": "Checkpoints",
             "embeddings": "Embeddings",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "סטטיסטיקה"
         },
         "search": {
@@ -191,7 +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:",
@@ -222,16 +220,12 @@
             "presetNamePlaceholder": "שם קביעה מראש...",
             "baseModel": "מודל בסיס",
             "modelTags": "תגיות (20 המובילות)",
-            "modelTypes": "סוגי מודלים",
+            "modelTypes": "Model Types",
             "license": "רישיון",
             "noCreditRequired": "ללא קרדיט נדרש",
             "allowSellingGeneratedContent": "אפשר מכירה",
             "noTags": "ללא תגיות",
-            "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",
@@ -261,27 +255,17 @@
             "contentFiltering": "סינון תוכן",
             "videoSettings": "הגדרות וידאו",
             "layoutSettings": "הגדרות פריסה",
-            "misc": "שונות",
-            "folderSettings": "תיקיות ברירת מחדל",
-            "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."
@@ -305,15 +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}"
-            }
-        },
         "layoutSettings": {
             "displayDensity": "צפיפות תצוגה",
             "displayDensityOptions": {
@@ -354,33 +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",
             "noDefault": "אין ברירת מחדל"
         },
-        "extraFolderPaths": {
-            "title": "נתיבי תיקיות נוספים",
-            "help": "הוסף תיקיות מודלים נוספות מחוץ לנתיבים הסטנדרטיים של ComfyUI. נתיבים אלה נשמרים בנפרד ונסרקים לצד תיקיות ברירת המחדל.",
-            "description": "הגדר תיקיות נוספות לסריקת מודלים. נתיבים אלה ספציפיים ל-LoRA Manager וימוזגו עם נתיבי ברירת המחדל של ComfyUI.",
-            "modelTypes": {
-                "lora": "נתיבי LoRA",
-                "checkpoint": "נתיבי Checkpoint",
-                "unet": "נתיבי מודל דיפוזיה",
-                "embedding": "נתיבי Embedding"
-            },
-            "pathPlaceholder": "/נתיב/למודלים/נוספים",
-            "saveSuccess": "נתיבי תיקיות נוספים עודכנו.",
-            "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))",
@@ -456,10 +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"
@@ -571,12 +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 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}...",
@@ -685,11 +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": {
@@ -744,6 +690,16 @@
     "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",
@@ -757,17 +713,7 @@
         "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": {
@@ -1079,19 +1025,12 @@
                 },
                 "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 נוכחית",
                     "inLibrary": "בספרייה",
                     "newer": "גרסה חדשה יותר",
-                    "earlyAccess": "גישה מוקדמת",
                     "ignored": "התעלם"
                 },
                 "actions": {
@@ -1099,7 +1038,6 @@
                     "delete": "מחיקה",
                     "ignore": "התעלם",
                     "unignore": "בטל התעלמות",
-                    "earlyAccessTooltip": "נדרש רכישת גישה מוקדמת",
                     "resumeModelUpdates": "המשך עדכונים עבור מודל זה",
                     "ignoreModelUpdates": "התעלם מעדכונים עבור מודל זה",
                     "viewLocalVersions": "הצג את כל הגרסאות המקומיות",
@@ -1178,6 +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": {
@@ -1237,12 +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"
         },
         "nodeSelector": {
             "recipe": "מתכון",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "החלף",
             "append": "הוסף",
             "selectTargetNode": "בחר צומת יעד",
@@ -1359,14 +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": {
@@ -1400,8 +1341,6 @@
             "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",
@@ -1458,11 +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",
@@ -1550,7 +1484,6 @@
             "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": {
@@ -1661,20 +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

@@ -1,17 +1,14 @@
 {
     "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"
@@ -134,8 +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"
@@ -183,6 +179,7 @@
             "recipes": "レシピ",
             "checkpoints": "Checkpoint",
             "embeddings": "Embedding",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "統計"
         },
         "search": {
@@ -191,7 +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：",
@@ -222,16 +220,12 @@
             "presetNamePlaceholder": "プリセット名...",
             "baseModel": "ベースモデル",
             "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",
-            "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",
@@ -261,27 +255,17 @@
             "contentFiltering": "コンテンツフィルタリング",
             "videoSettings": "動画設定",
             "layoutSettings": "レイアウト設定",
-            "misc": "その他",
-            "folderSettings": "デフォルトルート",
-            "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。"
@@ -305,15 +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}"
-            }
-        },
         "layoutSettings": {
             "displayDensity": "表示密度",
             "displayDensityOptions": {
@@ -354,33 +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",
             "noDefault": "デフォルトなし"
         },
-        "extraFolderPaths": {
-            "title": "追加フォルダーパス",
-            "help": "ComfyUIの標準パスの外部に追加のモデルフォルダを追加します。これらのパスは別々に保存され、デフォルトのフォルダと一緒にスキャンされます。",
-            "description": "モデルをスキャンするための追加フォルダを設定します。これらのパスはLoRA Manager固有であり、ComfyUIのデフォルトパスとマージされます。",
-            "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。",
-            "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))",
@@ -456,10 +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"
@@ -571,12 +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": "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...",
@@ -685,11 +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": {
@@ -744,6 +690,16 @@
     "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",
@@ -757,17 +713,7 @@
         "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": {
@@ -1079,19 +1025,12 @@
                 },
                 "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": "現在のバージョン",
                     "inLibrary": "ライブラリにあります",
                     "newer": "新しいバージョン",
-                    "earlyAccess": "早期アクセス",
                     "ignored": "無視中"
                 },
                 "actions": {
@@ -1099,7 +1038,6 @@
                     "delete": "削除",
                     "ignore": "無視",
                     "unignore": "無視を解除",
-                    "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",
@@ -1178,6 +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": {
@@ -1237,12 +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"
         },
         "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",
@@ -1359,14 +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": {
@@ -1400,8 +1341,6 @@
             "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",
@@ -1458,11 +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",
@@ -1550,7 +1484,6 @@
             "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": {
@@ -1661,20 +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",
@@ -134,8 +131,7 @@
         },
         "badges": {
             "update": "업데이트",
-            "updateAvailable": "업데이트 가능",
-            "skipRefresh": "메타데이터 새로고침 건너뜀"
+            "updateAvailable": "업데이트 가능"
         },
         "usage": {
             "timesUsed": "사용 횟수"
@@ -183,6 +179,7 @@
             "recipes": "레시피",
             "checkpoints": "Checkpoint",
             "embeddings": "Embedding",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "통계"
         },
         "search": {
@@ -191,7 +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:",
@@ -222,16 +220,12 @@
             "presetNamePlaceholder": "프리셋 이름...",
             "baseModel": "베이스 모델",
             "modelTags": "태그 (상위 20개)",
-            "modelTypes": "모델 유형",
+            "modelTypes": "Model Types",
             "license": "라이선스",
             "noCreditRequired": "크레딧 표기 없음",
             "allowSellingGeneratedContent": "판매 허용",
             "noTags": "태그 없음",
-            "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",
@@ -261,27 +255,17 @@
             "contentFiltering": "콘텐츠 필터링",
             "videoSettings": "비디오 설정",
             "layoutSettings": "레이아웃 설정",
-            "misc": "기타",
-            "folderSettings": "기본 루트",
-            "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."
@@ -305,15 +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}"
-            }
-        },
         "layoutSettings": {
             "displayDensity": "표시 밀도",
             "displayDensityOptions": {
@@ -354,33 +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",
             "noDefault": "기본값 없음"
         },
-        "extraFolderPaths": {
-            "title": "추가 폴다 경로",
-            "help": "ComfyUI의 표준 경로 외부에 추가 모델 폴드를 추가하세요. 이러한 경로는 별도로 저장되며 기본 폴와 함께 스캔됩니다.",
-            "description": "모델을 스캔하기 위한 추가 폴를 설정하세요. 이러한 경로는 LoRA Manager 특유의 것이며 ComfyUI의 기본 경로와 병합됩니다.",
-            "modelTypes": {
-                "lora": "LoRA 경로",
-                "checkpoint": "Checkpoint 경로",
-                "unet": "Diffusion 모델 경로",
-                "embedding": "Embedding 경로"
-            },
-            "pathPlaceholder": "/추가/모델/경로",
-            "saveSuccess": "추가 폴다 경로가 업데이트되었습니다.",
-            "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)).",
@@ -456,10 +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"
@@ -571,12 +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": "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...",
@@ -685,11 +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": {
@@ -744,6 +690,16 @@
     "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",
@@ -757,17 +713,7 @@
         "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": {
@@ -1079,19 +1025,12 @@
                 },
                 "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": "현재 버전",
                     "inLibrary": "라이브러리에 있음",
                     "newer": "최신 버전",
-                    "earlyAccess": "얼리 액세스",
                     "ignored": "무시됨"
                 },
                 "actions": {
@@ -1099,7 +1038,6 @@
                     "delete": "삭제",
                     "ignore": "무시",
                     "unignore": "무시 해제",
-                    "earlyAccessTooltip": "얼리 액세스 구매 필요",
                     "resumeModelUpdates": "이 모델 업데이트 재개",
                     "ignoreModelUpdates": "이 모델 업데이트 무시",
                     "viewLocalVersions": "로컬 버전 모두 보기",
@@ -1178,6 +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": {
@@ -1237,12 +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"
         },
         "nodeSelector": {
             "recipe": "레시피",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "교체",
             "append": "추가",
             "selectTargetNode": "대상 노드 선택",
@@ -1359,14 +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": {
@@ -1400,8 +1341,6 @@
             "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",
@@ -1458,11 +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",
@@ -1550,7 +1484,6 @@
             "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": {
@@ -1661,20 +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",
@@ -134,8 +131,7 @@
         },
         "badges": {
             "update": "Обновление",
-            "updateAvailable": "Доступно обновление",
-            "skipRefresh": "Обновление метаданных пропущено"
+            "updateAvailable": "Доступно обновление"
         },
         "usage": {
             "timesUsed": "Количество использований"
@@ -183,6 +179,7 @@
             "recipes": "Рецепты",
             "checkpoints": "Checkpoints",
             "embeddings": "Embeddings",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "Статистика"
         },
         "search": {
@@ -191,7 +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:",
@@ -222,16 +220,12 @@
             "presetNamePlaceholder": "Имя пресета...",
             "baseModel": "Базовая модель",
             "modelTags": "Теги (Топ 20)",
-            "modelTypes": "Типы моделей",
+            "modelTypes": "Model Types",
             "license": "Лицензия",
             "noCreditRequired": "Без указания авторства",
             "allowSellingGeneratedContent": "Продажа разрешена",
             "noTags": "Без тегов",
-            "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",
@@ -261,27 +255,17 @@
             "contentFiltering": "Фильтрация контента",
             "videoSettings": "Настройки видео",
             "layoutSettings": "Настройки макета",
-            "misc": "Разное",
-            "folderSettings": "Корневые папки",
-            "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."
@@ -305,15 +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}"
-            }
-        },
         "layoutSettings": {
             "displayDensity": "Плотность отображения",
             "displayDensityOptions": {
@@ -354,33 +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",
             "noDefault": "Не задано"
         },
-        "extraFolderPaths": {
-            "title": "Дополнительные пути к папкам",
-            "help": "Добавьте дополнительные папки моделей за пределами стандартных путей ComfyUI. Эти пути хранятся отдельно и сканируются вместе с папками по умолчанию.",
-            "description": "Настройте дополнительные папки для сканирования моделей. Эти пути специфичны для LoRA Manager и будут объединены с путями по умолчанию ComfyUI.",
-            "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.",
-            "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)).",
@@ -456,10 +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"
@@ -571,12 +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 все модели",
             "clear": "Очистить выбор",
-            "skipMetadataRefreshCount": "Пропустить（{count} моделей）",
-            "resumeMetadataRefreshCount": "Возобновить（{count} моделей）",
             "autoOrganizeProgress": {
                 "initializing": "Инициализация автоматической организации...",
                 "starting": "Запуск автоматической организации для {type}...",
@@ -685,11 +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": {
@@ -744,6 +690,16 @@
     "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",
@@ -757,17 +713,7 @@
         "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": {
@@ -1079,19 +1025,12 @@
                 },
                 "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": "Текущая версия",
                     "inLibrary": "В библиотеке",
                     "newer": "Более новая версия",
-                    "earlyAccess": "Ранний доступ",
                     "ignored": "Игнорируется"
                 },
                 "actions": {
@@ -1099,7 +1038,6 @@
                     "delete": "Удалить",
                     "ignore": "Игнорировать",
                     "unignore": "Перестать игнорировать",
-                    "earlyAccessTooltip": "Требуется покупка раннего доступа",
                     "resumeModelUpdates": "Возобновить обновления для этой модели",
                     "ignoreModelUpdates": "Игнорировать обновления для этой модели",
                     "viewLocalVersions": "Показать все локальные версии",
@@ -1178,6 +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": {
@@ -1237,12 +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"
         },
         "nodeSelector": {
             "recipe": "Рецепт",
             "lora": "LoRA",
+            "vae": "[TODO: Translate] VAE",
+            "upscaler": "[TODO: Translate] Upscaler",
             "replace": "Заменить",
             "append": "Добавить",
             "selectTargetNode": "Выберите целевой узел",
@@ -1359,14 +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": {
@@ -1400,8 +1341,6 @@
             "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",
@@ -1458,11 +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",
@@ -1550,7 +1484,6 @@
             "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": {
@@ -1661,20 +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",
@@ -134,8 +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"
@@ -162,11 +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",
@@ -183,6 +179,7 @@
             "recipes": "配方",
             "checkpoints": "Checkpoint",
             "embeddings": "Embedding",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "统计"
         },
         "search": {
@@ -191,7 +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：",
@@ -222,16 +220,12 @@
             "presetNamePlaceholder": "预设名称...",
             "baseModel": "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",
-            "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",
@@ -261,27 +255,17 @@
             "contentFiltering": "内容过滤",
             "videoSettings": "视频设置",
             "layoutSettings": "布局设置",
-            "misc": "其他",
-            "folderSettings": "默认根目录",
-            "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。"
@@ -305,15 +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}"
-            }
-        },
         "layoutSettings": {
             "displayDensity": "显示密度",
             "displayDensityOptions": {
@@ -354,33 +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",
             "noDefault": "无默认"
         },
-        "extraFolderPaths": {
-            "title": "额外文件夹路径",
-            "help": "在 ComfyUI 的标准路径之外添加额外的模型文件夹。这些路径单独存储，并与默认文件夹一起扫描。",
-            "description": "配置额外的文件夹以扫描模型。这些路径是 LoRA Manager 特有的，将与 ComfyUI 的默认路径合并。",
-            "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。",
-            "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))",
@@ -456,10 +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"
@@ -571,12 +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...",
@@ -685,11 +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": {
@@ -744,6 +690,16 @@
     "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",
@@ -757,17 +713,7 @@
         "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": {
@@ -1079,19 +1025,12 @@
                 },
                 "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": "当前版本",
                     "inLibrary": "已在库中",
                     "newer": "较新的版本",
-                    "earlyAccess": "抢先体验",
                     "ignored": "已忽略"
                 },
                 "actions": {
@@ -1099,7 +1038,6 @@
                     "delete": "删除",
                     "ignore": "忽略",
                     "unignore": "取消忽略",
-                    "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",
@@ -1178,6 +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": {
@@ -1237,12 +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"
         },
         "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",
@@ -1359,14 +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": {
@@ -1400,8 +1341,6 @@
             "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",
@@ -1458,11 +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",
@@ -1550,7 +1484,6 @@
             "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": {
@@ -1661,20 +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",
@@ -134,8 +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"
@@ -183,6 +179,7 @@
             "recipes": "配方",
             "checkpoints": "Checkpoint",
             "embeddings": "Embedding",
+            "misc": "[TODO: Translate] Misc",
             "statistics": "統計"
         },
         "search": {
@@ -191,7 +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：",
@@ -222,16 +220,12 @@
             "presetNamePlaceholder": "預設名稱...",
             "baseModel": "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",
-            "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",
@@ -261,27 +255,17 @@
             "contentFiltering": "內容過濾",
             "videoSettings": "影片設定",
             "layoutSettings": "版面設定",
-            "misc": "其他",
-            "folderSettings": "預設根目錄",
-            "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。"
@@ -305,15 +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}"
-            }
-        },
         "layoutSettings": {
             "displayDensity": "顯示密度",
             "displayDensityOptions": {
@@ -354,33 +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",
             "noDefault": "未設定預設"
         },
-        "extraFolderPaths": {
-            "title": "額外資料夾路徑",
-            "help": "在 ComfyUI 的標準路徑之外新增額外的模型資料夾。這些路徑單獨儲存，並與預設資料夾一起掃描。",
-            "description": "設定額外的資料夾以掃描模型。這些路徑是 LoRA Manager 特有的，將與 ComfyUI 的預設路徑合併。",
-            "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。",
-            "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))",
@@ -456,10 +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"
@@ -571,12 +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}...",
@@ -685,11 +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": {
@@ -744,6 +690,16 @@
     "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",
@@ -757,17 +713,7 @@
         "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": {
@@ -1079,19 +1025,12 @@
                 },
                 "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": "目前版本",
                     "inLibrary": "已在庫中",
                     "newer": "較新版本",
-                    "earlyAccess": "搶先體驗",
                     "ignored": "已忽略"
                 },
                 "actions": {
@@ -1099,7 +1038,6 @@
                     "delete": "刪除",
                     "ignore": "忽略",
                     "unignore": "取消忽略",
-                    "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",
@@ -1178,6 +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": {
@@ -1237,12 +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"
         },
         "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",
@@ -1359,14 +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": {
@@ -1400,8 +1341,6 @@
             "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",
@@ -1458,11 +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",
@@ -1550,7 +1484,6 @@
             "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": {
@@ -1661,20 +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.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/config.py

@@ -2,7 +2,7 @@ import os
 import platform
 import threading
 from pathlib import Path
-import folder_paths  # type: ignore
+import folder_paths # type: ignore
 from typing import Any, Dict, Iterable, List, Mapping, Optional, Set, Tuple
 import logging
 import json
@@ -10,23 +10,16 @@ import urllib.parse
 import time
 
 from .utils.cache_paths import CacheType, get_cache_file_path, get_legacy_cache_paths
-from .utils.settings_paths import (
-    ensure_settings_file,
-    get_settings_dir,
-    load_settings_template,
-)
+from .utils.settings_paths import ensure_settings_file, get_settings_dir, load_settings_template
 
 # Use an environment variable to control 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"
 
 logger = logging.getLogger(__name__)
 
 
 def _normalize_folder_paths_for_comparison(
-    folder_paths: Mapping[str, Iterable[str]],
+    folder_paths: Mapping[str, Iterable[str]]
 ) -> Dict[str, Set[str]]:
     """Normalize folder paths for comparison across libraries."""
 
@@ -56,7 +49,7 @@ def _normalize_folder_paths_for_comparison(
 
 
 def _normalize_library_folder_paths(
-    library_payload: Mapping[str, Any],
+    library_payload: Mapping[str, Any]
 ) -> Dict[str, Set[str]]:
     """Return normalized folder paths extracted from a library payload."""
 
@@ -81,17 +74,11 @@ def _get_template_folder_paths() -> Dict[str, Set[str]]:
 
 class Config:
     """Global configuration for LoRA Manager"""
-
+    
     def __init__(self):
-        self.templates_path = os.path.join(
-            os.path.dirname(os.path.dirname(__file__)), "templates"
-        )
-        self.static_path = os.path.join(
-            os.path.dirname(os.path.dirname(__file__)), "static"
-        )
-        self.i18n_path = os.path.join(
-            os.path.dirname(os.path.dirname(__file__)), "locales"
-        )
+        self.templates_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'templates')
+        self.static_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'static')
+        self.i18n_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'locales')
         # Path mapping dictionary, target to link mapping
         self._path_mappings: Dict[str, str] = {}
         # Normalized preview root directories used to validate preview access
@@ -102,16 +89,14 @@ class Config:
         self.checkpoints_roots = None
         self.unet_roots = None
         self.embeddings_roots = None
+        self.vae_roots = None
+        self.upscaler_roots = None
         self.base_models_roots = self._init_checkpoint_paths()
         self.embeddings_roots = self._init_embedding_paths()
-        # Extra paths (only for LoRA Manager, not shared with ComfyUI)
-        self.extra_loras_roots: List[str] = []
-        self.extra_checkpoints_roots: List[str] = []
-        self.extra_unet_roots: List[str] = []
-        self.extra_embeddings_roots: List[str] = []
+        self.misc_roots = self._init_misc_paths()
         # Scan symbolic links during initialization
         self._initialize_symlink_mappings()
-
+        
         if not standalone_mode:
             # Save the paths to settings.json when running in ComfyUI mode
             self.save_folder_paths_to_settings()
@@ -165,21 +150,19 @@ class Config:
                 default_library = libraries.get("default", {})
 
             target_folder_paths = {
-                "loras": list(self.loras_roots),
-                "checkpoints": list(self.checkpoints_roots or []),
-                "unet": list(self.unet_roots or []),
-                "embeddings": list(self.embeddings_roots or []),
+                'loras': list(self.loras_roots),
+                'checkpoints': list(self.checkpoints_roots or []),
+                'unet': list(self.unet_roots or []),
+                'embeddings': list(self.embeddings_roots or []),
+                'vae': list(self.vae_roots or []),
+                'upscale_models': list(self.upscaler_roots or []),
             }
 
