feat: update release notes and version to 0.8.18 with new features and improvements

Don't crash completely if a symlink resolve fails

.github/FUNDING.yml

@@ -0,0 +1,4 @@
+# These are supported funding model platforms
+
+ko_fi: pixelpawsai
+custom: ['paypal.me/pixelpawsai']

.gitignore

@@ -3,3 +3,4 @@ settings.json
 output/*
 py/run_test.py
 .vscode/
+cache/

README.md

@@ -10,16 +10,63 @@ A comprehensive toolset that streamlines organizing, downloading, and applying L
 
 ![Interface Preview](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/static/images/screenshot.png)
 
+One-click Integration:
+![One-Click Integration](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/static/images/one-click-send.jpg)
+
 ## 📺 Tutorial: One-Click LoRA Integration
 Watch this quick tutorial to learn how to use the new one-click LoRA integration feature:
 
-[![One-Click LoRA Integration Tutorial](https://img.youtube.com/vi/qS95OjX3e70/0.jpg)](https://youtu.be/qS95OjX3e70)
-[![LoRA Manager v0.8.10 - Checkpoint Management, Standalone Mode, and New Features!](https://img.youtube.com/vi/VKvTlCB78h4/0.jpg)](https://youtu.be/VKvTlCB78h4)
+[![One-Click LoRA Integration Tutorial](https://img.youtube.com/vi/hvKw31YpE-U/0.jpg)](https://youtu.be/hvKw31YpE-U)
 
 ---
 
 ## Release Notes
 
+### v0.8.18
+* **Custom Example Images** - Added ability to import your own example images for LoRAs and checkpoints with automatic metadata extraction from embedded information
+* **Enhanced Example Management** - New action buttons to set specific examples as previews or delete custom examples
+* **Improved Duplicate Detection** - Enhanced "Find Duplicates" with hash verification feature to eliminate false positives when identifying duplicate models
+* **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
+* **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 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
+* **Improved TriggerWord Control** - Enhanced TriggerWord Toggle node with new default_active switch to set the initial state (active/inactive) when trigger words are added
+* **Centralized Example Management** - Added "Migrate Existing Example Images" feature to consolidate downloaded example images from model folders into central storage with customizable naming patterns
+* **Intelligent Word Suggestions** - Implemented smart trigger word suggestions by reading class tokens and tag frequency from safetensors files, displaying recommendations when editing trigger words
+* **Model Version Management** - Added "Re-link to CivitAI" context menu option for connecting models to different CivitAI versions when needed
+
+### v0.8.16
+* **Dramatic Startup Speed Improvement** - Added cache serialization mechanism for significantly faster loading times, especially beneficial for large model collections
+* **Enhanced Refresh Options** - Extended functionality with "Full Rebuild (complete)" option alongside "Quick Refresh (incremental)" to fix potential memory cache issues without requiring application restart
+* **Customizable Display Density** - Replaced compact mode with adjustable display density settings for personalized layout customization
+* **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
+* **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
+* **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)"
+* **Improved LoRA Loader Controls** - Added header drag functionality for proportional strength adjustment of all LoRAs simultaneously (including CLIP strengths when expanded)
+* **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
+
+### v0.8.14
+* **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
+* **Compact Display Mode** - Added space-efficient view option that displays more cards per row (7 on 1080p, 8 on 2K, 10 on 4K)
+* **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
+
+### 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
@@ -37,71 +84,6 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 * **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
 
-### v0.8.9
-* **Favorites System** - New functionality to bookmark your favorite LoRAs and checkpoints for quick access and better organization
-* **Enhanced UI Controls** - Increased model card button sizes for improved usability and easier interaction
-* **Smoother Page Transitions** - Optimized interface switching between pages, eliminating flash issues particularly noticeable in dark theme
-* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
-
-### v0.8.8
-* **Real-time TriggerWord Updates** - Enhanced TriggerWord Toggle node to instantly update when connected Lora Loader or Lora Stacker nodes change, without requiring workflow execution
-* **Optimized Metadata Recovery** - Improved utilization of existing .civitai.info files for faster initialization and preservation of metadata from models deleted from CivitAI
-* **Migration Acceleration** - Further speed improvements for users transitioning from A1111/Forge environments
-* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
-
-### v0.8.7
-* **Enhanced Context Menu** - Added comprehensive context menu functionality to Recipes and Checkpoints pages for improved workflow
-* **Interactive LoRA Strength Control** - Implemented drag functionality in LoRA Loader for intuitive strength adjustment
-* **Metadata Collector Overhaul** - Rebuilt metadata collection system with optimized architecture for better performance
-* **Improved Save Image Node** - Enhanced metadata capture and image saving performance with the new metadata collector
-* **Streamlined Recipe Saving** - Optimized Save Recipe functionality to work independently without requiring Preview Image nodes
-* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
-
-### v0.8.6 Major Update
-* **Checkpoint Management** - Added comprehensive management for model checkpoints including scanning, searching, filtering, and deletion
-* **Enhanced Metadata Support** - New capabilities for retrieving and managing checkpoint metadata with improved operations
-* **Improved Initial Loading** - Optimized cache initialization with visual progress indicators for better user experience
-
-### v0.8.5
-* **Enhanced LoRA & Recipe Connectivity** - Added Recipes tab in LoRA details to see all recipes using a specific LoRA
-* **Improved Navigation** - New shortcuts to jump between related LoRAs and Recipes with one-click navigation
-* **Video Preview Controls** - Added "Autoplay Videos on Hover" setting to optimize performance and reduce resource usage
-* **UI Experience Refinements** - Smoother transitions between related content pages
-
-### v0.8.4
-* **Node Layout Improvements** - Fixed layout issues with LoRA Loader and Trigger Words Toggle nodes in newer ComfyUI frontend versions
-* **Recipe LoRA Reconnection** - Added ability to reconnect deleted LoRAs in recipes by clicking the "deleted" badge in recipe details
-* **Bug Fixes & Stability** - Resolved various issues for improved reliability
-
-### v0.8.3
-* **Enhanced Workflow Parser** - Rebuilt workflow analysis engine with improved support for ComfyUI core nodes and easier extensibility
-* **Improved Recipe System** - Refined the experimental Save Recipe functionality with better workflow integration
-* **New Save Image Node** - Added experimental node with metadata support for perfect CivitAI compatibility
-  * Supports dynamic filename prefixes with variables [1](https://github.com/nkchocoai/ComfyUI-SaveImageWithMetaData?tab=readme-ov-file#filename_prefix)
-* **Default LoRA Root Setting** - Added configuration option for setting your preferred LoRA directory
-
-### v0.8.2  
-* **Faster Initialization for Forge Users** - Improved first-run efficiency by utilizing existing `.json` and `.civitai.info` files from Forge’s CivitAI helper extension, making migration smoother.  
-* **LoRA Filename Editing** - Added support for renaming LoRA files directly within LoRA Manager.  
-* **Recipe Editing** - Users can now edit recipe names and tags.  
-* **Retain Deleted LoRAs in Recipes** - Deleted LoRAs will remain listed in recipes, allowing future functionality to reconnect them once re-obtained.  
-* **Download Missing LoRAs from Recipes** - Easily fetch missing LoRAs associated with a recipe.
-
-### v0.8.1
-* **Base Model Correction** - Added support for modifying base model associations to fix incorrect metadata for non-CivitAI LoRAs
-* **LoRA Loader Flexibility** - Made CLIP input optional for model-only workflows like Hunyuan video generation
-* **Expanded Recipe Support** - Added compatibility with 3 additional recipe metadata formats
-* **Enhanced Showcase Images** - Generation parameters now displayed alongside LoRA preview images
-* **UI Improvements & Bug Fixes** - Various interface refinements and stability enhancements
-
-### v0.8.0
-* **Introduced LoRA Recipes** - Create, import, save, and share your favorite LoRA combinations
-* **Recipe Management System** - Easily browse, search, and organize your LoRA recipes
-* **Workflow Integration** - Save recipes directly from your workflow with generation parameters preserved
-* **Simplified Workflow Application** - Quickly apply saved recipes to new projects
-* **Enhanced UI & UX** - Improved interface design and user experience
-* **Bug Fixes & Stability** - Resolved various issues and enhanced overall performance
-
 [View Update History](./update_logs.md)
 
 ---
@@ -166,7 +148,7 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 
 ### Option 2: **Portable Standalone Edition** (No ComfyUI required)
 
-1. Download the [Portable Package](https://github.com/willmiao/ComfyUI-Lora-Manager/releases/download/v0.8.10/lora_manager_portable.7z)
+1. Download the [Portable Package](https://github.com/willmiao/ComfyUI-Lora-Manager/releases/download/v0.8.15/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

py/lora_manager.py

@@ -1,5 +1,10 @@
 import asyncio
+import sys
+import os
+import logging
+from pathlib import Path
 from server import PromptServer # type: ignore
+
 from .config import config
 from .routes.lora_routes import LoraRoutes
 from .routes.api_routes import ApiRoutes
@@ -7,11 +12,10 @@ from .routes.recipe_routes import RecipeRoutes
 from .routes.checkpoints_routes import CheckpointsRoutes
 from .routes.update_routes import UpdateRoutes
 from .routes.misc_routes import MiscRoutes
+from .routes.example_images_routes import ExampleImagesRoutes
 from .services.service_registry import ServiceRegistry
 from .services.settings_manager import settings
-import logging
-import sys
-import os
+from .utils.example_images_migration import ExampleImagesMigration
 
 logger = logging.getLogger(__name__)
 
@@ -93,10 +97,14 @@ class LoraManager:
                     route_path = f'/loras_static/link_{link_idx["lora"]}/preview'
                     link_idx["lora"] += 1
                 
-                app.router.add_static(route_path, target_path)
-                logger.info(f"Added static route for link target {route_path} -> {target_path}")
-                config.add_route_mapping(target_path, route_path)
-                added_targets.add(target_path)
+                try:
+                    app.router.add_static(route_path, Path(target_path).resolve(strict=False))
+                    logger.info(f"Added static route for link target {route_path} -> {target_path}")
+                    config.add_route_mapping(target_path, route_path)
+                    added_targets.add(target_path)
+                except Exception as e:
+                    logger.warning(f"Failed to add static route on initialization for {target_path}: {e}")
+                    continue
         
         # Add static route for plugin assets
         app.router.add_static('/loras_static', config.static_path)
@@ -112,6 +120,7 @@ class LoraManager:
         RecipeRoutes.setup_routes(app)
         UpdateRoutes.setup_routes(app)  
         MiscRoutes.setup_routes(app)  # Register miscellaneous routes
+        ExampleImagesRoutes.setup_routes(app)  # Register example images routes
         
         # Schedule service initialization 
         app.on_startup.append(lambda app: cls._initialize_services())
@@ -128,26 +137,13 @@ class LoraManager:
             logging.getLogger('aiohttp.access').setLevel(logging.WARNING)
             
             # Initialize CivitaiClient first to ensure it's ready for other services
-            civitai_client = await ServiceRegistry.get_civitai_client()
-            
-            # Get file monitors through ServiceRegistry
-            lora_monitor = await ServiceRegistry.get_lora_monitor()
-            checkpoint_monitor = await ServiceRegistry.get_checkpoint_monitor()
-            
-            # Start monitors
-            lora_monitor.start()
-            logger.debug("Lora monitor started")
-            
-            # Make sure checkpoint monitor has paths before starting
-            await checkpoint_monitor.initialize_paths()
-            checkpoint_monitor.start()
-            logger.debug("Checkpoint monitor started")
+            await ServiceRegistry.get_civitai_client()
 
             # Register DownloadManager with ServiceRegistry
-            download_manager = await ServiceRegistry.get_download_manager()
+            await ServiceRegistry.get_download_manager()
             
             # Initialize WebSocket manager
-            ws_manager = await ServiceRegistry.get_websocket_manager()
+            await ServiceRegistry.get_websocket_manager()
             
             # Initialize scanners in background
             lora_scanner = await ServiceRegistry.get_lora_scanner()
@@ -166,6 +162,8 @@ class LoraManager:
             asyncio.create_task(lora_scanner.initialize_in_background(), name='lora_cache_init')
             asyncio.create_task(checkpoint_scanner.initialize_in_background(), name='checkpoint_cache_init')
             asyncio.create_task(recipe_scanner.initialize_in_background(), name='recipe_cache_init')
+
+            await ExampleImagesMigration.check_and_run_migrations()
             
             logger.info("LoRA Manager: All services initialized and background tasks scheduled")
                 
@@ -177,17 +175,6 @@ class LoraManager:
         """Cleanup resources using ServiceRegistry"""
         try:
             logger.info("LoRA Manager: Cleaning up services")
-            
-            # Get monitors from ServiceRegistry
-            lora_monitor = await ServiceRegistry.get_service("lora_monitor")
-            if lora_monitor:
-                lora_monitor.stop()
-                logger.info("Stopped LoRA monitor")
-                
-            checkpoint_monitor = await ServiceRegistry.get_service("checkpoint_monitor")
-            if checkpoint_monitor:
-                checkpoint_monitor.stop()
-                logger.info("Stopped checkpoint monitor")
                 
             # Close CivitaiClient gracefully
             civitai_client = await ServiceRegistry.get_service("civitai_client")

py/metadata_collector/constants.py

@@ -1,7 +1,5 @@
 """Constants used by the metadata collector"""
 
-# Metadata collection constants
-
 # Metadata categories
 MODELS = "models"
 PROMPTS = "prompts"
@@ -9,6 +7,7 @@ SAMPLING = "sampling"
 LORAS = "loras"
 SIZE = "size"
 IMAGES = "images"
+IS_SAMPLER = "is_sampler"  # New constant to mark sampler nodes
 
 # Complete list of categories to track
 METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES]

py/metadata_collector/metadata_processor.py

@@ -4,33 +4,109 @@ import sys
 # Check if running in standalone mode
 standalone_mode = 'nodes' not in sys.modules
 
-from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE
+from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IS_SAMPLER
 
 class MetadataProcessor:
     """Process and format collected metadata"""
     
     @staticmethod
-    def find_primary_sampler(metadata):
-        """Find the primary KSampler node (with highest denoise value)"""
+    def find_primary_sampler(metadata, downstream_id=None):
+        """
+        Find the primary KSampler node that executed before the given downstream node
+        
+        Parameters:
+        - metadata: The workflow metadata
+        - downstream_id: Optional ID of a downstream node to help identify the specific primary sampler
+        """
+        # If we have a downstream_id and execution_order, use it to narrow down potential samplers
+        if downstream_id and "execution_order" in metadata:
+            execution_order = metadata["execution_order"]
+            
+            # Find the index of the downstream node in the execution order
+            if downstream_id in execution_order:
+                downstream_index = execution_order.index(downstream_id)
+                
+                # Extract all sampler nodes that executed before the downstream node
+                candidate_samplers = {}
+                for i in range(downstream_index):
+                    node_id = execution_order[i]
+                    # Use IS_SAMPLER flag to identify true sampler nodes
+                    if node_id in metadata.get(SAMPLING, {}) and metadata[SAMPLING][node_id].get(IS_SAMPLER, False):
+                        candidate_samplers[node_id] = metadata[SAMPLING][node_id]
+                
+                # If we found candidate samplers, apply primary sampler logic to these candidates only
+                if candidate_samplers:
+                    # Collect potential primary samplers based on different criteria
+                    custom_advanced_samplers = []
+                    advanced_add_noise_samplers = []
+                    high_denoise_samplers = []
+                    max_denoise = -1
+                    high_denoise_id = None
+                    
+                    # First, check for SamplerCustomAdvanced among candidates
+                    prompt = metadata.get("current_prompt")
+                    if prompt and prompt.original_prompt:
+                        for node_id in candidate_samplers:
+                            node_info = prompt.original_prompt.get(node_id, {})
+                            if node_info.get("class_type") == "SamplerCustomAdvanced":
+                                custom_advanced_samplers.append(node_id)
+                    
+                    # Next, check for KSamplerAdvanced with add_noise="enable" among candidates
+                    for node_id, sampler_info in candidate_samplers.items():
+                        parameters = sampler_info.get("parameters", {})
+                        add_noise = parameters.get("add_noise")
+                        if add_noise == "enable":
+                            advanced_add_noise_samplers.append(node_id)
+                    
+                    # Find the sampler with highest denoise value among candidates
+                    for node_id, sampler_info in candidate_samplers.items():
+                        parameters = sampler_info.get("parameters", {})
+                        denoise = parameters.get("denoise")
+                        if denoise is not None and denoise > max_denoise:
+                            max_denoise = denoise
+                            high_denoise_id = node_id
+                    
+                    if high_denoise_id:
+                        high_denoise_samplers.append(high_denoise_id)
+                    
+                    # Combine all potential primary samplers
+                    potential_samplers = custom_advanced_samplers + advanced_add_noise_samplers + high_denoise_samplers
+                    
+                    # Find the most recent potential primary sampler (closest to downstream node)
+                    for i in range(downstream_index - 1, -1, -1):
+                        node_id = execution_order[i]
+                        if node_id in potential_samplers:
+                            return node_id, candidate_samplers[node_id]
+                    
+                    # If no potential sampler found from our criteria, return the most recent sampler
+                    if candidate_samplers:
+                        for i in range(downstream_index - 1, -1, -1):
+                            node_id = execution_order[i]
+                            if node_id in candidate_samplers:
+                                return node_id, candidate_samplers[node_id]
+        
+        # If no downstream_id provided or no suitable sampler found, fall back to original logic
         primary_sampler = None
         primary_sampler_id = None
-        max_denoise = -1  # Track the highest denoise value
+        max_denoise = -1
         
         # First, check for SamplerCustomAdvanced
         prompt = metadata.get("current_prompt")
         if prompt and prompt.original_prompt:
             for node_id, node_info in prompt.original_prompt.items():
                 if node_info.get("class_type") == "SamplerCustomAdvanced":
-                    # Found a SamplerCustomAdvanced node
-                    if node_id in metadata.get(SAMPLING, {}):
+                    # Check if the node is in SAMPLING and has IS_SAMPLER flag
+                    if node_id in metadata.get(SAMPLING, {}) and metadata[SAMPLING][node_id].get(IS_SAMPLER, False):
                         return node_id, metadata[SAMPLING][node_id]
         
-        # Next, check for KSamplerAdvanced with add_noise="enable"
+        # Next, check for KSamplerAdvanced with add_noise="enable" using IS_SAMPLER flag
         for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
+            # Skip if not marked as a sampler
+            if not sampler_info.get(IS_SAMPLER, False):
+                continue
+                
             parameters = sampler_info.get("parameters", {})
             add_noise = parameters.get("add_noise")
-            
-            # If add_noise is "enable", this is likely the primary sampler for KSamplerAdvanced
             if add_noise == "enable":
                 primary_sampler = sampler_info
                 primary_sampler_id = node_id
@@ -39,10 +115,12 @@ class MetadataProcessor:
         # If no specialized sampler found, find the sampler with highest denoise value
         if primary_sampler is None:
             for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
+                # Skip if not marked as a sampler
+                if not sampler_info.get(IS_SAMPLER, False):
+                    continue
+                    
                 parameters = sampler_info.get("parameters", {})
                 denoise = parameters.get("denoise")
-                
-                # If denoise exists and is higher than current max, use this sampler
                 if denoise is not None and denoise > max_denoise:
                     max_denoise = denoise
                     primary_sampler = sampler_info
@@ -74,13 +152,18 @@ class MetadataProcessor:
         current_node_id = node_id
         current_input = input_name
         
+        # If we're just tracing to origin (no target_class), keep track of the last valid node
+        last_valid_node = None
+        
         while current_depth < max_depth:
             if current_node_id not in prompt.original_prompt:
-                return None
+                return last_valid_node if not target_class else None
                 
             node_inputs = prompt.original_prompt[current_node_id].get("inputs", {})
             if current_input not in node_inputs:
-                return None
+                # We've reached a node without the specified input - this is our origin node
+                # if we're not looking for a specific target_class
+                return current_node_id if not target_class else None
                 
             input_value = node_inputs[current_input]
             # Input connections are formatted as [node_id, output_index]
@@ -91,9 +174,9 @@ class MetadataProcessor:
                 if target_class and prompt.original_prompt[found_node_id].get("class_type") == target_class:
                     return found_node_id
                 
-                # If we're not looking for a specific class or haven't found it yet
+                # If we're not looking for a specific class, update the last valid node
                 if not target_class:
-                    return found_node_id
+                    last_valid_node = found_node_id
                 
                 # Continue tracing through intermediate nodes
                 current_node_id = found_node_id
@@ -101,16 +184,17 @@ class MetadataProcessor:
                 if "conditioning" in prompt.original_prompt[current_node_id].get("inputs", {}):
                     current_input = "conditioning"
                 else:
-                    # If there's no "conditioning" input, we can't trace further
+                    # If there's no "conditioning" input, return the current node
+                    # if we're not looking for a specific target_class
                     return found_node_id if not target_class else None
             else:
                 # We've reached a node with no further connections
-                return None
+                return last_valid_node if not target_class else None
             
             current_depth += 1
             
         # If we've reached max depth without finding target_class
-        return None
+        return last_valid_node if not target_class else None
     
     @staticmethod
     def find_primary_checkpoint(metadata):
@@ -126,8 +210,52 @@ class MetadataProcessor:
         return None
     
     @staticmethod
-    def extract_generation_params(metadata):
-        """Extract generation parameters from metadata using node relationships"""
+    def match_conditioning_to_prompts(metadata, sampler_id):
+        """
+        Match conditioning objects from a sampler to prompts in metadata
+        
+        Parameters:
+        - metadata: The workflow metadata
+        - sampler_id: ID of the sampler node to match
+        
+        Returns:
+        - Dictionary with 'prompt' and 'negative_prompt' if found
+        """
+        result = {
+            "prompt": "",
+            "negative_prompt": ""
+        }
+        
+        # Check if we have stored conditioning objects for this sampler
+        if sampler_id in metadata.get(PROMPTS, {}) and (
+            "pos_conditioning" in metadata[PROMPTS][sampler_id] or 
+            "neg_conditioning" in metadata[PROMPTS][sampler_id]):
+            
+            pos_conditioning = metadata[PROMPTS][sampler_id].get("pos_conditioning")
+            neg_conditioning = metadata[PROMPTS][sampler_id].get("neg_conditioning")
+            
+            # Try to match conditioning objects with those stored by CLIPTextEncodeExtractor
+            for prompt_node_id, prompt_data in metadata[PROMPTS].items():
+                if "conditioning" not in prompt_data:
+                    continue
+                    
+                if pos_conditioning is not None and id(prompt_data["conditioning"]) == id(pos_conditioning):
+                    result["prompt"] = prompt_data.get("text", "")
+                
+                if neg_conditioning is not None and id(prompt_data["conditioning"]) == id(neg_conditioning):
+                    result["negative_prompt"] = prompt_data.get("text", "")
+            
+        return result
+    
+    @staticmethod
+    def extract_generation_params(metadata, id=None):
+        """
+        Extract generation parameters from metadata using node relationships
+        
+        Parameters:
+        - metadata: The workflow metadata
+        - id: Optional ID of a downstream node to help identify the specific primary sampler
+        """
         params = {
             "prompt": "",
             "negative_prompt": "",
@@ -147,13 +275,20 @@ class MetadataProcessor:
         prompt = metadata.get("current_prompt")
         
         # Find the primary KSampler node
-        primary_sampler_id, primary_sampler = MetadataProcessor.find_primary_sampler(metadata)
+        primary_sampler_id, primary_sampler = MetadataProcessor.find_primary_sampler(metadata, id)
         
         # Directly get checkpoint from metadata instead of tracing
         checkpoint = MetadataProcessor.find_primary_checkpoint(metadata)
         if checkpoint:
             params["checkpoint"] = checkpoint
         
+        # Check if guidance parameter exists in any sampling node
+        for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
+            parameters = sampler_info.get("parameters", {})
+            if "guidance" in parameters and parameters["guidance"] is not None:
+                params["guidance"] = parameters["guidance"]
+                break
+        
         if primary_sampler:
             # Extract sampling parameters
             sampling_params = primary_sampler.get("parameters", {})
@@ -164,7 +299,6 @@ class MetadataProcessor:
             params["sampler"] = sampling_params.get("sampler_name")
             params["scheduler"] = sampling_params.get("scheduler")
             
-            # Trace connections from the primary sampler
             if prompt and primary_sampler_id:
                 # Check if this is a SamplerCustomAdvanced node
                 is_custom_advanced = False
@@ -187,56 +321,61 @@ class MetadataProcessor:
                         sampler_params = metadata[SAMPLING][sampler_node_id].get("parameters", {})
                         params["sampler"] = sampler_params.get("sampler_name")
                     
-                    # 3. Trace guider input for FluxGuidance and CLIPTextEncode
+                    # 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:
-                        # Look for FluxGuidance along the guider path
-                        flux_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", "FluxGuidance", max_depth=5)
-                        if flux_node_id and flux_node_id in metadata.get(SAMPLING, {}):
-                            flux_params = metadata[SAMPLING][flux_node_id].get("parameters", {})
-                            params["guidance"] = flux_params.get("guidance")
-                        
-                        # Find CLIPTextEncode for positive prompt (through conditioning)
-                        positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", "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", "")
+                    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:
-                    # Original tracing for standard samplers
-                    # Trace positive prompt - look specifically for CLIPTextEncode
-                    positive_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_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", "")
-                    else:
-                        # If CLIPTextEncode is not found, try to find CLIPTextEncodeFlux
-                        positive_flux_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "CLIPTextEncodeFlux", max_depth=10)
-                        if positive_flux_node_id and positive_flux_node_id in metadata.get(PROMPTS, {}):
-                            params["prompt"] = metadata[PROMPTS][positive_flux_node_id].get("text", "")
-                            
-                            # Also extract guidance value if present in the sampling data
-                            if positive_flux_node_id in metadata.get(SAMPLING, {}):
-                                flux_params = metadata[SAMPLING][positive_flux_node_id].get("parameters", {})
-                                if "guidance" in flux_params:
-                                    params["guidance"] = flux_params.get("guidance")
-                    
-                    # Find any FluxGuidance nodes in the positive conditioning path
-                    flux_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "FluxGuidance", max_depth=5)
-                    if flux_node_id and flux_node_id in metadata.get(SAMPLING, {}):
-                        flux_params = metadata[SAMPLING][flux_node_id].get("parameters", {})
-                        params["guidance"] = flux_params.get("guidance")
-                    
-                    # Trace negative prompt - look specifically for CLIPTextEncode
-                    negative_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_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", "")
+                    # For standard samplers, match conditioning objects to prompts
+                    prompt_results = MetadataProcessor.match_conditioning_to_prompts(metadata, primary_sampler_id)
+                    params["prompt"] = prompt_results["prompt"]
+                    params["negative_prompt"] = prompt_results["negative_prompt"]
+
+                    # If prompts were still not found, fall back to tracing connections
+                    if not params["prompt"]:
+                        # Original tracing for standard samplers
+                        # Trace positive prompt - look specifically for CLIPTextEncode
+                        positive_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", 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:
+                            # If CLIPTextEncode is not found, try to find CLIPTextEncodeFlux
+                            positive_flux_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "CLIPTextEncodeFlux", max_depth=10)
+                            if positive_flux_node_id and positive_flux_node_id in metadata.get(PROMPTS, {}):
+                                params["prompt"] = metadata[PROMPTS][positive_flux_node_id].get("text", "")
+                        
+                        # Trace negative prompt - look specifically for CLIPTextEncode
+                        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", "")
                 
-                # Size extraction is same for all sampler types
-                # Check if the sampler itself has size information (from latent_image)
-                if primary_sampler_id in metadata.get(SIZE, {}):
-                    width = metadata[SIZE][primary_sampler_id].get("width")
-                    height = metadata[SIZE][primary_sampler_id].get("height")
-                    if width and height:
-                        params["size"] = f"{width}x{height}"
+            # Size extraction is same for all sampler types
+            # Check if the sampler itself has size information (from latent_image)
+            if primary_sampler_id in metadata.get(SIZE, {}):
+                width = metadata[SIZE][primary_sampler_id].get("width")
+                height = metadata[SIZE][primary_sampler_id].get("height")
+                if width and height:
+                    params["size"] = f"{width}x{height}"
         
         # Extract LoRAs using the standardized format
         lora_parts = []
@@ -256,13 +395,19 @@ class MetadataProcessor:
         return params
     
     @staticmethod
-    def to_dict(metadata):
-        """Convert extracted metadata to the ComfyUI output.json format"""           
+    def to_dict(metadata, id=None):
+        """
+        Convert extracted metadata to the ComfyUI output.json format
+        
+        Parameters:
+        - metadata: The workflow metadata
+        - id: Optional ID of a downstream node to help identify the specific primary sampler
+        """              
         if standalone_mode:
             # Return empty dictionary in standalone mode
             return {}
         
-        params = MetadataProcessor.extract_generation_params(metadata)
+        params = MetadataProcessor.extract_generation_params(metadata, id)
         
         # Convert all values to strings to match output.json format
         for key in params:
@@ -272,7 +417,7 @@ class MetadataProcessor:
         return params
     
     @staticmethod
-    def to_json(metadata):
+    def to_json(metadata, id=None):
         """Convert metadata to JSON string"""
-        params = MetadataProcessor.to_dict(metadata)
+        params = MetadataProcessor.to_dict(metadata, id)
         return json.dumps(params, indent=4)

py/metadata_collector/node_extractors.py

@@ -1,6 +1,6 @@
 import os
 
-from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES
+from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER
 
 
 class NodeMetadataExtractor:
@@ -47,6 +47,13 @@ class CLIPTextEncodeExtractor(NodeMetadataExtractor):
             "text": text,
             "node_id": node_id
         }
+
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        if outputs and isinstance(outputs, list) and len(outputs) > 0:
+            if isinstance(outputs[0], tuple) and len(outputs[0]) > 0:
+                conditioning = outputs[0][0]
+                metadata[PROMPTS][node_id]["conditioning"] = conditioning
         
 class SamplerExtractor(NodeMetadataExtractor):
     @staticmethod
@@ -61,8 +68,21 @@ class SamplerExtractor(NodeMetadataExtractor):
                 
         metadata[SAMPLING][node_id] = {
             "parameters": sampling_params,
-            "node_id": node_id
+            "node_id": node_id,
+            IS_SAMPLER: True  # Add sampler flag
         }
+
+        # Store the conditioning objects directly in metadata for later matching
+        pos_conditioning = inputs.get("positive", None)
+        neg_conditioning = inputs.get("negative", None)
+
+        # Save conditioning objects in metadata for later matching
+        if pos_conditioning is not None or neg_conditioning is not None:
+            if node_id not in metadata[PROMPTS]:
+                metadata[PROMPTS][node_id] = {"node_id": node_id}
+            
+            metadata[PROMPTS][node_id]["pos_conditioning"] = pos_conditioning
+            metadata[PROMPTS][node_id]["neg_conditioning"] = neg_conditioning
         
         # Extract latent image dimensions if available
         if "latent_image" in inputs and inputs["latent_image"] is not None:
@@ -98,9 +118,22 @@ class KSamplerAdvancedExtractor(NodeMetadataExtractor):
                 
         metadata[SAMPLING][node_id] = {
             "parameters": sampling_params,
-            "node_id": node_id
+            "node_id": node_id,
+            IS_SAMPLER: True  # Add sampler flag
         }
         
+        # Store the conditioning objects directly in metadata for later matching
+        pos_conditioning = inputs.get("positive", None)
+        neg_conditioning = inputs.get("negative", None)
+
+        # Save conditioning objects in metadata for later matching
+        if pos_conditioning is not None or neg_conditioning is not None:
+            if node_id not in metadata[PROMPTS]:
+                metadata[PROMPTS][node_id] = {"node_id": node_id}
+            
+            metadata[PROMPTS][node_id]["pos_conditioning"] = pos_conditioning
+            metadata[PROMPTS][node_id]["neg_conditioning"] = neg_conditioning
+        
         # Extract latent image dimensions if available
         if "latent_image" in inputs and inputs["latent_image"] is not None:
             latent = inputs["latent_image"]
@@ -269,7 +302,8 @@ class KSamplerSelectExtractor(NodeMetadataExtractor):
                 
         metadata[SAMPLING][node_id] = {
             "parameters": sampling_params,
-            "node_id": node_id
+            "node_id": node_id,
+            IS_SAMPLER: False  # Mark as non-primary sampler
         }
 
 class BasicSchedulerExtractor(NodeMetadataExtractor):
@@ -285,7 +319,8 @@ class BasicSchedulerExtractor(NodeMetadataExtractor):
                 
         metadata[SAMPLING][node_id] = {
             "parameters": sampling_params,
-            "node_id": node_id
+            "node_id": node_id,
+            IS_SAMPLER: False  # Mark as non-primary sampler
         }
 
 class SamplerCustomAdvancedExtractor(NodeMetadataExtractor):
@@ -303,7 +338,8 @@ class SamplerCustomAdvancedExtractor(NodeMetadataExtractor):
                 
         metadata[SAMPLING][node_id] = {
             "parameters": sampling_params,
-            "node_id": node_id
+            "node_id": node_id,
+            IS_SAMPLER: True  # Add sampler flag
         }
         
         # Extract latent image dimensions if available
@@ -338,11 +374,20 @@ class CLIPTextEncodeFluxExtractor(NodeMetadataExtractor):
         clip_l_text = inputs.get("clip_l", "")
         t5xxl_text = inputs.get("t5xxl", "")
         
-        # Create JSON string with T5 content first, then CLIP-L
-        combined_text = json.dumps({
-            "T5": t5xxl_text,
-            "CLIP-L": clip_l_text
-        })
+        # If both are empty, use empty string
+        if not clip_l_text and not t5xxl_text:
+            combined_text = ""
+        # If one is empty, use the non-empty one
+        elif not clip_l_text:
+            combined_text = t5xxl_text
+        elif not t5xxl_text:
+            combined_text = clip_l_text
+        # If both have content, use JSON format
+        else:
+            combined_text = json.dumps({
+                "T5": t5xxl_text,
+                "CLIP-L": clip_l_text
+            })
         
         metadata[PROMPTS][node_id] = {
             "text": combined_text,
@@ -362,6 +407,30 @@ class CLIPTextEncodeFluxExtractor(NodeMetadataExtractor):
                 
             metadata[SAMPLING][node_id]["parameters"]["guidance"] = guidance_value
 
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        if outputs and isinstance(outputs, list) and len(outputs) > 0:
+            if isinstance(outputs[0], tuple) and len(outputs[0]) > 0:
+                conditioning = outputs[0][0]
+                metadata[PROMPTS][node_id]["conditioning"] = conditioning
+
+class CFGGuiderExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs or "cfg" not in inputs:
+            return
+            
+        cfg_value = inputs.get("cfg")
+        
+        # Store the cfg value in SAMPLING category
+        if SAMPLING not in metadata:
+            metadata[SAMPLING] = {}
+            
+        if node_id not in metadata[SAMPLING]:
+            metadata[SAMPLING][node_id] = {"parameters": {}, "node_id": node_id}
+            
+        metadata[SAMPLING][node_id]["parameters"]["cfg"] = cfg_value
+
 # Registry of node-specific extractors
 NODE_EXTRACTORS = {
     # Sampling
@@ -373,16 +442,20 @@ NODE_EXTRACTORS = {
     "BasicScheduler": BasicSchedulerExtractor,  # Add BasicScheduler
     # Loaders
     "CheckpointLoaderSimple": CheckpointLoaderExtractor,
+    "comfyLoader": CheckpointLoaderExtractor,  # eeasy comfyLoader
     "UNETLoader": UNETLoaderExtractor,          # Updated to use dedicated extractor
+    "UnetLoaderGGUF": UNETLoaderExtractor,  # Updated to use dedicated extractor
     "LoraLoader": LoraLoaderExtractor,
     "LoraManagerLoader": LoraLoaderManagerExtractor,
     # Conditioning
     "CLIPTextEncode": CLIPTextEncodeExtractor,
     "CLIPTextEncodeFlux": CLIPTextEncodeFluxExtractor,  # Add CLIPTextEncodeFlux
+    "WAS_Text_to_Conditioning": CLIPTextEncodeExtractor,
     # Latent
     "EmptyLatentImage": ImageSizeExtractor,
     # Flux
     "FluxGuidance": FluxGuidanceExtractor,      # Add FluxGuidance
+    "CFGGuider": CFGGuiderExtractor,            # Add CFGGuider
     # Image
     "VAEDecode": VAEDecodeExtractor,  # Added VAEDecode extractor
     # Add other nodes as needed

py/nodes/debug_metadata.py

@@ -1,4 +1,5 @@
 import logging
+from server import PromptServer   # type: ignore
 from ..metadata_collector.metadata_processor import MetadataProcessor
 
 logger = logging.getLogger(__name__)
@@ -7,6 +8,7 @@ class DebugMetadata:
     NAME = "Debug Metadata (LoraManager)"
     CATEGORY = "Lora Manager/utils"
     DESCRIPTION = "Debug node to verify metadata_processor functionality"
+    OUTPUT_NODE = True
     
     @classmethod
     def INPUT_TYPES(cls):
@@ -14,22 +16,30 @@ class DebugMetadata:
             "required": {
                 "images": ("IMAGE",),
             },
+            "hidden": {
+                "id": "UNIQUE_ID",
+            },
         }
 
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("metadata_json",)
+    RETURN_TYPES = ()
     FUNCTION = "process_metadata"
 
-    def process_metadata(self, images):
+    def process_metadata(self, images, id):
         try:
             # Get the current execution context's metadata
             from ..metadata_collector import get_metadata
             metadata = get_metadata()
             
             # Use the MetadataProcessor to convert it to JSON string
-            metadata_json = MetadataProcessor.to_json(metadata)
+            metadata_json = MetadataProcessor.to_json(metadata, id)
+            
+            # Send metadata to frontend for display
+            PromptServer.instance.send_sync("metadata_update", {
+                "id": id,
+                "metadata": metadata_json
+            })
             
-            return (metadata_json,)
         except Exception as e:
             logger.error(f"Error processing metadata: {e}")
-            return ("{}",)  # Return empty JSON object in case of error
+        
+        return ()

py/nodes/lora_loader.py

@@ -1,10 +1,7 @@
 import logging
 from nodes import LoraLoader
 from comfy.comfy_types import IO # type: ignore
-from ..services.lora_scanner import LoraScanner
-from ..config import config
 import asyncio
-import os
 from .utils import FlexibleOptionalInputType, any_type, get_lora_info, extract_lora_name, get_loras_list
 
 logger = logging.getLogger(__name__)
@@ -51,7 +48,11 @@ class LoraManagerLoader:
                 _, trigger_words = asyncio.run(get_lora_info(lora_name))
                 
                 all_trigger_words.extend(trigger_words)
-                loaded_loras.append(f"{lora_name}: {model_strength}")
+                # Add clip strength to output if different from model strength
+                if abs(model_strength - clip_strength) > 0.001:
+                    loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
+                else:
+                    loaded_loras.append(f"{lora_name}: {model_strength}")
         
         # Then process loras from kwargs with support for both old and new formats
         loras_list = get_loras_list(kwargs)
@@ -60,14 +61,21 @@ class LoraManagerLoader:
                 continue
                 
             lora_name = lora['name']
-            strength = float(lora['strength'])
+            model_strength = float(lora['strength'])
+            # Get clip strength - use model strength as default if not specified
+            clip_strength = float(lora.get('clipStrength', model_strength))
             
             # Get lora path and trigger words
             lora_path, trigger_words = asyncio.run(get_lora_info(lora_name))
             
-            # Apply the LoRA using the resolved path
-            model, clip = LoraLoader().load_lora(model, clip, lora_path, strength, strength)
-            loaded_loras.append(f"{lora_name}: {strength}")
+            # Apply the LoRA using the resolved path with separate strengths
+            model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+            
+            # Include clip strength in output if different from model strength
+            if 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)
@@ -75,8 +83,23 @@ class LoraManagerLoader:
         # use ',, ' to separate trigger words for group mode
         trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
         
-        # Format loaded_loras as <lora:lora_name:strength> separated by spaces
-        formatted_loras = " ".join([f"<lora:{name.split(':')[0].strip()}:{str(strength).strip()}>" 
-                                  for name, strength in [item.split(':') for item in loaded_loras]])
+        # 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)
+        return (model, clip, trigger_words_text, formatted_loras_text)

py/nodes/lora_stacker.py

@@ -38,7 +38,7 @@ class LoraStacker:
         
         # Process existing lora_stack if available
         lora_stack = kwargs.get('lora_stack', None)
-        if lora_stack:
+        if (lora_stack):
             stack.extend(lora_stack)
             # Get trigger words from existing stack entries
             for lora_path, _, _ in lora_stack:
@@ -54,7 +54,8 @@ class LoraStacker:
                 
             lora_name = lora['name']
             model_strength = float(lora['strength'])
-            clip_strength = model_strength  # Using same strength for both as in the original loader
+            # Get clip strength - use model strength as default if not specified
+            clip_strength = float(lora.get('clipStrength', model_strength))
             
             # Get lora path and trigger words
             lora_path, trigger_words = asyncio.run(get_lora_info(lora_name))
@@ -62,15 +63,24 @@ class LoraStacker:
             # Add to stack without loading
             # replace '/' with os.sep to avoid different OS path format
             stack.append((lora_path.replace('/', os.sep), model_strength, clip_strength))
-            active_loras.append((lora_name, model_strength))
+            active_loras.append((lora_name, model_strength, clip_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 active_loras as <lora:lora_name:strength> separated by spaces
-        active_loras_text = " ".join([f"<lora:{name}:{str(strength).strip()}>" 
-                                  for name, strength in active_loras])
+        
+        # Format active_loras with support for both formats
+        formatted_loras = []
+        for name, model_strength, clip_strength in active_loras:
+            if abs(model_strength - clip_strength) > 0.001:
+                # Different model and clip strengths
+                formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}:{str(clip_strength).strip()}>")
+            else:
+                # Same strength for both
+                formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}>")
+                
+        active_loras_text = " ".join(formatted_loras)
 
         return (stack, trigger_words_text, active_loras_text)

py/nodes/save_image.py

@@ -31,16 +31,36 @@ class SaveImage:
         return {
             "required": {
                 "images": ("IMAGE",),
-                "filename_prefix": ("STRING", {"default": "ComfyUI"}),
-                "file_format": (["png", "jpeg", "webp"],),
+                "filename_prefix": ("STRING", {
+                    "default": "ComfyUI", 
+                    "tooltip": "Base filename for saved images. Supports format patterns like %seed%, %width%, %height%, %model%, etc."
+                }),
+                "file_format": (["png", "jpeg", "webp"], {
+                    "tooltip": "Image format to save as. PNG preserves quality, JPEG is smaller, WebP balances size and quality."
+                }),
             },
             "optional": {
-                "lossless_webp": ("BOOLEAN", {"default": False}),
-                "quality": ("INT", {"default": 100, "min": 1, "max": 100}),
-                "embed_workflow": ("BOOLEAN", {"default": False}),
-                "add_counter_to_filename": ("BOOLEAN", {"default": True}),
+                "lossless_webp": ("BOOLEAN", {
+                    "default": False,
+                    "tooltip": "When enabled, saves WebP images with lossless compression. Results in larger files but no quality loss."
+                }),
+                "quality": ("INT", {
+                    "default": 100, 
+                    "min": 1, 
+                    "max": 100,
+                    "tooltip": "Compression quality for JPEG and lossy WebP formats (1-100). Higher values mean better quality but larger files."
+                }),
+                "embed_workflow": ("BOOLEAN", {
+                    "default": False,
+                    "tooltip": "Embeds the complete workflow data into the image metadata. Only works with PNG and WebP formats."
+                }),
+                "add_counter_to_filename": ("BOOLEAN", {
+                    "default": True,
+                    "tooltip": "Adds an incremental counter to filenames to prevent overwriting previous images."
+                }),
             },
             "hidden": {
+                "id": "UNIQUE_ID",
                 "prompt": "PROMPT",
                 "extra_pnginfo": "EXTRA_PNGINFO",
             },
@@ -223,7 +243,7 @@ class SaveImage:
         if lora_hashes:
             lora_hash_parts = []
             for lora_name, hash_value in lora_hashes.items():
-                lora_hash_parts.append(f"{lora_name}: {hash_value}")
+                lora_hash_parts.append(f"{lora_name}: {hash_value[:10]}")
             
             if lora_hash_parts:
                 params.append(f"Lora hashes: \"{', '.join(lora_hash_parts)}\"")
@@ -300,14 +320,14 @@ class SaveImage:
                     
         return filename
 
-    def save_images(self, images, filename_prefix, file_format, prompt=None, extra_pnginfo=None, 
+    def save_images(self, images, filename_prefix, file_format, id, prompt=None, extra_pnginfo=None, 
                    lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
         """Save images with metadata"""
         results = []
-        
+
         # Get metadata using the metadata collector
         raw_metadata = get_metadata()
-        metadata_dict = MetadataProcessor.to_dict(raw_metadata)
+        metadata_dict = MetadataProcessor.to_dict(raw_metadata, id)
             
         # Get or create metadata asynchronously
         metadata = asyncio.run(self.format_metadata(metadata_dict))
@@ -378,14 +398,23 @@ class SaveImage:
                             print(f"Error adding EXIF data: {e}")
                     img.save(file_path, format="JPEG", **save_kwargs)
                 elif file_format == "webp":
-                    # For WebP, also use piexif for metadata
-                    if metadata:
-                        try:
-                            exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
-                            exif_bytes = piexif.dump(exif_dict)
-                            save_kwargs["exif"] = exif_bytes
-                        except Exception as e:
-                            print(f"Error adding EXIF data: {e}")
+                    try:
+                        # For WebP, use piexif for metadata
+                        exif_dict = {}
+
+                        if metadata:
+                            exif_dict['Exif'] = {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}
+                        
+                        # Add workflow if needed
+                        if embed_workflow and extra_pnginfo is not None:
+                            workflow_json = json.dumps(extra_pnginfo["workflow"]) 
+                            exif_dict['0th'] = {piexif.ImageIFD.ImageDescription: "Workflow:" + workflow_json}
+                            
+                        exif_bytes = piexif.dump(exif_dict)
+                        save_kwargs["exif"] = exif_bytes
+                    except Exception as e:
+                        print(f"Error adding EXIF data: {e}")
+                    
                     img.save(file_path, format="WEBP", **save_kwargs)
                 
                 results.append({
@@ -399,7 +428,7 @@ class SaveImage:
         
         return results
 
-    def process_image(self, images, filename_prefix="ComfyUI", file_format="png", prompt=None, extra_pnginfo=None,
+    def process_image(self, images, id, filename_prefix="ComfyUI", file_format="png", prompt=None, extra_pnginfo=None,
                      lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
         """Process and save image with metadata"""
         # Make sure the output directory exists
@@ -416,6 +445,7 @@ class SaveImage:
             images, 
             filename_prefix, 
             file_format, 
+            id,
             prompt, 
             extra_pnginfo,
             lossless_webp,

py/nodes/trigger_word_toggle.py

@@ -16,11 +16,18 @@ class TriggerWordToggle:
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "group_mode": ("BOOLEAN", {"default": True}),
+                "group_mode": ("BOOLEAN", {
+                    "default": True,
+                    "tooltip": "When enabled, treats each group of trigger words as a single toggleable unit."
+                }),
+                "default_active": ("BOOLEAN", {
+                    "default": True,
+                    "tooltip": "Sets the default initial state (active or inactive) when trigger words are added."
+                }),
             },
             "optional": FlexibleOptionalInputType(any_type),
             "hidden": {
-                "id": "UNIQUE_ID",  # 会被 ComfyUI 自动替换为唯一ID
+                "id": "UNIQUE_ID",
             },
         }
 
@@ -41,7 +48,7 @@ class TriggerWordToggle:
         else:
             return data
 
