chore(release): update version to 0.9.2 and add release notes for bulk auto-organization feature

Create wanvideo_lora_select_from_text.py

.github/copilot-instructions.md

@@ -0,0 +1 @@
+Always use English for comments.

README.md

@@ -34,87 +34,48 @@ Enhance your Civitai browsing experience with our companion browser extension! S
 
 ## Release Notes
 
-### v0.8.22
+### v0.9.2
-* **Embeddings Management** - Added Embeddings page for comprehensive embedding model management.
+* **Bulk Auto-Organization Action** - Added a new bulk auto-organization feature. You can now select multiple models and automatically organize them according to your current path template settings for streamlined management.
-* **Advanced Sorting Options** - Introduced flexible sorting controls, allowing sorting by name, added date, or file size in both ascending and descending order.
+* **Bug Fixes** - Addressed several bugs to improve stability and reliability.
-* **Custom Download Path Templates & Base Model Mapping** - Implemented UI settings for configuring download path templates and base model path mappings, allowing customized model organization and storage location when downloading models via LM Civitai Extension.
-* **LM Civitai Extension Enhancements** - Improved concurrent download performance and stability, with new support for canceling active downloads directly from the extension interface.
-* **Update Feature** - Added update functionality, allowing users to update LoRA Manager to the latest release version directly from the LoRA Manager UI.
-* **Bulk Operations: Refresh All** - Added bulk refresh functionality, allowing users to update Civitai metadata across multiple LoRAs.
 
-### v0.8.20
+### v0.9.1
-* **LM Civitai Extension** - Released [browser extension through Chrome Web Store](https://chromewebstore.google.com/detail/lm-civitai-extension/capigligggeijgmocnaflanlbghnamgm?utm_source=item-share-cb) that works seamlessly with LoRA Manager to enhance Civitai browsing experience, showing which models are already in your local library, enabling one-click downloads, and providing queue and parallel download support
+* **Enhanced Bulk Operations** - Improved bulk operations with Marquee Selection and a bulk operation context menu, providing a more intuitive, desktop-application-like user experience.
-* **Enhanced Lora Loader** - Added support for nunchaku, improving convenience when working with ComfyUI-nunchaku workflows, plus new template workflows for quick onboarding
+* **New Bulk Actions** - Added bulk operations for adding tags and setting base models to multiple models simultaneously.
-* **WanVideo Integration** - Introduced WanVideo Lora Select (LoraManager) node compatible with ComfyUI-WanVideoWrapper for streamlined lora usage in video workflows, including a template workflow to help you get started quickly
 
-### v0.8.19
+### v0.9.0
-* **Analytics Dashboard** - Added new Statistics page providing comprehensive visual analysis of model collection and usage patterns for better library insights
+* **UI Overhaul for Enhanced Navigation** - Replaced the top flat folder tags with a new folder sidebar and breadcrumb navigation system for a more intuitive folder browsing and selection experience.
-* **Target Node Selection** - Enhanced workflow integration with intelligent target choosing when sending LoRAs/recipes to workflows with multiple loader/stacker nodes; a visual selector now appears showing node color, type, ID, and title for precise targeting
+* **Dual-Mode Folder Sidebar** - The new folder sidebar offers two display modes: 'List Mode,' which mirrors the classic folder view, and 'Tree Mode,' which presents a hierarchical folder structure for effortless navigation through nested directories.
-* **Enhanced NSFW Controls** - Added support for setting NSFW levels on recipes with automatic content blurring based on user preferences
+* **Internationalization Support** - Introduced multi-language support, now available in English, Simplified Chinese, Traditional Chinese, Spanish, Japanese, Korean, French, Russian, and German. Feedback from native speakers is welcome to improve the translations.
-* **Customizable Card Display** - New display settings allowing users to choose whether card information and action buttons are always visible or only revealed on hover
+* **Automatic Filename Conflict Resolution** - Implemented automatic file renaming (`original name + short hash`) to prevent conflicts when downloading or moving models.
-* **Expanded Compatibility** - Added support for efficiency-nodes-comfyui in Save Recipe and Save Image nodes, plus fixed compatibility with ComfyUI_Custom_Nodes_AlekPet
+* **Performance Optimizations & Bug Fixes** - Various performance improvements and bug fixes for a more stable and responsive experience.
 
-### v0.8.18
+### v0.8.30
-* **Custom Example Images** - Added ability to import your own example images for LoRAs and checkpoints with automatic metadata extraction from embedded information
+* **Automatic Model Path Correction** - Added auto-correction for model paths in built-in nodes such as Load Checkpoint, Load Diffusion Model, Load LoRA, and other custom nodes with similar functionality. Workflows containing outdated or incorrect model paths will now be automatically updated to reflect the current location of your models.
-* **Enhanced Example Management** - New action buttons to set specific examples as previews or delete custom examples
+* **Node UI Enhancements** - Improved node interface for a smoother and more intuitive user experience.
-* **Improved Duplicate Detection** - Enhanced "Find Duplicates" with hash verification feature to eliminate false positives when identifying duplicate models
+* **Bug Fixes** - Addressed various bugs to enhance stability and reliability.
-* **Tag Management** - Added tag editing functionality allowing users to customize and manage model tags
-* **Advanced Selection Controls** - Implemented Ctrl+A shortcut for quickly selecting all filtered LoRAs, automatically entering bulk mode when needed
-* **Note**: Cache file functionality temporarily disabled pending rework
 
-### v0.8.17
+### v0.8.29
-* **Duplicate Model Detection** - Added "Find Duplicates" functionality for LoRAs and checkpoints using model file hash detection, enabling convenient viewing and batch deletion of duplicate models
+* **Enhanced Recipe Imports** - Improved recipe importing with new target folder selection, featuring path input autocomplete and interactive folder tree navigation. Added a "Use Default Path" option when downloading missing LoRAs.
-* **Enhanced URL Recipe Imports** - Optimized import recipe via URL functionality using CivitAI API calls instead of web scraping, now supporting all rated images (including NSFW) for recipe imports
+* **WanVideo Lora Select Node Update** - Updated the WanVideo Lora Select node with a 'merge_loras' option to match the counterpart node in the WanVideoWrapper node package.
-* **Improved TriggerWord Control** - Enhanced TriggerWord Toggle node with new default_active switch to set the initial state (active/inactive) when trigger words are added
+* **Autocomplete Conflict Resolution** - Resolved an autocomplete feature conflict in LoRA nodes with pysssss autocomplete.
-* **Centralized Example Management** - Added "Migrate Existing Example Images" feature to consolidate downloaded example images from model folders into central storage with customizable naming patterns
+* **Improved Download Functionality** - Enhanced download functionality with resumable downloads and improved error handling.
-* **Intelligent Word Suggestions** - Implemented smart trigger word suggestions by reading class tokens and tag frequency from safetensors files, displaying recommendations when editing trigger words
+* **Bug Fixes** - Addressed several bugs for improved stability and performance.
-* **Model Version Management** - Added "Re-link to CivitAI" context menu option for connecting models to different CivitAI versions when needed
 
-### v0.8.16
+### v0.8.28
-* **Dramatic Startup Speed Improvement** - Added cache serialization mechanism for significantly faster loading times, especially beneficial for large model collections
+* **Autocomplete for Node Inputs** - Instantly find and add LoRAs by filename directly in Lora Loader, Lora Stacker, and WanVideo Lora Select nodes. Autocomplete suggestions include preview tooltips and preset weights, allowing you to quickly select LoRAs without opening the LoRA Manager UI.
-* **Enhanced Refresh Options** - Extended functionality with "Full Rebuild (complete)" option alongside "Quick Refresh (incremental)" to fix potential memory cache issues without requiring application restart
+* **Duplicate Notification Control** - Added a switch to duplicates mode, enabling users to turn off duplicate model notifications for a more streamlined experience.
-* **Customizable Display Density** - Replaced compact mode with adjustable display density settings for personalized layout customization
+* **Download Example Images from Context Menu** - Introduced a new context menu option to download example images for individual models.
-* **Model Creator Information** - Added creator details to model information panels for better attribution
-* **Improved WebP Support** - Enhanced Save Image node with workflow embedding capability for WebP format images
-* **Direct Example Access** - Added "Open Example Images Folder" button to card interfaces for convenient browsing of downloaded model examples
-* **Enhanced Compatibility** - Full ComfyUI Desktop support for "Send lora or recipe to workflow" functionality
-* **Cache Management** - Added settings to clear existing cache files when needed
-* **Bug Fixes & Stability** - Various improvements for overall reliability and performance
 
-### v0.8.15
+### v0.8.27
-* **Enhanced One-Click Integration** - Replaced copy button with direct send button allowing LoRAs/recipes to be sent directly to your current ComfyUI workflow without needing to paste
+* **User Experience Enhancements** - Improved the model download target folder selection with path input autocomplete and interactive folder tree navigation, making it easier and faster to choose where models are saved.
-* **Flexible Workflow Integration** - Click to append LoRAs/recipes to existing loader nodes or Shift+click to replace content, with additional right-click menu options for "Send to Workflow (Append)" or "Send to Workflow (Replace)"
+* **Default Path Option for Downloads** - Added a "Use Default Path" option when downloading models. When enabled, models are automatically organized and stored according to your configured path template settings.
-* **Improved LoRA Loader Controls** - Added header drag functionality for proportional strength adjustment of all LoRAs simultaneously (including CLIP strengths when expanded)
+* **Advanced Download Path Templates** - Expanded path template settings, allowing users to set individual templates for LoRA, checkpoint, and embedding models for greater flexibility. Introduced the `{author}` placeholder, enabling automatic organization of model files by creator name.
-* **Keyboard Navigation Support** - Implemented Page Up/Down for page scrolling, Home key to jump to top, and End key to jump to bottom for faster browsing through large collections
+* **Bug Fixes & Stability Improvements** - Addressed various bugs and improved overall stability for a smoother experience.
 
-### v0.8.14
+### v0.8.26
-* **Virtualized Scrolling** - Completely rebuilt rendering mechanism for smooth browsing with no lag or freezing, now supporting virtually unlimited model collections with optimized layouts for large displays, improving space utilization and user experience
+* **Creator Search Option** - Added ability to search models by creator name, making it easier to find models from specific authors.
-* **Compact Display Mode** - Added space-efficient view option that displays more cards per row (7 on 1080p, 8 on 2K, 10 on 4K)
+* **Enhanced Node Usability** - Improved user experience for Lora Loader, Lora Stacker, and WanVideo Lora Select nodes by fixing the maximum height of the text input area. Users can now freely and conveniently adjust the LoRA region within these nodes.
-* **Enhanced LoRA Node Functionality** - Comprehensive improvements to LoRA loader/stacker nodes including real-time trigger word updates (reflecting any change anywhere in the LoRA chain for precise updates) and expanded context menu with "Copy Notes" and "Copy Trigger Words" options for faster workflow
+* **Compatibility Fixes** - Resolved compatibility issues with ComfyUI and certain custom nodes, including ComfyUI-Custom-Scripts, ensuring smoother integration and operation.
-
-### v0.8.13
-* **Enhanced Recipe Management** - Added "Find duplicates" feature to identify and batch delete duplicate recipes with duplicate detection notifications during imports
-* **Improved Source Tracking** - Source URLs are now saved with recipes imported via URL, allowing users to view original content with one click or manually edit links
-* **Advanced LoRA Control** - Double-click LoRAs in Loader/Stacker nodes to access expanded CLIP strength controls for more precise adjustments of model and CLIP strength separately
-* **Lycoris Model Support** - Added compatibility with Lycoris models for expanded creative options
-* **Bug Fixes & UX Improvements** - Resolved various issues and enhanced overall user experience with numerous optimizations
-
-### v0.8.12
-* **Enhanced Model Discovery** - Added alphabetical navigation bar to LoRAs page for faster browsing through large collections
-* **Optimized Example Images** - Improved download logic to automatically refresh stale metadata before fetching example images
-* **Model Exclusion System** - New right-click option to exclude specific LoRAs or checkpoints from management
-* **Improved Showcase Experience** - Enhanced interaction in LoRA and checkpoint showcase areas for better usability
-
-### v0.8.11
-* **Offline Image Support** - Added functionality to download and save all model example images locally, ensuring access even when offline or if images are removed from CivitAI or the site is down
-* **Resilient Download System** - Implemented pause/resume capability with checkpoint recovery that persists through restarts or unexpected exits
-* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
-
-### v0.8.10
-* **Standalone Mode** - Run LoRA Manager independently from ComfyUI for a lightweight experience that works even with other stable diffusion interfaces
-* **Portable Edition** - New one-click portable version for easy startup and updates in standalone mode
-* **Enhanced Metadata Collection** - Added support for SamplerCustomAdvanced node in the metadata collector module
-* **Improved UI Organization** - Optimized Lora Loader node height to display up to 5 LoRAs at once with scrolling capability for larger collections
 
 [View Update History](./update_logs.md)
 
@@ -173,10 +134,11 @@ Enhance your Civitai browsing experience with our companion browser extension! S
 
 ### Option 2: **Portable Standalone Edition** (No ComfyUI required)
 
-1. Download the [Portable Package](https://github.com/willmiao/ComfyUI-Lora-Manager/releases/download/v0.8.15/lora_manager_portable.7z)
+1. Download the [Portable Package](https://github.com/willmiao/ComfyUI-Lora-Manager/releases/download/v0.8.26/lora_manager_portable.7z)
 2. Copy the provided `settings.json.example` file to create a new file named `settings.json` in `comfyui-lora-manager` folder
 3. Edit `settings.json` to include your correct model folder paths and CivitAI API key
 4. Run run.bat
+    - To change the startup port, edit `run.bat` and modify the parameter (e.g. `--port 9001`)
 
 ### Option 3: **Manual Installation**
 
@@ -306,3 +268,6 @@ Join our Discord community for support, discussions, and updates:
 [Discord Server](https://discord.gg/vcqNrWVFvM)
 
 ---
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=willmiao/ComfyUI-Lora-Manager&type=Date)](https://star-history.com/#willmiao/ComfyUI-Lora-Manager&Date)

__init__.py

@@ -1,5 +1,5 @@
 from .py.lora_manager import LoraManager
-from .py.nodes.lora_loader import LoraManagerLoader
+from .py.nodes.lora_loader import LoraManagerLoader, LoraManagerTextLoader
 from .py.nodes.trigger_word_toggle import TriggerWordToggle
 from .py.nodes.lora_stacker import LoraStacker
 from .py.nodes.save_image import SaveImage
@@ -10,6 +10,7 @@ from .py.metadata_collector import init as init_metadata_collector
 
 NODE_CLASS_MAPPINGS = {
     LoraManagerLoader.NAME: LoraManagerLoader,
+    LoraManagerTextLoader.NAME: LoraManagerTextLoader,
     TriggerWordToggle.NAME: TriggerWordToggle,
     LoraStacker.NAME: LoraStacker,
     SaveImage.NAME: SaveImage,

docs/EventManagementImplementation.md

@@ -0,0 +1,182 @@
+# Event Management Implementation Summary
+
+## What Has Been Implemented
+
+### 1. Enhanced EventManager Class
+- **Location**: `static/js/utils/EventManager.js`
+- **Features**:
+  - Priority-based event handling
+  - Conditional execution based on application state
+  - Element filtering (target/exclude selectors)
+  - Mouse button filtering
+  - Automatic cleanup with cleanup functions
+  - State tracking for app modes
+  - Error handling for event handlers
+
+### 2. BulkManager Integration
+- **Location**: `static/js/managers/BulkManager.js`
+- **Migrated Events**:
+  - Global keyboard shortcuts (Ctrl+A, Escape, B key)
+  - Marquee selection events (mousedown, mousemove, mouseup, contextmenu)
+  - State synchronization with EventManager
+- **Benefits**:
+  - Centralized priority handling
+  - Conditional execution based on modal state
+  - Better coordination with other components
+
+### 3. UIHelpers Integration
+- **Location**: `static/js/utils/uiHelpers.js`
+- **Migrated Events**:
+  - Mouse position tracking for node selector positioning
+  - Node selector click events (outside clicks and selection)
+  - State management for node selector
+- **Benefits**:
+  - Reduced direct DOM listeners
+  - Coordinated state tracking
+  - Better cleanup
+
+### 4. ModelCard Integration
+- **Location**: `static/js/components/shared/ModelCard.js`
+- **Migrated Events**:
+  - Model card click delegation
+  - Action button handling (star, globe, copy, etc.)
+  - Better return value handling for event propagation
+- **Benefits**:
+  - Single event listener for all model cards
+  - Priority-based execution
+  - Better event flow control
+
+### 5. Documentation and Initialization
+- **EventManagerDocs.md**: Comprehensive documentation
+- **eventManagementInit.js**: Initialization and global handlers
+- **Features**:
+  - Global escape key handling
+  - Modal state synchronization
+  - Error handling
+  - Analytics integration points
+  - Cleanup on page unload
+
+## Application States Tracked
+
+1. **bulkMode**: When bulk selection mode is active
+2. **marqueeActive**: When marquee selection is in progress  
+3. **modalOpen**: When any modal dialog is open
+4. **nodeSelectorActive**: When node selector popup is visible
+
+## Priority Levels Used
+
+- **250+**: Critical system events (escape keys)
+- **200+**: High priority system events (modal close)
+- **100-199**: Application-level shortcuts (bulk operations)
+- **80-99**: UI interactions (marquee selection)
+- **60-79**: Component interactions (model cards)
+- **10-49**: Tracking and monitoring
+- **1-9**: Analytics and low-priority tasks
+
+## Event Flow Examples
+
+### Bulk Mode Toggle (B key)
+1. **Priority 100**: BulkManager keyboard handler catches 'b' key
+2. Toggles bulk mode state
+3. Updates EventManager state
+4. Updates UI accordingly
+5. Stops propagation (returns true)
+
+### Marquee Selection
+1. **Priority 80**: BulkManager mousedown handler (only in .models-container, excluding cards/buttons)
+2. Starts marquee selection
+3. **Priority 90**: BulkManager mousemove handler (only when marquee active)
+4. Updates selection rectangle
+5. **Priority 90**: BulkManager mouseup handler ends selection
+
+### Model Card Click
+1. **Priority 60**: ModelCard delegation handler checks for specific elements
+2. If action button: handles action and stops propagation
+3. If general card click: continues to other handlers
+4. Bulk selection may also handle the event if in bulk mode
+
+## Remaining Event Listeners (Not Yet Migrated)
+
+### High Priority for Migration
+1. **SearchManager keyboard events** - Global search shortcuts
+2. **ModalManager escape handling** - Already integrated with initialization
+3. **Scroll-based events** - Back to top, virtual scrolling
+4. **Resize events** - Panel positioning, responsive layouts
+
+### Medium Priority
+1. **Form input events** - Tag inputs, settings forms
+2. **Component-specific events** - Recipe modal, showcase view
+3. **Sidebar events** - Resize handling, toggle events
+
+### Low Priority (Can Remain As-Is)
+1. **VirtualScroller events** - Performance-critical, specialized
+2. **Component lifecycle events** - Modal open/close callbacks
+3. **One-time setup events** - Theme initialization, etc.
+
+## Benefits Achieved
+
+### Performance Improvements
+- **Reduced DOM listeners**: From ~15+ individual listeners to ~5 coordinated handlers
+- **Conditional execution**: Handlers only run when conditions are met
+- **Priority ordering**: Important events handled first
+- **Better memory management**: Automatic cleanup prevents leaks
+
+### Coordination Improvements
+- **State synchronization**: All components aware of app state
+- **Event flow control**: Proper propagation stopping
+- **Conflict resolution**: Priority system prevents conflicts
+- **Debugging**: Centralized event handling for easier debugging
+
+### Code Quality Improvements
+- **Consistent patterns**: All event handling follows same patterns
+- **Better separation of concerns**: Event logic separated from business logic
+- **Error handling**: Centralized error catching and reporting
+- **Documentation**: Clear patterns for future development
+
+## Next Steps (Recommendations)
+
+### 1. Migrate Search Events
+```javascript
+// In SearchManager.js
+eventManager.addHandler('keydown', 'search-shortcuts', (e) => {
+    if ((e.ctrlKey || e.metaKey) && e.key === 'f') {
+        this.focusSearchInput();
+        return true;
+    }
+}, { priority: 120 });
+```
+
+### 2. Integrate Resize Events
+```javascript
+// Create ResizeManager
+eventManager.addHandler('resize', 'layout-resize', debounce((e) => {
+    this.updateLayoutDimensions();
+}, 250), { priority: 50 });
+```
+
+### 3. Add Debug Mode
+```javascript
+// In EventManager.js
+if (window.DEBUG_EVENTS) {
+    console.log(`Event ${eventType} handled by ${source} (priority: ${priority})`);
+}
+```
+
+### 4. Create Event Analytics
+```javascript
+// Track event patterns for optimization
+eventManager.addHandler('*', 'analytics', (e) => {
+    this.trackEventUsage(e.type, performance.now());
+}, { priority: 1 });
+```
+
+## Testing Recommendations
+
+1. **Verify bulk mode interactions** work correctly
+2. **Test marquee selection** in various scenarios
+3. **Check modal state synchronization** 
+4. **Verify node selector** positioning and cleanup
+5. **Test keyboard shortcuts** don't conflict
+6. **Verify proper cleanup** when components are destroyed
+
+The centralized event management system provides a solid foundation for coordinated, efficient event handling across the application while maintaining good performance and code organization.

docs/EventManagerDocs.md

@@ -0,0 +1,301 @@
+# Centralized Event Management System
+
+This document describes the centralized event management system that coordinates event handling across the ComfyUI LoRA Manager application.
+
+## Overview
+
+The `EventManager` class provides a centralized way to handle DOM events with priority-based execution, conditional execution based on application state, and proper cleanup mechanisms.
+
+## Features
+
+- **Priority-based execution**: Handlers with higher priority run first
+- **Conditional execution**: Handlers can be executed based on application state
+- **Element filtering**: Handlers can target specific elements or exclude others
+- **Automatic cleanup**: Cleanup functions are called when handlers are removed
+- **State tracking**: Tracks application states like bulk mode, modal open, etc.
+
+## Basic Usage
+
+### Importing
+
+```javascript
+import { eventManager } from './EventManager.js';
+```
+
+### Adding Event Handlers
+
+```javascript
+eventManager.addHandler('click', 'myComponent', (event) => {
+    console.log('Button clicked!');
+    return true; // Stop propagation to other handlers
+}, {
+    priority: 100,
+    targetSelector: '.my-button',
+    skipWhenModalOpen: true
+});
+```
+
+### Removing Event Handlers
+
+```javascript
+// Remove specific handler
+eventManager.removeHandler('click', 'myComponent');
+
+// Remove all handlers for a component
+eventManager.removeAllHandlersForSource('myComponent');
+```
+
+### Updating Application State
+
+```javascript
+// Set state
+eventManager.setState('bulkMode', true);
+eventManager.setState('modalOpen', true);
+
+// Get state
+const isBulkMode = eventManager.getState('bulkMode');
+```
+
+## Available States
+
+- `bulkMode`: Whether bulk selection mode is active
+- `marqueeActive`: Whether marquee selection is in progress
+- `modalOpen`: Whether any modal is currently open
+- `nodeSelectorActive`: Whether the node selector popup is active
+
+## Handler Options
+
+### Priority
+Higher numbers = higher priority. Handlers run in descending priority order.
+
+```javascript
+{
+    priority: 100 // High priority
+}
+```
+
+### Conditional Execution
+
+```javascript
+{
+    onlyInBulkMode: true,               // Only run when bulk mode is active
+    onlyWhenMarqueeActive: true,        // Only run when marquee selection is active
+    skipWhenModalOpen: true,            // Skip when any modal is open
+    skipWhenNodeSelectorActive: true,   // Skip when node selector is active
+    onlyWhenNodeSelectorActive: true    // Only run when node selector is active
+}
+```
+
+### Element Filtering
+
+```javascript
+{
+    targetSelector: '.model-card',      // Only handle events on matching elements
+    excludeSelector: 'button, input',  // Exclude events from these elements
+    button: 0                          // Only handle specific mouse button (0=left, 1=middle, 2=right)
+}
+```
+
+### Cleanup Functions
+
+```javascript
+{
+    cleanup: () => {
+        // Custom cleanup logic
+        console.log('Handler cleaned up');
+    }
+}
+```
+
+## Integration Examples
+
+### BulkManager Integration
+
+```javascript
+class BulkManager {
+    registerEventHandlers() {
+        // High priority keyboard shortcuts
+        eventManager.addHandler('keydown', 'bulkManager-keyboard', (e) => {
+            return this.handleGlobalKeyboard(e);
+        }, {
+            priority: 100,
+            skipWhenModalOpen: true
+        });
+
+        // Marquee selection
+        eventManager.addHandler('mousedown', 'bulkManager-marquee-start', (e) => {
+            return this.handleMarqueeStart(e);
+        }, {
+            priority: 80,
+            skipWhenModalOpen: true,
+            targetSelector: '.models-container',
+            excludeSelector: '.model-card, button, input',
+            button: 0
+        });
+    }
+    
+    cleanup() {
+        eventManager.removeAllHandlersForSource('bulkManager-keyboard');
+        eventManager.removeAllHandlersForSource('bulkManager-marquee-start');
+    }
+}
+```
+
+### Modal Integration
+
+```javascript
+class ModalManager {
+    showModal(modalId) {
+        // Update state when modal opens
+        eventManager.setState('modalOpen', true);
+        this.displayModal(modalId);
+    }
+    
+    closeModal(modalId) {
+        // Update state when modal closes
+        eventManager.setState('modalOpen', false);
+        this.hideModal(modalId);
+    }
+}
+```
+
+### Component Event Delegation
+
+```javascript
+export function setupComponentEvents() {
+    eventManager.addHandler('click', 'myComponent-actions', (event) => {
+        const button = event.target.closest('.action-button');
+        if (!button) return false;
+        
+        this.handleAction(button.dataset.action);
+        return true; // Stop propagation
+    }, {
+        priority: 60,
+        targetSelector: '.component-container'
+    });
+}
+```
+
+## Best Practices
+
+### 1. Use Descriptive Source Names
+Use the format `componentName-purposeDescription`:
+```javascript
+// Good
+'bulkManager-marqueeSelection'
+'nodeSelector-clickOutside'
+'modelCard-delegation'
+
+// Avoid
+'bulk'
+'click'
+'handler1'
+```
+
+### 2. Set Appropriate Priorities
+- 200+: Critical system events (escape keys, critical modals)
+- 100-199: High priority application events (keyboard shortcuts)
+- 50-99: Normal UI interactions (buttons, cards)
+- 1-49: Low priority events (tracking, analytics)
+
+### 3. Use Conditional Execution
+Instead of checking state inside handlers, use options:
+```javascript
+// Good
+eventManager.addHandler('click', 'bulk-action', handler, {
+    onlyInBulkMode: true
+});
+
+// Avoid
+eventManager.addHandler('click', 'bulk-action', (e) => {
+    if (!state.bulkMode) return;
+    // handler logic
+});
+```
+
+### 4. Clean Up Properly
+Always clean up handlers when components are destroyed:
+```javascript
+class MyComponent {
+    constructor() {
+        this.registerEvents();
+    }
+    
+    destroy() {
+        eventManager.removeAllHandlersForSource('myComponent');
+    }
+}
+```
+
+### 5. Return Values Matter
+- Return `true` to stop event propagation to other handlers
+- Return `false` or `undefined` to continue with other handlers
+
+## Migration Guide
+
+### From Direct Event Listeners
+
+**Before:**
+```javascript
+document.addEventListener('click', (e) => {
+    if (e.target.closest('.my-button')) {
+        this.handleClick(e);
+    }
+});
+```
+
+**After:**
+```javascript
+eventManager.addHandler('click', 'myComponent-button', (e) => {
+    this.handleClick(e);
+}, {
+    targetSelector: '.my-button'
+});
+```
+
+### From Event Delegation
+
+**Before:**
+```javascript
+container.addEventListener('click', (e) => {
+    const card = e.target.closest('.model-card');
+    if (!card) return;
+    
+    if (e.target.closest('.action-btn')) {
+        this.handleAction(e);
+    }
+});
+```
+
+**After:**
+```javascript
+eventManager.addHandler('click', 'container-actions', (e) => {
+    const card = e.target.closest('.model-card');
+    if (!card) return false;
+    
+    if (e.target.closest('.action-btn')) {
+        this.handleAction(e);
+        return true;
+    }
+}, {
+    targetSelector: '.container'
+});
+```
+
+## Performance Benefits
+
+1. **Reduced DOM listeners**: Single listener per event type instead of multiple
+2. **Conditional execution**: Handlers only run when conditions are met
+3. **Priority ordering**: Important handlers run first, avoiding unnecessary work
+4. **Automatic cleanup**: Prevents memory leaks from orphaned listeners
+5. **Centralized debugging**: All event handling flows through one system
+
+## Debugging
+
+Enable debug logging to trace event handling:
+```javascript
+// Add to EventManager.js for debugging
+console.log(`Handling ${eventType} event with ${handlers.length} handlers`);
+```
+
+The event manager provides a foundation for coordinated, efficient event handling across the entire application.

py/config.py

@@ -5,6 +5,7 @@ from typing import List
 import logging
 import sys
 import json
+import urllib.parse
 
 # Check if running in standalone mode
 standalone_mode = 'nodes' not in sys.modules
@@ -17,6 +18,7 @@ class Config:
     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')
         # Path mapping dictionary, target to link mapping
         self._path_mappings = {}
         # Static route mapping dictionary, target to route mapping
@@ -59,6 +61,9 @@ class Config:
             
             if self.checkpoints_roots and len(self.checkpoints_roots) == 1 and "default_checkpoint_root" not in settings:
                 settings["default_checkpoint_root"] = self.checkpoints_roots[0]
+
+            if self.embeddings_roots and len(self.embeddings_roots) == 1 and "default_embedding_root" not in settings:
+                settings["default_embedding_root"] = self.embeddings_roots[0]
             
             # Save settings
             with open(settings_path, 'w', encoding='utf-8') as f:
@@ -201,16 +206,20 @@ class Config:
                     real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
                     unet_map[real_path] = unet_map.get(real_path, path.replace(os.sep, "/"))  # preserve first seen
             
+            # Merge both maps and deduplicate by real path
+            merged_map = {}
+            for real_path, orig_path in {**checkpoint_map, **unet_map}.items():
+                if real_path not in merged_map:
+                    merged_map[real_path] = orig_path
+
             # Now sort and use only the deduplicated real paths
-            unique_checkpoint_paths = sorted(checkpoint_map.values(), key=lambda p: p.lower())
+            unique_paths = sorted(merged_map.values(), key=lambda p: p.lower())
-            unique_unet_paths = sorted(unet_map.values(), key=lambda p: p.lower())
             
-            # Store individual paths in class properties
+            # Split back into checkpoints and unet roots for class properties
-            self.checkpoints_roots = unique_checkpoint_paths
+            self.checkpoints_roots = [p for p in unique_paths if p in checkpoint_map.values()]
-            self.unet_roots = unique_unet_paths
+            self.unet_roots = [p for p in unique_paths if p in unet_map.values()]
             
-            # Combine all checkpoint-related paths for return value
+            all_paths = unique_paths
-            all_paths = unique_checkpoint_paths + unique_unet_paths
             
             logger.info("Found checkpoint roots:" + ("\n - " + "\n - ".join(all_paths) if all_paths else "[]"))
             
@@ -260,17 +269,26 @@ class Config:
             return []
 
     def get_preview_static_url(self, preview_path: str) -> str:
-        """Convert local preview path to static URL"""
         if not preview_path:
             return ""
         
         real_path = os.path.realpath(preview_path).replace(os.sep, '/')
-
+        
+        # Find longest matching path (most specific match)
+        best_match = ""
+        best_route = ""
+        
         for path, route in self._route_mappings.items():
-            if real_path.startswith(path):
+            if real_path.startswith(path) and len(path) > len(best_match):
-                relative_path = os.path.relpath(real_path, path)
+                best_match = path
-                return f'{route}/{relative_path.replace(os.sep, "/")}'
+                best_route = route
-
+        
+        if best_match:
+            relative_path = os.path.relpath(real_path, best_match).replace(os.sep, '/')
+            safe_parts = [urllib.parse.quote(part) for part in relative_path.split('/')]
+            safe_path = '/'.join(safe_parts)
+            return f'{best_route}/{safe_path}'
+        
         return ""
 
 # Global config instance

py/lora_manager.py

@@ -145,7 +145,12 @@ class LoraManager:
                 except Exception as e:
                     logger.warning(f"Failed to add static route on initialization for {target_path}: {e}")
                     continue
-        
+
+        # 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}")
+
         # Add static route for plugin assets
         app.router.add_static('/loras_static', config.static_path)
         
@@ -198,18 +203,267 @@ class LoraManager:
             recipe_scanner = await ServiceRegistry.get_recipe_scanner()
             
             # Create low-priority initialization tasks
-            asyncio.create_task(lora_scanner.initialize_in_background(), name='lora_cache_init')
+            init_tasks = [
-            asyncio.create_task(checkpoint_scanner.initialize_in_background(), name='checkpoint_cache_init')
+                asyncio.create_task(lora_scanner.initialize_in_background(), name='lora_cache_init'),
-            asyncio.create_task(embedding_scanner.initialize_in_background(), name='embedding_cache_init')
+                asyncio.create_task(checkpoint_scanner.initialize_in_background(), name='checkpoint_cache_init'),
-            asyncio.create_task(recipe_scanner.initialize_in_background(), name='recipe_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')
+            ]
 
             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'
+            )
+            
             logger.info("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)
     
+    @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...")
+            
+            # 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...")
+            
+            # Run post-initialization tasks
+            post_tasks = [
+                asyncio.create_task(cls._cleanup_backup_files(), name='cleanup_bak_files'),
+                asyncio.create_task(cls._cleanup_example_images_folders(), name='cleanup_example_images'),
+                # 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}")
+                else:
+                    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)
+    
+    @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) 
+            all_roots.update(config.embeddings_roots)
+            
+            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)
+                    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)")
+                        
+                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")
+            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'):
+                                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}")
+                            
+            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):
+        """Clean up invalid or empty folders in example images directory"""
+        try:
+            example_images_path = settings.get('example_images_path')
+            if not example_images_path or not os.path.exists(example_images_path):
+                logger.debug("Example images path not configured or doesn't exist, skipping cleanup")
+                return
+            
+            logger.debug(f"Starting cleanup of example images folders in: {example_images_path}")
+            
+            # Get all scanner instances to check hash validity
+            lora_scanner = await ServiceRegistry.get_lora_scanner()
+            checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+            embedding_scanner = await ServiceRegistry.get_embedding_scanner()
+            
+            total_folders_checked = 0
+            empty_folders_removed = 0
+            invalid_hash_folders_removed = 0
+            
+            # Scan the example images directory
+            try:
+                with os.scandir(example_images_path) as it:
+                    for entry in it:
+                        if not entry.is_dir(follow_symlinks=False):
+                            continue
+                            
+                        folder_name = entry.name
+                        folder_path = entry.path
+                        total_folders_checked += 1
+                        
+                        try:
+                            # Check if folder is empty
+                            is_empty = cls._is_folder_empty(folder_path)
+                            if is_empty:
+                                logger.debug(f"Removing empty example images folder: {folder_name}")
+                                await cls._remove_folder_safely(folder_path)
+                                empty_folders_removed += 1
+                                continue
+                            
+                            # Check if folder name is a valid SHA256 hash (64 hex characters)
+                            if len(folder_name) != 64 or not all(c in '0123456789abcdefABCDEF' for c in folder_name):
+                                logger.debug(f"Removing invalid hash folder: {folder_name}")
+                                await cls._remove_folder_safely(folder_path)
+                                invalid_hash_folders_removed += 1
+                                continue
+                            
+                            # Check if hash exists in any of the scanners
+                            hash_exists = (
+                                lora_scanner.has_hash(folder_name) or 
+                                checkpoint_scanner.has_hash(folder_name) or 
+                                embedding_scanner.has_hash(folder_name)
+                            )
+                            
+                            if not hash_exists:
+                                logger.debug(f"Removing example images folder for deleted model: {folder_name}")
+                                await cls._remove_folder_safely(folder_path)
+                                invalid_hash_folders_removed += 1
+                                continue
+                                
+                            logger.debug(f"Keeping valid example images folder: {folder_name}")
+                            
+
+                        except Exception as e:
+                            logger.error(f"Error processing example images folder {folder_name}: {e}")
+                        
+                        # Yield control periodically
+                        await asyncio.sleep(0.01)
+                        
+            except Exception as e:
+                logger.error(f"Error scanning example images directory: {e}")
+                return
+            
+            # Log final cleanup report
+            total_removed = empty_folders_removed + invalid_hash_folders_removed
+            if total_removed > 0:
+                logger.info(f"Example images cleanup completed: checked {total_folders_checked} folders, "
+                           f"removed {empty_folders_removed} empty folders and {invalid_hash_folders_removed} "
+                           f"folders for deleted/invalid models (total: {total_removed} removed)")
+            else:
+                logger.info(f"Example images cleanup completed: checked {total_folders_checked} folders, "
+                           f"no cleanup needed")
+                
+        except Exception as e:
+            logger.error(f"Error during example images cleanup: {e}", exc_info=True)
+    
+    @classmethod
+    def _is_folder_empty(cls, folder_path: str) -> bool:
+        """Check if a folder is empty
+        
+        Args:
+            folder_path: Path to the folder to check
+            
+        Returns:
+            bool: True if folder is empty, False otherwise
+        """
+        try:
+            with os.scandir(folder_path) as it:
+                return not any(it)
+        except Exception as e:
+            logger.debug(f"Error checking if folder is empty {folder_path}: {e}")
+            return False
+    
+    @classmethod
+    async def _remove_folder_safely(cls, folder_path: str):
+        """Safely remove a folder and all its contents
+        
+        Args:
+            folder_path: Path to the folder to remove
+        """
+        try:
+            import shutil
+            loop = asyncio.get_event_loop()
+            await loop.run_in_executor(None, shutil.rmtree, folder_path)
+        except Exception as e:
+            logger.warning(f"Failed to remove folder {folder_path}: {e}")
+
     @classmethod
     async def _cleanup(cls, app):
         """Cleanup resources using ServiceRegistry"""

py/metadata_collector/metadata_hook.py

@@ -146,52 +146,40 @@ class MetadataHook:
         # Store the original _async_map_node_over_list function
         original_map_node_over_list = getattr(execution, map_node_func_name)
         
-        # Define the wrapped async function - NOTE: Updated signature with prompt_id and unique_id!
+        # Wrapped async function, compatible with both stable and nightly
-        async def async_map_node_over_list_with_metadata(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None):
+        async def async_map_node_over_list_with_metadata(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, *args, **kwargs):
+            hidden_inputs = kwargs.get('hidden_inputs', None)
             # Only collect metadata when calling the main function of nodes
             if func == obj.FUNCTION and hasattr(obj, '__class__'):
                 try:
-                    # Get the current prompt_id from the registry
                     registry = MetadataRegistry()
-                    # We now have prompt_id directly from the function parameters
-                    
                     if prompt_id is not None:
-                        # Get node class type
                         class_type = obj.__class__.__name__
-                        
-                        # Use the passed unique_id parameter instead of trying to extract it
                         node_id = unique_id
-                        
-                        # Record inputs before execution
                         if node_id is not None:
                             registry.record_node_execution(node_id, class_type, input_data_all, None)
                 except Exception as e:
                     print(f"Error collecting metadata (pre-execution): {str(e)}")
             
-            # Execute the original async function with ALL parameters in the correct order
+            # Call original function with all args/kwargs
-            results = await original_map_node_over_list(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt, execution_block_cb, pre_execute_cb)
+            results = await original_map_node_over_list(
+                prompt_id, unique_id, obj, input_data_all, func,
+                allow_interrupt, execution_block_cb, pre_execute_cb, *args, **kwargs
+            )
             
-            # After execution, collect outputs for relevant nodes
             if func == obj.FUNCTION and hasattr(obj, '__class__'):
                 try:
-                    # Get the current prompt_id from the registry
                     registry = MetadataRegistry()
-                    
                     if prompt_id is not None:
-                        # Get node class type
                         class_type = obj.__class__.__name__
-                        
-                        # Use the passed unique_id parameter 
                         node_id = unique_id
-                        
-                        # Record outputs after execution
                         if node_id is not None:
                             registry.update_node_execution(node_id, class_type, results)
                 except Exception as e:
                     print(f"Error collecting metadata (post-execution): {str(e)}")
             
             return results
-            
+        
         # Also hook the execute function to track the current prompt_id
         original_execute = execution.execute
         

py/metadata_collector/metadata_processor.py

@@ -339,44 +339,8 @@ class MetadataProcessor:
                     is_custom_advanced = prompt.original_prompt[primary_sampler_id].get("class_type") == "SamplerCustomAdvanced"
                 
                 if is_custom_advanced:
-                    # For SamplerCustomAdvanced, trace specific inputs
+                    # For SamplerCustomAdvanced, use the new handler method
-                    
+                    MetadataProcessor.handle_custom_advanced_sampler(metadata, prompt, primary_sampler_id, params)
-                    # 1. Trace sigmas input to find BasicScheduler
-                    scheduler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sigmas", "BasicScheduler", max_depth=5)
-                    if scheduler_node_id and scheduler_node_id in metadata.get(SAMPLING, {}):
-                        scheduler_params = metadata[SAMPLING][scheduler_node_id].get("parameters", {})
-                        params["steps"] = scheduler_params.get("steps")
-                        params["scheduler"] = scheduler_params.get("scheduler")
-                    
-                    # 2. Trace sampler input to find KSamplerSelect
-                    sampler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sampler", "KSamplerSelect", max_depth=5)
-                    if sampler_node_id and sampler_node_id in metadata.get(SAMPLING, {}):
-                        sampler_params = metadata[SAMPLING][sampler_node_id].get("parameters", {})
-                        params["sampler"] = sampler_params.get("sampler_name")
-                    
-                    # 3. Trace guider input for CFGGuider and CLIPTextEncode
-                    guider_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "guider", max_depth=5)
-                    if guider_node_id and guider_node_id in prompt.original_prompt:
-                        # Check if the guider node is a CFGGuider
-                        if prompt.original_prompt[guider_node_id].get("class_type") == "CFGGuider":
-                            # Extract cfg value from the CFGGuider
-                            if guider_node_id in metadata.get(SAMPLING, {}):
-                                cfg_params = metadata[SAMPLING][guider_node_id].get("parameters", {})
-                                params["cfg_scale"] = cfg_params.get("cfg")
-                            
-                            # Find CLIPTextEncode for positive prompt
-                            positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "positive", "CLIPTextEncode", max_depth=10)
-                            if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
-                                params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
-                            
-                            # Find CLIPTextEncode for negative prompt
-                            negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "negative", "CLIPTextEncode", max_depth=10)
-                            if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
-                                params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
-                        else:
-                            positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
-                            if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
-                                params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
                 
                 else:
                     # For standard samplers, match conditioning objects to prompts
@@ -401,6 +365,9 @@ class MetadataProcessor:
                         negative_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "negative", max_depth=10)
                         if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
                             params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
+                    
+                    # For SamplerCustom, handle any additional parameters
+                    MetadataProcessor.handle_custom_advanced_sampler(metadata, prompt, primary_sampler_id, params)
                 