-            normalized_target_paths = _normalize_folder_paths_for_comparison(
-                target_folder_paths
-            )
+            normalized_target_paths = _normalize_folder_paths_for_comparison(target_folder_paths)
 
             normalized_default_paths: Optional[Dict[str, Set[str]]] = None
             if isinstance(default_library, Mapping):
-                normalized_default_paths = _normalize_library_folder_paths(
-                    default_library
-                )
+                normalized_default_paths = _normalize_library_folder_paths(default_library)
 
             if (
                 not comfy_library
@@ -202,19 +185,13 @@ class Config:
                 default_lora_root = self.loras_roots[0]
 
             default_checkpoint_root = comfy_library.get("default_checkpoint_root", "")
-            if (
-                not default_checkpoint_root
-                and self.checkpoints_roots
-                and len(self.checkpoints_roots) == 1
-            ):
+            if (not default_checkpoint_root and self.checkpoints_roots and
+                    len(self.checkpoints_roots) == 1):
                 default_checkpoint_root = self.checkpoints_roots[0]
 
             default_embedding_root = comfy_library.get("default_embedding_root", "")
-            if (
-                not default_embedding_root
-                and self.embeddings_roots
-                and len(self.embeddings_roots) == 1
-            ):
+            if (not default_embedding_root and self.embeddings_roots and
+                    len(self.embeddings_roots) == 1):
                 default_embedding_root = self.embeddings_roots[0]
 
             metadata = dict(comfy_library.get("metadata", {}))
@@ -239,12 +216,11 @@ class Config:
         try:
             if os.path.islink(path):
                 return True
-            if platform.system() == "Windows":
+            if platform.system() == 'Windows':
                 try:
                     import ctypes
-
                     FILE_ATTRIBUTE_REPARSE_POINT = 0x400
-                    attrs = ctypes.windll.kernel32.GetFileAttributesW(str(path))  # type: ignore[attr-defined]
+                    attrs = ctypes.windll.kernel32.GetFileAttributesW(str(path))
                     return attrs != -1 and (attrs & FILE_ATTRIBUTE_REPARSE_POINT)
                 except Exception as e:
                     logger.error(f"Error checking Windows reparse point: {e}")
@@ -257,19 +233,18 @@ class Config:
         """Check if a directory entry is a symlink, including Windows junctions."""
         if entry.is_symlink():
             return True
-        if platform.system() == "Windows":
+        if platform.system() == 'Windows':
             try:
                 import ctypes
-
                 FILE_ATTRIBUTE_REPARSE_POINT = 0x400
-                attrs = ctypes.windll.kernel32.GetFileAttributesW(entry.path)  # type: ignore[attr-defined]
+                attrs = ctypes.windll.kernel32.GetFileAttributesW(entry.path)
                 return attrs != -1 and (attrs & FILE_ATTRIBUTE_REPARSE_POINT)
             except Exception:
                 pass
         return False
 
     def _normalize_path(self, path: str) -> str:
-        return os.path.normpath(path).replace(os.sep, "/")
+        return os.path.normpath(path).replace(os.sep, '/')
 
     def _get_symlink_cache_path(self) -> Path:
         canonical_path = get_cache_file_path(CacheType.SYMLINK, create_dir=True)
@@ -280,11 +255,7 @@ class Config:
         roots.extend(self.loras_roots or [])
         roots.extend(self.base_models_roots or [])
         roots.extend(self.embeddings_roots or [])
-        # Include extra paths for scanning symlinks
-        roots.extend(self.extra_loras_roots or [])
-        roots.extend(self.extra_checkpoints_roots or [])
-        roots.extend(self.extra_unet_roots or [])
-        roots.extend(self.extra_embeddings_roots or [])
+        roots.extend(self.misc_roots or [])
         return roots
 
     def _build_symlink_fingerprint(self) -> Dict[str, object]:
@@ -303,18 +274,19 @@ class Config:
                             if self._entry_is_symlink(entry):
                                 try:
                                     target = os.path.realpath(entry.path)
-                                    direct_symlinks.append(
-                                        [
-                                            self._normalize_path(entry.path),
-                                            self._normalize_path(target),
-                                        ]
-                                    )
+                                    direct_symlinks.append([
+                                        self._normalize_path(entry.path),
+                                        self._normalize_path(target)
+                                    ])
                                 except OSError:
                                     pass
             except (OSError, PermissionError):
                 pass
 
-        return {"roots": unique_roots, "direct_symlinks": sorted(direct_symlinks)}
+        return {
+            "roots": unique_roots,
+            "direct_symlinks": sorted(direct_symlinks)
+        }
 
     def _initialize_symlink_mappings(self) -> None:
         start = time.perf_counter()
@@ -331,14 +303,10 @@ class Config:
             cached_fingerprint = self._cached_fingerprint
 
             # Check 1: First-level symlinks unchanged (catches new symlinks at root)
-            fingerprint_valid = (
-                cached_fingerprint and current_fingerprint == cached_fingerprint
-            )
+            fingerprint_valid = cached_fingerprint and current_fingerprint == cached_fingerprint
 
             # Check 2: All cached mappings still valid (catches changes at any depth)
-            mappings_valid = (
-                self._validate_cached_mappings() if fingerprint_valid else False
-            )
+            mappings_valid = self._validate_cached_mappings() if fingerprint_valid else False
 
             if fingerprint_valid and mappings_valid:
                 return
@@ -398,9 +366,7 @@ class Config:
         for target, link in cached_mappings.items():
             if not isinstance(target, str) or not isinstance(link, str):
                 continue
-            normalized_mappings[self._normalize_path(target)] = self._normalize_path(
-                link
-            )
+            normalized_mappings[self._normalize_path(target)] = self._normalize_path(link)
 
         self._path_mappings = normalized_mappings
 
@@ -421,9 +387,7 @@ class Config:
                         parent_dir = loaded_path.parent
                         if parent_dir.name == "cache" and not any(parent_dir.iterdir()):
                             parent_dir.rmdir()
-                            logger.info(
-                                "Removed empty legacy cache directory: %s", parent_dir
-                            )
+                            logger.info("Removed empty legacy cache directory: %s", parent_dir)
                     except Exception:
                         pass
 
@@ -434,9 +398,7 @@ class Config:
                     exc,
                 )
         else:
-            logger.info(
-                "Symlink cache loaded with %d mappings", len(self._path_mappings)
-            )
+            logger.info("Symlink cache loaded with %d mappings", len(self._path_mappings))
 
         return True
 
@@ -448,7 +410,7 @@ class Config:
         """
         for target, link in self._path_mappings.items():
             # Convert normalized paths back to OS paths
-            link_path = link.replace("/", os.sep)
+            link_path = link.replace('/', os.sep)
 
             # Check if symlink still exists
             if not self._is_link(link_path):
@@ -461,9 +423,7 @@ class Config:
                 if actual_target != target:
                     logger.debug(
                         "Symlink target changed: %s -> %s (cached: %s)",
-                        link_path,
-                        actual_target,
-                        target,
+                        link_path, actual_target, target
                     )
                     return False
             except OSError:
@@ -482,64 +442,89 @@ class Config:
         try:
             with cache_path.open("w", encoding="utf-8") as handle:
                 json.dump(payload, handle, ensure_ascii=False, indent=2)
-            logger.debug(
-                "Symlink cache saved to %s with %d mappings",
-                cache_path,
-                len(self._path_mappings),
-            )
+            logger.debug("Symlink cache saved to %s with %d mappings", cache_path, len(self._path_mappings))
         except Exception as exc:
             logger.info("Failed to write symlink cache %s: %s", cache_path, exc)
 
     def _scan_symbolic_links(self):
-        """Scan symbolic links in LoRA, Checkpoint, and Embedding root directories.
-
-        Only scans the first level of each root directory to avoid performance
-        issues with large file systems. Detects symlinks and Windows junctions
-        at the root level only (not nested symlinks in subdirectories).
-        """
+        """Scan all symbolic links in LoRA, Checkpoint, and Embedding root directories"""
         start = time.perf_counter()
-
+        
         # Reset mappings before rescanning to avoid stale entries
         self._path_mappings.clear()
         self._seed_root_symlink_mappings()
+        visited_dirs: Set[str] = set()
         for root in self._symlink_roots():
-            self._scan_first_level_symlinks(root)
+            self._scan_directory_links(root, visited_dirs)
         logger.debug(
             "Symlink scan finished in %.2f ms with %d mappings",
             (time.perf_counter() - start) * 1000,
             len(self._path_mappings),
         )
 
-    def _scan_first_level_symlinks(self, root: str):
-        """Scan only the first level of a directory for symlinks.
-
-        This avoids traversing the entire directory tree which can be extremely
-        slow for large model collections. Only symlinks directly under the root
-        are detected.
-        """
+    def _scan_directory_links(self, root: str, visited_dirs: Set[str]):
+        """Iteratively scan directory symlinks to avoid deep recursion."""
         try:
-            with os.scandir(root) as it:
-                for entry in it:
-                    try:
-                        # Only detect symlinks including Windows junctions
-                        # Skip normal directories to avoid deep traversal
-                        if not self._entry_is_symlink(entry):
-                            continue
+            # Note: We only use realpath for the initial root if it's not already resolved
+            # to ensure we have a valid entry point.
+            root_real = self._normalize_path(os.path.realpath(root))
+        except OSError:
+            root_real = self._normalize_path(root)
+
+        if root_real in visited_dirs:
+            return
+
+        visited_dirs.add(root_real)
+        # Stack entries: (display_path, real_resolved_path)
+        stack: List[Tuple[str, str]] = [(root, root_real)]
+
+        while stack:
+            current_display, current_real = stack.pop()
+            try:
+                with os.scandir(current_display) as it:
+                    for entry in it:
+                        try:
+                            # 1. Detect symlinks including Windows junctions
+                            is_link = self._entry_is_symlink(entry)
+
+                            if is_link:
+                                # Only resolve realpath when we actually find a link
+                                target_path = os.path.realpath(entry.path)
+                                if not os.path.isdir(target_path):
+                                    continue
+
+                                normalized_target = self._normalize_path(target_path)
+                                self.add_path_mapping(entry.path, target_path)
+                                
+                                if normalized_target in visited_dirs:
+                                    continue
+                                    
+                                visited_dirs.add(normalized_target)
+                                stack.append((target_path, normalized_target))
+                                continue
+
+                            # 2. Process normal directories
+                            if not entry.is_dir(follow_symlinks=False):
+                                continue
+
+                            # For normal directories, we avoid realpath() call by 
+                            # incrementally building the real path relative to current_real.
+                            # This is safe because 'entry' is NOT a symlink.
+                            entry_real = self._normalize_path(os.path.join(current_real, entry.name))
+                            
+                            if entry_real in visited_dirs:
+                                continue
+                            
+                            visited_dirs.add(entry_real)
+                            stack.append((entry.path, entry_real))
+                        except Exception as inner_exc:
+                            logger.debug(
+                                "Error processing directory entry %s: %s", entry.path, inner_exc
+                            )
+            except Exception as e:
+                logger.error(f"Error scanning links in {current_display}: {e}")
 
-                        # Resolve the symlink target
-                        target_path = os.path.realpath(entry.path)
-                        if not os.path.isdir(target_path):
-                            continue
 
-                        self.add_path_mapping(entry.path, target_path)
-                    except Exception as inner_exc:
-                        logger.debug(
-                            "Error processing directory entry %s: %s",
-                            entry.path,
-                            inner_exc,
-                        )
-        except Exception as e:
-            logger.error(f"Error scanning links in {root}: {e}")
 
     def add_path_mapping(self, link_path: str, target_path: str):
         """Add a symbolic link path mapping
@@ -620,60 +605,49 @@ class Config:
             preview_roots.update(self._expand_preview_root(root))
         for root in self.embeddings_roots or []:
             preview_roots.update(self._expand_preview_root(root))
-        # Include extra paths for preview access
-        for root in self.extra_loras_roots or []:
-            preview_roots.update(self._expand_preview_root(root))
-        for root in self.extra_checkpoints_roots or []:
-            preview_roots.update(self._expand_preview_root(root))
-        for root in self.extra_unet_roots or []:
-            preview_roots.update(self._expand_preview_root(root))
-        for root in self.extra_embeddings_roots or []:
+        for root in self.misc_roots or []:
             preview_roots.update(self._expand_preview_root(root))
 
         for target, link in self._path_mappings.items():
             preview_roots.update(self._expand_preview_root(target))
             preview_roots.update(self._expand_preview_root(link))
 
-        self._preview_root_paths = {
-            path for path in preview_roots if path.is_absolute()
-        }
+        self._preview_root_paths = {path for path in preview_roots if path.is_absolute()}
         logger.debug(
-            "Preview roots rebuilt: %d paths from %d lora roots (%d extra), %d checkpoint roots (%d extra), %d embedding roots (%d extra), %d symlink mappings",
+            "Preview roots rebuilt: %d paths from %d lora roots, %d checkpoint roots, %d embedding roots, %d misc roots, %d symlink mappings",
             len(self._preview_root_paths),
             len(self.loras_roots or []),
-            len(self.extra_loras_roots or []),
             len(self.base_models_roots or []),
-            len(self.extra_checkpoints_roots or []),
             len(self.embeddings_roots or []),
-            len(self.extra_embeddings_roots or []),
+            len(self.misc_roots or []),
             len(self._path_mappings),
         )
 
     def map_path_to_link(self, path: str) -> str:
         """Map a target path back to its symbolic link path"""
-        normalized_path = os.path.normpath(path).replace(os.sep, "/")
+        normalized_path = os.path.normpath(path).replace(os.sep, '/')
         # Check if the path is contained in any mapped target path
         for target_path, link_path in self._path_mappings.items():
             # Match whole path components to avoid prefix collisions (e.g., /a/b vs /a/bc)
             if normalized_path == target_path:
                 return link_path
-
-            if normalized_path.startswith(target_path + "/"):
+            
+            if normalized_path.startswith(target_path + '/'):
                 # If the path starts with the target path, replace with link path
                 mapped_path = normalized_path.replace(target_path, link_path, 1)
                 return mapped_path
         return normalized_path
-
+    
     def map_link_to_path(self, link_path: str) -> str:
         """Map a symbolic link path back to the actual path"""
-        normalized_link = os.path.normpath(link_path).replace(os.sep, "/")
+        normalized_link = os.path.normpath(link_path).replace(os.sep, '/')
         # Check if the path is contained in any mapped target path
         for target_path, link_path_mapped in self._path_mappings.items():
             # Match whole path components
             if normalized_link == link_path_mapped:
                 return target_path
 
-            if normalized_link.startswith(link_path_mapped + "/"):
+            if normalized_link.startswith(link_path_mapped + '/'):
                 # If the path starts with the link path, replace with actual path
                 mapped_path = normalized_link.replace(link_path_mapped, target_path, 1)
                 return mapped_path
@@ -686,8 +660,8 @@ class Config:
                 continue
             if not os.path.exists(path):
                 continue
-            real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, "/")
-            normalized = os.path.normpath(path).replace(os.sep, "/")
+            real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
+            normalized = os.path.normpath(path).replace(os.sep, '/')
             if real_path not in dedup:
                 dedup[real_path] = normalized
         return dedup
@@ -697,9 +671,7 @@ class Config:
         unique_paths = sorted(path_map.values(), key=lambda p: p.lower())
 
         for original_path in unique_paths:
-            real_path = os.path.normpath(os.path.realpath(original_path)).replace(
-                os.sep, "/"
-            )
+            real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
             if real_path != original_path:
                 self.add_path_mapping(original_path, real_path)
 
@@ -711,23 +683,6 @@ class Config:
         checkpoint_map = self._dedupe_existing_paths(checkpoint_paths)
         unet_map = self._dedupe_existing_paths(unet_paths)
 
-        # Detect when checkpoints and unet share the same physical location
-        # This is a configuration issue that can cause duplicate model entries
-        overlapping_real_paths = set(checkpoint_map.keys()) & set(unet_map.keys())
-        if overlapping_real_paths:
-            logger.warning(
-                "Detected overlapping paths between 'checkpoints' and 'diffusion_models' (unet). "
-                "They should not point to the same physical folder as they are different model types. "
-                "Please fix your ComfyUI path configuration to separate these folders. "
-                "Falling back to 'checkpoints' for backward compatibility. "
-                "Overlapping real paths: %s",
-                [checkpoint_map.get(rp, rp) for rp in overlapping_real_paths],
-            )
-            # Remove overlapping paths from unet_map to prioritize checkpoints
-            for rp in overlapping_real_paths:
-                if rp in unet_map:
-                    del unet_map[rp]
-
         merged_map: Dict[str, str] = {}
         for real_path, original in {**checkpoint_map, **unet_map}.items():
             if real_path not in merged_map:
@@ -741,9 +696,7 @@ class Config:
         self.unet_roots = [p for p in unique_paths if p in unet_values]
 
         for original_path in unique_paths:
-            real_path = os.path.normpath(os.path.realpath(original_path)).replace(
-                os.sep, "/"
-            )
+            real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
             if real_path != original_path:
                 self.add_path_mapping(original_path, real_path)
 
@@ -754,83 +707,25 @@ class Config:
         unique_paths = sorted(path_map.values(), key=lambda p: p.lower())
 
         for original_path in unique_paths:
-            real_path = os.path.normpath(os.path.realpath(original_path)).replace(
-                os.sep, "/"
-            )
+            real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
             if real_path != original_path:
                 self.add_path_mapping(original_path, real_path)
 
         return unique_paths
 
-    def _apply_library_paths(
-        self,
-        folder_paths: Mapping[str, Iterable[str]],
-        extra_folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
-    ) -> None:
+    def _apply_library_paths(self, folder_paths: Mapping[str, Iterable[str]]) -> None:
         self._path_mappings.clear()
         self._preview_root_paths = set()
 
-        lora_paths = folder_paths.get("loras", []) or []
-        checkpoint_paths = folder_paths.get("checkpoints", []) or []
-        unet_paths = folder_paths.get("unet", []) or []
-        embedding_paths = folder_paths.get("embeddings", []) or []
+        lora_paths = folder_paths.get('loras', []) or []
+        checkpoint_paths = folder_paths.get('checkpoints', []) or []
+        unet_paths = folder_paths.get('unet', []) or []
+        embedding_paths = folder_paths.get('embeddings', []) or []
 
         self.loras_roots = self._prepare_lora_paths(lora_paths)
-        self.base_models_roots = self._prepare_checkpoint_paths(
-            checkpoint_paths, unet_paths
-        )
+        self.base_models_roots = self._prepare_checkpoint_paths(checkpoint_paths, unet_paths)
         self.embeddings_roots = self._prepare_embedding_paths(embedding_paths)
 
-        # Process extra paths (only for LoRA Manager, not shared with ComfyUI)
-        extra_paths = extra_folder_paths or {}
-        extra_lora_paths = extra_paths.get("loras", []) or []
-        extra_checkpoint_paths = extra_paths.get("checkpoints", []) or []
-        extra_unet_paths = extra_paths.get("unet", []) or []
-        extra_embedding_paths = extra_paths.get("embeddings", []) or []
-
-        self.extra_loras_roots = self._prepare_lora_paths(extra_lora_paths)
-        # Save main paths before processing extra paths ( _prepare_checkpoint_paths overwrites them)
-        saved_checkpoints_roots = self.checkpoints_roots
-        saved_unet_roots = self.unet_roots
-        self.extra_checkpoints_roots = self._prepare_checkpoint_paths(
-            extra_checkpoint_paths, extra_unet_paths
-        )
-        self.extra_unet_roots = (
-            self.unet_roots if self.unet_roots is not None else []
-        )  # unet_roots was set by _prepare_checkpoint_paths
-        # Restore main paths
-        self.checkpoints_roots = saved_checkpoints_roots
-        self.unet_roots = saved_unet_roots
-        self.extra_embeddings_roots = self._prepare_embedding_paths(
-            extra_embedding_paths
-        )
-
-        # Log extra folder paths
-        if self.extra_loras_roots:
-            logger.info(
-                "Found extra LoRA roots:"
-                + "\n - "
-                + "\n - ".join(self.extra_loras_roots)
-            )
-        if self.extra_checkpoints_roots:
-            logger.info(
-                "Found extra checkpoint roots:"
-                + "\n - "
-                + "\n - ".join(self.extra_checkpoints_roots)
-            )
-        if self.extra_unet_roots:
-            logger.info(
-                "Found extra diffusion model roots:"
-                + "\n - "
-                + "\n - ".join(self.extra_unet_roots)
-            )
-        if self.extra_embeddings_roots:
-            logger.info(
-                "Found extra embedding roots:"
-                + "\n - "
-                + "\n - ".join(self.extra_embeddings_roots)
-            )
-
         self._initialize_symlink_mappings()
 
     def _init_lora_paths(self) -> List[str]:
@@ -838,10 +733,7 @@ class Config:
         try:
             raw_paths = folder_paths.get_folder_paths("loras")
             unique_paths = self._prepare_lora_paths(raw_paths)
-            logger.info(
-                "Found LoRA roots:"
-                + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]")
-            )
+            logger.info("Found LoRA roots:" + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]"))
 
             if not unique_paths:
                 logger.warning("No valid loras folders found in ComfyUI configuration")
@@ -857,19 +749,12 @@ class Config:
         try:
             raw_checkpoint_paths = folder_paths.get_folder_paths("checkpoints")
             raw_unet_paths = folder_paths.get_folder_paths("unet")
-            unique_paths = self._prepare_checkpoint_paths(
-                raw_checkpoint_paths, raw_unet_paths
-            )
+            unique_paths = self._prepare_checkpoint_paths(raw_checkpoint_paths, raw_unet_paths)
 
-            logger.info(
-                "Found checkpoint roots:"
-                + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]")
-            )
+            logger.info("Found checkpoint roots:" + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]"))
 
             if not unique_paths:
-                logger.warning(
-                    "No valid checkpoint folders found in ComfyUI configuration"
-                )
+                logger.warning("No valid checkpoint folders found in ComfyUI configuration")
                 return []
 
             return unique_paths
@@ -882,15 +767,10 @@ class Config:
         try:
             raw_paths = folder_paths.get_folder_paths("embeddings")
             unique_paths = self._prepare_embedding_paths(raw_paths)
-            logger.info(
-                "Found embedding roots:"
-                + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]")
-            )
+            logger.info("Found embedding roots:" + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]"))
 
             if not unique_paths:
-                logger.warning(
-                    "No valid embeddings folders found in ComfyUI configuration"
-                )
+                logger.warning("No valid embeddings folders found in ComfyUI configuration")
                 return []
 
             return unique_paths
@@ -898,32 +778,59 @@ class Config:
             logger.warning(f"Error initializing embedding paths: {e}")
             return []
 
+    def _init_misc_paths(self) -> List[str]:
+        """Initialize and validate misc (VAE and upscaler) paths from ComfyUI settings"""
+        try:
+            raw_vae_paths = folder_paths.get_folder_paths("vae")
+            raw_upscaler_paths = folder_paths.get_folder_paths("upscale_models")
+            unique_paths = self._prepare_misc_paths(raw_vae_paths, raw_upscaler_paths)
+
+            logger.info("Found misc roots:" + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]"))
+
+            if not unique_paths:
+                logger.warning("No valid VAE or upscaler folders found in ComfyUI configuration")
+                return []
+
+            return unique_paths
+        except Exception as e:
+            logger.warning(f"Error initializing misc paths: {e}")
+            return []
+
+    def _prepare_misc_paths(
+        self, vae_paths: Iterable[str], upscaler_paths: Iterable[str]
+    ) -> List[str]:
+        vae_map = self._dedupe_existing_paths(vae_paths)
+        upscaler_map = self._dedupe_existing_paths(upscaler_paths)
+
+        merged_map: Dict[str, str] = {}
+        for real_path, original in {**vae_map, **upscaler_map}.items():
+            if real_path not in merged_map:
+                merged_map[real_path] = original
+
+        unique_paths = sorted(merged_map.values(), key=lambda p: p.lower())
+
+        vae_values = set(vae_map.values())
+        upscaler_values = set(upscaler_map.values())
+        self.vae_roots = [p for p in unique_paths if p in vae_values]
+        self.upscaler_roots = [p for p in unique_paths if p in upscaler_values]
+
+        for original_path in unique_paths:
+            real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
+            if real_path != original_path:
+                self.add_path_mapping(original_path, real_path)
+
+        return unique_paths
+
     def get_preview_static_url(self, preview_path: str) -> str:
         if not preview_path:
             return ""
 
-        normalized = os.path.normpath(preview_path).replace(os.sep, "/")
-        encoded_path = urllib.parse.quote(normalized, safe="")
-        return f"/api/lm/previews?path={encoded_path}"
+        normalized = os.path.normpath(preview_path).replace(os.sep, '/')
+        encoded_path = urllib.parse.quote(normalized, safe='')
+        return f'/api/lm/previews?path={encoded_path}'
 
     def is_preview_path_allowed(self, preview_path: str) -> bool:
-        """Return ``True`` if ``preview_path`` is within an allowed directory.
-
-        If the path is initially rejected, attempts to discover deep symlinks
-        that were not scanned during initialization. If a symlink is found,
-        updates the in-memory path mappings and retries the check.
-        """
-
-        if self._is_path_in_allowed_roots(preview_path):
-            return True
-
-        if self._try_discover_deep_symlink(preview_path):
-            return self._is_path_in_allowed_roots(preview_path)
-
-        return False
-
-    def _is_path_in_allowed_roots(self, preview_path: str) -> bool:
-        """Check if preview_path is within allowed preview roots without modification."""
+        """Return ``True`` if ``preview_path`` is within an allowed directory."""
 