-    def process_trigger_words(self, id, group_mode, **kwargs):
+    def process_trigger_words(self, id, group_mode, default_active, **kwargs):
         # Handle both old and new formats for trigger_words
         trigger_words_data = self._get_toggle_data(kwargs, 'trigger_words')
         trigger_words = trigger_words_data if isinstance(trigger_words_data, str) else ""

py/recipes/__init__.py

@@ -0,0 +1,24 @@
+"""Recipe metadata parser package for ComfyUI-Lora-Manager."""
+
+from .base import RecipeMetadataParser
+from .factory import RecipeParserFactory
+from .constants import GEN_PARAM_KEYS, VALID_LORA_TYPES
+from .parsers import (
+    RecipeFormatParser,
+    ComfyMetadataParser,
+    MetaFormatParser,
+    AutomaticMetadataParser,
+    CivitaiApiMetadataParser
+)
+
+__all__ = [
+    'RecipeMetadataParser',
+    'RecipeParserFactory',
+    'GEN_PARAM_KEYS',
+    'VALID_LORA_TYPES',
+    'RecipeFormatParser',
+    'ComfyMetadataParser',
+    'MetaFormatParser',
+    'AutomaticMetadataParser',
+    'CivitaiApiMetadataParser'
+]

py/recipes/base.py

@@ -0,0 +1,181 @@
+"""Base classes for recipe parsers."""
+
+import json
+import logging
+import os
+import re
+from typing import Dict, List, Any, Optional, Tuple
+from abc import ABC, abstractmethod
+from ..config import config
+from ..utils.constants import VALID_LORA_TYPES
+
+logger = logging.getLogger(__name__)
+
+class RecipeMetadataParser(ABC):
+    """Interface for parsing recipe metadata from image user comments"""
+
+    METADATA_MARKER = None
+
+    @abstractmethod
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the metadata format"""
+        pass
+    
+    @abstractmethod
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """
+        Parse metadata from user comment and return structured recipe data
+        
+        Args:
+            user_comment: The EXIF UserComment string from the image
+            recipe_scanner: Optional recipe scanner instance for local LoRA lookup
+            civitai_client: Optional Civitai client for fetching model information
+            
+        Returns:
+            Dict containing parsed recipe data with standardized format
+        """
+        pass
+    
+    async def populate_lora_from_civitai(self, lora_entry: Dict[str, Any], civitai_info_tuple: Tuple[Dict[str, Any], Optional[str]], 
+                                         recipe_scanner=None, base_model_counts=None, hash_value=None) -> Optional[Dict[str, Any]]:
+        """
+        Populate a lora entry with information from Civitai API response
+        
+        Args:
+            lora_entry: The lora entry to populate
+            civitai_info_tuple: The response tuple from Civitai API (data, error_msg)
+            recipe_scanner: Optional recipe scanner for local file lookup
+            base_model_counts: Optional dict to track base model counts
+            hash_value: Optional hash value to use if not available in civitai_info
+            
+        Returns:
+            The populated lora_entry dict if type is valid, None otherwise
+        """
+        try:
+            # Unpack the tuple to get the actual data
+            civitai_info, error_msg = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
+            
+            if not civitai_info or civitai_info.get("error") == "Model not found":
+                # Model not found or deleted
+                lora_entry['isDeleted'] = True
+                lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                return lora_entry
+                
+            # Get model type and validate
+            model_type = civitai_info.get('model', {}).get('type', '').lower()
+            lora_entry['type'] = model_type
+            if model_type not in VALID_LORA_TYPES:
+                logger.debug(f"Skipping non-LoRA model type: {model_type}")
+                return None
+
+            # Check if this is an early access lora
+            if civitai_info.get('earlyAccessEndsAt'):
+                # Convert earlyAccessEndsAt to a human-readable date
+                early_access_date = civitai_info.get('earlyAccessEndsAt', '')
+                lora_entry['isEarlyAccess'] = True
+                lora_entry['earlyAccessEndsAt'] = early_access_date
+                
+            # Update model name if available
+            if 'model' in civitai_info and 'name' in civitai_info['model']:
+                lora_entry['name'] = civitai_info['model']['name']
+            
+            # Update version if available
+            if 'name' in civitai_info:
+                lora_entry['version'] = civitai_info.get('name', '')
+            
+            # Get thumbnail URL from first image
+            if 'images' in civitai_info and civitai_info['images']:
+                lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
+            
+            # Get base model
+            current_base_model = civitai_info.get('baseModel', '')
+            lora_entry['baseModel'] = current_base_model
+            
+            # Update base model counts if tracking them
+            if base_model_counts is not None and current_base_model:
+                base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
+            
+            # Get download URL
+            lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
+            
+            # Process file information if available
+            if 'files' in civitai_info:
+                # Find the primary model file (type="Model" and primary=true) in the files list
+                model_file = next((file for file in civitai_info.get('files', []) 
+                                    if file.get('type') == 'Model' and file.get('primary') == True), None)
+                
+                if model_file:
+                    # Get size
+                    lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
+                    
+                    # Get SHA256 hash
+                    sha256 = model_file.get('hashes', {}).get('SHA256', hash_value)
+                    if sha256:
+                        lora_entry['hash'] = sha256.lower()
+                    
+                    # 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'])
+                        if exists_locally:
+                            try:
+                                local_path = lora_scanner.get_lora_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]
+                                
+                                # Get thumbnail from local preview if available
+                                lora_cache = await lora_scanner.get_cached_data()
+                                lora_item = next((item for item in lora_cache.raw_data 
+                                                    if item['sha256'].lower() == lora_entry['hash'].lower()), None)
+                                if lora_item and 'preview_url' in lora_item:
+                                    lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
+                            except Exception as e:
+                                logger.error(f"Error getting local lora path: {e}")
+                        else:
+                            # For missing LoRAs, get file_name from model_file.name
+                            file_name = model_file.get('name', '')
+                            lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else ''
+                
+        except Exception as e:
+            logger.error(f"Error populating lora from Civitai info: {e}")
+            
+        return lora_entry
+        
+    async def populate_checkpoint_from_civitai(self, checkpoint: Dict[str, Any], civitai_info: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Populate checkpoint information from Civitai API response
+        
+        Args:
+            checkpoint: The checkpoint entry to populate
+            civitai_info: The response from Civitai API
+            
+        Returns:
+            The populated checkpoint dict
+        """
+        try:
+            if civitai_info and civitai_info.get("error") != "Model not found":
+                # Update model name if available
+                if 'model' in civitai_info and 'name' in civitai_info['model']:
+                    checkpoint['name'] = civitai_info['model']['name']
+                
+                # Update version if available
+                if 'name' in civitai_info:
+                    checkpoint['version'] = civitai_info.get('name', '')
+                
+                # Get thumbnail URL from first image
+                if 'images' in civitai_info and civitai_info['images']:
+                    checkpoint['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
+                
+                # Get base model
+                checkpoint['baseModel'] = civitai_info.get('baseModel', '')
+                
+                # Get download URL
+                checkpoint['downloadUrl'] = civitai_info.get('downloadUrl', '')
+            else:
+                # Model not found or deleted
+                checkpoint['isDeleted'] = True
+        except Exception as e:
+            logger.error(f"Error populating checkpoint from Civitai info: {e}")
+            
+        return checkpoint

py/recipes/constants.py

@@ -0,0 +1,16 @@
+"""Constants used across recipe parsers."""
+
+# Import VALID_LORA_TYPES from utils.constants
+from ..utils.constants import VALID_LORA_TYPES
+
+# Constants for generation parameters
+GEN_PARAM_KEYS = [
+    'prompt',
+    'negative_prompt', 
+    'steps',
+    'sampler',
+    'cfg_scale',
+    'seed',
+    'size',
+    'clip_skip',
+]

py/recipes/factory.py

@@ -0,0 +1,64 @@
+"""Factory for creating recipe metadata parsers."""
+
+import logging
+from .parsers import (
+    RecipeFormatParser,
+    ComfyMetadataParser,
+    MetaFormatParser,
+    AutomaticMetadataParser,
+    CivitaiApiMetadataParser
+)
+from .base import RecipeMetadataParser
+
+logger = logging.getLogger(__name__)
+
+class RecipeParserFactory:
+    """Factory for creating recipe metadata parsers"""
+    
+    @staticmethod
+    def create_parser(metadata) -> RecipeMetadataParser:
+        """
+        Create appropriate parser based on the metadata content
+        
+        Args:
+            metadata: The metadata from the image (dict or str)
+            
+        Returns:
+            Appropriate RecipeMetadataParser implementation
+        """
+        # First, try CivitaiApiMetadataParser for dict input
+        if isinstance(metadata, dict):
+            try:
+                if CivitaiApiMetadataParser().is_metadata_matching(metadata):
+                    return CivitaiApiMetadataParser()
+            except Exception as e:
+                logger.debug(f"CivitaiApiMetadataParser check failed: {e}")
+                pass
+            
+            # Convert dict to string for other parsers that expect string input
+            try:
+                import json
+                metadata_str = json.dumps(metadata)
+            except Exception as e:
+                logger.debug(f"Failed to convert dict to JSON string: {e}")
+                return None
+        else:
+            metadata_str = metadata
+        
+        # Try ComfyMetadataParser which requires valid JSON
+        try:
+            if ComfyMetadataParser().is_metadata_matching(metadata_str):
+                return ComfyMetadataParser()
+        except Exception:
+            # If JSON parsing fails, move on to other parsers
+            pass
+        
+        # Check other parsers that expect string input
+        if RecipeFormatParser().is_metadata_matching(metadata_str):
+            return RecipeFormatParser()
+        elif AutomaticMetadataParser().is_metadata_matching(metadata_str):
+            return AutomaticMetadataParser()
+        elif MetaFormatParser().is_metadata_matching(metadata_str):
+            return MetaFormatParser()
+        else:
+            return None

py/recipes/parsers/__init__.py

@@ -0,0 +1,15 @@
+"""Recipe parsers package."""
+
+from .recipe_format import RecipeFormatParser
+from .comfy import ComfyMetadataParser
+from .meta_format import MetaFormatParser
+from .automatic import AutomaticMetadataParser
+from .civitai_image import CivitaiApiMetadataParser
+
+__all__ = [
+    'RecipeFormatParser',
+    'ComfyMetadataParser',
+    'MetaFormatParser',
+    'AutomaticMetadataParser',
+    'CivitaiApiMetadataParser',
+]

py/recipes/parsers/automatic.py

@@ -0,0 +1,304 @@
+"""Parser for Automatic1111 metadata format."""
+
+import re
+import json
+import logging
+from typing import Dict, Any
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+class AutomaticMetadataParser(RecipeMetadataParser):
+    """Parser for Automatic1111 metadata format"""
+    
+    METADATA_MARKER = r"Steps: \d+"
+    
+    # Regular expressions for extracting specific metadata
+    HASHES_REGEX = r', Hashes:\s*({[^}]+})'
+    LORA_HASHES_REGEX = r', Lora hashes:\s*"([^"]+)"'
+    CIVITAI_RESOURCES_REGEX = r', Civitai resources:\s*(\[\{.*?\}\])'
+    CIVITAI_METADATA_REGEX = r', Civitai metadata:\s*(\{.*?\})'
+    EXTRANETS_REGEX = r'<(lora|hypernet):([a-zA-Z0-9_\.\-]+):([0-9.]+)>'
+    MODEL_HASH_PATTERN = r'Model hash: ([a-zA-Z0-9]+)'
+    VAE_HASH_PATTERN = r'VAE hash: ([a-zA-Z0-9]+)'
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the Automatic1111 format"""
+        return re.search(self.METADATA_MARKER, user_comment) is not None
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from Automatic1111 format"""
+        try:
+            # Split on Negative prompt if it exists
+            if "Negative prompt:" in user_comment:
+                parts = user_comment.split('Negative prompt:', 1)
+                prompt = parts[0].strip()
+                negative_and_params = parts[1] if len(parts) > 1 else ""
+            else:
+                # No negative prompt section
+                param_start = re.search(self.METADATA_MARKER, user_comment)
+                if param_start:
+                    prompt = user_comment[:param_start.start()].strip()
+                    negative_and_params = user_comment[param_start.start():]
+                else:
+                    prompt = user_comment.strip()
+                    negative_and_params = ""
+            
+            # Initialize metadata
+            metadata = {
+                "prompt": prompt,
+                "loras": []
+            }
+            
+            # Extract negative prompt and parameters
+            if negative_and_params:
+                # If we split on "Negative prompt:", check for params section
+                if "Negative prompt:" in user_comment:
+                    param_start = re.search(r'Steps: ', negative_and_params)
+                    if param_start:
+                        neg_prompt = negative_and_params[:param_start.start()].strip()
+                        metadata["negative_prompt"] = neg_prompt
+                        params_section = negative_and_params[param_start.start():]
+                    else:
+                        metadata["negative_prompt"] = negative_and_params.strip()
+                        params_section = ""
+                else:
+                    # No negative prompt, entire section is params
+                    params_section = negative_and_params
+                
+                # Extract generation parameters
+                if params_section:
+                    # Extract Civitai resources
+                    civitai_resources_match = re.search(self.CIVITAI_RESOURCES_REGEX, params_section)
+                    if civitai_resources_match:
+                        try:
+                            civitai_resources = json.loads(civitai_resources_match.group(1))
+                            metadata["civitai_resources"] = civitai_resources
+                            params_section = params_section.replace(civitai_resources_match.group(0), '')
+                        except json.JSONDecodeError:
+                            logger.error("Error parsing Civitai resources JSON")
+                    
+                    # Extract Hashes
+                    hashes_match = re.search(self.HASHES_REGEX, params_section)
+                    if hashes_match:
+                        try:
+                            hashes = json.loads(hashes_match.group(1))
+                            # Process hash keys
+                            processed_hashes = {}
+                            for key, value in hashes.items():
+                                # Convert Model: or LORA: prefix to lowercase if present
+                                if ':' in key:
+                                    prefix, name = key.split(':', 1)
+                                    prefix = prefix.lower()
+                                else:
+                                    prefix = ''
+                                    name = key
+
+                                # Clean up the name part
+                                if '/' in name:
+                                    name = name.split('/')[-1]  # Get last part after /
+                                if '.safetensors' in name:
+                                    name = name.split('.safetensors')[0]  # Remove .safetensors
+                                
+                                # Reconstruct the key
+                                new_key = f"{prefix}:{name}" if prefix else name
+                                processed_hashes[new_key] = value
+
+                            metadata["hashes"] = processed_hashes
+                            # Remove hashes from params section to not interfere with other parsing
+                            params_section = params_section.replace(hashes_match.group(0), '')
+                        except json.JSONDecodeError:
+                            logger.error("Error parsing hashes JSON")
+                    
+                    # Extract Lora hashes in alternative format
+                    lora_hashes_match = re.search(self.LORA_HASHES_REGEX, params_section)
+                    if not hashes_match and lora_hashes_match:
+                        try:
+                            lora_hashes_str = lora_hashes_match.group(1)
+                            lora_hash_entries = lora_hashes_str.split(', ')
+                            
+                            # Initialize hashes dict if it doesn't exist
+                            if "hashes" not in metadata:
+                                metadata["hashes"] = {}
+                                
+                            # Parse each lora hash entry (format: "name: hash")
+                            for entry in lora_hash_entries:
+                                if ': ' in entry:
+                                    lora_name, lora_hash = entry.split(': ', 1)
+                                    # Add as lora type in the same format as regular hashes
+                                    metadata["hashes"][f"lora:{lora_name}"] = lora_hash.strip()
+                            
+                            # Remove lora hashes from params section
+                            params_section = params_section.replace(lora_hashes_match.group(0), '')
+                        except Exception as e:
+                            logger.error(f"Error parsing Lora hashes: {e}")
+                    
+                    # Extract basic parameters
+                    param_pattern = r'([A-Za-z\s]+): ([^,]+)'
+                    params = re.findall(param_pattern, params_section)
+                    gen_params = {}
+                    
+                    for key, value in params:
+                        clean_key = key.strip().lower().replace(' ', '_')
+                        
+                        # Skip if not in recognized gen param keys
+                        if clean_key not in GEN_PARAM_KEYS:
+                            continue
+                            
+                        # Convert numeric values
+                        if clean_key in ['steps', 'seed']:
+                            try:
+                                gen_params[clean_key] = int(value.strip())
+                            except ValueError:
+                                gen_params[clean_key] = value.strip()
+                        elif clean_key in ['cfg_scale']:
+                            try:
+                                gen_params[clean_key] = float(value.strip())
+                            except ValueError:
+                                gen_params[clean_key] = value.strip()
+                        else:
+                            gen_params[clean_key] = value.strip()
+                    
+                    # Extract size if available and add to gen_params if a recognized key
+                    size_match = re.search(r'Size: (\d+)x(\d+)', params_section)
+                    if size_match and 'size' in GEN_PARAM_KEYS:
+                        width, height = size_match.groups()
+                        gen_params['size'] = f"{width}x{height}"
+                    
+                    # Add prompt and negative_prompt to gen_params if they're in GEN_PARAM_KEYS
+                    if 'prompt' in GEN_PARAM_KEYS and 'prompt' in metadata:
+                        gen_params['prompt'] = metadata['prompt']
+                    if 'negative_prompt' in GEN_PARAM_KEYS and 'negative_prompt' in metadata:
+                        gen_params['negative_prompt'] = metadata['negative_prompt']
+                    
+                    metadata["gen_params"] = gen_params
+            
+            # Extract LoRA information 
+            loras = []
+            base_model_counts = {}
+            
+            # First use Civitai resources if available (more reliable source)
+            if metadata.get("civitai_resources"):
+                for resource in metadata.get("civitai_resources", []):
+                    if resource.get("type") in ["lora", "lycoris", "hypernet"] and resource.get("modelVersionId"):
+                        # Initialize lora entry
+                        lora_entry = {
+                            'id': str(resource.get("modelVersionId")),
+                            'modelId': str(resource.get("modelId")) if resource.get("modelId") else None,
+                            'name': resource.get("modelName", "Unknown LoRA"),
+                            'version': resource.get("modelVersionName", ""),
+                            'type': resource.get("type", "lora"),
+                            'weight': round(float(resource.get("weight", 1.0)), 2),
+                            'existsLocally': False,
+                            'thumbnailUrl': '/loras_static/images/no-preview.png',
+                            'baseModel': '',
+                            'size': 0,
+                            'downloadUrl': '',
+                            'isDeleted': False
+                        }
+                        
+                        # Get additional info from Civitai
+                        if civitai_client:
+                            try:
+                                civitai_info = await civitai_client.get_model_version_info(resource.get("modelVersionId"))
+                                populated_entry = await self.populate_lora_from_civitai(
+                                    lora_entry,
+                                    civitai_info,
+                                    recipe_scanner,
+                                    base_model_counts
+                                )
+                                if populated_entry is None:
+                                    continue  # Skip invalid LoRA types
+                                lora_entry = populated_entry
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for LoRA {lora_entry['name']}: {e}")
+                        
+                        loras.append(lora_entry)
+            
+            # If no LoRAs from Civitai resources or to supplement, extract from metadata["hashes"]
+            if not loras or len(loras) == 0:
+                # Extract lora weights from extranet tags in prompt (for later use)
+                lora_weights = {}
+                lora_matches = re.findall(self.EXTRANETS_REGEX, prompt)
+                for lora_type, lora_name, lora_weight in lora_matches:
+                    key = f"{lora_type}:{lora_name}"
+                    lora_weights[key] = round(float(lora_weight), 2)
+                
+                # Use hashes from metadata as the primary source
+                if metadata.get("hashes"):
+                    for hash_key, lora_hash in metadata.get("hashes", {}).items():
+                        # Only process lora or hypernet types
+                        if not hash_key.startswith(("lora:", "hypernet:")):
+                            continue
+                            
+                        lora_type, lora_name = hash_key.split(':', 1)
+                        
+                        # Get weight from extranet tags if available, else default to 1.0
+                        weight = lora_weights.get(hash_key, 1.0)
+                        
+                        # Initialize lora entry
+                        lora_entry = {
+                            'name': lora_name,
+                            'type': lora_type,  # 'lora' or 'hypernet'
+                            'weight': weight,
+                            '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 civitai_client:
+                            try:
+                                if lora_hash:
+                                    # If we have hash, use it for lookup
+                                    civitai_info = await civitai_client.get_model_by_hash(lora_hash)
+                                else:
+                                    civitai_info = None
+                                
+                                populated_entry = await self.populate_lora_from_civitai(
+                                    lora_entry, 
+                                    civitai_info, 
+                                    recipe_scanner,
+                                    base_model_counts,
+                                    lora_hash
+                                )
+                                if populated_entry is None:
+                                    continue  # Skip invalid LoRA types
+                                lora_entry = populated_entry
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for LoRA {lora_name}: {e}")
+                        
+                        loras.append(lora_entry)
+                
+            # Try to get base model from resources or make educated guess
+            base_model = None
+            if base_model_counts:
+                # Use the most common base model from the loras
+                base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
+            
+            # Prepare final result structure
+            # Make sure gen_params only contains recognized keys
+            filtered_gen_params = {}
+            for key in GEN_PARAM_KEYS:
+                if key in metadata.get("gen_params", {}):
+                    filtered_gen_params[key] = metadata["gen_params"][key]
+            
+            result = {
+                'base_model': base_model,
+                'loras': loras,
+                'gen_params': filtered_gen_params,
+                'from_automatic_metadata': True
+            }
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"Error parsing Automatic1111 metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}

py/recipes/parsers/civitai_image.py

@@ -0,0 +1,248 @@
+"""Parser for Civitai image metadata format."""
+
+import json
+import logging
+from typing import Dict, Any, Union
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+class CivitaiApiMetadataParser(RecipeMetadataParser):
+    """Parser for Civitai image metadata format"""
+    
+    def is_metadata_matching(self, metadata) -> bool:
+        """Check if the metadata matches the Civitai image metadata format
+        
+        Args:
+            metadata: The metadata from the image (dict)
+            
+        Returns:
+            bool: True if this parser can handle the metadata
+        """
+        if not metadata or not isinstance(metadata, dict):
+            return False
+            
+        # Check for key markers specific to Civitai image metadata
+        return any([
+            "resources" in metadata,
+            "civitaiResources" in metadata,
+            "additionalResources" in metadata
+        ])
+    
+    async def parse_metadata(self, metadata, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from Civitai image format
+        
+        Args:
+            metadata: The metadata from the image (dict)
+            recipe_scanner: Optional recipe scanner service
+            civitai_client: Optional Civitai API client
+            
+        Returns:
+            Dict containing parsed recipe data
+        """
+        try:
+            # Initialize result structure
+            result = {
+                'base_model': None,
+                'loras': [],
+                'gen_params': {},
+                'from_civitai_image': True
+            }
+            
+            # Extract prompt and negative prompt
+            if "prompt" in metadata:
+                result["gen_params"]["prompt"] = metadata["prompt"]
+            
+            if "negativePrompt" in metadata:
+                result["gen_params"]["negative_prompt"] = metadata["negativePrompt"]
+            
+            # Extract other generation parameters
+            param_mapping = {
+                "steps": "steps",
+                "sampler": "sampler",
+                "cfgScale": "cfg_scale",
+                "seed": "seed",
+                "Size": "size",
+                "clipSkip": "clip_skip",
+            }
+            
+            for civitai_key, our_key in param_mapping.items():
+                if civitai_key in metadata and our_key in GEN_PARAM_KEYS:
+                    result["gen_params"][our_key] = metadata[civitai_key]
+            
+            # Extract base model information - directly if available
+            if "baseModel" in metadata:
+                result["base_model"] = metadata["baseModel"]
+            elif "Model hash" in metadata and civitai_client:
+                model_hash = metadata["Model hash"]
+                model_info = await civitai_client.get_model_by_hash(model_hash)
+                if model_info:
+                    result["base_model"] = model_info.get("baseModel", "")
+            elif "Model" in metadata and isinstance(metadata.get("resources"), list):
+                # Try to find base model in resources
+                for resource in metadata.get("resources", []):
+                    if resource.get("type") == "model" and resource.get("name") == metadata.get("Model"):
+                        # This is likely the checkpoint model
+                        if civitai_client and resource.get("hash"):
+                            model_info = await civitai_client.get_model_by_hash(resource.get("hash"))
+                            if model_info:
+                                result["base_model"] = model_info.get("baseModel", "")
+            
+            base_model_counts = {}
+            
+            # Process standard resources array
+            if "resources" in metadata and isinstance(metadata["resources"], list):
+                for resource in metadata["resources"]:
+                    # Modified to process resources without a type field as potential LoRAs
+                    if resource.get("type", "lora") == "lora":
+                        lora_entry = {
+                            'name': resource.get("name", "Unknown LoRA"),
+                            'type': "lora",
+                            'weight': float(resource.get("weight", 1.0)),
+                            'hash': resource.get("hash", ""),
+                            'existsLocally': False,
+                            'localPath': None,
+                            'file_name': resource.get("name", "Unknown"),
+                            'thumbnailUrl': '/loras_static/images/no-preview.png',
+                            'baseModel': '',
+                            'size': 0,
+                            'downloadUrl': '',
+                            'isDeleted': False
+                        }
+                        
+                        # Try to get info from Civitai if hash is available
+                        if lora_entry['hash'] and civitai_client:
+                            try:
+                                lora_hash = lora_entry['hash']
+                                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:
+                                    continue  # Skip invalid LoRA types
+                                    
+                                lora_entry = populated_entry
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}")
+                        
+                        result["loras"].append(lora_entry)
+            
+            # Process civitaiResources array
+            if "civitaiResources" in metadata and isinstance(metadata["civitaiResources"], list):
+                for resource in metadata["civitaiResources"]:
+                    # Modified to process resources without a type field as potential LoRAs
+                    if resource.get("type") in ["lora", "lycoris"] or "type" not in resource:
+                        # Initialize lora entry with the same structure as in automatic.py
+                        lora_entry = {
+                            'id': str(resource.get("modelVersionId")),
+                            'modelId': str(resource.get("modelId")) if resource.get("modelId") else None,
+                            'name': resource.get("modelName", "Unknown LoRA"),
+                            'version': resource.get("modelVersionName", ""),
+                            'type': resource.get("type", "lora"),
+                            'weight': round(float(resource.get("weight", 1.0)), 2),
+                            'existsLocally': False,
+                            'thumbnailUrl': '/loras_static/images/no-preview.png',
+                            'baseModel': '',
+                            'size': 0,
+                            'downloadUrl': '',
+                            'isDeleted': False
+                        }
+                        
+                        # Try to get info from Civitai if modelVersionId is available
+                        if resource.get('modelVersionId') and civitai_client:
+                            try:
+                                version_id = str(resource.get('modelVersionId'))
+                                # Use get_model_version_info instead of get_model_version
+                                civitai_info, error = await civitai_client.get_model_version_info(version_id)
+                                
+                                if error:
+                                    logger.warning(f"Error getting model version info: {error}")
+                                    continue
+                                
+                                populated_entry = await self.populate_lora_from_civitai(
+                                    lora_entry,
+                                    civitai_info,
+                                    recipe_scanner,
+                                    base_model_counts
+                                )
+                                
+                                if populated_entry is None:
+                                    continue  # Skip invalid LoRA types
+                                    
+                                lora_entry = populated_entry
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for model version {resource.get('modelVersionId')}: {e}")
+                        
+                        result["loras"].append(lora_entry)
+            
+            # Process additionalResources array
+            if "additionalResources" in metadata and isinstance(metadata["additionalResources"], list):
+                for resource in metadata["additionalResources"]:
+                    # Modified to process resources without a type field as potential LoRAs
+                    if resource.get("type") in ["lora", "lycoris"] or "type" not in resource:
+                        lora_type = resource.get("type", "lora")
+                        name = resource.get("name", "")
+                        
+                        # Extract ID from URN format if available
+                        model_id = None
+                        if name and "civitai:" in name:
+                            parts = name.split("@")
+                            if len(parts) > 1:
+                                model_id = parts[1]
+                        
+                        lora_entry = {
+                            'name': name,
+                            'type': lora_type,
+                            'weight': float(resource.get("strength", 1.0)),
+                            'hash': "",
+                            'existsLocally': False,
+                            'localPath': None,
+                            'file_name': name,
+                            'thumbnailUrl': '/loras_static/images/no-preview.png',
+                            'baseModel': '',
+                            'size': 0,
+                            'downloadUrl': '',
+                            'isDeleted': False
+                        }
+                        
+                        # If we have a model ID and civitai client, try to get more info
+                        if model_id and civitai_client:
+                            try:
+                                # Use get_model_version_info with the model ID
+                                civitai_info, error = await civitai_client.get_model_version_info(model_id)
+                                
+                                if error:
+                                    logger.warning(f"Error getting model version info: {error}")
+                                else:
+                                    populated_entry = await self.populate_lora_from_civitai(
+                                        lora_entry,
+                                        civitai_info,
+                                        recipe_scanner,
+                                        base_model_counts
+                                    )
+                                    
+                                    if populated_entry is None:
+                                        continue  # Skip invalid LoRA types
+                                        
+                                    lora_entry = populated_entry
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for model ID {model_id}: {e}")
+                        
+                        result["loras"].append(lora_entry)
+            
+            # 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]
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"Error parsing Civitai image metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}

py/recipes/parsers/comfy.py

@@ -0,0 +1,216 @@
+"""Parser for ComfyUI metadata format."""
+
+import re
+import json
+import logging
+from typing import Dict, Any
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+class ComfyMetadataParser(RecipeMetadataParser):
+    """Parser for Civitai ComfyUI metadata JSON format"""
+    
+    METADATA_MARKER = r"class_type"
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the ComfyUI metadata format"""
+        try:
+            data = json.loads(user_comment)
+            # Check if it contains class_type nodes typical of ComfyUI workflow
+            return isinstance(data, dict) and any(isinstance(v, dict) and 'class_type' in v for v in data.values())
+        except (json.JSONDecodeError, TypeError):
+            return False
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from Civitai ComfyUI metadata format"""
+        try:
+            data = json.loads(user_comment)
+            loras = []
+            
+            # Find all LoraLoader nodes
+            lora_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'LoraLoader'}
+            
+            if not lora_nodes:
+                return {"error": "No LoRA information found in this ComfyUI workflow", "loras": []}
+            
+            # Process each LoraLoader node
+            for node_id, node in lora_nodes.items():
+                if 'inputs' not in node or 'lora_name' not in node['inputs']:
+                    continue
+                    
+                lora_name = node['inputs'].get('lora_name', '')
+                
+                # Parse the URN to extract model ID and version ID
+                # Format: "urn:air:sdxl:lora:civitai:1107767@1253442"
+                lora_id_match = re.search(r'civitai:(\d+)@(\d+)', lora_name)
+                if not lora_id_match:
+                    continue
+                    
+                model_id = lora_id_match.group(1)
+                model_version_id = lora_id_match.group(2)
+                
+                # Get strength from node inputs
+                weight = node['inputs'].get('strength_model', 1.0)
+                
+                # Initialize lora entry with default values
+                lora_entry = {
+                    'id': model_version_id,
+                    'modelId': model_id,
+                    'name': f"Lora {model_id}",  # Default name
+                    'version': '',
+                    'type': 'lora',
+                    'weight': weight,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': '',
+                    'hash': '',
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+                
+                # Get additional info from Civitai if client is available
+                if civitai_client:
+                    try:
+                        civitai_info_tuple = await civitai_client.get_model_version_info(model_version_id)
+                        # Populate lora entry with Civitai info
+                        populated_entry = await self.populate_lora_from_civitai(
+                            lora_entry, 
+                            civitai_info_tuple, 
+                            recipe_scanner
+                        )
+                        if populated_entry is None:
+                            continue  # Skip invalid LoRA types
+                        lora_entry = populated_entry
+                    except Exception as e:
+                        logger.error(f"Error fetching Civitai info for LoRA: {e}")
+                
+                loras.append(lora_entry)
+            
+            # Find checkpoint info
+            checkpoint_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'CheckpointLoaderSimple'}
+            checkpoint = None
+            checkpoint_id = None
+            checkpoint_version_id = None
+            
+            if checkpoint_nodes:
+                # Get the first checkpoint node
+                checkpoint_node = next(iter(checkpoint_nodes.values()))
+                if 'inputs' in checkpoint_node and 'ckpt_name' in checkpoint_node['inputs']:
+                    checkpoint_name = checkpoint_node['inputs']['ckpt_name']
+                    # Parse checkpoint URN
+                    checkpoint_match = re.search(r'civitai:(\d+)@(\d+)', checkpoint_name)
+                    if checkpoint_match:
+                        checkpoint_id = checkpoint_match.group(1)
+                        checkpoint_version_id = checkpoint_match.group(2)
+                        checkpoint = {
+                            'id': checkpoint_version_id,
+                            'modelId': checkpoint_id,
+                            'name': f"Checkpoint {checkpoint_id}",
+                            'version': '',
+                            'type': 'checkpoint'
+                        }
+                        
+                        # Get additional checkpoint info from Civitai
+                        if civitai_client:
+                            try:
+                                civitai_info_tuple = await civitai_client.get_model_version_info(checkpoint_version_id)
+                                civitai_info, _ = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
+                                # Populate checkpoint with Civitai info
+                                checkpoint = await self.populate_checkpoint_from_civitai(checkpoint, civitai_info)
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for checkpoint: {e}")
+            
+            # Extract generation parameters
+            gen_params = {}
+            
+            # First try to get from extraMetadata
+            if 'extraMetadata' in data:
+                try:
+                    # extraMetadata is a JSON string that needs to be parsed
+                    extra_metadata = json.loads(data['extraMetadata'])
+                    
+                    # Map fields from extraMetadata to our standard format
+                    mapping = {
+                        'prompt': 'prompt',
+                        'negativePrompt': 'negative_prompt',
+                        'steps': 'steps',
+                        'sampler': 'sampler',
+                        'cfgScale': 'cfg_scale',
+                        'seed': 'seed'
+                    }
+                    
+                    for src_key, dest_key in mapping.items():
+                        if src_key in extra_metadata:
+                            gen_params[dest_key] = extra_metadata[src_key]
+                    
+                    # If size info is available, format as "width x height"
+                    if 'width' in extra_metadata and 'height' in extra_metadata:
+                        gen_params['size'] = f"{extra_metadata['width']}x{extra_metadata['height']}"
+                    
+                except Exception as e:
+                    logger.error(f"Error parsing extraMetadata: {e}")
+            
+            # If extraMetadata doesn't have all the info, try to get from nodes
+            if not gen_params or len(gen_params) < 3:  # At least we want prompt, negative_prompt, and steps
+                # Find positive prompt node
+                positive_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and 
+                                v.get('class_type', '').endswith('CLIPTextEncode') and 
+                                v.get('_meta', {}).get('title') == 'Positive'}
+                
+                if positive_nodes:
+                    positive_node = next(iter(positive_nodes.values()))
+                    if 'inputs' in positive_node and 'text' in positive_node['inputs']:
+                        gen_params['prompt'] = positive_node['inputs']['text']
+                
+                # Find negative prompt node
+                negative_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and 
+                                v.get('class_type', '').endswith('CLIPTextEncode') and 
+                                v.get('_meta', {}).get('title') == 'Negative'}
+                
+                if negative_nodes:
+                    negative_node = next(iter(negative_nodes.values()))
+                    if 'inputs' in negative_node and 'text' in negative_node['inputs']:
+                        gen_params['negative_prompt'] = negative_node['inputs']['text']
+                
+                # Find KSampler node for other parameters
+                ksampler_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'KSampler'}
+                
+                if ksampler_nodes:
+                    ksampler_node = next(iter(ksampler_nodes.values()))
+                    if 'inputs' in ksampler_node:
+                        inputs = ksampler_node['inputs']
+                        if 'sampler_name' in inputs:
+                            gen_params['sampler'] = inputs['sampler_name']
+                        if 'steps' in inputs:
+                            gen_params['steps'] = inputs['steps']
+                        if 'cfg' in inputs:
+                            gen_params['cfg_scale'] = inputs['cfg']
+                        if 'seed' in inputs:
+                            gen_params['seed'] = inputs['seed']
+            
+            # Determine base model from loras info
+            base_model = None
+            if loras:
+                # Use the most common base model from loras
+                base_models = [lora['baseModel'] for lora in loras if lora.get('baseModel')]
+                if base_models:
+                    from collections import Counter
+                    base_model_counts = Counter(base_models)
+                    base_model = base_model_counts.most_common(1)[0][0]
+            
+            return {
+                'base_model': base_model,
+                'loras': loras,
+                'checkpoint': checkpoint,
+                'gen_params': gen_params,
+                'from_comfy_metadata': True
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing ComfyUI metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}

py/recipes/parsers/meta_format.py

@@ -0,0 +1,174 @@
+"""Parser for meta format (Lora_N Model hash) metadata."""
+
+import re
+import logging
+from typing import Dict, Any
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+class MetaFormatParser(RecipeMetadataParser):
+    """Parser for images with meta format metadata (Lora_N Model hash format)"""
+    
+    METADATA_MARKER = r'Lora_\d+ Model hash:'
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the metadata format"""
+        return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from images with meta format metadata"""
+        try:
+            # Extract prompt and negative prompt
+            parts = user_comment.split('Negative prompt:', 1)
+            prompt = parts[0].strip()
+            
+            # Initialize metadata
+            metadata = {"prompt": prompt, "loras": []}
+            
+            # Extract negative prompt and parameters if available
+            if len(parts) > 1:
+                negative_and_params = parts[1]
+                
+                # Extract negative prompt - everything until the first parameter (usually "Steps:")
+                param_start = re.search(r'([A-Za-z]+): ', negative_and_params)
+                if param_start:
+                    neg_prompt = negative_and_params[:param_start.start()].strip()
+                    metadata["negative_prompt"] = neg_prompt
+                    params_section = negative_and_params[param_start.start():]
+                else:
+                    params_section = negative_and_params
+                
+                # Extract key-value parameters (Steps, Sampler, Seed, etc.)
+                param_pattern = r'([A-Za-z_0-9 ]+): ([^,]+)'
+                params = re.findall(param_pattern, params_section)
+                for key, value in params:
+                    clean_key = key.strip().lower().replace(' ', '_')
+                    metadata[clean_key] = value.strip()
+            
+            # Extract LoRA information
+            # Pattern to match lora entries: Lora_0 Model name: ArtVador I.safetensors, Lora_0 Model hash: 08f7133a58, etc.
+            lora_pattern = r'Lora_(\d+) Model name: ([^,]+), Lora_\1 Model hash: ([^,]+), Lora_\1 Strength model: ([^,]+), Lora_\1 Strength clip: ([^,]+)'
+            lora_matches = re.findall(lora_pattern, user_comment)
+            
+            # If the regular pattern doesn't match, try a more flexible approach
+            if not lora_matches:
+                # First find all Lora indices
+                lora_indices = set(re.findall(r'Lora_(\d+)', user_comment))
+                
+                # For each index, extract the information
+                for idx in lora_indices:
+                    lora_info = {}
+                    
+                    # Extract model name
+                    name_match = re.search(f'Lora_{idx} Model name: ([^,]+)', user_comment)
+                    if name_match:
+                        lora_info['name'] = name_match.group(1).strip()
+                    
+                    # Extract model hash
+                    hash_match = re.search(f'Lora_{idx} Model hash: ([^,]+)', user_comment)
+                    if hash_match:
+                        lora_info['hash'] = hash_match.group(1).strip()
+                    
+                    # Extract strength model
+                    strength_model_match = re.search(f'Lora_{idx} Strength model: ([^,]+)', user_comment)
+                    if strength_model_match:
+                        lora_info['strength_model'] = float(strength_model_match.group(1).strip())
+                    
+                    # Extract strength clip
+                    strength_clip_match = re.search(f'Lora_{idx} Strength clip: ([^,]+)', user_comment)
+                    if strength_clip_match:
+                        lora_info['strength_clip'] = float(strength_clip_match.group(1).strip())
+                    
+                    # Only add if we have at least name and hash
+                    if 'name' in lora_info and 'hash' in lora_info:
+                        lora_matches.append((idx, lora_info['name'], lora_info['hash'], 
+                                            str(lora_info.get('strength_model', 1.0)), 
+                                            str(lora_info.get('strength_clip', 1.0))))
+            
+            # Process LoRAs
+            base_model_counts = {}
+            loras = []
+            
+            for match in lora_matches:
+                if len(match) == 5:  # Regular pattern match
+                    idx, name, hash_value, strength_model, strength_clip = match
+                else:  # Flexible approach match
+                    continue  # Should not happen now
+                
+                # Clean up the values
+                name = name.strip()
+                if name.endswith('.safetensors'):
+                    name = name[:-12]  # Remove .safetensors extension
+                    
+                hash_value = hash_value.strip()
+                weight = float(strength_model)  # Use model strength as weight
+                
+                # Initialize lora entry with default values
+                lora_entry = {
+                    'name': name,
+                    'type': 'lora',
+                    'weight': weight,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': name,
+                    'hash': hash_value,
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+                
+                # Get info from Civitai by hash if available
+                if civitai_client and hash_value:
+                    try:
+                        civitai_info = await civitai_client.get_model_by_hash(hash_value)
+                        # Populate lora entry with Civitai info
+                        populated_entry = await self.populate_lora_from_civitai(
+                            lora_entry, 
+                            civitai_info, 
+                            recipe_scanner,
+                            base_model_counts,
+                            hash_value
+                        )
+                        if populated_entry is None:
+                            continue  # Skip invalid LoRA types
+                        lora_entry = populated_entry
+                    except Exception as e:
+                        logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}")
+                
+                loras.append(lora_entry)
+            
+            # Extract model information
+            model = None
+            if 'model' in metadata:
+                model = metadata['model']
+            
+            # Set base_model to the most common one from civitai_info
+            base_model = None
+            if base_model_counts:
+                base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
+            
+            # Extract generation parameters for recipe metadata
+            gen_params = {}
+            for key in GEN_PARAM_KEYS:
+                if key in metadata:
+                    gen_params[key] = metadata.get(key, '')
+            
+            # Try to extract size information if available
+            if 'width' in metadata and 'height' in metadata:
+                gen_params['size'] = f"{metadata['width']}x{metadata['height']}"
+            
+            return {
+                'base_model': base_model,
+                'loras': loras,
+                'gen_params': gen_params,
+                'raw_metadata': metadata,
+                'from_meta_format': True
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing meta format metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}

py/recipes/parsers/recipe_format.py

@@ -0,0 +1,114 @@
+"""Parser for dedicated recipe metadata format."""
+
+import re
+import json
+import logging
+from typing import Dict, Any
+from ...config import config
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+class RecipeFormatParser(RecipeMetadataParser):
+    """Parser for images with dedicated recipe metadata format"""
+    
+    # Regular expression pattern for extracting recipe metadata
+    METADATA_MARKER = r'Recipe metadata: (\{.*\})'
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the metadata format"""
+        return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from images with dedicated recipe metadata format"""
+        try:
+            # Extract recipe metadata from user comment
+            try:
+                # Look for recipe metadata section
+                recipe_match = re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL)
+                if not recipe_match:
+                    recipe_metadata = None
+                else:
+                    recipe_json = recipe_match.group(1)
+                    recipe_metadata = json.loads(recipe_json)
+            except Exception as e:
+                logger.error(f"Error extracting recipe metadata: {e}")
+                recipe_metadata = None
+            if not recipe_metadata:
+                return {"error": "No recipe metadata found", "loras": []}
+                
+            # Process the recipe metadata
+            loras = []
+            for lora in recipe_metadata.get('loras', []):
+                # Convert recipe lora format to frontend format
+                lora_entry = {
+                    'id': lora.get('modelVersionId', ''),
+                    'name': lora.get('modelName', ''),
+                    'version': lora.get('modelVersionName', ''),
+                    'type': 'lora',
+                    'weight': lora.get('strength', 1.0),
+                    'file_name': lora.get('file_name', ''),
+                    'hash': lora.get('hash', '')
+                }
+                
+                # 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'])
+                    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)
+                        if lora_item:
+                            lora_entry['existsLocally'] = True
+                            lora_entry['localPath'] = lora_item['file_path']
+                            lora_entry['file_name'] = lora_item['file_name']
+                            lora_entry['size'] = lora_item['size']
+                            lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
+                            
+                    else:
+                        lora_entry['existsLocally'] = False
+                        lora_entry['localPath'] = None
+                        
+                        # Try to get additional info from Civitai if we have a model version ID
+                        if lora.get('modelVersionId') and civitai_client:
+                            try:
+                                civitai_info_tuple = await civitai_client.get_model_version_info(lora['modelVersionId'])
+                                # Populate lora entry with Civitai info
+                                populated_entry = await self.populate_lora_from_civitai(
+                                    lora_entry, 
+                                    civitai_info_tuple, 
+                                    recipe_scanner,
+                                    None,  # No need to track base model counts
+                                    lora['hash']
+                                )
+                                if populated_entry is None:
+                                    continue  # Skip invalid LoRA types
+                                lora_entry = populated_entry
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for LoRA: {e}")
+                                lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                
+                loras.append(lora_entry)
+
+            logger.info(f"Found {len(loras)} loras in recipe metadata")
+            
+            # Filter gen_params to only include recognized keys
+            filtered_gen_params = {}
+            if 'gen_params' in recipe_metadata:
+                for key, value in recipe_metadata['gen_params'].items():
+                    if key in GEN_PARAM_KEYS:
+                        filtered_gen_params[key] = value
+            
+            return {
+                'base_model': recipe_metadata.get('base_model', ''),
+                'loras': loras,
+                'gen_params': filtered_gen_params,
+                'tags': recipe_metadata.get('tags', []),
+                'title': recipe_metadata.get('title', ''),
+                'from_recipe_metadata': True
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing recipe format metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}

py/routes/api_routes.py

@@ -10,11 +10,11 @@ from ..nodes.utils import get_lora_info
 
 from ..config import config
 from ..services.websocket_manager import ws_manager
-from ..services.settings_manager import settings
 import asyncio
 from .update_routes import UpdateRoutes
-from ..utils.constants import PREVIEW_EXTENSIONS, CARD_PREVIEW_WIDTH
+from ..utils.constants import PREVIEW_EXTENSIONS, CARD_PREVIEW_WIDTH, VALID_LORA_TYPES
 from ..utils.exif_utils import ExifUtils
+from ..utils.metadata_manager import MetadataManager
 from ..services.service_registry import ServiceRegistry
 
 logger = logging.getLogger(__name__)
@@ -45,6 +45,7 @@ class ApiRoutes:
         app.router.add_post('/api/delete_model', routes.delete_model)
         app.router.add_post('/api/loras/exclude', routes.exclude_model)  # Add new exclude endpoint
         app.router.add_post('/api/fetch-civitai', routes.fetch_civitai)
+        app.router.add_post('/api/relink-civitai', routes.relink_civitai)  # Add new relink endpoint
         app.router.add_post('/api/replace_preview', routes.replace_preview)
         app.router.add_get('/api/loras', routes.get_loras)
         app.router.add_post('/api/fetch-all-civitai', routes.fetch_all_civitai)
@@ -72,10 +73,24 @@ class ApiRoutes:
 
         # Add new endpoint for letter counts
         app.router.add_get('/api/loras/letter-counts', routes.get_letter_counts)
+        
+        # Add new endpoints for copying lora data
+        app.router.add_get('/api/loras/get-notes', routes.get_lora_notes)
+        app.router.add_get('/api/loras/get-trigger-words', routes.get_lora_trigger_words)
 
         # Add update check routes
         UpdateRoutes.setup_routes(app)
 
+        # Add new endpoints for finding duplicates
+        app.router.add_get('/api/loras/find-duplicates', routes.find_duplicate_loras)
+        app.router.add_get('/api/loras/find-filename-conflicts', routes.find_filename_conflicts)
+
+        # Add new endpoint for bulk deleting loras
+        app.router.add_post('/api/loras/bulk-delete', routes.bulk_delete_loras)
+
+        # Add new endpoint for verifying duplicates
+        app.router.add_post('/api/loras/verify-duplicates', routes.verify_duplicates)
+
     async def delete_model(self, request: web.Request) -> web.Response:
         """Handle model deletion request"""
         if self.scanner is None:
@@ -92,7 +107,21 @@ class ApiRoutes:
         """Handle CivitAI metadata fetch request"""
         if self.scanner is None:
             self.scanner = await ServiceRegistry.get_lora_scanner()
-        return await ModelRouteUtils.handle_fetch_civitai(request, self.scanner)
+        
+        response = await ModelRouteUtils.handle_fetch_civitai(request, self.scanner)
+        
+        # If successful, format the metadata before returning
+        if response.status == 200:
+            data = json.loads(response.body.decode('utf-8'))
+            if data.get("success") and data.get("metadata"):
+                formatted_metadata = self._format_lora_response(data["metadata"])
+                return web.json_response({
+                    "success": True,
+                    "metadata": formatted_metadata
+                })
+        
+        # Otherwise, return the original response
+        return response
 
     async def replace_preview(self, request: web.Request) -> web.Response:
         """Handle preview image replacement request"""
@@ -102,8 +131,11 @@ class ApiRoutes:
     
     async def scan_loras(self, request: web.Request) -> web.Response:
         """Force a rescan of LoRA files"""
-        try:                
-            await self.scanner.get_cached_data(force_refresh=True)
+        try:
+            # Get full_rebuild parameter from query string, default to false
+            full_rebuild = request.query.get('full_rebuild', 'false').lower() == 'true'
+                
+            await self.scanner.get_cached_data(force_refresh=True, rebuild_cache=full_rebuild)
             return web.json_response({"status": "success", "message": "LoRA scan completed"})
         except Exception as e:
             logger.error(f"Error in scan_loras: {e}", exc_info=True)
@@ -214,66 +246,6 @@ class ApiRoutes:
             "civitai": ModelRouteUtils.filter_civitai_data(lora.get("civitai", {}))
         }
 
-    # Private helper methods
-    async def _read_preview_file(self, reader) -> tuple[bytes, str]:
-        """Read preview file and content type from multipart request"""
-        field = await reader.next()
-        if field.name != 'preview_file':
-            raise ValueError("Expected 'preview_file' field")
-        content_type = field.headers.get('Content-Type', 'image/png')
-        return await field.read(), content_type
-
-    async def _read_model_path(self, reader) -> str:
-        """Read model path from multipart request"""
-        field = await reader.next()
-        if field.name != 'model_path':
-            raise ValueError("Expected 'model_path' field")
-        return (await field.read()).decode()
-
-    async def _save_preview_file(self, model_path: str, preview_data: bytes, content_type: str) -> str:
-        """Save preview file and return its path"""
-        base_name = os.path.splitext(os.path.basename(model_path))[0]
-        folder = os.path.dirname(model_path)
-        
-        # Determine if content is video or image
-        if content_type.startswith('video/'):
-            # For videos, keep original format and use .mp4 extension
-            extension = '.mp4'
-            optimized_data = preview_data
-        else:
-            # For images, optimize and convert to WebP
-            optimized_data, _ = ExifUtils.optimize_image(
-                image_data=preview_data,
-                target_width=CARD_PREVIEW_WIDTH,
-                format='webp',
-                quality=85,
-                preserve_metadata=False
-            )
-            extension = '.webp'  # Use .webp without .preview part
-        
-        preview_path = os.path.join(folder, base_name + extension).replace(os.sep, '/')
-        
-        with open(preview_path, 'wb') as f:
-            f.write(optimized_data)
-            
-        return preview_path
-
-    async def _update_preview_metadata(self, model_path: str, preview_path: str):
-        """Update preview path in metadata"""
-        metadata_path = os.path.splitext(model_path)[0] + '.metadata.json'
-        if os.path.exists(metadata_path):
-            try:
-                with open(metadata_path, 'r', encoding='utf-8') as f:
-                    metadata = json.load(f)
-                
-                # Update preview_url directly in the metadata dict
-                metadata['preview_url'] = preview_path
-                
-                with open(metadata_path, 'w', encoding='utf-8') as f:
-                    json.dump(metadata, f, indent=2, ensure_ascii=False)
-            except Exception as e:
-                logger.error(f"Error updating metadata: {e}")
-
     async def fetch_all_civitai(self, request: web.Request) -> web.Response:
         """Fetch CivitAI metadata for all loras in the background"""
         try:
@@ -386,10 +358,10 @@ class ApiRoutes:
             versions = response.get('modelVersions', [])
             model_type = response.get('type', '')
             
-            # Check model type - should be LORA
-            if model_type.lower() != 'lora':
+            # Check model type - should be LORA, LoCon, or DORA
+            if model_type.lower() not in VALID_LORA_TYPES:
                 return web.json_response({
-                    'error': f"Model type mismatch. Expected LORA, got {model_type}"
+                    'error': f"Model type mismatch. Expected LORA or LoCon, got {model_type}"
                 }, status=400)
             
             # Check local availability for each version
@@ -508,7 +480,7 @@ class ApiRoutes:
                         logger.warning(f"Early access download failed: {error_message}")
                         return web.Response(
                             status=401,  # Use 401 status code to match Civitai's response
-                            text=f"Early Access Restriction: {error_message}"
+                            text=error_message
                         )
                     
                     return web.Response(status=500, text=error_message)
@@ -609,8 +581,7 @@ class ApiRoutes:
                     metadata[key] = value
 
             # Save updated metadata
-            with open(metadata_path, 'w', encoding='utf-8') as f:
-                json.dump(metadata, f, indent=2, ensure_ascii=False)
+            await MetadataManager.save_metadata(file_path, metadata)
 
             # Update cache
             await self.scanner.update_single_model_cache(file_path, file_path, metadata)
@@ -823,9 +794,7 @@ class ApiRoutes:
                             metadata['tags'] = tags
                             metadata['creator'] = creator
                             