             # Size extraction is same for all sampler types
             # Check if the sampler itself has size information (from latent_image)
@@ -454,3 +421,59 @@ class MetadataProcessor:
         """Convert metadata to JSON string"""
         params = MetadataProcessor.to_dict(metadata, id)
         return json.dumps(params, indent=4)
+    
+    @staticmethod
+    def handle_custom_advanced_sampler(metadata, prompt, primary_sampler_id, params):
+        """
+        Handle parameter extraction for SamplerCustomAdvanced nodes
+        
+        Parameters:
+        - metadata: The workflow metadata
+        - prompt: The prompt object containing node connections
+        - primary_sampler_id: ID of the SamplerCustomAdvanced node
+        - params: Parameters dictionary to update
+        """
+        if not prompt.original_prompt or primary_sampler_id not in prompt.original_prompt:
+            return
+            
+        sampler_inputs = prompt.original_prompt[primary_sampler_id].get("inputs", {})
+        
+        # 1. Trace sigmas input to find BasicScheduler (only if sigmas input exists)
+        if "sigmas" in sampler_inputs:
+            scheduler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sigmas", None, max_depth=5)
+            if scheduler_node_id and scheduler_node_id in metadata.get(SAMPLING, {}):
+                scheduler_params = metadata[SAMPLING][scheduler_node_id].get("parameters", {})
+                params["steps"] = scheduler_params.get("steps")
+                params["scheduler"] = scheduler_params.get("scheduler")
+        
+        # 2. Trace sampler input to find KSamplerSelect (only if sampler input exists)
+        if "sampler" in sampler_inputs:
+            sampler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sampler", "KSamplerSelect", max_depth=5)
+            if sampler_node_id and sampler_node_id in metadata.get(SAMPLING, {}):
+                sampler_params = metadata[SAMPLING][sampler_node_id].get("parameters", {})
+                params["sampler"] = sampler_params.get("sampler_name")
+        
+        # 3. Trace guider input for CFGGuider and CLIPTextEncode
+        if "guider" in sampler_inputs:
+            guider_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "guider", max_depth=5)
+            if guider_node_id and guider_node_id in prompt.original_prompt:
+                # Check if the guider node is a CFGGuider
+                if prompt.original_prompt[guider_node_id].get("class_type") == "CFGGuider":
+                    # Extract cfg value from the CFGGuider
+                    if guider_node_id in metadata.get(SAMPLING, {}):
+                        cfg_params = metadata[SAMPLING][guider_node_id].get("parameters", {})
+                        params["cfg_scale"] = cfg_params.get("cfg")
+                    
+                    # Find CLIPTextEncode for positive prompt
+                    positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "positive", "CLIPTextEncode", max_depth=10)
+                    if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
+                        params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
+                    
+                    # Find CLIPTextEncode for negative prompt
+                    negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "negative", "CLIPTextEncode", max_depth=10)
+                    if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
+                        params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
+                else:
+                    positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
+                    if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
+                        params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")

py/metadata_collector/node_extractors.py

@@ -642,7 +642,9 @@ NODE_EXTRACTORS = {
     # Sampling
     "KSampler": SamplerExtractor,
     "KSamplerAdvanced": KSamplerAdvancedExtractor,
+    "SamplerCustom": KSamplerAdvancedExtractor,
     "SamplerCustomAdvanced": SamplerCustomAdvancedExtractor,
+    "ClownsharKSampler_Beta": SamplerExtractor,
     "TSC_KSampler": TSCKSamplerExtractor,   # Efficient Nodes
     "TSC_KSamplerAdvanced": TSCKSamplerAdvancedExtractor,  # Efficient Nodes
     "KSamplerBasicPipe": KSamplerBasicPipeExtractor,    # comfyui-impact-pack
@@ -652,9 +654,11 @@ NODE_EXTRACTORS = {
     # Sampling Selectors
     "KSamplerSelect": KSamplerSelectExtractor,  # Add KSamplerSelect
     "BasicScheduler": BasicSchedulerExtractor,  # Add BasicScheduler
+    "AlignYourStepsScheduler": BasicSchedulerExtractor,  # Add AlignYourStepsScheduler
     # Loaders
     "CheckpointLoaderSimple": CheckpointLoaderExtractor,
     "comfyLoader": CheckpointLoaderExtractor,  # easy comfyLoader
+    "CheckpointLoaderSimpleWithImages": CheckpointLoaderExtractor,  # CheckpointLoader|pysssss
     "TSC_EfficientLoader": TSCCheckpointLoaderExtractor,  # Efficient Nodes
     "UNETLoader": UNETLoaderExtractor,          # Updated to use dedicated extractor
     "UnetLoaderGGUF": UNETLoaderExtractor,  # Updated to use dedicated extractor

py/nodes/lora_loader.py

@@ -1,4 +1,5 @@
 import logging
+import re
 from nodes import LoraLoader
 from comfy.comfy_types import IO # type: ignore
 from ..utils.utils import get_lora_info
@@ -17,7 +18,8 @@ class LoraManagerLoader:
                 "model": ("MODEL",),
                 # "clip": ("CLIP",),
                 "text": (IO.STRING, {
-                    "multiline": True, 
+                    "multiline": True,
+                    "pysssss.autocomplete": False, 
                     "dynamicPrompts": True, 
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                     "placeholder": "LoRA syntax input: <lora:name:strength>"
@@ -128,4 +130,142 @@ class LoraManagerLoader:
                 
         formatted_loras_text = " ".join(formatted_loras)
 
+        return (model, clip, trigger_words_text, formatted_loras_text)
+
+class LoraManagerTextLoader:
+    NAME = "LoRA Text Loader (LoraManager)"
+    CATEGORY = "Lora Manager/loaders"
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "lora_syntax": (IO.STRING, {
+                    "defaultInput": True,
+                    "forceInput": True,
+                    "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation"
+                }),
+            },
+            "optional": {
+                "clip": ("CLIP",),
+                "lora_stack": ("LORA_STACK",),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "CLIP", IO.STRING, IO.STRING)
+    RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
+    FUNCTION = "load_loras_from_text"
+    
+    def parse_lora_syntax(self, text):
+        """Parse LoRA syntax from text input."""
+        # Pattern to match <lora:name:strength> or <lora:name:model_strength:clip_strength>
+        pattern = r'<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>'
+        matches = re.findall(pattern, text, re.IGNORECASE)
+        
+        loras = []
+        for match in matches:
+            lora_name = match[0].strip()
+            model_strength = float(match[1])
+            clip_strength = float(match[2]) if match[2] else model_strength
+            
+            loras.append({
+                'name': lora_name,
+                'model_strength': model_strength,
+                'clip_strength': clip_strength
+            })
+        
+        return loras
+    
+    def load_loras_from_text(self, model, lora_syntax, clip=None, lora_stack=None):
+        """Load LoRAs based on text syntax input."""
+        loaded_loras = []
+        all_trigger_words = []
+        
+        # Check if model is a Nunchaku Flux model - simplified approach
+        is_nunchaku_model = False
+        
+        try:
+            model_wrapper = model.model.diffusion_model
+            # Check if model is a Nunchaku Flux model using only class name
+            if model_wrapper.__class__.__name__ == "ComfyFluxWrapper":
+                is_nunchaku_model = True
+                logger.info("Detected Nunchaku Flux model")
+        except (AttributeError, TypeError):
+            # Not a model with the expected structure
+            pass
+        
+        # First process lora_stack if available
+        if lora_stack:
+            for lora_path, model_strength, clip_strength in lora_stack:
+                # Apply the LoRA using the appropriate loader
+                if is_nunchaku_model:
+                    # Use our custom function for Flux models
+                    model = nunchaku_load_lora(model, lora_path, model_strength)
+                    # clip remains unchanged for Nunchaku models
+                else:
+                    # Use default loader for standard models
+                    model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+                
+                # Extract lora name for trigger words lookup
+                lora_name = extract_lora_name(lora_path)
+                _, trigger_words = get_lora_info(lora_name)
+                
+                all_trigger_words.extend(trigger_words)
+                # Add clip strength to output if different from model strength (except for Nunchaku models)
+                if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
+                    loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
+                else:
+                    loaded_loras.append(f"{lora_name}: {model_strength}")
+        
+        # Parse and process LoRAs from text syntax
+        parsed_loras = self.parse_lora_syntax(lora_syntax)
+        for lora in parsed_loras:
+            lora_name = lora['name']
+            model_strength = lora['model_strength']
+            clip_strength = lora['clip_strength']
+            
+            # Get lora path and trigger words
+            lora_path, trigger_words = get_lora_info(lora_name)
+            
+            # Apply the LoRA using the appropriate loader
+            if is_nunchaku_model:
+                # For Nunchaku models, use our custom function
+                model = nunchaku_load_lora(model, lora_path, model_strength)
+                # clip remains unchanged
+            else:
+                # Use default loader for standard models
+                model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+            
+            # Include clip strength in output if different from model strength and not a Nunchaku model
+            if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
+                loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
+            else:
+                loaded_loras.append(f"{lora_name}: {model_strength}")
+            
+            # Add trigger words to collection
+            all_trigger_words.extend(trigger_words)
+        
+        # use ',, ' to separate trigger words for group mode
+        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+
+        # Format loaded_loras with support for both formats
+        formatted_loras = []
+        for item in loaded_loras:
+            parts = item.split(":")
+            lora_name = parts[0].strip()
+            strength_parts = parts[1].strip().split(",")
+            
+            if len(strength_parts) > 1:
+                # Different model and clip strengths
+                model_str = strength_parts[0].strip()
+                clip_str = strength_parts[1].strip()
+                formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
+            else:
+                # Same strength for both
+                model_str = strength_parts[0].strip()
+                formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
+                
+        formatted_loras_text = " ".join(formatted_loras)
+
         return (model, clip, trigger_words_text, formatted_loras_text)

py/nodes/lora_stacker.py

@@ -17,6 +17,7 @@ class LoraStacker:
             "required": {
                 "text": (IO.STRING, {
                     "multiline": True, 
+                    "pysssss.autocomplete": False, 
                     "dynamicPrompts": True, 
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                     "placeholder": "LoRA syntax input: <lora:name:strength>"

py/nodes/save_image.py

@@ -1,6 +1,5 @@
 import json
 import os
-import asyncio
 import re
 import numpy as np
 import folder_paths # type: ignore
@@ -419,11 +418,15 @@ class SaveImage:
         # Make sure the output directory exists
         os.makedirs(self.output_dir, exist_ok=True)
         
-        # Ensure images is always a list of images
+        # If images is already a list or array of images, do nothing; otherwise, convert to list
-        if len(images.shape) == 3:  # Single image (height, width, channels)
+        if isinstance(images, (list, np.ndarray)):
-            images = [images]
+            pass
-        else:  # Multiple images (batch, height, width, channels)
+        else:
-            images = [img for img in images]
+            # Ensure images is always a list of images
+            if len(images.shape) == 3:  # Single image (height, width, channels)
+                images = [images]
+            else:  # Multiple images (batch, height, width, channels)
+                images = [img for img in images]
         
         # Save all images
         results = self.save_images(

py/nodes/wanvideo_lora_select.py

@@ -14,9 +14,11 @@ class WanVideoLoraSelect:
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "low_mem_load": ("BOOLEAN", {"default": False, "tooltip": "Load the LORA model with less VRAM usage, slower loading"}),
+                "low_mem_load": ("BOOLEAN", {"default": False, "tooltip": "Load LORA models with less VRAM usage, slower loading. This affects ALL LoRAs, not just the current ones. No effect if merge_loras is False"}),
+                "merge_loras": ("BOOLEAN", {"default": True, "tooltip": "Merge LoRAs into the model, otherwise they are loaded on the fly. Always disabled for GGUF and scaled fp8 models. This affects ALL LoRAs, not just the current one"}),
                 "text": (IO.STRING, {
                     "multiline": True, 
+                    "pysssss.autocomplete": False, 
                     "dynamicPrompts": True, 
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                     "placeholder": "LoRA syntax input: <lora:name:strength>"
@@ -29,7 +31,7 @@ class WanVideoLoraSelect:
     RETURN_NAMES = ("lora", "trigger_words", "active_loras")
     FUNCTION = "process_loras"
     
-    def process_loras(self, text, low_mem_load=False, **kwargs):
+    def process_loras(self, text, low_mem_load=False, merge_loras=True, **kwargs):
         loras_list = []
         all_trigger_words = []
         active_loras = []
@@ -38,6 +40,9 @@ class WanVideoLoraSelect:
         prev_lora = kwargs.get('prev_lora', None)
         if prev_lora is not None:
             loras_list.extend(prev_lora)
+
+        if not merge_loras:
+            low_mem_load = False  # Unmerged LoRAs don't need low_mem_load
         
         # Get blocks if available
         blocks = kwargs.get('blocks', {})
@@ -65,6 +70,7 @@ class WanVideoLoraSelect:
                 "blocks": selected_blocks,
                 "layer_filter": layer_filter,
                 "low_mem_load": low_mem_load,
+                "merge_loras": merge_loras,
             }
             
             # Add to list and collect active loras

py/nodes/wanvideo_lora_select_from_text.py

@@ -0,0 +1,128 @@
+from comfy.comfy_types import IO
+import folder_paths
+from ..utils.utils import get_lora_info
+from .utils import any_type 
+import logging
+
+# 初始化日志记录器
+logger = logging.getLogger(__name__)
+
+# 定义新节点的类
+class WanVideoLoraSelectFromText:
+    # 节点在UI中显示的名称
+    NAME = "WanVideo Lora Select From Text (LoraManager)"
+    # 节点所属的分类
+    CATEGORY = "Lora Manager/stackers"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "low_mem_load": ("BOOLEAN", {"default": False, "tooltip": "Load LORA models with less VRAM usage, slower loading. This affects ALL LoRAs, not just the current ones. No effect if merge_loras is False"}),
+                "merge_lora": ("BOOLEAN", {"default": True, "tooltip": "Merge LoRAs into the model, otherwise they are loaded on the fly. Always disabled for GGUF and scaled fp8 models. This affects ALL LoRAs, not just the current one"}),
+                "lora_syntax": (IO.STRING, {
+                    "multiline": True,
+                    "defaultInput": True,
+                    "forceInput": True,
+                    "tooltip": "Connect a TEXT output for LoRA syntax: <lora:name:strength>"
+                }),
+            },
+
+            "optional": {
+                "prev_lora": ("WANVIDLORA",), 
+                "blocks": ("BLOCKS",)        
+            }
+        }
+
+    RETURN_TYPES = ("WANVIDLORA", IO.STRING, IO.STRING)
+    RETURN_NAMES = ("lora", "trigger_words", "active_loras")
+
+    FUNCTION = "process_loras_from_syntax"
+    
+    def process_loras_from_syntax(self, lora_syntax, low_mem_load=False, merge_lora=True, **kwargs):
+        text_to_process = lora_syntax
+
+        blocks = kwargs.get('blocks', {})
+        selected_blocks = blocks.get("selected_blocks", {})
+        layer_filter = blocks.get("layer_filter", "")
+
+        loras_list = []
+        all_trigger_words = []
+        active_loras = []
+        
+        prev_lora = kwargs.get('prev_lora', None)
+        if prev_lora is not None:
+            loras_list.extend(prev_lora)
+
+        if not merge_lora:
+            low_mem_load = False
+
+        parts = text_to_process.split('<lora:')
+        for part in parts[1:]:
+            end_index = part.find('>')
+            if end_index == -1:
+                continue
+
+            content = part[:end_index]
+            lora_parts = content.split(':')
+            
+            lora_name_raw = ""
+            model_strength = 1.0
+            clip_strength = 1.0
+
+            if len(lora_parts) == 2:
+                lora_name_raw = lora_parts[0].strip()
+                try:
+                    model_strength = float(lora_parts[1])
+                    clip_strength = model_strength
+                except (ValueError, IndexError):
+                    logger.warning(f"Invalid strength for LoRA '{lora_name_raw}'. Skipping.")
+                    continue
+            elif len(lora_parts) >= 3:
+                lora_name_raw = lora_parts[0].strip()
+                try:
+                    model_strength = float(lora_parts[1])
+                    clip_strength = float(lora_parts[2])
+                except (ValueError, IndexError):
+                    logger.warning(f"Invalid strengths for LoRA '{lora_name_raw}'. Skipping.")
+                    continue
+            else:
+                continue
+
+            lora_path, trigger_words = get_lora_info(lora_name_raw)
+
+            lora_item = {
+                "path": folder_paths.get_full_path("loras", lora_path),
+                "strength": model_strength,
+                "name": lora_path.split(".")[0],
+                "blocks": selected_blocks,
+                "layer_filter": layer_filter,
+                "low_mem_load": low_mem_load,
+                "merge_loras": merge_lora,
+            }
+
+            loras_list.append(lora_item)
+            active_loras.append((lora_name_raw, model_strength, clip_strength))
+            all_trigger_words.extend(trigger_words)
+
+        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+        
+        formatted_loras = []
+        for name, model_strength, clip_strength in active_loras:
+            if abs(model_strength - clip_strength) > 0.001:
+                formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}:{str(clip_strength).strip()}>")
+            else:
+                formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}>")
+                
+        active_loras_text = " ".join(formatted_loras)
+
+        return (loras_list, trigger_words_text, active_loras_text)
+
+NODE_CLASS_MAPPINGS = {
+    "WanVideoLoraSelectFromText": WanVideoLoraSelectFromText
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "WanVideoLoraSelectFromText": "WanVideo Lora Select From Text (LoraManager)"
+}
+，

py/recipes/base.py

@@ -119,10 +119,10 @@ class RecipeMetadataParser(ABC):
                     # Check if exists locally
                     if recipe_scanner and lora_entry['hash']:
                         lora_scanner = recipe_scanner._lora_scanner
-                        exists_locally = lora_scanner.has_lora_hash(lora_entry['hash'])
+                        exists_locally = lora_scanner.has_hash(lora_entry['hash'])
                         if exists_locally:
                             try:
-                                local_path = lora_scanner.get_lora_path_by_hash(lora_entry['hash'])
+                                local_path = lora_scanner.get_path_by_hash(lora_entry['hash'])
                                 lora_entry['existsLocally'] = True
                                 lora_entry['localPath'] = local_path
                                 lora_entry['file_name'] = os.path.splitext(os.path.basename(local_path))[0]

py/recipes/parsers/automatic.py

@@ -181,13 +181,30 @@ class AutomaticMetadataParser(RecipeMetadataParser):
             # First use Civitai resources if available (more reliable source)
             if metadata.get("civitai_resources"):
                 for resource in metadata.get("civitai_resources", []):
+                    # --- Added: Parse 'air' field if present ---
+                    air = resource.get("air")
+                    if air:
+                        # Format: urn:air:sdxl:lora:civitai:1221007@1375651
+                        # Or: urn:air:sdxl:checkpoint:civitai:623891@2019115
+                        air_pattern = r"urn:air:[^:]+:(?P<type>[^:]+):civitai:(?P<modelId>\d+)@(?P<modelVersionId>\d+)"
+                        air_match = re.match(air_pattern, air)
+                        if air_match:
+                            air_type = air_match.group("type")
+                            air_modelId = int(air_match.group("modelId"))
+                            air_modelVersionId = int(air_match.group("modelVersionId"))
+                            # checkpoint/lycoris/lora/hypernet
+                            resource["type"] = air_type
+                            resource["modelId"] = air_modelId
+                            resource["modelVersionId"] = air_modelVersionId
+                    # --- End added ---
+
                     if resource.get("type") in ["lora", "lycoris", "hypernet"] and resource.get("modelVersionId"):
                         # 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", ""),
+                            'version': resource.get("modelVersionName", resource.get("versionName", "")),
                             'type': resource.get("type", "lora"),
                             'weight': round(float(resource.get("weight", 1.0)), 2),
                             'existsLocally': False,

py/recipes/parsers/civitai_image.py

@@ -101,6 +101,11 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     if resource.get("type", "lora") == "lora":
                         lora_hash = resource.get("hash", "")
                         
+                        # 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")
+                            continue
+                        
                         # Skip if we've already added this LoRA by hash
                         if lora_hash and lora_hash in added_loras:
                             continue
@@ -153,10 +158,6 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             # Process civitaiResources array
             if "civitaiResources" in metadata and isinstance(metadata["civitaiResources"], list):
                 for resource in metadata["civitaiResources"]:
-                    # Skip resources that aren't LoRAs or LyCORIS
-                    if resource.get("type") not in ["lora", "lycoris"] and "type" not in resource:
-                        continue
-                        
                     # Get unique identifier for deduplication
                     version_id = str(resource.get("modelVersionId", ""))
                     
@@ -275,6 +276,66 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     
                         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:
+                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))
+                
+                # 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
+                }
+                
+                # Try to get info from Civitai if hash is available
+                if lora_entry['hash'] and civitai_client:
+                    try:
+                        civitai_info = await civitai_client.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
+                        )
+                        
+                        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"])
+                    except Exception as 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]

py/recipes/parsers/recipe_format.py

@@ -55,7 +55,7 @@ class RecipeFormatParser(RecipeMetadataParser):
                 # Check if this LoRA exists locally by SHA256 hash
                 if lora.get('hash') and recipe_scanner:
                     lora_scanner = recipe_scanner._lora_scanner
-                    exists_locally = lora_scanner.has_lora_hash(lora['hash'])
+                    exists_locally = lora_scanner.has_hash(lora['hash'])
                     if exists_locally:
                         lora_cache = await lora_scanner.get_cached_data()
                         lora_item = next((item for item in lora_cache.raw_data if item['sha256'].lower() == lora['hash'].lower()), None)

py/routes/base_model_routes.py

@@ -1,5 +1,6 @@
 from abc import ABC, abstractmethod
 import asyncio
+import os
 import json
 import logging
 from aiohttp import web
@@ -10,6 +11,9 @@ import jinja2
 from ..utils.routes_common import ModelRouteUtils
 from ..services.websocket_manager import ws_manager
 from ..services.settings_manager import settings
+from ..services.server_i18n import server_i18n
+from ..utils.utils import calculate_relative_path_for_model
+from ..utils.constants import AUTO_ORGANIZE_BATCH_SIZE
 from ..config import config
 
 logger = logging.getLogger(__name__)
@@ -38,16 +42,22 @@ class BaseModelRoutes(ABC):
             prefix: URL prefix (e.g., 'loras', 'checkpoints')
         """
         # Common model management routes
-        app.router.add_get(f'/api/{prefix}', self.get_models)
+        app.router.add_get(f'/api/{prefix}/list', self.get_models)
         app.router.add_post(f'/api/{prefix}/delete', self.delete_model)
         app.router.add_post(f'/api/{prefix}/exclude', self.exclude_model)
         app.router.add_post(f'/api/{prefix}/fetch-civitai', self.fetch_civitai)
         app.router.add_post(f'/api/{prefix}/relink-civitai', self.relink_civitai)
         app.router.add_post(f'/api/{prefix}/replace-preview', self.replace_preview)
         app.router.add_post(f'/api/{prefix}/save-metadata', self.save_metadata)
+        app.router.add_post(f'/api/{prefix}/add-tags', self.add_tags)
         app.router.add_post(f'/api/{prefix}/rename', self.rename_model)
         app.router.add_post(f'/api/{prefix}/bulk-delete', self.bulk_delete_models)
         app.router.add_post(f'/api/{prefix}/verify-duplicates', self.verify_duplicates)
+        app.router.add_post(f'/api/{prefix}/move_model', self.move_model)
+        app.router.add_post(f'/api/{prefix}/move_models_bulk', self.move_models_bulk)
+        app.router.add_get(f'/api/{prefix}/auto-organize', self.auto_organize_models)
+        app.router.add_post(f'/api/{prefix}/auto-organize', self.auto_organize_models)
+        app.router.add_get(f'/api/{prefix}/auto-organize-progress', self.get_auto_organize_progress)
         
         # Common query routes
         app.router.add_get(f'/api/{prefix}/top-tags', self.get_top_tags)
@@ -55,8 +65,18 @@ class BaseModelRoutes(ABC):
         app.router.add_get(f'/api/{prefix}/scan', self.scan_models)
         app.router.add_get(f'/api/{prefix}/roots', self.get_model_roots)
         app.router.add_get(f'/api/{prefix}/folders', self.get_folders)
+        app.router.add_get(f'/api/{prefix}/folder-tree', self.get_folder_tree)
+        app.router.add_get(f'/api/{prefix}/unified-folder-tree', self.get_unified_folder_tree)
         app.router.add_get(f'/api/{prefix}/find-duplicates', self.find_duplicate_models)
         app.router.add_get(f'/api/{prefix}/find-filename-conflicts', self.find_filename_conflicts)
+        app.router.add_get(f'/api/{prefix}/get-notes', self.get_model_notes)
+        app.router.add_get(f'/api/{prefix}/preview-url', self.get_model_preview_url)
+        app.router.add_get(f'/api/{prefix}/civitai-url', self.get_model_civitai_url)
+        app.router.add_get(f'/api/{prefix}/metadata', self.get_model_metadata)
+        app.router.add_get(f'/api/{prefix}/model-description', self.get_model_description)
+        
+        # Autocomplete route
+        app.router.add_get(f'/api/{prefix}/relative-paths', self.get_relative_paths)
 
         # Common Download management
         app.router.add_post(f'/api/download-model', self.download_model)
@@ -96,30 +116,36 @@ class BaseModelRoutes(ABC):
             if not self.template_env or not template_name:
                 return web.Response(text="Template environment or template name not set", status=500)
 
-            if is_initializing:
+            # Get user's language setting
-                rendered = self.template_env.get_template(template_name).render(
+            user_language = settings.get('language', 'en')
-                    folders=[],
+            
-                    is_initializing=True,
+            # Set server-side i18n locale
-                    settings=settings,
+            server_i18n.set_locale(user_language)
-                    request=request
+            
-                )
+            # Add i18n filter to the template environment if not already added
-            else:
+            if not hasattr(self.template_env, '_i18n_filter_added'):
+                self.template_env.filters['t'] = server_i18n.create_template_filter()
+                self.template_env._i18n_filter_added = True
+            
+            # Prepare template context
+            template_context = {
+                'is_initializing': is_initializing,
+                'settings': settings,
+                'request': request,
+                'folders': [],
+                't': server_i18n.get_translation,
+            }
+
+            if not is_initializing:
                 try:
                     cache = await self.service.scanner.get_cached_data(force_refresh=False)
-                    rendered = self.template_env.get_template(template_name).render(
+                    template_context['folders'] = getattr(cache, "folders", [])
-                        folders=getattr(cache, "folders", []),
-                        is_initializing=False,
-                        settings=settings,
-                        request=request
-                    )
                 except Exception as cache_error:
                     logger.error(f"Error loading cache data: {cache_error}")
-                    rendered = self.template_env.get_template(template_name).render(
+                    template_context['is_initializing'] = True
-                        folders=[],
+
-                        is_initializing=True,
+            rendered = self.template_env.get_template(template_name).render(**template_context)
-                        settings=settings,
+            
-                        request=request
-                    )
             return web.Response(
                 text=rendered,
                 content_type='text/html'
@@ -175,7 +201,8 @@ class BaseModelRoutes(ABC):
             'filename': request.query.get('search_filename', 'true').lower() == 'true',
             'modelname': request.query.get('search_modelname', 'true').lower() == 'true',
             'tags': request.query.get('search_tags', 'false').lower() == 'true',
-            'recursive': request.query.get('recursive', 'false').lower() == 'true',
+            'creator': request.query.get('search_creator', 'false').lower() == 'true',
+            'recursive': request.query.get('recursive', 'true').lower() == 'true',
         }
         
         # Parse hash filters if provided
@@ -247,6 +274,10 @@ class BaseModelRoutes(ABC):
         """Handle saving metadata updates"""
         return await ModelRouteUtils.handle_save_metadata(request, self.service.scanner)
     
+    async def add_tags(self, request: web.Request) -> web.Response:
+        """Handle adding tags to model metadata"""
+        return await ModelRouteUtils.handle_add_tags(request, self.service.scanner)
+    
     async def rename_model(self, request: web.Request) -> web.Response:
         """Handle renaming a model file and its associated files"""
         return await ModelRouteUtils.handle_rename_model(request, self.service.scanner)
@@ -343,6 +374,43 @@ class BaseModelRoutes(ABC):
                 'error': str(e)
             }, status=500)
     
+    async def get_folder_tree(self, request: web.Request) -> web.Response:
+        """Get hierarchical folder tree structure for download modal"""
+        try:
+            model_root = request.query.get('model_root')
+            if not model_root:
+                return web.json_response({
+                    'success': False,
+                    'error': 'model_root parameter is required'
+                }, status=400)
+            
+            folder_tree = await self.service.get_folder_tree(model_root)
+            return web.json_response({
+                'success': True,
+                'tree': folder_tree
+            })
+        except Exception as e:
+            logger.error(f"Error getting folder tree: {e}")
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_unified_folder_tree(self, request: web.Request) -> web.Response:
+        """Get unified folder tree across all model roots"""
+        try:
+            unified_tree = await self.service.get_unified_folder_tree()
+            return web.json_response({
+                'success': True,
+                'tree': unified_tree
+            })
+        except Exception as e:
+            logger.error(f"Error getting unified folder tree: {e}")
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
     async def find_duplicate_models(self, request: web.Request) -> web.Response:
         """Find models with duplicate SHA256 hashes"""
         try:
@@ -408,7 +476,7 @@ class BaseModelRoutes(ABC):
                         group["models"].append(await self.service.format_response(model))
                 
                 # Find the model from the main index too
-                hash_val = self.service.scanner._hash_index.get_hash_by_filename(filename)
+                hash_val = self.service.scanner.get_hash_by_filename(filename)
                 if hash_val:
                     main_path = self.service.get_path_by_hash(hash_val)
                     if main_path and main_path not in paths:
@@ -616,4 +684,571 @@ class BaseModelRoutes(ABC):
         # This will be implemented by subclasses as they need CivitAI client access
         return web.json_response({
             "error": "Not implemented in base class"
-        }, status=501)
+        }, status=501)
+    
+    # Common model move handlers
+    async def move_model(self, request: web.Request) -> web.Response:
+        """Handle model move request"""
+        try:
+            data = await request.json()
+            file_path = data.get('file_path')
+            target_path = data.get('target_path')
+            if not file_path or not target_path:
+                return web.Response(text='File path and target path are required', status=400)
+            import os
+            source_dir = os.path.dirname(file_path)
+            if os.path.normpath(source_dir) == os.path.normpath(target_path):
+                logger.info(f"Source and target directories are the same: {source_dir}")
+                return web.json_response({
+                    'success': True, 
+                    'message': 'Source and target directories are the same',
+                    'original_file_path': file_path,
+                    'new_file_path': file_path
+                })
+
+            new_file_path = await self.service.scanner.move_model(file_path, target_path)
+            if new_file_path:
+                return web.json_response({
+                    'success': True, 
+                    'original_file_path': file_path,
+                    'new_file_path': new_file_path
+                })
+            else:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Failed to move model',
+                    'original_file_path': file_path,
+                    'new_file_path': None
+                }, status=500)
+        except Exception as e:
+            logger.error(f"Error moving model: {e}", exc_info=True)
+            return web.Response(text=str(e), status=500)
+
+    async def move_models_bulk(self, request: web.Request) -> web.Response:
+        """Handle bulk model move request"""
+        try:
+            data = await request.json()
+            file_paths = data.get('file_paths', [])
+            target_path = data.get('target_path')
+            if not file_paths or not target_path:
+                return web.Response(text='File paths and target path are required', status=400)
+            results = []
+            import os
+            for file_path in file_paths:
+                source_dir = os.path.dirname(file_path)
+                if os.path.normpath(source_dir) == os.path.normpath(target_path):
+                    results.append({
+                        "original_file_path": file_path,
+                        "new_file_path": file_path,
+                        "success": True, 
+                        "message": "Source and target directories are the same"
+                    })
+                    continue
+
+                new_file_path = await self.service.scanner.move_model(file_path, target_path)
+                if new_file_path:
+                    results.append({
+                        "original_file_path": file_path,
+                        "new_file_path": new_file_path,
+                        "success": True,
+                        "message": "Success"
+                    })
+                else:
+                    results.append({
+                        "original_file_path": file_path,
+                        "new_file_path": None,
+                        "success": False,
+                        "message": "Failed to move model"
+                    })
+            success_count = sum(1 for r in results if r["success"])
+            failure_count = len(results) - success_count
+            return web.json_response({
+                'success': True,
+                'message': f'Moved {success_count} of {len(file_paths)} models',
+                'results': results,
+                'success_count': success_count,
+                'failure_count': failure_count
+            })
+        except Exception as e:
+            logger.error(f"Error moving models in bulk: {e}", exc_info=True)
+            return web.Response(text=str(e), status=500)
+    
+    async def auto_organize_models(self, request: web.Request) -> web.Response:
+        """Auto-organize all models or a specific set of models based on current settings"""
+        try:
+            # Check if auto-organize is already running
+            if ws_manager.is_auto_organize_running():
+                return web.json_response({
+                    'success': False,
+                    'error': 'Auto-organize is already running. Please wait for it to complete.'
+                }, status=409)
+            
+            # Acquire lock to prevent concurrent auto-organize operations
+            auto_organize_lock = await ws_manager.get_auto_organize_lock()
+            
+            if auto_organize_lock.locked():
+                return web.json_response({
+                    'success': False,
+                    'error': 'Auto-organize is already running. Please wait for it to complete.'
+                }, status=409)
+            
+            # Get specific file paths from request if this is a POST with selected models
+            file_paths = None
+            if request.method == 'POST':
+                try:
+                    data = await request.json()
+                    file_paths = data.get('file_paths')
+                except Exception:
+                    pass  # Continue with all models if no valid JSON
+            
+            async with auto_organize_lock:
+                return await self._perform_auto_organize(file_paths)
+                
+        except Exception as e:
+            logger.error(f"Error in auto_organize_models: {e}", exc_info=True)
+            
+            # Send error message via WebSocket and cleanup
+            await ws_manager.broadcast_auto_organize_progress({
+                'type': 'auto_organize_progress',
+                'status': 'error',
+                'error': str(e)
+            })
+            
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def _perform_auto_organize(self, file_paths=None) -> web.Response:
+        """Perform the actual auto-organize operation
+        
+        Args:
+            file_paths: Optional list of specific file paths to organize. 
+                       If None, organizes all models.
+        """
+        try:
+            # Get all models from cache
+            cache = await self.service.scanner.get_cached_data()
+            all_models = cache.raw_data
+            
+            # Filter models if specific file paths are provided
+            if file_paths:
+                all_models = [model for model in all_models if model.get('file_path') in file_paths]
+                operation_type = 'bulk'
+            else:
+                operation_type = 'all'
+            
+            # Get model roots for this scanner
+            model_roots = self.service.get_model_roots()
+            if not model_roots:
+                await ws_manager.broadcast_auto_organize_progress({
+                    'type': 'auto_organize_progress',
+                    'status': 'error',
+                    'error': 'No model roots configured',
+                    'operation_type': operation_type
+                })
+                return web.json_response({
+                    'success': False,
+                    'error': 'No model roots configured'
+                }, status=400)
+            
+            # Check if flat structure is configured for this model type
+            path_template = settings.get_download_path_template(self.service.model_type)
+            is_flat_structure = not path_template
+            
+            # Prepare results tracking
+            results = []
+            total_models = len(all_models)
+            processed = 0
+            success_count = 0
+            failure_count = 0
+            skipped_count = 0
+            
+            # Send initial progress via WebSocket
+            await ws_manager.broadcast_auto_organize_progress({
+                'type': 'auto_organize_progress',
+                'status': 'started',
+                'total': total_models,
+                'processed': 0,
+                'success': 0,
+                'failures': 0,
+                'skipped': 0,
+                'operation_type': operation_type
+            })
+            
+            # Process models in batches
+            for i in range(0, total_models, AUTO_ORGANIZE_BATCH_SIZE):
+                batch = all_models[i:i + AUTO_ORGANIZE_BATCH_SIZE]
+                
+                for model in batch:
+                    try:
+                        file_path = model.get('file_path')
+                        if not file_path:
+                            if len(results) < 100:  # Limit detailed results
+                                results.append({
+                                    "model": model.get('model_name', 'Unknown'),
+                                    "success": False,
+                                    "message": "No file path found"
+                                })
+                            failure_count += 1
+                            processed += 1
+                            continue
+                        
+                        # Find which model root this file belongs to
+                        current_root = None
+                        for root in model_roots:
+                            # Normalize paths for comparison
+                            normalized_root = os.path.normpath(root).replace(os.sep, '/')
+                            normalized_file = os.path.normpath(file_path).replace(os.sep, '/')
+                            
+                            if normalized_file.startswith(normalized_root):
+                                current_root = root
+                                break
+                        
+                        if not current_root:
+                            if len(results) < 100:  # Limit detailed results
+                                results.append({
+                                    "model": model.get('model_name', 'Unknown'),
+                                    "success": False,
+                                    "message": "Model file not found in any configured root directory"
+                                })
+                            failure_count += 1
+                            processed += 1
+                            continue
+                        
+                        # Handle flat structure case
+                        if is_flat_structure:
+                            current_dir = os.path.dirname(file_path)
+                            # Check if already in root directory
+                            if os.path.normpath(current_dir) == os.path.normpath(current_root):
+                                skipped_count += 1
+                                processed += 1
+                                continue
+                            
+                            # Move to root directory for flat structure
+                            target_dir = current_root
+                        else:
+                            # Calculate new relative path based on settings
+                            new_relative_path = calculate_relative_path_for_model(model, self.service.model_type)
+                            
+                            # If no relative path calculated (insufficient metadata), skip
+                            if not new_relative_path:
+                                if len(results) < 100:  # Limit detailed results
+                                    results.append({
+                                        "model": model.get('model_name', 'Unknown'),
+                                        "success": False,
+                                        "message": "Skipped - insufficient metadata for organization"
+                                    })
+                                skipped_count += 1
+                                processed += 1
+                                continue
+                            
+                            # Calculate target directory
+                            target_dir = os.path.join(current_root, new_relative_path).replace(os.sep, '/')
+                        
+                        current_dir = os.path.dirname(file_path)
+                        
+                        # Skip if already in correct location
+                        if current_dir.replace(os.sep, '/') == target_dir.replace(os.sep, '/'):
+                            skipped_count += 1
+                            processed += 1
+                            continue
+                        
+                        # Check if target file would conflict
+                        file_name = os.path.basename(file_path)
+                        target_file_path = os.path.join(target_dir, file_name)
+                        
+                        if os.path.exists(target_file_path):
+                            if len(results) < 100:  # Limit detailed results
+                                results.append({
+                                    "model": model.get('model_name', 'Unknown'),
+                                    "success": False,
+                                    "message": f"Target file already exists: {target_file_path}"
+                                })
+                            failure_count += 1
+                            processed += 1
+                            continue
+                        
+                        # Perform the move
+                        success = await self.service.scanner.move_model(file_path, target_dir)
+                        
+                        if success:
+                            success_count += 1
+                        else:
+                            if len(results) < 100:  # Limit detailed results
+                                results.append({
+                                    "model": model.get('model_name', 'Unknown'),
+                                    "success": False,
+                                    "message": "Failed to move model"
+                                })
+                            failure_count += 1
+                        
+                        processed += 1
+                        
+                    except Exception as e:
+                        logger.error(f"Error processing model {model.get('model_name', 'Unknown')}: {e}", exc_info=True)
+                        if len(results) < 100:  # Limit detailed results
+                            results.append({
+                                "model": model.get('model_name', 'Unknown'),
+                                "success": False,
+                                "message": f"Error: {str(e)}"
+                            })
+                        failure_count += 1
+                        processed += 1
+                
+                # Send progress update after each batch
+                await ws_manager.broadcast_auto_organize_progress({
+                    'type': 'auto_organize_progress',
+                    'status': 'processing',
+                    'total': total_models,
+                    'processed': processed,
+                    'success': success_count,
+                    'failures': failure_count,
+                    'skipped': skipped_count,
+                    'operation_type': operation_type
+                })
+                
+                # Small delay between batches to prevent overwhelming the system
+                await asyncio.sleep(0.1)
+            
+            # Send completion message
+            await ws_manager.broadcast_auto_organize_progress({
+                'type': 'auto_organize_progress',
+                'status': 'cleaning',
+                'total': total_models,
+                'processed': processed,
+                'success': success_count,
+                'failures': failure_count,
+                'skipped': skipped_count,
+                'message': 'Cleaning up empty directories...',
+                'operation_type': operation_type
+            })
+            
+            # Clean up empty directories after organizing
+            from ..utils.utils import remove_empty_dirs
+            cleanup_counts = {}
+            for root in model_roots:
+                removed = remove_empty_dirs(root)
+                cleanup_counts[root] = removed
+                
+            # Send cleanup completed message
+            await ws_manager.broadcast_auto_organize_progress({
+                'type': 'auto_organize_progress',
+                'status': 'completed',
+                'total': total_models,
+                'processed': processed,
+                'success': success_count,
+                'failures': failure_count,
+                'skipped': skipped_count,
+                'cleanup': cleanup_counts,
+                'operation_type': operation_type
+            })
+            
+            # Prepare response with limited details
+            response_data = {
+                'success': True,
+                'message': f'Auto-organize {operation_type} completed: {success_count} moved, {skipped_count} skipped, {failure_count} failed out of {total_models} total',
+                'summary': {
+                    'total': total_models,
+                    'success': success_count,
+                    'skipped': skipped_count,
+                    'failures': failure_count,
+                    'organization_type': 'flat' if is_flat_structure else 'structured',
+                    'cleaned_dirs': cleanup_counts,
+                    'operation_type': operation_type
+                }
+            }
+            
+            # Only include detailed results if under limit
+            if len(results) <= 100:
+                response_data['results'] = results
+            else:
+                response_data['results_truncated'] = True
+                response_data['sample_results'] = results[:50]  # Show first 50 as sample
+            
+            return web.json_response(response_data)
+            
+        except Exception as e:
+            logger.error(f"Error in _perform_auto_organize: {e}", exc_info=True)
+            
+            # Send error message via WebSocket
+            await ws_manager.broadcast_auto_organize_progress({
+                'type': 'auto_organize_progress',
+                'status': 'error',
+                'error': str(e),
+                'operation_type': operation_type if 'operation_type' in locals() else 'unknown'
+            })
+            
+            raise e
+    
+    async def get_auto_organize_progress(self, request: web.Request) -> web.Response:
+        """Get current auto-organize progress for polling"""
+        try:
+            progress_data = ws_manager.get_auto_organize_progress()
+            
+            if progress_data is None:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No auto-organize operation in progress'
+                }, status=404)
+            
+            return web.json_response({
+                'success': True,
+                'progress': progress_data
+            })
+        except Exception as e:
+            logger.error(f"Error getting auto-organize progress: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_model_notes(self, request: web.Request) -> web.Response:
+        """Get notes for a specific model file"""
+        try:
+            model_name = request.query.get('name')
+            if not model_name:
+                return web.Response(text=f'{self.model_type.capitalize()} file name is required', status=400)
+            
+            notes = await self.service.get_model_notes(model_name)
+            if notes is not None:
+                return web.json_response({
+                    'success': True,
+                    'notes': notes
+                })
+            else:
+                return web.json_response({
+                    'success': False,
+                    'error': f'{self.model_type.capitalize()} not found in cache'
+                }, status=404)
+                
+        except Exception as e:
+            logger.error(f"Error getting {self.model_type} notes: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_model_preview_url(self, request: web.Request) -> web.Response:
+        """Get the static preview URL for a model file"""
+        try:
+            model_name = request.query.get('name')
+            if not model_name:
+                return web.Response(text=f'{self.model_type.capitalize()} file name is required', status=400)
+            
+            preview_url = await self.service.get_model_preview_url(model_name)
+            if preview_url:
+                return web.json_response({
+                    'success': True,
+                    'preview_url': preview_url
+                })
+            else:
+                return web.json_response({
+                    'success': False,
+                    'error': f'No preview URL found for the specified {self.model_type}'
+                }, status=404)
+                
+        except Exception as e:
+            logger.error(f"Error getting {self.model_type} preview URL: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_model_civitai_url(self, request: web.Request) -> web.Response:
+        """Get the Civitai URL for a model file"""
+        try:
+            model_name = request.query.get('name')
+            if not model_name:
+                return web.Response(text=f'{self.model_type.capitalize()} file name is required', status=400)
+            
+            result = await self.service.get_model_civitai_url(model_name)
+            if result['civitai_url']:
+                return web.json_response({
+                    'success': True,
+                    **result
+                })
+            else:
+                return web.json_response({
+                    'success': False,
+                    'error': f'No Civitai data found for the specified {self.model_type}'
+                }, status=404)
+                
+        except Exception as e:
+            logger.error(f"Error getting {self.model_type} Civitai URL: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_model_metadata(self, request: web.Request) -> web.Response:
+        """Get filtered CivitAI metadata for a model by file path"""
+        try:
+            file_path = request.query.get('file_path')
+            if not file_path:
+                return web.Response(text='File path is required', status=400)
+            
+            metadata = await self.service.get_model_metadata(file_path)
+            if metadata is not None:
+                return web.json_response({
+                    'success': True,
+                    'metadata': metadata
+                })
+            else:
+                return web.json_response({
+                    'success': False,
+                    'error': f'{self.model_type.capitalize()} not found or no CivitAI metadata available'
+                }, status=404)
+                
+        except Exception as e:
+            logger.error(f"Error getting {self.model_type} metadata: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_model_description(self, request: web.Request) -> web.Response:
+        """Get model description by file path"""
+        try:
+            file_path = request.query.get('file_path')
+            if not file_path:
+                return web.Response(text='File path is required', status=400)
+            
+            description = await self.service.get_model_description(file_path)
+            if description is not None:
+                return web.json_response({
+                    'success': True,
+                    'description': description
+                })
+            else:
+                return web.json_response({
+                    'success': False,
+                    'error': f'{self.model_type.capitalize()} not found or no description available'
+                }, status=404)
+                
+        except Exception as e:
+            logger.error(f"Error getting {self.model_type} description: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_relative_paths(self, request: web.Request) -> web.Response:
+        """Get model relative file paths for autocomplete functionality"""
+        try:
+            search = request.query.get('search', '').strip()
+            limit = min(int(request.query.get('limit', '15')), 50)  # Max 50 items
+            
+            matching_paths = await self.service.search_relative_paths(search, limit)
+            
+            return web.json_response({
+                'success': True,
+                'relative_paths': matching_paths
+            })
+            
+        except Exception as e:
+            logger.error(f"Error getting relative paths for autocomplete: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/routes/checkpoint_routes.py