         if not preview_path:
             return False
@@ -933,106 +840,45 @@ class Config:
         except Exception:
             return False
 
+        # Use os.path.normcase for case-insensitive comparison on Windows.
+        # On Windows, Path.relative_to() is case-sensitive for drive letters,
+        # causing paths like 'a:/folder' to not match 'A:/folder'.
         candidate_str = os.path.normcase(str(candidate))
         for root in self._preview_root_paths:
             root_str = os.path.normcase(str(root))
+            # Check if candidate is equal to or under the root directory
             if candidate_str == root_str or candidate_str.startswith(root_str + os.sep):
                 return True
 
-        logger.debug(
-            "Path not in allowed roots: %s (candidate=%s, num_roots=%d)",
-            preview_path,
-            candidate_str,
-            len(self._preview_root_paths),
-        )
-
-        return False
-
-    def _try_discover_deep_symlink(self, preview_path: str) -> bool:
-        """Attempt to discover a deep symlink that contains the preview_path.
-
-        Walks up from the preview path to the root directories, checking each
-        parent directory for symlinks. If a symlink is found, updates the
-        in-memory path mappings and preview roots.
-
-        Only updates in-memory state (self._path_mappings and self._preview_root_paths),
-        does not modify the persistent cache file.
-
-        Returns:
-            True if a symlink was discovered and mappings updated, False otherwise.
-        """
-        if not preview_path:
-            return False
-
-        try:
-            candidate = Path(preview_path).expanduser()
-        except Exception:
-            return False
-
-        current = candidate
-        while True:
-            try:
-                if self._is_link(str(current)):
-                    try:
-                        target = os.path.realpath(str(current))
-                        normalized_target = self._normalize_path(target)
-                        normalized_link = self._normalize_path(str(current))
-
-                        self._path_mappings[normalized_target] = normalized_link
-                        self._preview_root_paths.update(
-                            self._expand_preview_root(normalized_target)
-                        )
-                        self._preview_root_paths.update(
-                            self._expand_preview_root(normalized_link)
-                        )
-
-                        logger.debug(
-                            "Discovered deep symlink: %s -> %s (preview path: %s)",
-                            normalized_link,
-                            normalized_target,
-                            preview_path,
-                        )
-
-                        return True
-                    except OSError:
-                        pass
-            except OSError:
-                pass
-
-            parent = current.parent
-            if parent == current:
-                break
-            current = parent
+        if self._preview_root_paths:
+            logger.debug(
+                "Preview path rejected: %s (candidate=%s, num_roots=%d, first_root=%s)",
+                preview_path,
+                candidate_str,
+                len(self._preview_root_paths),
+                os.path.normcase(str(next(iter(self._preview_root_paths)))),
+            )
+        else:
+            logger.debug(
+                "Preview path rejected (no roots configured): %s",
+                preview_path,
+            )
 
         return False
 
     def apply_library_settings(self, library_config: Mapping[str, object]) -> None:
         """Update runtime paths to match the provided library configuration."""
-        folder_paths = (
-            library_config.get("folder_paths")
-            if isinstance(library_config, Mapping)
-            else {}
-        )
-        extra_folder_paths = (
-            library_config.get("extra_folder_paths")
-            if isinstance(library_config, Mapping)
-            else None
-        )
+        folder_paths = library_config.get('folder_paths') if isinstance(library_config, Mapping) else {}
         if not isinstance(folder_paths, Mapping):
             folder_paths = {}
-        if not isinstance(extra_folder_paths, Mapping):
-            extra_folder_paths = None
 
-        self._apply_library_paths(folder_paths, extra_folder_paths)
+        self._apply_library_paths(folder_paths)
 
         logger.info(
-            "Applied library settings with %d lora roots (%d extra), %d checkpoint roots (%d extra), and %d embedding roots (%d extra)",
+            "Applied library settings with %d lora roots, %d checkpoint roots, and %d embedding roots",
             len(self.loras_roots or []),
-            len(self.extra_loras_roots or []),
             len(self.base_models_roots or []),
-            len(self.extra_checkpoints_roots or []),
             len(self.embeddings_roots or []),
-            len(self.extra_embeddings_roots or []),
         )
 
     def get_library_registry_snapshot(self) -> Dict[str, object]:
@@ -1052,6 +898,5 @@ class Config:
             logger.debug("Failed to collect library registry snapshot: %s", exc)
             return {"active_library": "", "libraries": {}}
 
-
 # Global config instance
 config = Config()

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):
@@ -134,89 +124,50 @@ 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()
 
@@ -224,200 +175,166 @@ class LoraManager:
             await ServiceRegistry.get_download_manager()
 
             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."""
@@ -425,21 +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
@@ -447,9 +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
@@ -457,6 +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
             
@@ -162,7 +159,7 @@ 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)}")
             
             # Call original function with all args/kwargs
             results = await original_map_node_over_list(
@@ -179,7 +176,7 @@ 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
         

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/nodes/lora_cycler.py

@@ -126,7 +126,9 @@ class LoraCyclerLM:
                 "current_index": [clamped_index],
                 "next_index": [next_index],
                 "total_count": [total_count],
-                "current_lora_name": [current_lora["file_name"]],
+                "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["file_name"]],

py/nodes/lora_loader.py

@@ -1,8 +1,7 @@
 import logging
 import re
-import comfy.utils # type: ignore
-import comfy.sd # type: ignore
-from ..utils.utils import get_lora_info_absolute
+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__)
@@ -53,20 +52,18 @@ class LoraLoaderLM:
         # First process lora_stack if available
         if lora_stack:
             for lora_path, model_strength, clip_strength in lora_stack:
-                # Extract lora name and convert to absolute path
-                # lora_stack stores relative paths, but load_torch_file needs absolute paths
-                lora_name = extract_lora_name(lora_path)
-                absolute_lora_path, trigger_words = get_lora_info_absolute(lora_name)
-                
                 # 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 lower-level API to load LoRA directly without folder_paths validation
-                    lora = comfy.utils.load_torch_file(absolute_lora_path, safe_load=True)
-                    model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
+                    # 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)
@@ -87,7 +84,7 @@ class LoraLoaderLM:
             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)
             
             # Apply the LoRA using the appropriate loader
             if is_nunchaku_model:
@@ -95,9 +92,8 @@ class LoraLoaderLM:
                 model = nunchaku_load_lora(model, lora_path, model_strength)
                 # clip remains unchanged
             else:
-                # Use lower-level API to load LoRA directly without folder_paths validation
-                lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
-                model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
+                # 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:
@@ -197,20 +193,18 @@ class LoraTextLoaderLM:
         # First process lora_stack if available
         if lora_stack:
             for lora_path, model_strength, clip_strength in lora_stack:
-                # Extract lora name and convert to absolute path
-                # lora_stack stores relative paths, but load_torch_file needs absolute paths
-                lora_name = extract_lora_name(lora_path)
-                absolute_lora_path, trigger_words = get_lora_info_absolute(lora_name)
-                
                 # 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 lower-level API to load LoRA directly without folder_paths validation
-                    lora = comfy.utils.load_torch_file(absolute_lora_path, safe_load=True)
-                    model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
+                    # 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)
@@ -227,7 +221,7 @@ class LoraTextLoaderLM:
             clip_strength = lora['clip_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)
             
             # Apply the LoRA using the appropriate loader
             if is_nunchaku_model:
@@ -235,9 +229,8 @@ class LoraTextLoaderLM:
                 model = nunchaku_load_lora(model, lora_path, model_strength)
                 # clip remains unchanged
             else:
-                # Use lower-level API to load LoRA directly without folder_paths validation
-                lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
-                model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
+                # 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:

py/nodes/prompt.py

@@ -1,16 +1,4 @@
-from typing import Any
-import inspect
-
-
-class _AllContainer:
-    """Container that accepts any key for dynamic input validation."""
-
-    def __contains__(self, item):
-        return True
-
-    def __getitem__(self, key):
-        return ("STRING", {"forceInput": True})
-
+from typing import Any, Optional
 
 class PromptLM:
     """Encodes text (and optional trigger words) into CLIP conditioning."""
@@ -19,27 +7,11 @@ 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."
+        "to guide the diffusion model towards generating specific images."
     )
 
     @classmethod
     def INPUT_TYPES(cls):
-        dyn_inputs = {
-            "trigger_words1": (
-                "STRING",
-                {
-                    "forceInput": True,
-                    "tooltip": "Trigger words to prepend. Connect to add more inputs.",
-                },
-            ),
-        }
-
-        # Bypass validation for dynamic inputs during graph execution
-        stack = inspect.stack()
-        if len(stack) > 2 and stack[2].function == "get_input_info":
-            dyn_inputs = _AllContainer()
-
         return {
             "required": {
                 "text": (
@@ -51,34 +23,36 @@ class PromptLM:
                     },
                 ),
                 "clip": (
-                    "CLIP",
+                    'CLIP',
                     {"tooltip": "The CLIP model used for encoding the text."},
                 ),
             },
-            "optional": dyn_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"
 
-    def encode(self, text: str, clip: Any, **kwargs):
-        # Collect all trigger words from dynamic inputs
-        trigger_words = []
-        for key, value in kwargs.items():
-            if key.startswith("trigger_words") and value:
-                trigger_words.append(value)
-
-        # Build final prompt
+    def encode(self, text: str, clip: Any, trigger_words: Optional[str] = None):
+        prompt = text
         if trigger_words:
-            prompt = ", ".join(trigger_words + [text])
-        else:
-            prompt = 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

@@ -1,18 +1,13 @@
 import json
 import os
 import re
-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 PIL import Image, PngImagePlugin
 import piexif
-import logging
-
-logger = logging.getLogger(__name__)
-
 
 class SaveImageLM:
     NAME = "Save Image (LoraManager)"
@@ -25,60 +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.",
-                    },
-                ),
-                "add_counter_to_filename": (
-                    "BOOLEAN",
-                    {
-                        "default": True,
-                        "tooltip": "Adds an incremental counter to filenames to prevent overwriting previous images.",
-                    },
-                ),
+                "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",
@@ -95,59 +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)
@@ -155,114 +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)
 
@@ -272,36 +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)
 
@@ -315,7 +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}",
@@ -336,62 +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
 
-    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,
-    ):
+    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"
@@ -403,24 +359,17 @@ 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 metadata:
                         pnginfo.add_text("parameters", metadata)
                     if embed_workflow and extra_pnginfo is not None:
@@ -432,16 +381,11 @@ class SaveImageLM:
                     # For JPEG, use piexif
                     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:
@@ -449,52 +393,37 @@ class SaveImageLM:
                         exif_dict = {}
 
                         if metadata:
-                            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')}
+                        
                         # 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)
-
-                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,
-        add_counter_to_filename=True,
-    ):
+    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
@@ -504,19 +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,
-            add_counter_to_filename,
+            add_counter_to_filename
         )
-
+        
         return (images,)

py/nodes/trigger_word_toggle.py

@@ -60,22 +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 process_trigger_words(
         self,
         id,
@@ -97,7 +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,)

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
@@ -146,15 +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
+    
+    return ret_model

py/recipes/factory.py

@@ -6,24 +6,23 @@ from .parsers import (
     ComfyMetadataParser,
     MetaFormatParser,
     AutomaticMetadataParser,
-    CivitaiApiMetadataParser,
+    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
         """
@@ -35,18 +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):
@@ -54,7 +52,7 @@ class RecipeParserFactory:
         except Exception:
             # If JSON parsing fails, move on to other parsers
             pass
-
+        
         # Check other parsers that expect string input
         if RecipeFormatParser().is_metadata_matching(metadata_str):
             return RecipeFormatParser()

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",
@@ -41,7 +40,7 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                 "width",
                 "height",
                 "Model",
-                "Model hash",
+                "Model hash"
             )
             return any(key in payload for key in civitai_image_fields)
 
@@ -51,9 +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)
@@ -64,28 +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()
@@ -109,19 +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):
@@ -130,14 +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",
@@ -147,117 +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()
@@ -265,39 +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
@@ -310,35 +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:
@@ -346,87 +307,74 @@ 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)
 
             # If we found LoRA hashes in the metadata but haven't already
@@ -442,32 +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:
@@ -475,93 +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/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(

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/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/misc_handlers.py

@@ -9,7 +9,6 @@ objects that can be composed by the route controller.
 from __future__ import annotations
 
 import asyncio
-import json
 import logging
 import os
 import subprocess
@@ -193,7 +192,6 @@ class NodeRegistry:
                     "comfy_class": comfy_class,
                     "capabilities": capabilities,
                     "widget_names": widget_names,
-                    "mode": node.get("mode"),
                 }
             logger.debug("Registered %s nodes in registry", len(nodes))
             self._registry_updated.set()
@@ -219,148 +217,45 @@ class HealthCheckHandler:
         return web.json_response({"status": "ok"})
 
 
-class SupportersHandler:
-    """Handler for supporters data."""
-
-    def __init__(self, logger: logging.Logger | None = None) -> None:
-        self._logger = logger or logging.getLogger(__name__)
-
-    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}
-
-    async def get_supporters(self, request: web.Request) -> web.Response:
-        """Return supporters data as JSON."""
-        try:
-            supporters = self._load_supporters()
-            return web.json_response({"success": True, "supporters": supporters})
-        except Exception as exc:
-            self._logger.error("Error loading supporters: %s", exc, exc_info=True)
-            return web.json_response({"success": False, "error": str(exc)}, status=500)
-
-
-class ExampleWorkflowsHandler:
-    """Handler for example workflow templates."""
-
-    def __init__(self, logger: logging.Logger | None = None) -> None:
-        self._logger = logger or logging.getLogger(__name__)
-
-    def _get_workflows_dir(self) -> str:
-        """Get the example workflows directory path."""
-        current_file = os.path.abspath(__file__)
-        root_dir = os.path.dirname(
-            os.path.dirname(os.path.dirname(os.path.dirname(current_file)))
-        )
-        return os.path.join(root_dir, "example_workflows")
-
-    def _format_workflow_name(self, filename: str) -> str:
-        """Convert filename to human-readable name."""
-        name = os.path.splitext(filename)[0]
-        name = name.replace("_", " ")
-        return name
-
-    async def get_example_workflows(self, request: web.Request) -> web.Response:
-        """Return list of available example workflows."""
-        try:
-            workflows_dir = self._get_workflows_dir()
-            workflows = [
-                {
-                    "value": "Default",
-                    "label": "Default (Blank)",
-                    "path": None,
-                }
-            ]
-
-            if os.path.exists(workflows_dir):
-                for filename in sorted(os.listdir(workflows_dir)):
-                    if filename.endswith(".json"):
-                        workflows.append(
-                            {
-                                "value": filename,
-                                "label": self._format_workflow_name(filename),
-                                "path": f"example_workflows/{filename}",
-                            }
-                        )
-
-            return web.json_response({"success": True, "workflows": workflows})
-        except Exception as exc:
-            self._logger.error(
-                "Error listing example workflows: %s", exc, exc_info=True
-            )
-            return web.json_response({"success": False, "error": str(exc)}, status=500)
-
-    async def get_example_workflow(self, request: web.Request) -> web.Response:
-        """Return a specific example workflow JSON content."""
-        try:
-            filename = request.match_info.get("filename")
-            if not filename:
-                return web.json_response(
-                    {"success": False, "error": "Filename not provided"}, status=400
-                )
-
-            if filename == "Default":
-                return web.json_response(
-                    {
-                        "success": True,
-                        "workflow": {
-                            "last_node_id": 0,
-                            "last_link_id": 0,
-                            "nodes": [],
-                            "links": [],
-                            "groups": [],
-                            "config": {},
-                            "extra": {},
-                            "version": 0.4,
-                        },
-                    }
-                )
-
-            workflows_dir = self._get_workflows_dir()
-            filepath = os.path.join(workflows_dir, filename)
-
-            if not os.path.exists(filepath):
-                return web.json_response(
-                    {"success": False, "error": f"Workflow not found: {filename}"},
-                    status=404,
-                )
-
-            with open(filepath, "r", encoding="utf-8") as f:
-                workflow = json.load(f)
-
-            return web.json_response({"success": True, "workflow": workflow})
-        except Exception as exc:
-            self._logger.error("Error loading example workflow: %s", exc, exc_info=True)
-            return web.json_response({"success": False, "error": str(exc)}, status=500)
-
-
 class SettingsHandler:
     """Sync settings between backend and frontend."""
 
-    # Settings keys that should NOT be synced to frontend.
-    # All other settings are synced by default.
-    _NO_SYNC_KEYS = frozenset(
-        {
-            # Internal/performance settings (not used by frontend)
-            "hash_chunk_size_mb",
-            "download_stall_timeout_seconds",
-            # Complex internal structures retrieved via separate endpoints
-            "folder_paths",
-            "libraries",
-            "active_library",
-        }
+    _SYNC_KEYS = (
+        "civitai_api_key",
+        "default_lora_root",
+        "default_checkpoint_root",
+        "default_unet_root",
+        "default_embedding_root",
+        "base_model_path_mappings",
+        "download_path_templates",
+        "enable_metadata_archive_db",
+        "language",
+        "use_portable_settings",
+        "onboarding_completed",
+        "dismissed_banners",
+        "proxy_enabled",
+        "proxy_type",
+        "proxy_host",
+        "proxy_port",
+        "proxy_username",
+        "proxy_password",
+        "example_images_path",
+        "optimize_example_images",
+        "auto_download_example_images",
+        "blur_mature_content",
+        "autoplay_on_hover",
+        "display_density",
+        "card_info_display",
+        "show_folder_sidebar",
+        "include_trigger_words",
+        "show_only_sfw",
+        "compact_mode",
+        "priority_tags",
+        "model_card_footer_action",
+        "model_name_display",
+        "update_flag_strategy",
+        "auto_organize_exclusions",
+        "filter_presets",
     )
 
     _PROXY_KEYS = {
@@ -408,12 +303,10 @@ class SettingsHandler:
     async def get_settings(self, request: web.Request) -> web.Response:
         try:
             response_data = {}
-            # Sync all settings except those in _NO_SYNC_KEYS
-            for key in self._settings.keys():
-                if key not in self._NO_SYNC_KEYS:
-                    value = self._settings.get(key)
-                    if value is not None:
-                        response_data[key] = value
+            for key in self._SYNC_KEYS:
+                value = self._settings.get(key)
+                if value is not None:
+                    response_data[key] = value
             settings_file = getattr(self._settings, "settings_file", None)
             if settings_file:
                 response_data["settings_file"] = settings_file
@@ -1316,7 +1209,6 @@ class CustomWordsHandler:
 
     def __init__(self) -> None:
         from ...services.custom_words_service import get_custom_words_service
-
         self._service = get_custom_words_service()
 
     async def search_custom_words(self, request: web.Request) -> web.Response:
@@ -1325,7 +1217,6 @@ class CustomWordsHandler:
         Query parameters:
             search: The search term to match against.
             limit: Maximum number of results to return (default: 20).
-            offset: Number of results to skip (default: 0).
             category: Optional category filter. Can be:
                 - A category name (e.g., "character", "artist", "general")
                 - Comma-separated category IDs (e.g., "4,11" for character)
@@ -1335,7 +1226,6 @@ class CustomWordsHandler:
         try:
             search_term = request.query.get("search", "")
             limit = int(request.query.get("limit", "20"))
-            offset = max(0, int(request.query.get("offset", "0")))
             category_param = request.query.get("category", "")
             enriched_param = request.query.get("enriched", "").lower() == "true"
 
@@ -1345,14 +1235,13 @@ class CustomWordsHandler:
                 categories = self._parse_category_param(category_param)
 
             results = self._service.search_words(
-                search_term,
-                limit,
-                offset=offset,
-                categories=categories,
-                enriched=enriched_param,
+                search_term, limit, categories=categories, enriched=enriched_param
             )
 
-            return web.json_response({"success": True, "words": results})
+            return web.json_response({
+                "success": True,
+                "words": results
+            })
         except Exception as exc:
             logger.error("Error searching custom words: %s", exc, exc_info=True)
             return web.json_response({"error": str(exc)}, status=500)
@@ -1616,8 +1505,6 @@ class MiscHandlerSet:
         metadata_archive: MetadataArchiveHandler,
         filesystem: FileSystemHandler,
         custom_words: CustomWordsHandler,
-        supporters: SupportersHandler,
-        example_workflows: ExampleWorkflowsHandler,
     ) -> None:
         self.health = health
         self.settings = settings
@@ -1630,8 +1517,6 @@ class MiscHandlerSet:
         self.metadata_archive = metadata_archive
         self.filesystem = filesystem
         self.custom_words = custom_words
-        self.supporters = supporters
-        self.example_workflows = example_workflows
 
     def to_route_mapping(
         self,
@@ -1660,9 +1545,6 @@ class MiscHandlerSet:
             "open_file_location": self.filesystem.open_file_location,
             "open_settings_location": self.filesystem.open_settings_location,
             "search_custom_words": self.custom_words.search_custom_words,
-            "get_supporters": self.supporters.get_supporters,
-            "get_example_workflows": self.example_workflows.get_example_workflows,
-            "get_example_workflow": self.example_workflows.get_example_workflow,
         }
 
 

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
@@ -66,23 +65,6 @@ class ModelPageView:
         self._logger = logger
         self._app_version = self._get_app_version()
 
-    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}
-
     def _get_app_version(self) -> str:
         version = "1.0.0"
         short_hash = "stable"
@@ -287,11 +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:
@@ -320,7 +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,
@@ -400,34 +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)
 
@@ -547,153 +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()
@@ -836,7 +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})
@@ -950,22 +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(
@@ -973,23 +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)}
             )
@@ -1002,87 +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()
@@ -1268,11 +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}
             )
@@ -1335,7 +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:
@@ -1344,15 +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()
@@ -1736,13 +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(
@@ -1979,9 +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(
             {
@@ -2020,79 +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,
@@ -2259,15 +1877,6 @@ class ModelUpdateHandler:
         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,
@@ -2276,7 +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
@@ -2292,25 +1901,6 @@ class ModelUpdateHandler:
         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,
@@ -2320,8 +1910,6 @@ class ModelUpdateHandler:
             "previewUrl": preview_url,
             "isInLibrary": version.is_in_library,
             "shouldIgnore": version.should_ignore,
-            "earlyAccessEndsAt": version.early_access_ends_at,
-            "isEarlyAccess": is_early_access,
             "filePath": context.get("file_path"),
             "fileName": context.get("file_name"),
         }
@@ -2387,7 +1975,6 @@ class ModelHandlerSet:
             "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/handlers/recipe_handlers.py

@@ -412,11 +412,10 @@ class RecipeQueryHandler:
             if recipe_scanner is None:
                 raise RuntimeError("Recipe scanner unavailable")
 
-            fingerprint_groups = await recipe_scanner.find_all_duplicate_recipes()
-            url_groups = await recipe_scanner.find_duplicate_recipes_by_source()
+            duplicate_groups = await recipe_scanner.find_all_duplicate_recipes()
             response_data = []
 
-            for fingerprint, recipe_ids in fingerprint_groups.items():
+            for fingerprint, recipe_ids in duplicate_groups.items():
                 if len(recipe_ids) <= 1:
                     continue
 
@@ -440,44 +439,12 @@ class RecipeQueryHandler:
                     recipes.sort(key=lambda entry: entry.get("modified", 0), reverse=True)
                     response_data.append(
                         {
-                            "type": "fingerprint",
                             "fingerprint": fingerprint,
                             "count": len(recipes),
                             "recipes": recipes,
                         }
                     )
 