-                            with open(metadata_path, 'w', encoding='utf-8') as f:
-                                json.dump(metadata, f, indent=2, ensure_ascii=False)
-                                logger.info(f"Saved model metadata to file for {file_path}")
+                            await MetadataManager.save_metadata(file_path, metadata)
                         except Exception as e:
                             logger.error(f"Error saving model metadata: {e}")
             
@@ -941,6 +910,7 @@ class ApiRoutes:
             patterns = [
                 f"{old_file_name}.safetensors",  # Required
                 f"{old_file_name}.metadata.json",
+                f"{old_file_name}.metadata.json.bak",
             ]
             
             # Add all preview file extensions
@@ -967,17 +937,6 @@ class ApiRoutes:
             renamed_files = []
             new_metadata_path = None
             
-            # Notify file monitor to ignore these events
-            main_file_path = os.path.join(target_dir, f"{old_file_name}.safetensors")
-            if os.path.exists(main_file_path):
-                # Get lora monitor through ServiceRegistry instead of download_manager
-                lora_monitor = await ServiceRegistry.get_lora_monitor()
-                if lora_monitor:
-                    # Add old and new paths to ignore list
-                    file_size = os.path.getsize(main_file_path)
-                    lora_monitor.handler.add_ignore_path(main_file_path, file_size)
-                    lora_monitor.handler.add_ignore_path(new_file_path, file_size)
-            
             for old_path, pattern in existing_files:
                 # Get the file extension like .safetensors or .metadata.json
                 ext = ModelRouteUtils.get_multipart_ext(pattern)
@@ -1007,8 +966,7 @@ class ApiRoutes:
                     metadata['preview_url'] = new_preview
                 
                 # Save updated metadata
-                with open(new_metadata_path, 'w', encoding='utf-8') as f:
-                    json.dump(metadata, f, indent=2, ensure_ascii=False)
+                await MetadataManager.save_metadata(new_file_path, metadata)
             
             # Update the scanner cache
             if metadata:
@@ -1084,3 +1042,202 @@ class ApiRoutes:
                 'success': False,
                 'error': str(e)
             }, status=500)
+
+    async def get_lora_notes(self, request: web.Request) -> web.Response:
+        """Get notes for a specific LoRA file"""
+        try:
+            if self.scanner is None:
+                self.scanner = await ServiceRegistry.get_lora_scanner()
+                
+            # Get lora file name from query parameters
+            lora_name = request.query.get('name')
+            if not lora_name:
+                return web.Response(text='Lora file name is required', status=400)
+
+            # Get cache data
+            cache = await self.scanner.get_cached_data()
+            
+            # Search for the lora in cache data
+            for lora in cache.raw_data:
+                file_name = lora['file_name']
+                if file_name == lora_name:
+                    notes = lora.get('notes', '')
+                    
+                    return web.json_response({
+                        'success': True,
+                        'notes': notes
+                    })
+
+            # If lora not found
+            return web.json_response({
+                'success': False,
+                'error': 'LoRA not found in cache'
+            }, status=404)
+
+        except Exception as e:
+            logger.error(f"Error getting lora notes: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    async def get_lora_trigger_words(self, request: web.Request) -> web.Response:
+        """Get trigger words for a specific LoRA file"""
+        try:
+            if self.scanner is None:
+                self.scanner = await ServiceRegistry.get_lora_scanner()
+                
+            # Get lora file name from query parameters
+            lora_name = request.query.get('name')
+            if not lora_name:
+                return web.Response(text='Lora file name is required', status=400)
+
+            # Get cache data
+            cache = await self.scanner.get_cached_data()
+            
+            # Search for the lora in cache data
+            for lora in cache.raw_data:
+                file_name = lora['file_name']
+                if file_name == lora_name:
+                    # Get trigger words from civitai data
+                    civitai_data = lora.get('civitai', {})
+                    trigger_words = civitai_data.get('trainedWords', [])
+                    
+                    return web.json_response({
+                        'success': True,
+                        'trigger_words': trigger_words
+                    })
+
+            # If lora not found
+            return web.json_response({
+                'success': False,
+                'error': 'LoRA not found in cache'
+            }, status=404)
+
+        except Exception as e:
+            logger.error(f"Error getting lora trigger words: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    async def find_duplicate_loras(self, request: web.Request) -> web.Response:
+        """Find loras with duplicate SHA256 hashes"""
+        try:
+            if self.scanner is None:
+                self.scanner = await ServiceRegistry.get_lora_scanner()
+                
+            # Get duplicate hashes from hash index
+            duplicates = self.scanner._hash_index.get_duplicate_hashes()
+            
+            # Format the response
+            result = []
+            cache = await self.scanner.get_cached_data()
+            
+            for sha256, paths in duplicates.items():
+                group = {
+                    "hash": sha256,
+                    "models": []
+                }
+                # Find matching models for each duplicate path
+                for path in paths:
+                    model = next((m for m in cache.raw_data if m['file_path'] == path), None)
+                    if model:
+                        group["models"].append(self._format_lora_response(model))
+                
+                # Add the primary model too
+                primary_path = self.scanner._hash_index.get_path(sha256)
+                if primary_path and primary_path not in paths:
+                    primary_model = next((m for m in cache.raw_data if m['file_path'] == primary_path), None)
+                    if primary_model:
+                        group["models"].insert(0, self._format_lora_response(primary_model))
+                
+                if len(group["models"]) > 1:  # Only include if we found multiple models
+                    result.append(group)
+                
+            return web.json_response({
+                "success": True,
+                "duplicates": result,
+                "count": len(result)
+            })
+        except Exception as e:
+            logger.error(f"Error finding duplicate loras: {e}", exc_info=True)
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)
+
+    async def find_filename_conflicts(self, request: web.Request) -> web.Response:
+        """Find loras with conflicting filenames"""
+        try:
+            if self.scanner is None:
+                self.scanner = await ServiceRegistry.get_lora_scanner()
+                
+            # Get duplicate filenames from hash index
+            duplicates = self.scanner._hash_index.get_duplicate_filenames()
+            
+            # Format the response
+            result = []
+            cache = await self.scanner.get_cached_data()
+            
+            for filename, paths in duplicates.items():
+                group = {
+                    "filename": filename,
+                    "models": []
+                }
+                # Find matching models for each path
+                for path in paths:
+                    model = next((m for m in cache.raw_data if m['file_path'] == path), None)
+                    if model:
+                        group["models"].append(self._format_lora_response(model))
+                
+                # Find the model from the main index too
+                hash_val = self.scanner._hash_index.get_hash_by_filename(filename)
+                if hash_val:
+                    main_path = self.scanner._hash_index.get_path(hash_val)
+                    if main_path and main_path not in paths:
+                        main_model = next((m for m in cache.raw_data if m['file_path'] == main_path), None)
+                        if main_model:
+                            group["models"].insert(0, self._format_lora_response(main_model))
+                
+                if group["models"]:  # Only include if we found models
+                    result.append(group)
+                
+            return web.json_response({
+                "success": True,
+                "conflicts": result,
+                "count": len(result)
+            })
+        except Exception as e:
+            logger.error(f"Error finding filename conflicts: {e}", exc_info=True)
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)
+
+    async def bulk_delete_loras(self, request: web.Request) -> web.Response:
+        """Handle bulk deletion of lora models"""
+        try:
+            if self.scanner is None:
+                self.scanner = await ServiceRegistry.get_lora_scanner()
+            
+            return await ModelRouteUtils.handle_bulk_delete_models(request, self.scanner)
+                
+        except Exception as e:
+            logger.error(f"Error in bulk delete loras: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    async def relink_civitai(self, request: web.Request) -> web.Response:
+        """Handle CivitAI metadata re-linking request by model version ID for LoRAs"""
+        if self.scanner is None:
+            self.scanner = await ServiceRegistry.get_lora_scanner()
+        return await ModelRouteUtils.handle_relink_civitai(request, self.scanner)
+
+    async def verify_duplicates(self, request: web.Request) -> web.Response:
+        """Handle verification of duplicate lora hashes"""
+        if self.scanner is None:
+            self.scanner = await ServiceRegistry.get_lora_scanner()
+        return await ModelRouteUtils.handle_verify_duplicates(request, self.scanner)

py/routes/checkpoints_routes.py

@@ -7,6 +7,7 @@ import asyncio
 
 from ..utils.routes_common import ModelRouteUtils
 from ..utils.constants import NSFW_LEVELS
+from ..utils.metadata_manager import MetadataManager
 from ..services.websocket_manager import ws_manager
 from ..services.service_registry import ServiceRegistry
 from ..config import config
@@ -51,6 +52,7 @@ class CheckpointsRoutes:
         app.router.add_post('/api/checkpoints/delete', self.delete_model)
         app.router.add_post('/api/checkpoints/exclude', self.exclude_model)  # Add new exclude endpoint
         app.router.add_post('/api/checkpoints/fetch-civitai', self.fetch_civitai)
+        app.router.add_post('/api/checkpoints/relink-civitai', self.relink_civitai)  # Add new relink endpoint
         app.router.add_post('/api/checkpoints/replace-preview', self.replace_preview)
         app.router.add_post('/api/checkpoints/download', self.download_checkpoint)
         app.router.add_post('/api/checkpoints/save-metadata', self.save_metadata) # Add new route
@@ -58,6 +60,16 @@ class CheckpointsRoutes:
         # Add new WebSocket endpoint for checkpoint progress
         app.router.add_get('/ws/checkpoint-progress', ws_manager.handle_checkpoint_connection)
 
+        # Add new routes for finding duplicates and filename conflicts
+        app.router.add_get('/api/checkpoints/find-duplicates', self.find_duplicate_checkpoints)
+        app.router.add_get('/api/checkpoints/find-filename-conflicts', self.find_filename_conflicts)
+
+        # Add new endpoint for bulk deleting checkpoints
+        app.router.add_post('/api/checkpoints/bulk-delete', self.bulk_delete_checkpoints)
+
+        # Add new endpoint for verifying duplicates
+        app.router.add_post('/api/checkpoints/verify-duplicates', self.verify_duplicates)
+
     async def get_checkpoints(self, request):
         """Get paginated checkpoint data"""
         try:
@@ -420,7 +432,10 @@ class CheckpointsRoutes:
     async def scan_checkpoints(self, request):
         """Force a rescan of checkpoint files"""
         try:
-            await self.scanner.get_cached_data(force_refresh=True)
+            # Get the full_rebuild parameter and convert to bool, default to False
+            full_rebuild = request.query.get('full_rebuild', 'false').lower() == 'true'
+            
+            await self.scanner.get_cached_data(force_refresh=True, rebuild_cache=full_rebuild)
             return web.json_response({"status": "success", "message": "Checkpoint scan completed"})
         except Exception as e:
             logger.error(f"Error in scan_checkpoints: {e}", exc_info=True)
@@ -430,7 +445,7 @@ class CheckpointsRoutes:
         """Get detailed information for a specific checkpoint by name"""
         try:
             name = request.match_info.get('name', '')
-            checkpoint_info = await self.scanner.get_checkpoint_info_by_name(name)
+            checkpoint_info = await self.scanner.get_model_info_by_name(name)
             
             if checkpoint_info:
                 return web.json_response(checkpoint_info)
@@ -507,7 +522,20 @@ class CheckpointsRoutes:
     
     async def fetch_civitai(self, request: web.Request) -> web.Response:
         """Handle CivitAI metadata fetch request for checkpoints"""
-        return await ModelRouteUtils.handle_fetch_civitai(request, self.scanner)
+        response = await ModelRouteUtils.handle_fetch_civitai(request, self.scanner)
+        
+        # If successful, format the metadata before returning
+        if response.status == 200:
+            data = json.loads(response.body.decode('utf-8'))
+            if data.get("success") and data.get("metadata"):
+                formatted_metadata = self._format_checkpoint_response(data["metadata"])
+                return web.json_response({
+                    "success": True,
+                    "metadata": formatted_metadata
+                })
+        
+        # Otherwise, return the original response
+        return response
     
     async def replace_preview(self, request: web.Request) -> web.Response:
         """Handle preview image replacement for checkpoints"""
@@ -623,8 +651,7 @@ class CheckpointsRoutes:
             metadata.update(metadata_updates)
 
             # Save updated metadata
-            with open(metadata_path, 'w', encoding='utf-8') as f:
-                json.dump(metadata, f, indent=2, ensure_ascii=False)
+            await MetadataManager.save_metadata(file_path, metadata)
 
             # Update cache
             await self.scanner.update_single_model_cache(file_path, file_path, metadata)
@@ -692,3 +719,120 @@ class CheckpointsRoutes:
         except Exception as e:
             logger.error(f"Error fetching checkpoint model versions: {e}")
             return web.Response(status=500, text=str(e))
+
+    async def find_duplicate_checkpoints(self, request: web.Request) -> web.Response:
+        """Find checkpoints with duplicate SHA256 hashes"""
+        try:
+            if self.scanner is None:
+                self.scanner = await ServiceRegistry.get_checkpoint_scanner()
+                
+            # Get duplicate hashes from hash index
+            duplicates = self.scanner._hash_index.get_duplicate_hashes()
+            
+            # Format the response
+            result = []
+            cache = await self.scanner.get_cached_data()
+            
+            for sha256, paths in duplicates.items():
+                group = {
+                    "hash": sha256,
+                    "models": []
+                }
+                # Find matching models for each path
+                for path in paths:
+                    model = next((m for m in cache.raw_data if m['file_path'] == path), None)
+                    if model:
+                        group["models"].append(self._format_checkpoint_response(model))
+                
+                # Add the primary model too
+                primary_path = self.scanner._hash_index.get_path(sha256)
+                if primary_path and primary_path not in paths:
+                    primary_model = next((m for m in cache.raw_data if m['file_path'] == primary_path), None)
+                    if primary_model:
+                        group["models"].insert(0, self._format_checkpoint_response(primary_model))
+                
+                if len(group["models"]) > 1:  # Only include if we found multiple models
+                    result.append(group)
+                
+            return web.json_response({
+                "success": True,
+                "duplicates": result,
+                "count": len(result)
+            })
+        except Exception as e:
+            logger.error(f"Error finding duplicate checkpoints: {e}", exc_info=True)
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)
+
+    async def find_filename_conflicts(self, request: web.Request) -> web.Response:
+        """Find checkpoints with conflicting filenames"""
+        try:
+            if self.scanner is None:
+                self.scanner = await ServiceRegistry.get_checkpoint_scanner()
+                
+            # Get duplicate filenames from hash index
+            duplicates = self.scanner._hash_index.get_duplicate_filenames()
+            
+            # Format the response
+            result = []
+            cache = await self.scanner.get_cached_data()
+            
+            for filename, paths in duplicates.items():
+                group = {
+                    "filename": filename,
+                    "models": []
+                }
+                # Find matching models for each path
+                for path in paths:
+                    model = next((m for m in cache.raw_data if m['file_path'] == path), None)
+                    if model:
+                        group["models"].append(self._format_checkpoint_response(model))
+                
+                # Find the model from the main index too
+                hash_val = self.scanner._hash_index.get_hash_by_filename(filename)
+                if hash_val:
+                    main_path = self.scanner._hash_index.get_path(hash_val)
+                    if main_path and main_path not in paths:
+                        main_model = next((m for m in cache.raw_data if m['file_path'] == main_path), None)
+                        if main_model:
+                            group["models"].insert(0, self._format_checkpoint_response(main_model))
+                
+                if group["models"]:
+                    result.append(group)
+                
+            return web.json_response({
+                "success": True,
+                "conflicts": result,
+                "count": len(result)
+            })
+        except Exception as e:
+            logger.error(f"Error finding filename conflicts: {e}", exc_info=True)
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)
+
+    async def bulk_delete_checkpoints(self, request: web.Request) -> web.Response:
+        """Handle bulk deletion of checkpoint models"""
+        try:
+            if self.scanner is None:
+                self.scanner = await ServiceRegistry.get_checkpoint_scanner()
+            
+            return await ModelRouteUtils.handle_bulk_delete_models(request, self.scanner)
+                
+        except Exception as e:
+            logger.error(f"Error in bulk delete checkpoints: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    async def relink_civitai(self, request: web.Request) -> web.Response:
+        """Handle CivitAI metadata re-linking request by model version ID for checkpoints"""
+        return await ModelRouteUtils.handle_relink_civitai(request, self.scanner)
+
+    async def verify_duplicates(self, request: web.Request) -> web.Response:
+        """Handle verification of duplicate checkpoint hashes"""
+        return await ModelRouteUtils.handle_verify_duplicates(request, self.scanner)

py/routes/example_images_routes.py

@@ -0,0 +1,68 @@
+import logging
+from ..utils.example_images_download_manager import DownloadManager
+from ..utils.example_images_processor import ExampleImagesProcessor
+from ..utils.example_images_metadata import MetadataUpdater
+from ..utils.example_images_file_manager import ExampleImagesFileManager
+
+logger = logging.getLogger(__name__)
+
+class ExampleImagesRoutes:
+    """Routes for example images related functionality"""
+    
+    @staticmethod
+    def setup_routes(app):
+        """Register example images routes"""
+        app.router.add_post('/api/download-example-images', ExampleImagesRoutes.download_example_images)
+        app.router.add_post('/api/import-example-images', ExampleImagesRoutes.import_example_images)
+        app.router.add_get('/api/example-images-status', ExampleImagesRoutes.get_example_images_status)
+        app.router.add_post('/api/pause-example-images', ExampleImagesRoutes.pause_example_images)
+        app.router.add_post('/api/resume-example-images', ExampleImagesRoutes.resume_example_images)
+        app.router.add_post('/api/open-example-images-folder', ExampleImagesRoutes.open_example_images_folder)
+        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)
+
+    @staticmethod
+    async def download_example_images(request):
+        """Download example images for models from Civitai"""
+        return await DownloadManager.start_download(request)
+
+    @staticmethod
+    async def get_example_images_status(request):
+        """Get the current status of example images download"""
+        return await DownloadManager.get_status(request)
+
+    @staticmethod
+    async def pause_example_images(request):
+        """Pause the example images download"""
+        return await DownloadManager.pause_download(request)
+
+    @staticmethod
+    async def resume_example_images(request):
+        """Resume the example images download"""
+        return await DownloadManager.resume_download(request)
+        
+    @staticmethod
+    async def open_example_images_folder(request):
+        """Open the example images folder for a specific model"""
+        return await ExampleImagesFileManager.open_folder(request)
+
+    @staticmethod
+    async def get_example_image_files(request):
+        """Get list of example image files for a specific model"""
+        return await ExampleImagesFileManager.get_files(request)
+
+    @staticmethod
+    async def import_example_images(request):
+        """Import local example images for a model"""
+        return await ExampleImagesProcessor.import_images(request)
+        
+    @staticmethod
+    async def has_example_images(request):
+        """Check if example images folder exists and is not empty for a model"""
+        return await ExampleImagesFileManager.has_images(request)
+
+    @staticmethod
+    async def delete_example_image(request):
+        """Delete a custom example image for a model"""
+        return await ExampleImagesProcessor.delete_custom_image(request)

py/routes/lora_routes.py

@@ -70,8 +70,7 @@ class LoraRoutes:
             # It's initializing if the cache object doesn't exist yet,
             # OR if the scanner explicitly says it's initializing (background task running).
             is_initializing = (
-                self.scanner._cache is None or
-                (hasattr(self.scanner, '_is_initializing') and self.scanner._is_initializing)
+                self.scanner._cache is None or self.scanner.is_initializing()
             )
 
             if is_initializing:

py/routes/misc_routes.py

@@ -1,17 +1,13 @@
 import logging
 import os
-import asyncio
-import json
-import time
-import aiohttp
+from server import PromptServer # type: ignore
 from aiohttp import web
 from ..services.settings_manager import settings
 from ..utils.usage_stats import UsageStats
-from ..services.service_registry import ServiceRegistry
-from ..utils.exif_utils import ExifUtils
-from ..utils.constants import EXAMPLE_IMAGE_WIDTH, SUPPORTED_MEDIA_EXTENSIONS
-from ..services.civitai_client import CivitaiClient
-from ..utils.routes_common import ModelRouteUtils
+from ..utils.lora_metadata import extract_trained_words
+from ..config import config
+from ..utils.constants import SUPPORTED_MEDIA_EXTENSIONS
+import re
 
 logger = logging.getLogger(__name__)
 
@@ -38,17 +34,72 @@ class MiscRoutes:
     def setup_routes(app):
         """Register miscellaneous routes"""
         app.router.add_post('/api/settings', MiscRoutes.update_settings)
+        
+        # Add new route for clearing cache
+        app.router.add_post('/api/clear-cache', MiscRoutes.clear_cache)
 
         # Usage stats routes
         app.router.add_post('/api/update-usage-stats', MiscRoutes.update_usage_stats)
         app.router.add_get('/api/get-usage-stats', MiscRoutes.get_usage_stats)
         
-        # Example images download routes
-        app.router.add_post('/api/download-example-images', MiscRoutes.download_example_images)
-        app.router.add_get('/api/example-images-status', MiscRoutes.get_example_images_status)
-        app.router.add_post('/api/pause-example-images', MiscRoutes.pause_example_images)
-        app.router.add_post('/api/resume-example-images', MiscRoutes.resume_example_images)
+        # Lora code update endpoint
+        app.router.add_post('/api/update-lora-code', MiscRoutes.update_lora_code)
+
+        # Add new route for getting trained words
+        app.router.add_get('/api/trained-words', MiscRoutes.get_trained_words)
         
+        # Add new route for getting model example files
+        app.router.add_get('/api/model-example-files', MiscRoutes.get_model_example_files)
+
+    @staticmethod
+    async def clear_cache(request):
+        """Clear all cache files from the cache folder"""
+        try:
+            # Get the cache folder path (relative to project directory)
+            project_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+            cache_folder = os.path.join(project_dir, 'cache')
+            
+            # Check if cache folder exists
+            if not os.path.exists(cache_folder):
+                logger.info("Cache folder does not exist, nothing to clear")
+                return web.json_response({'success': True, 'message': 'No cache folder found'})
+            
+            # Get list of cache files before deleting for reporting
+            cache_files = [f for f in os.listdir(cache_folder) if os.path.isfile(os.path.join(cache_folder, f))]
+            deleted_files = []
+            
+            # Delete each .msgpack file in the cache folder
+            for filename in cache_files:
+                if filename.endswith('.msgpack'):
+                    file_path = os.path.join(cache_folder, filename)
+                    try:
+                        os.remove(file_path)
+                        deleted_files.append(filename)
+                        logger.info(f"Deleted cache file: {filename}")
+                    except Exception as e:
+                        logger.error(f"Failed to delete {filename}: {e}")
+                        return web.json_response({
+                            'success': False,
+                            'error': f"Failed to delete {filename}: {str(e)}"
+                        }, status=500)
+            
+            # If we want to completely remove the cache folder too (optional, 
+            # but we'll keep the folder structure in place here)
+            # shutil.rmtree(cache_folder)
+            
+            return web.json_response({
+                'success': True,
+                'message': f"Successfully cleared {len(deleted_files)} cache files",
+                'deleted_files': deleted_files
+            })
+            
+        except Exception as e:
+            logger.error(f"Error clearing cache files: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
     @staticmethod
     async def update_settings(request):
         """Update application settings"""
@@ -121,10 +172,14 @@ class MiscRoutes:
             usage_stats = UsageStats()
             stats = await usage_stats.get_stats()
             
-            return web.json_response({
+            # Add version information to help clients handle format changes
+            stats_response = {
                 'success': True,
-                'data': stats
-            })
+                'data': stats,
+                'format_version': 2  # Indicate this is the new format with history
+            }
+            
+            return web.json_response(stats_response)
             
         except Exception as e:
             logger.error(f"Failed to get usage stats: {e}", exc_info=True)
@@ -134,634 +189,217 @@ class MiscRoutes:
             }, status=500)
     
     @staticmethod
-    async def download_example_images(request):
+    async def update_lora_code(request):
         """
-        Download example images for models from Civitai
+        Update Lora code in ComfyUI nodes
         
         Expects a JSON body with:
         {
-            "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 to avoid rate limiting (default: 1.0)
+            "node_ids": [123, 456], # Optional - List of node IDs to update (for browser mode)
+            "lora_code": "<lora:modelname:1.0>", # The Lora code to send
+            "mode": "append" # or "replace" - whether to append or replace existing code
         }
         """
-        global download_task, is_downloading, download_progress
-        
-        if is_downloading:
-            # Create a copy for JSON serialization
-            response_progress = download_progress.copy()
-            response_progress['processed_models'] = list(download_progress['processed_models'])
-            response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
-            
-            return web.json_response({
-                'success': False,
-                'error': 'Download already in progress',
-                'status': response_progress
-            }, status=400)
-        
         try:
             # Parse the request body
             data = await request.json()
-            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))
+            node_ids = data.get('node_ids')
+            lora_code = data.get('lora_code', '')
+            mode = data.get('mode', 'append')
             
-            if not output_dir:
+            if not lora_code:
                 return web.json_response({
                     'success': False,
-                    'error': 'Missing output_dir parameter'
+                    'error': 'Missing lora_code parameter'
                 }, status=400)
             
-            # Create the output directory
-            os.makedirs(output_dir, exist_ok=True)
+            results = []
             
-            # Initialize progress tracking
-            download_progress['total'] = 0
-            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
-            
-            # Get the processed models list from a file if it exists
-            progress_file = os.path.join(output_dir, '.download_progress.json')
-            if os.path.exists(progress_file):
+            # Desktop mode: no specific node_ids provided
+            if node_ids is None:
                 try:
-                    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")
+                    # Send broadcast message with id=-1 to all Lora Loader nodes
+                    PromptServer.instance.send_sync("lora_code_update", {
+                        "id": -1,
+                        "lora_code": lora_code,
+                        "mode": mode
+                    })
+                    results.append({
+                        'node_id': 'broadcast',
+                        'success': True
+                    })
                 except Exception as e:
-                    logger.error(f"Failed to load progress file: {e}")
-                    download_progress['processed_models'] = set()
+                    logger.error(f"Error broadcasting lora code: {e}")
+                    results.append({
+                        'node_id': 'broadcast',
+                        'success': False,
+                        'error': str(e)
+                    })
             else:
-                download_progress['processed_models'] = set()
-            
-            # Start the download task
-            is_downloading = True
-            download_task = asyncio.create_task(
-                MiscRoutes._download_all_example_images(
-                    output_dir, 
-                    optimize, 
-                    model_types,
-                    delay
-                )
-            )
-            
-            # Create a copy for JSON serialization
-            response_progress = download_progress.copy()
-            response_progress['processed_models'] = list(download_progress['processed_models'])
-            response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
+                # Browser mode: send to specific nodes
+                for node_id in node_ids:
+                    try:
+                        # Send the message to the frontend
+                        PromptServer.instance.send_sync("lora_code_update", {
+                            "id": node_id,
+                            "lora_code": lora_code,
+                            "mode": mode
+                        })
+                        results.append({
+                            'node_id': node_id,
+                            'success': True
+                        })
+                    except Exception as e:
+                        logger.error(f"Error sending lora code to node {node_id}: {e}")
+                        results.append({
+                            'node_id': node_id,
+                            'success': False,
+                            'error': str(e)
+                        })
             
             return web.json_response({
                 'success': True,
-                'message': 'Download started',
-                'status': response_progress
+                'results': results
             })
             
         except Exception as e:
-            logger.error(f"Failed to start example images download: {e}", exc_info=True)
+            logger.error(f"Failed to update lora code: {e}", exc_info=True)
             return web.json_response({
                 'success': False,
                 'error': str(e)
             }, status=500)
 
     @staticmethod
-    async def get_example_images_status(request):
-        """Get the current status of example images download"""
-        global download_progress
+    async def get_trained_words(request):
+        """
+        Get trained words from a safetensors file, sorted by frequency
         
-        # Create a copy of the progress dict with the set converted to a list for JSON serialization
-        response_progress = download_progress.copy()
-        response_progress['processed_models'] = list(download_progress['processed_models'])
-        response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
-        
-        return web.json_response({
-            'success': True,
-            'is_downloading': is_downloading,
-            'status': response_progress
-        })
-
-    @staticmethod
-    async def pause_example_images(request):
-        """Pause the example images download"""
-        global download_progress
-        
-        if not is_downloading:
+        Expects a query parameter:
+        file_path: Path to the safetensors file
+        """
+        try:
+            # Get file path from query parameters
+            file_path = request.query.get('file_path')
+            
+            if not file_path:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing file_path parameter'
+                }, status=400)
+            
+            # Check if file exists and is a safetensors file
+            if not os.path.exists(file_path):
+                return web.json_response({
+                    'success': False,
+                    'error': f"File not found: {file_path}"
+                }, status=404)
+                
+            if not file_path.lower().endswith('.safetensors'):
+                return web.json_response({
+                    'success': False,
+                    'error': 'File is not a safetensors file'
+                }, status=400)
+            
+            # Extract trained words and class_tokens
+            trained_words, class_tokens = await extract_trained_words(file_path)
+            
+            # Return result with both trained words and class tokens
+            return web.json_response({
+                'success': True,
+                'trained_words': trained_words,
+                'class_tokens': class_tokens
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to get trained words: {e}", exc_info=True)
             return web.json_response({
                 'success': False,
-                'error': 'No download in progress'
-            }, status=400)
-        
-        download_progress['status'] = 'paused'
-        
-        return web.json_response({
-            'success': True,
-            'message': 'Download paused'
-        })
+                'error': str(e)
+            }, status=500)
 
     @staticmethod
-    async def resume_example_images(request):
-        """Resume the example images download"""
-        global download_progress
+    async def get_model_example_files(request):
+        """
+        Get list of example image files for a specific model based on file path
         
-        if not is_downloading:
-            return web.json_response({
-                'success': False,
-                'error': 'No download in progress'
-            }, status=400)
+        Expects:
+        - file_path in query parameters
         
-        if download_progress['status'] == 'paused':
-            download_progress['status'] = 'running'
+        Returns:
+        - List of image files with their paths as static URLs
+        """
+        try:
+            # Get the model file path from query parameters
+            file_path = request.query.get('file_path')
+            
+            if not file_path:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing file_path parameter'
+                }, status=400)
+            
+            # Extract directory and base filename
+            model_dir = os.path.dirname(file_path)
+            model_filename = os.path.basename(file_path)
+            model_name = os.path.splitext(model_filename)[0]
+            
+            # Check if the directory exists
+            if not os.path.exists(model_dir):
+                return web.json_response({
+                    'success': False, 
+                    'error': 'Model directory not found',
+                    'files': []
+                }, status=404)
+            
+            # Look for files matching the pattern modelname.example.<index>.<ext>
+            files = []
+            pattern = f"{model_name}.example."
+            
+            for file in os.listdir(model_dir):
+                file_lower = file.lower()
+                if file_lower.startswith(pattern.lower()):
+                    file_full_path = os.path.join(model_dir, file)
+                    if os.path.isfile(file_full_path):
+                        # Check if the file is a supported media file
+                        file_ext = os.path.splitext(file)[1].lower()
+                        if (file_ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or 
+                            file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']):
+                            
+                            # Extract the index from the filename
+                            try:
+                                # Extract the part after '.example.' and before file extension
+                                index_part = file[len(pattern):].split('.')[0]
+                                # Try to parse it as an integer
+                                index = int(index_part)
+                            except (ValueError, IndexError):
+                                # If we can't parse the index, use infinity to sort at the end
+                                index = float('inf')
+                            
+                            # Convert file path to static URL
+                            static_url = config.get_preview_static_url(file_full_path)
+                            
+                            files.append({
+                                'name': file,
+                                'path': static_url,
+                                'extension': file_ext,
+                                'is_video': file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos'],
+                                'index': index
+                            })
+            
+            # Sort files by their index for consistent ordering
+            files.sort(key=lambda x: x['index'])
+            # Remove the index field as it's only used for sorting
+            for file in files:
+                file.pop('index', None)
             
             return web.json_response({
                 'success': True,
-                'message': 'Download resumed'
+                'files': files
             })
-        else:
+            
+        except Exception as e:
+            logger.error(f"Failed to get model example files: {e}", exc_info=True)
             return web.json_response({
                 'success': False,
-                'error': f"Download is in '{download_progress['status']}' state, cannot resume"
-            }, status=400)
-
-    @staticmethod
-    async def _refresh_model_metadata(model_hash, model_name, scanner_type, scanner):
-        """Refresh model metadata from CivitAI
-        
-        Args:
-            model_hash: SHA256 hash of the model
-            model_name: Name of the model (for logging)
-            scanner_type: Type of scanner ('lora' or 'checkpoint')
-            scanner: Scanner instance for this model type
-            
-        Returns:
-            bool: True if metadata was successfully refreshed, False otherwise
-        """
-        global download_progress
-        
-        try:
-            # Find the model in the scanner cache
-            cache = await scanner.get_cached_data()
-            model_data = None
-            
-            for item in cache.raw_data:
-                if item.get('sha256') == model_hash:
-                    model_data = item
-                    break
-            
-            if not model_data:
-                logger.warning(f"Model {model_name} with hash {model_hash} not found in cache")
-                return False
-            
-            file_path = model_data.get('file_path')
-            if not file_path:
-                logger.warning(f"Model {model_name} has no file path")
-                return False
-            
-            # Track that we're refreshing this model
-            download_progress['refreshed_models'].add(model_hash)
-            
-            # Use ModelRouteUtils to refresh the metadata
-            async def update_cache_func(old_path, new_path, metadata):
-                return await scanner.update_single_model_cache(old_path, new_path, metadata)
-            
-            success = await ModelRouteUtils.fetch_and_update_model(
-                model_hash, 
-                file_path, 
-                model_data,
-                update_cache_func
-            )
-            
-            if success:
-                logger.info(f"Successfully refreshed metadata for {model_name}")
-                return True
-            else:
-                logger.warning(f"Failed to refresh metadata for {model_name}")
-                return False
-                
-        except Exception as e:
-            error_msg = f"Error refreshing metadata for {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
-    
-    @staticmethod
-    async def _process_model_images(model_hash, model_name, model_images, model_dir, optimize, independent_session, delay):
-        """Process and download images for a single model
-        
-        Args:
-            model_hash: SHA256 hash of the model
-            model_name: Name of the model
-            model_images: List of image objects from CivitAI
-            model_dir: Directory to save images to
-            optimize: Whether to optimize images
-            independent_session: aiohttp session for downloads
-            delay: Delay between downloads
-            
-        Returns:
-            bool: True if all images were processed successfully, False otherwise
-        """
-        global download_progress
-        
-        model_success = True
-        
-        for i, image in enumerate(model_images, 1):
-            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 both 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
-            
-            save_filename = f"image_{i}{image_ext}"
-            
-            # 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}")
-                
-                # Direct download using the independent session
-                async with independent_session.get(image_url, timeout=60) as response:
-                    if response.status == 200:
-                        if is_image and optimize:
-                            # For images, optimize if requested
-                            image_data = await response.read()
-                            optimized_data, ext = ExifUtils.optimize_image(
-                                image_data, 
-                                target_width=EXAMPLE_IMAGE_WIDTH, 
-                                format='webp', 
-                                quality=85, 
-                                preserve_metadata=False
-                            )
-                            
-                            # Update save filename if format changed
-                            if ext == '.webp':
-                                save_filename = os.path.splitext(save_filename)[0] + '.webp'
-                                save_path = os.path.join(model_dir, save_filename)
-                            
-                            # Save the optimized image
-                            with open(save_path, 'wb') as f:
-                                f.write(optimized_data)
-                        else:
-                            # For videos or unoptimized images, save directly
-                            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)
-                        download_progress['errors'].append(error_msg)
-                        download_progress['last_error'] = error_msg
-                        model_success = False  # Mark model as failed due to 404
-                        # Return early to trigger metadata refresh attempt
-                        return False, True  # (success, is_stale_metadata)
-                    else:
-                        error_msg = f"Failed to download file: {image_url}, status code: {response.status}"
-                        logger.warning(error_msg)
-                        download_progress['errors'].append(error_msg)
-                        download_progress['last_error'] = error_msg
-                        model_success = False  # Mark model as failed
-                
-                # Add a delay between downloads for remote files only
-                await asyncio.sleep(delay)
-            except Exception as e:
-                error_msg = f"Error downloading file {image_url}: {str(e)}"
-                logger.error(error_msg)
-                download_progress['errors'].append(error_msg)
-                download_progress['last_error'] = error_msg
-                model_success = False  # Mark model as failed
-        
-        return model_success, False  # (success, is_stale_metadata)
-    
-    @staticmethod
-    async def _process_local_example_images(model_file_path, model_file_name, model_name, model_dir, optimize):
-        """Process local example images for a model
-        
-        Args:
-            model_file_path: Path to the model file
-            model_file_name: Filename of the model
-            model_name: Name of the model
-            model_dir: Directory to save processed images to
-            optimize: Whether to optimize images
-            
-        Returns:
-            bool: True if local images were processed successfully, False otherwise
-        """
-        global download_progress
-        
-        try:
-            model_dir_path = os.path.dirname(model_file_path)
-            local_images = []
-            
-            # Look for files with pattern: filename.example.*.ext
-            if model_file_name:
-                example_prefix = f"{model_file_name}.example."
-                
-                if os.path.exists(model_dir_path):
-                    for file in os.listdir(model_dir_path):
-                        file_lower = file.lower()
-                        if file_lower.startswith(example_prefix.lower()):
-                            file_ext = os.path.splitext(file_lower)[1]
-                            is_supported = (file_ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or 
-                                           file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos'])
-                            
-                            if is_supported:
-                                local_images.append(os.path.join(model_dir_path, file))
-            
-            # Process local images if found
-            if local_images:
-                logger.info(f"Found {len(local_images)} local example images for {model_name}")
-                
-                for i, local_image_path in enumerate(local_images, 1):
-                    local_ext = os.path.splitext(local_image_path)[1].lower()
-                    save_filename = f"image_{i}{local_ext}"
-                    save_path = os.path.join(model_dir, save_filename)
-                    
-                    # Skip if already exists in output directory
-                    if os.path.exists(save_path):
-                        logger.debug(f"File already exists in output: {save_path}")
-                        continue
-                    
-                    # Handle image processing based on file type and optimize setting
-                    is_image = local_ext in SUPPORTED_MEDIA_EXTENSIONS['images']
-                    
-                    if is_image and optimize:
-                        # Optimize the image
-                        with open(local_image_path, 'rb') as img_file:
-                            image_data = img_file.read()
-                        
-                        optimized_data, ext = ExifUtils.optimize_image(
-                            image_data, 
-                            target_width=EXAMPLE_IMAGE_WIDTH, 
-                            format='webp', 
-                            quality=85, 
-                            preserve_metadata=False
-                        )
-                        
-                        # Update save filename if format changed
-                        if ext == '.webp':
-                            save_filename = os.path.splitext(save_filename)[0] + '.webp'
-                            save_path = os.path.join(model_dir, save_filename)
-                        
-                        # Save the optimized image
-                        with open(save_path, 'wb') as f:
-                            f.write(optimized_data)
-                    else:
-                        # For videos or unoptimized images, copy directly
-                        with open(local_image_path, 'rb') as src_file:
-                            with open(save_path, 'wb') as dst_file:
-                                dst_file.write(src_file.read())
-                
-                return True
-            return False
-        except Exception as e:
-            error_msg = f"Error processing local examples for {model_name}: {str(e)}"
-            logger.error(error_msg)
-            download_progress['errors'].append(error_msg)
-            download_progress['last_error'] = error_msg
-            return False
-
-    @staticmethod
-    async def _download_all_example_images(output_dir, optimize, model_types, delay):
-        """Download example images for all models
-        
-        Args:
-            output_dir: Base directory to save example images
-            optimize: Whether to optimize images
-            model_types: List of model types to process
-            delay: Delay between downloads to avoid rate limiting
-        """
-        global is_downloading, download_progress
-        
-        # Create an independent session for downloading example images
-        # This avoids interference with the CivitAI client's 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)
-        
-        # Create a dedicated session just for this download task
-        independent_session = aiohttp.ClientSession(
-            connector=connector,
-            trust_env=True,
-            timeout=timeout
-        )
-        
-        try:
-            # Get the 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))
-            
-            # Get all models from all scanners
-            all_models = []
-            for scanner_type, scanner in scanners:
-                cache = await scanner.get_cached_data()
-                if cache and cache.raw_data:
-                    for model in cache.raw_data:
-                        # Only process models with images and a valid sha256
-                        if model.get('civitai') and model.get('civitai', {}).get('images') and model.get('sha256'):
-                            all_models.append((scanner_type, model, scanner))
-            
-            # Update total count
-            download_progress['total'] = len(all_models)
-            logger.info(f"Found {download_progress['total']} models with example images")
-            
-            # Process each model
-            for scanner_type, model, scanner in all_models:
-                # 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']}")
-                    break
-                
-                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]})"
-                    
-                    # Skip if already processed
-                    if model_hash in download_progress['processed_models']:
-                        logger.debug(f"Skipping already processed model: {model_name}")
-                        download_progress['completed'] += 1
-                        continue
-                    
-                    # Create model directory
-                    model_dir = os.path.join(output_dir, model_hash)
-                    os.makedirs(model_dir, exist_ok=True)
-                    
-                    # Process images for this model
-                    images = model.get('civitai', {}).get('images', [])
-                    
-                    if not images:
-                        logger.debug(f"No images found for model: {model_name}")
-                        download_progress['processed_models'].add(model_hash)
-                        download_progress['completed'] += 1
-                        continue
-                    
-                    # First check if we have local example images for this model
-                    local_images_processed = False
-                    if model_file_path:
-                        local_images_processed = await MiscRoutes._process_local_example_images(
-                            model_file_path, 
-                            model_file_name, 
-                            model_name, 
-                            model_dir, 
-                            optimize
-                        )
-                        
-                        if local_images_processed:
-                            # Mark as successfully processed if all local images were processed
-                            download_progress['processed_models'].add(model_hash)
-                            logger.info(f"Successfully processed local examples for {model_name}")
-                    
-                    # If we didn't process local images, download from remote
-                    if not local_images_processed:
-                        # Try to download images
-                        model_success, is_stale_metadata = await MiscRoutes._process_model_images(
-                            model_hash, 
-                            model_name, 
-                            images, 
-                            model_dir, 
-                            optimize, 
-                            independent_session, 
-                            delay
-                        )
-                        
-                        # If metadata is stale (404 error), try to refresh it and download again
-                        if is_stale_metadata and model_hash not in download_progress['refreshed_models']:
-                            logger.info(f"Metadata seems stale for {model_name}, attempting to refresh...")
-                            
-                            # Refresh metadata from CivitAI
-                            refresh_success = await MiscRoutes._refresh_model_metadata(
-                                model_hash, 
-                                model_name, 
-                                scanner_type, 
-                                scanner
-                            )
-                            
-                            if refresh_success:
-                                # Get updated model data
-                                updated_cache = await scanner.get_cached_data()
-                                updated_model = None
-                                
-                                for item in updated_cache.raw_data:
-                                    if item.get('sha256') == model_hash:
-                                        updated_model = item
-                                        break
-                                
-                                if updated_model and updated_model.get('civitai', {}).get('images'):
-                                    # Try downloading with updated metadata
-                                    logger.info(f"Retrying download with refreshed metadata for {model_name}")
-                                    updated_images = updated_model.get('civitai', {}).get('images', [])
-                                    
-                                    # Retry download with new images
-                                    model_success, _ = await MiscRoutes._process_model_images(
-                                        model_hash, 
-                                        model_name, 
-                                        updated_images, 
-                                        model_dir, 
-                                        optimize, 
-                                        independent_session, 
-                                        delay
-                                    )
-                        
-                        # Only mark model as processed if all images downloaded successfully
-                        if model_success:
-                            download_progress['processed_models'].add(model_hash)
-                        else:
-                            logger.warning(f"Model {model_name} had download errors, will not mark as completed")
-                    
-                    # Save progress to file periodically
-                    if download_progress['completed'] % 10 == 0 or download_progress['completed'] == download_progress['total'] - 1:
-                        progress_file = os.path.join(output_dir, '.download_progress.json')
-                        with open(progress_file, 'w', encoding='utf-8') as f:
-                            json.dump({
-                                'processed_models': list(download_progress['processed_models']),
-                                'refreshed_models': list(download_progress['refreshed_models']),
-                                'completed': download_progress['completed'],
-                                'total': download_progress['total'],
-                                'last_update': time.time()
-                            }, f, indent=2)
-                
-                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
-                
-                # Update progress
-                download_progress['completed'] += 1
-            
-            # 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")
-        
-        except Exception as e:
-            error_msg = f"Error during 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()
-        
-        finally:
-            # Close the independent session
-            try:
-                await independent_session.close()
-            except Exception as e:
-                logger.error(f"Error closing download session: {e}")
-                
-            # Save final progress to file
-            try:
-                progress_file = os.path.join(output_dir, '.download_progress.json')
-                with open(progress_file, 'w', encoding='utf-8') as f:
-                    json.dump({
-                        'processed_models': list(download_progress['processed_models']),
-                        'refreshed_models': list(download_progress['refreshed_models']),
-                        'completed': download_progress['completed'],
-                        'total': download_progress['total'],
-                        'last_update': time.time(),
-                        'status': download_progress['status']
-                    }, f, indent=2)
-            except Exception as e:
-                logger.error(f"Failed to save progress file: {e}")
-            
-            # Set download status to not downloading
-            is_downloading = False
+                'error': str(e)
+            }, status=500)

py/routes/recipe_routes.py

@@ -1,5 +1,6 @@
 import os
 import time
+import base64
 import numpy as np
 from PIL import Image
 import torch
@@ -12,7 +13,7 @@ import json
 import asyncio
 import sys
 from ..utils.exif_utils import ExifUtils
-from ..utils.recipe_parsers import RecipeParserFactory
+from ..recipes import RecipeParserFactory
 from ..utils.constants import CARD_PREVIEW_WIDTH
 
 from ..config import config
@@ -56,6 +57,7 @@ class RecipeRoutes:
         app.router.add_get('/api/recipes', routes.get_recipes)
         app.router.add_get('/api/recipe/{recipe_id}', routes.get_recipe_detail)
         app.router.add_post('/api/recipes/analyze-image', routes.analyze_recipe_image)
+        app.router.add_post('/api/recipes/analyze-local-image', routes.analyze_local_image)
         app.router.add_post('/api/recipes/save', routes.save_recipe)
         app.router.add_delete('/api/recipe/{recipe_id}', routes.delete_recipe)
         
@@ -70,12 +72,18 @@ class RecipeRoutes:
         # Add new endpoint for getting recipe syntax
         app.router.add_get('/api/recipe/{recipe_id}/syntax', routes.get_recipe_syntax)
         
-        # Add new endpoint for updating recipe metadata (name and tags)
+        # Add new endpoint for updating recipe metadata (name, tags and source_path)
         app.router.add_put('/api/recipe/{recipe_id}/update', routes.update_recipe)
         
         # Add new endpoint for reconnecting deleted LoRAs
         app.router.add_post('/api/recipe/lora/reconnect', routes.reconnect_lora)
         
+        # Add new endpoint for finding duplicate recipes
+        app.router.add_get('/api/recipes/find-duplicates', routes.find_duplicates)
+        
+        # Add new endpoint for bulk deletion of recipes
+        app.router.add_post('/api/recipes/bulk-delete', routes.bulk_delete)
+        
         # Start cache initialization
         app.on_startup.append(routes._init_cache)
         
@@ -246,6 +254,7 @@ class RecipeRoutes:
             content_type = request.headers.get('Content-Type', '')
             
             is_url_mode = False
+            metadata = None  # Initialize metadata variable
             
             if 'multipart/form-data' in content_type:
                 # Handle image upload
@@ -279,17 +288,63 @@ class RecipeRoutes:
                         "loras": []
                     }, status=400)
                 
-                # Download image from URL
-                temp_path = download_civitai_image(url)
+                # Check if this is a Civitai image URL
+                import re
+                civitai_image_match = re.match(r'https://civitai\.com/images/(\d+)', url)
                 
-                if not temp_path:
-                    return web.json_response({
-                        "error": "Failed to download image from URL",
-                        "loras": []
-                    }, status=400)
+                if civitai_image_match:
+                    # Extract image ID and fetch image info using get_image_info
+                    image_id = civitai_image_match.group(1)
+                    image_info = await self.civitai_client.get_image_info(image_id)
+                    
+                    if not image_info:
+                        return web.json_response({
+                            "error": "Failed to fetch image information from Civitai",
+                            "loras": []
+                        }, status=400)
+                    
+                    # Get image URL from response
+                    image_url = image_info.get('url')
+                    if not image_url:
+                        return web.json_response({
+                            "error": "No image URL found in Civitai response",
+                            "loras": []
+                        }, status=400)
+                    
+                    # Download image directly from URL
+                    session = await self.civitai_client.session
+                    # Create a temporary file to save the downloaded image
+                    with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
+                        temp_path = temp_file.name
+                    
+                    async with session.get(image_url) as response:
+                        if response.status != 200:
+                            return web.json_response({
+                                "error": f"Failed to download image from URL: HTTP {response.status}",
+                                "loras": []
+                            }, status=400)
+                        
+                        with open(temp_path, 'wb') as f:
+                            f.write(await response.read())
+                    
+                    # 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)
             
-            # Extract metadata from the image using ExifUtils
-            metadata = ExifUtils.extract_image_metadata(temp_path)
+            # If metadata wasn't obtained from Civitai API, extract it from the image
+            if metadata is None:
+                # Extract metadata from the image using ExifUtils
+                metadata = ExifUtils.extract_image_metadata(temp_path)
             
             # If no metadata found, return a more specific error
             if not metadata:
@@ -300,7 +355,6 @@ class RecipeRoutes:
                 
                 # For URL mode, include the image data as base64
                 if is_url_mode and temp_path:
-                    import base64
                     with open(temp_path, "rb") as image_file:
                         result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
                     
@@ -317,7 +371,6 @@ class RecipeRoutes:
                 
                 # For URL mode, include the image data as base64
                 if is_url_mode and temp_path:
-                    import base64
                     with open(temp_path, "rb") as image_file:
                         result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
                     
@@ -332,7 +385,6 @@ class RecipeRoutes:
             
             # For URL mode, include the image data as base64
             if is_url_mode and temp_path:
-                import base64
                 with open(temp_path, "rb") as image_file:
                     result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
             
@@ -340,6 +392,21 @@ class RecipeRoutes:
             if "error" in result and not result.get("loras"):
                 return web.json_response(result, status=200)
             