@@ -4,6 +4,7 @@ from aiohttp import web
 from .base_model_routes import BaseModelRoutes
 from ..services.checkpoint_service import CheckpointService
 from ..services.service_registry import ServiceRegistry
+from ..config import config
 
 logger = logging.getLogger(__name__)
 
@@ -41,6 +42,10 @@ class CheckpointRoutes(BaseModelRoutes):
         
         # Checkpoint info by name
         app.router.add_get(f'/api/{prefix}/info/{{name}}', self.get_checkpoint_info)
+        
+        # Checkpoint roots and Unet roots
+        app.router.add_get(f'/api/{prefix}/checkpoints_roots', self.get_checkpoints_roots)
+        app.router.add_get(f'/api/{prefix}/unet_roots', self.get_unet_roots)
     
     async def get_checkpoint_info(self, request: web.Request) -> web.Response:
         """Get detailed information for a specific checkpoint by name"""
@@ -102,4 +107,34 @@ class CheckpointRoutes(BaseModelRoutes):
             return web.json_response(versions)
         except Exception as e:
             logger.error(f"Error fetching checkpoint model versions: {e}")
-            return web.Response(status=500, text=str(e))
+            return web.Response(status=500, text=str(e))
+    
+    async def get_checkpoints_roots(self, request: web.Request) -> web.Response:
+        """Return the list of checkpoint roots from config"""
+        try:
+            roots = config.checkpoints_roots
+            return web.json_response({
+                "success": True,
+                "roots": roots
+            })
+        except Exception as e:
+            logger.error(f"Error getting checkpoint roots: {e}", exc_info=True)
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)
+
+    async def get_unet_roots(self, request: web.Request) -> web.Response:
+        """Return the list of unet roots from config"""
+        try:
+            roots = config.unet_roots
+            return web.json_response({
+                "success": True,
+                "roots": roots
+            })
+        except Exception as e:
+            logger.error(f"Error getting unet roots: {e}", exc_info=True)
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)

py/routes/example_images_routes.py

@@ -2,6 +2,7 @@ import logging
 from ..utils.example_images_download_manager import DownloadManager
 from ..utils.example_images_processor import ExampleImagesProcessor
 from ..utils.example_images_file_manager import ExampleImagesFileManager
+from ..services.websocket_manager import ws_manager
 
 logger = logging.getLogger(__name__)
 
@@ -20,6 +21,7 @@ class ExampleImagesRoutes:
         app.router.add_get('/api/example-image-files', ExampleImagesRoutes.get_example_image_files)
         app.router.add_get('/api/has-example-images', ExampleImagesRoutes.has_example_images)
         app.router.add_post('/api/delete-example-image', ExampleImagesRoutes.delete_example_image)
+        app.router.add_post('/api/force-download-example-images', ExampleImagesRoutes.force_download_example_images)
 
     @staticmethod
     async def download_example_images(request):
@@ -64,4 +66,9 @@ class ExampleImagesRoutes:
     @staticmethod
     async def delete_example_image(request):
         """Delete a custom example image for a model"""
-        return await ExampleImagesProcessor.delete_custom_image(request)
+        return await ExampleImagesProcessor.delete_custom_image(request)
+
+    @staticmethod
+    async def force_download_example_images(request):
+        """Force download example images for specific models"""
+        return await DownloadManager.start_force_download(request)

py/routes/lora_routes.py

@@ -43,15 +43,8 @@ class LoraRoutes(BaseModelRoutes):
         """Setup LoRA-specific routes"""
         # LoRA-specific query routes
         app.router.add_get(f'/api/{prefix}/letter-counts', self.get_letter_counts)
-        app.router.add_get(f'/api/{prefix}/get-notes', self.get_lora_notes)
         app.router.add_get(f'/api/{prefix}/get-trigger-words', self.get_lora_trigger_words)
-        app.router.add_get(f'/api/{prefix}/preview-url', self.get_lora_preview_url)
+        app.router.add_get(f'/api/{prefix}/usage-tips-by-path', self.get_lora_usage_tips_by_path)
-        app.router.add_get(f'/api/{prefix}/civitai-url', self.get_lora_civitai_url)
-        app.router.add_get(f'/api/{prefix}/model-description', self.get_lora_model_description)
-        
-        # LoRA-specific management routes
-        app.router.add_post(f'/api/{prefix}/move_model', self.move_model)
-        app.router.add_post(f'/api/{prefix}/move_models_bulk', self.move_models_bulk)
         
         # CivitAI integration with LoRA-specific validation
         app.router.add_get(f'/api/{prefix}/civitai/versions/{{model_id}}', self.get_civitai_versions_lora)
@@ -147,6 +140,26 @@ class LoraRoutes(BaseModelRoutes):
                 'error': str(e)
             }, status=500)
     
+    async def get_lora_usage_tips_by_path(self, request: web.Request) -> web.Response:
+        """Get usage tips for a LoRA by its relative path"""
+        try:
+            relative_path = request.query.get('relative_path')
+            if not relative_path:
+                return web.Response(text='Relative path is required', status=400)
+            
+            usage_tips = await self.service.get_lora_usage_tips_by_relative_path(relative_path)
+            return web.json_response({
+                'success': True,
+                'usage_tips': usage_tips or ''
+            })
+            
+        except Exception as e:
+            logger.error(f"Error getting lora usage tips by path: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
     async def get_lora_preview_url(self, request: web.Request) -> web.Response:
         """Get the static preview URL for a LoRA file"""
         try:
@@ -284,173 +297,6 @@ class LoraRoutes(BaseModelRoutes):
                 "error": str(e)
             }, status=500)
     
-    # Model management methods
-    async def move_model(self, request: web.Request) -> web.Response:
-        """Handle model move request"""
-        try:
-            data = await request.json()
-            file_path = data.get('file_path')  # full path of the model file
-            target_path = data.get('target_path')  # folder path to move the model to
-            
-            if not file_path or not target_path:
-                return web.Response(text='File path and target path are required', status=400)
-
-            # Check if source and destination are the same
-            import os
-            source_dir = os.path.dirname(file_path)
-            if os.path.normpath(source_dir) == os.path.normpath(target_path):
-                logger.info(f"Source and target directories are the same: {source_dir}")
-                return web.json_response({'success': True, 'message': 'Source and target directories are the same'})
-
-            # Check if target file already exists
-            file_name = os.path.basename(file_path)
-            target_file_path = os.path.join(target_path, file_name).replace(os.sep, '/')
-            
-            if os.path.exists(target_file_path):
-                return web.json_response({
-                    'success': False, 
-                    'error': f"Target file already exists: {target_file_path}"
-                }, status=409)  # 409 Conflict
-
-            # Call scanner to handle the move operation
-            success = await self.service.scanner.move_model(file_path, target_path)
-            
-            if success:
-                return web.json_response({'success': True, 'new_file_path': target_file_path})
-            else:
-                return web.Response(text='Failed to move model', status=500)
-                
-        except Exception as e:
-            logger.error(f"Error moving model: {e}", exc_info=True)
-            return web.Response(text=str(e), status=500)
-    
-    async def move_models_bulk(self, request: web.Request) -> web.Response:
-        """Handle bulk model move request"""
-        try:
-            data = await request.json()
-            file_paths = data.get('file_paths', [])  # list of full paths of the model files
-            target_path = data.get('target_path')  # folder path to move the models to
-            
-            if not file_paths or not target_path:
-                return web.Response(text='File paths and target path are required', status=400)
-
-            results = []
-            import os
-            for file_path in file_paths:
-                # Check if source and destination are the same
-                source_dir = os.path.dirname(file_path)
-                if os.path.normpath(source_dir) == os.path.normpath(target_path):
-                    results.append({
-                        "path": file_path, 
-                        "success": True, 
-                        "message": "Source and target directories are the same"
-                    })
-                    continue
-                
-                # Check if target file already exists
-                file_name = os.path.basename(file_path)
-                target_file_path = os.path.join(target_path, file_name).replace(os.sep, '/')
-                
-                if os.path.exists(target_file_path):
-                    results.append({
-                        "path": file_path, 
-                        "success": False, 
-                        "message": f"Target file already exists: {target_file_path}"
-                    })
-                    continue
-                
-                # Try to move the model
-                success = await self.service.scanner.move_model(file_path, target_path)
-                results.append({
-                    "path": file_path, 
-                    "success": success,
-                    "message": "Success" if success else "Failed to move model"
-                })
-            
-            # Count successes and failures
-            success_count = sum(1 for r in results if r["success"])
-            failure_count = len(results) - success_count
-            
-            return web.json_response({
-                'success': True,
-                'message': f'Moved {success_count} of {len(file_paths)} models',
-                'results': results,
-                'success_count': success_count,
-                'failure_count': failure_count
-            })
-                
-        except Exception as e:
-            logger.error(f"Error moving models in bulk: {e}", exc_info=True)
-            return web.Response(text=str(e), status=500)
-    
-    async def get_lora_model_description(self, request: web.Request) -> web.Response:
-        """Get model description for a Lora model"""
-        try:
-            # Get parameters
-            model_id = request.query.get('model_id')
-            file_path = request.query.get('file_path')
-            
-            if not model_id:
-                return web.json_response({
-                    'success': False, 
-                    'error': 'Model ID is required'
-                }, status=400)
-            
-            # Check if we already have the description stored in metadata
-            description = None
-            tags = []
-            creator = {}
-            if file_path:
-                import os
-                from ..utils.metadata_manager import MetadataManager
-                metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
-                metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
-                description = metadata.get('modelDescription')
-                tags = metadata.get('tags', [])
-                creator = metadata.get('creator', {})
-            
-            # If description is not in metadata, fetch from CivitAI
-            if not description:
-                logger.info(f"Fetching model metadata for model ID: {model_id}")
-                model_metadata, _ = await self.civitai_client.get_model_metadata(model_id)
-                
-                if model_metadata:
-                    description = model_metadata.get('description')
-                    tags = model_metadata.get('tags', [])
-                    creator = model_metadata.get('creator', {})
-                
-                    # Save the metadata to file if we have a file path and got metadata
-                    if file_path:
-                        try:
-                            metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
-                            metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
-                            
-                            metadata['modelDescription'] = description
-                            metadata['tags'] = tags
-                            # Ensure the civitai dict exists
-                            if 'civitai' not in metadata:
-                                metadata['civitai'] = {}
-                            # Store creator in the civitai nested structure
-                            metadata['civitai']['creator'] = creator
-                            
-                            await MetadataManager.save_metadata(file_path, metadata, True)
-                        except Exception as e:
-                            logger.error(f"Error saving model metadata: {e}")
-            
-            return web.json_response({
-                'success': True,
-                'description': description or "<p>No model description available.</p>",
-                'tags': tags,
-                'creator': creator
-            })
-            
-        except Exception as e:
-            logger.error(f"Error getting model metadata: {e}")
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
     async def get_trigger_words(self, request: web.Request) -> web.Response:
         """Get trigger words for specified LoRA models"""
         try:

py/routes/misc_routes.py

@@ -1,4 +1,3 @@
-import json
 import logging
 import os
 import sys
@@ -167,6 +166,9 @@ class MiscRoutes:
             
             # Validate and update settings
             for key, value in data.items():
+                if value == settings.get(key):
+                    # No change, skip
+                    continue
                 # Special handling for example_images_path - verify path exists
                 if key == 'example_images_path' and value:
                     if not os.path.exists(value):
@@ -180,16 +182,6 @@ class MiscRoutes:
                     if old_path != value:
                         logger.info(f"Example images path changed to {value} - server restart required")
                 
-                # Special handling for base_model_path_mappings - parse JSON string
-                if key == 'base_model_path_mappings' and value:
-                    try:
-                        value = json.loads(value)
-                    except json.JSONDecodeError:
-                        return web.json_response({
-                            'success': False,
-                            'error': f"Invalid JSON format for base_model_path_mappings: {value}"
-                        })
-                
                 # Save to settings
                 settings.set(key, value)
             
@@ -651,13 +643,13 @@ class MiscRoutes:
                 exists = False
                 model_type = None
 
-                if await lora_scanner.check_model_version_exists(model_id, model_version_id):
+                if await lora_scanner.check_model_version_exists(model_version_id):
                     exists = True
                     model_type = 'lora'
-                elif checkpoint_scanner and await checkpoint_scanner.check_model_version_exists(model_id, model_version_id):
+                elif checkpoint_scanner and await checkpoint_scanner.check_model_version_exists(model_version_id):
                     exists = True
                     model_type = 'checkpoint'
-                elif embedding_scanner and await embedding_scanner.check_model_version_exists(model_id, model_version_id):
+                elif embedding_scanner and await embedding_scanner.check_model_version_exists(model_version_id):
                     exists = True
                     model_type = 'embedding'
                 

py/routes/recipe_routes.py

@@ -17,12 +17,12 @@ from ..recipes import RecipeParserFactory
 from ..utils.constants import CARD_PREVIEW_WIDTH
 
 from ..services.settings_manager import settings
+from ..services.server_i18n import server_i18n
 from ..config import config
 
 # Check if running in standalone mode
 standalone_mode = 'nodes' not in sys.modules
 
-from ..utils.utils import download_civitai_image
 from ..services.service_registry import ServiceRegistry  # Add ServiceRegistry import
 
 # Only import MetadataRegistry in non-standalone mode
@@ -128,6 +128,17 @@ class RecipeRoutes:
             # Ensure services are initialized
             await self.init_services()
             
+            # 获取用户语言设置
+            user_language = settings.get('language', 'en')
+            
+            # 设置服务端i18n语言
+            server_i18n.set_locale(user_language)
+            
+            # 为模板环境添加i18n过滤器
+            if not hasattr(self.template_env, '_i18n_filter_added'):
+                self.template_env.filters['t'] = server_i18n.create_template_filter()
+                self.template_env._i18n_filter_added = True
+            
             # Skip initialization check and directly try to get cached data
             try:
                 # Recipe scanner will initialize cache if needed
@@ -137,7 +148,9 @@ class RecipeRoutes:
                     recipes=[],  # Frontend will load recipes via API
                     is_initializing=False,
                     settings=settings,
-                    request=request
+                    request=request,
+                    # 添加服务端翻译函数
+                    t=server_i18n.get_translation,
                 )
             except Exception as cache_error:
                 logger.error(f"Error loading recipe cache data: {cache_error}")
@@ -146,7 +159,9 @@ class RecipeRoutes:
                 rendered = template.render(
                     is_initializing=True,
                     settings=settings,
-                    request=request
+                    request=request,
+                    # 添加服务端翻译函数
+                    t=server_i18n.get_translation,
                 )
                 logger.info("Recipe cache error, returning initialization page")
             
@@ -376,16 +391,6 @@ class RecipeRoutes:
                     # Use meta field from image_info as metadata
                     if 'meta' in image_info:
                         metadata = image_info['meta']
-                    
-                else:
-                    # Not a Civitai image URL, use the original download method
-                    temp_path = download_civitai_image(url)
-                    
-                    if not temp_path:
-                        return web.json_response({
-                            "error": "Failed to download image from URL",
-                            "loras": []
-                        }, status=400)
             
             # If metadata wasn't obtained from Civitai API, extract it from the image
             if metadata is None:
@@ -638,21 +643,6 @@ class RecipeRoutes:
                         image = base64.b64decode(image_base64)
                     except Exception as e:
                         return web.json_response({"error": f"Invalid base64 image data: {str(e)}"}, status=400)
-                elif image_url:
-                    # Download image from URL
-                    temp_path = download_civitai_image(image_url)
-                    if not temp_path:
-                        return web.json_response({"error": "Failed to download image from URL"}, status=400)
-                    
-                    # Read the downloaded image
-                    with open(temp_path, 'rb') as f:
-                        image = f.read()
-                    
-                    # Clean up temp file
-                    try:
-                        os.unlink(temp_path)
-                    except:
-                        pass
                 else:
                     return web.json_response({"error": "No image data provided"}, status=400)
             

py/routes/stats_routes.py

@@ -9,6 +9,7 @@ from typing import Dict, List, Any
 
 from ..config import config
 from ..services.settings_manager import settings
+from ..services.server_i18n import server_i18n
 from ..services.service_registry import ServiceRegistry
 from ..utils.usage_stats import UsageStats
 
@@ -58,11 +59,23 @@ class StatsRoutes:
             
             is_initializing = lora_initializing or checkpoint_initializing or embedding_initializing
 
+            # 获取用户语言设置
+            user_language = settings.get('language', 'en')
+            
+            # 设置服务端i18n语言
+            server_i18n.set_locale(user_language)
+            
+            # 为模板环境添加i18n过滤器
+            if not hasattr(self.template_env, '_i18n_filter_added'):
+                self.template_env.filters['t'] = server_i18n.create_template_filter()
+                self.template_env._i18n_filter_added = True
+
             template = self.template_env.get_template('statistics.html')
             rendered = template.render(
                 is_initializing=is_initializing,
                 settings=settings,
-                request=request
+                request=request,
+                t=server_i18n.get_translation,
             )
             
             return web.Response(

py/routes/update_routes.py

@@ -1,5 +1,4 @@
 import os
-import subprocess
 import aiohttp
 import logging
 import toml
@@ -7,7 +6,6 @@ import git
 import zipfile
 import shutil
 import tempfile
-from datetime import datetime
 from aiohttp import web
 from typing import Dict, List
 
@@ -157,7 +155,7 @@ class UpdateRoutes:
     async def _download_and_replace_zip(plugin_root: str) -> tuple[bool, str]:
         """
         Download latest release ZIP from GitHub and replace plugin files.
-        Skips settings.json.
+        Skips settings.json. Writes extracted file list to .tracking.
         """
         repo_owner = "willmiao"
         repo_name = "ComfyUI-Lora-Manager"
@@ -196,7 +194,6 @@ class UpdateRoutes:
                         src = os.path.join(extracted_root, item)
                         dst = os.path.join(plugin_root, item)
                         if os.path.isdir(src):
-                            # Remove old folder, then copy
                             if os.path.exists(dst):
                                 shutil.rmtree(dst)
                             shutil.copytree(src, dst, ignore=shutil.ignore_patterns('settings.json'))
@@ -205,6 +202,17 @@ class UpdateRoutes:
                                 continue
                             shutil.copy2(src, dst)
 
+                    # Write .tracking file: list all files under extracted_root, relative to extracted_root
+                    # for ComfyUI Manager to work properly
+                    tracking_info_file = os.path.join(plugin_root, '.tracking')
+                    tracking_files = []
+                    for root, dirs, files in os.walk(extracted_root):
+                        for file in files:
+                            rel_path = os.path.relpath(os.path.join(root, file), extracted_root)
+                            tracking_files.append(rel_path.replace("\\", "/"))
+                    with open(tracking_info_file, "w", encoding='utf-8') as file:
+                        file.write('\n'.join(tracking_files))
+
                 os.remove(zip_path)
                 logger.info(f"Updated plugin via ZIP to {version}")
                 return True, version
@@ -364,65 +372,28 @@ class UpdateRoutes:
         """Get Git repository information"""
         current_dir = os.path.dirname(os.path.abspath(__file__))
         plugin_root = os.path.dirname(os.path.dirname(current_dir))
-        
+
         git_info = {
             'commit_hash': 'unknown',
-            'short_hash': 'unknown',
+            'short_hash': 'stable',
             'branch': 'unknown',
             'commit_date': 'unknown'
         }
-        
+
         try:
             # Check if we're in a git repository
             if not os.path.exists(os.path.join(plugin_root, '.git')):
                 return git_info
-                
+
-            # Get current commit hash
+            repo = git.Repo(plugin_root)
-            result = subprocess.run(
+            commit = repo.head.commit
-                ['git', 'rev-parse', 'HEAD'],
+            git_info['commit_hash'] = commit.hexsha
-                cwd=plugin_root,
+            git_info['short_hash'] = commit.hexsha[:7]
-                stdout=subprocess.PIPE,
+            git_info['branch'] = repo.active_branch.name if not repo.head.is_detached else 'detached'
-                stderr=subprocess.PIPE,
+            git_info['commit_date'] = commit.committed_datetime.strftime('%Y-%m-%d')
-                text=True,
-                check=False
-            )
-            if result.returncode == 0:
-                git_info['commit_hash'] = result.stdout.strip()
-                git_info['short_hash'] = git_info['commit_hash'][:7]
-            
-            # Get current branch name
-            result = subprocess.run(
-                ['git', 'rev-parse', '--abbrev-ref', 'HEAD'],
-                cwd=plugin_root,
-                stdout=subprocess.PIPE,
-                stderr=subprocess.PIPE,
-                text=True,
-                check=False
-            )
-            if result.returncode == 0:
-                git_info['branch'] = result.stdout.strip()
-            
-            # Get commit date
-            result = subprocess.run(
-                ['git', 'show', '-s', '--format=%ci', 'HEAD'],
-                cwd=plugin_root,
-                stdout=subprocess.PIPE,
-                stderr=subprocess.PIPE,
-                text=True,
-                check=False
-            )
-            if result.returncode == 0:
-                commit_date = result.stdout.strip()
-                # Format the date nicely if possible
-                try:
-                    date_obj = datetime.strptime(commit_date, '%Y-%m-%d %H:%M:%S %z')
-                    git_info['commit_date'] = date_obj.strftime('%Y-%m-%d')
-                except:
-                    git_info['commit_date'] = commit_date
-                    
         except Exception as e:
             logger.warning(f"Error getting git info: {e}")
-            
+
         return git_info
     
     @staticmethod

py/services/base_model_service.py

@@ -1,8 +1,10 @@
 from abc import ABC, abstractmethod
 from typing import Dict, List, Optional, Type
 import logging
+import os
 
 from ..utils.models import BaseModelMetadata
+from ..utils.routes_common import ModelRouteUtils
 from ..utils.constants import NSFW_LEVELS
 from .settings_manager import settings
 from ..utils.utils import fuzzy_match
@@ -67,7 +69,7 @@ class BaseModelService(ABC):
                 'filename': True,
                 'modelname': True,
                 'tags': False,
-                'recursive': False,
+                'recursive': True,
             }
 
         # Get the base data set using new sort logic
@@ -138,12 +140,20 @@ class BaseModelService(ABC):
         
         # Apply folder filtering
         if folder is not None:
-            if search_options and search_options.get('recursive', False):
+            if search_options and search_options.get('recursive', True):
                 # Recursive folder filtering - include all subfolders
-                data = [
+                # Ensure we match exact folder or its subfolders by checking path boundaries
-                    item for item in data
+                if folder == "":
-                    if item['folder'].startswith(folder)
+                    # Empty folder means root - include all items
-                ]
+                    pass  # Don't filter anything
+                else:
+                    # Add trailing slash to ensure we match folder boundaries correctly
+                    folder_with_separator = folder + "/"
+                    data = [
+                        item for item in data
+                        if (item['folder'] == folder or 
+                            item['folder'].startswith(folder_with_separator))
+                    ]
             else:
                 # Exact folder filtering
                 data = [
@@ -199,6 +209,22 @@ class BaseModelService(ABC):
                        for tag in item['tags']):
                     search_results.append(item)
                     continue
+            
+            # Search by creator
+            civitai = item.get('civitai')
+            creator_username = ''
+            if civitai and isinstance(civitai, dict):
+                creator = civitai.get('creator')
+                if creator and isinstance(creator, dict):
+                    creator_username = creator.get('username', '')
+            if search_options.get('creator', False) and creator_username:
+                if fuzzy_search:
+                    if fuzzy_match(creator_username, search):
+                        search_results.append(item)
+                        continue
+                elif search.lower() in creator_username.lower():
+                    search_results.append(item)
+                    continue
         
         return search_results
     
@@ -256,4 +282,170 @@ class BaseModelService(ABC):
     
     def get_model_roots(self) -> List[str]:
         """Get model root directories"""
-        return self.scanner.get_model_roots()
+        return self.scanner.get_model_roots()
+    
+    async def get_folder_tree(self, model_root: str) -> Dict:
+        """Get hierarchical folder tree for a specific model root"""
+        cache = await self.scanner.get_cached_data()
+        
+        # Build tree structure from folders
+        tree = {}
+        
+        for folder in cache.folders:
+            # Check if this folder belongs to the specified model root
+            folder_belongs_to_root = False
+            for root in self.scanner.get_model_roots():
+                if root == model_root:
+                    folder_belongs_to_root = True
+                    break
+            
+            if not folder_belongs_to_root:
+                continue
+            
+            # Split folder path into components
+            parts = folder.split('/') if folder else []
+            current_level = tree
+            
+            for part in parts:
+                if part not in current_level:
+                    current_level[part] = {}
+                current_level = current_level[part]
+        
+        return tree
+    
+    async def get_unified_folder_tree(self) -> Dict:
+        """Get unified folder tree across all model roots"""
+        cache = await self.scanner.get_cached_data()
+        
+        # Build unified tree structure by analyzing all relative paths
+        unified_tree = {}
+        
+        # Get all model roots for path normalization
+        model_roots = self.scanner.get_model_roots()
+        
+        for folder in cache.folders:
+            if not folder:  # Skip empty folders
+                continue
+            
+            # Find which root this folder belongs to by checking the actual file paths
+            # This is a simplified approach - we'll use the folder as-is since it should already be relative
+            relative_path = folder
+            
+            # Split folder path into components
+            parts = relative_path.split('/')
+            current_level = unified_tree
+            
+            for part in parts:
+                if part not in current_level:
+                    current_level[part] = {}
+                current_level = current_level[part]
+        
+        return unified_tree
+
+    async def get_model_notes(self, model_name: str) -> Optional[str]:
+        """Get notes for a specific model file"""
+        cache = await self.scanner.get_cached_data()
+        
+        for model in cache.raw_data:
+            if model['file_name'] == model_name:
+                return model.get('notes', '')
+        
+        return None
+    
+    async def get_model_preview_url(self, model_name: str) -> Optional[str]:
+        """Get the static preview URL for a model file"""
+        cache = await self.scanner.get_cached_data()
+        
+        for model in cache.raw_data:
+            if model['file_name'] == model_name:
+                preview_url = model.get('preview_url')
+                if preview_url:
+                    from ..config import config
+                    return config.get_preview_static_url(preview_url)
+        
+        return None
+    
+    async def get_model_civitai_url(self, model_name: str) -> Dict[str, Optional[str]]:
+        """Get the Civitai URL for a model file"""
+        cache = await self.scanner.get_cached_data()
+        
+        for model in cache.raw_data:
+            if model['file_name'] == model_name:
+                civitai_data = model.get('civitai', {})
+                model_id = civitai_data.get('modelId')
+                version_id = civitai_data.get('id')
+                
+                if model_id:
+                    civitai_url = f"https://civitai.com/models/{model_id}"
+                    if version_id:
+                        civitai_url += f"?modelVersionId={version_id}"
+                    
+                    return {
+                        'civitai_url': civitai_url,
+                        'model_id': str(model_id),
+                        'version_id': str(version_id) if version_id else None
+                    }
+        
+        return {'civitai_url': None, 'model_id': None, 'version_id': None}
+
+    async def get_model_metadata(self, file_path: str) -> Optional[Dict]:
+        """Get filtered CivitAI metadata for a model by file path"""
+        cache = await self.scanner.get_cached_data()
+        
+        for model in cache.raw_data:
+            if model.get('file_path') == file_path:
+                return ModelRouteUtils.filter_civitai_data(model.get("civitai", {}))
+        
+        return None
+
+    async def get_model_description(self, file_path: str) -> Optional[str]:
+        """Get model description by file path"""
+        cache = await self.scanner.get_cached_data()
+        
+        for model in cache.raw_data:
+            if model.get('file_path') == file_path:
+                return model.get('modelDescription', '')
+        
+        return None
+
+    async def search_relative_paths(self, search_term: str, limit: int = 15) -> List[str]:
+        """Search model relative file paths for autocomplete functionality"""
+        cache = await self.scanner.get_cached_data()
+        
+        matching_paths = []
+        search_lower = search_term.lower()
+        
+        # Get model roots for path calculation
+        model_roots = self.scanner.get_model_roots()
+        
+        for model in cache.raw_data:
+            file_path = model.get('file_path', '')
+            if not file_path:
+                continue
+                
+            # Calculate relative path from model root
+            relative_path = None
+            for root in model_roots:
+                # Normalize paths for comparison
+                normalized_root = os.path.normpath(root)
+                normalized_file = os.path.normpath(file_path)
+                
+                if normalized_file.startswith(normalized_root):
+                    # Remove root and leading separator to get relative path
+                    relative_path = normalized_file[len(normalized_root):].lstrip(os.sep)
+                    break
+            
+            if relative_path and search_lower in relative_path.lower():
+                matching_paths.append(relative_path)
+                
+                if len(matching_paths) >= limit * 2:  # Get more for better sorting
+                    break
+        
+        # Sort by relevance (exact matches first, then by length)
+        matching_paths.sort(key=lambda x: (
+            not x.lower().startswith(search_lower),  # Exact prefix matches first
+            len(x),  # Then by length (shorter first)
+            x.lower()  # Then alphabetically
+        ))
+        
+        return matching_paths[:limit]

py/services/checkpoint_scanner.py

@@ -13,7 +13,7 @@ class CheckpointScanner(ModelScanner):
     
     def __init__(self):
         # Define supported file extensions
-        file_extensions = {'.safetensors', '.ckpt', '.pt', '.pth', '.sft', '.gguf'}
+        file_extensions = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft', '.gguf'}
         super().__init__(
             model_type="checkpoint",
             model_class=CheckpointMetadata,
@@ -21,6 +21,14 @@ class CheckpointScanner(ModelScanner):
             hash_index=ModelHashIndex()
         )
 
+    def adjust_metadata(self, metadata, file_path, root_path):
+        if hasattr(metadata, "model_type"):
+            if root_path in config.checkpoints_roots:
+                metadata.model_type = "checkpoint"
+            elif root_path in config.unet_roots:
+                metadata.model_type = "diffusion_model"
+        return metadata
+
     def get_model_roots(self) -> List[str]:
         """Get checkpoint root directories"""
         return config.base_models_roots

py/services/checkpoint_service.py

@@ -34,12 +34,11 @@ class CheckpointService(BaseModelService):
             "file_size": checkpoint_data.get("size", 0),
             "modified": checkpoint_data.get("modified", ""),
             "tags": checkpoint_data.get("tags", []),
-            "modelDescription": checkpoint_data.get("modelDescription", ""),
             "from_civitai": checkpoint_data.get("from_civitai", True),
             "notes": checkpoint_data.get("notes", ""),
             "model_type": checkpoint_data.get("model_type", "checkpoint"),
             "favorite": checkpoint_data.get("favorite", False),
-            "civitai": ModelRouteUtils.filter_civitai_data(checkpoint_data.get("civitai", {}))
+            "civitai": ModelRouteUtils.filter_civitai_data(checkpoint_data.get("civitai", {}), minimal=True)
         }
     
     def find_duplicate_hashes(self) -> Dict:

py/services/civitai_client.py

@@ -33,8 +33,8 @@ class CivitaiClient:
         }
         self._session = None
         self._session_created_at = None
-        # Set default buffer size to 1MB for higher throughput
+        # Adjust chunk size based on storage type - consider making this configurable
-        self.chunk_size = 1024 * 1024
+        self.chunk_size = 4 * 1024 * 1024   # 4MB chunks for better HDD throughput
     
     @property
     async def session(self) -> aiohttp.ClientSession:
@@ -49,8 +49,8 @@ class CivitaiClient:
                 enable_cleanup_closed=True
             )
             trust_env = True  # Allow using system environment proxy settings
-            # Configure timeout parameters - increase read timeout for large files
+            # Configure timeout parameters - increase read timeout for large files and remove sock_read timeout
-            timeout = aiohttp.ClientTimeout(total=None, connect=60, sock_read=120)
+            timeout = aiohttp.ClientTimeout(total=None, connect=60, sock_read=None)
             self._session = aiohttp.ClientSession(
                 connector=connector, 
                 trust_env=trust_env,
@@ -102,7 +102,7 @@ class CivitaiClient:
         return headers
 
     async def _download_file(self, url: str, save_dir: str, default_filename: str, progress_callback=None) -> Tuple[bool, str]:
-        """Download file with content-disposition support and progress tracking
+        """Download file with resumable downloads and retry mechanism
 
         Args:
             url: Download URL
@@ -113,73 +113,176 @@ class CivitaiClient:
         Returns:
             Tuple[bool, str]: (success, save_path or error message)
         """
-        logger.debug(f"Resolving DNS for: {url}")
+        max_retries = 5
+        retry_count = 0
+        base_delay = 2.0  # Base delay for exponential backoff
+        
+        # Initial setup
         session = await self._ensure_fresh_session()
-        try:
+        save_path = os.path.join(save_dir, default_filename)
-            headers = self._get_request_headers()
+        part_path = save_path + '.part'
-            
+        
-            # Add Range header to allow resumable downloads
+        # Get existing file size for resume
-            headers['Accept-Encoding'] = 'identity'  # Disable compression for better chunked downloads
+        resume_offset = 0
-            
+        if os.path.exists(part_path):
-            logger.debug(f"Starting download from: {url}")
+            resume_offset = os.path.getsize(part_path)
-            async with session.get(url, headers=headers, allow_redirects=True) as response:
+            logger.info(f"Resuming download from offset {resume_offset} bytes")
-                if response.status != 200:
+        
-                    # Handle 401 unauthorized responses
+        total_size = 0
-                    if response.status == 401:
+        filename = default_filename
+        
+        while retry_count <= max_retries:
+            try:
+                headers = self._get_request_headers()
+                
+                # Add Range header for resume if we have partial data
+                if resume_offset > 0:
+                    headers['Range'] = f'bytes={resume_offset}-'
+                
+                # Add Range header to allow resumable downloads
+                headers['Accept-Encoding'] = 'identity'  # Disable compression for better chunked downloads
+                
+                logger.debug(f"Download attempt {retry_count + 1}/{max_retries + 1} from: {url}")
+                if resume_offset > 0:
+                    logger.debug(f"Requesting range from byte {resume_offset}")
+                
+                async with session.get(url, headers=headers, allow_redirects=True) as response:
+                    # Handle different response codes
+                    if response.status == 200:
+                        # Full content response
+                        if resume_offset > 0:
+                            # Server doesn't support ranges, restart from beginning
+                            logger.warning("Server doesn't support range requests, restarting download")
+                            resume_offset = 0
+                            if os.path.exists(part_path):
+                                os.remove(part_path)
+                    elif response.status == 206:
+                        # Partial content response (resume successful)
+                        content_range = response.headers.get('Content-Range')
+                        if content_range:
+                            # Parse total size from Content-Range header (e.g., "bytes 1024-2047/2048")
+                            range_parts = content_range.split('/')
+                            if len(range_parts) == 2:
+                                total_size = int(range_parts[1])
+                        logger.info(f"Successfully resumed download from byte {resume_offset}")
+                    elif response.status == 416:
+                        # Range not satisfiable - file might be complete or corrupted
+                        if os.path.exists(part_path):
+                            part_size = os.path.getsize(part_path)
+                            logger.warning(f"Range not satisfiable. Part file size: {part_size}")
+                            # Try to get actual file size
+                            head_response = await session.head(url, headers=self._get_request_headers())
+                            if head_response.status == 200:
+                                actual_size = int(head_response.headers.get('content-length', 0))
+                                if part_size == actual_size:
+                                    # File is complete, just rename it
+                                    os.rename(part_path, save_path)
+                                    if progress_callback:
+                                        await progress_callback(100)
+                                    return True, save_path
+                            # Remove corrupted part file and restart
+                            os.remove(part_path)
+                            resume_offset = 0
+                            continue
+                    elif response.status == 401:
                         logger.warning(f"Unauthorized access to resource: {url} (Status 401)")
-                        
                         return False, "Invalid or missing CivitAI API key, or early access restriction."
-                    
+                    elif response.status == 403:
-                    # Handle other client errors that might be permission-related
-                    if response.status == 403:
                         logger.warning(f"Forbidden access to resource: {url} (Status 403)")
                         return False, "Access forbidden: You don't have permission to download this file."
+                    else:
+                        logger.error(f"Download failed for {url} with status {response.status}")
+                        return False, f"Download failed with status {response.status}"
                     