-            for url, recipe_ids in url_groups.items():
-                if len(recipe_ids) <= 1:
-                    continue
-
-                recipes = []
-                for recipe_id in recipe_ids:
-                    recipe = await recipe_scanner.get_recipe_by_id(recipe_id)
-                    if recipe:
-                        recipes.append(
-                            {
-                                "id": recipe.get("id"),
-                                "title": recipe.get("title"),
-                                "file_url": recipe.get("file_url")
-                                or self._format_recipe_file_url(recipe.get("file_path", "")),
-                                "modified": recipe.get("modified"),
-                                "created_date": recipe.get("created_date"),
-                                "lora_count": len(recipe.get("loras", [])),
-                            }
-                        )
-
-                if len(recipes) >= 2:
-                    recipes.sort(key=lambda entry: entry.get("modified", 0), reverse=True)
-                    response_data.append(
-                        {
-                            "type": "source_url",
-                            "fingerprint": url,
-                            "count": len(recipes),
-                            "recipes": recipes,
-                        }
-                    )
-
             response_data.sort(key=lambda entry: entry["count"], reverse=True)
             return web.json_response({"success": True, "duplicate_groups": response_data})
         except Exception as exc:
@@ -1054,7 +1021,7 @@ class RecipeManagementHandler:
             "exclude": False,
         }
 
-    async def _download_remote_media(self, image_url: str) -> tuple[bytes, str, Any]:
+    async def _download_remote_media(self, image_url: str) -> tuple[bytes, str]:
         civitai_client = self._civitai_client_getter()
         downloader = await self._downloader_factory()
         temp_path = None
@@ -1062,7 +1029,6 @@ class RecipeManagementHandler:
             with tempfile.NamedTemporaryFile(delete=False) as temp_file:
                 temp_path = temp_file.name
             download_url = image_url
-            image_info = None
             civitai_match = re.match(r"https://civitai\.com/images/(\d+)", image_url)
             if civitai_match:
                 if civitai_client is None:

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

@@ -26,7 +26,6 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     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("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"),
@@ -38,24 +37,12 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/get-registry", "get_registry"),
     RouteDefinition("GET", "/api/lm/check-model-exists", "check_model_exists"),
     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/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"
-    ),
 )
 
 
@@ -79,11 +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

@@ -19,7 +19,6 @@ from ..services.downloader import get_downloader
 from ..utils.usage_stats import UsageStats
 from .handlers.misc_handlers import (
     CustomWordsHandler,
-    ExampleWorkflowsHandler,
     FileSystemHandler,
     HealthCheckHandler,
     LoraCodeHandler,
@@ -30,7 +29,6 @@ from .handlers.misc_handlers import (
     NodeRegistry,
     NodeRegistryHandler,
     SettingsHandler,
-    SupportersHandler,
     TrainedWordsHandler,
     UsageStatsHandler,
     build_service_registry_adapter,
@@ -39,10 +37,9 @@ 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:
@@ -77,9 +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:
@@ -91,9 +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()
@@ -126,8 +119,6 @@ class MiscRoutes:
             metadata_provider_factory=self._metadata_provider_factory,
         )
         custom_words = CustomWordsHandler()
-        supporters = SupportersHandler()
-        example_workflows = ExampleWorkflowsHandler()
 
         return self._handler_set_factory(
             health=health,
@@ -141,8 +132,6 @@ class MiscRoutes:
             metadata_archive=metadata_archive,
             filesystem=filesystem,
             custom_words=custom_words,
-            supporters=supporters,
-            example_workflows=example_workflows,
         )
 
 

py/routes/model_route_registrar.py

@@ -1,5 +1,4 @@
 """Route registrar for model endpoints."""
-
 from __future__ import annotations
 
 from dataclasses import dataclass
@@ -28,9 +27,6 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     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"),
@@ -40,9 +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"),
@@ -50,60 +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"),
 )
@@ -130,18 +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/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/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
@@ -82,7 +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"""
@@ -111,7 +109,6 @@ class BaseModelService(ABC):
                 tags=tags,
                 favorites_only=favorites_only,
                 search_options=search_options,
-                tag_logic=tag_logic,
             )
 
             if search:
@@ -244,7 +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)
@@ -257,7 +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)
 
@@ -381,15 +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:
@@ -398,7 +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:
@@ -416,11 +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",
@@ -433,9 +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)
@@ -473,7 +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
@@ -597,19 +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(
@@ -822,61 +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()
@@ -887,7 +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:
@@ -916,12 +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/cache_entry_validator.py

@@ -1,263 +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),
-    }
-
-    @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
-        working_entry = dict(entry) if auto_repair else entry
-
-        for field_name, (default_value, is_required) in cls.CORE_FIELDS.items():
-            value = working_entry.get(field_name)
-
-            # Check if field is missing or None
-            if value is None:
-                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:
-                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', '')
-        hash_status = working_entry.get('hash_status', 'completed')
-        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:
-                working_entry['sha256'] = normalized_sha
-                repaired = True
-
-        # 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

@@ -1,12 +1,7 @@
-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
@@ -26,216 +21,6 @@ class CheckpointScanner(ModelScanner):
             hash_index=ModelHashIndex()
         )
 
-    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.
-        
-        Args:
-            file_path: Path to the model file
-            
-        Returns:
-            SHA256 hash string, or None if calculation failed
-        """
-        from ..utils.file_utils import calculate_sha256
-        
-        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
-            
-            # Load current metadata
-            metadata, _ = await MetadataManager.load_metadata(file_path, self.model_class)
-            if metadata is None:
-                logger.error(f"No metadata found for {file_path}")
-                return None
-            
-            # 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."""
         if not root_path:
@@ -266,16 +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

@@ -43,7 +43,6 @@ class CheckpointService(BaseModelService):
             "sub_type": sub_type,
             "favorite": checkpoint_data.get("favorite", 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_client.py

@@ -3,42 +3,36 @@ import copy
 import logging
 import os
 from typing import Any, Optional, Dict, Tuple, List, Sequence
-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.com/api/v1"
 
     async def _make_request(
@@ -81,10 +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:
@@ -98,48 +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 "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:
@@ -151,19 +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:
             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."""
@@ -190,17 +175,19 @@ class CivitaiClient:
         """Get all versions of a model with local availability info"""
         try:
             success, result = await self._make_request(
-                "GET", f"{self.base_url}/models/{model_id}", use_auth=True
+                'GET',
+                f"{self.base_url}/models/{model_id}",
+                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 message:
                 raise RuntimeError(message)
@@ -234,15 +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 {}
 
@@ -250,19 +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:
@@ -270,10 +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:
@@ -296,7 +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
@@ -308,9 +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
@@ -319,12 +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)
@@ -336,9 +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)
@@ -346,7 +323,9 @@ class CivitaiClient:
 
     async def _fetch_model_data(self, model_id: int) -> Optional[Dict]:
         success, data = await self._make_request(
-            "GET", f"{self.base_url}/models/{model_id}", use_auth=True
+            'GET',
+            f"{self.base_url}/models/{model_id}",
+            use_auth=True
         )
         if success:
             return data
@@ -358,7 +337,9 @@ class CivitaiClient:
             return None
 
         success, version = await self._make_request(
-            "GET", f"{self.base_url}/model-versions/{version_id}", use_auth=True
+            'GET',
+            f"{self.base_url}/model-versions/{version_id}",
+            use_auth=True
         )
         if success:
             return version
@@ -371,7 +352,9 @@ class CivitaiClient:
             return None
 
         success, version = await self._make_request(
-            "GET", f"{self.base_url}/model-versions/by-hash/{model_hash}", use_auth=True
+            'GET',
+            f"{self.base_url}/model-versions/by-hash/{model_hash}",
+            use_auth=True
         )
         if success:
             return version
@@ -379,17 +362,16 @@ class CivitaiClient:
         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(
@@ -401,50 +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
@@ -452,23 +430,25 @@ class CivitaiClient:
         """
         try:
             url = f"{self.base_url}/model-versions/{version_id}"
-
+            
             logger.debug(f"Resolving DNS for model version info: {url}")
-            success, result = await self._make_request("GET", url, use_auth=True)
-
+            success, result = await self._make_request(
+                'GET',
+                url,
+                use_auth=True
+            )
+            
             if success:
-                logger.debug(
-                    f"Successfully fetched model version info for: {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 "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)
@@ -484,23 +464,27 @@ class CivitaiClient:
 
         Args:
             image_id: The Civitai image ID
-
+            
         Returns:
             Optional[Dict]: The image data or None if not found
         """
         try:
             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)
-
+            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
-
+            
             logger.error(f"Failed to fetch image info for ID: {image_id}: {result}")
             return None
         except RateLimitError:
@@ -517,7 +501,11 @@ class CivitaiClient:
 
         try:
             url = f"{self.base_url}/models?username={username}"
-            success, result = await self._make_request("GET", url, use_auth=True)
+            success, result = await self._make_request(
+                'GET',
+                url,
+                use_auth=True
+            )
 
             if not success:
                 logger.error("Failed to fetch models for %s: %s", username, result)

py/services/custom_words_service.py

@@ -49,7 +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}")
@@ -60,16 +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).
@@ -79,9 +76,7 @@ class CustomWordsService:
         """
         tag_index = self._get_tag_index()
         if tag_index is not None:
-            results = tag_index.search(
-                search_term, 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")

py/services/download_coordinator.py

@@ -86,7 +86,6 @@ class DownloadCoordinator:
             progress_callback=progress_callback,
             download_id=download_id,
             source=payload.get("source"),
-            file_params=payload.get("file_params"),
         )
 
         result["download_id"] = download_id

py/services/download_manager.py

@@ -9,7 +9,7 @@ from collections import OrderedDict
 import uuid
 from typing import Dict, List, Optional, Set, Tuple
 from urllib.parse import urlparse
-from ..utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata
+from ..utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata, MiscMetadata
 from ..utils.constants import CARD_PREVIEW_WIDTH, DIFFUSION_MODEL_BASE_MODELS, VALID_LORA_TYPES
 from ..utils.civitai_utils import rewrite_preview_url
 from ..utils.preview_selection import select_preview_media
@@ -60,6 +60,10 @@ class DownloadManager:
         """Get the checkpoint scanner from registry"""
         return await ServiceRegistry.get_checkpoint_scanner()
 
+    async def _get_misc_scanner(self):
+        """Get the misc scanner from registry"""
+        return await ServiceRegistry.get_misc_scanner()
+
     async def download_from_civitai(
         self,
         model_id: int = None,
@@ -70,7 +74,6 @@ class DownloadManager:
         use_default_paths: bool = False,
         download_id: str = None,
         source: str = None,
-        file_params: Dict = None,
     ) -> Dict:
         """Download model from Civitai with task tracking and concurrency control
 
@@ -83,7 +86,6 @@ class DownloadManager:
             use_default_paths: Flag to use default paths
             download_id: Unique identifier for this download task
             source: Optional source parameter to specify metadata provider
-            file_params: Optional dict with file selection params (type, format, size, fp, isPrimary)
 
         Returns:
             Dict with download result
@@ -124,7 +126,6 @@ class DownloadManager:
                 progress_callback,
                 use_default_paths,
                 source,
-                file_params,
             )
         )
 
@@ -158,7 +159,6 @@ class DownloadManager:
         progress_callback=None,
         use_default_paths: bool = False,
         source: str = None,
-        file_params: Dict = None,
     ):
         """Execute download with semaphore to limit concurrency"""
         # Update status to waiting
@@ -219,7 +219,6 @@ class DownloadManager:
                         use_default_paths,
                         task_id,
                         source,
-                        file_params,
                     )
 
                     # Update status based on result
@@ -271,7 +270,6 @@ class DownloadManager:
         use_default_paths,
         download_id=None,
         source=None,
-        file_params=None,
     ):
         """Wrapper for original download_from_civitai implementation"""
         try:
@@ -281,6 +279,7 @@ class DownloadManager:
                 lora_scanner = await self._get_lora_scanner()
                 checkpoint_scanner = await self._get_checkpoint_scanner()
                 embedding_scanner = await ServiceRegistry.get_embedding_scanner()
+                misc_scanner = await self._get_misc_scanner()
 
                 # Check lora scanner first
                 if await lora_scanner.check_model_version_exists(model_version_id):
@@ -305,6 +304,13 @@ class DownloadManager:
                         "error": "Model version already exists in embedding library",
                     }
 
+                # Check misc scanner (VAE, Upscaler)
+                if await misc_scanner.check_model_version_exists(model_version_id):
+                    return {
+                        "success": False,
+                        "error": "Model version already exists in misc library",
+                    }
+
             # Use CivArchive provider directly when source is 'civarchive'
             # This prioritizes CivArchive metadata (with mirror availability info) over Civitai
             if source == "civarchive":
@@ -343,6 +349,10 @@ class DownloadManager:
                 model_type = "lora"
             elif model_type_from_info == "textualinversion":
                 model_type = "embedding"
+            elif model_type_from_info == "vae":
+                model_type = "misc"
+            elif model_type_from_info == "upscaler":
+                model_type = "misc"
             else:
                 return {
                     "success": False,
@@ -385,6 +395,14 @@ class DownloadManager:
                             "success": False,
                             "error": "Model version already exists in embedding library",
                         }
+                elif model_type == "misc":
+                    # Check misc scanner (VAE, Upscaler)
+                    misc_scanner = await self._get_misc_scanner()
+                    if await misc_scanner.check_model_version_exists(version_id):
+                        return {
+                            "success": False,
+                            "error": "Model version already exists in misc library",
+                        }
 
             # Handle use_default_paths
             if use_default_paths:
@@ -419,6 +437,26 @@ class DownloadManager:
                             "error": "Default embedding root path not set in settings",
                         }
                     save_dir = default_path
+                elif model_type == "misc":
+                    from ..config import config
+
+                    civitai_type = version_info.get("model", {}).get("type", "").lower()
+                    if civitai_type == "vae":
+                        default_paths = config.vae_roots
+                        error_msg = "VAE root path not configured"
+                    elif civitai_type == "upscaler":
+                        default_paths = config.upscaler_roots
+                        error_msg = "Upscaler root path not configured"
+                    else:
+                        default_paths = config.misc_roots
+                        error_msg = "Misc root path not configured"
+
+                    if not default_paths:
+                        return {
+                            "success": False,
+                            "error": error_msg,
+                        }
+                    save_dir = default_paths[0] if default_paths else ""
 
                 # Calculate relative path using template
                 relative_path = self._calculate_relative_path(version_info, model_type)
@@ -462,57 +500,16 @@ class DownloadManager:
                 await progress_callback(0)
 
             # 2. Get file information
-            files = version_info.get("files", [])
-            file_info = None
-            
-            # If file_params is provided, try to find matching file
-            if file_params and model_version_id:
-                target_type = file_params.get("type", "Model")
-                target_format = file_params.get("format", "SafeTensor")
-                target_size = file_params.get("size", "full")
-                target_fp = file_params.get("fp")
-                is_primary = file_params.get("isPrimary", False)
-                
-                if is_primary:
-                    # Find primary file
-                    file_info = next(
-                        (f for f in files if f.get("primary") and f.get("type") in ("Model", "Negative")),
-                        None
-                    )
-                else:
-                    # Match by metadata
-                    for f in files:
-                        f_type = f.get("type", "")
-                        f_meta = f.get("metadata", {})
-                        
-                        # Check type match
-                        if f_type != target_type:
-                            continue
-                        
-                        # Check metadata match
-                        if f_meta.get("format") != target_format:
-                            continue
-                        if f_meta.get("size") != target_size:
-                            continue
-                        if target_fp and f_meta.get("fp") != target_fp:
-                            continue
-                        
-                        file_info = f
-                        break
-            
-            # Fallback to primary file if no match found
+            file_info = next(
+                (
+                    f
+                    for f in version_info.get("files", [])
+                    if f.get("primary") and f.get("type") in ("Model", "Negative")
+                ),
+                None,
+            )
             if not file_info:
-                file_info = next(
-                    (
-                        f
-                        for f in files
-                        if f.get("primary") and f.get("type") in ("Model", "Negative")
-                    ),
-                    None,
-                )
-            
-            if not file_info:
-                return {"success": False, "error": "No suitable file found in metadata"}
+                return {"success": False, "error": "No primary file found in metadata"}
             mirrors = file_info.get("mirrors") or []
             download_urls = []
             if mirrors:
@@ -543,9 +540,7 @@ class DownloadManager:
                 return {"success": False, "error": "No mirror URL found"}
 
             # 3. Prepare download
-            file_name = file_info.get("name", "")
-            if not file_name:
-                return {"success": False, "error": "No filename found in file info"}
+            file_name = file_info["name"]
             save_path = os.path.join(save_dir, file_name)
 
             # 5. Prepare metadata based on model type
@@ -564,6 +559,11 @@ class DownloadManager:
                     version_info, file_info, save_path
                 )
                 logger.info(f"Creating EmbeddingMetadata for {file_name}")
+            elif model_type == "misc":
+                metadata = MiscMetadata.from_civitai_info(
+                    version_info, file_info, save_path
+                )
+                logger.info(f"Creating MiscMetadata for {file_name}")
 
             # 6. Start download process
             result = await self._execute_download(
@@ -669,6 +669,8 @@ class DownloadManager:
                 scanner = await self._get_checkpoint_scanner()
             elif model_type == "embedding":
                 scanner = await ServiceRegistry.get_embedding_scanner()
+            elif model_type == "misc":
+                scanner = await self._get_misc_scanner()
         except Exception as exc:
             logger.debug("Failed to acquire scanner for %s models: %s", model_type, exc)
 
@@ -1065,6 +1067,9 @@ class DownloadManager:
             elif model_type == "embedding":
                 scanner = await ServiceRegistry.get_embedding_scanner()
                 logger.info(f"Updating embedding cache for {actual_file_paths[0]}")
+            elif model_type == "misc":
+                scanner = await self._get_misc_scanner()
+                logger.info(f"Updating misc cache for {actual_file_paths[0]}")
 
             adjust_cached_entry = (
                 getattr(scanner, "adjust_cached_entry", None)
@@ -1174,6 +1179,14 @@ class DownloadManager:
                 ".pkl",
                 ".sft",
             }
+        if model_type == "misc":
+            return {
+                ".ckpt",
+                ".pt",
+                ".bin",
+                ".pth",
+                ".safetensors",
+            }
         return {".safetensors"}
 
     async def _extract_model_files_from_archive(

py/services/embedding_scanner.py

@@ -22,15 +22,5 @@ class EmbeddingScanner(ModelScanner):
         )
 
     def get_model_roots(self) -> List[str]:
-        """Get embedding root directories (including extra paths)"""
-        roots: List[str] = []
-        roots.extend(config.embeddings_roots or [])
-        roots.extend(config.extra_embeddings_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)
-        return unique_roots
+        """Get embedding root directories"""
+        return config.embeddings_roots

py/services/embedding_service.py

@@ -43,7 +43,6 @@ class EmbeddingService(BaseModelService):
             "sub_type": sub_type,
             "favorite": embedding_data.get("favorite", False),
             "update_available": bool(embedding_data.get("update_available", False)),
-            "skip_metadata_refresh": bool(embedding_data.get("skip_metadata_refresh", False)),
             "civitai": self.filter_civitai_data(embedding_data.get("civitai", {}), minimal=True)
         }
     

py/services/lora_scanner.py

@@ -25,51 +25,41 @@ class LoraScanner(ModelScanner):
         )
     
     def get_model_roots(self) -> List[str]:
-        """Get lora root directories (including extra paths)"""
-        roots: List[str] = []
-        roots.extend(config.loras_roots or [])
-        roots.extend(config.extra_loras_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)
-        return unique_roots
+        """Get lora root directories"""
+        return config.loras_roots
 
     async def diagnose_hash_index(self):
         """Diagnostic method to verify hash index functionality"""
-        logger.debug("\n\n*** DIAGNOSING LORA HASH INDEX ***\n\n")
+        print("\n\n*** DIAGNOSING LORA HASH INDEX ***\n\n", file=sys.stderr)
         
         # First check if the hash index has any entries
         if hasattr(self, '_hash_index'):
             index_entries = len(self._hash_index._hash_to_path)
-            logger.debug(f"Hash index has {index_entries} entries")
+            print(f"Hash index has {index_entries} entries", file=sys.stderr)
             
             # Print a few example entries if available
             if index_entries > 0:
-                logger.debug("\nSample hash index entries:")
+                print("\nSample hash index entries:", file=sys.stderr)
                 count = 0
                 for hash_val, path in self._hash_index._hash_to_path.items():
                     if count < 5:  # Just show the first 5
-                        logger.debug(f"Hash: {hash_val[:8]}... -> Path: {path}")
+                        print(f"Hash: {hash_val[:8]}... -> Path: {path}", file=sys.stderr)
                         count += 1
                     else:
                         break
         else:
-            logger.debug("Hash index not initialized")
+            print("Hash index not initialized", file=sys.stderr)
         
         # Try looking up by a known hash for testing
         if not hasattr(self, '_hash_index') or not self._hash_index._hash_to_path:
-            logger.debug("No hash entries to test lookup with")
+            print("No hash entries to test lookup with", file=sys.stderr)
             return
         
         test_hash = next(iter(self._hash_index._hash_to_path.keys()))
         test_path = self._hash_index.get_path(test_hash)
-        logger.debug(f"\nTest lookup by hash: {test_hash[:8]}... -> {test_path}")
+        print(f"\nTest lookup by hash: {test_hash[:8]}... -> {test_path}", file=sys.stderr)
         
         # Also test reverse lookup
         test_hash_result = self._hash_index.get_hash(test_path)
-        logger.debug(f"Test reverse lookup: {test_path} -> {test_hash_result[:8]}...\n\n")
+        print(f"Test reverse lookup: {test_path} -> {test_hash_result[:8]}...\n\n", file=sys.stderr)
 

py/services/lora_service.py

@@ -48,7 +48,6 @@ class LoraService(BaseModelService):
             "notes": lora_data.get("notes", ""),
             "favorite": lora_data.get("favorite", False),
             "update_available": bool(lora_data.get("update_available", False)),
-            "skip_metadata_refresh": bool(lora_data.get("skip_metadata_refresh", False)),
             "sub_type": sub_type,
             "civitai": self.filter_civitai_data(
                 lora_data.get("civitai", {}), minimal=True

py/services/metadata_service.py

@@ -44,8 +44,6 @@ async def initialize_metadata_providers():
                 logger.debug(f"SQLite metadata provider registered with database: {db_path}")
             else:
                 logger.warning("Metadata archive database is enabled but database file not found")
-                logger.info("Automatically disabling enable_metadata_archive_db setting")
-                settings_manager.set('enable_metadata_archive_db', False)
         except Exception as e:
             logger.error(f"Failed to initialize SQLite metadata provider: {e}")
     

py/services/metadata_sync_service.py

@@ -243,27 +243,17 @@ class MetadataSyncService:
                 last_error = error or last_error
 
             if civitai_metadata is None or metadata_provider is None:
-                # Track if we need to save metadata
-                needs_save = False
-
                 if sqlite_attempted:
                     model_data["db_checked"] = True
-                    needs_save = True
 
                 if civitai_api_not_found:
                     model_data["from_civitai"] = False
                     model_data["civitai_deleted"] = True
                     model_data["db_checked"] = sqlite_attempted or (enable_archive and model_data.get("db_checked", False))
                     model_data["last_checked_at"] = datetime.now().timestamp()
-                    needs_save = True
 