+            # Calculate fingerprint from parsed loras
+            from ..utils.utils import calculate_recipe_fingerprint
+            fingerprint = calculate_recipe_fingerprint(result.get("loras", []))
+            
+            # Add fingerprint to result
+            result["fingerprint"] = fingerprint
+            
+            # Find matching recipes with the same fingerprint
+            matching_recipes = []
+            if fingerprint:
+                matching_recipes = await self.recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+                
+            # Add matching recipes to result
+            result["matching_recipes"] = matching_recipes
+            
             return web.json_response(result)
             
         except Exception as e:
@@ -355,7 +422,100 @@ class RecipeRoutes:
                     os.unlink(temp_path)
                 except Exception as e:
                     logger.error(f"Error deleting temporary file: {e}")
+    
+    async def analyze_local_image(self, request: web.Request) -> web.Response:
+        """Analyze a local image file for recipe metadata"""
+        try:
+            # Ensure services are initialized 
+            await self.init_services()
+            
+            # Get JSON data from request
+            data = await request.json()
+            file_path = data.get('path')
+            
+            if not file_path:
+                return web.json_response({
+                    'error': 'No file path provided',
+                    'loras': []
+                }, status=400)
+                
+            # Normalize file path for cross-platform compatibility
+            file_path = os.path.normpath(file_path.strip('"').strip("'"))
+            
+            # Validate that the file exists
+            if not os.path.isfile(file_path):
+                return web.json_response({
+                    'error': 'File not found',
+                    'loras': []
+                }, status=404)
+                
+            # Extract metadata from the image using ExifUtils
+            metadata = ExifUtils.extract_image_metadata(file_path)
+            
+            # If no metadata found, return error
+            if not metadata:
+                # Get base64 image data
+                with open(file_path, "rb") as image_file:
+                    image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
+                    
+                return web.json_response({
+                    "error": "No metadata found in this image",
+                    "loras": [],  # Return empty loras array to prevent client-side errors
+                    "image_base64": image_base64
+                }, status=200)
+            
+            # Use the parser factory to get the appropriate parser
+            parser = RecipeParserFactory.create_parser(metadata)
 
+            if parser is None:
+                # Get base64 image data
+                with open(file_path, "rb") as image_file:
+                    image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
+                    
+                return web.json_response({
+                    "error": "No parser found for this image",
+                    "loras": [],  # Return empty loras array to prevent client-side errors
+                    "image_base64": image_base64
+                }, status=200)
+            
+            # Parse the metadata
+            result = await parser.parse_metadata(
+                metadata, 
+                recipe_scanner=self.recipe_scanner, 
+                civitai_client=self.civitai_client
+            )
+            
+            # Add base64 image data to result
+            with open(file_path, "rb") as image_file:
+                result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
+            
+            # Check for errors
+            if "error" in result and not result.get("loras"):
+                return web.json_response(result, status=200)
+            
+            # Calculate fingerprint from parsed loras
+            from ..utils.utils import calculate_recipe_fingerprint
+            fingerprint = calculate_recipe_fingerprint(result.get("loras", []))
+            
+            # Add fingerprint to result
+            result["fingerprint"] = fingerprint
+            
+            # Find matching recipes with the same fingerprint
+            matching_recipes = []
+            if fingerprint:
+                matching_recipes = await self.recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+                
+            # Add matching recipes to result
+            result["matching_recipes"] = matching_recipes
+            
+            return web.json_response(result)
+            
+        except Exception as e:
+            logger.error(f"Error analyzing local image: {e}", exc_info=True)
+            return web.json_response({
+                'error': str(e),
+                'loras': []  # Return empty loras array to prevent client-side errors
+            }, status=500)
 
     async def save_recipe(self, request: web.Request) -> web.Response:
         """Save a recipe to the recipes folder"""
@@ -425,7 +585,6 @@ class RecipeRoutes:
             if not image:
                 if image_base64:
                     # Convert base64 to binary
-                    import base64
                     try:
                         # Remove potential data URL prefix
                         if ',' in image_base64:
@@ -474,7 +633,7 @@ class RecipeRoutes:
             with open(image_path, 'wb') as f:
                 f.write(optimized_image)
             
-            # Create the recipe JSON
+            # Create the recipe data structure
             current_time = time.time()
             
             # Format loras data according to the recipe.json format
@@ -514,6 +673,10 @@ class RecipeRoutes:
                     "clip_skip": raw_metadata.get("clip_skip", "")
                 }
             
+            # Calculate recipe fingerprint 
+            from ..utils.utils import calculate_recipe_fingerprint
+            fingerprint = calculate_recipe_fingerprint(loras_data)
+            
             # Create the recipe data structure
             recipe_data = {
                 "id": recipe_id,
@@ -523,13 +686,18 @@ class RecipeRoutes:
                 "created_date": current_time,
                 "base_model": metadata.get("base_model", ""),
                 "loras": loras_data,
-                "gen_params": gen_params
+                "gen_params": gen_params,
+                "fingerprint": fingerprint
             }
             
             # Add tags if provided
             if tags:
                 recipe_data["tags"] = tags
             
+            # Add source_path if provided in metadata
+            if metadata.get("source_path"):
+                recipe_data["source_path"] = metadata.get("source_path")
+            
             # Save the recipe JSON
             json_filename = f"{recipe_id}.recipe.json"
             json_path = os.path.join(recipes_dir, json_filename)
@@ -539,6 +707,14 @@ class RecipeRoutes:
             # Add recipe metadata to the image
             ExifUtils.append_recipe_metadata(image_path, recipe_data)
             
+            # Check for duplicates
+            matching_recipes = []
+            if fingerprint:
+                matching_recipes = await self.recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+                # Remove current recipe from matches
+                if recipe_id in matching_recipes:
+                    matching_recipes.remove(recipe_id)
+            
             # Simplified cache update approach
             # Instead of trying to update the cache directly, just set it to None
             # to force a refresh on the next get_cached_data call
@@ -554,7 +730,8 @@ class RecipeRoutes:
                 'success': True,
                 'recipe_id': recipe_id,
                 'image_path': image_path,
-                'json_path': json_path
+                'json_path': json_path,
+                'matching_recipes': matching_recipes
             })
             
         except Exception as e:
@@ -1089,9 +1266,9 @@ class RecipeRoutes:
             data = await request.json()
             
             # Validate required fields
-            if 'title' not in data and 'tags' not in data:
+            if 'title' not in data and 'tags' not in data and 'source_path' not in data:
                 return web.json_response({
-                    "error": "At least one field to update must be provided (title or tags)"
+                    "error": "At least one field to update must be provided (title or tags or source_path)"
                 }, status=400)
             
             # Use the recipe scanner's update method
@@ -1186,6 +1363,10 @@ class RecipeRoutes:
                     
             if not found:
                 return web.json_response({"error": "Could not find matching deleted LoRA in recipe"}, status=404)
+            
+            # Recalculate recipe fingerprint after updating LoRA
+            from ..utils.utils import calculate_recipe_fingerprint
+            recipe_data['fingerprint'] = calculate_recipe_fingerprint(recipe_data.get('loras', []))
                 
             # Save updated recipe
             with open(recipe_path, 'w', encoding='utf-8') as f:
@@ -1201,6 +1382,8 @@ class RecipeRoutes:
                     if cache_item.get('id') == recipe_id:
                         # Replace loras array with updated version
                         cache_item['loras'] = recipe_data['loras']
+                        # Update fingerprint in cache
+                        cache_item['fingerprint'] = recipe_data['fingerprint']
                         
                         # Resort the cache
                         asyncio.create_task(scanner._cache.resort())
@@ -1211,11 +1394,20 @@ class RecipeRoutes:
             if image_path and os.path.exists(image_path):
                 from ..utils.exif_utils import ExifUtils
                 ExifUtils.append_recipe_metadata(image_path, recipe_data)
+            
+            # Find other recipes with the same fingerprint
+            matching_recipes = []
+            if 'fingerprint' in recipe_data:
+                matching_recipes = await scanner.find_recipes_by_fingerprint(recipe_data['fingerprint'])
+                # Remove current recipe from matches
+                if recipe_id in matching_recipes:
+                    matching_recipes.remove(recipe_id)
                 
             return web.json_response({
                 "success": True,
                 "recipe_id": recipe_id,
-                "updated_lora": updated_lora
+                "updated_lora": updated_lora,
+                "matching_recipes": matching_recipes
             })
             
         except Exception as e:
@@ -1291,3 +1483,150 @@ class RecipeRoutes:
                 'success': False,
                 'error': str(e)
             }, status=500)
+
+    async def find_duplicates(self, request: web.Request) -> web.Response:
+        """Find all duplicate recipes based on fingerprints"""
+        try:
+            # Ensure services are initialized
+            await self.init_services()
+            
+            # Get all duplicate recipes
+            duplicate_groups = await self.recipe_scanner.find_all_duplicate_recipes()
+            
+            # Create response data with additional recipe information
+            response_data = []
+            
+            for fingerprint, recipe_ids in duplicate_groups.items():
+                # Skip groups with only one recipe (not duplicates)
+                if len(recipe_ids) <= 1:
+                    continue
+                    
+                # Get recipe details for each recipe in the group
+                recipes = []
+                for recipe_id in recipe_ids:
+                    recipe = await self.recipe_scanner.get_recipe_by_id(recipe_id)
+                    if recipe:
+                        # Add only needed fields to keep response size manageable
+                        recipes.append({
+                            'id': recipe.get('id'),
+                            'title': recipe.get('title'),
+                            'file_url': recipe.get('file_url') or self._format_recipe_file_url(recipe.get('file_path', '')),
+                            'modified': recipe.get('modified'),
+                            'created_date': recipe.get('created_date'),
+                            'lora_count': len(recipe.get('loras', [])),
+                        })
+                        
+                # Only include groups with at least 2 valid recipes
+                if len(recipes) >= 2:
+                    # Sort recipes by modified date (newest first)
+                    recipes.sort(key=lambda x: x.get('modified', 0), reverse=True)
+                    
+                    response_data.append({
+                        'fingerprint': fingerprint,
+                        'count': len(recipes),
+                        'recipes': recipes
+                    })
+            
+            # Sort groups by count (highest first)
+            response_data.sort(key=lambda x: x['count'], reverse=True)
+            
+            return web.json_response({
+                'success': True,
+                'duplicate_groups': response_data
+            })
+            
+        except Exception as e:
+            logger.error(f"Error finding duplicate recipes: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def bulk_delete(self, request: web.Request) -> web.Response:
+        """Delete multiple recipes by ID"""
+        try:
+            # Ensure services are initialized
+            await self.init_services()
+            
+            # Parse request data
+            data = await request.json()
+            recipe_ids = data.get('recipe_ids', [])
+            
+            if not recipe_ids:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No recipe IDs provided'
+                }, status=400)
+                
+            # Get recipes directory
+            recipes_dir = self.recipe_scanner.recipes_dir
+            if not recipes_dir or not os.path.exists(recipes_dir):
+                return web.json_response({
+                    'success': False,
+                    'error': 'Recipes directory not found'
+                }, status=404)
+            
+            # Track deleted and failed recipes
+            deleted_recipes = []
+            failed_recipes = []
+            
+            # Process each recipe ID
+            for recipe_id in recipe_ids:
+                # Find recipe JSON file
+                recipe_json_path = os.path.join(recipes_dir, f"{recipe_id}.recipe.json")
+                
+                if not os.path.exists(recipe_json_path):
+                    failed_recipes.append({
+                        'id': recipe_id,
+                        'reason': 'Recipe not found'
+                    })
+                    continue
+                
+                try:
+                    # Load recipe data to get image path
+                    with open(recipe_json_path, 'r', encoding='utf-8') as f:
+                        recipe_data = json.load(f)
+                    
+                    # Get image path
+                    image_path = recipe_data.get('file_path')
+                    
+                    # Delete recipe JSON file
+                    os.remove(recipe_json_path)
+                    
+                    # Delete recipe image if it exists
+                    if image_path and os.path.exists(image_path):
+                        os.remove(image_path)
+                        
+                    deleted_recipes.append(recipe_id)
+                    
+                except Exception as e:
+                    failed_recipes.append({
+                        'id': recipe_id,
+                        'reason': str(e)
+                    })
+            
+            # Update cache if any recipes were deleted
+            if deleted_recipes and self.recipe_scanner._cache is not None:
+                # Remove deleted recipes from raw_data
+                self.recipe_scanner._cache.raw_data = [
+                    r for r in self.recipe_scanner._cache.raw_data 
+                    if r.get('id') not in deleted_recipes
+                ]
+                # Resort the cache
+                asyncio.create_task(self.recipe_scanner._cache.resort())
+                logger.info(f"Removed {len(deleted_recipes)} recipes from cache")
+            
+            return web.json_response({
+                'success': True,
+                'deleted': deleted_recipes,
+                'failed': failed_recipes,
+                'total_deleted': len(deleted_recipes),
+                'total_failed': len(failed_recipes)
+            })
+            
+        except Exception as e:
+            logger.error(f"Error performing bulk delete: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/routes/update_routes.py

@@ -2,6 +2,8 @@ import os
 import aiohttp
 import logging
 import toml
+import subprocess
+from datetime import datetime
 from aiohttp import web
 from typing import Dict, Any, List
 
@@ -13,7 +15,8 @@ class UpdateRoutes:
     @staticmethod
     def setup_routes(app):
         """Register update check routes"""
-        app.router.add_get('/loras/api/check-updates', UpdateRoutes.check_updates)
+        app.router.add_get('/api/check-updates', UpdateRoutes.check_updates)
+        app.router.add_get('/api/version-info', UpdateRoutes.get_version_info)
     
     @staticmethod
     async def check_updates(request):
@@ -24,6 +27,9 @@ class UpdateRoutes:
         try:
             # Read local version from pyproject.toml
             local_version = UpdateRoutes._get_local_version()
+            
+            # Get git info (commit hash, branch)
+            git_info = UpdateRoutes._get_git_info()
 
             # Fetch remote version from GitHub
             remote_version, changelog = await UpdateRoutes._get_remote_version()
@@ -39,7 +45,8 @@ class UpdateRoutes:
                 'current_version': local_version,
                 'latest_version': remote_version,
                 'update_available': update_available,
-                'changelog': changelog
+                'changelog': changelog,
+                'git_info': git_info
             })
             
         except Exception as e:
@@ -49,6 +56,34 @@ class UpdateRoutes:
                 'error': str(e)
             })
     
+    @staticmethod
+    async def get_version_info(request):
+        """
+        Returns the current version in the format 'version-short_hash'
+        """
+        try:
+            # Read local version from pyproject.toml
+            local_version = UpdateRoutes._get_local_version().replace('v', '')
+            
+            # Get git info (commit hash, branch)
+            git_info = UpdateRoutes._get_git_info()
+            short_hash = git_info['short_hash']
+            
+            # Format: version-short_hash
+            version_string = f"{local_version}-{short_hash}"
+            
+            return web.json_response({
+                'success': True,
+                'version': version_string
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to get version info: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            })
+    
     @staticmethod
     def _get_local_version() -> str:
         """Get local plugin version from pyproject.toml"""
@@ -72,6 +107,72 @@ class UpdateRoutes:
             logger.error(f"Failed to get local version: {e}", exc_info=True)
             return "v0.0.0"
     
+    @staticmethod
+    def _get_git_info() -> Dict[str, str]:
+        """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',
+            '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
+            result = subprocess.run(
+                ['git', 'rev-parse', 'HEAD'],
+                cwd=plugin_root,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+                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
     async def _get_remote_version() -> tuple[str, List[str]]:
         """

py/server_routes.py

@@ -1,26 +0,0 @@
-from aiohttp import web
-from server import PromptServer
-from .nodes.utils import get_lora_info
-
-@PromptServer.instance.routes.post("/loramanager/get_trigger_words")
-async def get_trigger_words(request):
-    json_data = await request.json()
-    lora_names = json_data.get("lora_names", [])
-    node_ids = json_data.get("node_ids", [])
-    
-    all_trigger_words = []
-    for lora_name in lora_names:
-        _, trigger_words = await get_lora_info(lora_name)
-        all_trigger_words.extend(trigger_words)
-    
-    # Format the trigger words
-    trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
-    
-    # Send update to all connected trigger word toggle nodes
-    for node_id in node_ids:
-        PromptServer.instance.send_sync("trigger_word_update", {
-            "id": node_id,
-            "message": trigger_words_text
-        })
-    
-    return web.json_response({"success": True})

py/services/civitai_client.py

@@ -224,6 +224,69 @@ class CivitaiClient:
         except Exception as e:
             logger.error(f"Error fetching model versions: {e}")
             return None
+            
+    async def get_model_version(self, model_id: str, version_id: str = "") -> Optional[Dict]:
+        """Get specific model version with additional metadata
+        
+        Args:
+            model_id: The Civitai model ID
+            version_id: Optional specific version ID to retrieve
+            
+        Returns:
+            Optional[Dict]: The model version data with additional fields or None if not found
+        """
+        try:
+            session = await self._ensure_fresh_session()
+            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', [])
+                
+                # Find matching version
+                matched_version = None
+                
+                if version_id:
+                    # If version_id provided, find exact match
+                    for version in model_versions:
+                        if str(version.get('id')) == str(version_id):
+                            matched_version = version
+                            break
+                else:
+                    # If no version_id then use the first version
+                    matched_version = model_versions[0] if model_versions else None
+                
+                # If no match found, return None
+                if not matched_version:
+                    return None
+                    
+                # Build result with modified fields
+                result = matched_version.copy()  # Copy to avoid modifying original
+                
+                # Replace index with modelId
+                if 'index' in result:
+                    del result['index']
+                result['modelId'] = model_id
+                
+                # Add model field with metadata from top level
+                result['model'] = {
+                    "name": data.get("name"),
+                    "type": data.get("type"),
+                    "nsfw": data.get("nsfw", False),
+                    "poi": data.get("poi", False),
+                    "description": data.get("description"),
+                    "tags": data.get("tags", [])
+                }
+                
+                # Add creator field from top level
+                result['creator'] = data.get("creator")
+                
+                return result
+                
+        except Exception as e:
+            logger.error(f"Error fetching model version: {e}")
+            return None
 
     async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict], Optional[str]]:
         """Fetch model version metadata from Civitai
@@ -346,3 +409,34 @@ class CivitaiClient:
         except Exception as e:
             logger.error(f"Error getting hash from Civitai: {e}")
             return None
+
+    async def get_image_info(self, image_id: str) -> Optional[Dict]:
+        """Fetch image information from Civitai API
+        
+        Args:
+            image_id: The Civitai image ID
+            
+        Returns:
+            Optional[Dict]: The image data or None if not found
+        """
+        try:
+            session = await self._ensure_fresh_session()
+            headers = self._get_request_headers()
+            url = f"{self.base_url}/images?imageId={image_id}&nsfw=X"
+            
+            logger.debug(f"Fetching image info for ID: {image_id}")
+            async with session.get(url, headers=headers) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    if data and "items" in data and len(data["items"]) > 0:
+                        logger.debug(f"Successfully fetched image info for ID: {image_id}")
+                        return data["items"][0]
+                    logger.warning(f"No image found with ID: {image_id}")
+                    return None
+                
+                logger.error(f"Failed to fetch image info for ID: {image_id} (status {response.status})")
+                return None
+        except Exception as e:
+            error_msg = f"Error fetching image info: {e}"
+            logger.error(error_msg)
+            return None

py/services/download_manager.py

@@ -2,11 +2,11 @@ import logging
 import os
 import json
 import asyncio
-from typing import Optional, Dict, Any
-from .civitai_client import CivitaiClient
+from typing import Dict
 from ..utils.models import LoraMetadata, CheckpointMetadata
 from ..utils.constants import CARD_PREVIEW_WIDTH
 from ..utils.exif_utils import ExifUtils
+from ..utils.metadata_manager import MetadataManager
 from .service_registry import ServiceRegistry
 
 # Download to temporary file first
@@ -39,14 +39,6 @@ class DownloadManager:
         if self._civitai_client is None:
             self._civitai_client = await ServiceRegistry.get_civitai_client()
         return self._civitai_client
-
-    async def _get_lora_monitor(self):
-        """Get the lora file monitor from registry"""
-        return await ServiceRegistry.get_lora_monitor()
-        
-    async def _get_checkpoint_monitor(self):
-        """Get the checkpoint file monitor from registry"""
-        return await ServiceRegistry.get_checkpoint_monitor()
     
     async def _get_lora_scanner(self):
         """Get the lora scanner from registry"""
@@ -136,15 +128,6 @@ class DownloadManager:
             # 3. Prepare download
             file_name = file_info['name']
             save_path = os.path.join(save_dir, file_name)
-            file_size = file_info.get('sizeKB', 0) * 1024
-
-            # 4. Notify file monitor - use normalized path and file size
-            file_monitor = await self._get_lora_monitor() if model_type == "lora" else await self._get_checkpoint_monitor()
-            if file_monitor and file_monitor.handler:
-                file_monitor.handler.add_ignore_path(
-                    save_path.replace(os.sep, '/'),
-                    file_size
-                )
 
             # 5. Prepare metadata based on model type
             if model_type == "checkpoint":
@@ -216,8 +199,6 @@ class DownloadManager:
                     if await civitai_client.download_preview_image(images[0]['url'], preview_path):
                         metadata.preview_url = preview_path.replace(os.sep, '/')
                         metadata.preview_nsfw_level = images[0].get('nsfwLevel', 0)
-                        with open(metadata_path, 'w', encoding='utf-8') as f:
-                            json.dump(metadata.to_dict(), f, indent=2, ensure_ascii=False)
                 else:
                     # For images, use WebP format for better performance
                     with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as temp_file:
@@ -244,8 +225,6 @@ class DownloadManager:
                         # Update metadata
                         metadata.preview_url = preview_path.replace(os.sep, '/')
                         metadata.preview_nsfw_level = images[0].get('nsfwLevel', 0)
-                        with open(metadata_path, 'w', encoding='utf-8') as f:
-                            json.dump(metadata.to_dict(), f, indent=2, ensure_ascii=False)
                         
                         # Remove temporary file
                         try:
@@ -276,8 +255,7 @@ class DownloadManager:
             metadata.update_file_info(save_path)
 
             # 5. Final metadata update
-            with open(metadata_path, 'w', encoding='utf-8') as f:
-                json.dump(metadata.to_dict(), f, indent=2, ensure_ascii=False)
+            await MetadataManager.save_metadata(save_path, metadata)
 
             # 6. Update cache based on model type
             if model_type == "checkpoint":
@@ -287,17 +265,11 @@ class DownloadManager:
                 scanner = await self._get_lora_scanner()
                 logger.info(f"Updating lora cache for {save_path}")
                 
-            cache = await scanner.get_cached_data()
+            # Convert metadata to dictionary
             metadata_dict = metadata.to_dict()
-            metadata_dict['folder'] = relative_path
-            cache.raw_data.append(metadata_dict)
-            await cache.resort()
-            all_folders = set(cache.folders)
-            all_folders.add(relative_path)
-            cache.folders = sorted(list(all_folders), key=lambda x: x.lower())
-            
-            # Update the hash index with the new model entry
-            scanner._hash_index.add_entry(metadata_dict['sha256'], metadata_dict['file_path'])
+
+            # Add model to cache and save to disk in a single operation
+            await scanner.add_model_to_cache(metadata_dict, relative_path)
 
             # Report 100% completion
             if progress_callback:

py/services/file_monitor.py

@@ -1,542 +0,0 @@
-import os
-import logging
-import asyncio
-import time
-from watchdog.observers import Observer
-from watchdog.events import FileSystemEventHandler
-from typing import List, Dict, Set, Optional
-from threading import Lock
-
-from ..config import config
-from .service_registry import ServiceRegistry
-
-logger = logging.getLogger(__name__)
-
-# Configuration constant to control file monitoring functionality
-ENABLE_FILE_MONITORING = False
-
-class BaseFileHandler(FileSystemEventHandler):
-    """Base handler for file system events"""
-    
-    def __init__(self, loop: asyncio.AbstractEventLoop):
-        self.loop = loop  # Store event loop reference
-        self.pending_changes = set()  # Pending changes
-        self.lock = Lock()  # Thread-safe lock
-        self.update_task = None  # Async update task
-        self._ignore_paths = set()  # Paths to ignore
-        self._min_ignore_timeout = 5  # Minimum timeout in seconds
-        self._download_speed = 1024 * 1024  # Assume 1MB/s as base speed
-        
-        # Track modified files with timestamps for debouncing
-        self.modified_files: Dict[str, float] = {}
-        self.debounce_timer = None
-        self.debounce_delay = 3.0  # Seconds to wait after last modification
-        
-        # Track files already scheduled for processing
-        self.scheduled_files: Set[str] = set()
-        
-        # File extensions to monitor - should be overridden by subclasses
-        self.file_extensions = set()
-
-    def _should_ignore(self, path: str) -> bool:
-        """Check if path should be ignored"""
-        real_path = os.path.realpath(path)  # Resolve any symbolic links
-        return real_path.replace(os.sep, '/') in self._ignore_paths
-
-    def add_ignore_path(self, path: str, file_size: int = 0):
-        """Add path to ignore list with dynamic timeout based on file size"""
-        real_path = os.path.realpath(path)  # Resolve any symbolic links
-        self._ignore_paths.add(real_path.replace(os.sep, '/'))
-        
-        # Short timeout (e.g. 5 seconds) is sufficient to ignore the CREATE event
-        timeout = 5
-        
-        self.loop.call_later(
-            timeout,
-            self._ignore_paths.discard,
-            real_path.replace(os.sep, '/')
-        )
-        
-    def on_created(self, event):
-        if event.is_directory:
-            return
-            
-        # Handle appropriate files based on extensions
-        file_ext = os.path.splitext(event.src_path)[1].lower()
-        if file_ext in self.file_extensions:
-            if self._should_ignore(event.src_path):
-                return
-            
-            # Process this file directly and ignore subsequent modifications
-            normalized_path = os.path.realpath(event.src_path).replace(os.sep, '/')
-            if normalized_path not in self.scheduled_files:
-                logger.info(f"File created: {event.src_path}")
-                self.scheduled_files.add(normalized_path)
-                self._schedule_update('add', event.src_path)
-                
-                # Ignore modifications for a short period after creation
-                self.loop.call_later(
-                    self.debounce_delay * 2,
-                    self.scheduled_files.discard,
-                    normalized_path
-                )
-            
-    def on_modified(self, event):
-        if event.is_directory:
-            return
-            
-        # Only process files with supported extensions
-        file_ext = os.path.splitext(event.src_path)[1].lower()
-        if file_ext in self.file_extensions:
-            if self._should_ignore(event.src_path):
-                return
-                
-            normalized_path = os.path.realpath(event.src_path).replace(os.sep, '/')
-            
-            # Skip if this file is already scheduled for processing
-            if normalized_path in self.scheduled_files:
-                return
-                
-            # Update the timestamp for this file
-            self.modified_files[normalized_path] = time.time()
-            
-            # Cancel any existing timer
-            if self.debounce_timer:
-                self.debounce_timer.cancel()
-                
-            # Set a new timer to process modified files after debounce period
-            self.debounce_timer = self.loop.call_later(
-                self.debounce_delay, 
-                self.loop.call_soon_threadsafe,
-                self._process_modified_files
-            )
-
-    def _process_modified_files(self):
-        """Process files that have been modified after debounce period"""
-        current_time = time.time()
-        files_to_process = []
-        
-        # Find files that haven't been modified for debounce_delay seconds
-        for file_path, last_modified in list(self.modified_files.items()):
-            if current_time - last_modified >= self.debounce_delay:
-                # Only process if not already scheduled
-                if file_path not in self.scheduled_files:
-                    files_to_process.append(file_path)
-                    self.scheduled_files.add(file_path)
-                    
-                    # Auto-remove from scheduled list after reasonable time
-                    self.loop.call_later(
-                        self.debounce_delay * 2,
-                        self.scheduled_files.discard,
-                        file_path
-                    )
-                
-                del self.modified_files[file_path]
-        
-        # Process stable files
-        for file_path in files_to_process:
-            logger.info(f"Processing modified file: {file_path}")
-            self._schedule_update('add', file_path)
-
-    def on_deleted(self, event):
-        if event.is_directory:
-            return
-            
-        file_ext = os.path.splitext(event.src_path)[1].lower()
-        if file_ext not in self.file_extensions:
-            return
-            
-        if self._should_ignore(event.src_path):
-            return
-        
-        # Remove from scheduled files if present
-        normalized_path = os.path.realpath(event.src_path).replace(os.sep, '/')
-        self.scheduled_files.discard(normalized_path)
-        
-        logger.info(f"File deleted: {event.src_path}")
-        self._schedule_update('remove', event.src_path)
-        
-    def on_moved(self, event):
-        """Handle file move/rename events"""
-        
-        src_ext = os.path.splitext(event.src_path)[1].lower()
-        dest_ext = os.path.splitext(event.dest_path)[1].lower()
-        
-        # If destination has supported extension, treat as new file
-        if dest_ext in self.file_extensions:
-            if self._should_ignore(event.dest_path):
-                return
-                
-            normalized_path = os.path.realpath(event.dest_path).replace(os.sep, '/')
-            
-            # Only process if not already scheduled
-            if normalized_path not in self.scheduled_files:
-                logger.info(f"File renamed/moved to: {event.dest_path}")
-                self.scheduled_files.add(normalized_path)
-                self._schedule_update('add', event.dest_path)
-                
-                # Auto-remove from scheduled list after reasonable time
-                self.loop.call_later(
-                    self.debounce_delay * 2, 
-                    self.scheduled_files.discard,
-                    normalized_path
-                )
-            
-        # If source was a supported file, treat it as deleted
-        if src_ext in self.file_extensions:
-            if self._should_ignore(event.src_path):
-                return
-                
-            normalized_path = os.path.realpath(event.src_path).replace(os.sep, '/')
-            self.scheduled_files.discard(normalized_path)
-            
-            logger.info(f"File moved/renamed from: {event.src_path}")
-            self._schedule_update('remove', event.src_path)
-
-    def _schedule_update(self, action: str, file_path: str):
-        """Schedule a cache update"""
-        with self.lock:
-            # Use config method to map path
-            mapped_path = config.map_path_to_link(file_path)
-            normalized_path = mapped_path.replace(os.sep, '/')
-            self.pending_changes.add((action, normalized_path))
-            
-            self.loop.call_soon_threadsafe(self._create_update_task)
-
-    def _create_update_task(self):
-        """Create update task in the event loop"""
-        if self.update_task is None or self.update_task.done():
-            self.update_task = asyncio.create_task(self._process_changes())
-    
-    async def _process_changes(self, delay: float = 2.0):
-        """Process pending changes with debouncing - should be implemented by subclasses"""
-        raise NotImplementedError("Subclasses must implement _process_changes")
-
-
-class LoraFileHandler(BaseFileHandler):
-    """Handler for LoRA file system events"""
-    
-    def __init__(self, loop: asyncio.AbstractEventLoop):
-        super().__init__(loop)
-        # Set supported file extensions for LoRAs
-        self.file_extensions = {'.safetensors'}
-    
-    async def _process_changes(self, delay: float = 2.0):
-        """Process pending changes with debouncing"""
-        await asyncio.sleep(delay)
-        
-        try:
-            with self.lock:
-                changes = self.pending_changes.copy()
-                self.pending_changes.clear()
-            
-            if not changes:
-                return
-            
-            logger.info(f"Processing {len(changes)} LoRA file changes")
-
-            # Get scanner through ServiceRegistry
-            scanner = await ServiceRegistry.get_lora_scanner()
-            cache = await scanner.get_cached_data()
-            needs_resort = False
-            new_folders = set()
-            
-            for action, file_path in changes:
-                try:
-                    if action == 'add':
-                        # Check if file already exists in cache
-                        existing = next((item for item in cache.raw_data if item['file_path'] == file_path), None)
-                        if existing:
-                            logger.info(f"File {file_path} already in cache, skipping")
-                            continue
-                            
-                        # Scan new file
-                        model_data = await scanner.scan_single_model(file_path)
-                        if model_data:
-                            # Update tags count
-                            for tag in model_data.get('tags', []):
-                                scanner._tags_count[tag] = scanner._tags_count.get(tag, 0) + 1
-                            
-                            cache.raw_data.append(model_data)
-                            new_folders.add(model_data['folder'])
-                            # Update hash index
-                            if 'sha256' in model_data:
-                                scanner._hash_index.add_entry(
-                                    model_data['sha256'], 
-                                    model_data['file_path']
-                                )
-                            needs_resort = True
-                            
-                    elif action == 'remove':
-                        # Find the model to remove so we can update tags count
-                        model_to_remove = next((item for item in cache.raw_data if item['file_path'] == file_path), None)
-                        if model_to_remove:
-                            # Update tags count by reducing counts
-                            for tag in model_to_remove.get('tags', []):
-                                if tag in scanner._tags_count:
-                                    scanner._tags_count[tag] = max(0, scanner._tags_count[tag] - 1)
-                                    if scanner._tags_count[tag] == 0:
-                                        del scanner._tags_count[tag]
-                        
-                        # Remove from cache and hash index
-                        logger.info(f"Removing {file_path} from cache")
-                        scanner._hash_index.remove_by_path(file_path)
-                        cache.raw_data = [
-                            item for item in cache.raw_data 
-                            if item['file_path'] != file_path
-                        ]
-                        needs_resort = True
-                        
-                except Exception as e:
-                    logger.error(f"Error processing {action} for {file_path}: {e}")
-            
-            if needs_resort:
-                await cache.resort()
-                
-                # Update folder list
-                all_folders = set(cache.folders) | new_folders
-                cache.folders = sorted(list(all_folders), key=lambda x: x.lower())
-                
-        except Exception as e:
-            logger.error(f"Error in process_changes for LoRA: {e}")
-
-
-class CheckpointFileHandler(BaseFileHandler):
-    """Handler for checkpoint file system events"""
-    
-    def __init__(self, loop: asyncio.AbstractEventLoop):
-        super().__init__(loop)
-        # Set supported file extensions for checkpoints
-        self.file_extensions = {'.safetensors', '.ckpt', '.pt', '.pth', '.sft', '.gguf'}
-    
-    async def _process_changes(self, delay: float = 2.0):
-        """Process pending changes with debouncing for checkpoint files"""
-        await asyncio.sleep(delay)
-        
-        try:
-            with self.lock:
-                changes = self.pending_changes.copy()
-                self.pending_changes.clear()
-            
-            if not changes:
-                return
-            
-            logger.info(f"Processing {len(changes)} checkpoint file changes")
-
-            # Get scanner through ServiceRegistry
-            scanner = await ServiceRegistry.get_checkpoint_scanner()
-            cache = await scanner.get_cached_data()
-            needs_resort = False
-            new_folders = set()
-            
-            for action, file_path in changes:
-                try:
-                    if action == 'add':
-                        # Check if file already exists in cache
-                        existing = next((item for item in cache.raw_data if item['file_path'] == file_path), None)
-                        if existing:
-                            logger.info(f"File {file_path} already in cache, skipping")
-                            continue
-                            
-                        # Scan new file
-                        model_data = await scanner.scan_single_model(file_path)
-                        if model_data:
-                            # Update tags count if applicable
-                            for tag in model_data.get('tags', []):
-                                scanner._tags_count[tag] = scanner._tags_count.get(tag, 0) + 1
-                            
-                            cache.raw_data.append(model_data)
-                            new_folders.add(model_data['folder'])
-                            # Update hash index
-                            if 'sha256' in model_data:
-                                scanner._hash_index.add_entry(
-                                    model_data['sha256'], 
-                                    model_data['file_path']
-                                )
-                            needs_resort = True
-                            
-                    elif action == 'remove':
-                        # Find the model to remove so we can update tags count
-                        model_to_remove = next((item for item in cache.raw_data if item['file_path'] == file_path), None)
-                        if model_to_remove:
-                            # Update tags count by reducing counts
-                            for tag in model_to_remove.get('tags', []):
-                                if tag in scanner._tags_count:
-                                    scanner._tags_count[tag] = max(0, scanner._tags_count[tag] - 1)
-                                    if scanner._tags_count[tag] == 0:
-                                        del scanner._tags_count[tag]
-                        
-                        # Remove from cache and hash index
-                        logger.info(f"Removing {file_path} from checkpoint cache")
-                        scanner._hash_index.remove_by_path(file_path)
-                        cache.raw_data = [
-                            item for item in cache.raw_data 
-                            if item['file_path'] != file_path
-                        ]
-                        needs_resort = True
-                        
-                except Exception as e:
-                    logger.error(f"Error processing checkpoint {action} for {file_path}: {e}")
-            
-            if needs_resort:
-                await cache.resort()
-                
-                # Update folder list
-                all_folders = set(cache.folders) | new_folders
-                cache.folders = sorted(list(all_folders), key=lambda x: x.lower())
-                
-        except Exception as e:
-            logger.error(f"Error in process_changes for checkpoint: {e}")
-
-
-class BaseFileMonitor:
-    """Base class for file monitoring"""
-    
-    def __init__(self, monitor_paths: List[str]):
-        self.observer = Observer()
-        self.loop = asyncio.get_event_loop()
-        self.monitor_paths = set()
-        
-        # Process monitor paths
-        for path in monitor_paths:
-            self.monitor_paths.add(os.path.realpath(path).replace(os.sep, '/'))
-
-        # Add mapped paths from config
-        for target_path in config._path_mappings.keys():
-            self.monitor_paths.add(target_path)
-    
-    def start(self):
-        """Start file monitoring"""
-        if not ENABLE_FILE_MONITORING:
-            logger.debug("File monitoring is disabled via ENABLE_FILE_MONITORING setting")
-            return
-            
-        for path in self.monitor_paths:
-            try:
-                self.observer.schedule(self.handler, path, recursive=True)
-                logger.info(f"Started monitoring: {path}")
-            except Exception as e:
-                logger.error(f"Error monitoring {path}: {e}")
-                
-        self.observer.start()
-    
-    def stop(self):
-        """Stop file monitoring"""
-        if not ENABLE_FILE_MONITORING:
-            return
-            
-        self.observer.stop()
-        self.observer.join()
-    
-    def rescan_links(self):
-        """Rescan links when new ones are added"""
-        if not ENABLE_FILE_MONITORING:
-            return
-            
-        # Find new paths not yet being monitored
-        new_paths = set()
-        for path in config._path_mappings.keys():
-            real_path = os.path.realpath(path).replace(os.sep, '/')
-            if real_path not in self.monitor_paths:
-                new_paths.add(real_path)
-                self.monitor_paths.add(real_path)
-        
-        # Add new paths to monitoring
-        for path in new_paths:
-            try:
-                self.observer.schedule(self.handler, path, recursive=True)
-                logger.info(f"Added new monitoring path: {path}")
-            except Exception as e:
-                logger.error(f"Error adding new monitor for {path}: {e}")
-
-
-class LoraFileMonitor(BaseFileMonitor):
-    """Monitor for LoRA file changes"""
-    
-    _instance = None
-    _lock = asyncio.Lock()
-    
-    def __new__(cls, monitor_paths=None):
-        if cls._instance is None:
-            cls._instance = super().__new__(cls)
-        return cls._instance
-    
-    def __init__(self, monitor_paths=None):
-        if not hasattr(self, '_initialized'):
-            if monitor_paths is None:
-                from ..config import config
-                monitor_paths = config.loras_roots
-                
-            super().__init__(monitor_paths)
-            self.handler = LoraFileHandler(self.loop)
-            self._initialized = True
-    
-    @classmethod
-    async def get_instance(cls):
-        """Get singleton instance with async support"""
-        async with cls._lock:
-            if cls._instance is None:
-                from ..config import config
-                cls._instance = cls(config.loras_roots)
-            return cls._instance
-
-
-class CheckpointFileMonitor(BaseFileMonitor):
-    """Monitor for checkpoint file changes"""
-    
-    _instance = None
-    _lock = asyncio.Lock()
-    
-    def __new__(cls, monitor_paths=None):
-        if cls._instance is None:
-            cls._instance = super().__new__(cls)
-        return cls._instance
-    
-    def __init__(self, monitor_paths=None):
-        if not hasattr(self, '_initialized'):
-            if monitor_paths is None:
-                # Get checkpoint roots from scanner
-                monitor_paths = []
-                # We'll initialize monitor paths later when scanner is available
-                
-            super().__init__(monitor_paths or [])
-            self.handler = CheckpointFileHandler(self.loop)
-            self._initialized = True
-    
-    @classmethod
-    async def get_instance(cls):
-        """Get singleton instance with async support"""
-        async with cls._lock:
-            if cls._instance is None:
-                cls._instance = cls([])
-                
-                # Now get checkpoint roots from scanner
-                from .checkpoint_scanner import CheckpointScanner
-                scanner = await CheckpointScanner.get_instance()
-                monitor_paths = scanner.get_model_roots()
-                
-                # Update monitor paths - but don't actually monitor them
-                for path in monitor_paths:
-                    real_path = os.path.realpath(path).replace(os.sep, '/')
-                    cls._instance.monitor_paths.add(real_path)
-                
-            return cls._instance
-    
-    def start(self):
-        """Override start to check global enable flag"""
-        if not ENABLE_FILE_MONITORING:
-            logger.debug("Checkpoint file monitoring is disabled via ENABLE_FILE_MONITORING setting")
-            return
-            
-        logger.debug("Checkpoint file monitoring is temporarily disabled")
-        # Skip the actual monitoring setup
-        pass
-    
-    async def initialize_paths(self):
-        """Initialize monitor paths from scanner - currently disabled"""
-        if not ENABLE_FILE_MONITORING:
-            logger.debug("Checkpoint path initialization skipped (monitoring disabled)")
-            return
-            
-        logger.debug("Checkpoint file path initialization skipped (monitoring disabled)")
-        pass

py/services/lora_cache.py

@@ -2,6 +2,7 @@ import asyncio
 from typing import List, Dict
 from dataclasses import dataclass
 from operator import itemgetter
+from natsort import natsorted
 
 @dataclass
 class LoraCache:
@@ -17,7 +18,7 @@ class LoraCache:
     async def resort(self, name_only: bool = False):
         """Resort all cached data views"""
         async with self._lock:
-            self.sorted_by_name = sorted(
+            self.sorted_by_name = natsorted(
                 self.raw_data, 
                 key=lambda x: x['model_name'].lower()  # Case-insensitive sort
             )

py/services/lora_hash_index.py

@@ -1,54 +0,0 @@
-from typing import Dict, Optional
-import logging
-from dataclasses import dataclass
-
-logger = logging.getLogger(__name__)
-
-@dataclass
-class LoraHashIndex:
-    """Index for mapping LoRA file hashes to their file paths"""
-    
-    def __init__(self):
-        self._hash_to_path: Dict[str, str] = {}
-        
-    def add_entry(self, sha256: str, file_path: str) -> None:
-        """Add or update a hash -> path mapping"""
-        if not sha256 or not file_path:
-            return
-        # Always store lowercase hashes for consistency
-        self._hash_to_path[sha256.lower()] = file_path
-        
-    def remove_entry(self, sha256: str) -> None:
-        """Remove a hash entry"""
-        if sha256:
-            self._hash_to_path.pop(sha256.lower(), None)
-        
-    def remove_by_path(self, file_path: str) -> None:
-        """Remove entry by file path"""
-        for sha256, path in list(self._hash_to_path.items()):
-            if path == file_path:
-                del self._hash_to_path[sha256]
-                break
-                
-    def get_path(self, sha256: str) -> Optional[str]:
-        """Get file path for a given hash"""
-        if not sha256:
-            return None
-        return self._hash_to_path.get(sha256.lower())
-        
-    def get_hash(self, file_path: str) -> Optional[str]:
-        """Get hash for a given file path"""
-        for sha256, path in self._hash_to_path.items():
-            if path == file_path:
-                return sha256
-        return None
-        
-    def has_hash(self, sha256: str) -> bool:
-        """Check if hash exists in index"""
-        if not sha256:
-            return False
-        return sha256.lower() in self._hash_to_path
-        
-    def clear(self) -> None:
-        """Clear all entries"""
-        self._hash_to_path.clear() 

py/services/lora_scanner.py

@@ -374,32 +374,6 @@ class LoraScanner(ModelScanner):
                 
         return letters
 
-    async def _update_metadata_paths(self, metadata_path: str, lora_path: str) -> Dict:
-        """Update file paths in metadata file"""
-        try:
-            with open(metadata_path, 'r', encoding='utf-8') as f:
-                metadata = json.load(f)
-            
-            # Update file_path
-            metadata['file_path'] = lora_path.replace(os.sep, '/')
-            
-            # Update preview_url if exists
-            if 'preview_url' in metadata:
-                preview_dir = os.path.dirname(lora_path)
-                preview_name = os.path.splitext(os.path.basename(metadata['preview_url']))[0]
-                preview_ext = os.path.splitext(metadata['preview_url'])[1]
-                new_preview_path = os.path.join(preview_dir, f"{preview_name}{preview_ext}")
-                metadata['preview_url'] = new_preview_path.replace(os.sep, '/')
-            
-            # Save updated metadata
-            with open(metadata_path, 'w', encoding='utf-8') as f:
-                json.dump(metadata, f, indent=2, ensure_ascii=False)
-
-            return metadata
-                
-        except Exception as e:
-            logger.error(f"Error updating metadata paths: {e}", exc_info=True)
-
     # Lora-specific hash index functionality
     def has_lora_hash(self, sha256: str) -> bool:
         """Check if a LoRA with given hash exists"""

py/services/model_cache.py

@@ -2,6 +2,7 @@ import asyncio
 from typing import List, Dict
 from dataclasses import dataclass
 from operator import itemgetter
+from natsort import natsorted
 
 @dataclass
 class ModelCache:
@@ -17,7 +18,7 @@ class ModelCache:
     async def resort(self, name_only: bool = False):
         """Resort all cached data views"""
         async with self._lock:
-            self.sorted_by_name = sorted(
+            self.sorted_by_name = natsorted(
                 self.raw_data, 
                 key=lambda x: x['model_name'].lower()  # Case-insensitive sort
             )
@@ -31,12 +32,13 @@ class ModelCache:
             all_folders = set(l['folder'] for l in self.raw_data)
             self.folders = sorted(list(all_folders), key=lambda x: x.lower())
 
-    async def update_preview_url(self, file_path: str, preview_url: str) -> bool:
+    async def update_preview_url(self, file_path: str, preview_url: str, preview_nsfw_level: int) -> bool:
         """Update preview_url for a specific model in all cached data
         
         Args:
             file_path: The file path of the model to update
             preview_url: The new preview URL
+            preview_nsfw_level: The NSFW level of the preview
             
         Returns:
             bool: True if the update was successful, False if the model wasn't found
@@ -46,19 +48,9 @@ class ModelCache:
             for item in self.raw_data:
                 if item['file_path'] == file_path:
                     item['preview_url'] = preview_url
+                    item['preview_nsfw_level'] = preview_nsfw_level
                     break
             else:
                 return False  # Model not found
-                
-            # Update in sorted lists (references to the same dict objects)
-            for item in self.sorted_by_name:
-                if item['file_path'] == file_path:
-                    item['preview_url'] = preview_url
-                    break
-                    
-            for item in self.sorted_by_date:
-                if item['file_path'] == file_path:
-                    item['preview_url'] = preview_url
-                    break
                     
             return True

py/services/model_hash_index.py

@@ -1,12 +1,15 @@
-from typing import Dict, Optional, Set
+from typing import Dict, Optional, Set, List
 import os
 
 class ModelHashIndex:
-    """Index for looking up models by hash or path"""
+    """Index for looking up models by hash or filename"""
     
     def __init__(self):
         self._hash_to_path: Dict[str, str] = {}
-        self._filename_to_hash: Dict[str, str] = {}  # Changed from path_to_hash to filename_to_hash
+        self._filename_to_hash: Dict[str, str] = {}
+        # New data structures for tracking duplicates
+        self._duplicate_hashes: Dict[str, List[str]] = {}  # sha256 -> list of paths
+        self._duplicate_filenames: Dict[str, List[str]] = {}  # filename -> list of paths
     
     def add_entry(self, sha256: str, file_path: str) -> None:
         """Add or update hash index entry"""
@@ -19,6 +22,26 @@ class ModelHashIndex:
         # Extract filename without extension
         filename = self._get_filename_from_path(file_path)
         
+        # Track duplicates by hash
+        if sha256 in self._hash_to_path:
+            old_path = self._hash_to_path[sha256]
+            if old_path != file_path:  # Only record if it's actually a different path
+                if sha256 not in self._duplicate_hashes:
+                    self._duplicate_hashes[sha256] = [old_path]
+                if file_path not in self._duplicate_hashes.get(sha256, []):
+                    self._duplicate_hashes.setdefault(sha256, []).append(file_path)
+        
+        # Track duplicates by filename
+        if filename in self._filename_to_hash:
+            old_hash = self._filename_to_hash[filename]
+            if old_hash != sha256:  # Different models with the same name
+                old_path = self._hash_to_path.get(old_hash)
+                if old_path:
+                    if filename not in self._duplicate_filenames:
+                        self._duplicate_filenames[filename] = [old_path]
+                    if file_path not in self._duplicate_filenames.get(filename, []):
+                        self._duplicate_filenames.setdefault(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]
@@ -40,24 +63,126 @@ class ModelHashIndex:
         """Extract filename without extension from path"""
         return os.path.splitext(os.path.basename(file_path))[0]
     