-                    # Generic error response for other status codes
+                    # Get total file size for progress calculation (if not set from Content-Range)
-                    logger.error(f"Download failed for {url} with status {response.status}")
+                    if total_size == 0:
-                    return False, f"Download failed with status {response.status}"
+                        total_size = int(response.headers.get('content-length', 0))
+                        if response.status == 206:
+                            # For partial content, add the offset to get total file size
+                            total_size += resume_offset
 
-                # Get filename from content-disposition header
+                    current_size = resume_offset
-                content_disposition = response.headers.get('Content-Disposition')
+                    last_progress_report_time = datetime.now()
-                filename = self._parse_content_disposition(content_disposition)
-                if not filename:
-                    filename = default_filename
-                
-                save_path = os.path.join(save_dir, filename)
-                
-                # Get total file size for progress calculation
-                total_size = int(response.headers.get('content-length', 0))
-                current_size = 0
-                last_progress_report_time = datetime.now()
 
-                # Stream download to file with progress updates using larger buffer
+                    # Stream download to file with progress updates using larger buffer
-                with open(save_path, 'wb') as f:
+                    loop = asyncio.get_running_loop()
-                    async for chunk in response.content.iter_chunked(self.chunk_size):
+                    mode = 'ab' if resume_offset > 0 else 'wb'
-                        if chunk:
+                    with open(part_path, mode) as f:
-                            f.write(chunk)
+                        async for chunk in response.content.iter_chunked(self.chunk_size):
-                            current_size += len(chunk)
+                            if chunk:
-                            
+                                # Run blocking file write in executor
-                            # Limit progress update frequency to reduce overhead
+                                await loop.run_in_executor(None, f.write, chunk)
-                            now = datetime.now()
+                                current_size += len(chunk)
-                            time_diff = (now - last_progress_report_time).total_seconds()
+                                
-                            
+                                # Limit progress update frequency to reduce overhead
-                            if progress_callback and total_size and time_diff >= 1.0:
+                                now = datetime.now()
-                                progress = (current_size / total_size) * 100
+                                time_diff = (now - last_progress_report_time).total_seconds()
-                                await progress_callback(progress)
+                                
-                                last_progress_report_time = now
+                                if progress_callback and total_size and time_diff >= 1.0:
-                
+                                    progress = (current_size / total_size) * 100
-                # Ensure 100% progress is reported
+                                    await progress_callback(progress)
-                if progress_callback:
+                                    last_progress_report_time = now
-                    await progress_callback(100)
+                    
+                    # Download completed successfully
+                    # Verify file size if total_size was provided
+                    final_size = os.path.getsize(part_path)
+                    if total_size > 0 and final_size != total_size:
+                        logger.warning(f"File size mismatch. Expected: {total_size}, Got: {final_size}")
+                        # Don't treat this as fatal error, rename anyway
+                    
+                    # Atomically rename .part to final file with retries
+                    max_rename_attempts = 5
+                    rename_attempt = 0
+                    rename_success = False
+                    
+                    while rename_attempt < max_rename_attempts and not rename_success:
+                        try:
+                            os.rename(part_path, save_path)
+                            rename_success = True
+                        except PermissionError as e:
+                            rename_attempt += 1
+                            if rename_attempt < max_rename_attempts:
+                                logger.info(f"File still in use, retrying rename in 2 seconds (attempt {rename_attempt}/{max_rename_attempts})")
+                                await asyncio.sleep(2)  # Wait before retrying
+                            else:
+                                logger.error(f"Failed to rename file after {max_rename_attempts} attempts: {e}")
+                                return False, f"Failed to finalize download: {str(e)}"
+                    
+                    # Ensure 100% progress is reported
+                    if progress_callback:
+                        await progress_callback(100)
                         
-                return True, save_path
+                    return True, save_path
+                    
+            except (aiohttp.ClientError, aiohttp.ClientPayloadError, 
+                    aiohttp.ServerDisconnectedError, asyncio.TimeoutError) as e:
+                retry_count += 1
+                logger.warning(f"Network error during download (attempt {retry_count}/{max_retries + 1}): {e}")
                 
-        except aiohttp.ClientError as e:
+                if retry_count <= max_retries:
-            logger.error(f"Network error during download: {e}")
+                    # Calculate delay with exponential backoff
-            return False, f"Network error: {str(e)}"
+                    delay = base_delay * (2 ** (retry_count - 1))
-        except Exception as e:
+                    logger.info(f"Retrying in {delay} seconds...")
-            logger.error(f"Download error: {e}")
+                    await asyncio.sleep(delay)
-            return False, str(e)
+                    
+                    # Update resume offset for next attempt
+                    if os.path.exists(part_path):
+                        resume_offset = os.path.getsize(part_path)
+                        logger.info(f"Will resume from byte {resume_offset}")
+                    
+                    # Refresh session to get new connection
+                    await self.close()
+                    session = await self._ensure_fresh_session()
+                    continue
+                else:
+                    logger.error(f"Max retries exceeded for download: {e}")
+                    return False, f"Network error after {max_retries + 1} attempts: {str(e)}"
+                    
+            except Exception as e:
+                logger.error(f"Unexpected download error: {e}")
+                return False, str(e)
+        
+        return False, f"Download failed after {max_retries + 1} attempts"
 
     async def get_model_by_hash(self, model_hash: str) -> Optional[Dict]:
         try:
@@ -223,11 +326,11 @@ class CivitaiClient:
             logger.error(f"Error fetching model versions: {e}")
             return None
             
-    async def get_model_version(self, model_id: int, 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
         
         Args:
-            model_id: The Civitai model ID
+            model_id: The Civitai model ID (optional if version_id is provided)
             version_id: Optional specific version ID to retrieve
             
         Returns:
@@ -235,37 +338,72 @@ class CivitaiClient:
         """
         try:
             session = await self._ensure_fresh_session()
-            
-            # Step 1: Get model data to find version_id if not provided and get additional metadata
-            async with session.get(f"{self.base_url}/models/{model_id}") as response:
-                if response.status != 200:
-                    return None
-                    
-                data = await response.json()
-                model_versions = data.get('modelVersions', [])
-                
-                # Step 2: Determine the version_id to use
-                target_version_id = version_id
-                if target_version_id is None:
-                    target_version_id = model_versions[0].get('id')
-            
-            # Step 3: Get detailed version info using the version_id
             headers = self._get_request_headers()
-            async with session.get(f"{self.base_url}/model-versions/{target_version_id}", headers=headers) as response:
+            
-                if response.status != 200:
+            # Case 1: Only version_id is provided
-                    return None
+            if model_id is None and version_id is not None:
+                # First get the version info to extract model_id
+                async with session.get(f"{self.base_url}/model-versions/{version_id}", headers=headers) as response:
+                    if response.status != 200:
+                        return None
+                    
+                    version = await response.json()
+                    model_id = version.get('modelId')
+                    
+                    if not model_id:
+                        logger.error(f"No modelId found in version {version_id}")
+                        return None
                 
-                version = await response.json()
+                # Now get the model data for additional metadata
+                async with session.get(f"{self.base_url}/models/{model_id}") as response:
+                    if response.status != 200:
+                        return version  # Return version without additional metadata
+                    
+                    model_data = await response.json()
+                    
+                    # Enrich version with model data
+                    version['model']['description'] = model_data.get("description")
+                    version['model']['tags'] = model_data.get("tags", [])
+                    version['creator'] = model_data.get("creator")
+                    
+                    return version
+            
+            # Case 2: model_id is provided (with or without version_id)
+            elif model_id is not None:
+                # Step 1: Get model data to find version_id if not provided and get additional metadata
+                async with session.get(f"{self.base_url}/models/{model_id}") as response:
+                    if response.status != 200:
+                        return None
+                        
+                    data = await response.json()
+                    model_versions = data.get('modelVersions', [])
+                    
+                    # Step 2: Determine the version_id to use
+                    target_version_id = version_id
+                    if target_version_id is None:
+                        target_version_id = model_versions[0].get('id')
                 
-                # Step 4: Enrich version_info with model data
+                # Step 3: Get detailed version info using the version_id
-                # Add description and tags from model data
+                async with session.get(f"{self.base_url}/model-versions/{target_version_id}", headers=headers) as response:
-                version['model']['description'] = data.get("description")
+                    if response.status != 200:
-                version['model']['tags'] = data.get("tags", [])
+                        return None
-                
+                    
-                # Add creator from model data
+                    version = await response.json()
-                version['creator'] = data.get("creator")
+                    
-                
+                    # Step 4: Enrich version_info with model data
-                return version
+                    # Add description and tags from model data
+                    version['model']['description'] = data.get("description")
+                    version['model']['tags'] = data.get("tags", [])
+                    
+                    # Add creator from model data
+                    version['creator'] = data.get("creator")
+                    
+                    return version
+            
+            # Case 3: Neither model_id nor version_id provided
+            else:
+                logger.error("Either model_id or version_id must be provided")
+                return None
                 
         except Exception as e:
             logger.error(f"Error fetching model version: {e}")

py/services/download_manager.py

@@ -54,15 +54,15 @@ class DownloadManager:
         """Get the checkpoint scanner from registry"""
         return await ServiceRegistry.get_checkpoint_scanner()
     
-    async def download_from_civitai(self, model_id: int, model_version_id: int, 
+    async def download_from_civitai(self, model_id: int = None, model_version_id: int = None, 
                                   save_dir: str = None, relative_path: str = '', 
                                   progress_callback=None, use_default_paths: bool = False,
                                   download_id: str = None) -> Dict:
         """Download model from Civitai with task tracking and concurrency control
         
         Args:
-            model_id: Civitai model ID
+            model_id: Civitai model ID (optional if model_version_id is provided)
-            model_version_id: Civitai model version ID
+            model_version_id: Civitai model version ID (optional if model_id is provided)
             save_dir: Directory to save the model
             relative_path: Relative path within save_dir
             progress_callback: Callback function for progress updates
@@ -72,6 +72,10 @@ class DownloadManager:
         Returns:
             Dict with download result
         """
+        # Validate that at least one identifier is provided
+        if not model_id and not model_version_id:
+            return {'success': False, 'error': 'Either model_id or model_version_id must be provided'}
+        
         # Use provided download_id or generate new one
         task_id = download_id or str(uuid.uuid4())
         
@@ -181,14 +185,19 @@ class DownloadManager:
                 # Check both scanners
                 lora_scanner = await self._get_lora_scanner()
                 checkpoint_scanner = await self._get_checkpoint_scanner()
+                embedding_scanner = await ServiceRegistry.get_embedding_scanner()
                 
                 # Check lora scanner first
-                if await lora_scanner.check_model_version_exists(model_id, model_version_id):
+                if await lora_scanner.check_model_version_exists(model_version_id):
                     return {'success': False, 'error': 'Model version already exists in lora library'}
                 
                 # Check checkpoint scanner
-                if await checkpoint_scanner.check_model_version_exists(model_id, model_version_id):
+                if await checkpoint_scanner.check_model_version_exists(model_version_id):
                     return {'success': False, 'error': 'Model version already exists in checkpoint library'}
+                
+                # Check embedding scanner
+                if await embedding_scanner.check_model_version_exists(model_version_id):
+                    return {'success': False, 'error': 'Model version already exists in embedding library'}
 
             # Get civitai client
             civitai_client = await self._get_civitai_client()
@@ -211,23 +220,22 @@ class DownloadManager:
             
             # Case 2: model_version_id was None, check after getting version_info
             if model_version_id is None:
-                version_model_id = version_info.get('modelId')
                 version_id = version_info.get('id')
                 
                 if model_type == 'lora':
                     # Check lora scanner
                     lora_scanner = await self._get_lora_scanner()
-                    if await lora_scanner.check_model_version_exists(version_model_id, version_id):
+                    if await lora_scanner.check_model_version_exists(version_id):
                         return {'success': False, 'error': 'Model version already exists in lora library'}
                 elif model_type == 'checkpoint':
                     # Check checkpoint scanner
                     checkpoint_scanner = await self._get_checkpoint_scanner()
-                    if await checkpoint_scanner.check_model_version_exists(version_model_id, version_id):
+                    if await checkpoint_scanner.check_model_version_exists(version_id):
                         return {'success': False, 'error': 'Model version already exists in checkpoint library'}
                 elif model_type == 'embedding':
                     # Embeddings are not checked in scanners, but we can still check if it exists
                     embedding_scanner = await ServiceRegistry.get_embedding_scanner()
-                    if await embedding_scanner.check_model_version_exists(version_model_id, version_id):
+                    if await embedding_scanner.check_model_version_exists(version_id):
                         return {'success': False, 'error': 'Model version already exists in embedding library'}
             
             # Handle use_default_paths
@@ -250,7 +258,7 @@ class DownloadManager:
                     save_dir = default_path
                     
                 # Calculate relative path using template
-                relative_path = self._calculate_relative_path(version_info)
+                relative_path = self._calculate_relative_path(version_info, model_type)
 
             # Update save directory with relative path if provided
             if relative_path:
@@ -266,9 +274,9 @@ class DownloadManager:
                     from datetime import datetime
                     date_obj = datetime.fromisoformat(early_access_date.replace('Z', '+00:00'))
                     formatted_date = date_obj.strftime('%Y-%m-%d')
-                    early_access_msg = f"This model requires early access payment (until {formatted_date}). "
+                    early_access_msg = f"This model requires payment (until {formatted_date}). "
                 except:
-                    early_access_msg = "This model requires early access payment. "
+                    early_access_msg = "This model requires payment. "
                 
                 early_access_msg += "Please ensure you have purchased early access and are logged in to Civitai."
                 logger.warning(f"Early access model detected: {version_info.get('name', 'Unknown')}")
@@ -313,6 +321,10 @@ class DownloadManager:
                 download_id=download_id
             )
 
+            # If early_access_msg exists and download failed, replace error message
+            if 'early_access_msg' in locals() and not result.get('success', False):
+                result['error'] = early_access_msg
+
             return result
 
         except Exception as e:
@@ -323,17 +335,18 @@ class DownloadManager:
                 return {'success': False, 'error': f"Early access restriction: {str(e)}. Please ensure you have purchased early access and are logged in to Civitai."}
             return {'success': False, 'error': str(e)}
 
-    def _calculate_relative_path(self, version_info: Dict) -> str:
+    def _calculate_relative_path(self, version_info: Dict, model_type: str = 'lora') -> str:
         """Calculate relative path using template from settings
         
         Args:
             version_info: Version info from Civitai API
+            model_type: Type of model ('lora', 'checkpoint', 'embedding')
             
         Returns:
             Relative path string
         """
-        # Get path template from settings, default to '{base_model}/{first_tag}'
+        # Get path template from settings for specific model type
-        path_template = settings.get('download_path_template', '{base_model}/{first_tag}')
+        path_template = settings.get_download_path_template(model_type)
         
         # If template is empty, return empty path (flat structure)
         if not path_template:
@@ -342,6 +355,13 @@ class DownloadManager:
         # Get base model name
         base_model = version_info.get('baseModel', '')
         
+        # Get author from creator data
+        creator_info = version_info.get('creator')
+        if creator_info and isinstance(creator_info, dict):
+            author = creator_info.get('username') or 'Anonymous'
+        else:
+            author = 'Anonymous'
+        
         # Apply mapping if available
         base_model_mappings = settings.get('base_model_path_mappings', {})
         mapped_base_model = base_model_mappings.get(base_model, base_model)
@@ -364,22 +384,51 @@ class DownloadManager:
         formatted_path = path_template
         formatted_path = formatted_path.replace('{base_model}', mapped_base_model)
         formatted_path = formatted_path.replace('{first_tag}', first_tag)
+        formatted_path = formatted_path.replace('{author}', author)
         
         return formatted_path
 
     async def _execute_download(self, download_url: str, save_dir: str, 
-                              metadata, version_info: Dict, 
+                          metadata, version_info: Dict, 
-                              relative_path: str, progress_callback=None,
+                          relative_path: str, progress_callback=None,
-                              model_type: str = "lora", download_id: str = None) -> Dict:
+                          model_type: str = "lora", download_id: str = None) -> Dict:
         """Execute the actual download process including preview images and model files"""
         try:
             civitai_client = await self._get_civitai_client()
-            save_path = metadata.file_path
+            
+            # Extract original filename details
+            original_filename = os.path.basename(metadata.file_path)
+            base_name, extension = os.path.splitext(original_filename)
+            
+            # Check for filename conflicts and generate unique filename if needed
+            # Use the hash from metadata for conflict resolution
+            def hash_provider():
+                return metadata.sha256
+            
+            unique_filename = metadata.generate_unique_filename(
+                save_dir, 
+                base_name, 
+                extension, 
+                hash_provider=hash_provider
+            )
+            
+            # Update paths if filename changed
+            if unique_filename != original_filename:
+                logger.info(f"Filename conflict detected. Changing '{original_filename}' to '{unique_filename}'")
+                save_path = os.path.join(save_dir, unique_filename)
+                # Update metadata with new file path and name
+                metadata.file_path = save_path.replace(os.sep, '/')
+                metadata.file_name = os.path.splitext(unique_filename)[0]
+            else:
+                save_path = metadata.file_path
+                
+            part_path = save_path + '.part'
             metadata_path = os.path.splitext(save_path)[0] + '.metadata.json'
             
-            # Store file path in active_downloads for potential cleanup
+            # Store file paths in active_downloads for potential cleanup
             if download_id and download_id in self._active_downloads:
                 self._active_downloads[download_id]['file_path'] = save_path
+                self._active_downloads[download_id]['part_path'] = part_path
 
             # Download preview image if available
             images = version_info.get('images', [])
@@ -446,17 +495,29 @@ class DownloadManager:
             )
 
             if not success:
-                # Clean up files on failure
+                # Clean up files on failure, but preserve .part file for resume
-                for path in [save_path, metadata_path, metadata.preview_url]:
+                cleanup_files = [metadata_path]
+                if metadata.preview_url and os.path.exists(metadata.preview_url):
+                    cleanup_files.append(metadata.preview_url)
+                
+                for path in cleanup_files:
                     if path and os.path.exists(path):
-                        os.remove(path)
+                        try:
+                            os.remove(path)
+                        except Exception as e:
+                            logger.warning(f"Failed to cleanup file {path}: {e}")
+                
+                # Log but don't remove .part file to allow resume
+                if os.path.exists(part_path):
+                    logger.info(f"Preserving partial download for resume: {part_path}")
+                
                 return {'success': False, 'error': result}
 
             # 4. Update file information (size and modified time)
             metadata.update_file_info(save_path)
 
             # 5. Final metadata update
-            await MetadataManager.save_metadata(save_path, metadata, True)
+            await MetadataManager.save_metadata(save_path, metadata)
 
             # 6. Update cache based on model type
             if model_type == "checkpoint":
@@ -485,10 +546,18 @@ class DownloadManager:
 
         except Exception as e:
             logger.error(f"Error in _execute_download: {e}", exc_info=True)
-            # Clean up partial downloads
+            # Clean up partial downloads except .part file
-            for path in [save_path, metadata_path]:
+            cleanup_files = [metadata_path]
+            if hasattr(metadata, 'preview_url') and metadata.preview_url and os.path.exists(metadata.preview_url):
+                cleanup_files.append(metadata.preview_url)
+            
+            for path in cleanup_files:
                 if path and os.path.exists(path):
-                    os.remove(path)
+                    try:
+                        os.remove(path)
+                    except Exception as e:
+                        logger.warning(f"Failed to cleanup file {path}: {e}")
+            
             return {'success': False, 'error': str(e)}
 
     async def _handle_download_progress(self, file_progress: float, progress_callback):
@@ -530,35 +599,48 @@ class DownloadManager:
             except (asyncio.CancelledError, asyncio.TimeoutError):
                 pass
             
-            # Clean up partial downloads
+            # Clean up ALL files including .part when user cancels
             download_info = self._active_downloads.get(download_id)
-            if download_info and 'file_path' in download_info:
+            if download_info:
-                # Delete the partial file
+                # Delete the main file
-                file_path = download_info['file_path']
+                if 'file_path' in download_info:
-                if os.path.exists(file_path):
+                    file_path = download_info['file_path']
-                    try:
+                    if os.path.exists(file_path):
-                        os.unlink(file_path)
+                        try:
-                        logger.debug(f"Deleted partial download: {file_path}")
+                            os.unlink(file_path)
-                    except Exception as e:
+                            logger.debug(f"Deleted cancelled download: {file_path}")
-                        logger.error(f"Error deleting partial file: {e}")
+                        except Exception as e:
+                            logger.error(f"Error deleting file: {e}")
+                
+                # Delete the .part file (only on user cancellation)
+                if 'part_path' in download_info:
+                    part_path = download_info['part_path']
+                    if os.path.exists(part_path):
+                        try:
+                            os.unlink(part_path)
+                            logger.debug(f"Deleted partial download: {part_path}")
+                        except Exception as e:
+                            logger.error(f"Error deleting part file: {e}")
                 
                 # Delete metadata file if exists
-                metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+                if 'file_path' in download_info:
-                if os.path.exists(metadata_path):
+                    file_path = download_info['file_path']
-                    try:
+                    metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
-                        os.unlink(metadata_path)
+                    if os.path.exists(metadata_path):
-                    except Exception as e:
-                        logger.error(f"Error deleting metadata file: {e}")
-
-                # Delete preview file if exists (.webp or .mp4)
-                for preview_ext in ['.webp', '.mp4']:
-                    preview_path = os.path.splitext(file_path)[0] + preview_ext
-                    if os.path.exists(preview_path):
                         try:
-                            os.unlink(preview_path)
+                            os.unlink(metadata_path)
-                            logger.debug(f"Deleted preview file: {preview_path}")
                         except Exception as e:
-                            logger.error(f"Error deleting preview file: {e}")
+                            logger.error(f"Error deleting metadata file: {e}")
+
+                    # Delete preview file if exists (.webp or .mp4)
+                    for preview_ext in ['.webp', '.mp4']:
+                        preview_path = os.path.splitext(file_path)[0] + preview_ext
+                        if os.path.exists(preview_path):
+                            try:
+                                os.unlink(preview_path)
+                                logger.debug(f"Deleted preview file: {preview_path}")
+                            except Exception as e:
+                                logger.error(f"Error deleting preview file: {e}")
             
             return {'success': True, 'message': 'Download cancelled successfully'}
         except Exception as e:

py/services/embedding_service.py

@@ -34,12 +34,11 @@ class EmbeddingService(BaseModelService):
             "file_size": embedding_data.get("size", 0),
             "modified": embedding_data.get("modified", ""),
             "tags": embedding_data.get("tags", []),
-            "modelDescription": embedding_data.get("modelDescription", ""),
             "from_civitai": embedding_data.get("from_civitai", True),
             "notes": embedding_data.get("notes", ""),
             "model_type": embedding_data.get("model_type", "embedding"),
             "favorite": embedding_data.get("favorite", False),
-            "civitai": ModelRouteUtils.filter_civitai_data(embedding_data.get("civitai", {}))
+            "civitai": ModelRouteUtils.filter_civitai_data(embedding_data.get("civitai", {}), minimal=True)
         }
     
     def find_duplicate_hashes(self) -> Dict:

py/services/lora_service.py

@@ -34,12 +34,11 @@ class LoraService(BaseModelService):
             "file_size": lora_data.get("size", 0),
             "modified": lora_data.get("modified", ""),
             "tags": lora_data.get("tags", []),
-            "modelDescription": lora_data.get("modelDescription", ""),
             "from_civitai": lora_data.get("from_civitai", True),
             "usage_tips": lora_data.get("usage_tips", ""),
             "notes": lora_data.get("notes", ""),
             "favorite": lora_data.get("favorite", False),
-            "civitai": ModelRouteUtils.filter_civitai_data(lora_data.get("civitai", {}))
+            "civitai": ModelRouteUtils.filter_civitai_data(lora_data.get("civitai", {}), minimal=True)
         }
     
     async def _apply_specific_filters(self, data: List[Dict], **kwargs) -> List[Dict]:
@@ -147,16 +146,6 @@ class LoraService(BaseModelService):
                 
         return letters
     
-    async def get_lora_notes(self, lora_name: str) -> Optional[str]:
-        """Get notes for a specific LoRA file"""
-        cache = await self.scanner.get_cached_data()
-        
-        for lora in cache.raw_data:
-            if lora['file_name'] == lora_name:
-                return lora.get('notes', '')
-        
-        return None
-    
     async def get_lora_trigger_words(self, lora_name: str) -> List[str]:
         """Get trigger words for a specific LoRA file"""
         cache = await self.scanner.get_cached_data()
@@ -168,41 +157,22 @@ class LoraService(BaseModelService):
         
         return []
     
-    async def get_lora_preview_url(self, lora_name: str) -> Optional[str]:
+    async def get_lora_usage_tips_by_relative_path(self, relative_path: str) -> Optional[str]:
-        """Get the static preview URL for a LoRA file"""
+        """Get usage tips for a LoRA by its relative path"""
         cache = await self.scanner.get_cached_data()
         
         for lora in cache.raw_data:
-            if lora['file_name'] == lora_name:
+            file_path = lora.get('file_path', '')
-                preview_url = lora.get('preview_url')
+            if file_path:
-                if preview_url:
+                # Convert to forward slashes and extract relative path
-                    return config.get_preview_static_url(preview_url)
+                file_path_normalized = file_path.replace('\\', '/')
+                relative_path = relative_path.replace('\\', '/')
+                # Find the relative path part by looking for the relative_path in the full path
+                if file_path_normalized.endswith(relative_path) or relative_path in file_path_normalized:
+                    return lora.get('usage_tips', '')
         
         return None
     
-    async def get_lora_civitai_url(self, lora_name: str) -> Dict[str, Optional[str]]:
-        """Get the Civitai URL for a LoRA file"""
-        cache = await self.scanner.get_cached_data()
-        
-        for lora in cache.raw_data:
-            if lora['file_name'] == lora_name:
-                civitai_data = lora.get('civitai', {})
-                model_id = civitai_data.get('modelId')
-                version_id = civitai_data.get('id')
-                
-                if model_id:
-                    civitai_url = f"https://civitai.com/models/{model_id}"
-                    if version_id:
-                        civitai_url += f"?modelVersionId={version_id}"
-                    
-                    return {
-                        'civitai_url': civitai_url,
-                        'model_id': str(model_id),
-                        'version_id': str(version_id) if version_id else None
-                    }
-        
-        return {'civitai_url': None, 'model_id': None, 'version_id': None}
-    
     def find_duplicate_hashes(self) -> Dict:
         """Find LoRAs with duplicate SHA256 hashes"""
         return self.scanner._hash_index.get_duplicate_hashes()

py/services/model_hash_index.py

@@ -31,29 +31,34 @@ class ModelHashIndex:
                 if file_path not in self._duplicate_hashes.get(sha256, []):
                     self._duplicate_hashes.setdefault(sha256, []).append(file_path)
         
-        # Track duplicates by filename
+        # Track duplicates by filename - FIXED LOGIC
         if filename in self._filename_to_hash:
-            old_hash = self._filename_to_hash[filename]
+            existing_hash = self._filename_to_hash[filename]
-            if old_hash != sha256:  # Different models with the same name
+            existing_path = self._hash_to_path.get(existing_hash)
-                old_path = self._hash_to_path.get(old_hash)
+            
-                if old_path:
+            # If this is a different file with the same filename
-                    if filename not in self._duplicate_filenames:
+            if existing_path and existing_path != file_path:
-                        self._duplicate_filenames[filename] = [old_path]
+                # Initialize duplicates tracking if needed
-                    if file_path not in self._duplicate_filenames.get(filename, []):
+                if filename not in self._duplicate_filenames:
-                        self._duplicate_filenames.setdefault(filename, []).append(file_path)
+                    self._duplicate_filenames[filename] = [existing_path]
+                
+                # Add current file to duplicates if not already present
+                if file_path not in self._duplicate_filenames[filename]:
+                    self._duplicate_filenames[filename].append(file_path)
         
         # Remove old path mapping if hash exists
         if sha256 in self._hash_to_path:
             old_path = self._hash_to_path[sha256]
             old_filename = self._get_filename_from_path(old_path)
-            if old_filename in self._filename_to_hash:
+            if old_filename in self._filename_to_hash and self._filename_to_hash[old_filename] == sha256:
                 del self._filename_to_hash[old_filename]
         
-        # Remove old hash mapping if filename exists
+        # Remove old hash mapping if filename exists and points to different hash
         if filename in self._filename_to_hash:
             old_hash = self._filename_to_hash[filename]
-            if old_hash in self._hash_to_path:
+            if old_hash != sha256 and old_hash in self._hash_to_path:
-                del self._hash_to_path[old_hash]
+                # Don't delete the old hash mapping, just update filename mapping
+                pass
         
         # Add new mappings
         self._hash_to_path[sha256] = file_path
@@ -199,8 +204,6 @@ class ModelHashIndex:
     
     def get_hash_by_filename(self, filename: str) -> Optional[str]:
         """Get hash for a filename without extension"""
-        # Strip extension if present to make the function more flexible
-        filename = os.path.splitext(filename)[0]
         return self._filename_to_hash.get(filename)
     
     def clear(self) -> None:

py/services/model_scanner.py

@@ -8,7 +8,7 @@ from typing import List, Dict, Optional, Type, Set
 
 from ..utils.models import BaseModelMetadata
 from ..config import config
-from ..utils.file_utils import find_preview_file
+from ..utils.file_utils import find_preview_file, get_preview_extension
 from ..utils.metadata_manager import MetadataManager
 from .model_cache import ModelCache
 from .model_hash_index import ModelHashIndex
@@ -302,6 +302,13 @@ class ModelScanner:
                         for tag in model_data['tags']:
                             self._tags_count[tag] = self._tags_count.get(tag, 0) + 1
                 
+                # Log duplicate filename warnings after building the index
+                # duplicate_filenames = self._hash_index.get_duplicate_filenames()
+                # if duplicate_filenames:
+                #     logger.warning(f"Found {len(duplicate_filenames)} filename(s) with duplicates during {self.model_type} cache build:")
+                #     for filename, paths in duplicate_filenames.items():
+                #         logger.warning(f"  Duplicate filename '{filename}': {paths}")
+                
                 # Update cache
                 self._cache.raw_data = raw_data
                 loop.run_until_complete(self._cache.resort())
@@ -367,6 +374,13 @@ class ModelScanner:
                     for tag in model_data['tags']:
                         self._tags_count[tag] = self._tags_count.get(tag, 0) + 1
             
+            # Log duplicate filename warnings after building the index
+            # duplicate_filenames = self._hash_index.get_duplicate_filenames()
+            # if duplicate_filenames:
+            #     logger.warning(f"Found {len(duplicate_filenames)} filename(s) with duplicates during {self.model_type} cache build:")
+            #     for filename, paths in duplicate_filenames.items():
+            #         logger.warning(f"  Duplicate filename '{filename}': {paths}")
+            
             # Update cache
             self._cache = ModelCache(
                 raw_data=raw_data,
@@ -569,12 +583,13 @@ class ModelScanner:
                     for entry in entries:
                         try:
                             if entry.is_file(follow_symlinks=True) and any(entry.name.endswith(ext) for ext in self.file_extensions):
-                                # Use original path instead of real path
                                 file_path = entry.path.replace(os.sep, "/")
-                                await self._process_single_file(file_path, original_root, models)
+                                result = await self._process_model_file(file_path, original_root)
+                                # Only add to models if result is not None (skip corrupted metadata)
+                                if result:
+                                    models.append(result)
                                 await asyncio.sleep(0)
                             elif entry.is_dir(follow_symlinks=True):
-                                # For directories, continue scanning with original path
                                 await scan_recursive(entry.path, visited_paths)
                         except Exception as e:
                             logger.error(f"Error processing entry {entry.path}: {e}")
@@ -583,15 +598,6 @@ class ModelScanner:
 
         await scan_recursive(root_path, set())
         return models
-
-    async def _process_single_file(self, file_path: str, root_path: str, models: list):
-        """Process a single file and add to results list"""
-        try:
-            result = await self._process_model_file(file_path, root_path)
-            if result:
-                models.append(result)
-        except Exception as e:
-            logger.error(f"Error processing {file_path}: {e}")
     
     def is_initializing(self) -> bool:
         """Check if the scanner is currently initializing"""
@@ -613,10 +619,18 @@ class ModelScanner:
                 return os.path.dirname(rel_path).replace(os.path.sep, '/')
         return ''
 
-    # Common methods shared between scanners
+    def adjust_metadata(self, metadata, file_path, root_path):
+        """Hook for subclasses: adjust metadata during scanning"""
+        return metadata
+
     async def _process_model_file(self, file_path: str, root_path: str) -> Dict:
         """Process a single model file and return its metadata"""
-        metadata = await MetadataManager.load_metadata(file_path, self.model_class)
+        metadata, should_skip = await MetadataManager.load_metadata(file_path, self.model_class)
+    
+        if should_skip:
+            # Metadata file exists but cannot be parsed - skip this model
+            logger.warning(f"Skipping model {file_path} due to corrupted metadata file")
+            return None
         
         if metadata is None:
             civitai_info_path = f"{os.path.splitext(file_path)[0]}.civitai.info"
@@ -632,7 +646,7 @@ class ModelScanner:
                     
                         metadata = self.model_class.from_civitai_info(version_info, file_info, file_path)
                         metadata.preview_url = find_preview_file(file_name, os.path.dirname(file_path))
-                        await MetadataManager.save_metadata(file_path, metadata, True)
+                        await MetadataManager.save_metadata(file_path, metadata)
                         logger.debug(f"Created metadata from .civitai.info for {file_path}")
                 except Exception as e:
                     logger.error(f"Error creating metadata from .civitai.info for {file_path}: {e}")
@@ -659,7 +673,7 @@ class ModelScanner:
                                 metadata.modelDescription = version_info['model']['description']
                         
                         # Save the updated metadata
-                        await MetadataManager.save_metadata(file_path, metadata, True)
+                        await MetadataManager.save_metadata(file_path, metadata)
                         logger.debug(f"Updated metadata with civitai info for {file_path}")
                     except Exception as e:
                         logger.error(f"Error restoring civitai data from .civitai.info for {file_path}: {e}")
@@ -667,12 +681,23 @@ class ModelScanner:
         if metadata is None:
             metadata = await self._create_default_metadata(file_path)
         
+        # Hook: allow subclasses to adjust metadata
+        metadata = self.adjust_metadata(metadata, file_path, root_path)
+        
         model_data = metadata.to_dict()
         
         # Skip excluded models
         if model_data.get('exclude', False):
             self._excluded_models.append(model_data['file_path'])
             return None
+        
+        # Check for duplicate filename before adding to hash index
+        filename = os.path.splitext(os.path.basename(file_path))[0]
+        existing_hash = self._hash_index.get_hash_by_filename(filename)
+        if existing_hash and existing_hash != model_data.get('sha256', '').lower():
+            existing_path = self._hash_index.get_path(existing_hash)
+            if existing_path and existing_path != file_path:
+                logger.warning(f"Duplicate filename detected: '{filename}' - files: '{existing_path}' and '{file_path}'")
             
         await self._fetch_missing_metadata(file_path, model_data)
         rel_path = os.path.relpath(file_path, root_path)
@@ -728,52 +753,10 @@ class ModelScanner:
 
                     model_data['civitai']['creator'] = model_metadata['creator']
                     
-                    await MetadataManager.save_metadata(file_path, model_data, True)
+                    await MetadataManager.save_metadata(file_path, model_data)
         except Exception as e:
             logger.error(f"Failed to update metadata from Civitai for {file_path}: {e}")
 
-    async def _scan_directory(self, root_path: str) -> List[Dict]:
-        """Base implementation for directory scanning"""
-        models = []
-        original_root = root_path
-
-        async def scan_recursive(path: str, visited_paths: set):
-            try:
-                real_path = os.path.realpath(path)
-                if real_path in visited_paths:
-                    logger.debug(f"Skipping already visited path: {path}")
-                    return
-                visited_paths.add(real_path)
-                
-                with os.scandir(path) as it:
-                    entries = list(it)
-                    for entry in entries:
-                        try:
-                            if entry.is_file(follow_symlinks=True):
-                                ext = os.path.splitext(entry.name)[1].lower()
-                                if ext in self.file_extensions:
-                                    file_path = entry.path.replace(os.sep, "/")
-                                    await self._process_single_file(file_path, original_root, models)
-                                    await asyncio.sleep(0)
-                            elif entry.is_dir(follow_symlinks=True):
-                                await scan_recursive(entry.path, visited_paths)
-                        except Exception as e:
-                            logger.error(f"Error processing entry {entry.path}: {e}")
-            except Exception as e:
-                logger.error(f"Error scanning {path}: {e}")
-        
-        await scan_recursive(root_path, set())
-        return models
-
-    async def _process_single_file(self, file_path: str, root_path: str, models_list: list):
-        """Process a single file and add to results list"""
-        try:
-            result = await self._process_model_file(file_path, root_path)
-            if result:
-                models_list.append(result)
-        except Exception as e:
-            logger.error(f"Error processing {file_path}: {e}")
-
     async def add_model_to_cache(self, metadata_dict: Dict, folder: str = '') -> bool:
         """Add a model to the cache
         
@@ -809,8 +792,16 @@ class ModelScanner:
             logger.error(f"Error adding model to cache: {e}")
             return False
     
-    async def move_model(self, source_path: str, target_path: str) -> bool:
+    async def move_model(self, source_path: str, target_path: str) -> Optional[str]:
-        """Move a model and its associated files to a new location"""
+        """Move a model and its associated files to a new location
+        
+        Args:
+            source_path: Original file path
+            target_path: Target directory path
+            
+        Returns:
+            Optional[str]: New file path if successful, None if failed
+        """
         try:
             source_path = source_path.replace(os.sep, '/')
             target_path = target_path.replace(os.sep, '/')
@@ -819,14 +810,28 @@ class ModelScanner:
             
             if not file_ext or file_ext.lower() not in self.file_extensions:
                 logger.error(f"Invalid file extension for model: {file_ext}")
-                return False
+                return None
                 
             base_name = os.path.splitext(os.path.basename(source_path))[0]
             source_dir = os.path.dirname(source_path)
             
             os.makedirs(target_path, exist_ok=True)
             
-            target_file = os.path.join(target_path, f"{base_name}{file_ext}").replace(os.sep, '/')
+            def get_source_hash():
+                return self.get_hash_by_path(source_path)
+            
+            # Check for filename conflicts and auto-rename if necessary
+            from ..utils.models import BaseModelMetadata
+            final_filename = BaseModelMetadata.generate_unique_filename(
+                target_path, base_name, file_ext, get_source_hash
+            )
+            
+            target_file = os.path.join(target_path, final_filename).replace(os.sep, '/')
+            final_base_name = os.path.splitext(final_filename)[0]
+            
+            # Log if filename was changed due to conflict
+            if final_filename != f"{base_name}{file_ext}":
+                logger.info(f"Renamed {base_name}{file_ext} to {final_filename} to avoid filename conflict")
 
             real_source = os.path.realpath(source_path)
             real_target = os.path.realpath(target_file)
@@ -843,12 +848,17 @@ class ModelScanner:
                 for file in os.listdir(source_dir):
                     if file.startswith(base_name + ".") and file != os.path.basename(source_path):
                         source_file_path = os.path.join(source_dir, file)
+                        # Generate new filename with the same base name as the model file
+                        file_suffix = file[len(base_name):]  # Get the part after base_name (e.g., ".metadata.json", ".preview.png")
+                        new_associated_filename = f"{final_base_name}{file_suffix}"
+                        target_associated_path = os.path.join(target_path, new_associated_filename)
+                        
                         # Store metadata file path for special handling
                         if file == f"{base_name}.metadata.json":
                             source_metadata = source_file_path
-                            moved_metadata_path = os.path.join(target_path, file)
+                            moved_metadata_path = target_associated_path
                         else:
-                            files_to_move.append((source_file_path, os.path.join(target_path, file)))
+                            files_to_move.append((source_file_path, target_associated_path))
             except Exception as e:
                 logger.error(f"Error listing files in {source_dir}: {e}")
             
@@ -870,11 +880,11 @@ class ModelScanner:
             
             await self.update_single_model_cache(source_path, target_file, metadata)
             
-            return True
+            return target_file
             
         except Exception as e:
             logger.error(f"Error moving model: {e}", exc_info=True)
-            return False
+            return None
     
     async def _update_metadata_paths(self, metadata_path: str, model_path: str) -> Dict:
         """Update file paths in metadata file"""
@@ -883,12 +893,15 @@ class ModelScanner:
                 metadata = json.load(f)
             
             metadata['file_path'] = model_path.replace(os.sep, '/')
+            # Update file_name to match the new filename
+            metadata['file_name'] = os.path.splitext(os.path.basename(model_path))[0]
             
             if 'preview_url' in metadata and metadata['preview_url']:
                 preview_dir = os.path.dirname(model_path)
-                preview_name = os.path.splitext(os.path.basename(metadata['preview_url']))[0]
+                # Update preview filename to match the new base name
-                preview_ext = os.path.splitext(metadata['preview_url'])[1]
+                new_base_name = os.path.splitext(os.path.basename(model_path))[0]
-                new_preview_path = os.path.join(preview_dir, f"{preview_name}{preview_ext}")
+                preview_ext = get_preview_extension(metadata['preview_url'])
+                new_preview_path = os.path.join(preview_dir, f"{new_base_name}{preview_ext}")
                 metadata['preview_url'] = new_preview_path.replace(os.sep, '/')
             
             await MetadataManager.save_metadata(metadata_path, metadata)
@@ -955,8 +968,16 @@ class ModelScanner:
         
     def get_hash_by_path(self, file_path: str) -> Optional[str]:
         """Get hash for a model by its file path"""
-        return self._hash_index.get_hash(file_path)
+        if self._cache is None or not self._cache.raw_data:
+            return None
+            
+        # Iterate through cache data to find matching file path
+        for model_data in self._cache.raw_data:
+            if model_data.get('file_path') == file_path:
+                return model_data.get('sha256')
         