-                # Save metadata if any state was updated
-                if needs_save:
                     data_to_save = model_data.copy()
                     data_to_save.pop("folder", None)
-                    # Update last_checked_at for sqlite-only attempts if not already set
-                    if "last_checked_at" not in data_to_save:
-                        data_to_save["last_checked_at"] = datetime.now().timestamp()
                     await self._metadata_manager.save_metadata(file_path, data_to_save)
 
                 default_error = (

py/services/misc_scanner.py

@@ -0,0 +1,55 @@
+import logging
+from typing import Any, Dict, List, Optional
+
+from ..utils.models import MiscMetadata
+from ..config import config
+from .model_scanner import ModelScanner
+from .model_hash_index import ModelHashIndex
+
+logger = logging.getLogger(__name__)
+
+class MiscScanner(ModelScanner):
+    """Service for scanning and managing misc files (VAE, Upscaler)"""
+
+    def __init__(self):
+        # Define supported file extensions (combined from VAE and upscaler)
+        file_extensions = {'.safetensors', '.pt', '.bin', '.ckpt', '.pth'}
+        super().__init__(
+            model_type="misc",
+            model_class=MiscMetadata,
+            file_extensions=file_extensions,
+            hash_index=ModelHashIndex()
+        )
+
+    def _resolve_sub_type(self, root_path: Optional[str]) -> Optional[str]:
+        """Resolve the sub-type based on the root path."""
+        if not root_path:
+            return None
+
+        if config.vae_roots and root_path in config.vae_roots:
+            return "vae"
+
+        if config.upscaler_roots and root_path in config.upscaler_roots:
+            return "upscaler"
+
+        return None
+
+    def adjust_metadata(self, metadata, file_path, root_path):
+        """Adjust metadata during scanning to set sub_type."""
+        sub_type = self._resolve_sub_type(root_path)
+        if sub_type:
+            metadata.sub_type = sub_type
+        return metadata
+
+    def adjust_cached_entry(self, entry: Dict[str, Any]) -> Dict[str, Any]:
+        """Adjust entries loaded from the persisted cache to ensure sub_type is set."""
+        sub_type = self._resolve_sub_type(
+            self._find_root_for_file(entry.get("file_path"))
+        )
+        if sub_type:
+            entry["sub_type"] = sub_type
+        return entry
+
+    def get_model_roots(self) -> List[str]:
+        """Get misc root directories (VAE and upscaler)"""
+        return config.misc_roots

py/services/misc_service.py

@@ -0,0 +1,55 @@
+import os
+import logging
+from typing import Dict
+
+from .base_model_service import BaseModelService
+from ..utils.models import MiscMetadata
+from ..config import config
+
+logger = logging.getLogger(__name__)
+
+class MiscService(BaseModelService):
+    """Misc-specific service implementation (VAE, Upscaler)"""
+
+    def __init__(self, scanner, update_service=None):
+        """Initialize Misc service
+
+        Args:
+            scanner: Misc scanner instance
+            update_service: Optional service for remote update tracking.
+        """
+        super().__init__("misc", scanner, MiscMetadata, update_service=update_service)
+
+    async def format_response(self, misc_data: Dict) -> Dict:
+        """Format Misc data for API response"""
+        # Get sub_type from cache entry (new canonical field)
+        sub_type = misc_data.get("sub_type", "vae")
+
+        return {
+            "model_name": misc_data["model_name"],
+            "file_name": misc_data["file_name"],
+            "preview_url": config.get_preview_static_url(misc_data.get("preview_url", "")),
+            "preview_nsfw_level": misc_data.get("preview_nsfw_level", 0),
+            "base_model": misc_data.get("base_model", ""),
+            "folder": misc_data["folder"],
+            "sha256": misc_data.get("sha256", ""),
+            "file_path": misc_data["file_path"].replace(os.sep, "/"),
+            "file_size": misc_data.get("size", 0),
+            "modified": misc_data.get("modified", ""),
+            "tags": misc_data.get("tags", []),
+            "from_civitai": misc_data.get("from_civitai", True),
+            "usage_count": misc_data.get("usage_count", 0),
+            "notes": misc_data.get("notes", ""),
+            "sub_type": sub_type,
+            "favorite": misc_data.get("favorite", False),
+            "update_available": bool(misc_data.get("update_available", False)),
+            "civitai": self.filter_civitai_data(misc_data.get("civitai", {}), minimal=True)
+        }
+
+    def find_duplicate_hashes(self) -> Dict:
+        """Find Misc models with duplicate SHA256 hashes"""
+        return self.scanner._hash_index.get_duplicate_hashes()
+
+    def find_duplicate_filenames(self) -> Dict:
+        """Find Misc models with conflicting filenames"""
+        return self.scanner._hash_index.get_duplicate_filenames()

py/services/model_cache.py

@@ -5,6 +5,7 @@ import logging
 logger = logging.getLogger(__name__)
 from typing import Any, Dict, List, Optional, Tuple
 from dataclasses import dataclass, field
+from operator import itemgetter
 from natsort import natsorted
 
 # Supported sort modes: (sort_key, order)
@@ -228,17 +229,17 @@ class ModelCache:
                 reverse=reverse
             )
         elif sort_key == 'date':
-            # Sort by modified timestamp (use .get() with default to handle missing fields)
+            # Sort by modified timestamp
             result = sorted(
                 data,
-                key=lambda x: x.get('modified', 0.0),
+                key=itemgetter('modified'),
                 reverse=reverse
             )
         elif sort_key == 'size':
-            # Sort by file size (use .get() with default to handle missing fields)
+            # Sort by file size
             result = sorted(
                 data,
-                key=lambda x: x.get('size', 0),
+                key=itemgetter('size'),
                 reverse=reverse
             )
         elif sort_key == 'usage':

py/services/model_metadata_provider.py

@@ -676,12 +676,10 @@ class ModelMetadataProviderManager:
         
     def _get_provider(self, provider_name: str = None) -> ModelMetadataProvider:
         """Get provider by name or default provider"""
-        if provider_name:
-            if provider_name not in self.providers:
-                raise ValueError(f"Provider '{provider_name}' is not registered")
+        if provider_name and provider_name in self.providers:
             return self.providers[provider_name]
-
+        
         if self.default_provider is None:
             raise ValueError("No default provider set and no valid provider specified")
-
+            
         return self.providers[self.default_provider]

py/services/model_query.py

@@ -99,7 +99,6 @@ class FilterCriteria:
     favorites_only: bool = False
     search_options: Optional[Dict[str, Any]] = None
     model_types: Optional[Sequence[str]] = None
-    tag_logic: str = "any"  # "any" (OR) or "all" (AND)
 
 
 class ModelCacheRepository:
@@ -301,29 +300,11 @@ class ModelFilterSet:
                 include_tags = {tag for tag in tag_filters if tag}
 
             if include_tags:
-                tag_logic = criteria.tag_logic.lower() if criteria.tag_logic else "any"
 
                 def matches_include(item_tags):
                     if not item_tags and "__no_tags__" in include_tags:
                         return True
-                    if tag_logic == "all":
-                        # AND logic: item must have ALL include tags
-                        # Special case: __no_tags__ is handled separately
-                        non_special_tags = include_tags - {"__no_tags__"}
-                        if "__no_tags__" in include_tags:
-                            # If __no_tags__ is selected along with other tags,
-                            # treat it as "no tags OR (all other tags)"
-                            if not item_tags:
-                                return True
-                            # Otherwise, check if all non-special tags match
-                            if non_special_tags:
-                                return all(tag in (item_tags or []) for tag in non_special_tags)
-                            return True
-                        # Normal case: all tags must match
-                        return all(tag in (item_tags or []) for tag in non_special_tags)
-                    else:
-                        # OR logic (default): item must have ANY include tag
-                        return any(tag in include_tags for tag in (item_tags or []))
+                    return any(tag in include_tags for tag in (item_tags or []))
 
                 items = [item for item in items if matches_include(item.get("tags"))]
 

py/services/model_scanner.py

@@ -20,8 +20,6 @@ from .service_registry import ServiceRegistry
 from .websocket_manager import ws_manager
 from .persistent_model_cache import get_persistent_cache
 from .settings_manager import get_settings_manager
-from .cache_entry_validator import CacheEntryValidator
-from .cache_health_monitor import CacheHealthMonitor, CacheHealthStatus
 
 logger = logging.getLogger(__name__)
 
@@ -248,7 +246,6 @@ class ModelScanner:
             'tags': tags_list,
             'civitai': civitai_slim,
             'civitai_deleted': bool(get_value('civitai_deleted', False)),
-            'skip_metadata_refresh': bool(get_value('skip_metadata_refresh', False)),
         }
 
         license_source: Dict[str, Any] = {}
@@ -282,11 +279,6 @@ class ModelScanner:
         sub_type = get_value('sub_type', None)
         if sub_type:
             entry['sub_type'] = sub_type
-        
-        # Handle hash_status for lazy hash calculation (checkpoints)
-        hash_status = get_value('hash_status', 'completed')
-        if hash_status:
-            entry['hash_status'] = hash_status
 
         return entry
 
@@ -476,39 +468,6 @@ class ModelScanner:
             for tag in adjusted_item.get('tags') or []:
                 tags_count[tag] = tags_count.get(tag, 0) + 1
 
-        # Validate cache entries and check health
-        valid_entries, invalid_entries = CacheEntryValidator.validate_batch(
-            adjusted_raw_data, auto_repair=True
-        )
-
-        if invalid_entries:
-            monitor = CacheHealthMonitor()
-            report = monitor.check_health(adjusted_raw_data, auto_repair=True)
-
-            if report.status != CacheHealthStatus.HEALTHY:
-                # Broadcast health warning to frontend
-                await ws_manager.broadcast_cache_health_warning(report, page_type)
-                logger.warning(
-                    f"{self.model_type.capitalize()} Scanner: Cache health issue detected - "
-                    f"{report.invalid_entries} invalid entries, {report.repaired_entries} repaired"
-                )
-
-            # Use only valid entries
-            adjusted_raw_data = valid_entries
-
-            # Rebuild tags count from valid entries only
-            tags_count = {}
-            for item in adjusted_raw_data:
-                for tag in item.get('tags') or []:
-                    tags_count[tag] = tags_count.get(tag, 0) + 1
-
-            # Remove invalid entries from hash index
-            for invalid_entry in invalid_entries:
-                file_path = CacheEntryValidator.get_file_path_safe(invalid_entry)
-                sha256 = CacheEntryValidator.get_sha256_safe(invalid_entry)
-                if file_path:
-                    hash_index.remove_by_path(file_path, sha256)
-
         scan_result = CacheBuildResult(
             raw_data=adjusted_raw_data,
             hash_index=hash_index,
@@ -692,6 +651,7 @@ class ModelScanner:
 
     async def _initialize_cache(self) -> None:
         """Initialize or refresh the cache"""
+        print("init start", flush=True)
         self._is_initializing = True  # Set flag
         try:
             start_time = time.time()
@@ -705,6 +665,7 @@ class ModelScanner:
             scan_result = await self._gather_model_data()
             await self._apply_scan_result(scan_result)
             await self._save_persistent_cache(scan_result)
+            print("init end", flush=True)
 
             logger.info(
                 f"{self.model_type.capitalize()} Scanner: Cache initialization completed in {time.time() - start_time:.2f} seconds, "
@@ -815,18 +776,6 @@ class ModelScanner:
                                     model_data = self.adjust_cached_entry(dict(model_data))
                                     if not model_data:
                                         continue
-
-                                    # Validate the new entry before adding
-                                    validation_result = CacheEntryValidator.validate(
-                                        model_data, auto_repair=True
-                                    )
-                                    if not validation_result.is_valid:
-                                        logger.warning(
-                                            f"Skipping invalid entry during reconcile: {path}"
-                                        )
-                                        continue
-                                    model_data = validation_result.entry
-
                                     self._ensure_license_flags(model_data)
                                     # Add to cache
                                     self._cache.raw_data.append(model_data)
@@ -1141,17 +1090,6 @@ class ModelScanner:
                             processed_files += 1
 
                             if result:
-                                # Validate the entry before adding
-                                validation_result = CacheEntryValidator.validate(
-                                    result, auto_repair=True
-                                )
-                                if not validation_result.is_valid:
-                                    logger.warning(
-                                        f"Skipping invalid scan result: {file_path}"
-                                    )
-                                    continue
-                                result = validation_result.entry
-
                                 self._ensure_license_flags(result)
                                 raw_data.append(result)
 
@@ -1453,7 +1391,7 @@ class ModelScanner:
         return None
         
     async def get_top_tags(self, limit: int = 20) -> List[Dict[str, any]]:
-        """Get top tags sorted by count. If limit is 0, return all tags."""
+        """Get top tags sorted by count"""
         await self.get_cached_data()
         
         sorted_tags = sorted(
@@ -1462,8 +1400,6 @@ class ModelScanner:
             reverse=True
         )
         
-        if limit == 0:
-            return sorted_tags
         return sorted_tags[:limit]
         
     async def get_base_models(self, limit: int = 20) -> List[Dict[str, any]]:

py/services/model_service_factory.py

@@ -118,19 +118,24 @@ class ModelServiceFactory:
 
 
 def register_default_model_types():
-    """Register the default model types (LoRA, Checkpoint, and Embedding)"""
+    """Register the default model types (LoRA, Checkpoint, Embedding, and Misc)"""
     from ..services.lora_service import LoraService
     from ..services.checkpoint_service import CheckpointService
     from ..services.embedding_service import EmbeddingService
+    from ..services.misc_service import MiscService
     from ..routes.lora_routes import LoraRoutes
     from ..routes.checkpoint_routes import CheckpointRoutes
     from ..routes.embedding_routes import EmbeddingRoutes
-    
+    from ..routes.misc_model_routes import MiscModelRoutes
+
     # Register LoRA model type
     ModelServiceFactory.register_model_type('lora', LoraService, LoraRoutes)
-    
+
     # Register Checkpoint model type
     ModelServiceFactory.register_model_type('checkpoint', CheckpointService, CheckpointRoutes)
-    
+
     # Register Embedding model type
-    ModelServiceFactory.register_model_type('embedding', EmbeddingService, EmbeddingRoutes)
+    ModelServiceFactory.register_model_type('embedding', EmbeddingService, EmbeddingRoutes)
+
+    # Register Misc model type (VAE, Upscaler)
+    ModelServiceFactory.register_model_type('misc', MiscService, MiscModelRoutes)

py/services/model_update_service.py

@@ -7,8 +7,7 @@ import os
 import sqlite3
 import time
 from dataclasses import dataclass, replace
-from datetime import datetime, timezone
-from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence
+from typing import Dict, Iterable, List, Mapping, Optional, Sequence
 
 from .errors import RateLimitError, ResourceNotFoundError
 from .settings_manager import get_settings_manager
@@ -65,9 +64,7 @@ class ModelVersionRecord:
     preview_url: Optional[str]
     is_in_library: bool
     should_ignore: bool
-    early_access_ends_at: Optional[str] = None
     sort_index: int = 0
-    is_early_access: bool = False
 
 
 @dataclass
@@ -100,12 +97,8 @@ class ModelUpdateRecord:
 
         return [version.version_id for version in self.versions if version.is_in_library]
 
-    def has_update(self, hide_early_access: bool = False) -> bool:
-        """Return True when a non-ignored remote version newer than the newest local copy is available.
-
-        Args:
-            hide_early_access: If True, exclude early access versions from update check.
-        """
+    def has_update(self) -> bool:
+        """Return True when a non-ignored remote version newer than the newest local copy is available."""
 
         if self.should_ignore_model:
             return False
@@ -117,56 +110,22 @@ class ModelUpdateRecord:
 
         if max_in_library is None:
             return any(
-                not version.is_in_library
-                and not version.should_ignore
-                and not (hide_early_access and ModelUpdateRecord._is_early_access_active(version))
-                for version in self.versions
+                not version.is_in_library and not version.should_ignore for version in self.versions
             )
 
         for version in self.versions:
             if version.is_in_library or version.should_ignore:
                 continue
-            if hide_early_access and ModelUpdateRecord._is_early_access_active(version):
-                continue
             if version.version_id > max_in_library:
                 return True
         return False
 
-    @staticmethod
-    def _is_early_access_active(version: ModelVersionRecord) -> bool:
-        """Check if a version is currently in early access period.
-
-        Uses two-phase detection:
-        1. If exact EA end time available (from single version API), use it for precise check
-        2. Otherwise fallback to basic EA flag (from bulk API)
-        """
-        # Phase 2: Precise check with exact end time
-        if version.early_access_ends_at:
-            try:
-                ea_date = datetime.fromisoformat(
-                    version.early_access_ends_at.replace("Z", "+00:00")
-                )
-                return ea_date > datetime.now(timezone.utc)
-            except (ValueError, AttributeError):
-                # If date parsing fails, treat as active EA (conservative)
-                return True
-
-        # Phase 1: Basic EA flag from bulk API
-        return version.is_early_access
-
     def has_update_for_base(
         self,
         local_version_id: Optional[int],
         local_base_model: Optional[str],
-        hide_early_access: bool = False,
     ) -> bool:
-        """Return True when a newer remote version with the same base model exists.
-
-        Args:
-            local_version_id: The current local version id.
-            local_base_model: The base model to filter by.
-            hide_early_access: If True, exclude early access versions from update check.
-        """
+        """Return True when a newer remote version with the same base model exists."""
 
         if self.should_ignore_model:
             return False
@@ -194,8 +153,6 @@ class ModelUpdateRecord:
         for version in self.versions:
             if version.is_in_library or version.should_ignore:
                 continue
-            if hide_early_access and ModelUpdateRecord._is_early_access_active(version):
-                continue
             version_base = _normalize_base_model(version.base_model)
             if version_base != normalized_base:
                 continue
@@ -311,14 +268,6 @@ class ModelUpdateService:
                 "ALTER TABLE model_update_versions "
                 "ADD COLUMN should_ignore INTEGER NOT NULL DEFAULT 0"
             ),
-            "early_access_ends_at": (
-                "ALTER TABLE model_update_versions "
-                "ADD COLUMN early_access_ends_at TEXT"
-            ),
-            "is_early_access": (
-                "ALTER TABLE model_update_versions "
-                "ADD COLUMN is_early_access INTEGER NOT NULL DEFAULT 0"
-            ),
         }
 
         for column, statement in migrations.items():
@@ -418,8 +367,6 @@ class ModelUpdateService:
                 preview_url TEXT,
                 is_in_library INTEGER NOT NULL DEFAULT 0,
                 should_ignore INTEGER NOT NULL DEFAULT 0,
-                early_access_ends_at TEXT,
-                is_early_access INTEGER NOT NULL DEFAULT 0,
                 PRIMARY KEY (model_id, version_id),
                 FOREIGN KEY(model_id) REFERENCES model_update_status(model_id) ON DELETE CASCADE
             )
@@ -437,8 +384,6 @@ class ModelUpdateService:
             "preview_url",
             "is_in_library",
             "should_ignore",
-            "early_access_ends_at",
-            "is_early_access",
         ]
         defaults = {
             "sort_index": "0",
@@ -449,8 +394,6 @@ class ModelUpdateService:
             "preview_url": "NULL",
             "is_in_library": "0",
             "should_ignore": "0",
-            "early_access_ends_at": "NULL",
-            "is_early_access": "0",
         }
 
         select_parts = []
@@ -724,8 +667,6 @@ class ModelUpdateService:
                         is_in_library=False,
                         should_ignore=should_ignore,
                         sort_index=len(versions),
-                        early_access_ends_at=None,
-                        is_early_access=False,
                     )
                 )
 
@@ -745,17 +686,16 @@ class ModelUpdateService:
         async with self._lock:
             return self._get_record(model_type, model_id)
 
-    async def has_update(self, model_type: str, model_id: int, hide_early_access: bool = False) -> bool:
+    async def has_update(self, model_type: str, model_id: int) -> bool:
         """Determine if a model has updates pending."""
 
         record = await self.get_record(model_type, model_id)
-        return record.has_update(hide_early_access=hide_early_access) if record else False
+        return record.has_update() if record else False
 
     async def has_updates_bulk(
         self,
         model_type: str,
         model_ids: Sequence[int],
-        hide_early_access: bool = False,
     ) -> Dict[int, bool]:
         """Return update availability for each model id in a single database pass."""
 