-    def remove_by_path(self, file_path: str) -> None:
+    def remove_by_path(self, file_path: str, hash_val: str = None) -> None:
         """Remove entry by file path"""
         filename = self._get_filename_from_path(file_path)
-        if filename in self._filename_to_hash:
-            hash_val = self._filename_to_hash[filename]
-            if hash_val in self._hash_to_path:
+        
+        # Find the hash for this file path
+        if hash_val is None:
+            for h, p in self._hash_to_path.items():
+                if p == file_path:
+                    hash_val = h
+                    break
+        
+        # If we didn't find a hash, nothing to do
+        if not hash_val:
+            return
+        
+        # Update duplicates tracking for hash
+        if hash_val in self._duplicate_hashes:
+            # Remove the current path from duplicates
+            self._duplicate_hashes[hash_val] = [p for p in self._duplicate_hashes[hash_val] if p != file_path]
+            
+            # Update or remove hash mapping based on remaining duplicates
+            if len(self._duplicate_hashes[hash_val]) > 0:
+                # Replace with one of the remaining paths
+                new_path = self._duplicate_hashes[hash_val][0]
+                new_filename = self._get_filename_from_path(new_path)
+                
+                # Update hash-to-path mapping
+                self._hash_to_path[hash_val] = new_path
+                
+                # IMPORTANT: Update filename-to-hash mapping for consistency
+                # Remove old filename mapping if it points to this hash
+                if filename in self._filename_to_hash and self._filename_to_hash[filename] == hash_val:
+                    del self._filename_to_hash[filename]
+                
+                # Add new filename mapping
+                self._filename_to_hash[new_filename] = hash_val
+                
+                # If only one duplicate left, remove from duplicates tracking
+                if len(self._duplicate_hashes[hash_val]) == 1:
+                    del self._duplicate_hashes[hash_val]
+            else:
+                # No duplicates left, remove hash entry completely
+                del self._duplicate_hashes[hash_val]
                 del self._hash_to_path[hash_val]
-            del self._filename_to_hash[filename]
+                
+                # Remove corresponding filename entry if it points to this hash
+                if filename in self._filename_to_hash and self._filename_to_hash[filename] == hash_val:
+                    del self._filename_to_hash[filename]
+        else:
+            # No duplicates, simply remove the hash entry
+            del self._hash_to_path[hash_val]
+            
+            # Remove corresponding filename entry if it points to this hash
+            if filename in self._filename_to_hash and self._filename_to_hash[filename] == hash_val:
+                del self._filename_to_hash[filename]
+        
+        # Update duplicates tracking for filename
+        if filename in self._duplicate_filenames:
+            # Remove the current path from duplicates
+            self._duplicate_filenames[filename] = [p for p in self._duplicate_filenames[filename] if p != file_path]
+            
+            # Update or remove filename mapping based on remaining duplicates
+            if len(self._duplicate_filenames[filename]) > 0:
+                # Get the hash for the first remaining duplicate path
+                first_dup_path = self._duplicate_filenames[filename][0]
+                first_dup_hash = None
+                for h, p in self._hash_to_path.items():
+                    if p == first_dup_path:
+                        first_dup_hash = h
+                        break
+                
+                # Update the filename to hash mapping if we found a hash
+                if first_dup_hash:
+                    self._filename_to_hash[filename] = first_dup_hash
+                
+                # If only one duplicate left, remove from duplicates tracking
+                if len(self._duplicate_filenames[filename]) == 1:
+                    del self._duplicate_filenames[filename]
+            else:
+                # No duplicates left, remove filename entry completely
+                del self._duplicate_filenames[filename]
+                if filename in self._filename_to_hash:
+                    del self._filename_to_hash[filename]
     
     def remove_by_hash(self, sha256: str) -> None:
         """Remove entry by hash"""
         sha256 = sha256.lower()
-        if sha256 in self._hash_to_path:
-            path = self._hash_to_path[sha256]
-            filename = self._get_filename_from_path(path)
-            if filename in self._filename_to_hash:
-                del self._filename_to_hash[filename]
-            del self._hash_to_path[sha256]
+        if sha256 not in self._hash_to_path:
+            return
+        
+        # Get the path and filename
+        path = self._hash_to_path[sha256]
+        filename = self._get_filename_from_path(path)
+        
+        # Get all paths for this hash (including duplicates)
+        paths_to_remove = [path]
+        if sha256 in self._duplicate_hashes:
+            paths_to_remove.extend(self._duplicate_hashes[sha256])
+            del self._duplicate_hashes[sha256]
+        
+        # Remove hash-to-path mapping
+        del self._hash_to_path[sha256]
+        
+        # Update filename-to-hash and duplicate filenames for all paths
+        for path_to_remove in paths_to_remove:
+            fname = self._get_filename_from_path(path_to_remove)
+            
+            # If this filename maps to the hash we're removing, remove it
+            if fname in self._filename_to_hash and self._filename_to_hash[fname] == sha256:
+                del self._filename_to_hash[fname]
+            
+            # Update duplicate filenames tracking
+            if fname in self._duplicate_filenames:
+                self._duplicate_filenames[fname] = [p for p in self._duplicate_filenames[fname] if p != path_to_remove]
+                
+                if not self._duplicate_filenames[fname]:
+                    del self._duplicate_filenames[fname]
+                elif len(self._duplicate_filenames[fname]) == 1:
+                    # If only one entry remains, it's no longer a duplicate
+                    del self._duplicate_filenames[fname]
     
     def has_hash(self, sha256: str) -> bool:
         """Check if hash exists in index"""
@@ -82,6 +207,8 @@ class ModelHashIndex:
         """Clear all entries"""
         self._hash_to_path.clear()
         self._filename_to_hash.clear()
+        self._duplicate_hashes.clear()
+        self._duplicate_filenames.clear()
     
     def get_all_hashes(self) -> Set[str]:
         """Get all hashes in the index"""
@@ -91,6 +218,14 @@ class ModelHashIndex:
         """Get all filenames in the index"""
         return set(self._filename_to_hash.keys())
     
+    def get_duplicate_hashes(self) -> Dict[str, List[str]]:
+        """Get dictionary of duplicate hashes and their paths"""    
+        return self._duplicate_hashes
+    
+    def get_duplicate_filenames(self) -> Dict[str, List[str]]:
+        """Get dictionary of duplicate filenames and their paths"""
+        return self._duplicate_filenames
+    
     def __len__(self) -> int:
         """Get number of entries"""
         return len(self._hash_to_path)

py/services/model_scanner.py

@@ -5,10 +5,12 @@ import asyncio
 import time
 import shutil
 from typing import List, Dict, Optional, Type, Set
+import msgpack  # Add MessagePack import for efficient serialization
 
 from ..utils.models import BaseModelMetadata
 from ..config import config
-from ..utils.file_utils import load_metadata, get_file_info, find_preview_file, save_metadata
+from ..utils.file_utils import find_preview_file
+from ..utils.metadata_manager import MetadataManager
 from .model_cache import ModelCache
 from .model_hash_index import ModelHashIndex
 from ..utils.constants import PREVIEW_EXTENSIONS
@@ -17,6 +19,13 @@ from .websocket_manager import ws_manager
 
 logger = logging.getLogger(__name__)
 
+# Define cache version to handle future format changes
+# Version history:
+# 1 - Initial version
+# 2 - Added duplicate_filenames and duplicate_hashes tracking
+# 3 - Added _excluded_models list to cache
+CACHE_VERSION = 3
+
 class ModelScanner:
     """Base service for scanning and managing model files"""
     
@@ -39,15 +48,203 @@ class ModelScanner:
         self._tags_count = {}  # Dictionary to store tag counts
         self._is_initializing = False  # Flag to track initialization state
         self._excluded_models = []  # List to track excluded models
+        self._dirs_last_modified = {}  # Track directory modification times
+        self._use_cache_files = False  # Flag to control cache file usage, default to disabled
+        
+        # Clear cache files if disabled
+        if not self._use_cache_files:
+            self._clear_cache_files()
         
         # Register this service
         asyncio.create_task(self._register_service())
-        
+    
+    def _clear_cache_files(self):
+        """Clear existing cache files if they exist"""
+        try:
+            cache_path = self._get_cache_file_path()
+            if cache_path and os.path.exists(cache_path):
+                os.remove(cache_path)
+                logger.info(f"Cleared {self.model_type} cache file: {cache_path}")
+        except Exception as e:
+            logger.error(f"Error clearing {self.model_type} cache file: {e}")
+    
     async def _register_service(self):
         """Register this instance with the ServiceRegistry"""
         service_name = f"{self.model_type}_scanner"
         await ServiceRegistry.register_service(service_name, self)
     
+    def _get_cache_file_path(self) -> Optional[str]:
+        """Get the path to the cache file"""
+        # Get the directory where this module is located
+        current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
+        
+        # Create a cache directory within the project if it doesn't exist
+        cache_dir = os.path.join(current_dir, "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        
+        # Create filename based on model type
+        cache_filename = f"lm_{self.model_type}_cache.msgpack"
+        return os.path.join(cache_dir, cache_filename)
+
+    def _prepare_for_msgpack(self, data):
+        """Preprocess data to accommodate MessagePack serialization limitations
+        
+        Converts integers exceeding safe range to strings
+        
+        Args:
+            data: Any type of data structure
+            
+        Returns:
+            Preprocessed data structure with large integers converted to strings
+        """
+        if isinstance(data, dict):
+            return {k: self._prepare_for_msgpack(v) for k, v in data.items()}
+        elif isinstance(data, list):
+            return [self._prepare_for_msgpack(item) for item in data]
+        elif isinstance(data, int) and (data > 9007199254740991 or data < -9007199254740991):
+            # Convert integers exceeding JavaScript's safe integer range (2^53-1) to strings
+            return str(data)
+        else:
+            return data
+
+    async def _save_cache_to_disk(self) -> bool:
+        """Save cache data to disk using MessagePack"""
+        if not self._use_cache_files:
+            logger.debug(f"Cache files disabled for {self.model_type}, skipping save")
+            return False
+            
+        if self._cache is None or not self._cache.raw_data:
+            logger.debug(f"No {self.model_type} cache data to save")
+            return False
+            
+        cache_path = self._get_cache_file_path()
+        if not cache_path:
+            logger.warning(f"Cannot determine {self.model_type} cache file location")
+            return False
+            
+        try:
+            # Create cache data structure
+            cache_data = {
+                "version": CACHE_VERSION,
+                "timestamp": time.time(),
+                "model_type": self.model_type,
+                "raw_data": self._cache.raw_data,
+                "hash_index": {
+                    "hash_to_path": self._hash_index._hash_to_path,
+                    "filename_to_hash": self._hash_index._filename_to_hash,  # Fix: changed from path_to_hash to filename_to_hash
+                    "duplicate_hashes": self._hash_index._duplicate_hashes,
+                    "duplicate_filenames": self._hash_index._duplicate_filenames
+                },
+                "tags_count": self._tags_count,
+                "dirs_last_modified": self._get_dirs_last_modified(),
+                "excluded_models": self._excluded_models  # Add excluded_models to cache data
+            }
+            
+            # Preprocess data to handle large integers
+            processed_cache_data = self._prepare_for_msgpack(cache_data)
+            
+            # Write to temporary file first (atomic operation)
+            temp_path = f"{cache_path}.tmp"
+            with open(temp_path, 'wb') as f:
+                msgpack.pack(processed_cache_data, f)
+                
+            # Replace the old file with the new one
+            if os.path.exists(cache_path):
+                os.replace(temp_path, cache_path)
+            else:
+                os.rename(temp_path, cache_path)
+                
+            logger.info(f"Saved {self.model_type} cache with {len(self._cache.raw_data)} models to {cache_path}")
+            logger.debug(f"Hash index stats - hash_to_path: {len(self._hash_index._hash_to_path)}, filename_to_hash: {len(self._hash_index._filename_to_hash)}, duplicate_hashes: {len(self._hash_index._duplicate_hashes)}, duplicate_filenames: {len(self._hash_index._duplicate_filenames)}")
+            return True
+        except Exception as e:
+            logger.error(f"Error saving {self.model_type} cache to disk: {e}")
+            # Try to clean up temp file if it exists
+            if 'temp_path' in locals() and os.path.exists(temp_path):
+                try:
+                    os.remove(temp_path)
+                except:
+                    pass
+            return False
+    
+    def _get_dirs_last_modified(self) -> Dict[str, float]:
+        """Get last modified time for all model directories"""
+        dirs_info = {}
+        for root in self.get_model_roots():
+            if os.path.exists(root):
+                dirs_info[root] = os.path.getmtime(root)
+                # Also check immediate subdirectories for changes
+                try:
+                    with os.scandir(root) as it:
+                        for entry in it:
+                            if entry.is_dir(follow_symlinks=True):
+                                dirs_info[entry.path] = entry.stat().st_mtime
+                except Exception as e:
+                    logger.error(f"Error getting directory info for {root}: {e}")
+        return dirs_info
+                
+    def _is_cache_valid(self, cache_data: Dict) -> bool:
+        """Validate if the loaded cache is still valid"""
+        if not cache_data or cache_data.get("version") != CACHE_VERSION:
+            logger.info(f"Cache invalid - version mismatch. Got: {cache_data.get('version')}, Expected: {CACHE_VERSION}")
+            return False
+            
+        if cache_data.get("model_type") != self.model_type:
+            logger.info(f"Cache invalid - model type mismatch. Got: {cache_data.get('model_type')}, Expected: {self.model_type}")
+            return False
+                
+        return True
+        
+    async def _load_cache_from_disk(self) -> bool:
+        """Load cache data from disk using MessagePack"""
+        if not self._use_cache_files:
+            logger.info(f"Cache files disabled for {self.model_type}, skipping load")
+            return False
+            
+        start_time = time.time()
+        cache_path = self._get_cache_file_path()
+        if not cache_path or not os.path.exists(cache_path):
+            return False
+            
+        try:
+            with open(cache_path, 'rb') as f:
+                cache_data = msgpack.unpack(f)
+                
+            # Validate cache data
+            if not self._is_cache_valid(cache_data):
+                logger.info(f"{self.model_type.capitalize()} cache file found but invalid or outdated")
+                return False
+                
+            # Load data into memory
+            self._cache = ModelCache(
+                raw_data=cache_data["raw_data"],
+                sorted_by_name=[],
+                sorted_by_date=[],
+                folders=[]
+            )
+            
+            # Load hash index
+            hash_index_data = cache_data.get("hash_index", {})
+            self._hash_index._hash_to_path = hash_index_data.get("hash_to_path", {})
+            self._hash_index._filename_to_hash = hash_index_data.get("filename_to_hash", {})  # Fix: changed from path_to_hash to filename_to_hash
+            self._hash_index._duplicate_hashes = hash_index_data.get("duplicate_hashes", {})
+            self._hash_index._duplicate_filenames = hash_index_data.get("duplicate_filenames", {})
+            
+            # Load tags count
+            self._tags_count = cache_data.get("tags_count", {})
+            
+            # Load excluded models
+            self._excluded_models = cache_data.get("excluded_models", [])
+            
+            # Resort the cache
+            await self._cache.resort()
+            
+            logger.info(f"Loaded {self.model_type} cache from disk with {len(self._cache.raw_data)} models in {time.time() - start_time:.2f} seconds")
+            return True
+        except Exception as e:
+            logger.error(f"Error loading {self.model_type} cache from disk: {e}")
+            return False
+
     async def initialize_in_background(self) -> None:
         """Initialize cache in background using thread pool"""
         try:
@@ -66,7 +263,31 @@ class ModelScanner:
             # Determine the page type based on model type
             page_type = 'loras' if self.model_type == 'lora' else 'checkpoints'
             
-            # First, count all model files to track progress
+            # First, try to load from cache
+            await ws_manager.broadcast_init_progress({
+                'stage': 'loading_cache',
+                'progress': 0,
+                'details': f"Loading {self.model_type} cache...",
+                'scanner_type': self.model_type,
+                'pageType': page_type
+            })
+            
+            cache_loaded = await self._load_cache_from_disk()
+            
+            if cache_loaded:
+                # Cache loaded successfully, broadcast complete message
+                await ws_manager.broadcast_init_progress({
+                    'stage': 'finalizing',
+                    'progress': 100,
+                    'status': 'complete',
+                    'details': f"Loaded {len(self._cache.raw_data)} {self.model_type} files from cache.",
+                    'scanner_type': self.model_type,
+                    'pageType': page_type
+                })
+                self._is_initializing = False
+                return
+                
+            # If cache loading failed, proceed with full scan
             await ws_manager.broadcast_init_progress({
                 'stage': 'scan_folders',
                 'progress': 0,
@@ -111,6 +332,9 @@ class ModelScanner:
             
             logger.info(f"{self.model_type.capitalize()} cache initialized in {time.time() - start_time:.2f} seconds. Found {len(self._cache.raw_data)} models")
             
+            # Save the cache to disk after initialization
+            await self._save_cache_to_disk()
+            
             # Send completion message
             await asyncio.sleep(0.5)  # Small delay to ensure final progress message is sent
             await ws_manager.broadcast_init_progress({
@@ -280,8 +504,13 @@ class ModelScanner:
             # Clean up the event loop
             loop.close()
 
-    async def get_cached_data(self, force_refresh: bool = False) -> ModelCache:
-        """Get cached model data, refresh if needed"""
+    async def get_cached_data(self, force_refresh: bool = False, rebuild_cache: bool = False) -> ModelCache:
+        """Get cached model data, refresh if needed
+        
+        Args:
+            force_refresh: Whether to refresh the cache
+            rebuild_cache: Whether to completely rebuild the cache by reloading from disk first
+        """
         # If cache is not initialized, return an empty cache
         # Actual initialization should be done via initialize_in_background
         if self._cache is None and not force_refresh:
@@ -293,10 +522,25 @@ class ModelScanner:
             )
 
         # If force refresh is requested, initialize the cache directly
-        if (force_refresh):
+        if force_refresh:
+            # If rebuild_cache is True, try to reload from disk before reconciliation
+            if rebuild_cache:
+                logger.info(f"{self.model_type.capitalize()} Scanner: Attempting to rebuild cache from disk...")
+                cache_loaded = await self._load_cache_from_disk()
+                if cache_loaded:
+                    logger.info(f"{self.model_type.capitalize()} Scanner: Successfully reloaded cache from disk")
+                else:
+                    logger.info(f"{self.model_type.capitalize()} Scanner: Could not reload cache from disk, proceeding with complete rebuild")
+                    # If loading from disk failed, do a complete rebuild and save to disk
+                    await self._initialize_cache()
+                    await self._save_cache_to_disk()
+                    return self._cache
+            
             if self._cache is None:
                 # For initial creation, do a full initialization
                 await self._initialize_cache()
+                # Save the newly built cache
+                await self._save_cache_to_disk()
             else:
                 # For subsequent refreshes, use fast reconciliation
                 await self._reconcile_cache()
@@ -426,26 +670,33 @@ class ModelScanner:
                     batch = new_files[i:i+batch_size]
                     for path in batch:
                         try:
-                            model_data = await self.scan_single_model(path)
-                            if model_data:
-                                # Add to cache
-                                self._cache.raw_data.append(model_data)
-                                
-                                # Update hash index if available
-                                if 'sha256' in model_data and 'file_path' in model_data:
-                                    self._hash_index.add_entry(model_data['sha256'].lower(), model_data['file_path'])
-                                
-                                # Update tags count
-                                if 'tags' in model_data and model_data['tags']:
-                                    for tag in model_data['tags']:
-                                        self._tags_count[tag] = self._tags_count.get(tag, 0) + 1
-                                        
-                                total_added += 1
+                            # Find the appropriate root path for this file
+                            root_path = None
+                            for potential_root in self.get_model_roots():
+                                if path.startswith(potential_root):
+                                    root_path = potential_root
+                                    break
+                            
+                            if root_path:
+                                model_data = await self._process_model_file(path, root_path)
+                                if model_data:
+                                    # Add to cache
+                                    self._cache.raw_data.append(model_data)
+                                    
+                                    # Update hash index if available
+                                    if 'sha256' in model_data and 'file_path' in model_data:
+                                        self._hash_index.add_entry(model_data['sha256'].lower(), model_data['file_path'])
+                                    
+                                    # Update tags count
+                                    if 'tags' in model_data and model_data['tags']:
+                                        for tag in model_data['tags']:
+                                            self._tags_count[tag] = self._tags_count.get(tag, 0) + 1
+                                            
+                                    total_added += 1
+                            else:
+                                logger.error(f"Could not determine root path for {path}")
                         except Exception as e:
                             logger.error(f"Error adding {path} to cache: {e}")
-                    
-                    # Yield control after each batch
-                    await asyncio.sleep(0)
             
             # Find missing files (in cache but not in filesystem)
             missing_files = cached_paths - found_paths
@@ -484,6 +735,9 @@ class ModelScanner:
                 # Resort cache
                 await self._cache.resort()
                 
+                # Save updated cache to disk
+                await self._save_cache_to_disk()
+                
             logger.info(f"{self.model_type.capitalize()} Scanner: Cache reconciliation completed in {time.time() - start_time:.2f} seconds. Added {total_added}, removed {total_removed} models.")
         except Exception as e:
             logger.error(f"{self.model_type.capitalize()} Scanner: Error reconciling cache: {e}", exc_info=True)
@@ -495,36 +749,17 @@ class ModelScanner:
         """Scan all model directories and return metadata"""
         raise NotImplementedError("Subclasses must implement scan_all_models")
     
+    def is_initializing(self) -> bool:
+        """Check if the scanner is currently initializing"""
+        return self._is_initializing
+    
     def get_model_roots(self) -> List[str]:
         """Get model root directories"""
         raise NotImplementedError("Subclasses must implement get_model_roots")
-
-    async def scan_single_model(self, file_path: str) -> Optional[Dict]:
-        """Scan a single model file and return its metadata"""
-        try:
-            if not os.path.exists(os.path.realpath(file_path)):
-                return None
-                
-            # Get basic file info
-            metadata = await self._get_file_info(file_path)
-            if not metadata:
-                return None
-                
-            folder = self._calculate_folder(file_path)
-                    
-            # Ensure folder field exists
-            metadata_dict = metadata.to_dict()
-            metadata_dict['folder'] = folder or ''
-            
-            return metadata_dict
-            
-        except Exception as e:
-            logger.error(f"Error scanning {file_path}: {e}")
-            return None
     
-    async def _get_file_info(self, file_path: str) -> Optional[BaseModelMetadata]:
+    async def _create_default_metadata(self, file_path: str) -> Optional[BaseModelMetadata]:
         """Get model file info and metadata (extensible for different model types)"""
-        return await get_file_info(file_path, self.model_class)
+        return await MetadataManager.create_default_metadata(file_path, self.model_class)
     
     def _calculate_folder(self, file_path: str) -> str:
         """Calculate the folder path for a model file"""
@@ -537,7 +772,7 @@ class ModelScanner:
     # Common methods shared between scanners
     async def _process_model_file(self, file_path: str, root_path: str) -> Dict:
         """Process a single model file and return its metadata"""
-        metadata = await load_metadata(file_path, self.model_class)
+        metadata = await MetadataManager.load_metadata(file_path, self.model_class)
         
         if metadata is None:
             civitai_info_path = f"{os.path.splitext(file_path)[0]}.civitai.info"
@@ -553,7 +788,7 @@ 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 save_metadata(file_path, metadata)
+                        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}")
@@ -580,13 +815,13 @@ class ModelScanner:
                                 metadata.modelDescription = version_info['model']['description']
                         
                         # Save the updated metadata
-                        await save_metadata(file_path, metadata)
+                        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}")
             
         if metadata is None:
-            metadata = await self._get_file_info(file_path)
+            metadata = await self._create_default_metadata(file_path)
         
         model_data = metadata.to_dict()
         
@@ -636,9 +871,7 @@ class ModelScanner:
                     logger.warning(f"Model {model_id} appears to be deleted from Civitai (404 response)")
                     model_data['civitai_deleted'] = True
                     
-                    metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
-                    with open(metadata_path, 'w', encoding='utf-8') as f:
-                        json.dump(model_data, f, indent=2, ensure_ascii=False)
+                    await MetadataManager.save_metadata(file_path, model_data)
                 
                 elif model_metadata:
                     logger.debug(f"Updating metadata for {file_path} with model ID {model_id}")
@@ -651,9 +884,7 @@ class ModelScanner:
 
                     model_data['civitai']['creator'] = model_metadata['creator']
                     
-                    metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
-                    with open(metadata_path, 'w', encoding='utf-8') as f:
-                        json.dump(model_data, f, indent=2, ensure_ascii=False)
+                    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}")
 
@@ -698,6 +929,44 @@ class ModelScanner:
                 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 and save to disk
+        
+        Args:
+            metadata_dict: The model metadata dictionary
+            folder: The relative folder path for the model
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            if self._cache is None:
+                await self.get_cached_data()
+                
+            # Update folder in metadata
+            metadata_dict['folder'] = folder
+            
+            # Add to cache
+            self._cache.raw_data.append(metadata_dict)
+            
+            # Resort cache data
+            await self._cache.resort()
+            
+            # Update folders list
+            all_folders = set(self._cache.folders)
+            all_folders.add(folder)
+            self._cache.folders = sorted(list(all_folders), key=lambda x: x.lower())
+            
+            # Update the hash index
+            self._hash_index.add_entry(metadata_dict['sha256'], metadata_dict['file_path'])
+                
+            # Save to disk
+            await self._save_cache_to_disk()
+            return True
+        except Exception as e:
+            logger.error(f"Error adding model to cache: {e}")
+            return False
     
     async def move_model(self, source_path: str, target_path: str) -> bool:
         """Move a model and its associated files to a new location"""
@@ -721,47 +990,42 @@ class ModelScanner:
             real_source = os.path.realpath(source_path)
             real_target = os.path.realpath(target_file)
             
-            file_size = os.path.getsize(real_source)
-            
-            # Get the appropriate file monitor through ServiceRegistry
-            if self.model_type == "lora":
-                monitor = await ServiceRegistry.get_lora_monitor()
-            elif self.model_type == "checkpoint":
-                monitor = await ServiceRegistry.get_checkpoint_monitor()
-            else:
-                monitor = None
-                
-            if monitor:
-                monitor.handler.add_ignore_path(
-                    real_source,
-                    file_size
-                )
-                monitor.handler.add_ignore_path(
-                    real_target,
-                    file_size
-                )
-            
             shutil.move(real_source, real_target)
             
-            source_metadata = os.path.join(source_dir, f"{base_name}.metadata.json")
+            # Move all associated files with the same base name
+            source_metadata = None
+            moved_metadata_path = None
+            
+            # Find all files with the same base name in the source directory
+            files_to_move = []
+            try:
+                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)
+                        # 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)
+                        else:
+                            files_to_move.append((source_file_path, os.path.join(target_path, file)))
+            except Exception as e:
+                logger.error(f"Error listing files in {source_dir}: {e}")
+            
+            # Move all associated files
             metadata = None
-            if os.path.exists(source_metadata):
-                target_metadata = os.path.join(target_path, f"{base_name}.metadata.json")
-                shutil.move(source_metadata, target_metadata)
-                metadata = await self._update_metadata_paths(target_metadata, target_file)
+            for source_file, target_file_path in files_to_move:
+                try:
+                    shutil.move(source_file, target_file_path)
+                except Exception as e:
+                    logger.error(f"Error moving associated file {source_file}: {e}")
             
-            # Move civitai.info file if exists
-            source_civitai = os.path.join(source_dir, f"{base_name}.civitai.info")
-            if os.path.exists(source_civitai):
-                target_civitai = os.path.join(target_path, f"{base_name}.civitai.info")
-                shutil.move(source_civitai, target_civitai)
-            
-            for ext in PREVIEW_EXTENSIONS:
-                source_preview = os.path.join(source_dir, f"{base_name}{ext}")
-                if os.path.exists(source_preview):
-                    target_preview = os.path.join(target_path, f"{base_name}{ext}")
-                    shutil.move(source_preview, target_preview)
-                    break
+            # Handle metadata file specially to update paths
+            if source_metadata and os.path.exists(source_metadata):
+                try:
+                    shutil.move(source_metadata, moved_metadata_path)
+                    metadata = await self._update_metadata_paths(moved_metadata_path, target_file)
+                except Exception as e:
+                    logger.error(f"Error moving metadata file: {e}")
             
             await self.update_single_model_cache(source_path, target_file, metadata)
             
@@ -779,15 +1043,14 @@ class ModelScanner:
             
             metadata['file_path'] = model_path.replace(os.sep, '/')
             
-            if 'preview_url' in metadata:
+            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]
                 preview_ext = os.path.splitext(metadata['preview_url'])[1]
                 new_preview_path = os.path.join(preview_dir, f"{preview_name}{preview_ext}")
                 metadata['preview_url'] = new_preview_path.replace(os.sep, '/')
             
-            with open(metadata_path, 'w', encoding='utf-8') as f:
-                json.dump(metadata, f, indent=2, ensure_ascii=False)
+            await MetadataManager.save_metadata(metadata_path, metadata)
 
             return metadata
                 
@@ -839,6 +1102,9 @@ class ModelScanner:
         
         await cache.resort()
         
+        # Save the updated cache
+        await self._save_cache_to_disk()
+        
         return True
         
     def has_hash(self, sha256: str) -> bool:
@@ -918,12 +1184,13 @@ class ModelScanner:
         """Get list of excluded model file paths"""
         return self._excluded_models.copy()
 
-    async def update_preview_in_cache(self, file_path: str, preview_url: str) -> bool:
+    async def update_preview_in_cache(self, file_path: str, preview_url: str, preview_nsfw_level: int) -> bool:
         """Update preview URL in cache for a specific lora
         
         Args:
             file_path: The file path of the lora to update
             preview_url: The new preview URL
+            preview_nsfw_level: The NSFW level of the preview
             
         Returns:
             bool: True if the update was successful, False if cache doesn't exist or lora wasn't found
@@ -931,4 +1198,167 @@ class ModelScanner:
         if self._cache is None:
             return False
 
-        return await self._cache.update_preview_url(file_path, preview_url)
+        updated = await self._cache.update_preview_url(file_path, preview_url, preview_nsfw_level)
+        if updated:
+            # Save updated cache to disk
+            await self._save_cache_to_disk()
+        return updated
+
+    async def bulk_delete_models(self, file_paths: List[str]) -> Dict:
+        """Delete multiple models and update cache in a batch operation
+        
+        Args:
+            file_paths: List of file paths to delete
+            
+        Returns:
+            Dict containing results of the operation
+        """
+        try:
+            if not file_paths:
+                return {
+                    'success': False,
+                    'error': 'No file paths provided for deletion',
+                    'results': []
+                }
+            
+            # Keep track of success and failures
+            results = []
+            total_deleted = 0
+            cache_updated = False
+            
+            # Get cache data
+            cache = await self.get_cached_data()
+            
+            # Track deleted models to update cache once
+            deleted_models = []
+            
+            for file_path in file_paths:
+                try:
+                    target_dir = os.path.dirname(file_path)
+                    file_name = os.path.splitext(os.path.basename(file_path))[0]
+                    
+                    # Delete all associated files for the model
+                    from ..utils.routes_common import ModelRouteUtils
+                    deleted_files = await ModelRouteUtils.delete_model_files(
+                        target_dir, 
+                        file_name
+                    )
+                    
+                    if deleted_files:
+                        deleted_models.append(file_path)
+                        results.append({
+                            'file_path': file_path,
+                            'success': True,
+                            'deleted_files': deleted_files
+                        })
+                        total_deleted += 1
+                    else:
+                        results.append({
+                            'file_path': file_path,
+                            'success': False,
+                            'error': 'No files deleted'
+                        })
+                except Exception as e:
+                    logger.error(f"Error deleting file {file_path}: {e}")
+                    results.append({
+                        'file_path': file_path,
+                        'success': False,
+                        'error': str(e)
+                    })
+            
+            # Batch update cache if any models were deleted
+            if deleted_models:
+                # Update the cache in a batch operation
+                cache_updated = await self._batch_update_cache_for_deleted_models(deleted_models)
+                
+            return {
+                'success': True,
+                'total_deleted': total_deleted,
+                'total_attempted': len(file_paths),
+                'cache_updated': cache_updated,
+                'results': results
+            }
+            
+        except Exception as e:
+            logger.error(f"Error in bulk delete: {e}", exc_info=True)
+            return {
+                'success': False,
+                'error': str(e),
+                'results': []
+            }
+    
+    async def _batch_update_cache_for_deleted_models(self, file_paths: List[str]) -> bool:
+        """Update cache after multiple models have been deleted
+        
+        Args:
+            file_paths: List of file paths that were deleted
+            
+        Returns:
+            bool: True if cache was updated and saved successfully
+        """
+        if not file_paths or self._cache is None:
+            return False
+            
+        try:
+            # Get all models that need to be removed from cache
+            models_to_remove = [item for item in self._cache.raw_data if item['file_path'] in file_paths]
+            
+            if not models_to_remove:
+                return False
+                
+            # Update tag counts
+            for model in models_to_remove:
+                for tag in model.get('tags', []):
+                    if tag in self._tags_count:
+                        self._tags_count[tag] = max(0, self._tags_count[tag] - 1)
+                        if self._tags_count[tag] == 0:
+                            del self._tags_count[tag]
+            
+            # Update hash index
+            for model in models_to_remove:
+                file_path = model['file_path']
+                if hasattr(self, '_hash_index') and self._hash_index:
+                    # Get the hash and filename before removal for duplicate checking
+                    file_name = os.path.splitext(os.path.basename(file_path))[0]
+                    hash_val = model.get('sha256', '').lower()
+                    
+                    # Remove from hash index
+                    self._hash_index.remove_by_path(file_path, hash_val)
+                    
+                    # Check and clean up duplicates
+                    self._cleanup_duplicates_after_removal(hash_val, file_name)
+            
+            # Update cache data
+            self._cache.raw_data = [item for item in self._cache.raw_data if item['file_path'] not in file_paths]
+            
+            # Resort cache
+            await self._cache.resort()
+            
+            # Save updated cache to disk
+            await self._save_cache_to_disk()
+            
+            return True
+            
+        except Exception as e:
+            logger.error(f"Error updating cache after bulk delete: {e}", exc_info=True)
+            return False
+    
+    def _cleanup_duplicates_after_removal(self, hash_val: str, file_name: str) -> None:
+        """Clean up duplicate entries in hash index after removing a model
+        
+        Args:
+            hash_val: SHA256 hash of the removed model
+            file_name: File name of the removed model without extension
+        """
+        if not hash_val or not file_name or not hasattr(self, '_hash_index'):
+            return
+            
+        # Clean up hash duplicates if only 0 or 1 entries remain
+        if hash_val in self._hash_index._duplicate_hashes:
+            if len(self._hash_index._duplicate_hashes[hash_val]) <= 1:
+                del self._hash_index._duplicate_hashes[hash_val]
+        
+        # Clean up filename duplicates if only 0 or 1 entries remain
+        if file_name in self._hash_index._duplicate_filenames:
+            if len(self._hash_index._duplicate_filenames[file_name]) <= 1:
+                del self._hash_index._duplicate_filenames[file_name]

py/services/recipe_cache.py

@@ -2,6 +2,7 @@ import asyncio
 from typing import List, Dict
 from dataclasses import dataclass
 from operator import itemgetter
+from natsort import natsorted
 
 @dataclass
 class RecipeCache:
@@ -16,7 +17,7 @@ class RecipeCache:
     async def resort(self, name_only: bool = False):
         """Resort all cached data views"""
         async with self._lock:
-            self.sorted_by_name = sorted(
+            self.sorted_by_name = natsorted(
                 self.raw_data, 
                 key=lambda x: x.get('title', '').lower()  # Case-insensitive sort
             )

py/services/recipe_scanner.py

@@ -9,6 +9,7 @@ from .recipe_cache import RecipeCache
 from .service_registry import ServiceRegistry
 from .lora_scanner import LoraScanner
 from ..utils.utils import fuzzy_match
+from natsort import natsorted
 import sys
 
 logger = logging.getLogger(__name__)
@@ -164,7 +165,7 @@ class RecipeScanner:
                 if hasattr(self._cache, "resort"):
                     try:
                         # Sort by name
-                        self._cache.sorted_by_name = sorted(
+                        self._cache.sorted_by_name = natsorted(
                             self._cache.raw_data,
                             key=lambda x: x.get('title', '').lower()
                         )
@@ -321,6 +322,20 @@ class RecipeScanner:
             
             # Update lora information with local paths and availability
             await self._update_lora_information(recipe_data)
+
+            # Calculate and update fingerprint if missing
+            if 'loras' in recipe_data and 'fingerprint' not in recipe_data:
+                from ..utils.utils import calculate_recipe_fingerprint
+                fingerprint = calculate_recipe_fingerprint(recipe_data['loras'])
+                recipe_data['fingerprint'] = fingerprint
+                
+                # Write updated recipe data back to file
+                try:
+                    with open(recipe_path, 'w', encoding='utf-8') as f:
+                        json.dump(recipe_data, f, indent=4, ensure_ascii=False)
+                    logger.info(f"Added fingerprint to recipe: {recipe_path}")
+                except Exception as e:
+                    logger.error(f"Error writing updated recipe with fingerprint: {e}")
             
             return recipe_data
         except Exception as e:
@@ -801,3 +816,60 @@ class RecipeScanner:
             logger.info(f"Resorted recipe cache after updating {cache_updated_count} items")
             
         return file_updated_count, cache_updated_count
+
+    async def find_recipes_by_fingerprint(self, fingerprint: str) -> list:
+        """Find recipes with a matching fingerprint
+        
+        Args:
+            fingerprint: The recipe fingerprint to search for
+            
+        Returns:
+            List of recipe details that match the fingerprint
+        """
+        if not fingerprint:
+            return []
+            
+        # Get all recipes from cache
+        cache = await self.get_cached_data()
+        
+        # Find recipes with matching fingerprint
+        matching_recipes = []
+        for recipe in cache.raw_data:
+            if recipe.get('fingerprint') == fingerprint:
+                recipe_details = {
+                    'id': recipe.get('id'),
+                    'title': recipe.get('title'),
+                    'file_url': self._format_file_url(recipe.get('file_path')),
+                    'modified': recipe.get('modified'),
+                    'created_date': recipe.get('created_date'),
+                    'lora_count': len(recipe.get('loras', []))
+                }
+                matching_recipes.append(recipe_details)
+        
+        return matching_recipes
+        
+    async def find_all_duplicate_recipes(self) -> dict:
+        """Find all recipe duplicates based on fingerprints
+        
+        Returns:
+            Dictionary where keys are fingerprints and values are lists of recipe IDs
+        """
+        # Get all recipes from cache
+        cache = await self.get_cached_data()
+        
+        # Group recipes by fingerprint
+        fingerprint_groups = {}
+        for recipe in cache.raw_data:
+            fingerprint = recipe.get('fingerprint')
+            if not fingerprint:
+                continue
+                
+            if fingerprint not in fingerprint_groups:
+                fingerprint_groups[fingerprint] = []
+                
+            fingerprint_groups[fingerprint].append(recipe.get('id'))
+        
+        # Filter to only include groups with more than one recipe
+        duplicate_groups = {k: v for k, v in fingerprint_groups.items() if len(v) > 1}
+        
+        return duplicate_groups

py/services/service_registry.py

@@ -58,26 +58,6 @@ class ServiceRegistry:
             scanner = await CheckpointScanner.get_instance()
             await cls.register_service("checkpoint_scanner", scanner)
         return scanner
-    
-    @classmethod
-    async def get_lora_monitor(cls):
-        """Get the LoraFileMonitor instance"""
-        from .file_monitor import LoraFileMonitor
-        monitor = await cls.get_service("lora_monitor")
-        if monitor is None:
-            monitor = await LoraFileMonitor.get_instance()
-            await cls.register_service("lora_monitor", monitor)
-        return monitor
-    
-    @classmethod
-    async def get_checkpoint_monitor(cls):
-        """Get the CheckpointFileMonitor instance"""
-        from .file_monitor import CheckpointFileMonitor
-        monitor = await cls.get_service("checkpoint_monitor")
-        if monitor is None:
-            monitor = await CheckpointFileMonitor.get_instance()
-            await cls.register_service("checkpoint_monitor", monitor)
-        return monitor
 
     @classmethod
     async def get_civitai_client(cls):
@@ -95,7 +75,6 @@ class ServiceRegistry:
         from .download_manager import DownloadManager
         manager = await cls.get_service("download_manager")
         if manager is None:
-            # We'll let DownloadManager.get_instance handle file_monitor parameter
             manager = await DownloadManager.get_instance()
             await cls.register_service("download_manager", manager)
         return manager

py/utils/constants.py

@@ -18,7 +18,9 @@ PREVIEW_EXTENSIONS = [
     '.png',
     '.jpeg',
     '.jpg',
-    '.mp4'
+    '.mp4',
+    '.gif',
+    '.webm'
 ]
 
 # Card preview image width
@@ -31,4 +33,7 @@ EXAMPLE_IMAGE_WIDTH = 832
 SUPPORTED_MEDIA_EXTENSIONS = {
     'images': ['.jpg', '.jpeg', '.png', '.webp', '.gif'],
     'videos': ['.mp4', '.webm']
-}
+}
+
+# Valid Lora types
+VALID_LORA_TYPES = ['lora', 'locon', 'dora']

py/utils/example_images_download_manager.py

@@ -0,0 +1,399 @@
+import logging
+import os
+import asyncio
+import json
+import time
+import aiohttp
+from aiohttp import web
+from ..services.service_registry import ServiceRegistry
+from .example_images_processor import ExampleImagesProcessor
+from .example_images_metadata import MetadataUpdater
+
+logger = logging.getLogger(__name__)
+
+# Download status tracking
+download_task = None
+is_downloading = False
+download_progress = {
+    'total': 0,
+    'completed': 0,
+    'current_model': '',
+    'status': 'idle',  # idle, running, paused, completed, error
+    'errors': [],
+    'last_error': None,
+    'start_time': None,
+    'end_time': None,
+    'processed_models': set(),  # Track models that have been processed
+    'refreshed_models': set()  # Track models that had metadata refreshed
+}
+
+class DownloadManager:
+    """Manages downloading example images for models"""
+    
+    @staticmethod
+    async def start_download(request):
+        """
+        Start downloading example images for models
+        
+        Expects a JSON body with:
+        {
+            "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 to avoid rate limiting (default: 1.0)
+        }
+        """
+        global download_task, is_downloading, download_progress
+        
+        if is_downloading:
+            # Create a copy for JSON serialization
+            response_progress = download_progress.copy()
+            response_progress['processed_models'] = list(download_progress['processed_models'])
+            response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
+            
+            return web.json_response({
+                'success': False,
+                'error': 'Download already in progress',
+                'status': response_progress
+            }, status=400)
+        
+        try:
+            # Parse the request body
+            data = await request.json()
+            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 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'] = 0
+            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
+            
+            # Get the processed models list from a file if it exists
+            progress_file = os.path.join(output_dir, '.download_progress.json')
+            if os.path.exists(progress_file):
+                try:
+                    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")
+                except Exception as e:
+                    logger.error(f"Failed to load progress file: {e}")
+                    download_progress['processed_models'] = set()
+            else:
+                download_progress['processed_models'] = set()
+            
+            # Start the download task
+            is_downloading = True
+            download_task = asyncio.create_task(
+                DownloadManager._download_all_example_images(
+                    output_dir, 
+                    optimize, 
+                    model_types,
+                    delay
+                )
+            )
+            
+            # Create a copy for JSON serialization
+            response_progress = download_progress.copy()
+            response_progress['processed_models'] = list(download_progress['processed_models'])
+            response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
+            
+            return web.json_response({
+                'success': True,
+                'message': 'Download started',
+                'status': response_progress
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to start example images download: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+            
+    @staticmethod
+    async def get_status(request):
+        """Get the current status of example images download"""
+        global download_progress
+        
+        # Create a copy of the progress dict with the set converted to a list for JSON serialization
+        response_progress = download_progress.copy()
+        response_progress['processed_models'] = list(download_progress['processed_models'])
+        response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
+        
+        return web.json_response({
+            'success': True,
+            'is_downloading': is_downloading,
+            'status': response_progress
+        })
+
+    @staticmethod
+    async def pause_download(request):
+        """Pause the example images download"""
+        global download_progress
+        
+        if not is_downloading:
+            return web.json_response({
+                'success': False,
+                'error': 'No download in progress'
+            }, status=400)
+        
+        download_progress['status'] = 'paused'
+        
+        return web.json_response({
+            'success': True,
+            'message': 'Download paused'
+        })
+
+    @staticmethod
+    async def resume_download(request):
+        """Resume the example images download"""
+        global download_progress
+        
+        if not is_downloading:
+            return web.json_response({
+                'success': False,
+                'error': 'No download in progress'
+            }, status=400)
+        
+        if download_progress['status'] == 'paused':
+            download_progress['status'] = 'running'
+            
+            return web.json_response({
+                'success': True,
+                'message': 'Download resumed'
+            })
+        else:
+            return web.json_response({
+                'success': False,
+                'error': f"Download is in '{download_progress['status']}' state, cannot resume"
+            }, status=400)
+    
+    @staticmethod
+    async def _download_all_example_images(output_dir, optimize, model_types, delay):
+        """Download example images for all models"""
+        global is_downloading, 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))
+            
+            # Get all models
+            all_models = []
+            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'):
+                            all_models.append((scanner_type, model, scanner))
+            
+            # Update total count
+            download_progress['total'] = len(all_models)
+            logger.info(f"Found {download_progress['total']} models to process")
+            
+            # Process each model
+            for i, (scanner_type, model, scanner) in enumerate(all_models):
+                # Main logic for processing model is here, but actual operations are delegated to other classes
+                was_remote_download = await DownloadManager._process_model(
+                    scanner_type, model, scanner, 
+                    output_dir, optimize, independent_session
+                )
+                
+                # Update progress
+                download_progress['completed'] += 1
+                
+                # Only add delay after remote download of models, and not after processing the last model
+                if was_remote_download and i < len(all_models) - 1 and download_progress['status'] == 'running':
+                    await asyncio.sleep(delay)
+            
+            # 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")
+            
+        except Exception as e:
+            error_msg = f"Error during 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()
+        
+        finally:
+            # Close the independent session
+            try:
+                await independent_session.close()
+            except Exception as e:
+                logger.error(f"Error closing download session: {e}")
+                
+            # Save final progress to file
+            try:
+                DownloadManager._save_progress(output_dir)
+            except Exception as e:
+                logger.error(f"Failed to save progress file: {e}")
+            
+            # Set download status to not downloading
+            is_downloading = False
+    
+    @staticmethod
+    async def _process_model(scanner_type, model, scanner, output_dir, optimize, independent_session):
+        """Process a single model download"""
+        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  # Return False to indicate no remote download happened
+        
+        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]})"
+            
+            # Skip if already processed
+            if model_hash in download_progress['processed_models']:
+                logger.debug(f"Skipping already processed model: {model_name}")
+                return False
+            
+            # 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 = await ExampleImagesProcessor.download_model_images(
+                    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, _ = await ExampleImagesProcessor.download_model_images(
+                            model_hash, model_name, updated_images, model_dir, optimize, independent_session
+                        )
+                
+                # Only mark as processed if all images were downloaded successfully
+                if success:
+                    download_progress['processed_models'].add(model_hash)
+                    
+                return True  # Return True to indicate a remote download happened
+            
+            # Save progress periodically
+            if download_progress['completed'] % 10 == 0 or download_progress['completed'] == download_progress['total'] - 1:
+                DownloadManager._save_progress(output_dir)
+                
+            return False  # Default return if no conditions met
+                
+        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
+    def _save_progress(output_dir):
+        """Save download progress to file"""
+        global download_progress
+        try:
+            progress_file = os.path.join(output_dir, '.download_progress.json')
+            
+            # Read existing progress file if it exists
+            existing_data = {}
+            if os.path.exists(progress_file):
+                try:
+                    with open(progress_file, 'r', encoding='utf-8') as f:
+                        existing_data = json.load(f)
+                except Exception as e:
+                    logger.warning(f"Failed to read existing progress file: {e}")
+            
+            # Create new progress data
+            progress_data = {
+                'processed_models': list(download_progress['processed_models']),
+                'refreshed_models': list(download_progress['refreshed_models']),
+                'completed': download_progress['completed'],
+                'total': download_progress['total'],
+                'last_update': time.time()
+            }
+            
+            # Preserve existing fields (especially naming_version)
+            for key, value in existing_data.items():
+                if key not in progress_data:
+                    progress_data[key] = value
+            
+            # Write updated progress data
+            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}")

py/utils/example_images_file_manager.py

@@ -0,0 +1,201 @@
+import logging
+import os
+import re
+import sys
+import subprocess
+from aiohttp import web
+from ..services.settings_manager import settings
+from ..utils.constants import SUPPORTED_MEDIA_EXTENSIONS
+
+logger = logging.getLogger(__name__)
+
+class ExampleImagesFileManager:
+    """Manages access and operations for example image files"""
+    
+    @staticmethod
+    async def open_folder(request):
+        """
+        Open the example images folder for a specific model
+        
+        Expects a JSON request body with:
+        {
+            "model_hash": "sha256_hash"  # SHA256 hash of the model
+        }
+        """
+        try:
+            # Parse request body
+            data = await request.json()
+            model_hash = data.get('model_hash')
+            
+            if not model_hash:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing model_hash parameter'
+                }, status=400)
+            
+            # Get example images path from settings
+            example_images_path = settings.get('example_images_path')
+            if not example_images_path:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No example images path configured. Please set it in the settings panel first.'
+                }, status=400)
+            
+            # Construct folder path for this model
+            model_folder = os.path.join(example_images_path, model_hash)
+            
+            # Check if folder exists
+            if not os.path.exists(model_folder):
+                return web.json_response({
+                    'success': False,
+                    'error': 'No example images found for this model. Download example images first.'
+                }, status=404)
+            
+            # Open folder in file explorer
+            if os.name == 'nt':  # Windows
+                os.startfile(model_folder)
+            elif os.name == 'posix':  # macOS and Linux
+                if sys.platform == 'darwin':  # macOS
+                    subprocess.Popen(['open', model_folder])
+                else:  # Linux
+                    subprocess.Popen(['xdg-open', model_folder])
+            
+            return web.json_response({
+                'success': True,
+                'message': f'Opened example images folder for model {model_hash}'
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to open example images folder: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    @staticmethod
+    async def get_files(request):
+        """
+        Get the list of example image files for a specific model
+        
+        Expects:
+        - model_hash in query parameters
+        
+        Returns:
+        - List of image files and their paths
+        """
+        try:
+            # Get model_hash from query parameters
+            model_hash = request.query.get('model_hash')
+            
+            if not model_hash:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing model_hash parameter'
+                }, status=400)
+            
+            # Get example images path from settings
+            example_images_path = settings.get('example_images_path')
+            if not example_images_path:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No example images path configured'
+                }, status=400)
+            
+            # Construct folder path for this model
+            model_folder = os.path.join(example_images_path, model_hash)
+            
+            # Check if folder exists
+            if not os.path.exists(model_folder):
+                return web.json_response({
+                    'success': False, 
+                    'error': 'No example images found for this model',
+                    'files': []
+                }, status=404)
+            
+            # Get list of files in the folder
+            files = []
+            for file in os.listdir(model_folder):
+                file_path = os.path.join(model_folder, file)
+                if os.path.isfile(file_path):
+                    # Check if file is a supported media file
+                    file_ext = os.path.splitext(file)[1].lower()
+                    if (file_ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or 
+                        file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']):
+                        files.append({
+                            'name': file,
+                            'path': f'/example_images_static/{model_hash}/{file}',
+                            'extension': file_ext,
+                            'is_video': file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']
+                        })
+            
+            return web.json_response({
+                'success': True,
+                'files': files
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to get example image files: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    @staticmethod
+    async def has_images(request):
+        """
+        Check if the example images folder for a model exists and is not empty
+        
+        Expects:
+        - model_hash in query parameters
+        
+        Returns:
+        - Boolean indicating whether the folder exists and contains images/videos
+        """
+        try:
+            # Get model_hash from query parameters
+            model_hash = request.query.get('model_hash')
+            
+            if not model_hash:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing model_hash parameter'
+                }, status=400)
+            
+            # Get example images path from settings
+            example_images_path = settings.get('example_images_path')
+            if not example_images_path:
+                return web.json_response({
+                    'has_images': False
+                })
+            
+            # Construct folder path for this model
+            model_folder = os.path.join(example_images_path, model_hash)
+            
+            # Check if folder exists
+            if not os.path.exists(model_folder) or not os.path.isdir(model_folder):
+                return web.json_response({
+                    'has_images': False
+                })
+            
+            # Check if folder contains any supported media files
+            for file in os.listdir(model_folder):
+                file_path = os.path.join(model_folder, file)
+                if os.path.isfile(file_path):
+                    file_ext = os.path.splitext(file)[1].lower()
+                    if (file_ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or 
+                        file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']):
+                        return web.json_response({
+                            'has_images': True
+                        })
+            
+            # If reached here, folder exists but has no supported media files
+            return web.json_response({
+                'has_images': False
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to check example images folder: {e}", exc_info=True)
+            return web.json_response({
+                'has_images': False,
+                'error': str(e)
+            })