+        return None
+    
     def get_hash_by_filename(self, filename: str) -> Optional[str]:
         """Get hash for a model by its filename without path"""
         return self._hash_index.get_hash_by_filename(filename)
@@ -1194,13 +1215,12 @@ class ModelScanner:
             if len(self._hash_index._duplicate_filenames[file_name]) <= 1:
                 del self._hash_index._duplicate_filenames[file_name]
 
-    async def check_model_version_exists(self, model_id: int, model_version_id: int) -> bool:
+    async def check_model_version_exists(self, model_version_id: int) -> bool:
         """Check if a specific model version exists in the cache
-        
+
         Args:
-            model_id: Civitai model ID
             model_version_id: Civitai model version ID
-            
+
         Returns:
             bool: True if the model version exists, False otherwise
         """
@@ -1208,13 +1228,11 @@ class ModelScanner:
             cache = await self.get_cached_data()
             if not cache or not cache.raw_data:
                 return False
-                
+
             for item in cache.raw_data:
-                if (item.get('civitai') and 
+                if item.get('civitai') and item['civitai'].get('id') == model_version_id:
-                    item['civitai'].get('modelId') == model_id and 
-                    item['civitai'].get('id') == model_version_id):
                     return True
-                    
+
             return False
         except Exception as e:
             logger.error(f"Error checking model version existence: {e}")

py/services/server_i18n.py

@@ -0,0 +1,114 @@
+import os
+import json
+import logging
+from typing import Dict, Any, Optional
+
+logger = logging.getLogger(__name__)
+
+class ServerI18nManager:
+    """Server-side internationalization manager for template rendering"""
+    
+    def __init__(self):
+        self.translations = {}
+        self.current_locale = 'en'
+        self._load_translations()
+    
+    def _load_translations(self):
+        """Load all translation files from the locales directory"""
+        i18n_path = os.path.join(
+            os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 
+            'locales'
+        )
+        
+        if not os.path.exists(i18n_path):
+            logger.warning(f"I18n directory not found: {i18n_path}")
+            return
+        
+        # Load all available locale files
+        for filename in os.listdir(i18n_path):
+            if filename.endswith('.json'):
+                locale_code = filename[:-5]  # Remove .json extension
+                try:
+                    self._load_locale_file(i18n_path, filename, locale_code)
+                except Exception as e:
+                    logger.error(f"Error loading locale file {filename}: {e}")
+    
+    def _load_locale_file(self, path: str, filename: str, locale_code: str):
+        """Load a single locale JSON file"""
+        file_path = os.path.join(path, filename)
+        
+        try:
+            with open(file_path, 'r', encoding='utf-8') as f:
+                translations = json.load(f)
+                
+            self.translations[locale_code] = translations
+            logger.debug(f"Loaded translations for {locale_code} from {filename}")
+            
+        except Exception as e:
+            logger.error(f"Error parsing locale file {filename}: {e}")
+    
+    def set_locale(self, locale: str):
+        """Set the current locale"""
+        if locale in self.translations:
+            self.current_locale = locale
+        else:
+            logger.warning(f"Locale {locale} not found, using 'en'")
+            self.current_locale = 'en'
+    
+    def get_translation(self, key: str, params: Dict[str, Any] = None, **kwargs) -> str:
+        """Get translation for a key with optional parameters (supports both dict and keyword args)"""
+        # Merge kwargs into params for convenience
+        if params is None:
+            params = {}
+        if kwargs:
+            params = {**params, **kwargs}
+        
+        if self.current_locale not in self.translations:
+            return key
+        
+        # Navigate through nested object using dot notation
+        keys = key.split('.')
+        value = self.translations[self.current_locale]
+        
+        for k in keys:
+            if isinstance(value, dict) and k in value:
+                value = value[k]
+            else:
+                # Fallback to English if current locale doesn't have the key
+                if self.current_locale != 'en' and 'en' in self.translations:
+                    en_value = self.translations['en']
+                    for k in keys:
+                        if isinstance(en_value, dict) and k in en_value:
+                            en_value = en_value[k]
+                        else:
+                            return key
+                    value = en_value
+                else:
+                    return key
+                break
+        
+        if not isinstance(value, str):
+            return key
+        
+        # Replace parameters if provided
+        if params:
+            for param_key, param_value in params.items():
+                placeholder = f"{{{param_key}}}"
+                double_placeholder = f"{{{{{param_key}}}}}"
+                value = value.replace(placeholder, str(param_value))
+                value = value.replace(double_placeholder, str(param_value))
+        
+        return value
+    
+    def get_available_locales(self) -> list:
+        """Get list of available locales"""
+        return list(self.translations.keys())
+    
+    def create_template_filter(self):
+        """Create a Jinja2 filter function for templates"""
+        def t_filter(key: str, **params) -> str:
+            return self.get_translation(key, params)
+        return t_filter
+
+# Create global instance
+server_i18n = ServerI18nManager()

py/services/settings_manager.py

@@ -9,6 +9,8 @@ class SettingsManager:
     def __init__(self):
         self.settings_file = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'settings.json')
         self.settings = self._load_settings()
+        self._migrate_download_path_template()
+        self._auto_set_default_roots()
         self._check_environment_variables()
 
     def _load_settings(self) -> Dict[str, Any]:
@@ -21,6 +23,46 @@ class SettingsManager:
                 logger.error(f"Error loading settings: {e}")
         return self._get_default_settings()
 
+    def _migrate_download_path_template(self):
+        """Migrate old download_path_template to new download_path_templates"""
+        old_template = self.settings.get('download_path_template')
+        templates = self.settings.get('download_path_templates')
+        
+        # If old template exists and new templates don't exist, migrate
+        if old_template is not None and not templates:
+            logger.info("Migrating download_path_template to download_path_templates")
+            self.settings['download_path_templates'] = {
+                'lora': old_template,
+                'checkpoint': old_template,
+                'embedding': old_template
+            }
+            # Remove old setting
+            del self.settings['download_path_template']
+            self._save_settings()
+            logger.info("Migration completed")
+
+    def _auto_set_default_roots(self):
+        """Auto set default root paths if only one folder is present and default is empty."""
+        folder_paths = self.settings.get('folder_paths', {})
+        updated = False
+        # loras
+        loras = folder_paths.get('loras', [])
+        if isinstance(loras, list) and len(loras) == 1 and not self.settings.get('default_lora_root'):
+            self.settings['default_lora_root'] = loras[0]
+            updated = True
+        # checkpoints
+        checkpoints = folder_paths.get('checkpoints', [])
+        if isinstance(checkpoints, list) and len(checkpoints) == 1 and not self.settings.get('default_checkpoint_root'):
+            self.settings['default_checkpoint_root'] = checkpoints[0]
+            updated = True
+        # embeddings
+        embeddings = folder_paths.get('embeddings', [])
+        if isinstance(embeddings, list) and len(embeddings) == 1 and not self.settings.get('default_embedding_root'):
+            self.settings['default_embedding_root'] = embeddings[0]
+            updated = True
+        if updated:
+            self._save_settings()
+
     def _check_environment_variables(self) -> None:
         """Check for environment variables and update settings if needed"""
         env_api_key = os.environ.get('CIVITAI_API_KEY')
@@ -38,7 +80,8 @@ class SettingsManager:
         """Return default settings"""
         return {
             "civitai_api_key": "",
-            "show_only_sfw": False
+            "show_only_sfw": False,
+            "language": "en"  # 添加默认语言设置
         }
 
     def get(self, key: str, default: Any = None) -> Any:
@@ -58,4 +101,53 @@ class SettingsManager:
         except Exception as e:
             logger.error(f"Error saving settings: {e}")
 
+    def get_download_path_template(self, model_type: str) -> str:
+        """Get download path template for specific model type
+        
+        Args:
+            model_type: The type of model ('lora', 'checkpoint', 'embedding')
+            
+        Returns:
+            Template string for the model type, defaults to '{base_model}/{first_tag}'
+        """
+        templates = self.settings.get('download_path_templates', {})
+        
+        # Handle edge case where templates might be stored as JSON string
+        if isinstance(templates, str):
+            try:
+                # Try to parse JSON string
+                parsed_templates = json.loads(templates)
+                if isinstance(parsed_templates, dict):
+                    # Update settings with parsed dictionary
+                    self.settings['download_path_templates'] = parsed_templates
+                    self._save_settings()
+                    templates = parsed_templates
+                    logger.info("Successfully parsed download_path_templates from JSON string")
+                else:
+                    raise ValueError("Parsed JSON is not a dictionary")
+            except (json.JSONDecodeError, ValueError) as e:
+                # If parsing fails, set default values
+                logger.warning(f"Failed to parse download_path_templates JSON string: {e}. Setting default values.")
+                default_template = '{base_model}/{first_tag}'
+                templates = {
+                    'lora': default_template,
+                    'checkpoint': default_template,
+                    'embedding': default_template
+                }
+                self.settings['download_path_templates'] = templates
+                self._save_settings()
+        
+        # Ensure templates is a dictionary
+        if not isinstance(templates, dict):
+            default_template = '{base_model}/{first_tag}'
+            templates = {
+                'lora': default_template,
+                'checkpoint': default_template,
+                'embedding': default_template
+            }
+            self.settings['download_path_templates'] = templates
+            self._save_settings()
+        
+        return templates.get(model_type, '{base_model}/{first_tag}')
+
 settings = SettingsManager()

py/services/websocket_manager.py

@@ -16,6 +16,9 @@ class WebSocketManager:
         self._download_websockets: Dict[str, web.WebSocketResponse] = {}  # New dict for download-specific clients
         # Add progress tracking dictionary
         self._download_progress: Dict[str, Dict] = {}
+        # Add auto-organize progress tracking
+        self._auto_organize_progress: Optional[Dict] = None
+        self._auto_organize_lock = asyncio.Lock()
         
     async def handle_connection(self, request: web.Request) -> web.WebSocketResponse:
         """Handle new WebSocket connection"""
@@ -134,6 +137,33 @@ class WebSocketManager:
         except Exception as e:
             logger.error(f"Error sending download progress: {e}")
             
+    async def broadcast_auto_organize_progress(self, data: Dict):
+        """Broadcast auto-organize progress to connected clients"""
+        # Store progress data in memory
+        self._auto_organize_progress = data
+        
+        # Broadcast via WebSocket
+        await self.broadcast(data)
+    
+    def get_auto_organize_progress(self) -> Optional[Dict]:
+        """Get current auto-organize progress"""
+        return self._auto_organize_progress
+    
+    def cleanup_auto_organize_progress(self):
+        """Clear auto-organize progress data"""
+        self._auto_organize_progress = None
+    
+    def is_auto_organize_running(self) -> bool:
+        """Check if auto-organize is currently running"""
+        if not self._auto_organize_progress:
+            return False
+        status = self._auto_organize_progress.get('status')
+        return status in ['started', 'processing', 'cleaning']
+    
+    async def get_auto_organize_lock(self):
+        """Get the auto-organize lock"""
+        return self._auto_organize_lock
+    
     def get_download_progress(self, download_id: str) -> Optional[Dict]:
         """Get progress information for a specific download"""
         return self._download_progress.get(download_id)

py/utils/constants.py

@@ -48,9 +48,13 @@ SUPPORTED_MEDIA_EXTENSIONS = {
 # Valid Lora types
 VALID_LORA_TYPES = ['lora', 'locon', 'dora']
 
+# Auto-organize settings
+AUTO_ORGANIZE_BATCH_SIZE = 50  # Process models in batches to avoid overwhelming the system
+
 # Civitai model tags in priority order for subfolder organization
 CIVITAI_MODEL_TAGS = [
-    'character', 'style', 'concept', 'clothing', 'base model', 
+    'character', 'style', 'concept', 'clothing', 
+    'realistic', 'anime', 'toon', 'furry',
     'poses', 'background', 'tool', 'vehicle', 'buildings', 
     'objects', 'assets', 'animal', 'action'
 ]

py/utils/example_images_download_manager.py

@@ -6,8 +6,10 @@ import time
 import aiohttp
 from aiohttp import web
 from ..services.service_registry import ServiceRegistry
+from ..utils.metadata_manager import MetadataManager
 from .example_images_processor import ExampleImagesProcessor
 from .example_images_metadata import MetadataUpdater
+from ..services.websocket_manager import ws_manager  # Add this import at the top
 
 logger = logging.getLogger(__name__)
 
@@ -24,7 +26,8 @@ download_progress = {
     'start_time': None,
     'end_time': None,
     'processed_models': set(),  # Track models that have been processed
-    'refreshed_models': set()  # Track models that had metadata refreshed
+    'refreshed_models': set(),  # Track models that had metadata refreshed
+    'failed_models': set()  # Track models that failed to download after metadata refresh
 }
 
 class DownloadManager:
@@ -50,6 +53,7 @@ class DownloadManager:
             response_progress = download_progress.copy()
             response_progress['processed_models'] = list(download_progress['processed_models'])
             response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
+            response_progress['failed_models'] = list(download_progress['failed_models'])
             
             return web.json_response({
                 'success': False,
@@ -64,6 +68,7 @@ class DownloadManager:
             optimize = data.get('optimize', True)
             model_types = data.get('model_types', ['lora', 'checkpoint'])
             delay = float(data.get('delay', 0.2)) # Default to 0.2 seconds
+            delay = 0 # Temporary: Disable delay to speed up downloads
             
             if not output_dir:
                 return web.json_response({
@@ -91,12 +96,15 @@ class DownloadManager:
                     with open(progress_file, 'r', encoding='utf-8') as f:
                         saved_progress = json.load(f)
                         download_progress['processed_models'] = set(saved_progress.get('processed_models', []))
-                        logger.info(f"Loaded previous progress, {len(download_progress['processed_models'])} models already processed")
+                        download_progress['failed_models'] = set(saved_progress.get('failed_models', []))
+                        logger.debug(f"Loaded previous progress, {len(download_progress['processed_models'])} models already processed, {len(download_progress['failed_models'])} models marked as failed")
                 except Exception as e:
                     logger.error(f"Failed to load progress file: {e}")
                     download_progress['processed_models'] = set()
+                    download_progress['failed_models'] = set()
             else:
                 download_progress['processed_models'] = set()
+                download_progress['failed_models'] = set()
             
             # Start the download task
             is_downloading = True
@@ -113,6 +121,7 @@ class DownloadManager:
             response_progress = download_progress.copy()
             response_progress['processed_models'] = list(download_progress['processed_models'])
             response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
+            response_progress['failed_models'] = list(download_progress['failed_models'])
             
             return web.json_response({
                 'success': True,
@@ -136,6 +145,7 @@ class DownloadManager:
         response_progress = download_progress.copy()
         response_progress['processed_models'] = list(download_progress['processed_models'])
         response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
+        response_progress['failed_models'] = list(download_progress['failed_models'])
         
         return web.json_response({
             'success': True,
@@ -230,7 +240,7 @@ class DownloadManager:
             
             # Update total count
             download_progress['total'] = len(all_models)
-            logger.info(f"Found {download_progress['total']} models to process")
+            logger.debug(f"Found {download_progress['total']} models to process")
             
             # Process each model
             for i, (scanner_type, model, scanner) in enumerate(all_models):
@@ -250,7 +260,7 @@ class DownloadManager:
             # Mark as completed
             download_progress['status'] = 'completed'
             download_progress['end_time'] = time.time()
-            logger.info(f"Example images download completed: {download_progress['completed']}/{download_progress['total']} models processed")
+            logger.debug(f"Example images download completed: {download_progress['completed']}/{download_progress['total']} models processed")
             
         except Exception as e:
             error_msg = f"Error during example images download: {str(e)}"
@@ -299,6 +309,11 @@ class DownloadManager:
             # Update current model info
             download_progress['current_model'] = f"{model_name} ({model_hash[:8]})"
             
+            # Skip if already in failed models
+            if model_hash in download_progress['failed_models']:
+                logger.debug(f"Skipping known failed model: {model_name}")
+                return False
+            
             # Skip if already processed AND directory exists with files
             if model_hash in download_progress['processed_models']:
                 model_dir = os.path.join(output_dir, model_hash)
@@ -308,6 +323,8 @@ class DownloadManager:
                     return False
                 else:
                     logger.info(f"Model {model_name} marked as processed but folder empty or missing, reprocessing")
+                    # Remove from processed models since we need to reprocess
+                    download_progress['processed_models'].discard(model_hash)
             
             # Create model directory
             model_dir = os.path.join(output_dir, model_hash)
@@ -351,12 +368,23 @@ class DownloadManager:
                         success, _ = await ExampleImagesProcessor.download_model_images(
                             model_hash, model_name, updated_images, model_dir, optimize, independent_session
                         )
+                    
+                    download_progress['refreshed_models'].add(model_hash)
                 
-                # Only mark as processed if all images were downloaded successfully
+                # Mark as processed if successful, or as failed if unsuccessful after refresh
                 if success:
                     download_progress['processed_models'].add(model_hash)
+                else:
+                    # If we refreshed metadata and still failed, mark as permanently failed
+                    if model_hash in download_progress['refreshed_models']:
+                        download_progress['failed_models'].add(model_hash)
+                        logger.info(f"Marking model {model_name} as failed after metadata refresh")
                     
                 return True  # Return True to indicate a remote download happened
+            else:
+                # No civitai data or images available, mark as failed to avoid future attempts
+                download_progress['failed_models'].add(model_hash)
+                logger.debug(f"No civitai images available for model {model_name}, marking as failed")
             
             # Save progress periodically
             if download_progress['completed'] % 10 == 0 or download_progress['completed'] == download_progress['total'] - 1:
@@ -391,6 +419,7 @@ class DownloadManager:
             progress_data = {
                 'processed_models': list(download_progress['processed_models']),
                 'refreshed_models': list(download_progress['refreshed_models']),
+                'failed_models': list(download_progress['failed_models']),
                 'completed': download_progress['completed'],
                 'total': download_progress['total'],
                 'last_update': time.time()
@@ -405,4 +434,364 @@ class DownloadManager:
             with open(progress_file, 'w', encoding='utf-8') as f:
                 json.dump(progress_data, f, indent=2)
         except Exception as e:
-            logger.error(f"Failed to save progress file: {e}")
+            logger.error(f"Failed to save progress file: {e}")
+    
+    @staticmethod
+    async def start_force_download(request):
+        """
+        Force download example images for specific models
+        
+        Expects a JSON body with:
+        {
+            "model_hashes": ["hash1", "hash2", ...],  # List of model hashes to download
+            "output_dir": "path/to/output",           # Base directory to save example images
+            "optimize": true,                         # Whether to optimize images (default: true)
+            "model_types": ["lora", "checkpoint"],    # Model types to process (default: both)
+            "delay": 1.0                              # Delay between downloads (default: 1.0)
+        }
+        """
+        global download_task, is_downloading, download_progress
+
+        if is_downloading:
+            return web.json_response({
+                'success': False,
+                'error': 'Download already in progress'
+            }, status=400)
+
+        try:
+            # Parse the request body
+            data = await request.json()
+            model_hashes = data.get('model_hashes', [])
+            output_dir = data.get('output_dir')
+            optimize = data.get('optimize', True)
+            model_types = data.get('model_types', ['lora', 'checkpoint'])
+            delay = float(data.get('delay', 0.2)) # Default to 0.2 seconds
+            
+            if not model_hashes:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing model_hashes parameter'
+                }, status=400)
+                
+            if not output_dir:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing output_dir parameter'
+                }, status=400)
+            
+            # Create the output directory
+            os.makedirs(output_dir, exist_ok=True)
+            
+            # Initialize progress tracking
+            download_progress['total'] = len(model_hashes)
+            download_progress['completed'] = 0
+            download_progress['current_model'] = ''
+            download_progress['status'] = 'running'
+            download_progress['errors'] = []
+            download_progress['last_error'] = None
+            download_progress['start_time'] = time.time()
+            download_progress['end_time'] = None
+            download_progress['processed_models'] = set()
+            download_progress['refreshed_models'] = set()
+            download_progress['failed_models'] = set()
+
+            # Set download status to downloading
+            is_downloading = True
+
+            # Execute the download function directly instead of creating a background task
+            result = await DownloadManager._download_specific_models_example_images_sync(
+                model_hashes,
+                output_dir, 
+                optimize, 
+                model_types,
+                delay
+            )
+
+            # Set download status to not downloading
+            is_downloading = False
+
+            return web.json_response({
+                'success': True,
+                'message': 'Force download completed',
+                'result': result
+            })
+
+        except Exception as e:
+            # Set download status to not downloading
+            is_downloading = False
+            logger.error(f"Failed during forced example images download: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    @staticmethod
+    async def _download_specific_models_example_images_sync(model_hashes, output_dir, optimize, model_types, delay):
+        """Download example images for specific models only - synchronous version"""
+        global download_progress
+        
+        # Create independent download session
+        connector = aiohttp.TCPConnector(
+            ssl=True,
+            limit=3,
+            force_close=False,
+            enable_cleanup_closed=True
+        )
+        timeout = aiohttp.ClientTimeout(total=None, connect=60, sock_read=60)
+        independent_session = aiohttp.ClientSession(
+            connector=connector,
+            trust_env=True,
+            timeout=timeout
+        )
+        
+        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))
+            
+            # Find the specified models
+            models_to_process = []
+            for scanner_type, scanner in scanners:
+                cache = await scanner.get_cached_data()
+                if cache and cache.raw_data:
+                    for model in cache.raw_data:
+                        if model.get('sha256') in model_hashes:
+                            models_to_process.append((scanner_type, model, scanner))
+            
+            # Update total count based on found models
+            download_progress['total'] = len(models_to_process)
+            logger.debug(f"Found {download_progress['total']} models to process")
+            
+            # Send initial progress via WebSocket
+            await ws_manager.broadcast({
+                'type': 'example_images_progress',
+                'processed': 0,
+                'total': download_progress['total'],
+                'status': 'running',
+                'current_model': ''
+            })
+            
+            # Process each model
+            success_count = 0
+            for i, (scanner_type, model, scanner) in enumerate(models_to_process):
+                # Force process this model regardless of previous status
+                was_successful = await DownloadManager._process_specific_model(
+                    scanner_type, model, scanner, 
+                    output_dir, optimize, independent_session
+                )
+                
+                if was_successful:
+                    success_count += 1
+                
+                # Update progress
+                download_progress['completed'] += 1
+                
+                # Send progress update via WebSocket
+                await ws_manager.broadcast({
+                    'type': 'example_images_progress',
+                    'processed': download_progress['completed'],
+                    'total': download_progress['total'],
+                    'status': 'running',
+                    'current_model': download_progress['current_model']
+                })
+                
+                # Only add delay after remote download, and not after processing the last model
+                if was_successful and i < len(models_to_process) - 1 and download_progress['status'] == 'running':
+                    await asyncio.sleep(delay)
+            
+            # Mark as completed
+            download_progress['status'] = 'completed'
+            download_progress['end_time'] = time.time()
+            logger.debug(f"Forced example images download completed: {download_progress['completed']}/{download_progress['total']} models processed")
+            
+            # Send final progress via WebSocket
+            await ws_manager.broadcast({
+                'type': 'example_images_progress',
+                'processed': download_progress['completed'],
+                'total': download_progress['total'],
+                'status': 'completed',
+                'current_model': ''
+            })
+            
+            return {
+                'total': download_progress['total'],
+                'processed': download_progress['completed'],
+                'successful': success_count,
+                'errors': download_progress['errors']
+            }
+            
+        except Exception as e:
+            error_msg = f"Error during forced example images download: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            download_progress['errors'].append(error_msg)
+            download_progress['last_error'] = error_msg
+            download_progress['status'] = 'error'
+            download_progress['end_time'] = time.time()
+            
+            # Send error status via WebSocket
+            await ws_manager.broadcast({
+                'type': 'example_images_progress',
+                'processed': download_progress['completed'],
+                'total': download_progress['total'],
+                'status': 'error',
+                'error': error_msg,
+                'current_model': ''
+            })
+            
+            raise
+        
+        finally:
+            # Close the independent session
+            try:
+                await independent_session.close()
+            except Exception as e:
+                logger.error(f"Error closing download session: {e}")
+    
+    @staticmethod
+    async def _process_specific_model(scanner_type, model, scanner, output_dir, optimize, independent_session):
+        """Process a specific model for forced download, ignoring previous download status"""
+        global download_progress
+        
+        # Check if download is paused
+        while download_progress['status'] == 'paused':
+            await asyncio.sleep(1)
+        
+        # Check if download should continue
+        if download_progress['status'] != 'running':
+            logger.info(f"Download stopped: {download_progress['status']}")
+            return False
+        
+        model_hash = model.get('sha256', '').lower()
+        model_name = model.get('model_name', 'Unknown')
+        model_file_path = model.get('file_path', '')
+        model_file_name = model.get('file_name', '')
+        
+        try:
+            # Update current model info
+            download_progress['current_model'] = f"{model_name} ({model_hash[:8]})"
+            
+            # Create model directory
+            model_dir = os.path.join(output_dir, model_hash)
+            os.makedirs(model_dir, exist_ok=True)
+            
+            # First check for local example images - local processing doesn't need delay
+            local_images_processed = await ExampleImagesProcessor.process_local_examples(
+                model_file_path, model_file_name, model_name, model_dir, optimize
+            )
+            
+            # If we processed local images, update metadata
+            if local_images_processed:
+                await MetadataUpdater.update_metadata_from_local_examples(
+                    model_hash, model, scanner_type, scanner, model_dir
+                )
+                download_progress['processed_models'].add(model_hash)
+                return False  # Return False to indicate no remote download happened
+            
+            # If no local images, try to download from remote
+            elif model.get('civitai') and model.get('civitai', {}).get('images'):
+                images = model.get('civitai', {}).get('images', [])
+                
+                success, is_stale, failed_images = await ExampleImagesProcessor.download_model_images_with_tracking(
+                    model_hash, model_name, images, model_dir, optimize, independent_session
+                )
+                
+                # If metadata is stale, try to refresh it
+                if is_stale and model_hash not in download_progress['refreshed_models']:
+                    await MetadataUpdater.refresh_model_metadata(
+                        model_hash, model_name, scanner_type, scanner
+                    )
+                    
+                    # Get the updated model data
+                    updated_model = await MetadataUpdater.get_updated_model(
+                        model_hash, scanner
+                    )
+                    
+                    if updated_model and updated_model.get('civitai', {}).get('images'):
+                        # Retry download with updated metadata
+                        updated_images = updated_model.get('civitai', {}).get('images', [])
+                        success, _, additional_failed_images = await ExampleImagesProcessor.download_model_images_with_tracking(
+                            model_hash, model_name, updated_images, model_dir, optimize, independent_session
+                        )
+                        
+                        # Combine failed images from both attempts
+                        failed_images.extend(additional_failed_images)
+                    
+                    download_progress['refreshed_models'].add(model_hash)
+                
+                # For forced downloads, remove failed images from metadata
+                if failed_images:
+                    # Create a copy of images excluding failed ones
+                    await DownloadManager._remove_failed_images_from_metadata(
+                        model_hash, model_name, failed_images, scanner
+                    )
+                
+                # Mark as processed
+                if success or failed_images:  # Mark as processed if we successfully downloaded some images or removed failed ones
+                    download_progress['processed_models'].add(model_hash)
+                    
+                return True  # Return True to indicate a remote download happened
+            else:
+                logger.debug(f"No civitai images available for model {model_name}")
+                return False
+                
+        except Exception as e:
+            error_msg = f"Error processing model {model.get('model_name')}: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            download_progress['errors'].append(error_msg)
+            download_progress['last_error'] = error_msg
+            return False  # Return False on exception
+
+    @staticmethod
+    async def _remove_failed_images_from_metadata(model_hash, model_name, failed_images, scanner):
+        """Remove failed images from model metadata"""
+        try:
+            # Get current model data
+            model_data = await MetadataUpdater.get_updated_model(model_hash, scanner)
+            if not model_data:
+                logger.warning(f"Could not find model data for {model_name} to remove failed images")
+                return
+                
+            if not model_data.get('civitai', {}).get('images'):
+                logger.warning(f"No images in metadata for {model_name}")
+                return
+                
+            # Get current images
+            current_images = model_data['civitai']['images']
+            
+            # Filter out failed images
+            updated_images = [img for img in current_images if img.get('url') not in failed_images]
+            
+            # If images were removed, update metadata
+            if len(updated_images) < len(current_images):
+                removed_count = len(current_images) - len(updated_images)
+                logger.info(f"Removing {removed_count} failed images from metadata for {model_name}")
+                
+                # Update the images list
+                model_data['civitai']['images'] = updated_images
+                
+                # Save metadata to file
+                file_path = model_data.get('file_path')
+                if file_path:
+                    # Create a copy of model data without 'folder' field
+                    model_copy = model_data.copy()
+                    model_copy.pop('folder', None)
+                    
+                    # Write metadata to file
+                    await MetadataManager.save_metadata(file_path, model_copy)
+                    logger.info(f"Saved updated metadata for {model_name} after removing failed images")
+                    
+                    # Update the scanner cache
+                    await scanner.update_single_model_cache(file_path, file_path, model_data)
+                    
+        except Exception as e:
+            logger.error(f"Error removing failed images from metadata for {model_name}: {e}", exc_info=True)

py/utils/example_images_file_manager.py

@@ -43,7 +43,15 @@ class ExampleImagesFileManager:
             
             # Construct folder path for this model
             model_folder = os.path.join(example_images_path, model_hash)
-            
+            model_folder = os.path.abspath(model_folder)  # Get absolute path
+
+            # Path validation: ensure model_folder is under example_images_path
+            if not model_folder.startswith(os.path.abspath(example_images_path)):
+                return web.json_response({
+                    'success': False,
+                    'error': 'Invalid model folder path'
+                }, status=400)
+
             # Check if folder exists
             if not os.path.exists(model_folder):
                 return web.json_response({

py/utils/example_images_processor.py

@@ -102,6 +102,78 @@ class ExampleImagesProcessor:
         
         return model_success, False  # (success, is_metadata_stale)
     
+    @staticmethod
+    async def download_model_images_with_tracking(model_hash, model_name, model_images, model_dir, optimize, independent_session):
+        """Download images for a single model with tracking of failed image URLs
+        
+        Returns:
+            tuple: (success, is_stale_metadata, failed_images) - whether download was successful, whether metadata is stale, list of failed image URLs
+        """
+        model_success = True
+        failed_images = []
+        
+        for i, image in enumerate(model_images):
+            image_url = image.get('url')
+            if not image_url:
+                continue
+            
+            # Get image filename from URL
+            image_filename = os.path.basename(image_url.split('?')[0])
+            image_ext = os.path.splitext(image_filename)[1].lower()
+            
+            # Handle images and videos
+            is_image = image_ext in SUPPORTED_MEDIA_EXTENSIONS['images']
+            is_video = image_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']
+            
+            if not (is_image or is_video):
+                logger.debug(f"Skipping unsupported file type: {image_filename}")
+                continue
+            
+            # Use 0-based indexing instead of 1-based indexing
+            save_filename = f"image_{i}{image_ext}"
+            
+            # If optimizing images and this is a Civitai image, use their pre-optimized WebP version
+            if is_image and optimize and 'civitai.com' in image_url:
+                image_url = ExampleImagesProcessor.get_civitai_optimized_url(image_url)
+                save_filename = f"image_{i}.webp"
+            
+            # Check if already downloaded
+            save_path = os.path.join(model_dir, save_filename)
+            if os.path.exists(save_path):
+                logger.debug(f"File already exists: {save_path}")
+                continue
+            
+            # Download the file
+            try:
+                logger.debug(f"Downloading {save_filename} for {model_name}")
+                
+                # Download directly using the independent session
+                async with independent_session.get(image_url, timeout=60) as response:
+                    if response.status == 200:
+                        with open(save_path, 'wb') as f:
+                            async for chunk in response.content.iter_chunked(8192):
+                                if chunk:
+                                    f.write(chunk)
+                    elif response.status == 404:
+                        error_msg = f"Failed to download file: {image_url}, status code: 404 - Model metadata might be stale"
+                        logger.warning(error_msg)
+                        model_success = False  # Mark the model as failed due to 404 error
+                        failed_images.append(image_url)  # Track failed URL
+                        # Return early to trigger metadata refresh attempt
+                        return False, True, failed_images  # (success, is_metadata_stale, failed_images)
+                    else:
+                        error_msg = f"Failed to download file: {image_url}, status code: {response.status}"
+                        logger.warning(error_msg)
+                        model_success = False  # Mark the model as failed
+                        failed_images.append(image_url)  # Track failed URL
+            except Exception as e:
+                error_msg = f"Error downloading file {image_url}: {str(e)}"
+                logger.error(error_msg)
+                model_success = False  # Mark the model as failed
+                failed_images.append(image_url)  # Track failed URL
+        
+        return model_success, False, failed_images  # (success, is_metadata_stale, failed_images)
+    
     @staticmethod
     async def process_local_examples(model_file_path, model_file_name, model_name, model_dir, optimize):
         """Process local example images

py/utils/file_utils.py

@@ -27,39 +27,58 @@ def find_preview_file(base_name: str, dir_path: str) -> str:
         full_pattern = os.path.join(dir_path, f"{base_name}{ext}")
         if os.path.exists(full_pattern):
             # Check if this is an image and not already webp
-            if ext.lower().endswith(('.jpg', '.jpeg', '.png')) and not ext.lower().endswith('.webp'):
+            # TODO: disable the optimization for now, maybe add a config option later
-                try:
+            # if ext.lower().endswith(('.jpg', '.jpeg', '.png')) and not ext.lower().endswith('.webp'):
-                    # Optimize the image to webp format
+            #     try:
-                    webp_path = os.path.join(dir_path, f"{base_name}.webp")
+            #         # Optimize the image to webp format
+            #         webp_path = os.path.join(dir_path, f"{base_name}.webp")
                     
-                    # Use ExifUtils to optimize the image
+            #         # Use ExifUtils to optimize the image
-                    with open(full_pattern, 'rb') as f:
+            #         with open(full_pattern, 'rb') as f:
-                        image_data = f.read()
+            #             image_data = f.read()
                     
-                    optimized_data, _ = ExifUtils.optimize_image(
+            #         optimized_data, _ = ExifUtils.optimize_image(
-                        image_data=image_data,
+            #             image_data=image_data,
-                        target_width=CARD_PREVIEW_WIDTH,
+            #             target_width=CARD_PREVIEW_WIDTH,
-                        format='webp',
+            #             format='webp',
-                        quality=85,
+            #             quality=85,
-                        preserve_metadata=False
+            #             preserve_metadata=False
-                    )
+            #         )
                     
-                    # Save the optimized webp file
+            #         # Save the optimized webp file
-                    with open(webp_path, 'wb') as f:
+            #         with open(webp_path, 'wb') as f:
-                        f.write(optimized_data)
+            #             f.write(optimized_data)
                     
-                    logger.debug(f"Optimized preview image from {full_pattern} to {webp_path}")
+            #         logger.debug(f"Optimized preview image from {full_pattern} to {webp_path}")
-                    return webp_path.replace(os.sep, "/")
+            #         return webp_path.replace(os.sep, "/")
-                except Exception as e:
+            #     except Exception as e:
-                    logger.error(f"Error optimizing preview image {full_pattern}: {e}")
+            #         logger.error(f"Error optimizing preview image {full_pattern}: {e}")
-                    # Fall back to original file if optimization fails
+            #         # Fall back to original file if optimization fails
-                    return full_pattern.replace(os.sep, "/")
+            #         return full_pattern.replace(os.sep, "/")
             
             # Return the original path for webp images or non-image files
             return full_pattern.replace(os.sep, "/")
     
     return ""
 
+def get_preview_extension(preview_path: str) -> str:
+    """Get the complete preview extension from a preview file path
+    
+    Args:
+        preview_path: Path to the preview file
+        
+    Returns:
+        str: The complete extension (e.g., '.preview.png', '.png', '.webp')
+    """
+    preview_path_lower = preview_path.lower()
+    
+    # Check for compound extensions first (longer matches first)
+    for ext in sorted(PREVIEW_EXTENSIONS, key=len, reverse=True):
+        if preview_path_lower.endswith(ext.lower()):
+            return ext
+    
+    return os.path.splitext(preview_path)[1]
+
 def normalize_path(path: str) -> str:
     """Normalize file path to use forward slashes"""
     return path.replace(os.sep, "/") if path else path

py/utils/metadata_manager.py

@@ -1,7 +1,6 @@
 from datetime import datetime
 import os
 import json
-import shutil
 import logging
 from typing import Dict, Optional, Type, Union
 
@@ -17,7 +16,7 @@ class MetadataManager:
     
     This class is responsible for:
     1. Loading metadata safely with fallback mechanisms
-    2. Saving metadata with atomic operations and backups
+    2. Saving metadata with atomic operations
     3. Creating default metadata for models
     4. Handling unknown fields gracefully
     """
@@ -25,81 +24,44 @@ class MetadataManager:
     @staticmethod
     async def load_metadata(file_path: str, model_class: Type[BaseModelMetadata] = LoraMetadata) -> Optional[BaseModelMetadata]:
         """
-        Load metadata with robust error handling and data preservation.
+        Load metadata safely.
         
-        Args:
-            file_path: Path to the model file
-            model_class: Class to instantiate (LoraMetadata, CheckpointMetadata, etc.)
-            
         Returns:
-            BaseModelMetadata instance or None if file doesn't exist
+            tuple: (metadata, should_skip)
+            - metadata: BaseModelMetadata instance or None
+            - should_skip: True if corrupted metadata file exists and model should be skipped
         """
         metadata_path = f"{os.path.splitext(file_path)[0]}.metadata.json"
-        backup_path = f"{metadata_path}.bak"
         
-        # Try loading the main metadata file
+        # Check if metadata file exists
-        if os.path.exists(metadata_path):
+        if not os.path.exists(metadata_path):
-            try:
+            return None, False
-                with open(metadata_path, 'r', encoding='utf-8') as f:
-                    data = json.load(f)
-                
-                # Create model instance
-                metadata = model_class.from_dict(data)
-                    
-                # Normalize paths
-                await MetadataManager._normalize_metadata_paths(metadata, file_path)
-                
-                return metadata
-                
-            except json.JSONDecodeError:
-                # JSON parsing error - try to restore from backup
-                logger.warning(f"Invalid JSON in metadata file: {metadata_path}")
-                return await MetadataManager._restore_from_backup(backup_path, file_path, model_class)
-                
-            except Exception as e:
-                # Other errors might be due to unknown fields or schema changes
-                logger.error(f"Error loading metadata from {metadata_path}: {str(e)}")
-                return await MetadataManager._restore_from_backup(backup_path, file_path, model_class)
         
-        return None
+        try:
-    
+            with open(metadata_path, 'r', encoding='utf-8') as f:
-    @staticmethod
+                data = json.load(f)
-    async def _restore_from_backup(backup_path: str, file_path: str, model_class: Type[BaseModelMetadata]) -> Optional[BaseModelMetadata]:
-        """
-        Try to restore metadata from backup file
-        
-        Args:
-            backup_path: Path to backup file
-            file_path: Path to the original model file
-            model_class: Class to instantiate
             
-        Returns:
+            # Create model instance
-            BaseModelMetadata instance or None if restoration fails
+            metadata = model_class.from_dict(data)
-        """
-        if os.path.exists(backup_path):
-            try:
-                logger.info(f"Attempting to restore metadata from backup: {backup_path}")
-                with open(backup_path, 'r', encoding='utf-8') as f:
-                    data = json.load(f)
                 
-                # Process data similarly to normal loading
+            # Normalize paths
-                metadata = model_class.from_dict(data)
+            await MetadataManager._normalize_metadata_paths(metadata, file_path)
-                await MetadataManager._normalize_metadata_paths(metadata, file_path)
+            
-                return metadata
+            return metadata, False
-            except Exception as e:
+            
-                logger.error(f"Failed to restore from backup: {str(e)}")
+        except (json.JSONDecodeError, Exception) as e:
-        
+            error_type = "Invalid JSON" if isinstance(e, json.JSONDecodeError) else "Parse error"
-        return None
+            logger.error(f"{error_type} in metadata file: {metadata_path}. Error: {str(e)}. Skipping model to preserve existing data.")
+            return None, True  # should_skip = True
     
     @staticmethod
-    async def save_metadata(path: str, metadata: Union[BaseModelMetadata, Dict], create_backup: bool = False) -> bool:
+    async def save_metadata(path: str, metadata: Union[BaseModelMetadata, Dict]) -> bool:
         """
-        Save metadata with atomic write operations and backup creation.
+        Save metadata with atomic write operations.
         