@@ -767,7 +707,7 @@ class ModelUpdateService:
             records = self._get_records_bulk(model_type, normalized_ids)
 
         return {
-            model_id: records.get(model_id).has_update(hide_early_access=hide_early_access) if records.get(model_id) else False
+            model_id: records.get(model_id).has_update() if records.get(model_id) else False
             for model_id in normalized_ids
         }
 
@@ -1047,8 +987,6 @@ class ModelUpdateService:
                         is_in_library=True,
                         should_ignore=ignore_map.get(missing_id, False),
                         sort_index=len(versions),
-                        early_access_ends_at=None,
-                        is_early_access=False,
                     )
                 )
 
@@ -1091,8 +1029,6 @@ class ModelUpdateService:
                     is_in_library=version_id in local_set,
                     should_ignore=ignore_map.get(version_id, remote_version.should_ignore),
                     sort_index=sort_map.get(version_id, index),
-                    early_access_ends_at=remote_version.early_access_ends_at,
-                    is_early_access=remote_version.is_early_access,
                 )
             )
 
@@ -1119,8 +1055,6 @@ class ModelUpdateService:
                             is_in_library=True,
                             should_ignore=ignore_map.get(version_id, False),
                             sort_index=len(versions),
-                            early_access_ends_at=None,
-                            is_early_access=False,
                         )
                     )
 
@@ -1186,11 +1120,6 @@ class ModelUpdateService:
         released_at = _normalize_string(entry.get("publishedAt") or entry.get("createdAt"))
         size_bytes = self._extract_size_bytes(entry.get("files"))
         preview_url = self._extract_preview_url(entry.get("images"))
-        early_access_ends_at = _normalize_string(entry.get("earlyAccessEndsAt"))
-
-        # Check availability field from bulk API for basic EA detection
-        availability = _normalize_string(entry.get("availability"))
-        is_early_access = availability == "EarlyAccess"
 
         return ModelVersionRecord(
             version_id=version_id,
@@ -1201,9 +1130,7 @@ class ModelUpdateService:
             preview_url=preview_url,
             is_in_library=False,
             should_ignore=False,
-            early_access_ends_at=early_access_ends_at,
             sort_index=index,
-            is_early_access=is_early_access,
         )
 
     def _extract_size_bytes(self, files) -> Optional[int]:
@@ -1304,8 +1231,7 @@ class ModelUpdateService:
             version_rows = conn.execute(
                 f"""
                 SELECT model_id, version_id, sort_index, name, base_model, released_at,
-                       size_bytes, preview_url, is_in_library, should_ignore, early_access_ends_at,
-                       is_early_access
+                       size_bytes, preview_url, is_in_library, should_ignore
                 FROM model_update_versions
                 WHERE model_id IN ({placeholders})
                 ORDER BY model_id ASC, sort_index ASC, version_id ASC
@@ -1326,9 +1252,7 @@ class ModelUpdateService:
                     preview_url=row["preview_url"],
                     is_in_library=bool(row["is_in_library"]),
                     should_ignore=bool(row["should_ignore"]),
-                    early_access_ends_at=row["early_access_ends_at"],
                     sort_index=_normalize_int(row["sort_index"]) or 0,
-                    is_early_access=bool(row["is_early_access"]),
                 )
             )
 
@@ -1384,9 +1308,8 @@ class ModelUpdateService:
                     """
                     INSERT INTO model_update_versions (
                         version_id, model_id, sort_index, name, base_model, released_at,
-                        size_bytes, preview_url, is_in_library, should_ignore, early_access_ends_at,
-                        is_early_access
-                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                        size_bytes, preview_url, is_in_library, should_ignore
+                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
                     """,
                     (
                         version.version_id,
@@ -1399,8 +1322,6 @@ class ModelUpdateService:
                         version.preview_url,
                         1 if version.is_in_library else 0,
                         1 if version.should_ignore else 0,
-                        version.early_access_ends_at,
-                        1 if version.is_early_access else 0,
                     ),
                 )
             conn.commit()

py/services/persistent_model_cache.py

@@ -52,7 +52,6 @@ class PersistentModelCache:
         "trained_words",
         "license_flags",
         "civitai_deleted",
-        "skip_metadata_refresh",
         "exclude",
         "db_checked",
         "last_checked_at",
@@ -184,7 +183,6 @@ class PersistentModelCache:
                 "tags": tags.get(file_path, []),
                 "civitai": civitai,
                 "civitai_deleted": bool(row["civitai_deleted"]),
-                "skip_metadata_refresh": bool(row["skip_metadata_refresh"]),
                 "license_flags": int(license_value),
             }
             raw_data.append(item)
@@ -493,7 +491,6 @@ class PersistentModelCache:
             "civitai_creator_username": "TEXT",
             "civitai_model_type": "TEXT",
             "civitai_deleted": "INTEGER DEFAULT 0",
-            "skip_metadata_refresh": "INTEGER DEFAULT 0",
             # Persisting without explicit flags should assume CivitAI's documented defaults (0b111001 == 57).
             "license_flags": f"INTEGER DEFAULT {DEFAULT_LICENSE_FLAGS}",
         }
@@ -566,7 +563,6 @@ class PersistentModelCache:
             trained_words_json,
             int(license_flags),
             1 if item.get("civitai_deleted") else 0,
-            1 if item.get("skip_metadata_refresh") else 0,
             1 if item.get("exclude") else 0,
             1 if item.get("db_checked") else 0,
             float(item.get("last_checked_at") or 0.0),

py/services/recipe_cache.py

@@ -4,7 +4,6 @@ from dataclasses import dataclass
 from operator import itemgetter
 from natsort import natsorted
 
-
 @dataclass
 class RecipeCache:
     """Cache structure for Recipe data"""
@@ -22,18 +21,11 @@ class RecipeCache:
         self.folder_tree = self.folder_tree or {}
 
     async def resort(self, name_only: bool = False):
-        """Resort all cached data views in a thread pool to avoid blocking the event loop."""
+        """Resort all cached data views"""
         async with self._lock:
-            loop = asyncio.get_event_loop()
-            await loop.run_in_executor(
-                None,
-                self._resort_locked,
-                name_only,
-            )
+            self._resort_locked(name_only=name_only)
 
-    async def update_recipe_metadata(
-        self, recipe_id: str, metadata: Dict, *, resort: bool = True
-    ) -> bool:
+    async def update_recipe_metadata(self, recipe_id: str, metadata: Dict, *, resort: bool = True) -> bool:
         """Update metadata for a specific recipe in all cached data
 
         Args:
@@ -45,7 +37,7 @@ class RecipeCache:
         """
         async with self._lock:
             for item in self.raw_data:
-                if str(item.get("id")) == str(recipe_id):
+                if str(item.get('id')) == str(recipe_id):
                     item.update(metadata)
                     if resort:
                         self._resort_locked()
@@ -60,9 +52,7 @@ class RecipeCache:
             if resort:
                 self._resort_locked()
 
-    async def remove_recipe(
-        self, recipe_id: str, *, resort: bool = False
-    ) -> Optional[Dict]:
+    async def remove_recipe(self, recipe_id: str, *, resort: bool = False) -> Optional[Dict]:
         """Remove a recipe from the cache by ID.
 
         Args:
@@ -74,16 +64,14 @@ class RecipeCache:
 
         async with self._lock:
             for index, recipe in enumerate(self.raw_data):
-                if str(recipe.get("id")) == str(recipe_id):
+                if str(recipe.get('id')) == str(recipe_id):
                     removed = self.raw_data.pop(index)
                     if resort:
                         self._resort_locked()
                     return removed
         return None
 
-    async def bulk_remove(
-        self, recipe_ids: Iterable[str], *, resort: bool = False
-    ) -> List[Dict]:
+    async def bulk_remove(self, recipe_ids: Iterable[str], *, resort: bool = False) -> List[Dict]:
         """Remove multiple recipes from the cache."""
 
         id_set = {str(recipe_id) for recipe_id in recipe_ids}
@@ -91,25 +79,21 @@ class RecipeCache:
             return []
 
         async with self._lock:
-            removed = [item for item in self.raw_data if str(item.get("id")) in id_set]
+            removed = [item for item in self.raw_data if str(item.get('id')) in id_set]
             if not removed:
                 return []
 
-            self.raw_data = [
-                item for item in self.raw_data if str(item.get("id")) not in id_set
-            ]
+            self.raw_data = [item for item in self.raw_data if str(item.get('id')) not in id_set]
             if resort:
                 self._resort_locked()
             return removed
 
-    async def replace_recipe(
-        self, recipe_id: str, new_data: Dict, *, resort: bool = False
-    ) -> bool:
+    async def replace_recipe(self, recipe_id: str, new_data: Dict, *, resort: bool = False) -> bool:
         """Replace cached data for a recipe."""
 
         async with self._lock:
             for index, recipe in enumerate(self.raw_data):
-                if str(recipe.get("id")) == str(recipe_id):
+                if str(recipe.get('id')) == str(recipe_id):
                     self.raw_data[index] = new_data
                     if resort:
                         self._resort_locked()
@@ -121,7 +105,7 @@ class RecipeCache:
 
         async with self._lock:
             for recipe in self.raw_data:
-                if str(recipe.get("id")) == str(recipe_id):
+                if str(recipe.get('id')) == str(recipe_id):
                     return dict(recipe)
         return None
 
@@ -131,13 +115,16 @@ class RecipeCache:
         async with self._lock:
             return [dict(item) for item in self.raw_data]
 
-    def _resort_locked(self, name_only: bool = False) -> None:
+    def _resort_locked(self, *, name_only: bool = False) -> None:
         """Sort cached views. Caller must hold ``_lock``."""
 
         self.sorted_by_name = natsorted(
-            self.raw_data, key=lambda x: x.get("title", "").lower()
+            self.raw_data,
+            key=lambda x: x.get('title', '').lower()
         )
         if not name_only:
             self.sorted_by_date = sorted(
-                self.raw_data, key=itemgetter("created_date", "file_path"), reverse=True
-            )
+                self.raw_data,
+                key=itemgetter('created_date', 'file_path'),
+                reverse=True
+            )

py/services/service_registry.py

@@ -233,23 +233,44 @@ class ServiceRegistry:
     async def get_embedding_scanner(cls):
         """Get or create Embedding scanner instance"""
         service_name = "embedding_scanner"
-        
+
         if service_name in cls._services:
             return cls._services[service_name]
-        
+
         async with cls._get_lock(service_name):
             # Double-check after acquiring lock
             if service_name in cls._services:
                 return cls._services[service_name]
-            
+
             # Import here to avoid circular imports
             from .embedding_scanner import EmbeddingScanner
-            
+
             scanner = await EmbeddingScanner.get_instance()
             cls._services[service_name] = scanner
             logger.debug(f"Created and registered {service_name}")
             return scanner
-    
+
+    @classmethod
+    async def get_misc_scanner(cls):
+        """Get or create Misc scanner instance (VAE, Upscaler)"""
+        service_name = "misc_scanner"
+
+        if service_name in cls._services:
+            return cls._services[service_name]
+
+        async with cls._get_lock(service_name):
+            # Double-check after acquiring lock
+            if service_name in cls._services:
+                return cls._services[service_name]
+
+            # Import here to avoid circular imports
+            from .misc_scanner import MiscScanner
+
+            scanner = await MiscScanner.get_instance()
+            cls._services[service_name] = scanner
+            logger.debug(f"Created and registered {service_name}")
+            return scanner
+
     @classmethod
     def clear_services(cls):
         """Clear all registered services - mainly for testing"""

py/services/settings_manager.py

@@ -28,9 +28,6 @@ CORE_USER_SETTING_KEYS: Tuple[str, ...] = (
     "folder_paths",
 )
 
-# Threshold for aggressive cleanup: if file contains this many default keys, clean it up
-DEFAULT_KEYS_CLEANUP_THRESHOLD = 10
-
 
 DEFAULT_SETTINGS: Dict[str, Any] = {
     "civitai_api_key": "",
@@ -54,7 +51,6 @@ DEFAULT_SETTINGS: Dict[str, Any] = {
     "base_model_path_mappings": {},
     "download_path_templates": {},
     "folder_paths": {},
-    "extra_folder_paths": {},
     "example_images_path": "",
     "optimize_example_images": True,
     "auto_download_example_images": False,
@@ -67,10 +63,9 @@ DEFAULT_SETTINGS: Dict[str, Any] = {
     "compact_mode": False,
     "priority_tags": DEFAULT_PRIORITY_TAG_CONFIG.copy(),
     "model_name_display": "model_name",
-    "model_card_footer_action": "replace_preview",
+    "model_card_footer_action": "example_images",
     "update_flag_strategy": "same_base",
     "auto_organize_exclusions": [],
-    "metadata_refresh_skip_paths": [],
 }
 
 
@@ -100,9 +95,6 @@ class SettingsManager:
         if self._needs_initial_save:
             self._save_settings()
             self._needs_initial_save = False
-        else:
-            # Clean up existing settings file by removing default values
-            self._cleanup_default_values_from_disk()
 
     def _detect_standalone_mode(self) -> bool:
         """Return ``True`` when running in standalone mode."""
@@ -234,7 +226,7 @@ class SettingsManager:
         return merged
 
     def _ensure_default_settings(self) -> None:
-        """Ensure all default settings keys exist in memory (but don't save defaults to disk)"""
+        """Ensure all default settings keys exist"""
         defaults = self._get_default_settings()
         updated_existing = False
         inserted_defaults = False
@@ -263,17 +255,6 @@ class SettingsManager:
             self.settings["auto_organize_exclusions"] = []
             inserted_defaults = True
 
-        if "metadata_refresh_skip_paths" in self.settings:
-            normalized_skip_paths = self.normalize_metadata_refresh_skip_paths(
-                self.settings.get("metadata_refresh_skip_paths")
-            )
-            if normalized_skip_paths != self.settings.get("metadata_refresh_skip_paths"):
-                self.settings["metadata_refresh_skip_paths"] = normalized_skip_paths
-                updated_existing = True
-        else:
-            self.settings["metadata_refresh_skip_paths"] = []
-            inserted_defaults = True
-
         for key, value in defaults.items():
             if key == "priority_tags":
                 continue
@@ -284,10 +265,10 @@ class SettingsManager:
                     self.settings[key] = value
                 inserted_defaults = True
 
-        # Save only if existing values were normalized/updated
-        if updated_existing:
+        if updated_existing or (
+            inserted_defaults and self._bootstrap_reason in {"invalid", "unreadable"}
+        ):
             self._save_settings()
-        # Note: inserted_defaults no longer triggers save - defaults stay in memory only
 
     def _migrate_to_library_registry(self) -> None:
         """Ensure settings include the multi-library registry structure."""
@@ -403,7 +384,6 @@ class SettingsManager:
         active_library = libraries.get(active_name, {})
         folder_paths = copy.deepcopy(active_library.get("folder_paths", {}))
         self.settings["folder_paths"] = folder_paths
-        self.settings["extra_folder_paths"] = copy.deepcopy(active_library.get("extra_folder_paths", {}))
         self.settings["default_lora_root"] = active_library.get("default_lora_root", "")
         self.settings["default_checkpoint_root"] = active_library.get("default_checkpoint_root", "")
         self.settings["default_unet_root"] = active_library.get("default_unet_root", "")
@@ -419,7 +399,6 @@ class SettingsManager:
         self,
         *,
         folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
-        extra_folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
         default_lora_root: Optional[str] = None,
         default_checkpoint_root: Optional[str] = None,
         default_unet_root: Optional[str] = None,
@@ -435,11 +414,6 @@ class SettingsManager:
         else:
             payload.setdefault("folder_paths", {})
 
-        if extra_folder_paths is not None:
-            payload["extra_folder_paths"] = self._normalize_folder_paths(extra_folder_paths)
-        else:
-            payload.setdefault("extra_folder_paths", {})
-
         if default_lora_root is not None:
             payload["default_lora_root"] = default_lora_root
         else:
@@ -554,7 +528,6 @@ class SettingsManager:
         self,
         *,
         folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
-        extra_folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
         default_lora_root: Optional[str] = None,
         default_checkpoint_root: Optional[str] = None,
         default_unet_root: Optional[str] = None,
@@ -574,12 +547,6 @@ class SettingsManager:
                 library["folder_paths"] = normalized_paths
                 changed = True
 
-        if extra_folder_paths is not None:
-            normalized_extra_paths = self._normalize_folder_paths(extra_folder_paths)
-            if library.get("extra_folder_paths") != normalized_extra_paths:
-                library["extra_folder_paths"] = normalized_extra_paths
-                changed = True
-
         if default_lora_root is not None and library.get("default_lora_root") != default_lora_root:
             library["default_lora_root"] = default_lora_root
             changed = True
@@ -744,42 +711,6 @@ class SettingsManager:
 
         self._startup_messages.append(payload)
 
-    def _cleanup_default_values_from_disk(self) -> None:
-        """Remove default values from existing settings.json to keep it clean.
-
-        Only performs cleanup if the file contains a significant number of default
-        values (indicating it's "bloated"). Small files (like template-based configs)
-        are preserved as-is to avoid unexpected changes.
-        """
-        # Only cleanup existing files (not new ones)
-        if self._bootstrap_reason == "missing" or self._original_disk_payload is None:
-            return
-
-        defaults = self._get_default_settings()
-        disk_keys = set(self._original_disk_payload.keys())
-
-        # Count how many keys on disk are set to their default values
-        default_value_keys = set()
-        for key in disk_keys:
-            if key in CORE_USER_SETTING_KEYS:
-                continue  # Core keys don't count as "cleanup candidates"
-            disk_value = self._original_disk_payload.get(key)
-            default_value = defaults.get(key)
-            # Compare using JSON serialization for complex objects
-            if json.dumps(disk_value, sort_keys=True, default=str) == json.dumps(default_value, sort_keys=True, default=str):
-                default_value_keys.add(key)
-
-        # Only cleanup if there are "many" default keys (indicating a bloated file)
-        # This preserves small/template-based configs while cleaning up legacy bloated files
-        if len(default_value_keys) >= DEFAULT_KEYS_CLEANUP_THRESHOLD:
-            logger.info(
-                "Cleaning up %d default value(s) from settings.json to keep it minimal",
-                len(default_value_keys)
-            )
-            self._save_settings()
-            # Update original payload to match what we just saved
-            self._original_disk_payload = self._serialize_settings_for_disk()
-
     def _collect_configuration_warnings(self) -> None:
         if not self._standalone_mode:
             return
@@ -831,14 +762,11 @@ class SettingsManager:
         defaults['download_path_templates'] = {}
         defaults['priority_tags'] = DEFAULT_PRIORITY_TAG_CONFIG.copy()
         defaults.setdefault('folder_paths', {})
-        defaults.setdefault('extra_folder_paths', {})
         defaults['auto_organize_exclusions'] = []
-        defaults['metadata_refresh_skip_paths'] = []
 
         library_name = defaults.get("active_library") or "default"
         default_library = self._build_library_payload(
             folder_paths=defaults.get("folder_paths", {}),
-            extra_folder_paths=defaults.get("extra_folder_paths", {}),
             default_lora_root=defaults.get("default_lora_root"),
             default_checkpoint_root=defaults.get("default_checkpoint_root"),
             default_embedding_root=defaults.get("default_embedding_root"),
@@ -906,73 +834,6 @@ class SettingsManager:
             self._save_settings()
         return exclusions
 
-    def normalize_metadata_refresh_skip_paths(self, value: Any) -> List[str]:
-        if value is None:
-            return []
-
-        if isinstance(value, str):
-            candidates: Iterable[str] = (
-                value.replace("\n", ",").replace(";", ",").split(",")
-            )
-        elif isinstance(value, Sequence) and not isinstance(value, (bytes, bytearray, str)):
-            candidates = value
-        else:
-            return []
-
-        paths: List[str] = []
-        for raw in candidates:
-            if isinstance(raw, str):
-                token = raw.replace("\\", "/").strip().strip("/")
-                if token:
-                    paths.append(token)
-
-        unique_paths: List[str] = []
-        seen = set()
-        for path in paths:
-            if path not in seen:
-                seen.add(path)
-                unique_paths.append(path)
-
-        return unique_paths
-
-    def get_metadata_refresh_skip_paths(self) -> List[str]:
-        skip_paths = self.normalize_metadata_refresh_skip_paths(
-            self.settings.get("metadata_refresh_skip_paths")
-        )
-        if skip_paths != self.settings.get("metadata_refresh_skip_paths"):
-            self.settings["metadata_refresh_skip_paths"] = skip_paths
-            self._save_settings()
-        return skip_paths
-
-    def get_extra_folder_paths(self) -> Dict[str, List[str]]:
-        """Get extra folder paths for the active library.
-
-        These paths are only used by LoRA Manager and not shared with ComfyUI.
-        Returns a dictionary with keys like 'loras', 'checkpoints', 'embeddings', 'unet'.
-        """
-        extra_paths = self.settings.get("extra_folder_paths", {})
-        if not isinstance(extra_paths, dict):
-            return {}
-        return self._normalize_folder_paths(extra_paths)
-
-    def update_extra_folder_paths(
-        self,
-        extra_folder_paths: Mapping[str, Iterable[str]],
-    ) -> None:
-        """Update extra folder paths for the active library.
-
-        These paths are only used by LoRA Manager and not shared with ComfyUI.
-        Validates that extra paths don't overlap with other libraries' paths.
-        """
-        active_name = self.get_active_library_name()
-        self._validate_folder_paths(active_name, extra_folder_paths)
-
-        normalized_paths = self._normalize_folder_paths(extra_folder_paths)
-        self.settings["extra_folder_paths"] = normalized_paths
-        self._update_active_library_entry(extra_folder_paths=normalized_paths)
-        self._save_settings()
-        logger.info("Updated extra folder paths for library '%s'", active_name)
-
     def get_startup_messages(self) -> List[Dict[str, Any]]:
         return [message.copy() for message in self._startup_messages]
 
@@ -1010,8 +871,6 @@ class SettingsManager:
         """Set setting value and save"""
         if key == "auto_organize_exclusions":
             value = self.normalize_auto_organize_exclusions(value)
-        elif key == "metadata_refresh_skip_paths":
-            value = self.normalize_metadata_refresh_skip_paths(value)
         self.settings[key] = value
         portable_switch_pending = False
         if key == "use_portable_settings" and isinstance(value, bool):
@@ -1019,8 +878,6 @@ class SettingsManager:
             self._prepare_portable_switch(value)
         if key == 'folder_paths' and isinstance(value, Mapping):
             self._update_active_library_entry(folder_paths=value)  # type: ignore[arg-type]
-        elif key == 'extra_folder_paths' and isinstance(value, Mapping):
-            self._update_active_library_entry(extra_folder_paths=value)  # type: ignore[arg-type]
         elif key == 'default_lora_root':
             self._update_active_library_entry(default_lora_root=str(value))
         elif key == 'default_checkpoint_root':
@@ -1042,10 +899,6 @@ class SettingsManager:
             self._save_settings()
             logger.info(f"Deleted setting: {key}")
 
-    def keys(self) -> Iterable[str]:
-        """Return all setting keys."""
-        return self.settings.keys()
-
     def _prepare_portable_switch(self, use_portable: bool) -> None:
         """Prepare switching the settings storage location."""
 
@@ -1248,12 +1101,7 @@ class SettingsManager:
             self._seed_template = None
 
     def _serialize_settings_for_disk(self) -> Dict[str, Any]:
-        """Return the settings payload that should be persisted to disk.
-
-        Only saves settings that differ from defaults, keeping the config file
-        clean and focused on user customizations. Default values are still
-        available at runtime via _get_default_settings().
-        """
+        """Return the settings payload that should be persisted to disk."""
 
         if self._bootstrap_reason == "missing":
             minimal: Dict[str, Any] = {}
@@ -1267,25 +1115,7 @@ class SettingsManager:
 
             return minimal
 
-        # Only save settings that differ from defaults
-        defaults = self._get_default_settings()
-        minimal = {}
-
-        for key, value in self.settings.items():
-            default_value = defaults.get(key)
-
-            # Core settings are always saved (even if equal to default)
-            if key in CORE_USER_SETTING_KEYS:
-                minimal[key] = copy.deepcopy(value)
-            # Complex objects need deep comparison
-            elif isinstance(value, (dict, list)) and default_value is not None:
-                if json.dumps(value, sort_keys=True, default=str) != json.dumps(default_value, sort_keys=True, default=str):
-                    minimal[key] = copy.deepcopy(value)
-            # Simple values use direct comparison
-            elif value != default_value:
-                minimal[key] = copy.deepcopy(value)
-
-        return minimal
+        return copy.deepcopy(self.settings)
 
     def get_libraries(self) -> Dict[str, Dict[str, Any]]:
         """Return a copy of the registered libraries."""
@@ -1332,7 +1162,6 @@ class SettingsManager:
         library_name: str,
         *,
         folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
-        extra_folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
         default_lora_root: Optional[str] = None,
         default_checkpoint_root: Optional[str] = None,
         default_unet_root: Optional[str] = None,
@@ -1349,15 +1178,11 @@ class SettingsManager:
         if folder_paths is not None:
             self._validate_folder_paths(name, folder_paths)
 
-        if extra_folder_paths is not None:
-            self._validate_folder_paths(name, extra_folder_paths)
-
         libraries = self.settings.setdefault("libraries", {})
         existing = libraries.get(name, {})
 
         payload = self._build_library_payload(
             folder_paths=folder_paths if folder_paths is not None else existing.get("folder_paths"),
-            extra_folder_paths=extra_folder_paths if extra_folder_paths is not None else existing.get("extra_folder_paths"),
             default_lora_root=default_lora_root if default_lora_root is not None else existing.get("default_lora_root"),
             default_checkpoint_root=(
                 default_checkpoint_root
@@ -1396,7 +1221,6 @@ class SettingsManager:
         library_name: str,
         *,
         folder_paths: Mapping[str, Iterable[str]],
-        extra_folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
         default_lora_root: str = "",
         default_checkpoint_root: str = "",
         default_unet_root: str = "",
@@ -1413,7 +1237,6 @@ class SettingsManager:
         return self.upsert_library(
             library_name,
             folder_paths=folder_paths,
-            extra_folder_paths=extra_folder_paths,
             default_lora_root=default_lora_root,
             default_checkpoint_root=default_checkpoint_root,
             default_unet_root=default_unet_root,
@@ -1472,7 +1295,6 @@ class SettingsManager:
         self,
         folder_paths: Mapping[str, Iterable[str]],
         *,
-        extra_folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
         default_lora_root: Optional[str] = None,
         default_checkpoint_root: Optional[str] = None,
         default_unet_root: Optional[str] = None,
@@ -1484,7 +1306,6 @@ class SettingsManager:
         self.upsert_library(
             active_name,
             folder_paths=folder_paths,
-            extra_folder_paths=extra_folder_paths,
             default_lora_root=default_lora_root,
             default_checkpoint_root=default_checkpoint_root,
             default_unet_root=default_unet_root,

py/services/tag_fts_index.py

@@ -69,9 +69,7 @@ class TagFTSIndex:
     _DEFAULT_FILENAME = "tag_fts.sqlite"
     _CSV_FILENAME = "danbooru_e621_merged.csv"
 
-    def __init__(
-        self, db_path: Optional[str] = None, csv_path: Optional[str] = None
-    ) -> None:
+    def __init__(self, db_path: Optional[str] = None, csv_path: Optional[str] = None) -> None:
         """Initialize the FTS index.
 