py/utils/example_images_metadata.py

@@ -0,0 +1,390 @@
+import logging
+import os
+import re
+from ..utils.metadata_manager import MetadataManager
+from ..utils.routes_common import ModelRouteUtils
+from ..utils.constants import SUPPORTED_MEDIA_EXTENSIONS
+from ..utils.exif_utils import ExifUtils
+from ..recipes.constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+class MetadataUpdater:
+    """Handles updating model metadata related to example images"""
+    
+    @staticmethod
+    async def refresh_model_metadata(model_hash, model_name, scanner_type, scanner):
+        """Refresh model metadata from CivitAI
+        
+        Args:
+            model_hash: SHA256 hash of the model
+            model_name: Model name (for logging)
+            scanner_type: Scanner type ('lora' or 'checkpoint')
+            scanner: Scanner instance for this model type
+            
+        Returns:
+            bool: True if metadata was successfully refreshed, False otherwise
+        """
+        from ..utils.example_images_download_manager import download_progress
+        
+        try:
+            # Find the model in the scanner cache
+            cache = await scanner.get_cached_data()
+            model_data = None
+            
+            for item in cache.raw_data:
+                if item.get('sha256') == model_hash:
+                    model_data = item
+                    break
+            
+            if not model_data:
+                logger.warning(f"Model {model_name} with hash {model_hash} not found in cache")
+                return False
+            
+            file_path = model_data.get('file_path')
+            if not file_path:
+                logger.warning(f"Model {model_name} has no file path")
+                return False
+            
+            # Track that we're refreshing this model
+            download_progress['refreshed_models'].add(model_hash)
+            
+            # Use ModelRouteUtils to refresh metadata
+            async def update_cache_func(old_path, new_path, metadata):
+                return await scanner.update_single_model_cache(old_path, new_path, metadata)
+            
+            success = await ModelRouteUtils.fetch_and_update_model(
+                model_hash, 
+                file_path, 
+                model_data,
+                update_cache_func
+            )
+            
+            if success:
+                logger.info(f"Successfully refreshed metadata for {model_name}")
+                return True
+            else:
+                logger.warning(f"Failed to refresh metadata for {model_name}")
+                return False
+                
+        except Exception as e:
+            error_msg = f"Error refreshing metadata for {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
+    
+    @staticmethod
+    async def get_updated_model(model_hash, scanner):
+        """Get updated model data
+        
+        Args:
+            model_hash: SHA256 hash of the model
+            scanner: Scanner instance
+            
+        Returns:
+            dict: Updated model data or None if not found
+        """
+        cache = await scanner.get_cached_data()
+        for item in cache.raw_data:
+            if item.get('sha256') == model_hash:
+                return item
+        return None
+    
+    @staticmethod
+    async def update_metadata_from_local_examples(model_hash, model, scanner_type, scanner, model_dir):
+        """Update model metadata with local example image information
+        
+        Args:
+            model_hash: SHA256 hash of the model
+            model: Model data dictionary
+            scanner_type: Scanner type ('lora' or 'checkpoint')
+            scanner: Scanner instance for this model type
+            model_dir: Model images directory
+            
+        Returns:
+            bool: True if metadata was successfully updated, False otherwise
+        """
+        try:
+            # Collect local image paths
+            local_images_paths = []
+            if os.path.exists(model_dir):
+                for file in os.listdir(model_dir):
+                    file_path = os.path.join(model_dir, file)
+                    if os.path.isfile(file_path):
+                        file_ext = os.path.splitext(file)[1].lower()
+                        is_supported = (file_ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or
+                                       file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos'])
+                        if is_supported:
+                            local_images_paths.append(file_path)
+            
+            # Check if metadata update is needed (no civitai field or empty images)
+            needs_update = not model.get('civitai') or not model.get('civitai', {}).get('images')
+            
+            if needs_update and local_images_paths:
+                logger.debug(f"Found {len(local_images_paths)} local example images for {model.get('model_name')}, updating metadata")
+                
+                # Create or get civitai field
+                if not model.get('civitai'):
+                    model['civitai'] = {}
+                
+                # Create images array
+                images = []
+                
+                # Generate metadata for each local image/video
+                for path in local_images_paths:
+                    # Determine if video or image
+                    file_ext = os.path.splitext(path)[1].lower()
+                    is_video = file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']
+                    
+                    # Create image metadata entry
+                    image_entry = {
+                        "url": "",  # Empty URL as required
+                        "nsfwLevel": 0,
+                        "width": 720,  # Default dimensions
+                        "height": 1280,
+                        "type": "video" if is_video else "image",
+                        "meta": None,
+                        "hasMeta": False,
+                        "hasPositivePrompt": False
+                    }
+                    
+                    # If it's an image, try to get actual dimensions (optional enhancement)
+                    try:
+                        from PIL import Image
+                        if not is_video and os.path.exists(path):
+                            with Image.open(path) as img:
+                                image_entry["width"], image_entry["height"] = img.size
+                    except:
+                        # If PIL fails or is unavailable, use default dimensions
+                        pass
+                        
+                    images.append(image_entry)
+                
+                # Update the model's civitai.images field
+                model['civitai']['images'] = images
+                
+                # Save metadata to .metadata.json file
+                file_path = model.get('file_path')
+                try:
+                    # Create a copy of model data without 'folder' field
+                    model_copy = model.copy()
+                    model_copy.pop('folder', None)
+                    
+                    # Write metadata to file
+                    await MetadataManager.save_metadata(file_path, model_copy)
+                    logger.info(f"Saved metadata for {model.get('model_name')}")
+                except Exception as e:
+                    logger.error(f"Failed to save metadata for {model.get('model_name')}: {str(e)}")
+                
+                # Save updated metadata to scanner cache
+                success = await scanner.update_single_model_cache(file_path, file_path, model)
+                if success:
+                    logger.info(f"Successfully updated metadata for {model.get('model_name')} with {len(images)} local examples")
+                    return True
+                else:
+                    logger.warning(f"Failed to update metadata for {model.get('model_name')}")
+            
+            return False
+        except Exception as e:
+            logger.error(f"Error updating metadata from local examples: {str(e)}", exc_info=True)
+            return False
+    
+    @staticmethod
+    async def update_metadata_after_import(model_hash, model_data, scanner, newly_imported_paths):
+        """Update model metadata after importing example images
+        
+        Args:
+            model_hash: SHA256 hash of the model
+            model_data: Model data dictionary
+            scanner: Scanner instance (lora or checkpoint)
+            newly_imported_paths: List of paths to newly imported files
+            
+        Returns:
+            tuple: (regular_images, custom_images) - Both image arrays
+        """
+        try:
+            # Ensure civitai field exists in model_data
+            if not model_data.get('civitai'):
+                model_data['civitai'] = {}
+            
+            # Ensure customImages array exists
+            if not model_data['civitai'].get('customImages'):
+                model_data['civitai']['customImages'] = []
+            
+            # Get current customImages array
+            custom_images = model_data['civitai']['customImages']
+            
+            # Add new image entry for each imported file
+            for path_tuple in newly_imported_paths:
+                path, short_id = path_tuple
+
+                # Determine if video or image
+                file_ext = os.path.splitext(path)[1].lower()
+                is_video = file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']
+                
+                # Create image metadata entry
+                image_entry = {
+                    "url": "",  # Empty URL as requested
+                    "id": short_id,
+                    "nsfwLevel": 0,
+                    "width": 720,  # Default dimensions
+                    "height": 1280,
+                    "type": "video" if is_video else "image",
+                    "meta": None,
+                    "hasMeta": False,
+                    "hasPositivePrompt": False
+                }
+                
+                # Extract and parse metadata if this is an image
+                if not is_video:
+                    try:
+                        # Extract metadata from image
+                        extracted_metadata = ExifUtils.extract_image_metadata(path)
+                        
+                        if extracted_metadata:
+                            # Parse the extracted metadata to get generation parameters
+                            parsed_meta = MetadataUpdater._parse_image_metadata(extracted_metadata)
+                            
+                            if parsed_meta:
+                                image_entry["meta"] = parsed_meta
+                                image_entry["hasMeta"] = True
+                                image_entry["hasPositivePrompt"] = bool(parsed_meta.get("prompt", ""))
+                                logger.debug(f"Extracted metadata from {os.path.basename(path)}")
+                    except Exception as e:
+                        logger.warning(f"Failed to extract metadata from {os.path.basename(path)}: {e}")
+                
+                # If it's an image, try to get actual dimensions
+                try:
+                    from PIL import Image
+                    if not is_video and os.path.exists(path):
+                        with Image.open(path) as img:
+                            image_entry["width"], image_entry["height"] = img.size
+                except:
+                    # If PIL fails or is unavailable, use default dimensions
+                    pass
+                    
+                # Append to existing customImages array
+                custom_images.append(image_entry)
+            
+            # Save metadata to .metadata.json file
+            file_path = model_data.get('file_path')
+            if file_path:
+                try:
+                    # 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 metadata for {model_data.get('model_name')}")
+                except Exception as e:
+                    logger.error(f"Failed to save metadata: {str(e)}")
+            
+            # Save updated metadata to scanner cache
+            if file_path:
+                await scanner.update_single_model_cache(file_path, file_path, model_data)
+            
+            # Get regular images array (might be None)
+            regular_images = model_data['civitai'].get('images', [])
+            
+            # Return both image arrays
+            return regular_images, custom_images
+                
+        except Exception as e:
+            logger.error(f"Failed to update metadata after import: {e}", exc_info=True)
+            return [], []
+    
+    @staticmethod
+    def _parse_image_metadata(user_comment):
+        """Parse metadata from image to extract generation parameters
+        
+        Args:
+            user_comment: Metadata string extracted from image
+            
+        Returns:
+            dict: Parsed metadata with generation parameters
+        """
+        if not user_comment:
+            return None
+            
+        try:
+            # Initialize metadata dictionary
+            metadata = {}
+            
+            # Split on Negative prompt if it exists
+            if "Negative prompt:" in user_comment:
+                parts = user_comment.split('Negative prompt:', 1)
+                prompt = parts[0].strip()
+                negative_and_params = parts[1] if len(parts) > 1 else ""
+            else:
+                # No negative prompt section
+                param_start = re.search(r'Steps: \d+', user_comment)
+                if param_start:
+                    prompt = user_comment[:param_start.start()].strip()
+                    negative_and_params = user_comment[param_start.start():]
+                else:
+                    prompt = user_comment.strip()
+                    negative_and_params = ""
+            
+            # Add prompt if it's in GEN_PARAM_KEYS
+            if 'prompt' in GEN_PARAM_KEYS:
+                metadata['prompt'] = prompt
+            
+            # Extract negative prompt and parameters
+            if negative_and_params:
+                # If we split on "Negative prompt:", check for params section
+                if "Negative prompt:" in user_comment:
+                    param_start = re.search(r'Steps: ', negative_and_params)
+                    if param_start:
+                        neg_prompt = negative_and_params[:param_start.start()].strip()
+                        if 'negative_prompt' in GEN_PARAM_KEYS:
+                            metadata['negative_prompt'] = neg_prompt
+                        params_section = negative_and_params[param_start.start():]
+                    else:
+                        if 'negative_prompt' in GEN_PARAM_KEYS:
+                            metadata['negative_prompt'] = negative_and_params.strip()
+                        params_section = ""
+                else:
+                    # No negative prompt, entire section is params
+                    params_section = negative_and_params
+                
+                # Extract generation parameters
+                if params_section:
+                    # Extract basic parameters
+                    param_pattern = r'([A-Za-z\s]+): ([^,]+)'
+                    params = re.findall(param_pattern, params_section)
+                    
+                    for key, value in params:
+                        clean_key = key.strip().lower().replace(' ', '_')
+                        
+                        # Skip if not in recognized gen param keys
+                        if clean_key not in GEN_PARAM_KEYS:
+                            continue
+                            
+                        # Convert numeric values
+                        if clean_key in ['steps', 'seed']:
+                            try:
+                                metadata[clean_key] = int(value.strip())
+                            except ValueError:
+                                metadata[clean_key] = value.strip()
+                        elif clean_key in ['cfg_scale']:
+                            try:
+                                metadata[clean_key] = float(value.strip())
+                            except ValueError:
+                                metadata[clean_key] = value.strip()
+                        else:
+                            metadata[clean_key] = value.strip()
+                    
+                    # Extract size if available and add if a recognized key
+                    size_match = re.search(r'Size: (\d+)x(\d+)', params_section)
+                    if size_match and 'size' in GEN_PARAM_KEYS:
+                        width, height = size_match.groups()
+                        metadata['size'] = f"{width}x{height}"
+            
+            # Return metadata if we have any entries
+            return metadata if metadata else None
+            
+        except Exception as e:
+            logger.error(f"Error parsing image metadata: {e}", exc_info=True)
+            return None

py/utils/example_images_migration.py

@@ -0,0 +1,318 @@
+import asyncio
+import logging
+import os
+import re
+import json
+from ..services.settings_manager import settings
+from ..services.service_registry import ServiceRegistry
+from ..utils.metadata_manager import MetadataManager
+from ..utils.example_images_processor import ExampleImagesProcessor
+from ..utils.constants import SUPPORTED_MEDIA_EXTENSIONS
+
+logger = logging.getLogger(__name__)
+
+CURRENT_NAMING_VERSION = 2  # Increment this when naming conventions change
+
+class ExampleImagesMigration:
+    """Handles migrations for example images naming conventions"""
+    
+    @staticmethod
+    async def check_and_run_migrations():
+        """Check if migrations are needed and run them in background"""
+        example_images_path = settings.get('example_images_path')
+        if not example_images_path or not os.path.exists(example_images_path):
+            logger.debug("No example images path configured or path doesn't exist, skipping migrations")
+            return
+        
+        # Check current version from progress file
+        current_version = 0
+        progress_file = os.path.join(example_images_path, '.download_progress.json')
+        if os.path.exists(progress_file):
+            try:
+                with open(progress_file, 'r', encoding='utf-8') as f:
+                    progress_data = json.load(f)
+                    current_version = progress_data.get('naming_version', 0)
+            except Exception as e:
+                logger.error(f"Failed to load progress file for migration check: {e}")
+        
+        # If current version is less than target version, start migration
+        if current_version < CURRENT_NAMING_VERSION:
+            logger.info(f"Starting example images naming migration from v{current_version} to v{CURRENT_NAMING_VERSION}")
+            # Start migration in background task
+            asyncio.create_task(
+                ExampleImagesMigration.run_migrations(example_images_path, current_version, CURRENT_NAMING_VERSION)
+            )
+    
+    @staticmethod
+    async def run_migrations(example_images_path, from_version, to_version):
+        """Run necessary migrations based on version difference"""
+        try:
+            # Get all model folders
+            model_folders = []
+            for item in os.listdir(example_images_path):
+                item_path = os.path.join(example_images_path, item)
+                if os.path.isdir(item_path) and len(item) == 64:  # SHA256 hash is 64 chars
+                    model_folders.append(item_path)
+            
+            logger.info(f"Found {len(model_folders)} model folders to check for migration")
+            
+            # Apply migrations sequentially
+            if from_version < 1 and to_version >= 1:
+                await ExampleImagesMigration._migrate_to_v1(model_folders)
+            
+            if from_version < 2 and to_version >= 2:
+                await ExampleImagesMigration._migrate_to_v2(model_folders)
+            
+            # Update version in progress file
+            progress_file = os.path.join(example_images_path, '.download_progress.json')
+            try:
+                progress_data = {}
+                if os.path.exists(progress_file):
+                    with open(progress_file, 'r', encoding='utf-8') as f:
+                        progress_data = json.load(f)
+                
+                progress_data['naming_version'] = to_version
+                
+                with open(progress_file, 'w', encoding='utf-8') as f:
+                    json.dump(progress_data, f, indent=2)
+                    
+                logger.info(f"Example images naming migration to v{to_version} completed")
+                
+            except Exception as e:
+                logger.error(f"Failed to update version in progress file: {e}")
+                
+        except Exception as e:
+            logger.error(f"Error during migration: {e}", exc_info=True)
+    
+    @staticmethod
+    async def _migrate_to_v1(model_folders):
+        """Migrate from 1-based to 0-based indexing"""
+        count = 0
+        for folder in model_folders:
+            has_one_based = False
+            has_zero_based = False
+            files_to_rename = []
+            
+            # Check naming pattern in this folder
+            for file in os.listdir(folder):
+                if re.match(r'image_1\.\w+$', file):
+                    has_one_based = True
+                if re.match(r'image_0\.\w+$', file):
+                    has_zero_based = True
+            
+            # Only migrate folders with 1-based indexing and no 0-based
+            if has_one_based and not has_zero_based:
+                # Create rename mapping
+                for file in os.listdir(folder):
+                    match = re.match(r'image_(\d+)\.(\w+)$', file)
+                    if match:
+                        index = int(match.group(1))
+                        ext = match.group(2)
+                        if index > 0:  # Only rename if index is positive
+                            files_to_rename.append((
+                                file,
+                                f"image_{index-1}.{ext}"
+                            ))
+                
+                # Use temporary names to avoid conflicts
+                for old_name, new_name in files_to_rename:
+                    old_path = os.path.join(folder, old_name)
+                    temp_path = os.path.join(folder, f"temp_{old_name}")
+                    try:
+                        os.rename(old_path, temp_path)
+                    except Exception as e:
+                        logger.error(f"Failed to rename {old_path} to {temp_path}: {e}")
+                
+                # Rename from temporary names to final names
+                for old_name, new_name in files_to_rename:
+                    temp_path = os.path.join(folder, f"temp_{old_name}")
+                    new_path = os.path.join(folder, new_name)
+                    try:
+                        os.rename(temp_path, new_path)
+                        logger.debug(f"Renamed {old_name} to {new_name} in {folder}")
+                    except Exception as e:
+                        logger.error(f"Failed to rename {temp_path} to {new_path}: {e}")
+                
+                count += 1
+                
+                # Give other tasks a chance to run
+                if count % 10 == 0:
+                    await asyncio.sleep(0)
+        
+        logger.info(f"Migrated {count} folders from 1-based to 0-based indexing")
+    
+    @staticmethod
+    async def _migrate_to_v2(model_folders):
+        """
+        Migrate to v2 naming scheme:
+        - Move custom examples from images array to customImages array
+        - Rename files from image_<index>.<ext> to custom_<short_id>.<ext>
+        - Add id field to each custom image entry
+        """
+        count = 0
+        updated_models = 0
+        migration_errors = 0
+        
+        # Get scanner instances
+        lora_scanner = await ServiceRegistry.get_lora_scanner()
+        checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+        
+        # Wait until scanners are initialized
+        scanners = [lora_scanner, checkpoint_scanner]
+        for scanner in scanners:
+            if scanner.is_initializing():
+                logger.info("Waiting for scanners to complete initialization before starting migration...")
+                initialized = False
+                retry_count = 0
+                while not initialized and retry_count < 120:  # Wait up to 120 seconds
+                    await asyncio.sleep(1)
+                    initialized = not scanner.is_initializing()
+                    retry_count += 1
+                
+                if not initialized:
+                    logger.warning("Scanner initialization timeout - proceeding with migration anyway")
+        
+        logger.info(f"Starting migration to v2 naming scheme for {len(model_folders)} model folders")
+        
+        for folder in model_folders:
+            try:
+                # Extract model hash from folder name
+                model_hash = os.path.basename(folder)
+                if not model_hash or len(model_hash) != 64:
+                    continue
+                
+                # Find the model in scanner cache
+                model_data = None
+                scanner = None
+                
+                for scan_obj in scanners:
+                  if scan_obj.has_hash(model_hash):
+                    cache = await scan_obj.get_cached_data()
+                    for item in cache.raw_data:
+                      if item.get('sha256') == model_hash:
+                        model_data = item
+                        scanner = scan_obj
+                        break
+                  if model_data:
+                    break
+                
+                if not model_data or not scanner:
+                    logger.debug(f"Model with hash {model_hash} not found in cache, skipping migration")
+                    continue
+                
+                # Clone model data to avoid modifying the cache directly
+                model_metadata = model_data.copy()
+                
+                # Check if model has civitai metadata
+                if not model_metadata.get('civitai'):
+                    continue
+                
+                # Get images array
+                images = model_metadata.get('civitai', {}).get('images', [])
+                if not images:
+                    continue
+                
+                # Initialize customImages array if it doesn't exist
+                if not model_metadata['civitai'].get('customImages'):
+                    model_metadata['civitai']['customImages'] = []
+                
+                # Find custom examples (entries with empty url)
+                custom_indices = []
+                for i, image in enumerate(images):
+                    if image.get('url') == "":
+                        custom_indices.append(i)
+                
+                if not custom_indices:
+                    continue
+                    
+                logger.debug(f"Found {len(custom_indices)} custom examples in {model_hash}")
+                
+                # Process each custom example
+                for index in custom_indices:
+                    try:
+                        image_entry = images[index]
+                        
+                        # Determine media type based on the entry type
+                        media_type = 'videos' if image_entry.get('type') == 'video' else 'images'
+                        extensions_to_try = SUPPORTED_MEDIA_EXTENSIONS[media_type]
+                        
+                        # Find the image file by trying possible extensions
+                        old_path = None
+                        old_filename = None
+                        found = False
+                        
+                        for ext in extensions_to_try:
+                            test_path = os.path.join(folder, f"image_{index}{ext}")
+                            if os.path.exists(test_path):
+                                old_path = test_path
+                                old_filename = f"image_{index}{ext}"
+                                found = True
+                                break
+                                
+                        if not found:
+                            logger.warning(f"Could not find file for index {index} in {model_hash}, skipping")
+                            continue
+                        
+                        # Generate short ID for the custom example
+                        short_id = ExampleImagesProcessor.generate_short_id()
+                        
+                        # Get file extension
+                        file_ext = os.path.splitext(old_path)[1]
+                        
+                        # Create new filename
+                        new_filename = f"custom_{short_id}{file_ext}"
+                        new_path = os.path.join(folder, new_filename)
+                        
+                        # Rename the file
+                        try:
+                            os.rename(old_path, new_path)
+                            logger.debug(f"Renamed {old_filename} to {new_filename} in {folder}")
+                        except Exception as e:
+                            logger.error(f"Failed to rename {old_path} to {new_path}: {e}")
+                            continue
+                        
+                        # Create a copy of the image entry with the id field
+                        custom_entry = image_entry.copy()
+                        custom_entry['id'] = short_id
+                        
+                        # Add to customImages array
+                        model_metadata['civitai']['customImages'].append(custom_entry)
+                        
+                        count += 1
+                        
+                    except Exception as e:
+                        logger.error(f"Error migrating custom example at index {index} for {model_hash}: {e}")
+                
+                # Remove custom examples from the original images array
+                model_metadata['civitai']['images'] = [
+                    img for i, img in enumerate(images) if i not in custom_indices
+                ]
+                
+                # Save the updated metadata
+                file_path = model_data.get('file_path')
+                if file_path:
+                    try:
+                        # Create a copy of model data without 'folder' field
+                        model_copy = model_metadata.copy()
+                        model_copy.pop('folder', None)
+                        
+                        # Save metadata to file
+                        await MetadataManager.save_metadata(file_path, model_copy)
+                        
+                        # Update scanner cache
+                        await scanner.update_single_model_cache(file_path, file_path, model_metadata)
+                        
+                        updated_models += 1
+                    except Exception as e:
+                        logger.error(f"Failed to save metadata for {model_hash}: {e}")
+                        migration_errors += 1
+                
+                # Give other tasks a chance to run
+                if count % 10 == 0:
+                    await asyncio.sleep(0)
+                    
+            except Exception as e:
+                logger.error(f"Error migrating folder {folder}: {e}")
+                migration_errors += 1
+        
+        logger.info(f"Migration to v2 complete: migrated {count} custom examples across {updated_models} models with {migration_errors} errors")

py/utils/example_images_processor.py

@@ -0,0 +1,494 @@
+import logging
+import os
+import re
+import tempfile
+import random
+import string
+from aiohttp import web
+from ..utils.constants import SUPPORTED_MEDIA_EXTENSIONS
+from ..services.service_registry import ServiceRegistry
+from ..services.settings_manager import settings
+from .example_images_metadata import MetadataUpdater
+from ..utils.metadata_manager import MetadataManager
+
+logger = logging.getLogger(__name__)
+
+class ExampleImagesProcessor:
+    """Processes and manipulates example images"""
+
+    @staticmethod
+    def generate_short_id(length=8):
+        """Generate a short random alphanumeric identifier"""
+        chars = string.ascii_lowercase + string.digits
+        return ''.join(random.choice(chars) for _ in range(length))
+    
+    @staticmethod
+    def get_civitai_optimized_url(image_url):
+        """Convert Civitai image URL to its optimized WebP version"""
+        base_pattern = r'(https://image\.civitai\.com/[^/]+/[^/]+)'
+        match = re.match(base_pattern, image_url)
+        
+        if match:
+            base_url = match.group(1)
+            return f"{base_url}/optimized=true/image.webp"
+        
+        return image_url
+    
+    @staticmethod
+    async def download_model_images(model_hash, model_name, model_images, model_dir, optimize, independent_session):
+        """Download images for a single model
+        
+        Returns:
+            tuple: (success, is_stale_metadata) - whether download was successful, whether metadata is stale
+        """
+        model_success = True
+        
+        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
+                        # Return early to trigger metadata refresh attempt
+                        return False, True  # (success, is_metadata_stale)
+                    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
+            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
+        
+        return model_success, False  # (success, is_metadata_stale)
+    
+    @staticmethod
+    async def process_local_examples(model_file_path, model_file_name, model_name, model_dir, optimize):
+        """Process local example images
+        
+        Returns:
+            bool: True if local images were processed successfully, False otherwise
+        """
+        try:
+            if not model_file_path or not os.path.exists(os.path.dirname(model_file_path)):
+                return False
+                
+            model_dir_path = os.path.dirname(model_file_path)
+            local_images = []
+            
+            # Look for files with pattern: filename.example.*.ext
+            if model_file_name:
+                example_prefix = f"{model_file_name}.example."
+                
+                if os.path.exists(model_dir_path):
+                    for file in os.listdir(model_dir_path):
+                        file_lower = file.lower()
+                        if file_lower.startswith(example_prefix.lower()):
+                            file_ext = os.path.splitext(file_lower)[1]
+                            is_supported = (file_ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or 
+                                           file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos'])
+                            
+                            if is_supported:
+                                local_images.append(os.path.join(model_dir_path, file))
+            
+            # Process local images if found
+            if local_images:
+                logger.info(f"Found {len(local_images)} local example images for {model_name}")
+                
+                for local_image_path in local_images:
+                    # Extract index from filename
+                    file_name = os.path.basename(local_image_path)
+                    example_prefix = f"{model_file_name}.example."
+                    
+                    try:
+                        # Extract the part between '.example.' and the file extension
+                        index_part = file_name[len(example_prefix):].split('.')[0]
+                        # Try to parse it as an integer
+                        index = int(index_part)
+                        local_ext = os.path.splitext(local_image_path)[1].lower()
+                        save_filename = f"image_{index}{local_ext}"
+                    except (ValueError, IndexError):
+                        # If we can't parse the index, fall back to sequential numbering
+                        logger.warning(f"Could not extract index from {file_name}, using sequential numbering")
+                        local_ext = os.path.splitext(local_image_path)[1].lower()
+                        save_filename = f"image_{len(local_images)}{local_ext}"
+                    
+                    save_path = os.path.join(model_dir, save_filename)
+                    
+                    # Skip if already exists in output directory
+                    if os.path.exists(save_path):
+                        logger.debug(f"File already exists in output: {save_path}")
+                        continue
+                    
+                    # Copy the file
+                    with open(local_image_path, 'rb') as src_file:
+                        with open(save_path, 'wb') as dst_file:
+                            dst_file.write(src_file.read())
+                
+                return True
+            return False
+        except Exception as e:
+            logger.error(f"Error processing local examples for {model_name}: {str(e)}")
+            return False
+            
+    @staticmethod
+    async def import_images(request):
+        """
+        Import local example images
+        
+        Accepts:
+        - multipart/form-data form with model_hash and files fields
+        or
+        - JSON request with model_hash and file_paths
+        
+        Returns:
+        - Success status and list of imported files
+        """
+        try:
+            model_hash = None
+            files_to_import = []
+            temp_files_to_cleanup = []
+            
+            # Check if it's a multipart form-data request (direct file upload)
+            if request.content_type and 'multipart/form-data' in request.content_type:
+                reader = await request.multipart()
+                
+                # First get model_hash
+                field = await reader.next()
+                if field.name == 'model_hash':
+                    model_hash = await field.text()
+                
+                # Then process all files
+                while True:
+                    field = await reader.next()
+                    if field is None:
+                        break
+                        
+                    if field.name == 'files':
+                        # Create a temporary file with appropriate suffix for type detection
+                        file_name = field.filename
+                        file_ext = os.path.splitext(file_name)[1].lower()
+                        
+                        with tempfile.NamedTemporaryFile(suffix=file_ext, delete=False) as tmp_file:
+                            temp_path = tmp_file.name
+                            temp_files_to_cleanup.append(temp_path)  # Track for cleanup
+                            
+                            # Write chunks to the temporary file
+                            while True:
+                                chunk = await field.read_chunk()
+                                if not chunk:
+                                    break
+                                tmp_file.write(chunk)
+                        
+                        # Add to the list of files to process
+                        files_to_import.append(temp_path)
+            else:
+                # Parse JSON request (legacy method using file paths)
+                data = await request.json()
+                model_hash = data.get('model_hash')
+                files_to_import = data.get('file_paths', [])
+            
+            if not model_hash:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing model_hash parameter'
+                }, status=400)
+            
+            if not files_to_import:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No files provided to import'
+                }, status=400)
+            
+            # Get example images path
+            example_images_path = settings.get('example_images_path')
+            if not example_images_path:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No example images path configured'
+                }, status=400)
+            
+            # Find the model and get current metadata
+            lora_scanner = await ServiceRegistry.get_lora_scanner()
+            checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+            
+            model_data = None
+            scanner = None
+            
+            # Check both scanners to find the model
+            for scan_obj in [lora_scanner, checkpoint_scanner]:
+                cache = await scan_obj.get_cached_data()
+                for item in cache.raw_data:
+                    if item.get('sha256') == model_hash:
+                        model_data = item
+                        scanner = scan_obj
+                        break
+                if model_data:
+                    break
+            
+            if not model_data:
+                return web.json_response({
+                    'success': False,
+                    'error': f"Model with hash {model_hash} not found in cache"
+                }, status=404)
+            
+            # Create model folder
+            model_folder = os.path.join(example_images_path, model_hash)
+            os.makedirs(model_folder, exist_ok=True)
+            
+            imported_files = []
+            errors = []
+            newly_imported_paths = []
+            
+            # Process each file path
+            for file_path in files_to_import:
+                try:
+                    # Ensure the file exists
+                    if not os.path.isfile(file_path):
+                        errors.append(f"File not found: {file_path}")
+                        continue
+                    
+                    # Check if file type is supported
+                    file_ext = os.path.splitext(file_path)[1].lower()
+                    if not (file_ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or 
+                            file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']):
+                        errors.append(f"Unsupported file type: {file_path}")
+                        continue
+                    
+                    # Generate new filename using short ID instead of UUID
+                    short_id = ExampleImagesProcessor.generate_short_id()
+                    new_filename = f"custom_{short_id}{file_ext}"
+                    
+                    dest_path = os.path.join(model_folder, new_filename)
+                    
+                    # Copy the file
+                    import shutil
+                    shutil.copy2(file_path, dest_path)
+                    # Store both the dest_path and the short_id
+                    newly_imported_paths.append((dest_path, short_id))
+                    
+                    # Add to imported files list
+                    imported_files.append({
+                        'name': new_filename,
+                        'path': f'/example_images_static/{model_hash}/{new_filename}',
+                        'extension': file_ext,
+                        'is_video': file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']
+                    })
+                except Exception as e:
+                    errors.append(f"Error importing {file_path}: {str(e)}")
+            
+            # Update metadata with new example images
+            regular_images, custom_images = await MetadataUpdater.update_metadata_after_import(
+                model_hash, 
+                model_data,
+                scanner,
+                newly_imported_paths
+            )
+            
+            return web.json_response({
+                'success': len(imported_files) > 0,
+                'message': f'Successfully imported {len(imported_files)} files' + 
+                        (f' with {len(errors)} errors' if errors else ''),
+                'files': imported_files,
+                'errors': errors,
+                'regular_images': regular_images,
+                'custom_images': custom_images,
+                "model_file_path": model_data.get('file_path', ''),
+            })
+                
+        except Exception as e:
+            logger.error(f"Failed to import example images: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+        finally:
+            # Clean up temporary files
+            for temp_file in temp_files_to_cleanup:
+                try:
+                    os.remove(temp_file)
+                except Exception as e:
+                    logger.error(f"Failed to remove temporary file {temp_file}: {e}")
+
+    @staticmethod
+    async def delete_custom_image(request):
+        """
+        Delete a custom example image for a model
+        
+        Accepts:
+        - JSON request with model_hash and short_id
+        
+        Returns:
+        - Success status and updated image lists
+        """
+        try:
+            # Parse request data
+            data = await request.json()
+            model_hash = data.get('model_hash')
+            short_id = data.get('short_id')
+            
+            if not model_hash or not short_id:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing required parameters: model_hash and short_id'
+                }, status=400)
+            
+            # Get example images path
+            example_images_path = settings.get('example_images_path')
+            if not example_images_path:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No example images path configured'
+                }, status=400)
+            
+            # Find the model and get current metadata
+            lora_scanner = await ServiceRegistry.get_lora_scanner()
+            checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+            
+            model_data = None
+            scanner = None
+            
+            # Check both scanners to find the model
+            for scan_obj in [lora_scanner, checkpoint_scanner]:
+                if scan_obj.has_hash(model_hash):
+                    cache = await scan_obj.get_cached_data()
+                    for item in cache.raw_data:
+                        if item.get('sha256') == model_hash:
+                            model_data = item
+                            scanner = scan_obj
+                            break
+                if model_data:
+                    break
+            
+            if not model_data:
+                return web.json_response({
+                    'success': False,
+                    'error': f"Model with hash {model_hash} not found in cache"
+                }, status=404)
+            
+            # Check if model has custom images
+            if not model_data.get('civitai', {}).get('customImages'):
+                return web.json_response({
+                    'success': False,
+                    'error': f"Model has no custom images"
+                }, status=404)
+            
+            # Find the custom image with matching short_id
+            custom_images = model_data['civitai']['customImages']
+            matching_image = None
+            new_custom_images = []
+            
+            for image in custom_images:
+                if image.get('id') == short_id:
+                    matching_image = image
+                else:
+                    new_custom_images.append(image)
+            
+            if not matching_image:
+                return web.json_response({
+                    'success': False,
+                    'error': f"Custom image with id {short_id} not found"
+                }, status=404)
+            
+            # Find and delete the actual file
+            model_folder = os.path.join(example_images_path, model_hash)
+            file_deleted = False
+            
+            if os.path.exists(model_folder):
+                for filename in os.listdir(model_folder):
+                    if f"custom_{short_id}" in filename:
+                        file_path = os.path.join(model_folder, filename)
+                        try:
+                            os.remove(file_path)
+                            file_deleted = True
+                            logger.info(f"Deleted custom example file: {file_path}")
+                            break
+                        except Exception as e:
+                            return web.json_response({
+                                'success': False,
+                                'error': f"Failed to delete file: {str(e)}"
+                            }, status=500)
+            
+            if not file_deleted:
+                logger.warning(f"File for custom example with id {short_id} not found, but metadata will still be updated")
+            
+            # Update metadata
+            model_data['civitai']['customImages'] = new_custom_images
+            
+            # Save updated metadata to file
+            file_path = model_data.get('file_path')
+            if file_path:
+                try:
+                    # 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.debug(f"Saved updated metadata for {model_data.get('model_name')}")
+                except Exception as e:
+                    logger.error(f"Failed to save metadata: {str(e)}")
+                    return web.json_response({
+                        'success': False,
+                        'error': f"Failed to save metadata: {str(e)}"
+                    }, status=500)
+            
+                # Update cache
+                await scanner.update_single_model_cache(file_path, file_path, model_data)
+            
+            # Get regular images array (might be None)
+            regular_images = model_data['civitai'].get('images', [])
+            
+            return web.json_response({
+                'success': True,
+                'regular_images': regular_images,
+                'custom_images': new_custom_images,
+                'model_file_path': model_data.get('file_path', '')
+            })
+                
+        except Exception as e:
+            logger.error(f"Failed to delete custom example image: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/utils/exif_utils.py

@@ -31,7 +31,7 @@ class ExifUtils:
                 # Method 2: Check EXIF UserComment field
                 if img.format not in ['JPEG', 'TIFF', 'WEBP']:
                     # For non-JPEG/TIFF/WEBP images, try to get EXIF through PIL
-                    exif = img._getexif()
+                    exif = img.getexif()
                     if exif and piexif.ExifIFD.UserComment in exif:
                         user_comment = exif[piexif.ExifIFD.UserComment]
                         if isinstance(user_comment, bytes):

py/utils/file_utils.py

@@ -1,13 +1,7 @@
 import logging
 import os
 import hashlib
-import json
-import time
-from typing import Dict, Optional, Type
 
-from .model_utils import determine_base_model
-from .lora_metadata import extract_lora_metadata, extract_checkpoint_metadata
-from .models import BaseModelMetadata, LoraMetadata, CheckpointMetadata
 from .constants import PREVIEW_EXTENSIONS, CARD_PREVIEW_WIDTH
 from .exif_utils import ExifUtils
 
@@ -24,7 +18,12 @@ async def calculate_sha256(file_path: str) -> str:
 def find_preview_file(base_name: str, dir_path: str) -> str:
     """Find preview file for given base name in directory"""
     
-    for ext in PREVIEW_EXTENSIONS:
+    temp_extensions = PREVIEW_EXTENSIONS.copy()
+    # Add example extension for compatibility
+    # https://github.com/willmiao/ComfyUI-Lora-Manager/issues/225
+    # The preview image will be optimized to lora-name.webp, so it won't affect other logic
+    temp_extensions.append(".example.0.jpeg")
+    for ext in temp_extensions:
         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
@@ -42,7 +41,7 @@ def find_preview_file(base_name: str, dir_path: str) -> str:
                         target_width=CARD_PREVIEW_WIDTH,
                         format='webp',
                         quality=85,
-                        preserve_metadata=False  # Changed from True to False
+                        preserve_metadata=False
                     )
                     
                     # Save the optimized webp file
@@ -63,188 +62,4 @@ def find_preview_file(base_name: str, dir_path: str) -> str:
 
 def normalize_path(path: str) -> str:
     """Normalize file path to use forward slashes"""
-    return path.replace(os.sep, "/") if path else path
-
-async def get_file_info(file_path: str, model_class: Type[BaseModelMetadata] = LoraMetadata) -> Optional[BaseModelMetadata]:
-    """Get basic file information as a model metadata object"""
-    # First check if file actually exists and resolve symlinks
-    try:
-        real_path = os.path.realpath(file_path)
-        if not os.path.exists(real_path):
-            return None
-    except Exception as e:
-        logger.error(f"Error checking file existence for {file_path}: {e}")
-        return None
-        
-    base_name = os.path.splitext(os.path.basename(file_path))[0]
-    dir_path = os.path.dirname(file_path)
-    
-    preview_url = find_preview_file(base_name, dir_path)
-    
-    # Check if a .json file exists with SHA256 hash to avoid recalculation
-    json_path = f"{os.path.splitext(file_path)[0]}.json"
-    sha256 = None
-    if os.path.exists(json_path):
-        try:
-            with open(json_path, 'r', encoding='utf-8') as f:
-                json_data = json.load(f)
-                if 'sha256' in json_data:
-                    sha256 = json_data['sha256'].lower()
-                    logger.debug(f"Using SHA256 from .json file for {file_path}")
-        except Exception as e:
-            logger.error(f"Error reading .json file for {file_path}: {e}")
-    
-    # If SHA256 is still not found, check for a .sha256 file
-    if sha256 is None:
-        sha256_file = f"{os.path.splitext(file_path)[0]}.sha256"
-        if os.path.exists(sha256_file):
-            try:
-                with open(sha256_file, 'r', encoding='utf-8') as f:
-                    sha256 = f.read().strip().lower()
-                    logger.debug(f"Using SHA256 from .sha256 file for {file_path}")
-            except Exception as e:
-                logger.error(f"Error reading .sha256 file for {file_path}: {e}")
-
-    try:
-        # If we didn't get SHA256 from the .json file, calculate it
-        if not sha256:
-            start_time = time.time()
-            sha256 = await calculate_sha256(real_path)
-            logger.debug(f"Calculated SHA256 for {file_path} in {time.time() - start_time:.2f} seconds")
-        
-        # Create default metadata based on model class
-        if model_class == CheckpointMetadata:
-            metadata = CheckpointMetadata(
-                file_name=base_name,
-                model_name=base_name,
-                file_path=normalize_path(file_path),
-                size=os.path.getsize(real_path),
-                modified=os.path.getmtime(real_path),
-                sha256=sha256,
-                base_model="Unknown",  # Will be updated later
-                preview_url=normalize_path(preview_url),
-                tags=[],
-                modelDescription="",
-                model_type="checkpoint"
-            )
-            
-            # Extract checkpoint-specific metadata
-            # model_info = await extract_checkpoint_metadata(real_path)
-            # metadata.base_model = model_info['base_model']
-            # if 'model_type' in model_info:
-            #     metadata.model_type = model_info['model_type']
-            
-        else:  # Default to LoraMetadata
-            metadata = LoraMetadata(
-                file_name=base_name,
-                model_name=base_name,
-                file_path=normalize_path(file_path),
-                size=os.path.getsize(real_path),
-                modified=os.path.getmtime(real_path),
-                sha256=sha256,
-                base_model="Unknown",  # Will be updated later
-                usage_tips="{}",
-                preview_url=normalize_path(preview_url),
-                tags=[],
-                modelDescription=""
-            )
-            
-            # Extract lora-specific metadata
-            model_info = await extract_lora_metadata(real_path)
-            metadata.base_model = model_info['base_model']
-
-        # Save metadata to file
-        await save_metadata(file_path, metadata)
-        
-        return metadata
-    except Exception as e:
-        logger.error(f"Error getting file info for {file_path}: {e}")
-        return None
-
-async def save_metadata(file_path: str, metadata: BaseModelMetadata) -> None:
-    """Save metadata to .metadata.json file"""
-    metadata_path = f"{os.path.splitext(file_path)[0]}.metadata.json"
-    try:
-        metadata_dict = metadata.to_dict()
-        metadata_dict['file_path'] = normalize_path(metadata_dict['file_path'])
-        metadata_dict['preview_url'] = normalize_path(metadata_dict['preview_url'])
-        
-        with open(metadata_path, 'w', encoding='utf-8') as f:
-            json.dump(metadata_dict, f, indent=2, ensure_ascii=False)
-    except Exception as e:
-        print(f"Error saving metadata to {metadata_path}: {str(e)}")
-
-async def load_metadata(file_path: str, model_class: Type[BaseModelMetadata] = LoraMetadata) -> Optional[BaseModelMetadata]:
-    """Load metadata from .metadata.json file"""
-    metadata_path = f"{os.path.splitext(file_path)[0]}.metadata.json"
-    try:
-        if os.path.exists(metadata_path):
-            with open(metadata_path, 'r', encoding='utf-8') as f:
-                data = json.load(f)
-                
-                needs_update = False
-
-                # Check and normalize base model name
-                normalized_base_model = determine_base_model(data['base_model'])
-                if data['base_model'] != normalized_base_model:
-                    data['base_model'] = normalized_base_model
-                    needs_update = True
-                
-                # Compare paths without extensions
-                stored_path_base = os.path.splitext(data['file_path'])[0]
-                current_path_base = os.path.splitext(normalize_path(file_path))[0]
-                if stored_path_base != current_path_base:
-                    data['file_path'] = normalize_path(file_path)
-                    needs_update = True
-                
-                # TODO: optimize preview image to webp format if not already done
-                preview_url = data.get('preview_url', '')
-                if not preview_url or not os.path.exists(preview_url):
-                    base_name = os.path.splitext(os.path.basename(file_path))[0]
-                    dir_path = os.path.dirname(file_path)
-                    new_preview_url = normalize_path(find_preview_file(base_name, dir_path))
-                    if new_preview_url != preview_url:
-                        data['preview_url'] = new_preview_url
-                        needs_update = True
-                else:
-                    # Compare preview paths without extensions
-                    stored_preview_base = os.path.splitext(preview_url)[0]
-                    current_preview_base = os.path.splitext(normalize_path(preview_url))[0]
-                    if stored_preview_base != current_preview_base:
-                        data['preview_url'] = normalize_path(preview_url)
-                        needs_update = True
-
-                # Ensure all fields are present
-                if 'tags' not in data:
-                    data['tags'] = []
-                    needs_update = True
-                    
-                if 'modelDescription' not in data:
-                    data['modelDescription'] = ""
-                    needs_update = True
-                    
-                # For checkpoint metadata
-                if model_class == CheckpointMetadata and 'model_type' not in data:
-                    data['model_type'] = "checkpoint"
-                    needs_update = True
-                
-                # For lora metadata
-                if model_class == LoraMetadata and 'usage_tips' not in data:
-                    data['usage_tips'] = "{}"
-                    needs_update = True
-                
-                if needs_update:
-                    with open(metadata_path, 'w', encoding='utf-8') as f:
-                        json.dump(data, f, indent=2, ensure_ascii=False)
-                
-                return model_class.from_dict(data)
-                
-    except Exception as e:
-        print(f"Error loading metadata from {metadata_path}: {str(e)}")
-    return None
-
-async def update_civitai_metadata(file_path: str, civitai_data: Dict) -> None:
-    """Update metadata file with Civitai data"""
-    metadata = await load_metadata(file_path)
-    metadata['civitai'] = civitai_data
-    await save_metadata(file_path, metadata)
+    return path.replace(os.sep, "/") if path else path

py/utils/lora_metadata.py

@@ -1,7 +1,11 @@
 from safetensors import safe_open
-from typing import Dict
+from typing import Dict, List, Tuple
 from .model_utils import determine_base_model
 import os
+import logging
+import json
+
+logger = logging.getLogger(__name__)
 
 async def extract_lora_metadata(file_path: str) -> Dict:
     """Extract essential metadata from safetensors file"""
@@ -77,4 +81,53 @@ async def extract_checkpoint_metadata(file_path: str) -> dict:
     except Exception as e:
         logger.error(f"Error extracting checkpoint metadata for {file_path}: {e}")
         # Return default values
-        return {'base_model': 'Unknown', 'model_type': 'checkpoint'}
+        return {'base_model': 'Unknown', 'model_type': 'checkpoint'}
+
+async def extract_trained_words(file_path: str) -> Tuple[List[Tuple[str, int]], str]:
+    """Extract trained words from a safetensors file and sort by frequency
+    
+    Args:
+        file_path: Path to the safetensors file
+        
+    Returns:
+        Tuple of:
+        - List of (word, frequency) tuples sorted by frequency (highest first)
+        - class_tokens value (or None if not found)
+    """
+    class_tokens = None
+    
+    try:
+        with safe_open(file_path, framework="pt", device="cpu") as f:
+            metadata = f.metadata()
+            
+            # Extract class_tokens from ss_datasets if present
+            if metadata and "ss_datasets" in metadata:
+                try:
+                    datasets_data = json.loads(metadata["ss_datasets"])
+                    # Look for class_tokens in the first subset
+                    if datasets_data and isinstance(datasets_data, list) and datasets_data[0].get("subsets"):
+                        subsets = datasets_data[0].get("subsets", [])
+                        if subsets and isinstance(subsets, list) and len(subsets) > 0:
+                            class_tokens = subsets[0].get("class_tokens")
+                except Exception as e:
+                    logger.error(f"Error parsing ss_datasets for class_tokens: {str(e)}")
+            
+            # Extract tag frequency as before
+            if metadata and "ss_tag_frequency" in metadata:
+                # Parse the JSON string into a dictionary
+                tag_data = json.loads(metadata["ss_tag_frequency"])
+                
+                # The structure may have an outer key (like "image_dir" or "img")
+                # We need to get the inner dictionary with the actual word frequencies
+                if tag_data:
+                    # Get the first key (usually "image_dir" or "img")
+                    first_key = list(tag_data.keys())[0]
+                    words_dict = tag_data[first_key]
+                    
+                    # Sort words by frequency (highest first)
+                    sorted_words = sorted(words_dict.items(), key=lambda x: x[1], reverse=True)
+                    return sorted_words, class_tokens
+    except Exception as e:
+        logger.error(f"Error extracting trained words from {file_path}: {str(e)}")
+    
+    return [], class_tokens