         Args:
           path: Path to the model file or directly to the metadata file
           metadata: Metadata to save (either BaseModelMetadata object or dict)
-          create_backup: Whether to create a new backup of existing file if a backup doesn't already exist
           
         Returns:
           bool: Success or failure
@@ -112,19 +74,8 @@ class MetadataManager:
             file_path = path
             metadata_path = f"{os.path.splitext(file_path)[0]}.metadata.json"
         temp_path = f"{metadata_path}.tmp"
-        backup_path = f"{metadata_path}.bak"
         
         try:
-            # Create backup if file exists and either:
-            # 1. create_backup is True, OR
-            # 2. backup file doesn't already exist
-            if os.path.exists(metadata_path) and (create_backup or not os.path.exists(backup_path)):
-                try:
-                    shutil.copy2(metadata_path, backup_path)
-                    logger.debug(f"Created metadata backup at: {backup_path}")
-                except Exception as e:
-                    logger.warning(f"Failed to create metadata backup: {str(e)}")
-            
             # Convert to dict if needed
             if isinstance(metadata, BaseModelMetadata):
                 metadata_dict = metadata.to_dict()
@@ -240,7 +191,7 @@ class MetadataManager:
             # await MetadataManager._enrich_metadata(metadata, real_path)
             
             # Save the created metadata
-            await MetadataManager.save_metadata(file_path, metadata, create_backup=False)
+            await MetadataManager.save_metadata(file_path, metadata)
             
             return metadata
             
@@ -310,4 +261,4 @@ class MetadataManager:
         
         # If path attributes were changed, save the metadata back to disk
         if need_update:
-            await MetadataManager.save_metadata(file_path, metadata, create_backup=False)
+            await MetadataManager.save_metadata(file_path, metadata)

py/utils/models.py

@@ -83,6 +83,50 @@ class BaseModelMetadata:
             self.size = os.path.getsize(file_path)
             self.modified = os.path.getmtime(file_path)
             self.file_path = file_path.replace(os.sep, '/')
+            # Update file_name when file_path changes
+            self.file_name = os.path.splitext(os.path.basename(file_path))[0]
+
+    @staticmethod
+    def generate_unique_filename(target_dir: str, base_name: str, extension: str, hash_provider: callable = None) -> str:
+        """Generate a unique filename to avoid conflicts
+        
+        Args:
+            target_dir: Target directory path
+            base_name: Base filename without extension
+            extension: File extension including the dot
+            hash_provider: A callable that returns the SHA256 hash when needed
+            
+        Returns:
+            str: Unique filename that doesn't conflict with existing files
+        """
+        original_filename = f"{base_name}{extension}"
+        target_path = os.path.join(target_dir, original_filename)
+        
+        # If no conflict, return original filename
+        if not os.path.exists(target_path):
+            return original_filename
+            
+        # Only compute hash when needed
+        if hash_provider:
+            sha256_hash = hash_provider()
+        else:
+            sha256_hash = "0000"
+        
+        # Generate short hash (first 4 characters of SHA256)
+        short_hash = sha256_hash[:4] if sha256_hash else "0000"
+        
+        # Try with short hash suffix
+        unique_filename = f"{base_name}-{short_hash}{extension}"
+        unique_path = os.path.join(target_dir, unique_filename)
+        
+        # If still conflicts, add incremental number
+        counter = 1
+        while os.path.exists(unique_path):
+            unique_filename = f"{base_name}-{short_hash}-{counter}{extension}"
+            unique_path = os.path.join(target_dir, unique_filename)
+            counter += 1
+            
+        return unique_filename
 
 @dataclass
 class LoraMetadata(BaseModelMetadata):

py/utils/routes_common.py

@@ -156,7 +156,7 @@ class ModelRouteUtils:
                                 local_metadata['preview_nsfw_level'] = first_preview.get('nsfwLevel', 0)
 
         # Save updated metadata
-        await MetadataManager.save_metadata(metadata_path, local_metadata, True)
+        await MetadataManager.save_metadata(metadata_path, local_metadata)
 
     @staticmethod
     async def fetch_and_update_model(
@@ -229,13 +229,13 @@ class ModelRouteUtils:
             await client.close()
     
     @staticmethod
-    def filter_civitai_data(data: Dict) -> Dict:
+    def filter_civitai_data(data: Dict, minimal: bool = False) -> Dict:
         """Filter relevant fields from CivitAI data"""
         if not data:
             return {}
-            
+
-        fields = [
+        fields = ["id", "modelId", "name", "trainedWords"] if minimal else [
-            "id", "modelId", "name", "createdAt", "updatedAt", 
+            "id", "modelId", "name", "createdAt", "updatedAt",
             "publishedAt", "trainedWords", "baseModel", "description",
             "model", "images", "customImages", "creator"
         ]
@@ -580,16 +580,19 @@ class ModelRouteUtils:
                 })
             
             # Check which identifier is provided and convert to int
-            try:
+            model_id = None
-                model_id = int(data.get('model_id'))
+            model_version_id = None
-            except (TypeError, ValueError):
+            
-                return web.json_response({
+            if data.get('model_id'):
-                    'success': False,
+                try:
-                    'error': "Invalid model_id: Must be an integer"
+                    model_id = int(data.get('model_id'))
-                }, status=400)
+                except (TypeError, ValueError):
+                    return web.json_response({
+                        'success': False,
+                        'error': "Invalid model_id: Must be an integer"
+                    }, status=400)
 
             # Convert model_version_id to int if provided
-            model_version_id = None 
             if data.get('model_version_id'):
                 try:
                     model_version_id = int(data.get('model_version_id'))
@@ -599,11 +602,11 @@ class ModelRouteUtils:
                         'error': "Invalid model_version_id: Must be an integer"
                     }, status=400)
             
-            # Only model_id is required, model_version_id is optional
+            # At least one identifier is required
-            if not model_id:
+            if not model_id and not model_version_id:
                 return web.json_response({
                     'success': False,
-                    'error': "Missing required parameter: Please provide 'model_id'"
+                    'error': "Missing required parameter: Please provide either 'model_id' or 'model_version_id'"
                 }, status=400)
             
             use_default_paths = data.get('use_default_paths', False)
@@ -625,15 +628,6 @@ class ModelRouteUtils:
             if not result.get('success', False):
                 error_message = result.get('error', 'Unknown error')
                 
-                # Return 401 for early access errors
-                if 'early access' in error_message.lower():
-                    logger.warning(f"Early access download failed: {error_message}")
-                    return web.json_response({
-                        'success': False,
-                        'error': f"Early Access Restriction: {error_message}",
-                        'download_id': download_id
-                    }, status=401)
-                
                 return web.json_response({
                     'success': False,
                     'error': error_message,
@@ -876,11 +870,11 @@ class ModelRouteUtils:
                     metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
                     
                     # Compare hashes
-                    stored_hash = metadata.get('sha256', '').lower()
+                    stored_hash = metadata.get('sha256', '').lower();
                     
                     # Set expected hash from first file if not yet set
                     if not expected_hash:
-                        expected_hash = stored_hash
+                        expected_hash = stored_hash;
                     
                     # Check if hash matches expected hash
                     if actual_hash != expected_hash:
@@ -984,10 +978,11 @@ class ModelRouteUtils:
             if os.path.exists(metadata_path):
                 metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
                 hash_value = metadata.get('sha256')
-            
+            logger.info(f"hash_value: {hash_value}, metadata_path: {metadata_path}, metadata: {metadata}")
             # Rename all files
             renamed_files = []
             new_metadata_path = None
+            new_preview = None
             
             for old_path, pattern in existing_files:
                 # Get the file extension like .safetensors or .metadata.json
@@ -1098,3 +1093,63 @@ class ModelRouteUtils:
         except Exception as e:
             logger.error(f"Error saving metadata: {e}", exc_info=True)
             return web.Response(text=str(e), status=500)
+
+    @staticmethod
+    async def handle_add_tags(request: web.Request, scanner) -> web.Response:
+        """Handle adding tags to model metadata
+        
+        Args:
+            request: The aiohttp request
+            scanner: The model scanner instance
+            
+        Returns:
+            web.Response: The HTTP response
+        """
+        try:
+            data = await request.json()
+            file_path = data.get('file_path')
+            new_tags = data.get('tags', [])
+            
+            if not file_path:
+                return web.Response(text='File path is required', status=400)
+            
+            if not isinstance(new_tags, list):
+                return web.Response(text='Tags must be a list', status=400)
+            
+            # Get metadata file path
+            metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+            
+            # Load existing metadata
+            metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
+            
+            # Get existing tags (case insensitive)
+            existing_tags = metadata.get('tags', [])
+            existing_tags_lower = [tag.lower() for tag in existing_tags]
+            
+            # Add new tags that don't already exist (case insensitive check)
+            tags_added = []
+            for tag in new_tags:
+                if isinstance(tag, str) and tag.strip():
+                    tag_stripped = tag.strip()
+                    if tag_stripped.lower() not in existing_tags_lower:
+                        existing_tags.append(tag_stripped)
+                        existing_tags_lower.append(tag_stripped.lower())
+                        tags_added.append(tag_stripped)
+            
+            # Update metadata with combined tags
+            metadata['tags'] = existing_tags
+            
+            # Save updated metadata
+            await MetadataManager.save_metadata(file_path, metadata)
+            
+            # Update cache
+            await scanner.update_single_model_cache(file_path, file_path, metadata)
+            
+            return web.json_response({
+                'success': True,
+                'tags': existing_tags
+            })
+            
+        except Exception as e:
+            logger.error(f"Error adding tags: {e}", exc_info=True)
+            return web.Response(text=str(e), status=500)

py/utils/utils.py

@@ -1,10 +1,10 @@
 from difflib import SequenceMatcher
-import requests
-import tempfile
 import os
-from bs4 import BeautifulSoup
+from typing import Dict
 from ..services.service_registry import ServiceRegistry
 from ..config import config
+from ..services.settings_manager import settings
+from .constants import CIVITAI_MODEL_TAGS
 import asyncio
 
 def get_lora_info(lora_name):
@@ -50,82 +50,7 @@ def get_lora_info(lora_name):
         # No event loop is running, we can use asyncio.run()
         return asyncio.run(_get_lora_info_async())
 
-def download_twitter_image(url):
+def fuzzy_match(text: str, pattern: str, threshold: float = 0.85) -> bool:
-    """Download image from a URL containing twitter:image meta tag
-    
-    Args:
-        url (str): The URL to download image from
-        
-    Returns:
-        str: Path to downloaded temporary image file
-    """
-    try:
-        # Download page content
-        response = requests.get(url)
-        response.raise_for_status()
-        
-        # Parse HTML
-        soup = BeautifulSoup(response.text, 'html.parser')
-        
-        # Find twitter:image meta tag
-        meta_tag = soup.find('meta', attrs={'property': 'twitter:image'})
-        if not meta_tag:
-            return None
-            
-        image_url = meta_tag['content']
-        
-        # Download image
-        image_response = requests.get(image_url)
-        image_response.raise_for_status()
-        
-        # Save to temp file
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
-            temp_file.write(image_response.content)
-            return temp_file.name
-            
-    except Exception as e:
-        print(f"Error downloading twitter image: {e}")
-        return None
-
-def download_civitai_image(url):
-    """Download image from a URL containing avatar image with specific class and style attributes
-    
-    Args:
-        url (str): The URL to download image from
-        
-    Returns:
-        str: Path to downloaded temporary image file
-    """
-    try:
-        # Download page content
-        response = requests.get(url)
-        response.raise_for_status()
-        
-        # Parse HTML
-        soup = BeautifulSoup(response.text, 'html.parser')
-        
-        # Find image with specific class and style attributes
-        image = soup.select_one('img.EdgeImage_image__iH4_q.max-h-full.w-auto.max-w-full')
-        
-        if not image or 'src' not in image.attrs:
-            return None
-            
-        image_url = image['src']
-        
-        # Download image
-        image_response = requests.get(image_url)
-        image_response.raise_for_status()
-        
-        # Save to temp file
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
-            temp_file.write(image_response.content)
-            return temp_file.name
-            
-    except Exception as e:
-        print(f"Error downloading civitai avatar: {e}")
-        return None
-
-def fuzzy_match(text: str, pattern: str, threshold: float = 0.7) -> bool:
         """
         Check if text matches pattern using fuzzy matching.
         Returns True if similarity ratio is above threshold.
@@ -206,3 +131,95 @@ def calculate_recipe_fingerprint(loras):
     fingerprint = "|".join([f"{hash_value}:{strength}" for hash_value, strength in valid_loras])
     
     return fingerprint
+
+def calculate_relative_path_for_model(model_data: Dict, model_type: str = 'lora') -> str:
+    """Calculate relative path for existing model using template from settings
+
+    Args:
+        model_data: Model data from scanner cache
+        model_type: Type of model ('lora', 'checkpoint', 'embedding')
+
+    Returns:
+        Relative path string (empty string for flat structure)
+    """
+    # Get path template from settings for specific model type
+    path_template = settings.get_download_path_template(model_type)
+
+    # If template is empty, return empty path (flat structure)
+    if not path_template:
+        return ''
+
+    # Get base model name from model metadata
+    civitai_data = model_data.get('civitai', {})
+
+    # For CivitAI models, prefer civitai data only if 'id' exists; for non-CivitAI models, use model_data directly
+    if civitai_data and civitai_data.get('id') is not None:
+        base_model = civitai_data.get('baseModel', '')
+        # Get author from civitai creator data
+        creator_info = civitai_data.get('creator') or {}
+        author = creator_info.get('username') or 'Anonymous'
+    else:
+        # Fallback to model_data fields for non-CivitAI models
+        base_model = model_data.get('base_model', '')
+        author = 'Anonymous'  # Default for non-CivitAI models
+
+    model_tags = model_data.get('tags', [])
+
+    # Apply mapping if available
+    base_model_mappings = settings.get('base_model_path_mappings', {})
+    mapped_base_model = base_model_mappings.get(base_model, base_model)
+
+    # Find the first Civitai model tag that exists in model_tags
+    first_tag = ''
+    for civitai_tag in CIVITAI_MODEL_TAGS:
+        if civitai_tag in model_tags:
+            first_tag = civitai_tag
+            break
+
+    # If no Civitai model tag found, fallback to first tag
+    if not first_tag and model_tags:
+        first_tag = model_tags[0]
+
+    if not first_tag:
+        first_tag = 'no tags'  # Default if no tags available
+
+    # Format the template with available data
+    formatted_path = path_template
+    formatted_path = formatted_path.replace('{base_model}', mapped_base_model)
+    formatted_path = formatted_path.replace('{first_tag}', first_tag)
+    formatted_path = formatted_path.replace('{author}', author)
+
+    return formatted_path
+
+def remove_empty_dirs(path):
+    """Recursively remove empty directories starting from the given path.
+    
+    Args:
+        path (str): Root directory to start cleaning from
+        
+    Returns:
+        int: Number of empty directories removed
+    """
+    removed_count = 0
+    
+    if not os.path.isdir(path):
+        return removed_count
+        
+    # List all files in directory
+    files = os.listdir(path)
+    
+    # Process all subdirectories first
+    for file in files:
+        full_path = os.path.join(path, file)
+        if os.path.isdir(full_path):
+            removed_count += remove_empty_dirs(full_path)
+    
+    # Check if directory is now empty (after processing subdirectories)
+    if not os.listdir(path):
+        try:
+            os.rmdir(path)
+            removed_count += 1
+        except OSError:
+            pass
+    
+    return removed_count

pyproject.toml

@@ -1,17 +1,15 @@
 [project]
 name = "comfyui-lora-manager"
 description = "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!"
-version = "0.8.22"
+version = "0.9.2"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",
     "jinja2", 
     "safetensors",
-    "beautifulsoup4",
     "piexif",
     "Pillow",
     "olefile", # for getting rid of warning message
-    "requests",
     "toml",
     "natsort",
     "GitPython"

requirements.txt

@@ -1,13 +1,10 @@
 aiohttp
 jinja2
 safetensors
-beautifulsoup4
 piexif
 Pillow
 olefile
-requests
 toml
 numpy
 natsort
-pyyaml
 GitPython

scripts/sync_translation_keys.py

@@ -0,0 +1,305 @@
+#!/usr/bin/env python3
+"""
+Translation Key Synchronization Script
+
+This script synchronizes new translation keys from en.json to all other locale files
+while maintaining exact formatting consistency to pass test_i18n.py validation.
+
+Features:
+- Preserves exact line-by-line formatting
+- Maintains proper indentation and structure
+- Adds missing keys with placeholder translations
+- Handles nested objects correctly
+- Ensures all locale files have identical structure
+
+Usage:
+    python scripts/sync_translation_keys.py [--dry-run] [--verbose]
+"""
+
+import os
+import sys
+import json
+import re
+import argparse
+from typing import Dict, List, Set, Tuple, Any, Optional
+from collections import OrderedDict
+
+# Add the parent directory to the path so we can import modules if needed
+sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
+
+class TranslationKeySynchronizer:
+    """Synchronizes translation keys across locale files while maintaining formatting."""
+    
+    def __init__(self, locales_dir: str, verbose: bool = False):
+        self.locales_dir = locales_dir
+        self.verbose = verbose
+        self.reference_locale = 'en'
+        self.target_locales = ['zh-CN', 'zh-TW', 'ja', 'ru', 'de', 'fr', 'es', 'ko']
+        
+    def log(self, message: str, level: str = 'INFO'):
+        """Log a message if verbose mode is enabled."""
+        if self.verbose or level == 'ERROR':
+            print(f"[{level}] {message}")
+    
+    def load_json_preserve_order(self, file_path: str) -> Tuple[Dict[str, Any], List[str]]:
+        """
+        Load a JSON file preserving the exact order and formatting.
+        Returns both the parsed data and the original lines.
+        """
+        with open(file_path, 'r', encoding='utf-8') as f:
+            lines = f.readlines()
+            content = ''.join(lines)
+        
+        # Parse JSON while preserving order
+        data = json.loads(content, object_pairs_hook=OrderedDict)
+        return data, lines
+    
+    def get_all_leaf_keys(self, data: Any, prefix: str = '') -> Dict[str, Any]:
+        """
+        Extract all leaf keys (non-object values) with their full paths.
+        Returns a dictionary mapping full key paths to their values.
+        """
+        keys = {}
+        
+        if isinstance(data, (dict, OrderedDict)):
+            for key, value in data.items():
+                full_key = f"{prefix}.{key}" if prefix else key
+                
+                if isinstance(value, (dict, OrderedDict)):
+                    # Recursively get nested keys
+                    keys.update(self.get_all_leaf_keys(value, full_key))
+                else:
+                    # Leaf node - actual translatable value
+                    keys[full_key] = value
+                    
+        return keys
+    
+    def merge_json_structures(self, reference_data: Dict[str, Any], target_data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Merge the reference JSON structure with existing target translations.
+        This creates a new structure that matches the reference exactly but preserves 
+        existing translations where available. Keys not in reference are removed.
+        """
+        def merge_recursive(ref_obj, target_obj):
+            if isinstance(ref_obj, (dict, OrderedDict)):
+                result = OrderedDict()
+                # Only include keys that exist in the reference
+                for key, ref_value in ref_obj.items():
+                    if key in target_obj and isinstance(target_obj[key], type(ref_value)):
+                        # Key exists in target with same type
+                        if isinstance(ref_value, (dict, OrderedDict)):
+                            # Recursively merge nested objects
+                            result[key] = merge_recursive(ref_value, target_obj[key])
+                        else:
+                            # Use existing translation
+                            result[key] = target_obj[key]
+                    else:
+                        # Key missing in target or type mismatch
+                        if isinstance(ref_value, (dict, OrderedDict)):
+                            # Recursively handle nested objects
+                            result[key] = merge_recursive(ref_value, {})
+                        else:
+                            # Create placeholder translation
+                            result[key] = f"[TODO: Translate] {ref_value}"
+                return result
+            else:
+                # For non-dict values, use reference (this shouldn't happen at root level)
+                return ref_obj
+        
+        return merge_recursive(reference_data, target_data)
+    
+    def format_json_like_reference(self, data: Dict[str, Any], reference_lines: List[str]) -> List[str]:
+        """
+        Format the merged JSON data to match the reference file's formatting exactly.
+        """
+        # Use json.dumps with proper formatting to match the reference style
+        formatted_json = json.dumps(data, indent=4, ensure_ascii=False, separators=(',', ': '))
+        
+        # Split into lines and ensure consistent line endings
+        formatted_lines = [line + '\n' for line in formatted_json.split('\n')]
+        
+        # Make sure the last line doesn't have extra newlines
+        if formatted_lines and formatted_lines[-1].strip() == '':
+            formatted_lines = formatted_lines[:-1]
+        
+        # Ensure the last line ends with just a newline
+        if formatted_lines and not formatted_lines[-1].endswith('\n'):
+            formatted_lines[-1] += '\n'
+        
+        return formatted_lines
+    
+    def synchronize_locale_simple(self, locale: str, reference_data: Dict[str, Any], 
+                                  reference_lines: List[str], dry_run: bool = False) -> bool:
+        """
+        Synchronize a locale file using JSON structure merging.
+        Handles both addition of missing keys and removal of obsolete keys.
+        """
+        locale_file = os.path.join(self.locales_dir, f'{locale}.json')
+        
+        if not os.path.exists(locale_file):
+            self.log(f"Locale file {locale_file} does not exist!", 'ERROR')
+            return False
+        
+        try:
+            target_data, _ = self.load_json_preserve_order(locale_file)
+        except Exception as e:
+            self.log(f"Error loading {locale_file}: {e}", 'ERROR')
+            return False
+        
+        # Get keys to check for differences
+        ref_keys = self.get_all_leaf_keys(reference_data)
+        target_keys = self.get_all_leaf_keys(target_data)
+        missing_keys = set(ref_keys.keys()) - set(target_keys.keys())
+        obsolete_keys = set(target_keys.keys()) - set(ref_keys.keys())
+        
+        if not missing_keys and not obsolete_keys:
+            self.log(f"Locale {locale} is already up to date")
+            return False
+        
+        # Report changes
+        if missing_keys:
+            self.log(f"Found {len(missing_keys)} missing keys in {locale}:")
+            for key in sorted(missing_keys):
+                self.log(f"  + {key}")
+        
+        if obsolete_keys:
+            self.log(f"Found {len(obsolete_keys)} obsolete keys in {locale}:")
+            for key in sorted(obsolete_keys):
+                self.log(f"  - {key}")
+        
+        if dry_run:
+            total_changes = len(missing_keys) + len(obsolete_keys)
+            self.log(f"DRY RUN: Would update {locale} with {len(missing_keys)} additions and {len(obsolete_keys)} deletions ({total_changes} total changes)")
+            return True
+        
+        # Merge the structures (this will both add missing keys and remove obsolete ones)
+        try:
+            merged_data = self.merge_json_structures(reference_data, target_data)
+            
+            # Format to match reference style
+            new_lines = self.format_json_like_reference(merged_data, reference_lines)
+            
+            # Validate that the result is valid JSON
+            reconstructed_content = ''.join(new_lines)
+            json.loads(reconstructed_content)  # This will raise an exception if invalid
+            
+            # Write the updated file
+            with open(locale_file, 'w', encoding='utf-8') as f:
+                f.writelines(new_lines)
+            
+            total_changes = len(missing_keys) + len(obsolete_keys)
+            self.log(f"Successfully updated {locale} with {len(missing_keys)} additions and {len(obsolete_keys)} deletions ({total_changes} total changes)")
+            return True
+            
+        except json.JSONDecodeError as e:
+            self.log(f"Generated invalid JSON for {locale}: {e}", 'ERROR')
+            return False
+        except Exception as e:
+            self.log(f"Error updating {locale_file}: {e}", 'ERROR')
+            return False
+    
+    def synchronize_all(self, dry_run: bool = False) -> bool:
+        """
+        Synchronize all locale files with the reference.
+        Returns True if all operations were successful.
+        """
+        # Load reference file
+        reference_file = os.path.join(self.locales_dir, f'{self.reference_locale}.json')
+        
+        if not os.path.exists(reference_file):
+            self.log(f"Reference file {reference_file} does not exist!", 'ERROR')
+            return False
+        
+        try:
+            reference_data, reference_lines = self.load_json_preserve_order(reference_file)
+            reference_keys = self.get_all_leaf_keys(reference_data)
+        except Exception as e:
+            self.log(f"Error loading reference file: {e}", 'ERROR')
+            return False
+        
+        self.log(f"Loaded reference file with {len(reference_keys)} keys")
+        
+        success = True
+        changes_made = False
+        
+        # Synchronize each target locale
+        for locale in self.target_locales:
+            try:
+                if self.synchronize_locale_simple(locale, reference_data, reference_lines, dry_run):
+                    changes_made = True
+            except Exception as e:
+                self.log(f"Error synchronizing {locale}: {e}", 'ERROR')
+                success = False
+        
+        if changes_made:
+            self.log("Synchronization completed with changes")
+        else:
+            self.log("All locale files are already up to date")
+            
+        return success
+
+def main():
+    """Main entry point for the script."""
+    parser = argparse.ArgumentParser(
+        description='Synchronize translation keys from en.json to all other locale files'
+    )
+    parser.add_argument(
+        '--dry-run', 
+        action='store_true',
+        help='Show what would be changed without making actual changes'
+    )
+    parser.add_argument(
+        '--verbose', '-v',
+        action='store_true',
+        help='Enable verbose output'
+    )
+    parser.add_argument(
+        '--locales-dir',
+        default=None,
+        help='Path to locales directory (default: auto-detect from script location)'
+    )
+    
+    args = parser.parse_args()
+    
+    # Determine locales directory
+    if args.locales_dir:
+        locales_dir = args.locales_dir
+    else:
+        # Auto-detect based on script location
+        script_dir = os.path.dirname(os.path.abspath(__file__))
+        locales_dir = os.path.join(os.path.dirname(script_dir), 'locales')
+    
+    if not os.path.exists(locales_dir):
+        print(f"ERROR: Locales directory not found: {locales_dir}")
+        sys.exit(1)
+    
+    print(f"Translation Key Synchronization")
+    print(f"Locales directory: {locales_dir}")
+    print(f"Mode: {'DRY RUN' if args.dry_run else 'LIVE UPDATE'}")
+    print("-" * 50)
+    
+    # Create synchronizer and run
+    synchronizer = TranslationKeySynchronizer(locales_dir, args.verbose)
+    
+    try:
+        success = synchronizer.synchronize_all(args.dry_run)
+        
+        if success:
+            print("\n✅ Synchronization completed successfully!")
+            if not args.dry_run:
+                print("💡 Run 'python test_i18n.py' to verify formatting consistency")
+        else:
+            print("\n❌ Synchronization completed with errors!")
+            sys.exit(1)
+            
+    except KeyboardInterrupt:
+        print("\n⚠️  Operation cancelled by user")
+        sys.exit(1)
+    except Exception as e:
+        print(f"\n❌ Unexpected error: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+
+if __name__ == '__main__':
+    main()

settings.json.example

@@ -9,6 +9,10 @@
     "checkpoints": [
       "C:/path/to/your/checkpoints_folder",
       "C:/path/to/another/checkpoints_folder"
+    ],
+    "embeddings": [
+      "C:/path/to/your/embeddings_folder",
+      "C:/path/to/another/embeddings_folder"
     ]
   }
 }

standalone.py

@@ -339,6 +339,11 @@ class StandaloneLoraManager(LoraManager):
                     logger.warning(f"Failed to add static route on initialization for {target_path}: {e}")
                     continue
         
+        # 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}")
+            
         # Add static route for plugin assets
         app.router.add_static('/loras_static', config.static_path)
         

static/css/base.css

@@ -46,7 +46,7 @@ html, body {
 
     /* Composed Colors */
     --lora-accent: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h));
-    --lora-surface: oklch(100% 0 0 / 0.98);
+    --lora-surface: oklch(97% 0 0 / 0.95);
     --lora-border: oklch(90% 0.02 256 / 0.15);
     --lora-text: oklch(95% 0.02 256);
     --lora-error: oklch(75% 0.32 29);

static/css/components/bulk.css

@@ -1,77 +1,3 @@
-/* Bulk Operations Styles */
-.bulk-operations-panel {
-    position: fixed;
-    bottom: 20px;
-    left: 50%;
-    transform: translateY(100px) translateX(-50%);
-    background: var(--card-bg);
-    border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-base);
-    padding: 12px 16px;
-    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
-    z-index: var(--z-overlay);
-    display: flex;
-    flex-direction: column;
-    min-width: 300px;
-    transition: all 0.4s cubic-bezier(0.175, 0.885, 0.32, 1.275);
-    opacity: 0;
-}
-
-.bulk-operations-header {
-    display: flex;
-    justify-content: space-between;
-    align-items: center;
-    margin-bottom: 12px;
-    gap: 20px; /* Increase space between count and buttons */
-}
-
-#selectedCount {
-    font-weight: 500;
-    background: var(--bg-color);
-    padding: 6px 12px;
-    border-radius: var(--border-radius-xs);
-    border: 1px solid var(--border-color);
-    min-width: 80px;
-    text-align: center;
-}
-
-.bulk-operations-actions {
-    display: flex;
-    gap: 8px;
-}
-
-.bulk-operations-actions button {
-    padding: 6px 12px;
-    border-radius: var(--border-radius-xs);
-    background: var(--bg-color);
-    border: 1px solid var(--border-color);
-    color: var(--text-color);
-    cursor: pointer;
-    font-size: 14px;
-    display: flex;
-    align-items: center;
-    gap: 6px;
-    transition: all 0.2s ease;
-}
-
-.bulk-operations-actions button:hover {
-    background: var(--lora-accent);
-    color: white;
-    border-color: var(--lora-accent);
-}
-
-/* Danger button style - updated to use proper theme variables */
-.bulk-operations-actions button.danger-btn {
-    background: oklch(70% 0.2 29); /* Light red background that works in both themes */
-    color: oklch(98% 0.01 0); /* Almost white text for good contrast */
-    border-color: var(--lora-error);
-}
-
-.bulk-operations-actions button.danger-btn:hover {
-    background: var(--lora-error);
-    color: oklch(100% 0 0); /* Pure white text on hover for maximum contrast */
-}
-
 /* Style for selected cards */
 .model-card.selected {
     box-shadow: 0 0 0 2px var(--lora-accent);
@@ -95,201 +21,61 @@
     z-index: 1;
 }
 
-/* Update bulk operations button to match others when active */
+/* Marquee selection styles */
-#bulkOperationsBtn.active {
+.marquee-selection {
-    background: var(--lora-accent);
-    color: white;
-    border-color: var(--lora-accent);
-}
-
-@media (max-width: 768px) {
-    .bulk-operations-panel {
-        width: calc(100% - 40px);
-        left: 20px;
-        transform: none;
-        border-radius: var(--border-radius-sm);
-    }
-    
-    .bulk-operations-actions {
-        flex-wrap: wrap;
-    }
-}
-
-.bulk-operations-panel.visible {
-    transform: translateY(0) translateX(-50%);
-    opacity: 1;
-}
-
-/* Thumbnail Strip Styles */
-.selected-thumbnails-strip {
     position: fixed;
-    bottom: 80px; /* Position above the bulk operations panel */
+    border: 2px dashed var(--lora-accent, #007bff);
-    left: 50%;
+    background: rgba(0, 123, 255, 0.1);
-    transform: translateX(-50%) translateY(20px);
+    pointer-events: none;
-    background: var(--card-bg);
+    z-index: 9999;
-    border: 1px solid var(--border-color);
+    border-radius: 2px;
-    border-radius: var(--border-radius-base);
-    box-shadow: 0 4px 16px rgba(0, 0, 0, 0.15);
-    z-index: calc(var(--z-overlay) - 1); /* Just below the bulk panel z-index */
-    padding: 16px;
-    max-width: 80%;
-    width: auto;
-    transition: all 0.3s ease;
-    opacity: 0;
-    overflow: hidden;
 }
 
-.selected-thumbnails-strip.visible {
+/* Visual feedback when marquee selecting */
-    opacity: 1;
+.marquee-selecting {
-    transform: translateX(-50%) translateY(0);
+    cursor: crosshair;
+    user-select: none;
+    -webkit-user-select: none;
+    -moz-user-select: none;
+    -ms-user-select: none;
 }
 
-.thumbnails-container {
+/* Prevent text selection during marquee */
-    display: flex;
+.marquee-selecting * {
-    gap: 12px;
+    user-select: none;
-    overflow-x: auto;
+    -webkit-user-select: none;
-    padding-bottom: 8px; /* Space for scrollbar */
+    -moz-user-select: none;
+    -ms-user-select: none;
+}
+
+/* Remove bulk base model modal specific styles - now using shared components */
+/* Use shared metadata editing styles instead */
+
+/* Override for bulk base model select to ensure proper width */
+.bulk-base-model-select {
+    width: 100%;
     max-width: 100%;
-    align-items: flex-start;
+    padding: 6px 10px;
-}
-
-.selected-thumbnail {
-    position: relative;
-    width: 80px;
-    min-width: 80px; /* Prevent shrinking */
     border-radius: var(--border-radius-xs);
     border: 1px solid var(--border-color);
-    overflow: hidden;
+    background-color: var(--lora-surface);
-    cursor: pointer;
-    background: var(--bg-color);
-    transition: transform 0.2s ease, box-shadow 0.2s ease;
-}
-
-.selected-thumbnail:hover {
-    transform: translateY(-2px);
-    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
-}
-
-.selected-thumbnail img,
-.selected-thumbnail video {
-    width: 100%;
-    aspect-ratio: 1 / 1;
-    object-fit: cover;
-    display: block;
-}
-
-.thumbnail-name {
-    position: absolute;
-    bottom: 0;
-    left: 0;
-    right: 0;
-    background: rgba(0, 0, 0, 0.6);
-    color: white;
-    font-size: 10px;
-    padding: 3px 5px;
-    text-overflow: ellipsis;
-    white-space: nowrap;
-    overflow: hidden;
-}
-
-.thumbnail-remove {
-    position: absolute;
-    top: 3px;
-    right: 3px;
-    width: 18px;
-    height: 18px;
-    border-radius: 50%;
-    background: rgba(0, 0, 0, 0.5);
-    color: white;
-    border: none;
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    cursor: pointer;
-    font-size: 10px;
-    opacity: 0.7;
-    transition: opacity 0.2s ease, background-color 0.2s ease;
-}
-
-.thumbnail-remove:hover {
-    opacity: 1;
-    background: var(--lora-error);
-}
-
-.strip-close-btn {
-    position: absolute;
-    top: 5px;
-    right: 5px;
-    width: 20px;
-    height: 20px;
-    background: none;
-    border: none;
     color: var(--text-color);
-    cursor: pointer;
+    font-size: 0.95em;
-    display: flex;
+    height: 32px;
-    align-items: center;
-    justify-content: center;
-    opacity: 0.7;
-    transition: opacity 0.2s ease;
 }
 
-.strip-close-btn:hover {
+.bulk-base-model-select:focus {
-    opacity: 1;
+    border-color: var(--lora-accent);
+    outline: none;
 }
 
-/* Style the selectedCount to indicate it's clickable */
+/* Dark theme support for bulk base model select */
-.selectable-count {
+[data-theme="dark"] .bulk-base-model-select {
-    display: flex;
+    background-color: rgba(30, 30, 30, 0.9);
-    align-items: center;
+    color: var(--text-color);
-    gap: 5px;
-    cursor: pointer;
-    transition: background-color 0.2s ease;
 }
 
-.selectable-count:hover {
+[data-theme="dark"] .bulk-base-model-select option {
-    background: var(--lora-border);
+    background-color: #2d2d2d;
-}
+    color: var(--text-color);
-
-.dropdown-caret {
-    font-size: 12px;
-    visibility: hidden; /* Will be shown via JS when items are selected */
-}
-
-/* Scrollbar styling for the thumbnails container */
-.thumbnails-container::-webkit-scrollbar {
-    height: 6px;
-}
-
-.thumbnails-container::-webkit-scrollbar-track {
-    background: var(--bg-color);
-    border-radius: 3px;
-}
-
-.thumbnails-container::-webkit-scrollbar-thumb {
-    background: var(--border-color);
-    border-radius: 3px;
-}
-
-.thumbnails-container::-webkit-scrollbar-thumb:hover {
-    background: var(--lora-accent);
-}
-
-/* Mobile optimizations */
-@media (max-width: 768px) {
-    .selected-thumbnails-strip {
-        width: calc(100% - 40px);
-        max-width: none;
-        left: 20px;
-        transform: translateY(20px);
-        border-radius: var(--border-radius-sm);
-    }
-    
-    .selected-thumbnails-strip.visible {
-        transform: translateY(0);
-    }
-    
-    .selected-thumbnail {
-        width: 70px;
-        min-width: 70px;
-    }
 }

static/css/components/card.css

@@ -41,7 +41,7 @@
 }
 
 /* Responsive adjustments for 1440p screens (2K) */
-@media (min-width: 2000px) {
+@media (min-width: 2150px) {
     .card-grid {
         max-width: 1800px; /* Increased for 2K screens */
         grid-template-columns: repeat(auto-fill, minmax(270px, 1fr));
@@ -424,6 +424,33 @@
     font-size: 0.85em;
 }
 
+/* Style for version name */
+.version-name {
+    display: inline-block;
+    color: rgba(255,255,255,0.8); /* Muted white */
+    text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.5);
+    font-size: 0.85em;
+    word-break: break-word;
+    overflow: hidden;
+    line-height: 1.4;
+    margin-top: 2px;
+    opacity: 0.8; /* Slightly transparent for better readability */
+    border: 1px solid rgba(255,255,255,0.25); /* Subtle border */
+    border-radius: var(--border-radius-xs);
+    padding: 1px 6px;
+    background: rgba(0,0,0,0.18); /* Optional: subtle background for contrast */
+}
+
+/* Medium density adjustments for version name */
+.medium-density .version-name {
+    font-size: 0.8em;
+}
+
+/* Compact density adjustments for version name */
+.compact-density .version-name {
+    font-size: 0.75em;
+}
+
 /* Prevent text selection on cards and interactive elements */
 .model-card,
 .model-card *,
@@ -498,7 +525,7 @@
 }
 
 /* For larger screens, allow more space for the cards */
-@media (min-width: 2000px) {
+@media (min-width: 2150px) {
     .card-grid.virtual-scroll {
         max-width: 1800px;
     }

static/css/components/download-modal.css

@@ -1,197 +0,0 @@
-/* Download Modal Styles */
-.download-step {
-    margin: var(--space-2) 0;
-}
-
-.input-group {
-    margin-bottom: var(--space-2);
-}
-
-.input-group label {
-    display: block;
-    margin-bottom: 8px;
-    color: var(--text-color);
-}
-
-.input-group input,
-.input-group select {
-    width: 100%;
-    padding: 8px;
-    border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-xs);
-    background: var(--bg-color);
-    color: var(--text-color);
-}
-
-/* Version List Styles */
-.version-list {
-    max-height: 400px;
-    overflow-y: auto;
-    margin: var(--space-2) 0;
-    display: flex;
-    flex-direction: column;
-    gap: 12px;
-    padding: 1px;
-}
-
-.version-item {
-    display: flex;
-    gap: var(--space-2);
-    padding: var(--space-2);
-    border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-sm);
-    cursor: pointer;
-    transition: all 0.2s ease;
-    background: var(--bg-color);
-    margin: 1px;
-    position: relative;
-}
-
-.version-item:hover {
-    border-color: var(--lora-accent);
-    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
-    z-index: 1;
-}
-
-.version-item.selected {
-    border: 2px solid var(--lora-accent);
-    background: oklch(var(--lora-accent) / 0.05);
-}
-
-.version-thumbnail {
-    width: 80px;
-    height: 80px;
-    flex-shrink: 0;
-    border-radius: var(--border-radius-xs);
-    overflow: hidden;
-    background: var(--bg-color);
-}
-
-.version-thumbnail img {
-    width: 100%;
-    height: 100%;
-    object-fit: cover;
-}
-
-.version-content {
-    display: flex;
-    flex-direction: column;
-    gap: 8px;
-    flex: 1;
-    min-width: 0;
-}
-
-.version-header {
-    display: flex;
-    align-items: flex-start;
-    justify-content: space-between;
-    gap: var(--space-2);
-}
-
-.version-content h3 {
-    margin: 0;
-    font-size: 1.1em;
-    color: var(--text-color);
-    flex: 1;
-}
-
-.version-content .version-info {
-    display: flex;
-    flex-wrap: wrap;
-    flex-direction: row !important;
-    gap: 8px;
-    align-items: center;
-    font-size: 0.9em;
-}
-
-.version-content .version-info .base-model {
-    background: oklch(var(--lora-accent) / 0.1);
-    color: var(--lora-accent);
-    padding: 2px 8px;
-    border-radius: var(--border-radius-xs);
-}
-
-.version-meta {
-    display: flex;
-    gap: 12px;
-    font-size: 0.85em;
-    color: var(--text-color);
-    opacity: 0.7;
-}
-
-.version-meta span {
-    display: flex;
-    align-items: center;
-    gap: 4px;
-}
-
-/* Folder Browser Styles */
-.folder-browser {
-    border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-xs);
-    padding: var(--space-1);
-    max-height: 200px;
-    overflow-y: auto;
-}
-
-.folder-item {
-    padding: 8px;
-    cursor: pointer;
-    border-radius: var(--border-radius-xs);
-    transition: background-color 0.2s;
-}
-
-.folder-item:hover {
-    background: var(--lora-surface);
-}
-
-.folder-item.selected {
-    background: oklch(var(--lora-accent) / 0.1);
-    border: 1px solid var(--lora-accent);
-}
-
-/* Path Preview Styles */
-.path-preview {
-    margin-bottom: var(--space-3);
-    padding: var(--space-2);
-    background: var(--bg-color);
-    border-radius: var(--border-radius-sm);
-    border: 1px dashed var(--border-color);
-}
-
-.path-preview label {
-    display: block;
-    margin-bottom: 8px;
-    color: var(--text-color);
-    font-size: 0.9em;
-    opacity: 0.8;
-}
-
-.path-display {
-    padding: var(--space-1);
-    color: var(--text-color);
-    font-family: monospace;
-    font-size: 0.9em;
-    line-height: 1.4;
-    white-space: pre-wrap;
-    word-break: break-all;
-    opacity: 0.85;
-    background: var(--lora-surface);
-    border-radius: var(--border-radius-xs);
-}
-
-/* Dark theme adjustments */
-[data-theme="dark"] .version-item {
-    background: var(--lora-surface);
-}
-
-[data-theme="dark"] .local-path {
-    background: var(--lora-surface);
-    border-color: var(--lora-border);
-}
-
-/* Enhance the local badge to make it more noticeable */
-.version-item.exists-locally {
-    background: oklch(var(--lora-accent) / 0.05);
-    border-left: 4px solid var(--lora-accent);
-}

static/css/components/duplicates.css

@@ -27,7 +27,7 @@
 }
 
 /* Responsive container for larger screens - match container in layout.css */
-@media (min-width: 2000px) {
+@media (min-width: 2150px) {
     .duplicates-banner .banner-content {
         max-width: 1800px;
     }
@@ -130,7 +130,7 @@
 }
 