         Args:
@@ -94,9 +92,7 @@ class TagFTSIndex:
             if directory:
                 os.makedirs(directory, exist_ok=True)
         except Exception as exc:
-            logger.warning(
-                "Could not create FTS index directory %s: %s", directory, exc
-            )
+            logger.warning("Could not create FTS index directory %s: %s", directory, exc)
 
     def _resolve_default_db_path(self) -> str:
         """Resolve the default database path."""
@@ -177,15 +173,13 @@ class TagFTSIndex:
                     # Set schema version
                     conn.execute(
                         "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
-                        ("schema_version", str(SCHEMA_VERSION)),
+                        ("schema_version", str(SCHEMA_VERSION))
                     )
                     conn.commit()
 
                     self._schema_initialized = True
                     self._needs_rebuild = needs_rebuild
-                    logger.debug(
-                        "Tag FTS index schema initialized at %s", self._db_path
-                    )
+                    logger.debug("Tag FTS index schema initialized at %s", self._db_path)
                 finally:
                     conn.close()
             except Exception as exc:
@@ -212,20 +206,13 @@ class TagFTSIndex:
             row = cursor.fetchone()
             if not row:
                 # Old schema without version, needs rebuild
-                logger.info(
-                    "Migrating tag FTS index to schema version %d (adding alias support)",
-                    SCHEMA_VERSION,
-                )
+                logger.info("Migrating tag FTS index to schema version %d (adding alias support)", SCHEMA_VERSION)
                 self._drop_old_tables(conn)
                 return True
 
             current_version = int(row[0])
             if current_version < SCHEMA_VERSION:
-                logger.info(
-                    "Migrating tag FTS index from version %d to %d",
-                    current_version,
-                    SCHEMA_VERSION,
-                )
+                logger.info("Migrating tag FTS index from version %d to %d", current_version, SCHEMA_VERSION)
                 self._drop_old_tables(conn)
                 return True
 
@@ -259,9 +246,7 @@ class TagFTSIndex:
             return
 
         if not os.path.exists(self._csv_path):
-            logger.warning(
-                "CSV file not found at %s, cannot build tag index", self._csv_path
-            )
+            logger.warning("CSV file not found at %s, cannot build tag index", self._csv_path)
             return
 
         self._indexing_in_progress = True
@@ -329,24 +314,22 @@ class TagFTSIndex:
                     # Update metadata
                     conn.execute(
                         "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
-                        ("last_build_time", str(time.time())),
+                        ("last_build_time", str(time.time()))
                     )
                     conn.execute(
                         "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
-                        ("tag_count", str(total_inserted)),
+                        ("tag_count", str(total_inserted))
                     )
                     conn.execute(
                         "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
-                        ("schema_version", str(SCHEMA_VERSION)),
+                        ("schema_version", str(SCHEMA_VERSION))
                     )
 
                     conn.commit()
                     elapsed = time.time() - start_time
                     logger.info(
                         "Tag FTS index built: %d tags indexed (%d with aliases) in %.2fs",
-                        total_inserted,
-                        tags_with_aliases,
-                        elapsed,
+                        total_inserted, tags_with_aliases, elapsed
                     )
                 finally:
                     conn.close()
@@ -367,7 +350,7 @@ class TagFTSIndex:
         # Insert into tags table (with aliases)
         conn.executemany(
             "INSERT OR IGNORE INTO tags (tag_name, category, post_count, aliases) VALUES (?, ?, ?, ?)",
-            rows,
+            rows
         )
 
         # Build a map of tag_name -> aliases for FTS insertion
@@ -379,7 +362,7 @@ class TagFTSIndex:
         placeholders = ",".join("?" * len(tag_names))
         cursor = conn.execute(
             f"SELECT rowid, tag_name FROM tags WHERE tag_name IN ({placeholders})",
-            tag_names,
+            tag_names
         )
 
         # Build FTS rows with (rowid, searchable_text) = (tags.rowid, "tag_name alias1 alias2 ...")
@@ -396,17 +379,13 @@ class TagFTSIndex:
                         alias = alias[1:]  # Remove leading slash
                     if alias:
                         alias_parts.append(alias)
-                searchable_text = (
-                    f"{tag_name} {' '.join(alias_parts)}" if alias_parts else tag_name
-                )
+                searchable_text = f"{tag_name} {' '.join(alias_parts)}" if alias_parts else tag_name
             else:
                 searchable_text = tag_name
             fts_rows.append((rowid, searchable_text))
 
         if fts_rows:
-            conn.executemany(
-                "INSERT INTO tag_fts (rowid, searchable_text) VALUES (?, ?)", fts_rows
-            )
+            conn.executemany("INSERT INTO tag_fts (rowid, searchable_text) VALUES (?, ?)", fts_rows)
 
     def ensure_ready(self) -> bool:
         """Ensure the index is ready, building if necessary.
@@ -441,8 +420,7 @@ class TagFTSIndex:
         self,
         query: str,
         categories: Optional[List[int]] = None,
-        limit: int = 20,
-        offset: int = 0,
+        limit: int = 20
     ) -> List[Dict]:
         """Search tags using FTS5 with prefix matching.
 
@@ -453,7 +431,6 @@ class TagFTSIndex:
             query: The search query string.
             categories: Optional list of category IDs to filter by.
             limit: Maximum number of results to return.
-            offset: Number of results to skip.
 
         Returns:
             List of dictionaries with tag_name, category, post_count,
@@ -489,9 +466,9 @@ class TagFTSIndex:
                             )
                             AND t.category IN ({placeholders})
                             ORDER BY t.post_count DESC
-                            LIMIT ? OFFSET ?
+                            LIMIT ?
                         """
-                        params = [fts_query] + categories + [limit, offset]
+                        params = [fts_query] + categories + [limit]
                     else:
                         sql = """
                             SELECT t.tag_name, t.category, t.post_count, t.aliases
@@ -499,9 +476,9 @@ class TagFTSIndex:
                             JOIN tags t ON f.rowid = t.rowid
                             WHERE f.searchable_text MATCH ?
                             ORDER BY t.post_count DESC
-                            LIMIT ? OFFSET ?
+                            LIMIT ?
                         """
-                        params = [fts_query, limit, offset]
+                        params = [fts_query, limit]
 
                     cursor = conn.execute(sql, params)
                     results = []
@@ -525,9 +502,7 @@ class TagFTSIndex:
             logger.debug("Tag FTS search error for query '%s': %s", query, exc)
             return []
 
-    def _find_matched_alias(
-        self, query: str, tag_name: str, aliases_str: str
-    ) -> Optional[str]:
+    def _find_matched_alias(self, query: str, tag_name: str, aliases_str: str) -> Optional[str]:
         """Find which alias matched the query, if any.
 
         Args:

py/services/use_cases/bulk_metadata_refresh_use_case.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Any, Dict, List, Optional, Protocol, Sequence
+from typing import Any, Dict, Optional, Protocol, Sequence
 
 from ..metadata_sync_service import MetadataSyncService
 from ...utils.metadata_manager import MetadataManager
@@ -43,21 +43,14 @@ class BulkMetadataRefreshUseCase:
         total_models = len(cache.raw_data)
 
         enable_metadata_archive_db = self._settings.get("enable_metadata_archive_db", False)
-        skip_paths = self._settings.get("metadata_refresh_skip_paths", [])
         to_process: Sequence[Dict[str, Any]] = [
             model
             for model in cache.raw_data
-            if not model.get("skip_metadata_refresh", False)
-            and not self._is_in_skip_path(model.get("folder", ""), skip_paths)
+            if model.get("sha256")
             and (not model.get("civitai") or not model["civitai"].get("id"))
-            and not (
-                # Skip models confirmed not on CivitAI when no need to retry
-                model.get("from_civitai") is False
-                and model.get("civitai_deleted") is True
-                and (
-                    not enable_metadata_archive_db
-                    or model.get("db_checked", False)
-                )
+            and (
+                (enable_metadata_archive_db and not model.get("db_checked", False))
+                or (not enable_metadata_archive_db and model.get("from_civitai") is True)
             )
         ]
 
@@ -84,36 +77,6 @@ class BulkMetadataRefreshUseCase:
                 return {"success": False, "message": "Operation cancelled", "processed": processed, "updated": success, "total": total_models}
             try:
                 original_name = model.get("model_name")
-
-                # Handle lazy hash calculation for models with pending hash status
-                sha256 = model.get("sha256", "")
-                hash_status = model.get("hash_status", "completed")
-                file_path = model.get("file_path")
-
-                if not sha256 and hash_status == "pending" and file_path:
-                    self._logger.info(f"Calculating pending hash for {file_path}")
-                    # Check if scanner has calculate_hash_for_model method (CheckpointScanner)
-                    calculate_hash_method = getattr(self._service.scanner, "calculate_hash_for_model", None)
-                    if calculate_hash_method:
-                        sha256 = await calculate_hash_method(file_path)
-                        if sha256:
-                            model["sha256"] = sha256
-                            model["hash_status"] = "completed"
-                        else:
-                            self._logger.error(f"Failed to calculate hash for {file_path}")
-                            processed += 1
-                            continue
-                    else:
-                        self._logger.warning(f"Scanner does not support lazy hash calculation for {file_path}")
-                        processed += 1
-                        continue
-
-                # Skip models without valid hash
-                if not model.get("sha256"):
-                    self._logger.warning(f"Skipping model without hash: {file_path}")
-                    processed += 1
-                    continue
-
                 await MetadataManager.hydrate_model_data(model)
                 result, _ = await self._metadata_sync.fetch_and_update_model(
                     sha256=model["sha256"],
@@ -152,21 +115,6 @@ class BulkMetadataRefreshUseCase:
 
         return {"success": True, "message": message, "processed": processed, "updated": success, "total": total_models}
 
-    @staticmethod
-    def _is_in_skip_path(folder: str, skip_paths: List[str]) -> bool:
-        if not skip_paths or not folder:
-            return False
-        normalized = folder.replace("\\", "/").strip("/")
-        if not normalized:
-            return False
-        for sp in skip_paths:
-            nsp = sp.replace("\\", "/").strip("/")
-            if not nsp:
-                continue
-            if normalized == nsp or normalized.startswith(nsp + "/"):
-                return True
-        return False
-
     async def execute_with_error_handling(
         self,
         *,

py/services/websocket_manager.py

@@ -255,42 +255,6 @@ class WebSocketManager:
             self._download_progress.pop(download_id, None)
             logger.debug(f"Cleaned up old download progress for {download_id}")
             
-    async def broadcast_cache_health_warning(self, report: 'HealthReport', page_type: str = None):
-        """
-        Broadcast cache health warning to frontend.
-
-        Args:
-            report: HealthReport instance from CacheHealthMonitor
-            page_type: The page type (loras, checkpoints, embeddings)
-        """
-        from .cache_health_monitor import CacheHealthStatus
-
-        # Only broadcast if there are issues
-        if report.status == CacheHealthStatus.HEALTHY:
-            return
-
-        payload = {
-            'type': 'cache_health_warning',
-            'status': report.status.value,
-            'message': report.message,
-            'pageType': page_type,
-            'details': {
-                'total': report.total_entries,
-                'valid': report.valid_entries,
-                'invalid': report.invalid_entries,
-                'repaired': report.repaired_entries,
-                'corruption_rate': f"{report.corruption_rate:.1%}",
-                'invalid_paths': report.invalid_paths[:5],  # Limit to first 5
-            }
-        }
-
-        logger.info(
-            f"Broadcasting cache health warning: {report.status.value} "
-            f"({report.invalid_entries} invalid entries)"
-        )
-
-        await self.broadcast(payload)
-
     def get_connected_clients_count(self) -> int:
         """Get number of connected clients"""
         return len(self._websockets)

py/utils/constants.py

@@ -49,6 +49,7 @@ SUPPORTED_MEDIA_EXTENSIONS = {
 VALID_LORA_SUB_TYPES = ["lora", "locon", "dora"]
 VALID_CHECKPOINT_SUB_TYPES = ["checkpoint", "diffusion_model"]
 VALID_EMBEDDING_SUB_TYPES = ["embedding"]
+VALID_MISC_SUB_TYPES = ["vae", "upscaler"]
 
 # Backward compatibility alias
 VALID_LORA_TYPES = VALID_LORA_SUB_TYPES
@@ -94,6 +95,7 @@ DEFAULT_PRIORITY_TAG_CONFIG = {
     "lora": ", ".join(CIVITAI_MODEL_TAGS),
     "checkpoint": ", ".join(CIVITAI_MODEL_TAGS),
     "embedding": ", ".join(CIVITAI_MODEL_TAGS),
+    "misc": ", ".join(CIVITAI_MODEL_TAGS),
 }
 
 # baseModel values from CivitAI that should be treated as diffusion models (unet)

py/utils/example_images_download_manager.py

@@ -121,71 +121,101 @@ class DownloadManager:
     async def start_download(self, options: dict):
         """Start downloading example images for models."""
 
-        # Step 1: Parse options (fast, non-blocking)
-        data = options or {}
-        auto_mode = data.get("auto_mode", False)
-        optimize = data.get("optimize", True)
-        model_types = data.get("model_types", ["lora", "checkpoint"])
-        delay = float(data.get("delay", 0.2))
-        force = data.get("force", False)
-
-        # Step 2: Validate configuration (fast lookup)
-        settings_manager = get_settings_manager()
-        base_path = settings_manager.get("example_images_path")
-
-        if not base_path:
-            error_msg = "Example images path not configured in settings"
-            if auto_mode:
-                logger.debug(error_msg)
-                return {
-                    "success": True,
-                    "message": "Example images path not configured, skipping auto download",
-                }
-            raise DownloadConfigurationError(error_msg)
-
-        active_library = settings_manager.get_active_library_name()
-        output_dir = self._resolve_output_dir(active_library)
-        if not output_dir:
-            raise DownloadConfigurationError(
-                "Example images path not configured in settings"
-            )
-
-        # Step 3: Load progress file (I/O operation, done outside lock)
-        processed_models = set()
-        failed_models = set()
-        
-        try:
-            progress_file, processed_models, failed_models = await self._load_progress_file(output_dir)
-            logger.debug(
-                "Loaded previous progress, %s models already processed, %s models marked as failed",
-                len(processed_models),
-                len(failed_models),
-            )
-        except Exception as e:
-            logger.error(f"Failed to load progress file: {e}")
-            # Continue with empty sets
-
-        # Step 4: Quick state check and update (minimal lock time)
         async with self._state_lock:
             if self._is_downloading:
                 raise DownloadInProgressError(self._progress.snapshot())
 
             try:
-                # Reset progress with loaded data
+                data = options or {}
+                auto_mode = data.get("auto_mode", False)
+                optimize = data.get("optimize", True)
+                model_types = data.get("model_types", ["lora", "checkpoint"])
+                delay = float(data.get("delay", 0.2))
+                force = data.get("force", False)
+
+                settings_manager = get_settings_manager()
+                base_path = settings_manager.get("example_images_path")
+
+                if not base_path:
+                    error_msg = "Example images path not configured in settings"
+                    if auto_mode:
+                        logger.debug(error_msg)
+                        return {
+                            "success": True,
+                            "message": "Example images path not configured, skipping auto download",
+                        }
+                    raise DownloadConfigurationError(error_msg)
+
+                active_library = get_settings_manager().get_active_library_name()
+                output_dir = self._resolve_output_dir(active_library)
+                if not output_dir:
+                    raise DownloadConfigurationError(
+                        "Example images path not configured in settings"
+                    )
+
                 self._progress.reset()
-                self._progress["processed_models"] = processed_models
-                self._progress["failed_models"] = failed_models
                 self._stop_requested = False
                 self._progress["status"] = "running"
                 self._progress["start_time"] = time.time()
                 self._progress["end_time"] = None
 
-                self._is_downloading = True
-                snapshot = self._progress.snapshot()
+                progress_file = os.path.join(output_dir, ".download_progress.json")
+                progress_source = progress_file
+                if uses_library_scoped_folders():
+                    legacy_root = (
+                        get_settings_manager().get("example_images_path") or ""
+                    )
+                    legacy_progress = (
+                        os.path.join(legacy_root, ".download_progress.json")
+                        if legacy_root
+                        else ""
+                    )
+                    if (
+                        legacy_progress
+                        and os.path.exists(legacy_progress)
+                        and not os.path.exists(progress_file)
+                    ):
+                        try:
+                            os.makedirs(output_dir, exist_ok=True)
+                            shutil.move(legacy_progress, progress_file)
+                            logger.info(
+                                "Migrated legacy download progress file '%s' to '%s'",
+                                legacy_progress,
+                                progress_file,
+                            )
+                        except OSError as exc:
+                            logger.warning(
+                                "Failed to migrate download progress file from '%s' to '%s': %s",
+                                legacy_progress,
+                                progress_file,
+                                exc,
+                            )
+                            progress_source = legacy_progress
 
-                # Create the download task without awaiting it
-                # This ensures the HTTP response is returned immediately
-                # while the actual processing happens in the background
+                if os.path.exists(progress_source):
+                    try:
+                        with open(progress_source, "r", encoding="utf-8") as f:
+                            saved_progress = json.load(f)
+                            self._progress["processed_models"] = set(
+                                saved_progress.get("processed_models", [])
+                            )
+                            self._progress["failed_models"] = set(
+                                saved_progress.get("failed_models", [])
+                            )
+                            logger.debug(
+                                "Loaded previous progress, %s models already processed, %s models marked as failed",
+                                len(self._progress["processed_models"]),
+                                len(self._progress["failed_models"]),
+                            )
+                    except Exception as e:
+                        logger.error(f"Failed to load progress file: {e}")
+                        self._progress["processed_models"] = set()
+                        self._progress["failed_models"] = set()
+                else:
+                    self._progress["processed_models"] = set()
+                    self._progress["failed_models"] = set()
+
+                self._is_downloading = True
                 self._download_task = asyncio.create_task(
                     self._download_all_example_images(
                         output_dir,
@@ -197,10 +227,7 @@ class DownloadManager:
                     )
                 )
 
-                # Add a callback to handle task completion/errors
-                self._download_task.add_done_callback(
-                    lambda t: self._handle_download_task_done(t, output_dir)
-                )
+                snapshot = self._progress.snapshot()
             except ExampleImagesDownloadError:
                 # Re-raise our own exception types without wrapping
                 self._is_downloading = False
@@ -214,26 +241,11 @@ class DownloadManager:
                 )
                 raise ExampleImagesDownloadError(str(e)) from e
 
-        # Broadcast progress in the background without blocking the response
-        # This ensures the HTTP response is returned immediately
-        asyncio.create_task(self._broadcast_progress(status="running"))
+        await self._broadcast_progress(status="running")
 
         return {"success": True, "message": "Download started", "status": snapshot}
 
-    def _handle_download_task_done(self, task: asyncio.Task, output_dir: str) -> None:
-        """Handle download task completion, including saving progress on error."""
-        try:
-            # This will re-raise any exception from the task
-            task.result()
-        except Exception as e:
-            logger.error(f"Download task failed with error: {e}", exc_info=True)
-            # Ensure progress is saved even on failure
-            try:
-                self._save_progress(output_dir)
-            except Exception as save_error:
-                logger.error(f"Failed to save progress after task failure: {save_error}")
-
-    async def get_status(self, request) -> dict:
+    async def get_status(self, request):
         """Get the current status of example images download."""
 
         return {
@@ -242,198 +254,6 @@ class DownloadManager:
             "status": self._progress.snapshot(),
         }
 
-    async def _load_progress_file(self, output_dir: str) -> tuple[str, set, set]:
-        """Load progress file from disk. Returns (progress_file_path, processed_models, failed_models).
-
-        This is a separate async method to allow running in executor to avoid blocking event loop.
-        """
-        loop = asyncio.get_event_loop()
-        return await loop.run_in_executor(
-            None, self._load_progress_file_sync, output_dir
-        )
-
-    def _load_progress_file_sync(self, output_dir: str) -> tuple[str, set, set]:
-        """Synchronous implementation of progress file loading."""
-        progress_file = os.path.join(output_dir, ".download_progress.json")
-        progress_source = progress_file
-
-        # Handle legacy migration if needed
-        if uses_library_scoped_folders():
-            legacy_root = get_settings_manager().get("example_images_path") or ""
-            legacy_progress = (
-                os.path.join(legacy_root, ".download_progress.json")
-                if legacy_root
-                else ""
-            )
-            if (
-                legacy_progress
-                and os.path.exists(legacy_progress)
-                and not os.path.exists(progress_file)
-            ):
-                try:
-                    os.makedirs(output_dir, exist_ok=True)
-                    shutil.move(legacy_progress, progress_file)
-                    logger.info(
-                        "Migrated legacy download progress file '%s' to '%s'",
-                        legacy_progress,
-                        progress_file,
-                    )
-                except OSError as exc:
-                    logger.warning(
-                        "Failed to migrate download progress file from '%s' to '%s': %s",
-                        legacy_progress,
-                        progress_file,
-                        exc,
-                    )
-                    progress_source = legacy_progress
-
-        processed_models = set()
-        failed_models = set()
-
-        if os.path.exists(progress_source):
-            try:
-                with open(progress_source, "r", encoding="utf-8") as f:
-                    saved_progress = json.load(f)
-                    processed_models = set(saved_progress.get("processed_models", []))
-                    failed_models = set(saved_progress.get("failed_models", []))
-            except Exception:
-                # Return empty sets on error
-                pass
-
-        return progress_file, processed_models, failed_models
-
-    def _load_progress_sets_sync(self, progress_file: str) -> tuple[set, set]:
-        """Load only the processed and failed model sets from progress file.
-
-        This is a lighter version for quick checks without legacy migration.
-        Returns (processed_models, failed_models).
-        """
-        processed_models = set()
-        failed_models = set()
-
-        if os.path.exists(progress_file):
-            try:
-                with open(progress_file, "r", encoding="utf-8") as f:
-                    saved_progress = json.load(f)
-                    processed_models = set(saved_progress.get("processed_models", []))
-                    failed_models = set(saved_progress.get("failed_models", []))
-            except Exception:
-                # Return empty sets on error
-                pass
-
-        return processed_models, failed_models
-
-    async def check_pending_models(self, model_types: list[str]) -> dict:
-        """Quickly check how many models need example images downloaded.
-        
-        This is a lightweight check that avoids the overhead of starting
-        a full download task when no work is needed.
-        
-        Returns:
-            dict with keys:
-                - total_models: Total number of models across specified types
-                - pending_count: Number of models needing example images
-                - processed_count: Number of already processed models
-                - failed_count: Number of models marked as failed
-                - needs_download: True if there are pending models to process
-        """
-        from ..services.service_registry import ServiceRegistry
-
-        if self._is_downloading:
-            return {
-                "success": True,
-                "is_downloading": True,
-                "total_models": 0,
-                "pending_count": 0,
-                "processed_count": 0,
-                "failed_count": 0,
-                "needs_download": False,
-                "message": "Download already in progress",
-            }
-
-        try:
-            # Get scanners
-            scanners = []
-            if "lora" in model_types:
-                lora_scanner = await ServiceRegistry.get_lora_scanner()
-                scanners.append(("lora", lora_scanner))
-
-            if "checkpoint" in model_types:
-                checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
-                scanners.append(("checkpoint", checkpoint_scanner))
-
-            if "embedding" in model_types:
-                embedding_scanner = await ServiceRegistry.get_embedding_scanner()
-                scanners.append(("embedding", embedding_scanner))
-
-            # Load progress file to check processed models (async to avoid blocking)
-            settings_manager = get_settings_manager()
-            active_library = settings_manager.get_active_library_name()
-            output_dir = self._resolve_output_dir(active_library)
-
-            processed_models: set[str] = set()
-            failed_models: set[str] = set()
-
-            if output_dir:
-                progress_file = os.path.join(output_dir, ".download_progress.json")
-                loop = asyncio.get_event_loop()
-                processed_models, failed_models = await loop.run_in_executor(
-                    None, self._load_progress_sets_sync, progress_file
-                )
-
-            # Collect all models and count in a single pass per scanner
-            total_models = 0
-            all_models_with_hash: list[tuple[str, str]] = []  # (hash, name) pairs
-
-            for scanner_type, scanner in scanners:
-                cache = await scanner.get_cached_data()
-                if cache and cache.raw_data:
-                    for model in cache.raw_data:
-                        total_models += 1
-                        raw_hash = model.get("sha256")
-                        if raw_hash:
-                            model_hash = raw_hash.lower()
-                            all_models_with_hash.append((model_hash, model.get("model_name", "Unknown")))
-
-            models_with_hash = len(all_models_with_hash)
-
-            # Calculate pending count: check which models actually need processing
-            # A model is pending if it has a hash, is not in processed_models,
-            # and its folder doesn't exist or is empty
-            pending_hashes = set()
-            for model_hash, model_name in all_models_with_hash:
-                if model_hash not in processed_models:
-                    # Check if model folder exists with files
-                    model_dir = ExampleImagePathResolver.get_model_folder(
-                        model_hash, active_library
-                    )
-                    if not _model_directory_has_files(model_dir):
-                        pending_hashes.add(model_hash)
-
-            pending_count = len(pending_hashes)
-
-            return {
-                "success": True,
-                "is_downloading": False,
-                "total_models": total_models,
-                "pending_count": pending_count,
-                "processed_count": len(processed_models),
-                "failed_count": len(failed_models),
-                "needs_download": pending_count > 0,
-            }
-
-        except Exception as e:
-            logger.error(f"Error checking pending models: {e}", exc_info=True)
-            return {
-                "success": False,
-                "error": str(e),
-                "total_models": 0,
-                "pending_count": 0,
-                "processed_count": 0,
-                "failed_count": 0,
-                "needs_download": False,
-            }
-
     async def pause_download(self, request):
         """Pause the example images download."""
 