py/utils/metadata_manager.py

@@ -0,0 +1,275 @@
+import os
+import json
+import shutil
+import logging
+from typing import Dict, Optional, Type, Union
+
+from .models import BaseModelMetadata, LoraMetadata
+from .file_utils import normalize_path, find_preview_file, calculate_sha256
+from .lora_metadata import extract_lora_metadata, extract_checkpoint_metadata
+
+logger = logging.getLogger(__name__)
+
+class MetadataManager:
+    """
+    Centralized manager for all metadata operations.
+    
+    This class is responsible for:
+    1. Loading metadata safely with fallback mechanisms
+    2. Saving metadata with atomic operations and backups
+    3. Creating default metadata for models
+    4. Handling unknown fields gracefully
+    """
+    
+    @staticmethod
+    async def load_metadata(file_path: str, model_class: Type[BaseModelMetadata] = LoraMetadata) -> Optional[BaseModelMetadata]:
+        """
+        Load metadata with robust error handling and data preservation.
+        
+        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
+        """
+        metadata_path = f"{os.path.splitext(file_path)[0]}.metadata.json"
+        backup_path = f"{metadata_path}.bak"
+        
+        # Try loading the main metadata file
+        if os.path.exists(metadata_path):
+            try:
+                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
+    
+    @staticmethod
+    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:
+            BaseModelMetadata instance or None if restoration fails
+        """
+        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
+                metadata = model_class.from_dict(data)
+                await MetadataManager._normalize_metadata_paths(metadata, file_path)
+                return metadata
+            except Exception as e:
+                logger.error(f"Failed to restore from backup: {str(e)}")
+        
+        return None
+    
+    @staticmethod
+    async def save_metadata(path: str, metadata: Union[BaseModelMetadata, Dict], create_backup: bool = True) -> bool:
+        """
+        Save metadata with atomic write operations and backup creation.
+        
+        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 backup of existing file
+          
+        Returns:
+          bool: Success or failure
+        """
+        # Determine if the input is a metadata path or a model file path
+        if path.endswith('.metadata.json'):
+            metadata_path = path
+        else:
+            # Use existing logic for model file paths
+            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 requested and file exists
+            if create_backup and os.path.exists(metadata_path):
+                try:
+                    shutil.copy2(metadata_path, 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()
+                # Preserve unknown fields if present
+                if hasattr(metadata, '_unknown_fields'):
+                    metadata_dict.update(metadata._unknown_fields)
+            else:
+                metadata_dict = metadata.copy()
+            
+            # Normalize paths
+            if 'file_path' in metadata_dict:
+                metadata_dict['file_path'] = normalize_path(metadata_dict['file_path'])
+            if 'preview_url' in metadata_dict:
+                metadata_dict['preview_url'] = normalize_path(metadata_dict['preview_url'])
+            
+            # Write to temporary file first
+            with open(temp_path, 'w', encoding='utf-8') as f:
+                json.dump(metadata_dict, f, indent=2, ensure_ascii=False)
+            
+            # Atomic rename operation
+            os.replace(temp_path, metadata_path)
+            return True
+            
+        except Exception as e:
+            logger.error(f"Error saving metadata to {metadata_path}: {str(e)}")
+            # Clean up temporary file if it exists
+            if os.path.exists(temp_path):
+                try:
+                    os.remove(temp_path)
+                except:
+                    pass
+            return False
+    
+    @staticmethod
+    async def create_default_metadata(file_path: str, model_class: Type[BaseModelMetadata] = LoraMetadata) -> Optional[BaseModelMetadata]:
+        """
+        Create basic metadata structure for a model file.
+        This replaces the old get_file_info function with a more appropriately named method.
+        
+        Args:
+            file_path: Path to the model file
+            model_class: Class to instantiate
+            
+        Returns:
+            BaseModelMetadata instance or None if file doesn't exist
+        """
+        # First check if file actually exists and resolve symlinks
+        try:
+            real_path = os.path.realpath(file_path)
+            if not os.path.exists(real_path):
+                return None
+        except Exception as e:
+            logger.error(f"Error checking file existence for {file_path}: {e}")
+            return None
+            
+        try:
+            base_name = os.path.splitext(os.path.basename(file_path))[0]
+            dir_path = os.path.dirname(file_path)
+            
+            # Find preview image
+            preview_url = find_preview_file(base_name, dir_path)
+            
+            # Calculate file hash
+            sha256 = await calculate_sha256(real_path)
+            
+            # Create instance based on model type
+            if model_class.__name__ == "CheckpointMetadata":
+                metadata = model_class(
+                    file_name=base_name,
+                    model_name=base_name,
+                    file_path=normalize_path(file_path),
+                    size=os.path.getsize(real_path),
+                    modified=os.path.getmtime(real_path),
+                    sha256=sha256,
+                    base_model="Unknown",
+                    preview_url=normalize_path(preview_url),
+                    tags=[],
+                    modelDescription="",
+                    model_type="checkpoint",
+                    from_civitai=True
+                )
+            else:  # Default to LoraMetadata
+                metadata = model_class(
+                    file_name=base_name,
+                    model_name=base_name,
+                    file_path=normalize_path(file_path),
+                    size=os.path.getsize(real_path),
+                    modified=os.path.getmtime(real_path),
+                    sha256=sha256,
+                    base_model="Unknown",
+                    preview_url=normalize_path(preview_url),
+                    tags=[],
+                    modelDescription="",
+                    from_civitai=True,
+                    usage_tips="{}"
+                )
+            
+            # Try to extract model-specific metadata
+            await MetadataManager._enrich_metadata(metadata, real_path)
+            
+            # Save the created metadata
+            await MetadataManager.save_metadata(file_path, metadata, create_backup=False)
+            
+            return metadata
+            
+        except Exception as e:
+            logger.error(f"Error creating default metadata for {file_path}: {e}")
+            return None
+    
+    @staticmethod
+    async def _enrich_metadata(metadata: BaseModelMetadata, file_path: str) -> None:
+        """
+        Enrich metadata with model-specific information
+        
+        Args:
+            metadata: Metadata to enrich
+            file_path: Path to the model file
+        """
+        try:
+            if metadata.__class__.__name__ == "LoraMetadata":
+                model_info = await extract_lora_metadata(file_path)
+                metadata.base_model = model_info['base_model']
+            
+            # elif metadata.__class__.__name__ == "CheckpointMetadata":
+            #     model_info = await extract_checkpoint_metadata(file_path)
+            #     metadata.base_model = model_info['base_model']
+            #     if 'model_type' in model_info:
+            #         metadata.model_type = model_info['model_type']
+        except Exception as e:
+            logger.error(f"Error enriching metadata: {str(e)}")
+    
+    @staticmethod
+    async def _normalize_metadata_paths(metadata: BaseModelMetadata, file_path: str) -> None:
+        """
+        Normalize paths in metadata object
+        
+        Args:
+            metadata: Metadata object to update
+            file_path: Current file path for the model
+        """
+        # Check if file path is different from what's in metadata
+        if normalize_path(file_path) != metadata.file_path:
+            metadata.file_path = normalize_path(file_path)
+        
+        # Check if preview exists at the current location
+        preview_url = metadata.preview_url
+        if preview_url and not os.path.exists(preview_url):
+            base_name = os.path.splitext(os.path.basename(file_path))[0]
+            dir_path = os.path.dirname(file_path)
+            new_preview_url = find_preview_file(base_name, dir_path)
+            if new_preview_url:
+                metadata.preview_url = normalize_path(new_preview_url)

py/utils/models.py

@@ -1,5 +1,5 @@
-from dataclasses import dataclass, asdict
-from typing import Dict, Optional, List
+from dataclasses import dataclass, asdict, field
+from typing import Dict, Optional, List, Any
 from datetime import datetime
 import os
 from .model_utils import determine_base_model
@@ -24,6 +24,7 @@ class BaseModelMetadata:
     civitai_deleted: bool = False  # Whether deleted from Civitai
     favorite: bool = False      # Whether the model is a favorite
     exclude: bool = False       # Whether to exclude this model from the cache
+    _unknown_fields: Dict[str, Any] = field(default_factory=dict, repr=False, compare=False)  # Store unknown fields
 
     def __post_init__(self):
         # Initialize empty lists to avoid mutable default parameter issue
@@ -34,11 +35,43 @@ class BaseModelMetadata:
     def from_dict(cls, data: Dict) -> 'BaseModelMetadata':
         """Create instance from dictionary"""
         data_copy = data.copy()
-        return cls(**data_copy)
+        
+        # Use cached fields if available, otherwise compute them
+        if not hasattr(cls, '_known_fields_cache'):
+            known_fields = set()
+            for c in cls.mro():
+                if hasattr(c, '__annotations__'):
+                    known_fields.update(c.__annotations__.keys())
+            cls._known_fields_cache = known_fields
+        
+        known_fields = cls._known_fields_cache
+        
+        # Extract fields that match our class attributes
+        fields_to_use = {k: v for k, v in data_copy.items() if k in known_fields}
+        
+        # Store unknown fields separately
+        unknown_fields = {k: v for k, v in data_copy.items() if k not in known_fields and not k.startswith('_')}
+        
+        # Create instance with known fields
+        instance = cls(**fields_to_use)
+        
+        # Add unknown fields as a separate attribute
+        instance._unknown_fields = unknown_fields
+        
+        return instance
 
     def to_dict(self) -> Dict:
         """Convert to dictionary for JSON serialization"""
-        return asdict(self)
+        result = asdict(self)
+        
+        # Remove private fields
+        result = {k: v for k, v in result.items() if not k.startswith('_')}
+        
+        # Add back unknown fields if they exist
+        if hasattr(self, '_unknown_fields'):
+            result.update(self._unknown_fields)
+            
+        return result
 
     @property
     def modified_datetime(self) -> datetime:

py/utils/routes_common.py

@@ -9,6 +9,7 @@ from .constants import PREVIEW_EXTENSIONS, CARD_PREVIEW_WIDTH
 from ..config import config
 from ..services.civitai_client import CivitaiClient
 from ..utils.exif_utils import ExifUtils
+from ..utils.metadata_manager import MetadataManager
 from ..services.download_manager import DownloadManager
 
 logger = logging.getLogger(__name__)
@@ -32,27 +33,60 @@ class ModelRouteUtils:
     async def handle_not_found_on_civitai(metadata_path: str, local_metadata: Dict) -> None:
         """Handle case when model is not found on CivitAI"""
         local_metadata['from_civitai'] = False
-        with open(metadata_path, 'w', encoding='utf-8') as f:
-            json.dump(local_metadata, f, indent=2, ensure_ascii=False)
+        await MetadataManager.save_metadata(metadata_path, local_metadata)
 
     @staticmethod
     async def update_model_metadata(metadata_path: str, local_metadata: Dict, 
                                   civitai_metadata: Dict, client: CivitaiClient) -> None:
         """Update local metadata with CivitAI data"""
-        local_metadata['civitai'] = civitai_metadata
+        # Save existing trainedWords and customImages if they exist
+        existing_civitai = local_metadata.get('civitai') or {}  # Use empty dict if None
+
+        # Create a new civitai metadata by updating existing with new
+        merged_civitai = existing_civitai.copy()
+        merged_civitai.update(civitai_metadata)
+
+        # Special handling for trainedWords - ensure we don't lose any existing trained words
+        if 'trainedWords' in existing_civitai:
+            existing_trained_words = existing_civitai.get('trainedWords', [])
+            new_trained_words = civitai_metadata.get('trainedWords', [])
+            # Use a set to combine words without duplicates, then convert back to list
+            merged_trained_words = list(set(existing_trained_words + new_trained_words))
+            merged_civitai['trainedWords'] = merged_trained_words
+
+        # Update local metadata with merged civitai data
+        local_metadata['civitai'] = merged_civitai
+        local_metadata['from_civitai'] = True
         
         # Update model name if available
         if 'model' in civitai_metadata:
             if civitai_metadata.get('model', {}).get('name'):
                 local_metadata['model_name'] = civitai_metadata['model']['name']
         
-            # Fetch additional model metadata (description and tags) if we have model ID
-            model_id = civitai_metadata['modelId']
-            if model_id:
-                model_metadata, _ = await client.get_model_metadata(str(model_id))
-                if model_metadata:
-                    local_metadata['modelDescription'] = model_metadata.get('description', '')
-                    local_metadata['tags'] = model_metadata.get('tags', [])
+            # Extract model metadata directly from civitai_metadata if available
+            model_metadata = None
+            
+            if 'model' in civitai_metadata and civitai_metadata.get('model'):
+                # Data is already available in the response from get_model_version
+                model_metadata = {
+                    'description': civitai_metadata.get('model', {}).get('description', ''),
+                    'tags': civitai_metadata.get('model', {}).get('tags', []),
+                    'creator': civitai_metadata.get('creator', {})
+                }
+            
+            # If we have modelId and don't have enough metadata, fetch additional data
+            if not model_metadata or not model_metadata.get('description'):
+                model_id = civitai_metadata.get('modelId')
+                if model_id:
+                    fetched_metadata, _ = await client.get_model_metadata(str(model_id))
+                    if fetched_metadata:
+                        model_metadata = fetched_metadata
+            
+            # Update local metadata with the model information
+            if model_metadata:
+                local_metadata['modelDescription'] = model_metadata.get('description', '')
+                local_metadata['tags'] = model_metadata.get('tags', [])
+                if 'creator' in model_metadata and model_metadata['creator']:
                     local_metadata['civitai']['creator'] = model_metadata['creator']
         
         # Update base model
@@ -61,7 +95,7 @@ class ModelRouteUtils:
         # Update preview if needed
         if not local_metadata.get('preview_url') or not os.path.exists(local_metadata['preview_url']):
             first_preview = next((img for img in civitai_metadata.get('images', [])), None)
-            if first_preview:
+            if (first_preview):
                 # Determine if content is video or image
                 is_video = first_preview['type'] == 'video'
                 
@@ -120,8 +154,7 @@ class ModelRouteUtils:
                                 local_metadata['preview_nsfw_level'] = first_preview.get('nsfwLevel', 0)
 
         # Save updated metadata
-        with open(metadata_path, 'w', encoding='utf-8') as f:
-            json.dump(local_metadata, f, indent=2, ensure_ascii=False)
+        await MetadataManager.save_metadata(metadata_path, local_metadata)
 
     @staticmethod
     async def fetch_and_update_model(
@@ -143,6 +176,11 @@ class ModelRouteUtils:
         """
         client = CivitaiClient()
         try:
+            # Validate input parameters
+            if not isinstance(model_data, dict):
+                logger.error(f"Invalid model_data type: {type(model_data)}")
+                return False
+
             metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
             
             # Check if model metadata exists
@@ -154,8 +192,7 @@ class ModelRouteUtils:
                 # Mark as not from CivitAI if not found
                 local_metadata['from_civitai'] = False
                 model_data['from_civitai'] = False
-                with open(metadata_path, 'w', encoding='utf-8') as f:
-                    json.dump(local_metadata, f, indent=2, ensure_ascii=False)
+                await MetadataManager.save_metadata(file_path, local_metadata)
                 return False
 
             # Update metadata
@@ -166,21 +203,25 @@ class ModelRouteUtils:
                 client
             )
             
-            # Update cache object directly
-            model_data.update({
+            # Update cache object directly using safe .get() method
+            update_dict = {
                 'model_name': local_metadata.get('model_name'),
                 'preview_url': local_metadata.get('preview_url'),
                 'from_civitai': True,
                 'civitai': civitai_metadata
-            })
+            }
+            model_data.update(update_dict)
             
             # Update cache using the provided function
             await update_cache_func(file_path, file_path, local_metadata)
                 
             return True
 
+        except KeyError as e:
+            logger.error(f"Error fetching CivitAI data - Missing key: {e} in model_data={model_data}")
+            return False
         except Exception as e:
-            logger.error(f"Error fetching CivitAI data: {e}")
+            logger.error(f"Error fetching CivitAI data: {str(e)}", exc_info=True)  # Include stack trace
             return False
         finally:
             await client.close()
@@ -194,18 +235,17 @@ class ModelRouteUtils:
         fields = [
             "id", "modelId", "name", "createdAt", "updatedAt", 
             "publishedAt", "trainedWords", "baseModel", "description",
-            "model", "images"
+            "model", "images", "customImages", "creator"
         ]
         return {k: data[k] for k in fields if k in data}
 
     @staticmethod
-    async def delete_model_files(target_dir: str, file_name: str, file_monitor=None) -> List[str]:
+    async def delete_model_files(target_dir: str, file_name: str) -> List[str]:
         """Delete model and associated files
         
         Args:
             target_dir: Directory containing the model files
             file_name: Base name of the model file without extension
-            file_monitor: Optional file monitor to ignore delete events
             
         Returns:
             List of deleted file paths
@@ -223,11 +263,7 @@ class ModelRouteUtils:
         main_file = patterns[0]
         main_path = os.path.join(target_dir, main_file).replace(os.sep, '/')
         
-        if os.path.exists(main_path):
-            # Notify file monitor to ignore delete event if available
-            if file_monitor:
-                file_monitor.handler.add_ignore_path(main_path, 0)
-            
+        if os.path.exists(main_path):     
             # Delete file
             os.remove(main_path)
             deleted.append(main_path)
@@ -248,10 +284,12 @@ class ModelRouteUtils:
     
     @staticmethod
     def get_multipart_ext(filename):
-        """Get extension that may have multiple parts like .metadata.json"""
+        """Get extension that may have multiple parts like .metadata.json or .metadata.json.bak"""
         parts = filename.split(".")
-        if len(parts) > 2:  # If contains multi-part extension
+        if len(parts) == 3:  # If contains 2-part extension
             return "." + ".".join(parts[-2:])  # Take the last two parts, like ".metadata.json"
+        elif len(parts) >= 4:  # If contains 3-part or more extensions
+            return "." + ".".join(parts[-3:])  # Take the last three parts, like ".metadata.json.bak"
         return os.path.splitext(filename)[1]  # Otherwise take the regular extension, like ".safetensors"
 
     # New common endpoint handlers
@@ -276,13 +314,9 @@ class ModelRouteUtils:
             target_dir = os.path.dirname(file_path)
             file_name = os.path.splitext(os.path.basename(file_path))[0]
             
-            # Get the file monitor from the scanner if available
-            file_monitor = getattr(scanner, 'file_monitor', None)
-            
             deleted_files = await ModelRouteUtils.delete_model_files(
                 target_dir, 
-                file_name,
-                file_monitor
+                file_name
             )
             
             # Remove from cache
@@ -293,6 +327,8 @@ class ModelRouteUtils:
             # Update hash index if available
             if hasattr(scanner, '_hash_index') and scanner._hash_index:
                 scanner._hash_index.remove_by_path(file_path)
+
+            await scanner._save_cache_to_disk()
             
             return web.json_response({
                 'success': True,
@@ -312,7 +348,7 @@ class ModelRouteUtils:
             scanner: The model scanner instance with cache management methods
             
         Returns:
-            web.Response: The HTTP response
+            web.Response: The HTTP response with metadata on success
         """
         try:
             data = await request.json()
@@ -337,7 +373,8 @@ class ModelRouteUtils:
                 # Update the cache
                 await scanner.update_single_model_cache(data['file_path'], data['file_path'], local_metadata)
                 
-                return web.json_response({"success": True})
+                # Return the updated metadata along with success status
+                return web.json_response({"success": True, "metadata": local_metadata})
             finally:
                 await client.close()
 
@@ -347,15 +384,7 @@ class ModelRouteUtils:
 
     @staticmethod
     async def handle_replace_preview(request: web.Request, scanner) -> web.Response:
-        """Handle preview image replacement request
-        
-        Args:
-            request: The aiohttp request
-            scanner: The model scanner instance with methods to update cache
-            
-        Returns:
-            web.Response: The HTTP response
-        """
+        """Handle preview image replacement request"""
         try:
             reader = await request.multipart()
             
@@ -364,6 +393,15 @@ class ModelRouteUtils:
             if field.name != 'preview_file':
                 raise ValueError("Expected 'preview_file' field")
             content_type = field.headers.get('Content-Type', 'image/png')
+            
+            # Try to get original filename if available
+            content_disposition = field.headers.get('Content-Disposition', '')
+            original_filename = None
+            import re
+            filename_match = re.search(r'filename="(.*?)"', content_disposition)
+            if filename_match:
+                original_filename = filename_match.group(1)
+                
             preview_data = await field.read()
             
             # Read model path
@@ -372,17 +410,47 @@ class ModelRouteUtils:
                 raise ValueError("Expected 'model_path' field")
             model_path = (await field.read()).decode()
             
+            # Read NSFW level
+            nsfw_level = 0  # Default to 0 (unknown)
+            field = await reader.next()
+            if field and field.name == 'nsfw_level':
+                try:
+                    nsfw_level = int((await field.read()).decode())
+                except (ValueError, TypeError):
+                    logger.warning("Invalid NSFW level format, using default 0")
+            
             # Save preview file
             base_name = os.path.splitext(os.path.basename(model_path))[0]
             folder = os.path.dirname(model_path)
             
-            # Determine if content is video or image
+            # Determine format based on content type and original filename
+            is_gif = False
+            if original_filename and original_filename.lower().endswith('.gif'):
+                is_gif = True
+            elif content_type.lower() == 'image/gif':
+                is_gif = True
+                
+            # Determine if content is video or image and handle specific formats
             if content_type.startswith('video/'):
-                # For videos, keep original format and use .mp4 extension
-                extension = '.mp4'
+                # For videos, preserve original format if possible
+                if original_filename:
+                    extension = os.path.splitext(original_filename)[1].lower()
+                    # Default to .mp4 if no extension or unrecognized
+                    if not extension or extension not in ['.mp4', '.webm', '.mov', '.avi']:
+                        extension = '.mp4'
+                else:
+                    # Try to determine extension from content type
+                    if 'webm' in content_type:
+                        extension = '.webm'
+                    else:
+                        extension = '.mp4'  # Default
+                optimized_data = preview_data  # No optimization for videos
+            elif is_gif:
+                # Preserve GIF format without optimization
+                extension = '.gif'
                 optimized_data = preview_data
             else:
-                # For images, optimize and convert to WebP
+                # For other images, optimize and convert to WebP
                 optimized_data, _ = ExifUtils.optimize_image(
                     image_data=preview_data,
                     target_width=CARD_PREVIEW_WIDTH,
@@ -390,35 +458,45 @@ class ModelRouteUtils:
                     quality=85,
                     preserve_metadata=False
                 )
-                extension = '.webp'  # Use .webp without .preview part
+                extension = '.webp'
+            
+            # Delete any existing preview files for this model
+            for ext in PREVIEW_EXTENSIONS:
+                existing_preview = os.path.join(folder, base_name + ext)
+                if os.path.exists(existing_preview):
+                    try:
+                        os.remove(existing_preview)
+                        logger.debug(f"Deleted existing preview: {existing_preview}")
+                    except Exception as e:
+                        logger.warning(f"Failed to delete existing preview {existing_preview}: {e}")
             
             preview_path = os.path.join(folder, base_name + extension).replace(os.sep, '/')
             
             with open(preview_path, 'wb') as f:
                 f.write(optimized_data)
             
-            # Update preview path in metadata
+            # Update preview path and NSFW level in metadata
             metadata_path = os.path.splitext(model_path)[0] + '.metadata.json'
             if os.path.exists(metadata_path):
                 try:
                     with open(metadata_path, 'r', encoding='utf-8') as f:
                         metadata = json.load(f)
                     
-                    # Update preview_url directly in the metadata dict
+                    # Update preview_url and preview_nsfw_level in the metadata dict
                     metadata['preview_url'] = preview_path
+                    metadata['preview_nsfw_level'] = nsfw_level
                     
-                    with open(metadata_path, 'w', encoding='utf-8') as f:
-                        json.dump(metadata, f, indent=2, ensure_ascii=False)
+                    await MetadataManager.save_metadata(model_path, metadata)
                 except Exception as e:
                     logger.error(f"Error updating metadata: {e}")
             
             # Update preview URL in scanner cache
-            if hasattr(scanner, 'update_preview_in_cache'):
-                await scanner.update_preview_in_cache(model_path, preview_path)
+            await scanner.update_preview_in_cache(model_path, preview_path, nsfw_level)
             
             return web.json_response({
                 "success": True,
-                "preview_url": config.get_preview_static_url(preview_path)
+                "preview_url": config.get_preview_static_url(preview_path),
+                "preview_nsfw_level": nsfw_level
             })
             
         except Exception as e:
@@ -448,8 +526,7 @@ class ModelRouteUtils:
             metadata['exclude'] = True
             
             # Save updated metadata
-            with open(metadata_path, 'w', encoding='utf-8') as f:
-                json.dump(metadata, f, indent=2, ensure_ascii=False)
+            await MetadataManager.save_metadata(file_path, metadata)
 
             # Update cache
             cache = await scanner.get_cached_data()
@@ -474,6 +551,8 @@ class ModelRouteUtils:
             
             # Add to excluded models list
             scanner._excluded_models.append(file_path)
+
+            await scanner._save_cache_to_disk()
             
             return web.json_response({
                 'success': True,
@@ -561,3 +640,199 @@ class ModelRouteUtils:
             
             logger.error(f"Error downloading {model_type}: {error_message}")
             return web.Response(status=500, text=error_message)
+
+    @staticmethod
+    async def handle_bulk_delete_models(request: web.Request, scanner) -> web.Response:
+        """Handle bulk deletion of models
+        
+        Args:
+            request: The aiohttp request
+            scanner: The model scanner instance with cache management methods
+            
+        Returns:
+            web.Response: The HTTP response
+        """
+        try:
+            data = await request.json()
+            file_paths = data.get('file_paths', [])
+            
+            if not file_paths:
+                return web.json_response({
+                    'success': False, 
+                    'error': 'No file paths provided for deletion'
+                }, status=400)
+            
+            # Use the scanner's bulk delete method to handle all cache and file operations
+            result = await scanner.bulk_delete_models(file_paths)
+            
+            return web.json_response({
+                'success': result.get('success', False),
+                'total_deleted': result.get('total_deleted', 0),
+                'total_attempted': result.get('total_attempted', len(file_paths)),
+                'results': result.get('results', [])
+            })
+            
+        except Exception as e:
+            logger.error(f"Error in bulk delete: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    @staticmethod
+    async def handle_relink_civitai(request: web.Request, scanner) -> web.Response:
+        """Handle CivitAI metadata re-linking request by model ID and/or version ID
+        
+        Args:
+            request: The aiohttp request
+            scanner: The model scanner instance with cache management methods
+            
+        Returns:
+            web.Response: The HTTP response
+        """
+        try:
+            data = await request.json()
+            file_path = data.get('file_path')
+            model_id = data.get('model_id')
+            model_version_id = data.get('model_version_id')
+            
+            if not file_path or not model_id:
+                return web.json_response({"success": False, "error": "Both file_path and model_id are required"}, status=400)
+            
+            metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+            
+            # Check if model metadata exists
+            local_metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
+            
+            # Create a client for fetching from Civitai
+            client = await CivitaiClient.get_instance()
+            try:
+                # Fetch metadata using get_model_version which includes more comprehensive data
+                civitai_metadata = await client.get_model_version(model_id, model_version_id)
+                if not civitai_metadata:
+                    error_msg = f"Model version not found on CivitAI for ID: {model_id}"
+                    if model_version_id:
+                        error_msg += f" with version: {model_version_id}"
+                    return web.json_response({"success": False, "error": error_msg}, status=404)
+                
+                # Try to find the primary model file to get the SHA256 hash
+                primary_model_file = None
+                for file in civitai_metadata.get('files', []):
+                    if file.get('primary', False) and file.get('type') == 'Model':
+                        primary_model_file = file
+                        break
+                
+                # Update the SHA256 hash in local metadata if available
+                if primary_model_file and primary_model_file.get('hashes', {}).get('SHA256'):
+                    local_metadata['sha256'] = primary_model_file['hashes']['SHA256'].lower()
+                
+                # Update metadata with CivitAI information
+                await ModelRouteUtils.update_model_metadata(metadata_path, local_metadata, civitai_metadata, client)
+                
+                # Update the cache
+                await scanner.update_single_model_cache(file_path, file_path, local_metadata)
+                
+                return web.json_response({
+                    "success": True,
+                    "message": f"Model successfully re-linked to Civitai model {model_id}" + 
+                               (f" version {model_version_id}" if model_version_id else ""),
+                    "hash": local_metadata.get('sha256', '')
+                })
+                
+            finally:
+                await client.close()
+
+        except Exception as e:
+            logger.error(f"Error re-linking to CivitAI: {e}", exc_info=True)
+            return web.json_response({"success": False, "error": str(e)}, status=500)
+
+    @staticmethod
+    async def handle_verify_duplicates(request: web.Request, scanner) -> web.Response:
+        """Handle verification of duplicate model hashes
+        
+        Args:
+            request: The aiohttp request
+            scanner: The model scanner instance with cache management methods
+            
+        Returns:
+            web.Response: The HTTP response with verification results
+        """
+        try:
+            data = await request.json()
+            file_paths = data.get('file_paths', [])
+            
+            if not file_paths:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No file paths provided for verification'
+                }, status=400)
+            
+            # Results tracking
+            results = {
+                'verified_as_duplicates': True,  # Start true, set to false if any mismatch
+                'mismatched_files': [],
+                'new_hash_map': {}
+            }
+            
+            # Get expected hash from the first file's metadata
+            expected_hash = None
+            first_metadata_path = os.path.splitext(file_paths[0])[0] + '.metadata.json'
+            first_metadata = await ModelRouteUtils.load_local_metadata(first_metadata_path)
+            if first_metadata and 'sha256' in first_metadata:
+                expected_hash = first_metadata['sha256'].lower()
+            
+            # Process each file
+            for file_path in file_paths:
+                # Skip files that don't exist
+                if not os.path.exists(file_path):
+                    continue
+                    
+                # Calculate actual hash
+                try:
+                    from .file_utils import calculate_sha256
+                    actual_hash = await calculate_sha256(file_path)
+                    
+                    # Get metadata
+                    metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+                    metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
+                    
+                    # Compare hashes
+                    stored_hash = metadata.get('sha256', '').lower()
+                    
+                    # Set expected hash from first file if not yet set
+                    if not expected_hash:
+                        expected_hash = stored_hash
+                    
+                    # Check if hash matches expected hash
+                    if actual_hash != expected_hash:
+                        results['verified_as_duplicates'] = False
+                        results['mismatched_files'].append(file_path)
+                        results['new_hash_map'][file_path] = actual_hash
+                        
+                    # Check if stored hash needs updating
+                    if actual_hash != stored_hash:
+                        # Update metadata with actual hash
+                        metadata['sha256'] = actual_hash
+                        
+                        # Save updated metadata
+                        await MetadataManager.save_metadata(file_path, metadata)
+                        
+                        # Update cache
+                        await scanner.update_single_model_cache(file_path, file_path, metadata)
+                except Exception as e:
+                    logger.error(f"Error verifying hash for {file_path}: {e}")
+                    results['mismatched_files'].append(file_path)
+                    results['new_hash_map'][file_path] = "error_calculating_hash"
+                    results['verified_as_duplicates'] = False
+            
+            return web.json_response({
+                'success': True,
+                **results
+            })
+            
+        except Exception as e:
+            logger.error(f"Error verifying duplicate models: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/utils/usage_stats.py

@@ -4,6 +4,8 @@ import sys
 import time
 import asyncio
 import logging
+import datetime
+import shutil
 from typing import Dict, Set
 
 from ..config import config
@@ -26,6 +28,7 @@ class UsageStats:
     
     # Default stats file name
     STATS_FILENAME = "lora_manager_stats.json"
+    BACKUP_SUFFIX = ".backup"
     
     def __new__(cls):
         if cls._instance is None:
@@ -39,8 +42,8 @@ class UsageStats:
             
         # Initialize stats storage
         self.stats = {
-            "checkpoints": {},  # sha256 -> count
-            "loras": {},        # sha256 -> count
+            "checkpoints": {},  # sha256 -> { total: count, history: { date: count } }
+            "loras": {},        # sha256 -> { total: count, history: { date: count } }
             "total_executions": 0,
             "last_save_time": 0
         }
@@ -70,6 +73,68 @@ class UsageStats:
         # Use the first lora root
         return os.path.join(config.loras_roots[0], self.STATS_FILENAME)
     
+    def _backup_old_stats(self):
+        """Backup the old stats file before conversion"""
+        if os.path.exists(self._stats_file_path):
+            backup_path = f"{self._stats_file_path}{self.BACKUP_SUFFIX}"
+            try:
+                shutil.copy2(self._stats_file_path, backup_path)
+                logger.info(f"Backed up old stats file to {backup_path}")
+                return True
+            except Exception as e:
+                logger.error(f"Failed to backup stats file: {e}")
+        return False
+
+    def _convert_old_format(self, old_stats):
+        """Convert old stats format to new format with history"""
+        new_stats = {
+            "checkpoints": {},
+            "loras": {},
+            "total_executions": old_stats.get("total_executions", 0),
+            "last_save_time": old_stats.get("last_save_time", time.time())
+        }
+        
+        # Get today's date in YYYY-MM-DD format
+        today = datetime.datetime.now().strftime("%Y-%m-%d")
+        
+        # Convert checkpoint stats
+        if "checkpoints" in old_stats and isinstance(old_stats["checkpoints"], dict):
+            for hash_id, count in old_stats["checkpoints"].items():
+                new_stats["checkpoints"][hash_id] = {
+                    "total": count,
+                    "history": {
+                        today: count
+                    }
+                }
+        
+        # Convert lora stats
+        if "loras" in old_stats and isinstance(old_stats["loras"], dict):
+            for hash_id, count in old_stats["loras"].items():
+                new_stats["loras"][hash_id] = {
+                    "total": count,
+                    "history": {
+                        today: count
+                    }
+                }
+        
+        logger.info("Successfully converted stats from old format to new format with history")
+        return new_stats
+    
+    def _is_old_format(self, stats):
+        """Check if the stats are in the old format (direct count values)"""
+        # Check if any lora or checkpoint entry is a direct number instead of an object
+        if "loras" in stats and isinstance(stats["loras"], dict):
+            for hash_id, data in stats["loras"].items():
+                if isinstance(data, (int, float)):
+                    return True
+                
+        if "checkpoints" in stats and isinstance(stats["checkpoints"], dict):
+            for hash_id, data in stats["checkpoints"].items():
+                if isinstance(data, (int, float)):
+                    return True
+        
+        return False
+    
     def _load_stats(self):
         """Load existing statistics from file"""
         try:
@@ -77,18 +142,27 @@ class UsageStats:
                 with open(self._stats_file_path, 'r', encoding='utf-8') as f:
                     loaded_stats = json.load(f)
                 
-                # Update our stats with loaded data
-                if isinstance(loaded_stats, dict):
-                    # Update individual sections to maintain structure
-                    if "checkpoints" in loaded_stats and isinstance(loaded_stats["checkpoints"], dict):
-                        self.stats["checkpoints"] = loaded_stats["checkpoints"]
-                    
-                    if "loras" in loaded_stats and isinstance(loaded_stats["loras"], dict):
-                        self.stats["loras"] = loaded_stats["loras"]
-                    
-                    if "total_executions" in loaded_stats:
-                        self.stats["total_executions"] = loaded_stats["total_executions"]
-                    
+                # Check if old format and needs conversion
+                if self._is_old_format(loaded_stats):
+                    logger.info("Detected old stats format, performing conversion")
+                    self._backup_old_stats()
+                    self.stats = self._convert_old_format(loaded_stats)
+                else:
+                    # Update our stats with loaded data (already in new format)
+                    if isinstance(loaded_stats, dict):
+                        # Update individual sections to maintain structure
+                        if "checkpoints" in loaded_stats and isinstance(loaded_stats["checkpoints"], dict):
+                            self.stats["checkpoints"] = loaded_stats["checkpoints"]
+                        
+                        if "loras" in loaded_stats and isinstance(loaded_stats["loras"], dict):
+                            self.stats["loras"] = loaded_stats["loras"]
+                        
+                        if "total_executions" in loaded_stats:
+                            self.stats["total_executions"] = loaded_stats["total_executions"]
+                        
+                        if "last_save_time" in loaded_stats:
+                            self.stats["last_save_time"] = loaded_stats["last_save_time"]
+                
                 logger.info(f"Loaded usage statistics from {self._stats_file_path}")
         except Exception as e:
             logger.error(f"Error loading usage statistics: {e}")
@@ -174,15 +248,18 @@ class UsageStats:
         # Increment total executions count
         self.stats["total_executions"] += 1
         
+        # Get today's date in YYYY-MM-DD format
+        today = datetime.datetime.now().strftime("%Y-%m-%d")
+        
         # Process checkpoints
         if MODELS in metadata and isinstance(metadata[MODELS], dict):
-            await self._process_checkpoints(metadata[MODELS])
+            await self._process_checkpoints(metadata[MODELS], today)
         
         # Process loras
         if LORAS in metadata and isinstance(metadata[LORAS], dict):
-            await self._process_loras(metadata[LORAS])
+            await self._process_loras(metadata[LORAS], today)
     
-    async def _process_checkpoints(self, models_data):
+    async def _process_checkpoints(self, models_data, today_date):
         """Process checkpoint models from metadata"""
         try:
             # Get checkpoint scanner service
@@ -208,12 +285,24 @@ class UsageStats:
                     # Get hash for this checkpoint
                     model_hash = checkpoint_scanner.get_hash_by_filename(model_filename)
                     if model_hash:
-                        # Update stats for this checkpoint
-                        self.stats["checkpoints"][model_hash] = self.stats["checkpoints"].get(model_hash, 0) + 1
+                        # Update stats for this checkpoint with date tracking
+                        if model_hash not in self.stats["checkpoints"]:
+                            self.stats["checkpoints"][model_hash] = {
+                                "total": 0,
+                                "history": {}
+                            }
+                        
+                        # Increment total count
+                        self.stats["checkpoints"][model_hash]["total"] += 1
+                        
+                        # Increment today's count
+                        if today_date not in self.stats["checkpoints"][model_hash]["history"]:
+                            self.stats["checkpoints"][model_hash]["history"][today_date] = 0
+                        self.stats["checkpoints"][model_hash]["history"][today_date] += 1
         except Exception as e:
             logger.error(f"Error processing checkpoint usage: {e}", exc_info=True)
     
-    async def _process_loras(self, loras_data):
+    async def _process_loras(self, loras_data, today_date):
         """Process LoRA models from metadata"""
         try:
             # Get LoRA scanner service
@@ -239,8 +328,20 @@ class UsageStats:
                     # Get hash for this LoRA
                     lora_hash = lora_scanner.get_hash_by_filename(lora_name)
                     if lora_hash:
-                        # Update stats for this LoRA
-                        self.stats["loras"][lora_hash] = self.stats["loras"].get(lora_hash, 0) + 1
+                        # Update stats for this LoRA with date tracking
+                        if lora_hash not in self.stats["loras"]:
+                            self.stats["loras"][lora_hash] = {
+                                "total": 0,
+                                "history": {}
+                            }
+                        
+                        # Increment total count
+                        self.stats["loras"][lora_hash]["total"] += 1
+                        
+                        # Increment today's count
+                        if today_date not in self.stats["loras"][lora_hash]["history"]:
+                            self.stats["loras"][lora_hash]["history"][today_date] = 0
+                        self.stats["loras"][lora_hash]["history"][today_date] += 1
         except Exception as e:
             logger.error(f"Error processing LoRA usage: {e}", exc_info=True)
     
@@ -251,9 +352,11 @@ class UsageStats:
     async def get_model_usage_count(self, model_type, sha256):
         """Get usage count for a specific model by hash"""
         if model_type == "checkpoint":
-            return self.stats["checkpoints"].get(sha256, 0)
+            if sha256 in self.stats["checkpoints"]:
+                return self.stats["checkpoints"][sha256]["total"]
         elif model_type == "lora":
-            return self.stats["loras"].get(sha256, 0)
+            if sha256 in self.stats["loras"]:
+                return self.stats["loras"][sha256]["total"]
         return 0
     
     async def process_execution(self, prompt_id):

py/utils/utils.py

@@ -114,3 +114,49 @@ def fuzzy_match(text: str, pattern: str, threshold: float = 0.7) -> bool:
         
         # All words found either as substrings or fuzzy matches
         return True
+
+def calculate_recipe_fingerprint(loras):
+    """
+    Calculate a unique fingerprint for a recipe based on its LoRAs.
+    
+    The fingerprint is created by sorting LoRA hashes, filtering invalid entries,
+    normalizing strength values to 2 decimal places, and joining in format:
+    hash1:strength1|hash2:strength2|...
+    
+    Args:
+        loras (list): List of LoRA dictionaries with hash and strength values
+        
+    Returns:
+        str: The calculated fingerprint
+    """
+    if not loras:
+        return ""
+    
+    # Filter valid entries and extract hash and strength
+    valid_loras = []
+    for lora in loras:
+        # Skip excluded loras
+        if lora.get("exclude", False):
+            continue
+            
+        # Get the hash - use modelVersionId as fallback if hash is empty
+        hash_value = lora.get("hash", "").lower()
+        if not hash_value and lora.get("isDeleted", False) and lora.get("modelVersionId"):
+            hash_value = lora.get("modelVersionId")
+            
+        # Skip entries without a valid hash
+        if not hash_value:
+            continue
+            
+        # Normalize strength to 2 decimal places (check both strength and weight fields)
+        strength = round(float(lora.get("strength", lora.get("weight", 1.0))), 2)
+        
+        valid_loras.append((hash_value, strength))
+    
+    # Sort by hash
+    valid_loras.sort()
+    
+    # Join in format hash1:strength1|hash2:strength2|...
+    fingerprint = "|".join([f"{hash_value}:{strength}" for hash_value, strength in valid_loras])
+    
+    return fingerprint

py/workflow/__init__.py

@@ -1,3 +0,0 @@
-"""
-ComfyUI workflow parsing module to extract generation parameters
-""" 

py/workflow/cli.py

@@ -1,58 +0,0 @@
-"""
-Command-line interface for the ComfyUI workflow parser
-"""
-import argparse
-import json
-import os
-import logging
-import sys
-from .parser import parse_workflow
-
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    handlers=[logging.StreamHandler()]
-)
-logger = logging.getLogger(__name__)
-
-def main():
-    """Entry point for the CLI"""
-    parser = argparse.ArgumentParser(description='Parse ComfyUI workflow files')
-    parser.add_argument('input', help='Input workflow JSON file path')
-    parser.add_argument('-o', '--output', help='Output JSON file path')
-    parser.add_argument('-p', '--pretty', action='store_true', help='Pretty print JSON output')
-    parser.add_argument('--debug', action='store_true', help='Enable debug logging')
-    
-    args = parser.parse_args()
-    
-    # Set logging level
-    if args.debug:
-        logging.getLogger().setLevel(logging.DEBUG)
-    
-    # Validate input file
-    if not os.path.isfile(args.input):
-        logger.error(f"Input file not found: {args.input}")
-        sys.exit(1)
-    
-    # Parse workflow
-    try:
-        result = parse_workflow(args.input, args.output)
-        
-        # Print result to console if output file not specified
-        if not args.output:
-            if args.pretty:
-                print(json.dumps(result, indent=4))
-            else:
-                print(json.dumps(result))
-        else:
-            logger.info(f"Output saved to: {args.output}")
-            
-    except Exception as e:
-        logger.error(f"Error parsing workflow: {e}")
-        if args.debug:
-            import traceback
-            traceback.print_exc()
-        sys.exit(1)
-
-if __name__ == "__main__":
-    main() 

py/workflow/ext/__init__.py

@@ -1,3 +0,0 @@
-"""
-Extension directory for custom node mappers
-""" 

py/workflow/ext/comfyui_core.py

@@ -1,285 +0,0 @@
-"""
-ComfyUI Core nodes mappers extension for workflow parsing
-"""
-import logging
-from typing import Dict, Any, List
-
-logger = logging.getLogger(__name__)
-
-# =============================================================================
-# Transform Functions
-# =============================================================================
-
-def transform_random_noise(inputs: Dict) -> Dict:
-    """Transform function for RandomNoise node"""
-    return {"seed": str(inputs.get("noise_seed", ""))}
-
-def transform_ksampler_select(inputs: Dict) -> Dict:
-    """Transform function for KSamplerSelect node"""
-    return {"sampler": inputs.get("sampler_name", "")}
-
-def transform_basic_scheduler(inputs: Dict) -> Dict:
-    """Transform function for BasicScheduler node"""
-    result = {
-        "scheduler": inputs.get("scheduler", ""),
-        "denoise": str(inputs.get("denoise", "1.0"))
-    }
-    
-    # Get steps from inputs or steps input
-    if "steps" in inputs:
-        if isinstance(inputs["steps"], str):
-            result["steps"] = inputs["steps"]
-        elif isinstance(inputs["steps"], dict) and "value" in inputs["steps"]:
-            result["steps"] = str(inputs["steps"]["value"])
-        else:
-            result["steps"] = str(inputs["steps"])
-    
-    return result
-
-def transform_basic_guider(inputs: Dict) -> Dict:
-    """Transform function for BasicGuider node"""
-    result = {}
-    
-    # Process conditioning
-    if "conditioning" in inputs:
-        if isinstance(inputs["conditioning"], str):
-            result["prompt"] = inputs["conditioning"]
-        elif isinstance(inputs["conditioning"], dict):
-            result["conditioning"] = inputs["conditioning"]
-    
-    # Get model information if needed
-    if "model" in inputs and isinstance(inputs["model"], dict):
-        result["model"] = inputs["model"]
-    
-    return result
-
-def transform_model_sampling_flux(inputs: Dict) -> Dict:
-    """Transform function for ModelSamplingFlux - mostly a pass-through node"""
-    # This node is primarily used for routing, so we mostly pass through values
-    
-    return inputs["model"]
-
-def transform_sampler_custom_advanced(inputs: Dict) -> Dict:
-    """Transform function for SamplerCustomAdvanced node"""
-    result = {}
-    
-    # Extract seed from noise
-    if "noise" in inputs and isinstance(inputs["noise"], dict):
-        result["seed"] = str(inputs["noise"].get("seed", ""))
-    
-    # Extract sampler info
-    if "sampler" in inputs and isinstance(inputs["sampler"], dict):
-        sampler = inputs["sampler"].get("sampler", "")
-        if sampler:
-            result["sampler"] = sampler
-    
-    # Extract scheduler, steps, denoise from sigmas
-    if "sigmas" in inputs and isinstance(inputs["sigmas"], dict):
-        sigmas = inputs["sigmas"]
-        result["scheduler"] = sigmas.get("scheduler", "")
-        result["steps"] = str(sigmas.get("steps", ""))
-        result["denoise"] = str(sigmas.get("denoise", "1.0"))
-    
-    # Extract prompt and guidance from guider
-    if "guider" in inputs and isinstance(inputs["guider"], dict):
-        guider = inputs["guider"]
-        
-        # Get prompt from conditioning
-        if "conditioning" in guider and isinstance(guider["conditioning"], str):
-            result["prompt"] = guider["conditioning"]
-        elif "conditioning" in guider and isinstance(guider["conditioning"], dict):
-            result["guidance"] = guider["conditioning"].get("guidance", "")
-            result["prompt"] = guider["conditioning"].get("prompt", "")
-
-        if "model" in guider and isinstance(guider["model"], dict):
-            result["checkpoint"] = guider["model"].get("checkpoint", "")
-            result["loras"] = guider["model"].get("loras", "")
-            result["clip_skip"] = str(int(guider["model"].get("clip_skip", "-1")) * -1)
-    
-    # Extract dimensions from latent_image
-    if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
-        latent = inputs["latent_image"]
-        width = latent.get("width", 0)
-        height = latent.get("height", 0)
-        if width and height:
-            result["width"] = width
-            result["height"] = height
-            result["size"] = f"{width}x{height}"
-    
-    return result
-
-def transform_ksampler(inputs: Dict) -> Dict:
-    """Transform function for KSampler nodes"""
-    result = {
-        "seed": str(inputs.get("seed", "")),
-        "steps": str(inputs.get("steps", "")),
-        "cfg": str(inputs.get("cfg", "")),
-        "sampler": inputs.get("sampler_name", ""),
-        "scheduler": inputs.get("scheduler", ""),
-    }
-    
-    # Process positive prompt
-    if "positive" in inputs:
-        result["prompt"] = inputs["positive"]
-    
-    # Process negative prompt
-    if "negative" in inputs:
-        result["negative_prompt"] = inputs["negative"]
-    
-    # Get dimensions from latent image
-    if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
-        width = inputs["latent_image"].get("width", 0)
-        height = inputs["latent_image"].get("height", 0)
-        if width and height:
-            result["size"] = f"{width}x{height}"
-    
-    # Add clip_skip if present
-    if "clip_skip" in inputs:
-        result["clip_skip"] = str(inputs.get("clip_skip", ""))
-
-    # Add guidance if present
-    if "guidance" in inputs:
-        result["guidance"] = str(inputs.get("guidance", ""))
-
-    # Add model if present
-    if "model" in inputs:
-        result["checkpoint"] = inputs.get("model", {}).get("checkpoint", "")
-        result["loras"] = inputs.get("model", {}).get("loras", "")
-        result["clip_skip"] = str(inputs.get("model", {}).get("clip_skip", -1) * -1)
-        
-    return result
-
-def transform_empty_latent(inputs: Dict) -> Dict:
-    """Transform function for EmptyLatentImage nodes"""
-    width = inputs.get("width", 0)
-    height = inputs.get("height", 0)
-    return {"width": width, "height": height, "size": f"{width}x{height}"}
-
-def transform_clip_text(inputs: Dict) -> Any:
-    """Transform function for CLIPTextEncode nodes"""
-    return inputs.get("text", "")
-
-def transform_flux_guidance(inputs: Dict) -> Dict:
-    """Transform function for FluxGuidance nodes"""
-    result = {}
-    
-    if "guidance" in inputs:
-        result["guidance"] = inputs["guidance"]
-    
-    if "conditioning" in inputs:
-        conditioning = inputs["conditioning"]
-        if isinstance(conditioning, str):
-            result["prompt"] = conditioning
-        else:
-            result["prompt"] = "Unknown prompt"
-    
-    return result
-
-def transform_unet_loader(inputs: Dict) -> Dict:
-    """Transform function for UNETLoader node"""
-    unet_name = inputs.get("unet_name", "")
-    return {"checkpoint": unet_name} if unet_name else {}
-
-def transform_checkpoint_loader(inputs: Dict) -> Dict:
-    """Transform function for CheckpointLoaderSimple node"""
-    ckpt_name = inputs.get("ckpt_name", "")
-    return {"checkpoint": ckpt_name} if ckpt_name else {}
-
-def transform_latent_upscale_by(inputs: Dict) -> Dict:
-    """Transform function for LatentUpscaleBy node"""
-    result = {}
-    
-    width = inputs["samples"].get("width", 0) * inputs["scale_by"]
-    height = inputs["samples"].get("height", 0) * inputs["scale_by"]
-    result["width"] = width
-    result["height"] = height
-    result["size"] = f"{width}x{height}"
-    
-    return result
-
-def transform_clip_set_last_layer(inputs: Dict) -> Dict:
-    """Transform function for CLIPSetLastLayer node"""
-    result = {}
-    
-    if "stop_at_clip_layer" in inputs:
-        result["clip_skip"] = inputs["stop_at_clip_layer"]
-    
-    return result
-
-# =============================================================================
-# Node Mapper Definitions
-# =============================================================================
-
-# Define the mappers for ComfyUI core nodes not in main mapper
-NODE_MAPPERS_EXT = {
-    # KSamplers
-    "SamplerCustomAdvanced": {
-        "inputs_to_track": ["noise", "guider", "sampler", "sigmas", "latent_image"],
-        "transform_func": transform_sampler_custom_advanced
-    },
-    "KSampler": {
-        "inputs_to_track": [
-            "seed", "steps", "cfg", "sampler_name", "scheduler", 
-            "denoise", "positive", "negative", "latent_image",
-            "model", "clip_skip"
-        ],
-        "transform_func": transform_ksampler
-    },
-    # ComfyUI core nodes
-    "EmptyLatentImage": {
-        "inputs_to_track": ["width", "height", "batch_size"],
-        "transform_func": transform_empty_latent
-    },
-    "EmptySD3LatentImage": {
-        "inputs_to_track": ["width", "height", "batch_size"],
-        "transform_func": transform_empty_latent
-    },
-    "CLIPTextEncode": {
-        "inputs_to_track": ["text", "clip"],
-        "transform_func": transform_clip_text
-    },
-    "FluxGuidance": {
-        "inputs_to_track": ["guidance", "conditioning"],
-        "transform_func": transform_flux_guidance
-    },
-    "RandomNoise": {
-        "inputs_to_track": ["noise_seed"],
-        "transform_func": transform_random_noise
-    },
-    "KSamplerSelect": {
-        "inputs_to_track": ["sampler_name"],
-        "transform_func": transform_ksampler_select
-    },
-    "BasicScheduler": {
-        "inputs_to_track": ["scheduler", "steps", "denoise", "model"],
-        "transform_func": transform_basic_scheduler
-    },
-    "BasicGuider": {
-        "inputs_to_track": ["model", "conditioning"],
-        "transform_func": transform_basic_guider
-    },
-    "ModelSamplingFlux": {
-        "inputs_to_track": ["max_shift", "base_shift", "width", "height", "model"],
-        "transform_func": transform_model_sampling_flux
-    },
-    "UNETLoader": {
-        "inputs_to_track": ["unet_name"],
-        "transform_func": transform_unet_loader
-    },
-    "CheckpointLoaderSimple": {
-        "inputs_to_track": ["ckpt_name"],
-        "transform_func": transform_checkpoint_loader
-    },
-    "LatentUpscale": {
-        "inputs_to_track": ["width", "height"],
-        "transform_func": transform_empty_latent
-    },
-    "LatentUpscaleBy": {
-        "inputs_to_track": ["samples", "scale_by"],
-        "transform_func": transform_latent_upscale_by
-    },
-    "CLIPSetLastLayer": {
-        "inputs_to_track": ["clip", "stop_at_clip_layer"],
-        "transform_func": transform_clip_set_last_layer
-    }
-} 