 /* Add responsive container adjustments for duplicate groups - match container in banner */
-@media (min-width: 2000px) {
+@media (min-width: 2150px) {
     .duplicate-group {
         max-width: 1800px;
     }

static/css/components/header.css

@@ -6,7 +6,7 @@
   z-index: var(--z-header);
   height: 48px; /* Reduced height */
   width: 100%;
-  box-shadow: 0 1px 3px rgba(0,0,0,0.05);
+  box-shadow: 0 2px 4px rgba(0,0,0,0.1); /* Slightly stronger shadow */
 }
 
 .header-container {
@@ -19,6 +19,18 @@
   height: 100%;
 }
 
+/* Responsive header container for larger screens */
+@media (min-width: 2150px) {
+  .header-container {
+    max-width: 1800px;
+  }
+}
+@media (min-width: 3000px) {
+  .header-container {
+    max-width: 2400px;
+  }
+}
+
 /* Logo and title styling */
 .header-branding {
   display: flex;

static/css/components/import-modal.css

@@ -337,72 +337,7 @@
     margin-left: 8px;
 }
 
-/* Location Selection Styles */
-.location-selection {
-    margin: var(--space-2) 0;
-    padding: var(--space-2);
-    background: var(--lora-surface);
-    border-radius: var(--border-radius-sm);
-}
-
-/* Reuse folder browser and path preview styles from download-modal.css */
-.folder-browser {
-    border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-xs);
-    padding: var(--space-1);
-    max-height: 200px;
-    overflow-y: auto;
-}
-
-.folder-item {
-    padding: 8px;
-    cursor: pointer;
-    border-radius: var(--border-radius-xs);
-    transition: background-color 0.2s;
-}
-
-.folder-item:hover {
-    background: var(--lora-surface);
-}
-
-.folder-item.selected {
-    background: oklch(var(--lora-accent) / 0.1);
-    border: 1px solid var(--lora-accent);
-}
-
-.path-preview {
-    margin-bottom: var(--space-3);
-    padding: var(--space-2);
-    background: var(--bg-color);
-    border-radius: var(--border-radius-sm);
-    border: 1px dashed var(--border-color);
-}
-
-.path-preview label {
-    display: block;
-    margin-bottom: 8px;
-    color: var(--text-color);
-    font-size: 0.9em;
-    opacity: 0.8;
-}
-
-.path-display {
-    padding: var(--space-1);
-    color: var(--text-color);
-    font-family: monospace;
-    font-size: 0.9em;
-    line-height: 1.4;
-    white-space: pre-wrap;
-    word-break: break-all;
-    opacity: 0.85;
-    background: var(--lora-surface);
-    border-radius: var(--border-radius-xs);
-}
-
 /* Input Group Styles */
-.input-group {
-    margin-bottom: var(--space-2);
-}
 
 .input-with-button {
     display: flex;
@@ -430,22 +365,6 @@
     background: oklch(from var(--lora-accent) l c h / 0.9);
 }
 
-.input-group label {
-    display: block;
-    margin-bottom: 8px;
-    color: var(--text-color);
-}
-
-.input-group input,
-.input-group select {
-    width: 100%;
-    padding: 8px;
-    border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-xs);
-    background: var(--bg-color);
-    color: var(--text-color);
-}
-
 /* Dark theme adjustments */
 [data-theme="dark"] .lora-item {
     background: var(--lora-surface);

static/css/components/keyboard-nav.css

@@ -40,10 +40,10 @@
     border-radius: var(--border-radius-xs);
     padding: 8px;
     position: absolute;
-    z-index: 9999; /* 确保在卡片上方显示 */
+    z-index: 9999; /* Ensure tooltip appears above cards */
-    left: 120%; /* 将tooltip显示在图标右侧 */
+    left: 120%; /* Position tooltip to the right of the icon */
-    top: 50%; /* 垂直居中 */
+    top: 50%; /* Vertically center */
-    transform: translateY(-50%); /* 垂直居中 */
+    transform: translateY(-15%); /* Vertically center */
     opacity: 0;
     transition: opacity 0.3s;
     box-shadow: 0 3px 8px rgba(0, 0, 0, 0.15);
@@ -55,12 +55,12 @@
 .tooltip .tooltiptext::after {
     content: "";
     position: absolute;
-    top: 50%; /* 箭头垂直居中 */
+    top: 50%; /* Vertically center arrow */
-    right: 100%; /* 箭头在左侧 */
+    right: 100%; /* Arrow on the left side */
     margin-top: -5px;
     border-width: 5px;
     border-style: solid;
-    border-color: transparent var(--lora-border) transparent transparent; /* 箭头指向左侧 */
+    border-color: transparent var(--lora-border) transparent transparent; /* Arrow points left */
 }
 
 .tooltip:hover .tooltiptext {

static/css/components/lora-modal/lora-modal.css

@@ -183,7 +183,11 @@
     outline: none;
 }
 
-.edit-file-name-btn {
+/* 合并编辑按钮样式 */
+.edit-model-name-btn,
+.edit-file-name-btn,
+.edit-base-model-btn,
+.edit-model-description-btn {
     background: transparent;
     border: none;
     color: var(--text-color);
@@ -195,17 +199,28 @@
     margin-left: var(--space-1);
 }
 
+.edit-model-name-btn.visible,
 .edit-file-name-btn.visible,
-.file-name-wrapper:hover .edit-file-name-btn {
+.edit-base-model-btn.visible,
+.edit-model-description-btn.visible,
+.model-name-header:hover .edit-model-name-btn,
+.file-name-wrapper:hover .edit-file-name-btn,
+.base-model-display:hover .edit-base-model-btn,
+.model-name-header:hover .edit-model-description-btn {
     opacity: 0.5;
 }
 
-.edit-file-name-btn:hover {
+.edit-model-name-btn:hover,
+.edit-file-name-btn:hover,
+.edit-base-model-btn:hover,
+.edit-model-description-btn:hover {
     opacity: 0.8 !important;
     background: rgba(0, 0, 0, 0.05);
 }
 
-[data-theme="dark"] .edit-file-name-btn:hover {
+[data-theme="dark"] .edit-model-name-btn:hover,
+[data-theme="dark"] .edit-file-name-btn:hover,
+[data-theme="dark"] .edit-base-model-btn:hover {
     background: rgba(255, 255, 255, 0.05);
 }
 
@@ -234,32 +249,6 @@
     flex: 1;
 }
 
-.edit-base-model-btn {
-    background: transparent;
-    border: none;
-    color: var(--text-color);
-    opacity: 0;
-    cursor: pointer;
-    padding: 2px 5px;
-    border-radius: var(--border-radius-xs);
-    transition: all 0.2s ease;
-    margin-left: var(--space-1);
-}
-
-.edit-base-model-btn.visible,
-.base-model-display:hover .edit-base-model-btn {
-    opacity: 0.5;
-}
-
-.edit-base-model-btn:hover {
-    opacity: 0.8 !important;
-    background: rgba(0, 0, 0, 0.05);
-}
-
-[data-theme="dark"] .edit-base-model-btn:hover {
-    background: rgba(255, 255, 255, 0.05);
-}
-
 .base-model-selector {
     width: 100%;
     padding: 3px 5px;
@@ -316,32 +305,6 @@
     background: var(--bg-color);
 }
 
-.edit-model-name-btn {
-    background: transparent;
-    border: none;
-    color: var(--text-color);
-    opacity: 0;
-    cursor: pointer;
-    padding: 2px 5px;
-    border-radius: var(--border-radius-xs);
-    transition: all 0.2s ease;
-    margin-left: var(--space-1);
-}
-
-.edit-model-name-btn.visible,
-.model-name-header:hover .edit-model-name-btn {
-    opacity: 0.5;
-}
-
-.edit-model-name-btn:hover {
-    opacity: 0.8 !important;
-    background: rgba(0, 0, 0, 0.05);
-}
-
-[data-theme="dark"] .edit-model-name-btn:hover {
-    background: rgba(255, 255, 255, 0.05);
-}
-
 /* Tab System Styling */
 .showcase-tabs {
     display: flex;

static/css/components/menu.css

@@ -176,11 +176,6 @@
     background: linear-gradient(45deg, #4a90e2, #357abd);
 }
 
-/* Remove old node-color-indicator styles */
-.node-color-indicator {
-    display: none;
-}
-
 .send-all-item {
     border-top: 1px solid var(--border-color);
     font-weight: 500;
@@ -217,4 +212,24 @@
     font-size: 12px;
     color: var(--text-muted);
     font-style: italic;
+}
+
+/* Bulk Context Menu Header */
+.bulk-context-header {
+    padding: 10px 12px;
+    background: var(--card-bg); /* Use card background for subtlety */
+    color: var(--text-color);   /* Use standard text color */
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    font-weight: 500;
+    font-size: 14px;
+    border-radius: var(--border-radius-xs) var(--border-radius-xs) 0 0;
+    border-bottom: 1px solid var(--border-color); /* Add subtle separator */
+}
+
+.bulk-context-header i {
+    width: 16px;
+    text-align: center;
+    color: var(--lora-accent); /* Accent only the icon for a hint of color */
 }

static/css/components/modal/_base.css

@@ -23,7 +23,7 @@ body.modal-open {
     position: relative;
     max-width: 800px;
     height: auto;
-    max-height: calc(90vh - 48px); /* Adjust to account for header height */
+    max-height: calc(90vh);
     margin: 1rem auto; /* Keep reduced top margin */
     background: var(--lora-surface);
     border-radius: var(--border-radius-base);
@@ -37,6 +37,10 @@ body.modal-open {
     overflow-x: hidden; /* 防止水平滚动条 */
 }
 
+.modal-content-large {
+    min-height: 480px;
+}
+
 /* 当 modal 打开时锁定 body */
 body.modal-open {
     overflow: hidden !important; /* 覆盖 base.css 中的 scroll */

static/css/components/modal/download-modal.css

@@ -0,0 +1,505 @@
+/* Download Modal Styles */
+.input-group {
+    margin-bottom: var(--space-2);
+}
+
+.input-group label {
+    display: block;
+    margin-bottom: 8px;
+    color: var(--text-color);
+}
+
+.input-group input,
+.input-group select {
+    width: 100%;
+    padding: 8px;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    color: var(--text-color);
+}
+
+/* Version List Styles */
+.version-list {
+    max-height: 400px;
+    overflow-y: auto;
+    margin: var(--space-2) 0;
+    display: flex;
+    flex-direction: column;
+    gap: 12px;
+    padding: 1px;
+}
+
+.version-item {
+    display: flex;
+    gap: var(--space-2);
+    padding: var(--space-2);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    cursor: pointer;
+    transition: all 0.2s ease;
+    background: var(--bg-color);
+    margin: 1px;
+    position: relative;
+}
+
+.version-item:hover {
+    border-color: var(--lora-accent);
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+    z-index: 1;
+}
+
+.version-item.selected {
+    border: 2px solid var(--lora-accent);
+    background: oklch(var(--lora-accent) / 0.05);
+}
+
+.version-thumbnail {
+    width: 80px;
+    height: 80px;
+    flex-shrink: 0;
+    border-radius: var(--border-radius-xs);
+    overflow: hidden;
+    background: var(--bg-color);
+}
+
+.version-thumbnail img {
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+.version-content {
+    display: flex;
+    flex-direction: column;
+    gap: 8px;
+    flex: 1;
+    min-width: 0;
+}
+
+.version-header {
+    display: flex;
+    align-items: flex-start;
+    justify-content: space-between;
+    gap: var(--space-2);
+}
+
+.version-content h3 {
+    margin: 0;
+    font-size: 1.1em;
+    color: var(--text-color);
+    flex: 1;
+}
+
+.version-content .version-info {
+    display: flex;
+    flex-wrap: wrap;
+    flex-direction: row !important;
+    gap: 8px;
+    align-items: center;
+    font-size: 0.9em;
+}
+
+.version-content .version-info .base-model {
+    background: oklch(var(--lora-accent) / 0.1);
+    color: var(--lora-accent);
+    padding: 2px 8px;
+    border-radius: var(--border-radius-xs);
+}
+
+.version-meta {
+    display: flex;
+    gap: 12px;
+    font-size: 0.85em;
+    color: var(--text-color);
+    opacity: 0.7;
+}
+
+.version-meta span {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+}
+
+.folder-item {
+    padding: 8px;
+    cursor: pointer;
+    border-radius: var(--border-radius-xs);
+    transition: background-color 0.2s;
+}
+
+.folder-item:hover {
+    background: var(--lora-surface);
+}
+
+.folder-item.selected {
+    background: oklch(var(--lora-accent) / 0.1);
+    border: 1px solid var(--lora-accent);
+}
+
+/* Path Input Styles */
+.path-input-container {
+    position: relative;
+    display: flex;
+    gap: 8px;
+    align-items: center;
+}
+
+.path-input-container input {
+    flex: 1;
+}
+
+.create-folder-btn {
+    padding: 8px;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    color: var(--text-color);
+    cursor: pointer;
+    transition: all 0.2s ease;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    width: 36px;
+    height: 36px;
+}
+
+.create-folder-btn:hover {
+    border-color: var(--lora-accent);
+    background: oklch(var(--lora-accent) / 0.05);
+}
+
+.path-suggestions {
+    position: absolute;
+    top: 46%;
+    left: 0;
+    right: 0;
+    z-index: 1000;
+    margin: 0 24px;
+    background: var(--bg-color);
+    border: 1px solid var(--border-color);
+    border-top: none;
+    border-radius: 0 0 var(--border-radius-xs) var(--border-radius-xs);
+    max-height: 200px;
+    overflow-y: auto;
+}
+
+.path-suggestion {
+    padding: 8px 12px;
+    cursor: pointer;
+    transition: background-color 0.2s;
+    border-bottom: 1px solid var(--border-color);
+}
+
+.path-suggestion:last-child {
+    border-bottom: none;
+}
+
+.path-suggestion:hover {
+    background: var(--lora-surface);
+}
+
+.path-suggestion.active {
+    background: oklch(var(--lora-accent) / 0.1);
+    color: var(--lora-accent);
+}
+
+/* Breadcrumb Navigation Styles */
+.breadcrumb-nav {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+    margin-bottom: var(--space-2);
+    padding: var(--space-1);
+    background: var(--lora-surface);
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    overflow-x: auto;
+    white-space: nowrap;
+}
+
+.breadcrumb-item {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+    cursor: pointer;
+    transition: all 0.2s ease;
+    color: var(--text-color);
+    opacity: 0.7;
+    text-decoration: none;
+}
+
+.breadcrumb-item:hover {
+    background: var(--bg-color);
+    opacity: 1;
+}
+
+.breadcrumb-item.active {
+    background: oklch(var(--lora-accent) / 0.1);
+    color: var(--lora-accent);
+    opacity: 1;
+}
+
+.breadcrumb-separator {
+    color: var(--text-color);
+    opacity: 0.5;
+    margin: 0 4px;
+}
+
+/* Folder Tree Styles */
+.folder-tree-container {
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    max-height: 300px;
+    overflow-y: auto;
+}
+
+.folder-tree {
+    padding: var(--space-1);
+}
+
+.tree-node {
+    user-select: none;
+}
+
+.tree-node-content {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+    padding: 4px 8px;
+    cursor: pointer;
+    border-radius: var(--border-radius-xs);
+    transition: all 0.2s ease;
+    position: relative;
+}
+
+.tree-node-content:hover {
+    background: var(--lora-surface);
+}
+
+.tree-node-content.selected {
+    background: oklch(var(--lora-accent) / 0.1);
+    border: 1px solid var(--lora-accent);
+}
+
+.tree-expand-icon {
+    width: 16px;
+    height: 16px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    cursor: pointer;
+    border-radius: 2px;
+    transition: all 0.2s ease;
+}
+
+.tree-expand-icon:hover {
+    background: var(--lora-surface);
+}
+
+.tree-expand-icon.expanded {
+    transform: rotate(90deg);
+}
+
+.tree-folder-icon {
+    width: 16px;
+    height: 16px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    color: var(--lora-accent);
+}
+
+.tree-folder-name {
+    flex: 1;
+    font-size: 0.9em;
+    color: var(--text-color);
+}
+
+.tree-children {
+    margin-left: 20px;
+    display: none;
+}
+
+.tree-children.expanded {
+    display: block;
+}
+
+.tree-node.has-children > .tree-node-content .tree-expand-icon {
+    opacity: 1;
+}
+
+.tree-node:not(.has-children) > .tree-node-content .tree-expand-icon {
+    opacity: 0;
+    pointer-events: none;
+}
+
+/* Create folder inline form */
+.create-folder-form {
+    display: flex;
+    gap: 8px;
+    margin-left: 20px;
+    align-items: center;
+    height: 21px;
+}
+
+.create-folder-form input {
+    flex: 1;
+    padding: 4px 8px;
+    border: 1px solid var(--lora-accent);
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    color: var(--text-color);
+    font-size: 0.9em;
+}
+
+.create-folder-form button {
+    padding: 4px 8px;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    color: var(--text-color);
+    cursor: pointer;
+    font-size: 0.8em;
+    transition: all 0.2s ease;
+}
+
+.create-folder-form button.confirm {
+    background: var(--lora-accent);
+    color: white;
+    border-color: var(--lora-accent);
+}
+
+.create-folder-form button:hover {
+    background: var(--lora-surface);
+}
+
+/* Path Preview Styles */
+.path-preview {
+    margin-bottom: var(--space-3);
+    padding: var(--space-2);
+    background: var(--bg-color);
+    border-radius: var(--border-radius-sm);
+    border: 1px dashed var(--border-color);
+}
+
+.path-preview-header {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    margin-bottom: 12px;
+    gap: var(--space-2);
+}
+
+.path-preview-header label {
+    margin: 0;
+    color: var(--text-color);
+    font-size: 0.9em;
+    opacity: 0.8;
+}
+
+.path-display {
+    padding: var(--space-1);
+    color: var(--text-color);
+    font-family: monospace;
+    font-size: 0.9em;
+    line-height: 1.4;
+    white-space: pre-wrap;
+    word-break: break-all;
+    opacity: 0.85;
+    background: var(--lora-surface);
+    border-radius: var(--border-radius-xs);
+}
+
+/* Inline Toggle Styles */
+.inline-toggle-container {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    cursor: pointer;
+    user-select: none;
+    position: relative;
+}
+
+.inline-toggle-label {
+    font-size: 0.85em;
+    color: var(--text-color);
+    opacity: 0.9;
+    white-space: nowrap;
+}
+
+.inline-toggle-container .toggle-switch {
+    position: relative;
+    width: 36px;
+    height: 18px;
+    flex-shrink: 0;
+}
+
+.inline-toggle-container .toggle-switch input {
+    opacity: 0;
+    width: 0;
+    height: 0;
+    position: absolute;
+}
+
+.inline-toggle-container .toggle-slider {
+    position: absolute;
+    cursor: pointer;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background-color: var(--border-color);
+    transition: all 0.3s ease;
+    border-radius: 18px;
+}
+
+.inline-toggle-container .toggle-slider:before {
+    position: absolute;
+    content: "";
+    height: 12px;
+    width: 12px;
+    left: 3px;
+    bottom: 3px;
+    background-color: white;
+    transition: all 0.3s ease;
+    border-radius: 50%;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.2);
+}
+
+.inline-toggle-container .toggle-switch input:checked + .toggle-slider {
+    background-color: var(--lora-accent);
+}
+
+.inline-toggle-container .toggle-switch input:checked + .toggle-slider:before {
+    transform: translateX(18px);
+}
+
+/* Dark theme adjustments */
+[data-theme="dark"] .version-item {
+    background: var(--lora-surface);
+}
+
+[data-theme="dark"] .local-path {
+    background: var(--lora-surface);
+    border-color: var(--lora-border);
+}
+
+[data-theme="dark"] .toggle-slider:before {
+    background-color: #f0f0f0;
+}
+
+/* Enhance the local badge to make it more noticeable */
+.version-item.exists-locally {
+    background: oklch(var(--lora-accent) / 0.05);
+    border-left: 4px solid var(--lora-accent);
+}
+
+.manual-path-selection.disabled {
+    opacity: 0.5;
+    pointer-events: none;
+    user-select: none;
+}

static/css/components/modal/settings-modal.css

@@ -482,4 +482,99 @@ input:checked + .toggle-slider:before {
 [data-theme="dark"] .base-model-select option {
     background-color: #2d2d2d;
     color: var(--text-color);
+}
+
+/* Template Configuration Styles */
+.placeholder-info {
+    margin-top: var(--space-1);
+    display: flex;
+    flex-wrap: wrap;
+    align-items: center;
+    gap: var(--space-1);
+}
+
+.placeholder-tag {
+    display: inline-block;
+    background: var(--lora-accent);
+    color: white;
+    padding: 2px 6px;
+    border-radius: 3px;
+    font-family: monospace;
+    font-size: 1em;
+    font-weight: 500;
+}
+
+.template-custom-row {
+    margin-top: 8px;
+    animation: slideDown 0.2s ease-out;
+}
+
+@keyframes slideDown {
+    from {
+        opacity: 0;
+        transform: translateY(-10px);
+    }
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
+}
+
+.template-custom-input {
+    width: 96%;
+    padding: 6px 10px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    background-color: var(--lora-surface);
+    color: var(--text-color);
+    font-size: 0.95em;
+    font-family: monospace;
+    height: 24px;
+    transition: border-color 0.2s;
+}
+
+.template-custom-input:focus {
+    border-color: var(--lora-accent);
+    outline: none;
+    box-shadow: 0 0 0 2px rgba(var(--lora-accent-rgb, 79, 70, 229), 0.1);
+}
+
+.template-custom-input::placeholder {
+    color: var(--text-color);
+    opacity: 0.5;
+    font-family: inherit;
+}
+
+.template-validation {
+    margin-top: 6px;
+    font-size: 0.85em;
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    min-height: 20px;
+}
+
+.template-validation.valid {
+    color: var(--lora-success, #22c55e);
+}
+
+.template-validation.invalid {
+    color: var(--lora-error, #ef4444);
+}
+
+.template-validation i {
+    width: 12px;
+}
+
+/* Dark theme specific adjustments */
+[data-theme="dark"] .template-custom-input {
+    background-color: rgba(30, 30, 30, 0.9);
+}
+
+/* Responsive adjustments */
+@media (max-width: 768px) {
+    .placeholder-info {
+        flex-direction: column;
+        align-items: flex-start;
+    }
 }

static/css/components/search-filter.css

@@ -445,69 +445,6 @@
     border-color: var(--lora-accent);
 }
 
-/* Switch styles */
-.search-option-switch {
-    display: flex;
-    justify-content: space-between;
-    align-items: center;
-    padding: 4px 0;
-}
-
-.switch {
-    position: relative;
-    display: inline-block;
-    width: 46px;
-    height: 24px;
-}
-
-.switch input {
-    opacity: 0;
-    width: 0;
-    height: 0;
-}
-
-.slider {
-    position: absolute;
-    cursor: pointer;
-    top: 0;
-    left: 0;
-    right: 0;
-    bottom: 0;
-    background-color: #ccc;
-    transition: .4s;
-}
-
-.slider:before {
-    position: absolute;
-    content: "";
-    height: 18px;
-    width: 18px;
-    left: 3px;
-    bottom: 3px;
-    background-color: white;
-    transition: .4s;
-}
-
-input:checked + .slider {
-    background-color: var(--lora-accent);
-}
-
-input:focus + .slider {
-    box-shadow: 0 0 1px var(--lora-accent);
-}
-
-input:checked + .slider:before {
-    transform: translateX(22px);
-}
-
-.slider.round {
-    border-radius: 34px;
-}
-
-.slider.round:before {
-    border-radius: 50%;
-}
-
 /* Mobile adjustments */
 @media (max-width: 768px) {
     .search-options-panel,

static/css/components/shared/edit-metadata.css

@@ -80,6 +80,7 @@
   align-items: flex-start;
   margin-bottom: var(--space-2);
   width: 100%;
+  min-height: 30px; /* Ensure some height even if empty to prevent layout shifts */
 }
 
 /* Individual Item */
@@ -153,17 +154,42 @@
 }
 
 .metadata-save-btn,
-.save-tags-btn {
+.save-tags-btn,
+.append-tags-btn,
+.replace-tags-btn {
   background: var(--lora-accent) !important;
   color: white !important;
   border-color: var(--lora-accent) !important;
 }
 
 .metadata-save-btn:hover,
-.save-tags-btn:hover {
+.save-tags-btn:hover,
+.append-tags-btn:hover,
+.replace-tags-btn:hover {
   opacity: 0.9;
 }
 
+/* Specific styling for bulk tag action buttons */
+.bulk-append-tags-btn {
+  background: var(--lora-accent) !important;
+  color: white !important;
+  border-color: var(--lora-accent) !important;
+}
+
+.bulk-replace-tags-btn {
+  background: var(--lora-warning, #f59e0b) !important;
+  color: white !important;
+  border-color: var(--lora-warning, #f59e0b) !important;
+}
+
+.bulk-append-tags-btn:hover {
+  opacity: 0.9;
+}
+
+.bulk-replace-tags-btn:hover {
+  background: var(--lora-warning-dark, #d97706) !important;
+}
+
 /* Add Form */
 .metadata-add-form {
   display: flex;

static/css/components/sidebar.css

@@ -0,0 +1,554 @@
+.folder-sidebar {
+    position: fixed;
+    top: 68px; /* Below header */
+    left: 0px;
+    width: 230px;
+    height: calc(100vh - 88px);
+    background: var(--bg-color);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    overflow: hidden;
+    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+    flex-shrink: 0;
+    z-index: var(--z-overlay);
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.08);
+    display: flex;
+    flex-direction: column;
+    backdrop-filter: blur(8px);
+    /* Default state: hidden off-screen */
+    transform: translateX(-100%);
+    opacity: 0;
+    pointer-events: none;
+}
+
+/* Visible state */
+.folder-sidebar.visible {
+    transform: translateX(0);
+    opacity: 1;
+    pointer-events: all;
+}
+
+/* Auto-hide states */
+.folder-sidebar.auto-hide {
+    transform: translateX(-100%);
+    opacity: 0;
+    pointer-events: none;
+}
+
+.folder-sidebar.auto-hide.hover-active {
+    transform: translateX(0);
+    opacity: 1;
+    pointer-events: all;
+}
+
+.folder-sidebar.collapsed {
+    transform: translateX(-100%);
+    opacity: 0;
+    pointer-events: none;
+}
+
+/* Hover detection area for auto-hide */
+.sidebar-hover-area {
+    position: fixed;
+    top: 68px;
+    left: 0;
+    width: 20px;
+    height: calc(100vh - 88px);
+    z-index: calc(var(--z-overlay) - 1);
+    background: transparent;
+    pointer-events: all;
+}
+
+.sidebar-hover-area.disabled {
+    pointer-events: none;
+}
+
+.sidebar-header {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    padding: 12px 16px;
+    background: var(--bg-color);
+    color: var(--text-color);
+    font-weight: 500;
+    font-size: 0.9em;
+    flex-shrink: 0;
+    border-bottom: 1px solid var(--border-color);
+    cursor: pointer;
+    transition: all 0.2s ease;
+}
+
+.sidebar-header:hover {
+    background: var(--lora-surface);
+}
+
+.sidebar-header.root-selected {
+    background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+    color: var(--lora-accent);
+}
+
+.sidebar-header h3 {
+    margin: 0;
+    font-size: 0.9em;
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    font-weight: 500;
+    flex: 1;
+    pointer-events: none;
+}
+
+.sidebar-header-actions {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+}
+
+.sidebar-action-btn {
+    background: none;
+    border: none;
+    color: var(--text-muted);
+    cursor: pointer;
+    padding: 4px;
+    border-radius: 4px;
+    opacity: 0.6;
+    transition: all 0.2s ease;
+    width: 24px;
+    height: 24px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.sidebar-action-btn:hover {
+    opacity: 1;
+    background: var(--lora-surface);
+    color: var(--text-color);
+}
+
+.sidebar-action-btn.active {
+    opacity: 1;
+    background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.15);
+    color: var(--lora-accent);
+}
+
+.sidebar-action-btn.disabled {
+    opacity: 0.3;
+    cursor: not-allowed;
+}
+
+/* Remove old close button styles */
+.sidebar-toggle-close {
+    display: none;
+}
+
+.sidebar-content {
+    flex: 1;
+    overflow: hidden;
+    display: flex;
+    flex-direction: column;
+}
+
+.sidebar-tree-container {
+    flex: 1;
+    overflow-y: auto;
+    padding: 8px 0;
+}
+
+/* Sidebar Tree Node Styles */
+.sidebar-tree-node {
+    position: relative;
+    user-select: none;
+}
+
+.sidebar-tree-node-content {
+    display: flex;
+    align-items: center;
+    padding: 8px 16px;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    font-size: 0.85em;
+    border-left: 3px solid transparent;
+    color: var(--text-color);
+}
+
+.sidebar-tree-node-content:hover {
+    background: var(--lora-surface);
+    color: var(--text-color);
+}
+
+.sidebar-tree-node-content.selected {
+    background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+    color: var(--lora-accent);
+    border-left-color: var(--lora-accent);
+    font-weight: 500;
+}
+
+.sidebar-tree-expand-icon {
+    width: 16px;
+    height: 16px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    margin-right: 4px;
+    transition: transform 0.2s ease;
+    opacity: 0.6;
+}
+
+.sidebar-tree-expand-icon.expanded {
+    transform: rotate(90deg);
+}
+
+.sidebar-tree-expand-icon i {
+    font-size: 10px;
+}
+
+.sidebar-tree-folder-icon {
+    margin-right: 8px;
+    color: var(--text-muted);
+    opacity: 0.7;
+}
+
+.sidebar-tree-node-content.selected .sidebar-tree-folder-icon {
+    color: var(--lora-accent);
+    opacity: 1;
+}
+
+.sidebar-tree-node-content:hover .sidebar-tree-folder-icon {
+    color: var(--text-color);
+    opacity: 0.9;
+}
+
+.sidebar-tree-folder-name {
+    flex: 1;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+}
+
+.sidebar-tree-children {
+    overflow: hidden;
+    max-height: 0;
+    transition: max-height 0.3s ease;
+}
+
+.sidebar-tree-children.expanded {
+    max-height: 1000px;
+}
+
+.sidebar-tree-children .sidebar-tree-node-content {
+    padding-left: 32px;
+}
+
+.sidebar-tree-children .sidebar-tree-children .sidebar-tree-node-content {
+    padding-left: 48px;
+}
+
+.sidebar-tree-children .sidebar-tree-children .sidebar-tree-children .sidebar-tree-node-content {
+    padding-left: 64px;
+}
+
+/* Enhanced Sidebar Breadcrumb Styles */
+.sidebar-breadcrumb-container {
+    margin-top: 8px;
+    padding: 8px 0;
+    border-bottom: 1px solid var(--border-color);
+    background: var(--bg-color);
+    border-radius: var(--border-radius-xs);
+}
+
+.sidebar-breadcrumb-nav {
+    display: flex;
+    align-items: center;
+    flex-wrap: wrap;
+    gap: 4px;
+    font-size: 0.85em;
+    padding: 0 8px;
+}
+
+.sidebar-breadcrumb-item {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+    cursor: pointer;
+    transition: all 0.2s ease;
+    color: var(--text-muted);
+    position: relative;
+}
+
+.sidebar-breadcrumb-item:hover {
+    background: var(--lora-surface);
+    color: var(--text-color);
+}
+
+.sidebar-breadcrumb-item.active {
+    background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+    color: var(--lora-accent);
+    font-weight: 500;
+}
+
+.sidebar-breadcrumb-separator {
+    color: var(--text-muted);
+    opacity: 0.6;
+    margin: 0 2px;
+}
+
+/* New Breadcrumb Dropdown Styles */
+.breadcrumb-dropdown {
+    position: relative;
+    display: inline-flex;
+    align-items: center;
+}
+
+.breadcrumb-dropdown-indicator {
+    margin-left: 6px;
+    color: inherit;
+    opacity: 0.6;
+    transition: all 0.2s ease;
+    pointer-events: none;
+    font-size: 0.9em;
+}
+
+.sidebar-breadcrumb-item:hover .breadcrumb-dropdown-indicator {
+    opacity: 0.9;
+}
+
+.sidebar-breadcrumb-item.placeholder {
+    color: var(--text-muted);
+    font-style: italic;
+}
+
+.sidebar-breadcrumb-item.placeholder:hover {
+    background: var(--lora-surface);
+    color: var(--text-color);
+}
+
+.breadcrumb-dropdown.open .breadcrumb-dropdown-indicator {
+    transform: rotate(180deg);
+    opacity: 1;
+}
+
+.breadcrumb-dropdown-menu {
+    position: absolute;
+    top: 100%;
+    left: 0;
+    min-width: 160px;
+    max-width: 240px;
+    background: var(--bg-color);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    box-shadow: 0 3px 8px rgba(0,0,0,0.15);
+    z-index: calc(var(--z-overlay) + 20);
+    overflow-y: auto;
+    max-height: 450px;
+    display: none;
+    margin-top: 4px;
+}
+
+.breadcrumb-dropdown.open .breadcrumb-dropdown-menu {
+    display: block;
+}
+
+.breadcrumb-dropdown-item {
+    padding: 6px 12px;
+    cursor: pointer;
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    transition: all 0.2s ease;
+}
+
+.breadcrumb-dropdown-item:hover {
+    background: var(--lora-surface);
+}
+
+.breadcrumb-dropdown-item.active {
+    background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+    color: var(--lora-accent);
+    font-weight: 500;
+}
+
+/* Folder List Mode Styles */
+.sidebar-folder-item {
+    position: relative;
+    user-select: none;
+}
+
+.sidebar-node-content {
+    display: flex;
+    align-items: center;
+    padding: 8px 16px;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    font-size: 0.85em;
+    border-left: 3px solid transparent;
+    color: var(--text-color);
+}
+
+.sidebar-node-content:hover {
+    background: var(--lora-surface);
+    color: var(--text-color);
+}
+
+.sidebar-folder-item.selected .sidebar-node-content {
+    background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+    color: var(--lora-accent);
+    border-left-color: var(--lora-accent);
+    font-weight: 500;
+}
+
+.sidebar-folder-icon {
+    margin-right: 8px;
+    color: var(--text-muted);
+    opacity: 0.7;
+    width: 16px;
+    text-align: center;
+}
+
+.sidebar-folder-item.selected .sidebar-folder-icon {
+    color: var(--lora-accent);
+    opacity: 1;
+}
+
+.sidebar-node-content:hover .sidebar-folder-icon {
+    color: var(--text-color);
+    opacity: 0.9;
+}
+
+.sidebar-folder-name {
+    flex: 1;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+}
+
+/* Responsive Design */
+@media (min-width: 2150px) {
+    .folder-sidebar {
+        width: 280px;
+        left: 0px;
+    }
+}
+
+@media (min-width: 3000px) {
+    .folder-sidebar {
+        width: 320px;
+        left: 0px;
+    }
+}
+
+@media (max-width: 1400px) {
+    .folder-sidebar {
+        width: 260px;
+        left: 0px;
+    }
+}
+
+/* Empty State */
+.sidebar-tree-placeholder {
+    padding: 24px 16px;
+    text-align: center;
+    color: var(--text-muted);
+    opacity: 0.7;
+}
+
+.sidebar-tree-placeholder i {
+    font-size: 2em;
+    opacity: 0.5;
+    margin-bottom: 8px;
+    display: block;
+}
+
+/* Smooth transitions for tree nodes */
+.sidebar-tree-node {
+    overflow: hidden;
+}
+
+.sidebar-tree-children {
+    transition: max-height 0.25s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+.sidebar-tree-expand-icon {
+    transition: transform 0.2s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+/* Visual separator for nested levels */
+.sidebar-tree-children .sidebar-tree-node-content {
+    position: relative;
+}
+
+.sidebar-tree-children .sidebar-tree-node-content::before {
+    content: '';
+    position: absolute;
+    left: 8px;
+    top: 0;
+    bottom: 0;
+    width: 1px;
+    background: var(--border-color);
+    opacity: 0.3;
+}
+
+/* Responsive Design */
+@media (max-width: 1024px) {
+    .folder-sidebar {
+        top: 68px;
+        left: 0px;
+        width: calc(100vw - 32px);
+        max-width: 320px;
+        height: calc(100vh - 88px);
+        z-index: calc(var(--z-overlay) + 10);
+    }
+    
+    .folder-sidebar.collapsed {
+        transform: translateX(-100%);
+    }
+    
+    /* Mobile overlay */
+    .folder-sidebar:not(.collapsed)::before {
+        content: '';
+        position: fixed;
+        top: 0;
+        left: 0;
+        right: 0;
+        bottom: 0;
+        background: rgba(0, 0, 0, 0.3);
+        z-index: -1;
+        backdrop-filter: blur(2px);
+    }
+}
+
+@media (max-width: 768px) {
+    .folder-sidebar {
+        width: calc(100vw - 32px);
+        max-width: 280px;
+        left: 0px;
+    }
+    
+    .sidebar-breadcrumb-nav {
+        font-size: 0.8em;
+    }
+    
+    .sidebar-breadcrumb-item {
+        padding: 3px 6px;
+    }
+}
+
+/* Hide scrollbar but keep functionality */
+.sidebar-tree-container::-webkit-scrollbar {
+    width: 6px;
+}
+
+.sidebar-tree-container::-webkit-scrollbar-track {
+    background: transparent;
+}
+
+.sidebar-tree-container::-webkit-scrollbar-thumb {
+    background: var(--border-color);
+    border-radius: 3px;
+}
+
+.sidebar-tree-container::-webkit-scrollbar-thumb:hover {
+    background: var(--text-muted);
+}

static/css/components/statistics.css

@@ -7,6 +7,7 @@
     display: grid;
     grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
     gap: var(--space-2);
+    margin-top: var(--space-2);
     margin-bottom: var(--space-3);
 }
 

static/css/layout.css

@@ -9,14 +9,24 @@
 
 .container {
     max-width: 1400px;
-    margin: 20px auto;
+    margin: 0 auto;
     padding: 0 15px;
     position: relative;
     z-index: var(--z-base);
 }
 
+/* Sticky controls container */
+.controls {
+    position: sticky;
+    top: -54px;
+    z-index: calc(var(--z-header) - 1);
+    background: var(--bg-color);
+    padding: var(--space-2) 0;
+    box-shadow: 0 1px 3px rgba(0,0,0,0.05);
+}
+
 /* Responsive container for larger screens */
-@media (min-width: 2000px) {
+@media (min-width: 2150px) {
     .container {
         max-width: 1800px;
     }
@@ -28,13 +38,6 @@
     }
 }
 
-.controls {
-    display: flex;
-    flex-direction: column;
-    gap: 8px;
-    margin-bottom: var(--space-2);
-}
-
 .controls-right {
     display: flex;
     align-items: center;
@@ -225,63 +228,6 @@
     display: none !important;
 }
 
-.folder-tags-container {
-    position: relative;
-    width: 100%;
-    margin-bottom: 8px; /* Add margin to ensure space for the button */
-}
-
-.folder-tags {
-    display: flex;
-    gap: 4px;
-    padding: 2px 0;
-    flex-wrap: wrap;
-    transition: max-height 0.3s ease, opacity 0.2s ease;
-    max-height: 150px; /* Limit height to prevent overflow */
-    opacity: 1;
-    overflow-y: auto; /* Enable vertical scrolling */
-    margin-bottom: 5px; /* Add margin below the tags */
-}
-
-.folder-tags.collapsed {
-    max-height: 0;
-    opacity: 0;
-    margin: 0;
-    padding-bottom: 0;
-    overflow: hidden;
-}
-
-.toggle-folders-container {
-    margin-left: auto;
-}
-
-/* Toggle Folders Button */
-.toggle-folders-btn {
-    width: 36px;
-    height: 36px;
-    border-radius: 50%;
-    background: var(--card-bg);
-    border: 1px solid var(--border-color);
-    color: var(--text-color);
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    cursor: pointer;
-    transition: all 0.3s ease;
-    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
-}
-
-.toggle-folders-btn:hover {
-    background: var(--lora-accent);
-    color: white;
-    transform: translateY(-2px);
-    box-shadow: 0 3px 6px rgba(0, 0, 0, 0.1);
-}
-
-.toggle-folders-btn i {
-    transition: transform 0.3s ease;
-}
-
 /* Icon-only button style */
 .icon-only {
     min-width: unset !important;
@@ -290,55 +236,6 @@
     height: 32px !important;
 }
 
-/* Rotate icon when folders are collapsed */
-.folder-tags.collapsed ~ .actions .toggle-folders-btn i {
-    transform: rotate(180deg);
-}
-
-/* Add custom scrollbar for better visibility */
-.folder-tags::-webkit-scrollbar {
-    width: 6px;
-}
-
-.folder-tags::-webkit-scrollbar-track {
-    background: var(--card-bg);
-    border-radius: 3px;
-}
-
-.folder-tags::-webkit-scrollbar-thumb {
-    background: var(--border-color);
-    border-radius: 3px;
-}
-
-.folder-tags::-webkit-scrollbar-thumb:hover {
-    background: var(--lora-accent);
-}
-
-.tag {
-    cursor: pointer;
-    padding: 2px 8px;
-    margin: 2px;
-    border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-xs);
-    display: inline-block;
-    line-height: 1.2;
-    font-size: 14px;
-    background-color: var(--card-bg);
-    transition: all 0.2s ease;
-}
-
-.tag:hover {
-    border-color: var(--lora-accent);
-    background-color: oklch(var(--lora-accent) / 0.1);
-    transform: translateY(-1px);
-}
-
-.tag.active {
-    background-color: var(--lora-accent);
-    color: white;
-    border-color: var(--lora-accent);
-}
-
 /* Back to Top Button */
 .back-to-top {
     position: fixed;
@@ -376,10 +273,8 @@
 }
 