py/utils/example_images_processor.py

@@ -43,15 +43,8 @@ class ExampleImagesProcessor:
         return media_url
     
     @staticmethod
-    def _get_file_extension_from_content_or_headers(content, headers, fallback_url=None, media_type_hint=None):
-        """Determine file extension from content magic bytes or headers
-        
-        Args:
-            content: File content bytes
-            headers: HTTP response headers
-            fallback_url: Original URL for extension extraction
-            media_type_hint: Optional media type hint from metadata (e.g., "video" or "image")
-        """
+    def _get_file_extension_from_content_or_headers(content, headers, fallback_url=None):
+        """Determine file extension from content magic bytes or headers"""
         # Check magic bytes for common formats
         if content:
             if content.startswith(b'\xFF\xD8\xFF'):
@@ -89,10 +82,6 @@ class ExampleImagesProcessor:
             if ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or ext in SUPPORTED_MEDIA_EXTENSIONS['videos']:
                 return ext
         
-        # Use media type hint from metadata if available
-        if media_type_hint == "video":
-            return '.mp4'
-        
         # Default fallback
         return '.jpg'
 
@@ -147,7 +136,7 @@ class ExampleImagesProcessor:
                 if success:
                     # Determine file extension from content or headers
                     media_ext = ExampleImagesProcessor._get_file_extension_from_content_or_headers(
-                        content, headers, original_url, image.get("type")
+                        content, headers, original_url
                     )
                     
                     # Check if the detected file type is supported
@@ -230,7 +219,7 @@ class ExampleImagesProcessor:
                 if success:
                     # Determine file extension from content or headers
                     media_ext = ExampleImagesProcessor._get_file_extension_from_content_or_headers(
-                        content, headers, original_url, image.get("type")
+                        content, headers, original_url
                     )
                     
                     # Check if the detected file type is supported

py/utils/lora_metadata.py

@@ -17,7 +17,7 @@ async def extract_lora_metadata(file_path: str) -> Dict:
                 base_model = determine_base_model(metadata.get("ss_base_model_version"))
                 return {"base_model": base_model}
     except Exception as e:
-        logger.error(f"Error reading metadata from {file_path}: {str(e)}")
+        print(f"Error reading metadata from {file_path}: {str(e)}")
     return {"base_model": "Unknown"}
 
 async def extract_checkpoint_metadata(file_path: str) -> dict:

py/utils/metadata_manager.py

@@ -223,7 +223,7 @@ class MetadataManager:
                     preview_url=normalize_path(preview_url),
                     tags=[],
                     modelDescription="",
-                    sub_type="checkpoint",
+                    model_type="checkpoint",
                     from_civitai=True
                 )
             elif model_class.__name__ == "EmbeddingMetadata":
@@ -238,7 +238,6 @@ class MetadataManager:
                     preview_url=normalize_path(preview_url),
                     tags=[],
                     modelDescription="",
-                    sub_type="embedding",
                     from_civitai=True
                 )
             else:  # Default to LoraMetadata

py/utils/models.py

@@ -25,10 +25,8 @@ class BaseModelMetadata:
     favorite: bool = False      # Whether the model is a favorite
     exclude: bool = False       # Whether to exclude this model from the cache
     db_checked: bool = False    # Whether checked in archive DB
-    skip_metadata_refresh: bool = False  # Whether to skip this model during bulk metadata refresh
     metadata_source: Optional[str] = None  # Last provider that supplied metadata
     last_checked_at: float = 0  # Last checked timestamp
-    hash_status: str = "completed"  # Hash calculation status: pending | calculating | completed | failed
     _unknown_fields: Dict[str, Any] = field(default_factory=dict, repr=False, compare=False)  # Store unknown fields
 
     def __post_init__(self):
@@ -144,27 +142,27 @@ class LoraMetadata(BaseModelMetadata):
     @classmethod
     def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'LoraMetadata':
         """Create LoraMetadata instance from Civitai version info"""
-        file_name = file_info.get('name', '')
+        file_name = file_info['name']
         base_model = determine_base_model(version_info.get('baseModel', ''))
-
+        
         # Extract tags and description if available
         tags = []
         description = ""
-        model_data = version_info.get('model') or {}
-        if 'tags' in model_data:
-            tags = model_data['tags']
-        if 'description' in model_data:
-            description = model_data['description']
-
+        if 'model' in version_info:
+            if 'tags' in version_info['model']:
+                tags = version_info['model']['tags']
+            if 'description' in version_info['model']:
+                description = version_info['model']['description']
+        
         return cls(
             file_name=os.path.splitext(file_name)[0],
-            model_name=model_data.get('name', os.path.splitext(file_name)[0]),
+            model_name=version_info.get('model').get('name', os.path.splitext(file_name)[0]),
             file_path=save_path.replace(os.sep, '/'),
             size=file_info.get('sizeKB', 0) * 1024,
             modified=datetime.now().timestamp(),
-            sha256=(file_info.get('hashes') or {}).get('SHA256', '').lower(),
+            sha256=file_info['hashes'].get('SHA256', '').lower(),
             base_model=base_model,
-            preview_url='',  # Will be updated after preview download
+            preview_url=None,  # Will be updated after preview download
             preview_nsfw_level=0, # Will be updated after preview download
             from_civitai=True,
             civitai=version_info,
@@ -180,28 +178,28 @@ class CheckpointMetadata(BaseModelMetadata):
     @classmethod
     def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'CheckpointMetadata':
         """Create CheckpointMetadata instance from Civitai version info"""
-        file_name = file_info.get('name', '')
+        file_name = file_info['name']
         base_model = determine_base_model(version_info.get('baseModel', ''))
         sub_type = version_info.get('type', 'checkpoint')
-
+        
         # Extract tags and description if available
         tags = []
         description = ""
-        model_data = version_info.get('model') or {}
-        if 'tags' in model_data:
-            tags = model_data['tags']
-        if 'description' in model_data:
-            description = model_data['description']
-
+        if 'model' in version_info:
+            if 'tags' in version_info['model']:
+                tags = version_info['model']['tags']
+            if 'description' in version_info['model']:
+                description = version_info['model']['description']
+        
         return cls(
             file_name=os.path.splitext(file_name)[0],
-            model_name=model_data.get('name', os.path.splitext(file_name)[0]),
+            model_name=version_info.get('model').get('name', os.path.splitext(file_name)[0]),
             file_path=save_path.replace(os.sep, '/'),
             size=file_info.get('sizeKB', 0) * 1024,
             modified=datetime.now().timestamp(),
-            sha256=(file_info.get('hashes') or {}).get('SHA256', '').lower(),
+            sha256=file_info['hashes'].get('SHA256', '').lower(),
             base_model=base_model,
-            preview_url='',  # Will be updated after preview download
+            preview_url=None,  # Will be updated after preview download
             preview_nsfw_level=0,
             from_civitai=True,
             civitai=version_info,
@@ -218,28 +216,74 @@ class EmbeddingMetadata(BaseModelMetadata):
     @classmethod
     def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'EmbeddingMetadata':
         """Create EmbeddingMetadata instance from Civitai version info"""
-        file_name = file_info.get('name', '')
+        file_name = file_info['name']
         base_model = determine_base_model(version_info.get('baseModel', ''))
         sub_type = version_info.get('type', 'embedding')
 
         # Extract tags and description if available
         tags = []
         description = ""
-        model_data = version_info.get('model') or {}
-        if 'tags' in model_data:
-            tags = model_data['tags']
-        if 'description' in model_data:
-            description = model_data['description']
+        if 'model' in version_info:
+            if 'tags' in version_info['model']:
+                tags = version_info['model']['tags']
+            if 'description' in version_info['model']:
+                description = version_info['model']['description']
 
         return cls(
             file_name=os.path.splitext(file_name)[0],
-            model_name=model_data.get('name', os.path.splitext(file_name)[0]),
+            model_name=version_info.get('model').get('name', os.path.splitext(file_name)[0]),
             file_path=save_path.replace(os.sep, '/'),
             size=file_info.get('sizeKB', 0) * 1024,
             modified=datetime.now().timestamp(),
-            sha256=(file_info.get('hashes') or {}).get('SHA256', '').lower(),
+            sha256=file_info['hashes'].get('SHA256', '').lower(),
             base_model=base_model,
-            preview_url='',  # Will be updated after preview download
+            preview_url=None,  # Will be updated after preview download
+            preview_nsfw_level=0,
+            from_civitai=True,
+            civitai=version_info,
+            sub_type=sub_type,
+            tags=tags,
+            modelDescription=description
+        )
+
+@dataclass
+class MiscMetadata(BaseModelMetadata):
+    """Represents the metadata structure for a Misc model (VAE, Upscaler)"""
+    sub_type: str = "vae"
+
+    @classmethod
+    def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'MiscMetadata':
+        """Create MiscMetadata instance from Civitai version info"""
+        file_name = file_info['name']
+        base_model = determine_base_model(version_info.get('baseModel', ''))
+
+        # Determine sub_type from CivitAI model type
+        civitai_type = version_info.get('model', {}).get('type', '').lower()
+        if civitai_type == 'vae':
+            sub_type = 'vae'
+        elif civitai_type == 'upscaler':
+            sub_type = 'upscaler'
+        else:
+            sub_type = 'vae'  # Default to vae
+
+        # Extract tags and description if available
+        tags = []
+        description = ""
+        if 'model' in version_info:
+            if 'tags' in version_info['model']:
+                tags = version_info['model']['tags']
+            if 'description' in version_info['model']:
+                description = version_info['model']['description']
+
+        return cls(
+            file_name=os.path.splitext(file_name)[0],
+            model_name=version_info.get('model').get('name', os.path.splitext(file_name)[0]),
+            file_path=save_path.replace(os.sep, '/'),
+            size=file_info.get('sizeKB', 0) * 1024,
+            modified=datetime.now().timestamp(),
+            sha256=file_info['hashes'].get('SHA256', '').lower(),
+            base_model=base_model,
+            preview_url=None,  # Will be updated after preview download
             preview_nsfw_level=0,
             from_civitai=True,
             civitai=version_info,

py/utils/usage_stats.py

@@ -57,9 +57,6 @@ class UsageStats:
             "last_save_time": 0
         }
         
-        # Track if stats have been modified since last save
-        self._is_dirty = False
-        
         # Queue for prompt_ids to process
         self.pending_prompt_ids = set()
         
@@ -183,39 +180,27 @@ class UsageStats:
     async def save_stats(self, force=False):
         """Save statistics to file"""
         try:
-            # Only save if:
-            # 1. force is True, OR
-            # 2. stats have been modified (is_dirty) AND save_interval has passed
+            # Only save if it's been at least save_interval since last save or force is True
             current_time = time.time()
-            time_since_last_save = current_time - self.stats.get("last_save_time", 0)
-
-            if not force:
-                if not self._is_dirty:
-                    # No changes to save
-                    return False
-                if time_since_last_save < self.save_interval:
-                    # Too soon since last save
-                    return False
-
+            if not force and (current_time - self.stats.get("last_save_time", 0)) < self.save_interval:
+                return False
+                
             # Use a lock to prevent concurrent writes
             async with self._lock:
                 # Update last save time
                 self.stats["last_save_time"] = current_time
-
+                
                 # Create directory if it doesn't exist
                 os.makedirs(os.path.dirname(self._stats_file_path), exist_ok=True)
-
+                
                 # Write to a temporary file first, then move it to avoid corruption
                 temp_path = f"{self._stats_file_path}.tmp"
                 with open(temp_path, 'w', encoding='utf-8') as f:
                     json.dump(self.stats, f, indent=2, ensure_ascii=False)
-
+                
                 # Replace the old file with the new one
                 os.replace(temp_path, self._stats_file_path)
-
-                # Clear dirty flag since we've saved
-                self._is_dirty = False
-
+                
                 logger.debug(f"Saved usage statistics to {self._stats_file_path}")
                 return True
         except Exception as e:
@@ -233,32 +218,25 @@ class UsageStats:
             while True:
                 # Wait a short interval before checking for new prompt_ids
                 await asyncio.sleep(5)  # Check every 5 seconds
-
+                
                 # Process any pending prompt_ids
                 if self.pending_prompt_ids:
                     async with self._lock:
                         # Get a copy of the set and clear original
                         prompt_ids = self.pending_prompt_ids.copy()
                         self.pending_prompt_ids.clear()
-
-                    # Process each prompt_id
-                    try:
-                        registry = MetadataRegistry()
-                    except NameError:
-                        # MetadataRegistry not available (standalone mode)
-                        registry = None
                     
-                    if registry:
-                        for prompt_id in prompt_ids:
-                            try:
-                                metadata = registry.get_metadata(prompt_id)
-                                await self._process_metadata(metadata)
-                            except Exception as e:
-                                logger.error(f"Error processing prompt_id {prompt_id}: {e}")
-
-                # Periodically save stats (only if there are changes)
-                if self._is_dirty:
-                    await self.save_stats()
+                    # Process each prompt_id
+                    registry = MetadataRegistry()
+                    for prompt_id in prompt_ids:
+                        try:
+                            metadata = registry.get_metadata(prompt_id)
+                            await self._process_metadata(metadata)
+                        except Exception as e:
+                            logger.error(f"Error processing prompt_id {prompt_id}: {e}")
+                
+                # Periodically save stats
+                await self.save_stats()
         except asyncio.CancelledError:
             # Task was cancelled, clean up
             await self.save_stats(force=True)
@@ -276,10 +254,9 @@ class UsageStats:
         """Process metadata from an execution"""
         if not metadata or not isinstance(metadata, dict):
             return
-
+            
         # Increment total executions count
         self.stats["total_executions"] += 1
-        self._is_dirty = True
         
         # Get today's date in YYYY-MM-DD format
         today = datetime.datetime.now().strftime("%Y-%m-%d")
@@ -396,11 +373,7 @@ class UsageStats:
         """Process a prompt execution immediately (synchronous approach)"""
         if not prompt_id:
             return
-
-        if standalone_mode:
-            # Usage statistics are not available in standalone mode
-            return
-
+            
         try:
             # Process metadata for this prompt_id
             registry = MetadataRegistry()

py/utils/utils.py

@@ -50,52 +50,6 @@ def get_lora_info(lora_name):
         # No event loop is running, we can use asyncio.run()
         return asyncio.run(_get_lora_info_async())
 
-
-def get_lora_info_absolute(lora_name):
-    """Get the absolute lora path and trigger words from cache
-    
-    Returns:
-        tuple: (absolute_path, trigger_words) where absolute_path is the full 
-               file system path to the LoRA file, or original lora_name if not found
-    """
-    async def _get_lora_info_absolute_async():
-        scanner = await ServiceRegistry.get_lora_scanner()
-        cache = await scanner.get_cached_data()
-        
-        for item in cache.raw_data:
-            if item.get('file_name') == lora_name:
-                file_path = item.get('file_path')
-                if file_path:
-                    # Return absolute path directly
-                    # Get trigger words from civitai metadata
-                    civitai = item.get('civitai', {})
-                    trigger_words = civitai.get('trainedWords', []) if civitai else []
-                    return file_path, trigger_words
-        return lora_name, []
-    
-    try:
-        # Check if we're already in an event loop
-        loop = asyncio.get_running_loop()
-        # If we're in a running loop, we need to use a different approach
-        # Create a new thread to run the async code
-        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_lora_info_absolute_async())
-            finally:
-                new_loop.close()
-        
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            future = executor.submit(run_in_thread)
-            return future.result()
-            
-    except RuntimeError:
-        # No event loop is running, we can use asyncio.run()
-        return asyncio.run(_get_lora_info_absolute_async())
-
 def fuzzy_match(text: str, pattern: str, threshold: float = 0.85) -> bool:
         """
         Check if text matches pattern using fuzzy matching.
@@ -184,28 +138,24 @@ def calculate_recipe_fingerprint(loras):
     if not loras:
         return ""
     
+    # Filter valid entries and extract hash and strength
     valid_loras = []
     for lora in loras:
+        # Skip excluded loras
         if lora.get("exclude", False):
             continue
             
-        hash_value = lora.get("hash", "")
-        if isinstance(hash_value, str):
-            hash_value = hash_value.lower()
-        else:
-            hash_value = str(hash_value).lower() if hash_value else ""
-        if not hash_value and lora.get("modelVersionId"):
+        # Get the hash - use modelVersionId as fallback if hash is empty
+        hash_value = lora.get("hash", "").lower()
+        if not hash_value and lora.get("isDeleted", False) and lora.get("modelVersionId"):
             hash_value = str(lora.get("modelVersionId"))
-
+            
+        # Skip entries without a valid hash
         if not hash_value:
             continue
-
+            
         # Normalize strength to 2 decimal places (check both strength and weight fields)
-        strength_val = lora.get("strength", lora.get("weight", 1.0))
-        try:
-            strength = round(float(strength_val), 2)
-        except (ValueError, TypeError):
-            strength = 1.0
+        strength = round(float(lora.get("strength", lora.get("weight", 1.0))), 2)
         
         valid_loras.append((hash_value, strength))
     

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-lora-manager"
 description = "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!"
-version = "1.0.0"
+version = "0.9.13"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",

pytest.ini

@@ -1,17 +1,12 @@
 [pytest]
-addopts = -v --import-mode=importlib -m "not performance"
+addopts = -v --import-mode=importlib
 testpaths = tests
 python_files = test_*.py
 python_classes = Test*
 python_functions = test_*
-# Asyncio configuration
-asyncio_mode = auto
-asyncio_default_fixture_loop_scope = function
-# Register markers
+# Register async marker for coroutine-style tests
 markers =
     asyncio: execute test within asyncio event loop
     no_settings_dir_isolation: allow tests to use real settings paths
-    integration: integration tests requiring external resources
-    performance: performance benchmarks (slow, skip by default)
 # Skip problematic directories to avoid import conflicts
-norecursedirs = .git .tox dist build *.egg __pycache__ py .hypothesis
+norecursedirs = .git .tox dist build *.egg __pycache__ py

requirements-dev.txt

@@ -1,7 +1,3 @@
 -r requirements.txt
 pytest>=7.4
 pytest-cov>=4.1
-pytest-asyncio>=0.21.0
-hypothesis>=6.0
-syrupy>=5.0
-pytest-benchmark>=5.0

scripts/update_supporters.py

@@ -1,63 +0,0 @@
-import json
-import os
-import re
-
-def update_readme():
-    # 1. Read JSON data
-    json_path = 'data/supporters.json'
-    if not os.path.exists(json_path):
-        print(f"Error: {json_path} not found.")
-        return
-
-    with open(json_path, 'r', encoding='utf-8') as f:
-        data = json.load(f)
-    
-    # 2. Generate Markdown content
-    special_thanks = data.get('specialThanks', [])
-    all_supporters = data.get('allSupporters', [])
-    total_count = data.get('totalCount', len(all_supporters))
-    
-    md_content = "\n### 🌟 Special Thanks\n\n"
-    if special_thanks:
-        md_content += ", ".join([f"**{name}**" for name in special_thanks]) + "\n\n"
-    else:
-        md_content += "*None yet*\n\n"
-    
-    md_content += f"### 💖 Supporters ({total_count})\n\n"
-    if all_supporters:
-        # Using a details block for the long list of supporters
-        md_content += "<details>\n<summary>Click to view all awesome supporters</summary>\n<br>\n\n"
-        md_content += ", ".join(all_supporters)
-        md_content += "\n\n</details>\n"
-    else:
-        md_content += "*No supporters listed yet*\n"
-
-    # 3. Read existing README.md
-    readme_path = 'README.md'
-    with open(readme_path, 'r', encoding='utf-8') as f:
-        readme = f.read()
-
-    # 4. Replace content between placeholders
-    start_tag = '<!-- SUPPORTERS-START -->'
-    end_tag = '<!-- SUPPORTERS-END -->'
-    
-    if start_tag not in readme or end_tag not in readme:
-        print(f"Error: Placeholders {start_tag} and {end_tag} not found in {readme_path}")
-        return
-
-    # Using non-regex replacement to avoid issues with special characters in names
-    parts = readme.split(start_tag)
-    before_start = parts[0]
-    after_start = parts[1].split(end_tag)
-    after_end = after_start[1]
-    
-    new_readme = f"{before_start}{start_tag}\n{md_content}\n{end_tag}{after_end}"
-
-    # 5. Write back to README.md
-    with open(readme_path, 'w', encoding='utf-8') as f:
-        f.write(new_readme)
-        
-    print(f"Successfully updated {readme_path} with {len(all_supporters)} supporters!")
-
-if __name__ == '__main__':
-    update_readme()