 /* Prevent text selection in control and header areas */
-.tag,
 .control-group button,
 .control-group select,
-.toggle-folders-btn,
 .bulk-operations-panel,
 .app-header,
 .header-branding,
@@ -387,8 +282,7 @@
 .main-nav,
 .nav-item,
 .header-actions button,
-.header-controls,
+.header-controls {
-.toggle-folders-container button {
     -webkit-user-select: none;
     -moz-user-select: none;
     -ms-user-select: none;
@@ -472,18 +366,6 @@
         justify-content: flex-end;
         margin-top: 8px;
     }
-    
-    .toggle-folders-container {
-        margin-left: 0;
-    }
-    
-    .folder-tags-container {
-        order: -1;
-    }
-    
-    .toggle-folders-btn:hover {
-        transform: none; /* Disable hover effects on mobile */
-    }
 
     .control-group button:hover {
         transform: none; /* Disable hover effects on mobile */
@@ -493,10 +375,6 @@
         transform: none; /* Disable hover effects on mobile */
     }
 
-    .tag:hover {
-        transform: none; /* Disable hover effects on mobile */
-    }
-
     .back-to-top {
         bottom: 60px; /* Give some extra space from bottom on mobile */
     }
@@ -505,4 +383,9 @@
         left: auto;
         right: 0; /* Align to right on mobile */
     }
+
+    /* Adjust controls padding on mobile */
+    .controls {
+        padding: 10px 0;
+    }
 }

static/css/onboarding.css

@@ -0,0 +1,252 @@
+/* Onboarding Tutorial Styles */
+.onboarding-overlay {
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    background: rgba(0, 0, 0, 0.8);
+    z-index: var(--z-overlay);
+    display: none;
+    /* Use mask to create cutout for highlighted element */
+    mask-composite: subtract;
+    -webkit-mask-composite: subtract;
+}
+
+.onboarding-overlay.active {
+    display: block;
+}
+
+.onboarding-spotlight {
+    position: absolute;
+    background: transparent;
+    border: 3px solid var(--lora-accent);
+    border-radius: var(--border-radius-base);
+    z-index: calc(var(--z-overlay) + 1);
+    pointer-events: none;
+    transition: all 0.3s ease;
+    /* Add glow effect */
+    box-shadow: 
+        0 0 0 2px rgba(24, 144, 255, 0.3),
+        0 0 20px rgba(24, 144, 255, 0.2),
+        inset 0 0 0 1px rgba(255, 255, 255, 0.1);
+}
+
+/* Target element highlighting */
+.onboarding-target-highlight {
+    position: relative;
+    z-index: calc(var(--z-overlay) + 2) !important;
+    pointer-events: auto !important;
+}
+
+/* Ensure highlighted elements are interactive */
+.onboarding-target-highlight * {
+    pointer-events: auto !important;
+}
+
+.onboarding-popup {
+    position: absolute;
+    background: var(--lora-surface);
+    border: 1px solid var(--lora-border);
+    border-radius: var(--border-radius-base);
+    padding: var(--space-3);
+    min-width: 320px;
+    max-width: 400px;
+    z-index: calc(var(--z-overlay) + 3);
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.3);
+    backdrop-filter: blur(10px);
+}
+
+.onboarding-popup h3 {
+    margin: 0 0 var(--space-2) 0;
+    color: var(--lora-accent);
+    font-size: 1.2em;
+    font-weight: 600;
+}
+
+.onboarding-popup p {
+    margin: 0 0 var(--space-3) 0;
+    color: var(--text-color);
+    line-height: 1.5;
+}
+
+.onboarding-controls {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    gap: var(--space-2);
+}
+
+.onboarding-progress {
+    display: flex;
+    align-items: center;
+    gap: var(--space-1);
+    font-size: 0.85em;
+    color: var(--text-muted);
+}
+
+.onboarding-actions {
+    display: flex;
+    gap: var(--space-2);
+}
+
+.onboarding-btn {
+    padding: var(--space-1) var(--space-2);
+    border: 1px solid var(--lora-border);
+    border-radius: var(--border-radius-sm);
+    background: var(--card-bg);
+    color: var(--text-color);
+    cursor: pointer;
+    font-size: 0.9em;
+    transition: all 0.2s ease;
+}
+
+.onboarding-btn:hover {
+    background: var(--lora-accent);
+    color: var(--lora-text);
+    border-color: var(--lora-accent);
+}
+
+.onboarding-btn.primary {
+    background: var(--lora-accent);
+    color: var(--lora-text);
+    border-color: var(--lora-accent);
+}
+
+.onboarding-btn.primary:hover {
+    opacity: 0.9;
+}
+
+/* Language Selection Modal */
+.language-selection-modal {
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    background: rgba(0, 0, 0, 0.9);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    z-index: calc(var(--z-overlay) + 10);
+}
+
+.language-selection-content {
+    background: var(--lora-surface);
+    border: 1px solid var(--lora-border);
+    border-radius: var(--border-radius-base);
+    padding: var(--space-3);
+    min-width: 510px;
+    text-align: center;
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.4);
+    backdrop-filter: blur(10px);
+}
+
+.language-selection-content h2 {
+    margin: 0 0 var(--space-2) 0;
+    color: var(--lora-accent);
+    font-size: 1.5em;
+}
+
+.language-selection-content p {
+    margin: 0 0 var(--space-3) 0;
+    color: var(--text-color);
+    line-height: 1.5;
+}
+
+.language-grid {
+    display: grid;
+    grid-template-columns: repeat(3, 1fr);
+    gap: var(--space-2);
+    margin-bottom: var(--space-3);
+}
+
+.language-option {
+    padding: var(--space-2);
+    border: 2px solid var(--lora-border);
+    border-radius: var(--border-radius-sm);
+    background: var(--card-bg);
+    cursor: pointer;
+    transition: all 0.2s ease;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    gap: var(--space-1);
+}
+
+.language-option:hover {
+    border-color: var(--lora-accent);
+    background: var(--lora-surface);
+}
+
+.language-option.selected {
+    border-color: var(--lora-accent);
+    background: var(--lora-accent);
+    color: var(--lora-text);
+}
+
+.language-flag {
+    font-size: 1.5em;
+}
+
+.language-name {
+    font-size: 0.9em;
+    font-weight: 500;
+}
+
+.language-actions {
+    display: flex;
+    gap: var(--space-2);
+    justify-content: center;
+}
+
+/* Shortcut Key Highlighting */
+.onboarding-shortcut {
+    display: inline-block;
+    background: var(--shortcut-bg);
+    border: 1px solid var(--shortcut-border);
+    border-radius: var(--border-radius-xs);
+    padding: 2px 6px;
+    font-size: 0.8em;
+    font-weight: 600;
+    color: var(--shortcut-text);
+    margin: 0 2px;
+}
+
+/* Animation for highlighting elements */
+.onboarding-highlight {
+    animation: onboarding-pulse 2s infinite;
+}
+
+@keyframes onboarding-pulse {
+    0%, 100% {
+        box-shadow: 
+            0 0 0 2px rgba(24, 144, 255, 0.4),
+            0 0 20px rgba(24, 144, 255, 0.3),
+            inset 0 0 0 1px rgba(255, 255, 255, 0.1);
+    }
+    50% {
+        box-shadow: 
+            0 0 0 4px rgba(24, 144, 255, 0.6),
+            0 0 30px rgba(24, 144, 255, 0.4),
+            inset 0 0 0 1px rgba(255, 255, 255, 0.2);
+    }
+}
+
+/* Responsive adjustments */
+@media (max-width: 768px) {
+    .onboarding-popup {
+        min-width: 280px;
+        max-width: calc(100vw - 40px);
+        padding: var(--space-2);
+    }
+    
+    .language-grid {
+        grid-template-columns: repeat(2, 1fr);
+    }
+    
+    .language-selection-content {
+        min-width: calc(100vw - 40px);
+        max-width: 400px;
+    }
+}

static/css/style.css

@@ -16,7 +16,7 @@
 @import 'components/modal/relink-civitai-modal.css';
 @import 'components/modal/example-access-modal.css';
 @import 'components/modal/support-modal.css';
-@import 'components/download-modal.css';
+@import 'components/modal/download-modal.css';
 @import 'components/toast.css';
 @import 'components/loading.css';
 @import 'components/menu.css';
@@ -34,10 +34,10 @@
 @import 'components/filter-indicator.css';
 @import 'components/initialization.css';
 @import 'components/progress-panel.css';
-@import 'components/alphabet-bar.css'; /* Add alphabet bar component */
 @import 'components/duplicates.css'; /* Add duplicates component */
 @import 'components/keyboard-nav.css'; /* Add keyboard navigation component */
 @import 'components/statistics.css'; /* Add statistics component */
+@import 'components/sidebar.css'; /* Add sidebar component */
 
 .initialization-notice {
     display: flex;

static/js/api/apiConfig.js

@@ -29,7 +29,7 @@ export const MODEL_CONFIG = {
         defaultPageSize: 100,
         supportsLetterFilter: false,
         supportsBulkOperations: true,
-        supportsMove: false,
+        supportsMove: true,
         templateName: 'checkpoints.html'
     },
     [MODEL_TYPES.EMBEDDING]: {
@@ -55,7 +55,7 @@ export function getApiEndpoints(modelType) {
     
     return {
         // Base CRUD operations
-        list: `/api/${modelType}`,
+        list: `/api/${modelType}/list`,
         delete: `/api/${modelType}/delete`,
         exclude: `/api/${modelType}/exclude`,
         rename: `/api/${modelType}/rename`,
@@ -64,6 +64,13 @@ export function getApiEndpoints(modelType) {
         // Bulk operations
         bulkDelete: `/api/${modelType}/bulk-delete`,
         
+        // Tag operations
+        addTags: `/api/${modelType}/add-tags`,
+
+        // Move operations (now common for all model types that support move)
+        moveModel: `/api/${modelType}/move_model`,
+        moveBulk: `/api/${modelType}/move_models_bulk`,
+        
         // CivitAI integration
         fetchCivitai: `/api/${modelType}/fetch-civitai`,
         fetchAllCivitai: `/api/${modelType}/fetch-all-civitai`,
@@ -79,9 +86,17 @@ export function getApiEndpoints(modelType) {
         baseModels: `/api/${modelType}/base-models`,
         roots: `/api/${modelType}/roots`,
         folders: `/api/${modelType}/folders`,
+        folderTree: `/api/${modelType}/folder-tree`,
+        unifiedFolderTree: `/api/${modelType}/unified-folder-tree`,
         duplicates: `/api/${modelType}/find-duplicates`,
         conflicts: `/api/${modelType}/find-filename-conflicts`,
         verify: `/api/${modelType}/verify-duplicates`,
+        metadata: `/api/${modelType}/metadata`,
+        modelDescription: `/api/${modelType}/model-description`,
+        
+        // Auto-organize operations
+        autoOrganize: `/api/${modelType}/auto-organize`,
+        autoOrganizeProgress: `/api/${modelType}/auto-organize-progress`,
         
         // Model-specific endpoints (will be merged with specific configs)
         specific: {}
@@ -98,17 +113,19 @@ export const MODEL_SPECIFIC_ENDPOINTS = {
         triggerWords: `/api/${MODEL_TYPES.LORA}/get-trigger-words`,
         previewUrl: `/api/${MODEL_TYPES.LORA}/preview-url`,
         civitaiUrl: `/api/${MODEL_TYPES.LORA}/civitai-url`,
-        modelDescription: `/api/${MODEL_TYPES.LORA}/model-description`,
+        metadata: `/api/${MODEL_TYPES.LORA}/metadata`,
-        moveModel: `/api/${MODEL_TYPES.LORA}/move_model`,
-        moveBulk: `/api/${MODEL_TYPES.LORA}/move_models_bulk`,
         getTriggerWordsPost: `/api/${MODEL_TYPES.LORA}/get_trigger_words`,
         civitaiModelByVersion: `/api/${MODEL_TYPES.LORA}/civitai/model/version`,
         civitaiModelByHash: `/api/${MODEL_TYPES.LORA}/civitai/model/hash`,
     },
     [MODEL_TYPES.CHECKPOINT]: {
         info: `/api/${MODEL_TYPES.CHECKPOINT}/info`,
+        checkpoints_roots: `/api/${MODEL_TYPES.CHECKPOINT}/checkpoints_roots`,
+        unet_roots: `/api/${MODEL_TYPES.CHECKPOINT}/unet_roots`,
+        metadata: `/api/${MODEL_TYPES.CHECKPOINT}/metadata`,
     },
     [MODEL_TYPES.EMBEDDING]: {
+        metadata: `/api/${MODEL_TYPES.EMBEDDING}/metadata`,
     }
 };
 
@@ -159,7 +176,8 @@ export const DOWNLOAD_ENDPOINTS = {
     download: '/api/download-model',
     downloadGet: '/api/download-model-get',
     cancelGet: '/api/cancel-download-get',
-    progress: '/api/download-progress'
+    progress: '/api/download-progress',
+    exampleImages: '/api/force-download-example-images' // New endpoint for downloading example images
 };
 
 // WebSocket endpoints

static/js/api/checkpointApi.js

@@ -0,0 +1,92 @@
+import { BaseModelApiClient } from './baseModelApi.js';
+
+/**
+ * Checkpoint-specific API client
+ */
+export class CheckpointApiClient extends BaseModelApiClient {
+    /**
+     * Get checkpoint information
+     */
+    async getCheckpointInfo(filePath) {
+        try {
+            const response = await fetch(this.apiConfig.endpoints.specific.info, {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ file_path: filePath })
+            });
+            
+            if (!response.ok) {
+                throw new Error('Failed to fetch checkpoint info');
+            }
+            
+            return await response.json();
+        } catch (error) {
+            console.error('Error fetching checkpoint info:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * Get checkpoint roots
+     */
+    async getCheckpointsRoots() {
+        try {
+            const response = await fetch(this.apiConfig.endpoints.specific.checkpoints_roots, {
+                method: 'GET'
+            });
+            if (!response.ok) {
+                throw new Error('Failed to fetch checkpoints roots');
+            }
+            return await response.json();
+        } catch (error) {
+            console.error('Error fetching checkpoints roots:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * Get unet roots
+     */
+    async getUnetRoots() {
+        try {
+            const response = await fetch(this.apiConfig.endpoints.specific.unet_roots, {
+                method: 'GET'
+            });
+            if (!response.ok) {
+                throw new Error('Failed to fetch unet roots');
+            }
+            return await response.json();
+        } catch (error) {
+            console.error('Error fetching unet roots:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * Get appropriate roots based on model type
+     */
+    async fetchModelRoots(modelType = 'checkpoint') {
+        try {
+            let response;
+            if (modelType === 'diffusion_model') {
+                response = await fetch(this.apiConfig.endpoints.specific.unet_roots, {
+                    method: 'GET'
+                });
+            } else {
+                response = await fetch(this.apiConfig.endpoints.specific.checkpoints_roots, {
+                    method: 'GET'
+                });
+            }
+            
+            if (!response.ok) {
+                throw new Error(`Failed to fetch ${modelType} roots`);
+            }
+            return await response.json();
+        } catch (error) {
+            console.error(`Error fetching ${modelType} roots:`, error);
+            throw error;
+        }
+    }
+}

static/js/api/embeddingApi.js

@@ -0,0 +1,7 @@
+import { BaseModelApiClient } from './baseModelApi.js';
+
+/**
+ * Embedding-specific API client
+ */
+export class EmbeddingApiClient extends BaseModelApiClient {
+}

static/js/api/loraApi.js

@@ -0,0 +1,93 @@
+import { BaseModelApiClient } from './baseModelApi.js';
+import { getSessionItem } from '../utils/storageHelpers.js';
+
+/**
+ * LoRA-specific API client
+ */
+export class LoraApiClient extends BaseModelApiClient {
+    /**
+     * Add LoRA-specific parameters to query
+     */
+    _addModelSpecificParams(params, pageState) {
+        const filterLoraHash = getSessionItem('recipe_to_lora_filterLoraHash');
+        const filterLoraHashes = getSessionItem('recipe_to_lora_filterLoraHashes');
+
+        if (filterLoraHash) {
+            params.append('lora_hash', filterLoraHash);
+        } else if (filterLoraHashes) {
+            try {
+                if (Array.isArray(filterLoraHashes) && filterLoraHashes.length > 0) {
+                    params.append('lora_hashes', filterLoraHashes.join(','));
+                }
+            } catch (error) {
+                console.error('Error parsing lora hashes from session storage:', error);
+            }
+        }
+    }
+
+    /**
+     * Get LoRA notes
+     */
+    async getLoraNote(filePath) {
+        try {
+            const response = await fetch(this.apiConfig.endpoints.specific.notes,
+                {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json'
+                    },
+                    body: JSON.stringify({ file_path: filePath })
+                }
+            );
+            
+            if (!response.ok) {
+                throw new Error('Failed to fetch LoRA notes');
+            }
+            
+            return await response.json();
+        } catch (error) {
+            console.error('Error fetching LoRA notes:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * Get LoRA trigger words
+     */
+    async getLoraTriggerWords(filePath) {
+        try {
+            const response = await fetch(this.apiConfig.endpoints.specific.triggerWords, {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ file_path: filePath })
+            });
+            
+            if (!response.ok) {
+                throw new Error('Failed to fetch trigger words');
+            }
+            
+            return await response.json();
+        } catch (error) {
+            console.error('Error fetching trigger words:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * Get letter counts for LoRAs
+     */
+    async getLetterCounts() {
+        try {
+            const response = await fetch(this.apiConfig.endpoints.specific.letterCounts);
+            if (!response.ok) {
+                throw new Error('Failed to fetch letter counts');
+            }
+            return await response.json();
+        } catch (error) {
+            console.error('Error fetching letter counts:', error);
+            throw error;
+        }
+    }
+}

static/js/api/modelApiFactory.js

@@ -0,0 +1,35 @@
+import { LoraApiClient } from './loraApi.js';
+import { CheckpointApiClient } from './checkpointApi.js';
+import { EmbeddingApiClient } from './embeddingApi.js';
+import { MODEL_TYPES } from './apiConfig.js';
+import { state } from '../state/index.js';
+
+export function createModelApiClient(modelType) {
+    switch (modelType) {
+        case MODEL_TYPES.LORA:
+            return new LoraApiClient();
+        case MODEL_TYPES.CHECKPOINT:
+            return new CheckpointApiClient();
+        case MODEL_TYPES.EMBEDDING:
+            return new EmbeddingApiClient();
+        default:
+            throw new Error(`Unsupported model type: ${modelType}`);
+    }
+}
+
+let _singletonClients = new Map();
+
+export function getModelApiClient(modelType = null) {
+    const targetType = modelType || state.currentPageType;
+    
+    if (!_singletonClients.has(targetType)) {
+        _singletonClients.set(targetType, createModelApiClient(targetType));
+    }
+    
+    return _singletonClients.get(targetType);
+}
+
+export function resetAndReload(updateFolders = false) {
+    const client = getModelApiClient();
+    return client.loadMoreWithVirtualScroll(true, updateFolders);
+}

static/js/api/recipeApi.js

@@ -89,7 +89,7 @@ export async function fetchRecipesPage(page = 1, pageSize = 100) {
         };
     } catch (error) {
         console.error('Error fetching recipes:', error);
-        showToast(`Failed to fetch recipes: ${error.message}`, 'error');
+        showToast('toast.recipes.fetchFailed', { message: error.message }, 'error');
         throw error;
     }
 }
@@ -131,7 +131,7 @@ export async function resetAndReloadWithVirtualScroll(options = {}) {
         return result;
     } catch (error) {
         console.error(`Error reloading ${modelType}s:`, error);
-        showToast(`Failed to reload ${modelType}s: ${error.message}`, 'error');
+        showToast('toast.recipes.reloadFailed', { modelType: modelType, message: error.message }, 'error');
         throw error;
     } finally {
         pageState.isLoading = false;
@@ -179,7 +179,7 @@ export async function loadMoreWithVirtualScroll(options = {}) {
         return result;
     } catch (error) {
         console.error(`Error loading ${modelType}s:`, error);
-        showToast(`Failed to load ${modelType}s: ${error.message}`, 'error');
+        showToast('toast.recipes.loadFailed', { modelType: modelType, message: error.message }, 'error');
         throw error;
     } finally {
         pageState.isLoading = false;
@@ -217,10 +217,10 @@ export async function refreshRecipes() {
         // After successful cache rebuild, reload the recipes
         await resetAndReload();
         
-        showToast('Refresh complete', 'success');
+        showToast('toast.recipes.refreshComplete', {}, 'success');
     } catch (error) {
         console.error('Error refreshing recipes:', error);
-        showToast(error.message || 'Failed to refresh recipes', 'error');
+        showToast('toast.recipes.refreshFailed', { message: error.message }, 'error');
     } finally {
         state.loadingManager.hide();
         state.loadingManager.restoreProgressBar();
@@ -285,7 +285,7 @@ export async function updateRecipeMetadata(filePath, updates) {
         const data = await response.json();
 
         if (!data.success) {
-            showToast(`Failed to update recipe: ${data.error}`, 'error');
+            showToast('toast.recipes.updateFailed', { error: data.error }, 'error');
             throw new Error(data.error || 'Failed to update recipe');
         }
 
@@ -294,7 +294,7 @@ export async function updateRecipeMetadata(filePath, updates) {
         return data;
     } catch (error) {
         console.error('Error updating recipe:', error);
-        showToast(`Error updating recipe: ${error.message}`, 'error');
+        showToast('toast.recipes.updateError', { message: error.message }, 'error');
         throw error;
     } finally {
         state.loadingManager.hide();

static/js/checkpoints.js

@@ -1,7 +1,6 @@
 import { appCore } from './core.js';
 import { confirmDelete, closeDeleteModal, confirmExclude, closeExcludeModal } from './utils/modalUtils.js';
 import { createPageControls } from './components/controls/index.js';
-import { CheckpointContextMenu } from './components/ContextMenu/index.js';
 import { ModelDuplicatesManager } from './components/ModelDuplicatesManager.js';
 import { MODEL_TYPES } from './api/apiConfig.js';
 
@@ -30,14 +29,7 @@ class CheckpointsPageManager {
     }
     
     async initialize() {
-        // Initialize page-specific components
+        // Initialize common page features (including context menus)
-        this.pageControls.restoreFolderFilter();
-        this.pageControls.initFolderTagsVisibility();
-        
-        // Initialize context menu
-        new CheckpointContextMenu();
-        
-        // Initialize common page features
         appCore.initializePageFeatures();
         
         console.log('Checkpoints Manager initialized');
@@ -52,4 +44,4 @@ document.addEventListener('DOMContentLoaded', async () => {
     // Initialize checkpoints page
     const checkpointsPage = new CheckpointsPageManager();
     await checkpointsPage.initialize();
-});
+});

static/js/components/ContextMenu/BaseContextMenu.js

@@ -15,17 +15,6 @@ export class BaseContextMenu {
     init() {
         // Hide menu on regular clicks
         document.addEventListener('click', () => this.hideMenu());
-        
-        // Show menu on right-click on cards
-        document.addEventListener('contextmenu', (e) => {
-            const card = e.target.closest(this.cardSelector);
-            if (!card) {
-                this.hideMenu();
-                return;
-            }
-            e.preventDefault();
-            this.showMenu(e.clientX, e.clientY, card);
-        });
 
         // Handle menu item clicks
         this.menu.addEventListener('click', (e) => {

static/js/components/ContextMenu/BulkContextMenu.js

@@ -0,0 +1,117 @@
+import { BaseContextMenu } from './BaseContextMenu.js';
+import { state } from '../../state/index.js';
+import { bulkManager } from '../../managers/BulkManager.js';
+import { updateElementText } from '../../utils/i18nHelpers.js';
+
+export class BulkContextMenu extends BaseContextMenu {
+    constructor() {
+        super('bulkContextMenu', '.model-card.selected');
+        this.setupBulkMenuItems();
+    }
+
+    setupBulkMenuItems() {
+        if (!this.menu) return;
+
+        // Update menu items visibility based on current model type
+        this.updateMenuItemsForModelType();
+        
+        // Update selected count in header
+        this.updateSelectedCountHeader();
+    }
+
+    updateMenuItemsForModelType() {
+        const currentModelType = state.currentPageType;
+        const config = bulkManager.actionConfig[currentModelType];
+        
+        if (!config) return;
+
+        // Update button visibility based on model type
+        const addTagsItem = this.menu.querySelector('[data-action="add-tags"]');
+        const setBaseModelItem = this.menu.querySelector('[data-action="set-base-model"]');
+        const sendToWorkflowAppendItem = this.menu.querySelector('[data-action="send-to-workflow-append"]');
+        const sendToWorkflowReplaceItem = this.menu.querySelector('[data-action="send-to-workflow-replace"]');
+        const copyAllItem = this.menu.querySelector('[data-action="copy-all"]');
+        const refreshAllItem = this.menu.querySelector('[data-action="refresh-all"]');
+        const moveAllItem = this.menu.querySelector('[data-action="move-all"]');
+        const autoOrganizeItem = this.menu.querySelector('[data-action="auto-organize"]');
+        const deleteAllItem = this.menu.querySelector('[data-action="delete-all"]');
+
+        if (sendToWorkflowAppendItem) {
+            sendToWorkflowAppendItem.style.display = config.sendToWorkflow ? 'flex' : 'none';
+        }
+        if (sendToWorkflowReplaceItem) {
+            sendToWorkflowReplaceItem.style.display = config.sendToWorkflow ? 'flex' : 'none';
+        }
+        if (copyAllItem) {
+            copyAllItem.style.display = config.copyAll ? 'flex' : 'none';
+        }
+        if (refreshAllItem) {
+            refreshAllItem.style.display = config.refreshAll ? 'flex' : 'none';
+        }
+        if (moveAllItem) {
+            moveAllItem.style.display = config.moveAll ? 'flex' : 'none';
+        }
+        if (autoOrganizeItem) {
+            autoOrganizeItem.style.display = config.autoOrganize ? 'flex' : 'none';
+        }
+        if (deleteAllItem) {
+            deleteAllItem.style.display = config.deleteAll ? 'flex' : 'none';
+        }
+        if (addTagsItem) {
+            addTagsItem.style.display = config.addTags ? 'flex' : 'none';
+        }
+        if (setBaseModelItem) {
+            setBaseModelItem.style.display = 'flex'; // Base model editing is available for all model types
+        }
+    }
+
+    updateSelectedCountHeader() {
+        const headerElement = this.menu.querySelector('.bulk-context-header');
+        if (headerElement) {
+            updateElementText(headerElement, 'loras.bulkOperations.selected', { count: state.selectedModels.size });
+        }
+    }
+
+    showMenu(x, y, card) {
+        this.updateMenuItemsForModelType();
+        this.updateSelectedCountHeader();
+        super.showMenu(x, y, card);
+    }
+
+    handleMenuAction(action, menuItem) {
+        switch (action) {
+            case 'add-tags':
+                bulkManager.showBulkAddTagsModal();
+                break;
+            case 'set-base-model':
+                bulkManager.showBulkBaseModelModal();
+                break;
+            case 'send-to-workflow-append':
+                bulkManager.sendAllModelsToWorkflow(false);
+                break;
+            case 'send-to-workflow-replace':
+                bulkManager.sendAllModelsToWorkflow(true);
+                break;
+            case 'copy-all':
+                bulkManager.copyAllModelsSyntax();
+                break;
+            case 'refresh-all':
+                bulkManager.refreshAllMetadata();
+                break;
+            case 'move-all':
+                window.moveManager.showMoveModal('bulk');
+                break;
+            case 'auto-organize':
+                bulkManager.autoOrganizeSelectedModels();
+                break;
+            case 'delete-all':
+                bulkManager.showBulkDeleteModal();
+                break;
+            case 'clear':
+                bulkManager.clearSelection();
+                break;
+            default:
+                console.warn(`Unknown bulk action: ${action}`);
+        }
+    }
+}

static/js/components/ContextMenu/CheckpointContextMenu.js

@@ -1,8 +1,8 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
 import { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
-import { getModelApiClient, resetAndReload } from '../../api/baseModelApi.js';
+import { getModelApiClient, resetAndReload } from '../../api/modelApiFactory.js';
-import { showToast } from '../../utils/uiHelpers.js';
 import { showDeleteModal, showExcludeModal } from '../../utils/modalUtils.js';
+import { moveManager } from '../../managers/MoveManager.js';
 
 export class CheckpointContextMenu extends BaseContextMenu {
     constructor() {
@@ -54,8 +54,7 @@ export class CheckpointContextMenu extends BaseContextMenu {
                 apiClient.refreshSingleModelMetadata(this.currentCard.dataset.filepath);
                 break;
             case 'move':
-                // Move to folder (placeholder)
+                moveManager.showMoveModal(this.currentCard.dataset.filepath, this.currentCard.dataset.model_type);
-                showToast('Move to folder feature coming soon', 'info');
                 break;
             case 'exclude':
                 showExcludeModal(this.currentCard.dataset.filepath);

static/js/components/ContextMenu/EmbeddingContextMenu.js

@@ -1,7 +1,7 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
 import { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
-import { getModelApiClient, resetAndReload } from '../../api/baseModelApi.js';
+import { getModelApiClient, resetAndReload } from '../../api/modelApiFactory.js';
-import { showToast } from '../../utils/uiHelpers.js';
+import { moveManager } from '../../managers/MoveManager.js';
 import { showDeleteModal, showExcludeModal } from '../../utils/modalUtils.js';
 
 export class EmbeddingContextMenu extends BaseContextMenu {
@@ -54,8 +54,7 @@ export class EmbeddingContextMenu extends BaseContextMenu {
                 apiClient.refreshSingleModelMetadata(this.currentCard.dataset.filepath);
                 break;
             case 'move':
-                // Move to folder (placeholder)
+                moveManager.showMoveModal(this.currentCard.dataset.filepath);
-                showToast('Move to folder feature coming soon', 'info');
                 break;
             case 'exclude':
                 showExcludeModal(this.currentCard.dataset.filepath);

static/js/components/ContextMenu/LoraContextMenu.js

@@ -1,8 +1,9 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
 import { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
-import { getModelApiClient, resetAndReload } from '../../api/baseModelApi.js';
+import { getModelApiClient, resetAndReload } from '../../api/modelApiFactory.js';
-import { copyToClipboard, sendLoraToWorkflow } from '../../utils/uiHelpers.js';
+import { copyLoraSyntax, sendLoraToWorkflow } from '../../utils/uiHelpers.js';
 import { showExcludeModal, showDeleteModal } from '../../utils/modalUtils.js';
+import { moveManager } from '../../managers/MoveManager.js';
 
 export class LoraContextMenu extends BaseContextMenu {
     constructor() {
@@ -36,7 +37,7 @@ export class LoraContextMenu extends BaseContextMenu {
                 break;
             case 'copyname':
                 // Generate and copy LoRA syntax
-                this.copyLoraSyntax();
+                copyLoraSyntax(this.currentCard);
                 break;
             case 'sendappend':
                 // Send LoRA to workflow (append mode)
@@ -66,16 +67,6 @@ export class LoraContextMenu extends BaseContextMenu {
         }
     }
 
-    // Specific LoRA methods
-    copyLoraSyntax() {
-        const card = this.currentCard;
-        const usageTips = JSON.parse(card.dataset.usage_tips || '{}');
-        const strength = usageTips.strength || 1;
-        const loraSyntax = `<lora:${card.dataset.file_name}:${strength}>`;
-        
-        copyToClipboard(loraSyntax, 'LoRA syntax copied to clipboard');
-    }
-
     sendLoraToWorkflow(replaceMode) {
         const card = this.currentCard;
         const usageTips = JSON.parse(card.dataset.usage_tips || '{}');

static/js/components/ContextMenu/ModelContextMenuMixin.js

@@ -1,6 +1,7 @@
 import { showToast, getNSFWLevelName, openExampleImagesFolder } from '../../utils/uiHelpers.js';
 import { modalManager } from '../../managers/ModalManager.js';
 import { state } from '../../state/index.js';
+import { getModelApiClient } from '../../api/modelApiFactory.js';
 
 // Mixin with shared functionality for LoraContextMenu and CheckpointContextMenu
 export const ModelContextMenuMixin = {
@@ -24,10 +25,10 @@ export const ModelContextMenuMixin = {
                 try {
                     await this.saveModelMetadata(filePath, { preview_nsfw_level: level });
                     
-                    showToast(`Content rating set to ${getNSFWLevelName(level)}`, 'success');
+                    showToast('toast.contextMenu.contentRatingSet', { level: getNSFWLevelName(level) }, 'success');
                     this.nsfwSelector.style.display = 'none';
                 } catch (error) {
-                    showToast(`Failed to set content rating: ${error.message}`, 'error');
+                    showToast('toast.contextMenu.contentRatingFailed', { message: error.message }, 'error');
                 }
             });
         });
@@ -146,7 +147,7 @@ export const ModelContextMenuMixin = {
                 const data = await response.json();
                 
                 if (data.success) {
-                    showToast('Model successfully re-linked to Civitai', 'success');
+                    showToast('toast.contextMenu.relinkSuccess', {}, 'success');
                     // Reload the current view to show updated data
                     await this.resetAndReload();
                 } else {
@@ -154,7 +155,7 @@ export const ModelContextMenuMixin = {
                 }
             } catch (error) {
                 console.error('Error re-linking model:', error);
-                showToast(`Error: ${error.message}`, 'error');
+                showToast('toast.contextMenu.relinkFailed', { message: error.message }, 'error');
             } finally {
                 state.loadingManager.hide();
             }
@@ -202,15 +203,18 @@ export const ModelContextMenuMixin = {
             case 'preview':
                 openExampleImagesFolder(this.currentCard.dataset.sha256);
                 return true;
+            case 'download-examples':
+                this.downloadExampleImages();
+                return true;
             case 'civitai':
                 if (this.currentCard.dataset.from_civitai === 'true') {
                     if (this.currentCard.querySelector('.fa-globe')) {
                         this.currentCard.querySelector('.fa-globe').click();
                     } else {
-                        showToast('Please fetch metadata from CivitAI first', 'info');
+                        showToast('toast.contextMenu.fetchMetadataFirst', {}, 'info');
                     }
                 } else {
-                    showToast('No CivitAI information available', 'info');
+                    showToast('toast.contextMenu.noCivitaiInfo', {}, 'info');
                 }
                 return true;
             case 'relink-civitai':
@@ -222,5 +226,21 @@ export const ModelContextMenuMixin = {
             default:
                 return false;
         }
+    },
+
+    // Download example images method
+    async downloadExampleImages() {
+        const modelHash = this.currentCard.dataset.sha256;
+        if (!modelHash) {
+            showToast('toast.contextMenu.missingHash', {}, 'error');
+            return;
+        }
+
+        try { 
+            const apiClient = getModelApiClient();
+            await apiClient.downloadExampleImages([modelHash]);
+        } catch (error) {
+            console.error('Error downloading example images:', error);
+        }
     }
 };

static/js/components/ContextMenu/RecipeContextMenu.js

@@ -99,7 +99,7 @@ export class RecipeContextMenu extends BaseContextMenu {
     copyRecipeSyntax() {
         const recipeId = this.currentCard.dataset.id;
         if (!recipeId) {
-            showToast('Cannot copy recipe: Missing recipe ID', 'error');
+            showToast('recipes.contextMenu.copyRecipe.missingId', {}, 'error');
             return;
         }
 
@@ -114,7 +114,7 @@ export class RecipeContextMenu extends BaseContextMenu {
             })
             .catch(err => {
                 console.error('Failed to copy recipe syntax: ', err);
-                showToast('Failed to copy recipe syntax', 'error');
+                showToast('recipes.contextMenu.copyRecipe.failed', {}, 'error');
             });
     }
     
@@ -122,7 +122,7 @@ export class RecipeContextMenu extends BaseContextMenu {
     sendRecipeToWorkflow(replaceMode) {
         const recipeId = this.currentCard.dataset.id;
         if (!recipeId) {
-            showToast('Cannot send recipe: Missing recipe ID', 'error');
+            showToast('recipes.contextMenu.sendRecipe.missingId', {}, 'error');
             return;
         }
 
@@ -137,14 +137,14 @@ export class RecipeContextMenu extends BaseContextMenu {
             })
             .catch(err => {
                 console.error('Failed to send recipe to workflow: ', err);
-                showToast('Failed to send recipe to workflow', 'error');
+                showToast('recipes.contextMenu.sendRecipe.failed', {}, 'error');
             });
     }
     
     // View all LoRAs in the recipe
     viewRecipeLoRAs(recipeId) {
         if (!recipeId) {
-            showToast('Cannot view LoRAs: Missing recipe ID', 'error');
+            showToast('recipes.contextMenu.viewLoras.missingId', {}, 'error');
             return;
         }
         
@@ -171,19 +171,19 @@ export class RecipeContextMenu extends BaseContextMenu {
                     // Navigate to the LoRAs page
                     window.location.href = '/loras';
                 } else {
-                    showToast('No LoRAs found in this recipe', 'info');
+                    showToast('recipes.contextMenu.viewLoras.noLorasFound', {}, 'info');
                 }
             })
             .catch(error => {
                 console.error('Error loading recipe LoRAs:', error);
-                showToast('Error loading recipe LoRAs: ' + error.message, 'error');
+                showToast('recipes.contextMenu.viewLoras.loadError', { message: error.message }, 'error');
             });
     }
     
     // Download missing LoRAs
     async downloadMissingLoRAs(recipeId) {
         if (!recipeId) {
-            showToast('Cannot download LoRAs: Missing recipe ID', 'error');
+            showToast('recipes.contextMenu.downloadMissing.missingId', {}, 'error');
             return;
         }
         
@@ -196,7 +196,7 @@ export class RecipeContextMenu extends BaseContextMenu {
             const missingLoras = recipe.loras.filter(lora => !lora.inLibrary && !lora.isDeleted);
             
             if (missingLoras.length === 0) {
-                showToast('No missing LoRAs to download', 'info');
+                showToast('recipes.contextMenu.downloadMissing.noMissingLoras', {}, 'info');
                 return;
             }
             
@@ -234,7 +234,7 @@ export class RecipeContextMenu extends BaseContextMenu {
             const validLoras = lorasWithVersionInfo.filter(lora => lora !== null);
             
             if (validLoras.length === 0) {
-                showToast('Failed to get information for missing LoRAs', 'error');
+                showToast('recipes.contextMenu.downloadMissing.getInfoFailed', {}, 'error');
                 return;
             }
             
@@ -275,7 +275,7 @@ export class RecipeContextMenu extends BaseContextMenu {
             window.importManager.downloadMissingLoras(recipeData, recipeId);
         } catch (error) {
             console.error('Error downloading missing LoRAs:', error);
-            showToast('Error preparing LoRAs for download: ' + error.message, 'error');
+            showToast('recipes.contextMenu.downloadMissing.prepareError', { message: error.message }, 'error');
         } finally {
             if (state.loadingManager) {
                 state.loadingManager.hide();

static/js/components/ContextMenu/index.js

@@ -2,4 +2,25 @@ export { LoraContextMenu } from './LoraContextMenu.js';
 export { RecipeContextMenu } from './RecipeContextMenu.js';
 export { CheckpointContextMenu } from './CheckpointContextMenu.js';
 export { EmbeddingContextMenu } from './EmbeddingContextMenu.js';
-export { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
+export { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
+
+import { LoraContextMenu } from './LoraContextMenu.js';
+import { RecipeContextMenu } from './RecipeContextMenu.js';
+import { CheckpointContextMenu } from './CheckpointContextMenu.js';
+import { EmbeddingContextMenu } from './EmbeddingContextMenu.js';
+
+// Factory method to create page-specific context menu instances
+export function createPageContextMenu(pageType) {
+    switch (pageType) {
+        case 'loras':
+            return new LoraContextMenu();
+        case 'recipes':
+            return new RecipeContextMenu();
+        case 'checkpoints':
+            return new CheckpointContextMenu();
+        case 'embeddings':
+            return new EmbeddingContextMenu();
+        default:
+            return null;
+    }
+}

static/js/components/DuplicatesManager.js

@@ -26,7 +26,7 @@ export class DuplicatesManager {
             this.duplicateGroups = data.duplicate_groups || [];
             
             if (this.duplicateGroups.length === 0) {
-                showToast('No duplicate recipes found', 'info');
+                showToast('toast.duplicates.noDuplicatesFound', { type: 'recipes' }, 'info');
                 return false;
             }
             
@@ -34,7 +34,7 @@ export class DuplicatesManager {
             return true;
         } catch (error) {
             console.error('Error finding duplicates:', error);
-            showToast('Failed to find duplicates: ' + error.message, 'error');
+            showToast('toast.duplicates.findFailed', { message: error.message }, 'error');
             return false;
         }
     }
@@ -325,7 +325,7 @@ export class DuplicatesManager {
     
     async deleteSelectedDuplicates() {
         if (this.selectedForDeletion.size === 0) {
-            showToast('No recipes selected for deletion', 'info');
+            showToast('toast.duplicates.noItemsSelected', { type: 'recipes' }, 'info');
             return;
         }
         
@@ -340,7 +340,7 @@ export class DuplicatesManager {
             modalManager.showModal('duplicateDeleteModal');
         } catch (error) {
             console.error('Error preparing delete:', error);
-            showToast('Error: ' + error.message, 'error');
+            showToast('toast.duplicates.deleteError', { message: error.message }, 'error');
         }
     }
     
@@ -371,7 +371,7 @@ export class DuplicatesManager {
                 throw new Error(data.error || 'Unknown error deleting recipes');
             }
             
-            showToast(`Successfully deleted ${data.total_deleted} recipes`, 'success');
+            showToast('toast.duplicates.deleteSuccess', { count: data.total_deleted, type: 'recipes' }, 'success');
             
             // Exit duplicate mode if deletions were successful
             if (data.total_deleted > 0) {
@@ -380,7 +380,7 @@ export class DuplicatesManager {
             
         } catch (error) {
             console.error('Error deleting recipes:', error);
-            showToast('Failed to delete recipes: ' + error.message, 'error');
+            showToast('toast.duplicates.deleteFailed', { type: 'recipes', message: error.message }, 'error');
         }
     }
 }