py/workflow/ext/kjnodes.py

@@ -1,74 +0,0 @@
-"""
-KJNodes mappers extension for ComfyUI workflow parsing
-"""
-import logging
-import re
-from typing import Dict, Any
-
-logger = logging.getLogger(__name__)
-
-# =============================================================================
-# Transform Functions
-# =============================================================================
-
-def transform_join_strings(inputs: Dict) -> str:
-    """Transform function for JoinStrings nodes"""
-    string1 = inputs.get("string1", "")
-    string2 = inputs.get("string2", "")
-    delimiter = inputs.get("delimiter", "")
-    return f"{string1}{delimiter}{string2}"
-
-def transform_string_constant(inputs: Dict) -> str:
-    """Transform function for StringConstant nodes"""
-    return inputs.get("string", "")
-
-def transform_empty_latent_presets(inputs: Dict) -> Dict:
-    """Transform function for EmptyLatentImagePresets nodes"""
-    dimensions = inputs.get("dimensions", "")
-    invert = inputs.get("invert", False)
-    
-    # Extract width and height from dimensions string
-    # Expected format: "width x height (ratio)" or similar
-    width = 0
-    height = 0
-    
-    if dimensions:
-        # Try to extract dimensions using regex
-        match = re.search(r'(\d+)\s*x\s*(\d+)', dimensions)
-        if match:
-            width = int(match.group(1))
-            height = int(match.group(2))
-    
-    # If invert is True, swap width and height
-    if invert and width and height:
-        width, height = height, width
-    
-    return {"width": width, "height": height, "size": f"{width}x{height}"}
-
-def transform_int_constant(inputs: Dict) -> int:
-    """Transform function for INTConstant nodes"""
-    return inputs.get("value", 0)
-
-# =============================================================================
-# Node Mapper Definitions
-# =============================================================================
-
-# Define the mappers for KJNodes
-NODE_MAPPERS_EXT = {
-    "JoinStrings": {
-        "inputs_to_track": ["string1", "string2", "delimiter"],
-        "transform_func": transform_join_strings
-    },
-    "StringConstantMultiline": {
-        "inputs_to_track": ["string"],
-        "transform_func": transform_string_constant
-    },
-    "EmptyLatentImagePresets": {
-        "inputs_to_track": ["dimensions", "invert", "batch_size"],
-        "transform_func": transform_empty_latent_presets
-    },
-    "INTConstant": {
-        "inputs_to_track": ["value"],
-        "transform_func": transform_int_constant
-    }
-} 

py/workflow/main.py

@@ -1,37 +0,0 @@
-"""
-Main entry point for the workflow parser module
-"""
-import os
-import sys
-import logging
-from typing import Dict, Optional, Union
-
-# Add the parent directory to sys.path to enable imports
-SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.abspath(os.path.join(SCRIPT_DIR, '..', '..'))
-sys.path.insert(0, os.path.dirname(SCRIPT_DIR))
-
-from .parser import parse_workflow
-
-logger = logging.getLogger(__name__)
-
-def parse_comfyui_workflow(
-    workflow_path: str, 
-    output_path: Optional[str] = None
-) -> Dict:
-    """
-    Parse a ComfyUI workflow file and extract generation parameters
-    
-    Args:
-        workflow_path: Path to the workflow JSON file
-        output_path: Optional path to save the output JSON
-        
-    Returns:
-        Dictionary containing extracted parameters
-    """
-    return parse_workflow(workflow_path, output_path)
-
-if __name__ == "__main__":
-    # If run directly, use the CLI
-    from .cli import main
-    main() 

py/workflow/mappers.py

@@ -1,282 +0,0 @@
-"""
-Node mappers for ComfyUI workflow parsing
-"""
-import logging
-import os
-import importlib.util
-import inspect
-from typing import Dict, List, Any, Optional, Union, Type, Callable, Tuple
-
-logger = logging.getLogger(__name__)
-
-# Global mapper registry
-_MAPPER_REGISTRY: Dict[str, Dict] = {}
-
-# =============================================================================
-# Mapper Definition Functions
-# =============================================================================
-
-def create_mapper(
-    node_type: str,
-    inputs_to_track: List[str],
-    transform_func: Callable[[Dict], Any] = None
-) -> Dict:
-    """Create a mapper definition for a node type"""
-    mapper = {
-        "node_type": node_type,
-        "inputs_to_track": inputs_to_track,
-        "transform": transform_func or (lambda inputs: inputs)
-    }
-    return mapper
-
-def register_mapper(mapper: Dict) -> None:
-    """Register a node mapper in the global registry"""
-    _MAPPER_REGISTRY[mapper["node_type"]] = mapper
-    logger.debug(f"Registered mapper for node type: {mapper['node_type']}")
-
-def get_mapper(node_type: str) -> Optional[Dict]:
-    """Get a mapper for the specified node type"""
-    return _MAPPER_REGISTRY.get(node_type)
-
-def get_all_mappers() -> Dict[str, Dict]:
-    """Get all registered mappers"""
-    return _MAPPER_REGISTRY.copy()
-
-# =============================================================================
-# Node Processing Function
-# =============================================================================
-
-def process_node(node_id: str, node_data: Dict, workflow: Dict, parser: 'WorkflowParser') -> Any: # type: ignore
-    """Process a node using its mapper and extract relevant information"""
-    node_type = node_data.get("class_type")
-    mapper = get_mapper(node_type)
-    
-    if not mapper:
-        logger.warning(f"No mapper found for node type: {node_type}")
-        return None
-        
-    result = {}
-    
-    # Extract inputs based on the mapper's tracked inputs
-    for input_name in mapper["inputs_to_track"]:
-        if input_name in node_data.get("inputs", {}):
-            input_value = node_data["inputs"][input_name]
-            
-            # Check if input is a reference to another node's output
-            if isinstance(input_value, list) and len(input_value) == 2:
-                try:
-                    # Format is [node_id, output_slot]
-                    ref_node_id, output_slot = input_value
-                    # Convert node_id to string if it's an integer
-                    if isinstance(ref_node_id, int):
-                        ref_node_id = str(ref_node_id)
-                    
-                    # Recursively process the referenced node
-                    ref_value = parser.process_node(ref_node_id, workflow)
-                    
-                    if ref_value is not None:
-                        result[input_name] = ref_value
-                    else:
-                        # If we couldn't get a value from the reference, store the raw value
-                        result[input_name] = input_value
-                except Exception as e:
-                    logger.error(f"Error processing reference in node {node_id}, input {input_name}: {e}")
-                    result[input_name] = input_value
-            else:
-                # Direct value
-                result[input_name] = input_value
-    
-    # Apply the transform function
-    try:
-        return mapper["transform"](result)
-    except Exception as e:
-        logger.error(f"Error in transform function for node {node_id} of type {node_type}: {e}")
-        return result
-
-# =============================================================================
-# Transform Functions
-# =============================================================================
-
-
-
-def transform_lora_loader(inputs: Dict) -> Dict:
-    """Transform function for LoraLoader nodes"""
-    loras_data = inputs.get("loras", [])
-    lora_stack = inputs.get("lora_stack", {}).get("lora_stack", [])
-    
-    lora_texts = []
-    
-    # Process loras array
-    if isinstance(loras_data, dict) and "__value__" in loras_data:
-        loras_list = loras_data["__value__"]
-    elif isinstance(loras_data, list):
-        loras_list = loras_data
-    else:
-        loras_list = []
-        
-    # Process each active lora entry
-    for lora in loras_list:
-        if isinstance(lora, dict) and lora.get("active", False):
-            lora_name = lora.get("name", "")
-            strength = lora.get("strength", 1.0)
-            lora_texts.append(f"<lora:{lora_name}:{strength}>")
-    
-    # Process lora_stack if valid
-    if lora_stack and isinstance(lora_stack, list):
-        if not (len(lora_stack) == 2 and isinstance(lora_stack[0], (str, int)) and isinstance(lora_stack[1], int)):
-            for stack_entry in lora_stack:
-                lora_name = stack_entry[0]
-                strength = stack_entry[1]
-                lora_texts.append(f"<lora:{lora_name}:{strength}>")
-
-    result = {
-        "checkpoint": inputs.get("model", {}).get("checkpoint", ""),
-        "loras": " ".join(lora_texts)
-    }
-
-    if "clip" in inputs and isinstance(inputs["clip"], dict):
-        result["clip_skip"] = inputs["clip"].get("clip_skip", "-1")
-    
-    return result
-
-def transform_lora_stacker(inputs: Dict) -> Dict:
-    """Transform function for LoraStacker nodes"""
-    loras_data = inputs.get("loras", [])
-    result_stack = []
-    
-    # Handle existing stack entries
-    existing_stack = []
-    lora_stack_input = inputs.get("lora_stack", [])
-    
-    if isinstance(lora_stack_input, dict) and "lora_stack" in lora_stack_input:
-        existing_stack = lora_stack_input["lora_stack"]
-    elif isinstance(lora_stack_input, list):
-        if not (len(lora_stack_input) == 2 and isinstance(lora_stack_input[0], (str, int)) and 
-               isinstance(lora_stack_input[1], int)):
-            existing_stack = lora_stack_input
-    
-    # Add existing entries
-    if existing_stack:
-        result_stack.extend(existing_stack)
-    
-    # Process new loras
-    if isinstance(loras_data, dict) and "__value__" in loras_data:
-        loras_list = loras_data["__value__"]
-    elif isinstance(loras_data, list):
-        loras_list = loras_data
-    else:
-        loras_list = []
-        
-    for lora in loras_list:
-        if isinstance(lora, dict) and lora.get("active", False):
-            lora_name = lora.get("name", "")
-            strength = float(lora.get("strength", 1.0))
-            result_stack.append((lora_name, strength))
-    
-    return {"lora_stack": result_stack}
-
-def transform_trigger_word_toggle(inputs: Dict) -> str:
-    """Transform function for TriggerWordToggle nodes"""
-    toggle_data = inputs.get("toggle_trigger_words", [])
-
-    if isinstance(toggle_data, dict) and "__value__" in toggle_data:
-        toggle_words = toggle_data["__value__"]
-    elif isinstance(toggle_data, list):
-        toggle_words = toggle_data
-    else:
-        toggle_words = []
-    
-    # Filter active trigger words
-    active_words = []
-    for item in toggle_words:
-        if isinstance(item, dict) and item.get("active", False):
-            word = item.get("text", "")
-            if word and not word.startswith("__dummy"):
-                active_words.append(word)
-    
-    return ", ".join(active_words)
-
-# =============================================================================
-# Node Mapper Definitions
-# =============================================================================
-
-# Central definition of all supported node types and their configurations
-NODE_MAPPERS = {
-    
-    # LoraManager nodes
-    "Lora Loader (LoraManager)": {
-        "inputs_to_track": ["model", "clip", "loras", "lora_stack"],
-        "transform_func": transform_lora_loader
-    },
-    "Lora Stacker (LoraManager)": {
-        "inputs_to_track": ["loras", "lora_stack"],
-        "transform_func": transform_lora_stacker
-    },
-    "TriggerWord Toggle (LoraManager)": {
-        "inputs_to_track": ["toggle_trigger_words"],
-        "transform_func": transform_trigger_word_toggle
-    }   
-}
-
-def register_all_mappers() -> None:
-    """Register all mappers from the NODE_MAPPERS dictionary"""
-    for node_type, config in NODE_MAPPERS.items():
-        mapper = create_mapper(
-            node_type=node_type,
-            inputs_to_track=config["inputs_to_track"],
-            transform_func=config["transform_func"]
-        )
-        register_mapper(mapper)
-    logger.info(f"Registered {len(NODE_MAPPERS)} node mappers")
-
-# =============================================================================
-# Extension Loading
-# =============================================================================
-
-def load_extensions(ext_dir: str = None) -> None:
-    """
-    Load mapper extensions from the specified directory
-    
-    Extension files should define a NODE_MAPPERS_EXT dictionary containing mapper configurations.
-    These will be added to the global NODE_MAPPERS dictionary and registered automatically.
-    """
-    # Use default path if none provided
-    if ext_dir is None:
-        # Get the directory of this file
-        current_dir = os.path.dirname(os.path.abspath(__file__))
-        ext_dir = os.path.join(current_dir, 'ext')
-    
-    # Ensure the extension directory exists
-    if not os.path.exists(ext_dir):
-        os.makedirs(ext_dir, exist_ok=True)
-        logger.info(f"Created extension directory: {ext_dir}")
-        return
-    
-    # Load each Python file in the extension directory
-    for filename in os.listdir(ext_dir):
-        if filename.endswith('.py') and not filename.startswith('_'):
-            module_path = os.path.join(ext_dir, filename)
-            module_name = f"workflow.ext.{filename[:-3]}"  # Remove .py
-            
-            try:
-                # Load the module
-                spec = importlib.util.spec_from_file_location(module_name, module_path)
-                if spec and spec.loader:
-                    module = importlib.util.module_from_spec(spec)
-                    spec.loader.exec_module(module)
-                    
-                    # Check if the module defines NODE_MAPPERS_EXT
-                    if hasattr(module, 'NODE_MAPPERS_EXT'):
-                        # Add the extension mappers to the global NODE_MAPPERS dictionary
-                        NODE_MAPPERS.update(module.NODE_MAPPERS_EXT)
-                        logger.info(f"Added {len(module.NODE_MAPPERS_EXT)} mappers from extension: {filename}")
-                    else:
-                        logger.warning(f"Extension {filename} does not define NODE_MAPPERS_EXT dictionary")
-            except Exception as e:
-                logger.warning(f"Error loading extension {filename}: {e}")
-    
-    # Re-register all mappers after loading extensions
-    register_all_mappers()
-
-# Initialize the registry with default mappers
-# register_default_mappers() 

py/workflow/parser.py

@@ -1,181 +0,0 @@
-"""
-Main workflow parser implementation for ComfyUI
-"""
-import json
-import logging
-from typing import Dict, List, Any, Optional, Union, Set
-from .mappers import get_mapper, get_all_mappers, load_extensions, process_node
-from .utils import (
-    load_workflow, save_output, find_node_by_type,
-    trace_model_path
-)
-
-logger = logging.getLogger(__name__)
-
-class WorkflowParser:
-    """Parser for ComfyUI workflows"""
-    
-    def __init__(self):
-        """Initialize the parser with mappers"""
-        self.processed_nodes: Set[str] = set()  # Track processed nodes to avoid cycles
-        self.node_results_cache: Dict[str, Any] = {}  # Cache for processed node results
-        
-        # Load extensions
-        load_extensions()
-    
-    def process_node(self, node_id: str, workflow: Dict) -> Any:
-        """Process a single node and extract relevant information"""
-        # Return cached result if available
-        if node_id in self.node_results_cache:
-            return self.node_results_cache[node_id]
-            
-        # Check if we're in a cycle
-        if node_id in self.processed_nodes:
-            return None
-        
-        # Mark this node as being processed (to detect cycles)
-        self.processed_nodes.add(node_id)
-        
-        if node_id not in workflow:
-            self.processed_nodes.remove(node_id)
-            return None
-        
-        node_data = workflow[node_id]
-        node_type = node_data.get("class_type")
-        
-        result = None
-        if get_mapper(node_type):
-            try:
-                result = process_node(node_id, node_data, workflow, self)
-                # Cache the result
-                self.node_results_cache[node_id] = result
-            except Exception as e:
-                logger.error(f"Error processing node {node_id} of type {node_type}: {e}", exc_info=True)
-                # Return a partial result or None depending on how we want to handle errors
-                result = {}
-        
-        # Remove node from processed set to allow it to be processed again in a different context
-        self.processed_nodes.remove(node_id)
-        return result
-    
-    def find_primary_sampler_node(self, workflow: Dict) -> Optional[str]:
-        """
-        Find the primary sampler node in the workflow.
-        
-        Priority:
-        1. First try to find a SamplerCustomAdvanced node
-        2. If not found, look for KSampler nodes with denoise=1.0
-        3. If still not found, use the first KSampler node
-        
-        Args:
-            workflow: The workflow data as a dictionary
-            
-        Returns:
-            The node ID of the primary sampler node, or None if not found
-        """
-        # First check for SamplerCustomAdvanced nodes
-        sampler_advanced_nodes = []
-        ksampler_nodes = []
-        
-        # Scan workflow for sampler nodes
-        for node_id, node_data in workflow.items():
-            node_type = node_data.get("class_type")
-            
-            if node_type == "SamplerCustomAdvanced":
-                sampler_advanced_nodes.append(node_id)
-            elif node_type == "KSampler":
-                ksampler_nodes.append(node_id)
-        
-        # If we found SamplerCustomAdvanced nodes, return the first one
-        if sampler_advanced_nodes:
-            logger.debug(f"Found SamplerCustomAdvanced node: {sampler_advanced_nodes[0]}")
-            return sampler_advanced_nodes[0]
-        
-        # If we have KSampler nodes, look for one with denoise=1.0
-        if ksampler_nodes:
-            for node_id in ksampler_nodes:
-                node_data = workflow[node_id]
-                inputs = node_data.get("inputs", {})
-                denoise = inputs.get("denoise", 0)
-                
-                # Check if denoise is 1.0 (allowing for small floating point differences)
-                if abs(float(denoise) - 1.0) < 0.001:
-                    logger.debug(f"Found KSampler node with denoise=1.0: {node_id}")
-                    return node_id
-            
-            # If no KSampler with denoise=1.0 found, use the first one
-            logger.debug(f"No KSampler with denoise=1.0 found, using first KSampler: {ksampler_nodes[0]}")
-            return ksampler_nodes[0]
-        
-        # No sampler nodes found
-        logger.warning("No sampler nodes found in workflow")
-        return None
-    
-    def parse_workflow(self, workflow_data: Union[str, Dict], output_path: Optional[str] = None) -> Dict:
-        """
-        Parse the workflow and extract generation parameters
-        
-        Args:
-            workflow_data: The workflow data as a dictionary or a file path
-            output_path: Optional path to save the output JSON
-            
-        Returns:
-            Dictionary containing extracted parameters
-        """
-        # Load workflow from file if needed
-        if isinstance(workflow_data, str):
-            workflow = load_workflow(workflow_data)
-        else:
-            workflow = workflow_data
-            
-        # Reset the processed nodes tracker and cache
-        self.processed_nodes = set()
-        self.node_results_cache = {}
-        
-        # Find the primary sampler node
-        sampler_node_id = self.find_primary_sampler_node(workflow)
-        if not sampler_node_id:
-            logger.warning("No suitable sampler node found in workflow")
-            return {}
-        
-        # Process sampler node to extract parameters
-        sampler_result = self.process_node(sampler_node_id, workflow)
-        if not sampler_result:
-            return {}
-        
-        # Return the sampler result directly - it's already in the format we need
-        # This simplifies the structure and makes it easier to use in recipe_routes.py
-        
-        # Handle standard ComfyUI names vs our output format
-        if "cfg" in sampler_result:
-            sampler_result["cfg_scale"] = sampler_result.pop("cfg")
-            
-        # Add clip_skip = 1 to match reference output if not already present
-        if "clip_skip" not in sampler_result:
-            sampler_result["clip_skip"] = "1"
-        
-        # Ensure the prompt is a string and not a nested dictionary
-        if "prompt" in sampler_result and isinstance(sampler_result["prompt"], dict):
-            if "prompt" in sampler_result["prompt"]:
-                sampler_result["prompt"] = sampler_result["prompt"]["prompt"]
-        
-        # Save the result if requested
-        if output_path:
-            save_output(sampler_result, output_path)
-            
-        return sampler_result
-
-
-def parse_workflow(workflow_path: str, output_path: Optional[str] = None) -> Dict:
-    """
-    Parse a ComfyUI workflow file and extract generation parameters
-    
-    Args:
-        workflow_path: Path to the workflow JSON file
-        output_path: Optional path to save the output JSON
-        
-    Returns:
-        Dictionary containing extracted parameters
-    """
-    parser = WorkflowParser()
-    return parser.parse_workflow(workflow_path, output_path)

py/workflow/test.py

@@ -1,63 +0,0 @@
-"""
-Test script for the ComfyUI workflow parser
-"""
-import os
-import json
-import logging
-from .parser import parse_workflow
-
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    handlers=[logging.StreamHandler()]
-)
-logger = logging.getLogger(__name__)
-
-# Configure paths
-SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.abspath(os.path.join(SCRIPT_DIR, '..', '..'))
-REFS_DIR = os.path.join(ROOT_DIR, 'refs')
-OUTPUT_DIR = os.path.join(ROOT_DIR, 'output')
-
-def test_parse_flux_workflow():
-    """Test parsing the flux example workflow"""
-    # Ensure output directory exists
-    os.makedirs(OUTPUT_DIR, exist_ok=True)
-    
-    # Define input and output paths
-    input_path = os.path.join(REFS_DIR, 'flux_prompt.json')
-    output_path = os.path.join(OUTPUT_DIR, 'parsed_flux_output.json')
-    
-    # Parse workflow
-    logger.info(f"Parsing workflow: {input_path}")
-    result = parse_workflow(input_path, output_path)
-    
-    # Print result summary
-    logger.info(f"Output saved to: {output_path}")
-    logger.info(f"Parsing completed. Result summary:")
-    logger.info(f"  LoRAs: {result.get('loras', '')}")
-    
-    gen_params = result.get('gen_params', {})
-    logger.info(f"  Prompt: {gen_params.get('prompt', '')[:50]}...")
-    logger.info(f"  Steps: {gen_params.get('steps', '')}")
-    logger.info(f"  Sampler: {gen_params.get('sampler', '')}")
-    logger.info(f"  Size: {gen_params.get('size', '')}")
-    
-    # Compare with reference output
-    ref_output_path = os.path.join(REFS_DIR, 'flux_output.json')
-    try:
-        with open(ref_output_path, 'r') as f:
-            ref_output = json.load(f)
-            
-        # Simple validation
-        loras_match = result.get('loras', '') == ref_output.get('loras', '')
-        prompt_match = gen_params.get('prompt', '') == ref_output.get('gen_params', {}).get('prompt', '')
-        
-        logger.info(f"Validation against reference:")
-        logger.info(f"  LoRAs match: {loras_match}")
-        logger.info(f"  Prompt match: {prompt_match}")
-    except Exception as e:
-        logger.warning(f"Failed to compare with reference output: {e}")
-
-if __name__ == "__main__":
-    test_parse_flux_workflow() 

py/workflow/utils.py

@@ -1,120 +0,0 @@
-"""
-Utility functions for ComfyUI workflow parsing
-"""
-import json
-import os
-import logging
-from typing import Dict, List, Any, Optional, Union, Set, Tuple
-
-logger = logging.getLogger(__name__)
-
-def load_workflow(workflow_path: str) -> Dict:
-    """Load a workflow from a JSON file"""
-    try:
-        with open(workflow_path, 'r', encoding='utf-8') as f:
-            return json.load(f)
-    except Exception as e:
-        logger.error(f"Error loading workflow from {workflow_path}: {e}")
-        raise
-
-def save_output(output: Dict, output_path: str) -> None:
-    """Save the parsed output to a JSON file"""
-    os.makedirs(os.path.dirname(os.path.abspath(output_path)), exist_ok=True)
-    try:
-        with open(output_path, 'w', encoding='utf-8') as f:
-            json.dump(output, f, indent=4)
-    except Exception as e:
-        logger.error(f"Error saving output to {output_path}: {e}")
-        raise
-
-def find_node_by_type(workflow: Dict, node_type: str) -> Optional[str]:
-    """Find a node of the specified type in the workflow"""
-    for node_id, node_data in workflow.items():
-        if node_data.get("class_type") == node_type:
-            return node_id
-    return None
-
-def find_nodes_by_type(workflow: Dict, node_type: str) -> List[str]:
-    """Find all nodes of the specified type in the workflow"""
-    return [node_id for node_id, node_data in workflow.items() 
-            if node_data.get("class_type") == node_type]
-
-def get_input_node_ids(workflow: Dict, node_id: str) -> Dict[str, Tuple[str, int]]:
-    """
-    Get the node IDs for all inputs of the given node
-    
-    Returns a dictionary mapping input names to (node_id, output_slot) tuples
-    """
-    result = {}
-    if node_id not in workflow:
-        return result
-        
-    node_data = workflow[node_id]
-    for input_name, input_value in node_data.get("inputs", {}).items():
-        # Check if this input is connected to another node
-        if isinstance(input_value, list) and len(input_value) == 2:
-            # Input is connected to another node's output
-            # Format: [node_id, output_slot]
-            ref_node_id, output_slot = input_value
-            result[input_name] = (str(ref_node_id), output_slot)
-            
-    return result
-
-def trace_model_path(workflow: Dict, start_node_id: str) -> List[str]:
-    """
-    Trace the model path backward from KSampler to find all LoRA nodes
-    
-    Args:
-        workflow: The workflow data
-        start_node_id: The starting node ID (usually KSampler)
-        
-    Returns:
-        List of node IDs in the model path
-    """
-    model_path_nodes = []
-    
-    # Get the model input from the start node
-    if start_node_id not in workflow:
-        return model_path_nodes
-    
-    # Track visited nodes to avoid cycles
-    visited = set()
-    
-    # Stack for depth-first search
-    stack = []
-    
-    # Get model input reference if available
-    start_node = workflow[start_node_id]
-    if "inputs" in start_node and "model" in start_node["inputs"] and isinstance(start_node["inputs"]["model"], list):
-        model_ref = start_node["inputs"]["model"]
-        stack.append(str(model_ref[0]))
-    
-    # Perform depth-first search
-    while stack:
-        node_id = stack.pop()
-        
-        # Skip if already visited
-        if node_id in visited:
-            continue
-        
-        # Mark as visited
-        visited.add(node_id)
-        
-        # Skip if node doesn't exist
-        if node_id not in workflow:
-            continue
-        
-        node = workflow[node_id]
-        node_type = node.get("class_type", "")
-        
-        # Add current node to result list if it's a LoRA node
-        if "Lora" in node_type:
-            model_path_nodes.append(node_id)
-        
-        # Add all input nodes that have a "model" or "lora_stack" output to the stack
-        if "inputs" in node:
-            for input_name, input_value in node["inputs"].items():
-                if input_name in ["model", "lora_stack"] and isinstance(input_value, list) and len(input_value) == 2:
-                    stack.append(str(input_value[0]))
-    
-    return model_path_nodes 

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-lora-manager"
 description = "LoRA Manager for ComfyUI - Access it at http://localhost:8188/loras for managing LoRA models with previews and metadata integration."
-version = "0.8.12"
+version = "0.8.18"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",
@@ -13,7 +13,9 @@ dependencies = [
     "Pillow",
     "olefile", # for getting rid of warning message
     "requests",
-    "toml"
+    "toml",
+    "natsort",
+    "msgpack"
 ]
 
 [project.urls]
@@ -23,4 +25,4 @@ Repository = "https://github.com/willmiao/ComfyUI-Lora-Manager"
 [tool.comfy]
 PublisherId = "willmiao"
 DisplayName = "ComfyUI-Lora-Manager"
-Icon = ""
+Icon = "https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/static/images/android-chrome-512x512.png?raw=true"

refs/output.json

@@ -1,11 +1,258 @@
 {
-    "loras": "<lora:ck-neon-retrowave-IL-000012:0.8> <lora:aorunIllstrious:1> <lora:ck-shadow-circuit-IL-000012:0.78> <lora:MoriiMee_Gothic_Niji_Style_Illustrious_r1:0.45> <lora:ck-nc-cyberpunk-IL-000011:0.4>",
-    "prompt": "in the style of ck-rw, aorun, scales, makeup, bare shoulders, pointy ears, dress, claws, in the style of cksc, artist:moriimee, in the style of cknc, masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject, close up, stylized, in gold and neon shades, wabi sabi, 1girl, rainbow angel wings, looking at viewer, dynamic angle, from below, from side, relaxing",
-    "negative_prompt": "bad quality, worst quality, worst detail, sketch ,signature, watermark, patreon logo, nsfw",
-    "steps": "20",
-    "sampler": "euler_ancestral",
-    "cfg_scale": "8",
-    "seed": "241",
-    "size": "832x1216",
-    "clip_skip": "2"
+    "id": 649516,
+    "name": "Cynthia -シロナ - Pokemon Diamond and Pearl - PDXL LORA",
+    "description": "<p><strong>Warning: Without Adetailer eyes are fucked (rainbow color and artefact)</strong></p><p><span style=\"color:rgb(193, 194, 197)\">Trained on </span><a target=\"_blank\" rel=\"ugc\" href=\"https://civitai.com/models/257749/horsefucker-diffusion-v6-xl\"><strong>Pony Diffusion V6 XL</strong></a> with 63 pictures.<br />Best result with weight between : 0.8-1.</p><p><span style=\"color:rgb(193, 194, 197)\">Basic prompts : </span><code>1girl, cynthia \\(pokemon\\), blonde hair, hair over one eye, very long hair, grey eyes, eyelashes, hair ornament</code> <br /><span style=\"color:rgb(193, 194, 197)\">Outfit prompts : </span><code>fur collar, black coat, fur-trimmed coat, long sleeves, black pants, black shirt, high heels</code></p><p>Reviews are really appreciated, i love to see the community use my work, that's why I share it.<br />If you like my work, you can tip me <a target=\"_blank\" rel=\"ugc\" href=\"https://ko-fi.com/konan49773\"><strong>here.</strong></a></p><p>Got a specific request ? I'm open for commission on my <a target=\"_blank\" rel=\"ugc\" href=\"https://ko-fi.com/konan49773/commissions\"><strong>kofi</strong></a> or<strong> </strong><a target=\"_blank\" rel=\"ugc\" href=\"https://www.fiverr.com/konanai/create-lora-model-for-you\"><strong>fiverr gig</strong></a> *! If you provide enough data, OCs are accepted</p>",
+    "allowNoCredit": true,
+    "allowCommercialUse": [
+        "Image",
+        "RentCivit"
+    ],
+    "allowDerivatives": true,
+    "allowDifferentLicense": true,
+    "type": "LORA",
+    "minor": false,
+    "sfwOnly": false,
+    "poi": false,
+    "nsfw": false,
+    "nsfwLevel": 29,
+    "availability": "Public",
+    "cosmetic": null,
+    "supportsGeneration": true,
+    "stats": {
+        "downloadCount": 811,
+        "favoriteCount": 0,
+        "thumbsUpCount": 175,
+        "thumbsDownCount": 0,
+        "commentCount": 4,
+        "ratingCount": 0,
+        "rating": 0,
+        "tippedAmountCount": 10
+    },
+    "creator": {
+        "username": "Konan",
+        "image": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7cd552a1-60fe-4baf-a0e4-f7d5d5381711/width=96/Konan.jpeg"
+    },
+    "tags": [
+        "anime",
+        "character",
+        "cynthia",
+        "woman",
+        "pokemon",
+        "pokegirl"
+    ],
+    "modelVersions": [
+        {
+            "id": 726676,
+            "index": 0,
+            "name": "v1.0",
+            "baseModel": "Pony",
+            "createdAt": "2024-08-16T01:13:16.099Z",
+            "publishedAt": "2024-08-16T01:14:44.984Z",
+            "status": "Published",
+            "availability": "Public",
+            "nsfwLevel": 29,
+            "trainedWords": [
+                "1girl, cynthia \\(pokemon\\), blonde hair, hair over one eye, very long hair, grey eyes, eyelashes, hair ornament",
+                "fur collar, black coat, fur-trimmed coat, long sleeves, black pants, black shirt, high heels"
+            ],
+            "covered": true,
+            "stats": {
+                "downloadCount": 811,
+                "ratingCount": 0,
+                "rating": 0,
+                "thumbsUpCount": 175,
+                "thumbsDownCount": 0
+            },
+            "files": [
+                {
+                    "id": 641092,
+                    "sizeKB": 56079.65234375,
+                    "name": "CynthiaXL.safetensors",
+                    "type": "Model",
+                    "pickleScanResult": "Success",
+                    "pickleScanMessage": "No Pickle imports",
+                    "virusScanResult": "Success",
+                    "virusScanMessage": null,
+                    "scannedAt": "2024-08-16T01:17:19.087Z",
+                    "metadata": {
+                        "format": "SafeTensor"
+                    },
+                    "hashes": {},
+                    "downloadUrl": "https://civitai.com/api/download/models/726676",
+                    "primary": true
+                }
+            ],
+            "images": [
+                {
+                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/b346d757-2b59-4aeb-9f09-3bee2724519d/width=1248/24511993.jpeg",
+                    "nsfwLevel": 1,
+                    "width": 1248,
+                    "height": 1824,
+                    "hash": "UqNc==RP.9s+~pxvIst7kWWBWBjY%MWBt7WB",
+                    "type": "image",
+                    "minor": false,
+                    "poi": false,
+                    "hasMeta": true,
+                    "hasPositivePrompt": true,
+                    "onSite": false,
+                    "remixOfId": null
+                },
+                {
+                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/fc132ac0-cc1c-4b68-a1d7-5b97b0996ac2/width=1248/24511997.jpeg",
+                    "nsfwLevel": 1,
+                    "width": 1248,
+                    "height": 1824,
+                    "hash": "UMGSS+?tTw.60MIX9cbb~WxHRRR-NEtLRiR%",
+                    "type": "image",
+                    "minor": false,
+                    "poi": false,
+                    "hasMeta": true,
+                    "hasPositivePrompt": true,
+                    "onSite": false,
+                    "remixOfId": null
+                },
+                {
+                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7b3237d1-e672-466a-85d0-cc5dd42ab130/width=1160/24512001.jpeg",
+                    "nsfwLevel": 4,
+                    "width": 1160,
+                    "height": 1696,
+                    "hash": "U9NA6f~o00%h00wvIYt74:ER-=D%5600DiE1",
+                    "type": "image",
+                    "minor": false,
+                    "poi": false,
+                    "hasMeta": true,
+                    "hasPositivePrompt": true,
+                    "onSite": false,
+                    "remixOfId": null
+                },
+                {
+                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/ccd7d11d-4fa9-4434-85a1-fb999312e60d/width=1248/24511991.jpeg",
+                    "nsfwLevel": 1,
+                    "width": 1248,
+                    "height": 1824,
+                    "hash": "UyNTg.j?~qxu?aoLRkj]%MfkM{jZaya}a#ax",
+                    "type": "image",
+                    "minor": false,
+                    "poi": false,
+                    "hasMeta": true,
+                    "hasPositivePrompt": true,
+                    "onSite": false,
+                    "remixOfId": null
+                },
+                {
+                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/1743be6d-7fe5-4b55-9f19-c931618fa259/width=1248/24511996.jpeg",
+                    "nsfwLevel": 4,
+                    "width": 1248,
+                    "height": 1824,
+                    "hash": "UGOC~n^+?w~6Tx_4oM^$yYEkMds74:9F#*xY",
+                    "type": "image",
+                    "minor": false,
+                    "poi": false,
+                    "hasMeta": true,
+                    "hasPositivePrompt": true,
+                    "onSite": false,
+                    "remixOfId": null
+                },
+                {
+                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/91693c98-d037-4489-882c-100eb26019a0/width=1160/24512010.jpeg",
+                    "nsfwLevel": 4,
+                    "width": 1160,
+                    "height": 1696,
+                    "hash": "UJI}kp^-Kl%hXAIX4;Nf^+M|9GRP0Mt8%L%2",
+                    "type": "image",
+                    "minor": false,
+                    "poi": false,
+                    "hasMeta": true,
+                    "hasPositivePrompt": true,
+                    "onSite": false,
+                    "remixOfId": null
+                },
+                {
+                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/49c7a294-ac5b-4832-98e5-2acd0f1a8782/width=1248/24512017.jpeg",
+                    "nsfwLevel": 4,
+                    "width": 1248,
+                    "height": 1824,
+                    "hash": "UML;8Qn|9G%3mnWA4nWFMf%N?Hae~qog-oNF",
+                    "type": "image",
+                    "minor": false,
+                    "poi": false,
+                    "hasMeta": true,
+                    "hasPositivePrompt": true,
+                    "onSite": false,
+                    "remixOfId": null
+                },
+                {
+                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d7b442f2-6ead-4a7a-9578-54d9ec2ff148/width=1248/24512015.jpeg",
+                    "nsfwLevel": 1,
+                    "width": 1248,
+                    "height": 1824,
+                    "hash": "UPGR#kt8xw%M0LWC9bWC?wxtR*NLM^jrxWM|",
+                    "type": "image",
+                    "minor": false,
+                    "poi": false,
+                    "hasMeta": true,
+                    "hasPositivePrompt": true,
+                    "onSite": false,
+                    "remixOfId": null
+                },
+                {
+                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d840f1e9-3dd3-4531-b83a-1ba2c6b7feaa/width=1160/24512004.jpeg",
+                    "nsfwLevel": 8,
+                    "width": 1160,
+                    "height": 1696,
+                    "hash": "ULNm1i_39wi^*I%hDiM_tlo#xuV?^kNIxCs,",
+                    "type": "image",
+                    "minor": false,
+                    "poi": false,
+                    "hasMeta": true,
+                    "hasPositivePrompt": true,
+                    "onSite": false,
+                    "remixOfId": null
+                },
+                {
+                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/520387ae-c176-43e3-92bd-5cd2a672475e/width=1248/24512012.jpeg",
+                    "nsfwLevel": 4,
+                    "width": 1248,
+                    "height": 1824,
+                    "hash": "URM%l.%M.9Ip~poIkExu_3V@M|xuD%oJM{D*",
+                    "type": "image",
+                    "minor": false,
+                    "poi": false,
+                    "hasMeta": true,
+                    "hasPositivePrompt": true,
+                    "onSite": false,
+                    "remixOfId": null
+                },
+                {
+                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/9ea28b94-f326-4776-83ff-851cc203c627/width=1248/24511988.jpeg",
+                    "nsfwLevel": 1,
+                    "width": 1248,
+                    "height": 1824,
+                    "hash": "U-PZloog_Nxut6j]WXWB-;j?IVa#ofaxj]j]",
+                    "type": "image",
+                    "minor": false,
+                    "poi": false,
+                    "hasMeta": true,
+                    "hasPositivePrompt": true,
+                    "onSite": false,
+                    "remixOfId": null
+                },
+                {
+                    "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/2e749dbb-7d5a-48f1-8e29-fea5022a5fe9/width=1248/24522268.jpeg",
+                    "nsfwLevel": 16,
+                    "width": 1248,
+                    "height": 1824,
+                    "hash": "UPLgtm9Z0z=|0yRRE2-A9rWAoNE1~DwOr=t7",
+                    "type": "image",
+                    "minor": false,
+                    "poi": false,
+                    "hasMeta": true,
+                    "hasPositivePrompt": true,
+                    "onSite": false,
+                    "remixOfId": null
+                }
+            ],
+            "downloadUrl": "https://civitai.com/api/download/models/726676"
+        }
+    ]
 }

requirements.txt

@@ -9,4 +9,6 @@ olefile
 requests
 toml
 numpy
-torch
+torch
+natsort
+msgpack

standalone.py

@@ -1,3 +1,4 @@
+from pathlib import Path
 import os
 import sys
 import json
@@ -280,10 +281,14 @@ class StandaloneLoraManager(LoraManager):
                 # Display path with forward slashes for consistency
                 display_target = target_path.replace('\\', '/')
                 
-                app.router.add_static(route_path, target_path)
-                logger.info(f"Added static route for link target {route_path} -> {display_target}")
-                config.add_route_mapping(target_path, route_path)
-                added_targets.add(norm_target)
+                try:
+                    app.router.add_static(route_path, Path(target_path).resolve(strict=False))
+                    logger.info(f"Added static route for link target {route_path} -> {display_target}")
+                    config.add_route_mapping(target_path, route_path)
+                    added_targets.add(norm_target)
+                except Exception as e:
+                    logger.warning(f"Failed to add static route on initialization for {target_path}: {e}")
+                    continue
         
         # Add static route for plugin assets
         app.router.add_static('/loras_static', config.static_path)
@@ -295,6 +300,7 @@ class StandaloneLoraManager(LoraManager):
         from py.routes.checkpoints_routes import CheckpointsRoutes
         from py.routes.update_routes import UpdateRoutes
         from py.routes.misc_routes import MiscRoutes
+        from py.routes.example_images_routes import ExampleImagesRoutes
         
         lora_routes = LoraRoutes()
         checkpoints_routes = CheckpointsRoutes()
@@ -306,6 +312,7 @@ class StandaloneLoraManager(LoraManager):
         RecipeRoutes.setup_routes(app)
         UpdateRoutes.setup_routes(app)
         MiscRoutes.setup_routes(app)
+        ExampleImagesRoutes.setup_routes(app)
         
         # Schedule service initialization
         app.on_startup.append(lambda app: cls._initialize_services())

static/css/base.css

@@ -32,13 +32,25 @@ html, body {
     --card-bg: #ffffff;
     --border-color: #e0e0e0;
 
-    /* Color System */
-    --lora-accent: oklch(68% 0.28 256);
+    /* Color Components */
+    --lora-accent-l: 68%;
+    --lora-accent-c: 0.28;
+    --lora-accent-h: 256;
+    --lora-warning-l: 75%;
+    --lora-warning-c: 0.25;
+    --lora-warning-h: 80;
+    --lora-success-l: 70%;
+    --lora-success-c: 0.2;
+    --lora-success-h: 140;
+
+    /* 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-border: oklch(90% 0.02 256 / 0.15);
     --lora-text: oklch(95% 0.02 256);
     --lora-error: oklch(75% 0.32 29);
-    --lora-warning: oklch(75% 0.25 80); /* Add warning color for deleted LoRAs */
+    --lora-warning: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h)); /* Modified to be used with oklch() */
+    --lora-success: oklch(var(--lora-success-l) var(--lora-success-c) var(--lora-success-h)); /* New green success color */
     
     /* Spacing Scale */
     --space-1: calc(8px * 1);
@@ -79,7 +91,7 @@ html[data-theme="light"] {
     --lora-surface: oklch(25% 0.02 256 / 0.98);
     --lora-border: oklch(90% 0.02 256 / 0.15);
     --lora-text: oklch(98% 0.02 256);
-    --lora-warning: oklch(75% 0.25 80); /* Add warning color for dark theme too */
+    --lora-warning: oklch(75% 0.25 80); /* Modified to be used with oklch() */
 }
 
 body {

static/css/components/bulk.css

@@ -60,6 +60,18 @@
     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 */
 .lora-card.selected {
     box-shadow: 0 0 0 2px var(--lora-accent);
@@ -262,83 +274,6 @@
     background: var(--lora-accent);
 }
 
-/* NSFW Level Selector */
-.nsfw-level-selector {
-    position: fixed;
-    top: 50%;
-    left: 50%;
-    transform: translate(-50%, -50%);
-    background: var(--card-bg);
-    border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-base);
-    padding: 16px;
-    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.2);
-    z-index: var(--z-modal);
-    width: 300px;
-    display: none;
-}
-
-.nsfw-level-header {
-    display: flex;
-    justify-content: space-between;
-    align-items: center;
-    margin-bottom: 16px;
-}
-
-.nsfw-level-header h3 {
-    margin: 0;
-    font-size: 16px;
-    font-weight: 500;
-}
-
-.close-nsfw-selector {
-    background: transparent;
-    border: none;
-    color: var(--text-color);
-    cursor: pointer;
-    padding: 4px;
-    border-radius: var(--border-radius-xs);
-}
-
-.close-nsfw-selector:hover {
-    background: var(--border-color);
-}
-
-.current-level {
-    margin-bottom: 12px;
-    padding: 8px;
-    background: var(--bg-color);
-    border-radius: var(--border-radius-xs);
-    border: 1px solid var(--border-color);
-}
-
-.nsfw-level-options {
-    display: flex;
-    flex-wrap: wrap;
-    gap: 8px;
-}
-
-.nsfw-level-btn {
-    flex: 1 0 calc(33% - 8px);
-    padding: 8px;
-    border-radius: var(--border-radius-xs);
-    background: var(--bg-color);
-    border: 1px solid var(--border-color);
-    color: var(--text-color);
-    cursor: pointer;
-    transition: all 0.2s ease;
-}
-
-.nsfw-level-btn:hover {
-    background: var(--lora-border);
-}
-
-.nsfw-level-btn.active {
-    background: var(--lora-accent);
-    color: white;
-    border-color: var(--lora-accent);
-}
-
 /* Mobile optimizations */
 @media (max-width: 768px) {
     .selected-thumbnails-strip {

static/css/components/card.css

@@ -1,14 +1,17 @@
 /* 卡片网格布局 */
 .card-grid {
     display: grid;
-    grid-template-columns: repeat(auto-fill, minmax(260px, 1fr)); /* Adjusted from 320px */
-    gap: 12px; /* Reduced from var(--space-2) for tighter horizontal spacing */
+    grid-template-columns: repeat(auto-fill, minmax(260px, 1fr)); /* Base size */
+    gap: 12px; /* Consistent gap for both row and column spacing */
+    row-gap: 20px; /* Increase vertical spacing between rows */
     margin-top: var(--space-2);
     padding-top: 4px; /* 添加顶部内边距，为悬停动画提供空间 */
     padding-bottom: 4px; /* 添加底部内边距，为悬停动画提供空间 */
-    max-width: 1400px; /* Container width control */
+    width: 100%; /* Ensure it takes full width of container */
+    max-width: 1400px; /* Base container width */
     margin-left: auto;
     margin-right: auto;
+    box-sizing: border-box; /* Include padding in width calculation */
 }
 
 .lora-card {
@@ -17,13 +20,14 @@
     border-radius: var(--border-radius-base);
     backdrop-filter: blur(16px);
     transition: transform 160ms ease-out;
-    aspect-ratio: 896/1152;
-    max-width: 260px; /* Adjusted from 320px to fit 5 cards */
+    aspect-ratio: 896/1152; /* Preserve aspect ratio */
+    max-width: 260px; /* Base size */
+    width: 100%;
     margin: 0 auto;
-    cursor: pointer; /* Added from recipe-card */
-    display: flex; /* Added from recipe-card */
-    flex-direction: column; /* Added from recipe-card */
-    overflow: hidden; /* Add overflow hidden to contain children */
+    cursor: pointer;
+    display: flex;
+    flex-direction: column;
+    overflow: hidden;
 }
 
 .lora-card:hover {
@@ -36,6 +40,30 @@
     outline-offset: 2px;
 }
 
+/* Responsive adjustments for 1440p screens (2K) */
+@media (min-width: 2000px) {
+    .card-grid {
+        max-width: 1800px; /* Increased for 2K screens */
+        grid-template-columns: repeat(auto-fill, minmax(270px, 1fr));
+    }
+    
+    .lora-card {
+        max-width: 270px;
+    }
+}
+
+/* Responsive adjustments for 4K screens */
+@media (min-width: 3000px) {
+    .card-grid {
+        max-width: 2400px; /* Increased for 4K screens */
+        grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
+    }
+    
+    .lora-card {
+        max-width: 280px;
+    }
+}
+
 /* Responsive adjustments */
 @media (max-width: 1400px) {
     .card-grid {
@@ -58,6 +86,42 @@
     min-height: 0; /* Fix for potential flexbox sizing issue in Firefox */
 }
 
+/* Smaller text for medium density */
+.medium-density .model-name {
+    font-size: 0.95em;
+    max-height: 3em; /* Increased from 2.6em */
+}
+
+.medium-density .base-model-label {
+    font-size: 0.85em;
+    max-width: 120px;
+}
+
+.medium-density .card-actions i {
+    font-size: 0.98em;
+    padding: 4px;
+}
+
+/* Smaller text for compact mode */
+.compact-density .model-name {
+    font-size: 0.9em;
+    max-height: 2.8em; /* Increased from 2.4em */
+}
+
+.compact-density .base-model-label {
+    font-size: 0.8em;
+    max-width: 110px;
+}
+
+.compact-density .card-actions i {
+    font-size: 0.95em;
+    padding: 3px;
+}
+
+.compact-density .model-info {
+    padding-bottom: 2px;
+}
+
 .card-preview img,
 .card-preview video {
     width: 100%;
@@ -103,6 +167,38 @@
     text-shadow: 1px 1px 1px rgba(0, 0, 0, 0.5);
 }
 
+/* NSFW warning adjustments for medium density */
+.medium-density .nsfw-warning {
+    padding: calc(var(--space-2) * 0.85);
+    max-width: 70%;
+}
+
+.medium-density .nsfw-warning p {
+    font-size: 0.95em;
+    margin-bottom: calc(var(--space-1) * 0.85);
+}
+
+.medium-density .show-content-btn {
+    font-size: 0.85em;
+    padding: 3px calc(var(--space-1) * 0.85);
+}
+
+/* NSFW warning adjustments for compact density */
+.compact-density .nsfw-warning {
+    padding: calc(var(--space-2) * 0.7);
+    max-width: 60%;
+}
+
+.compact-density .nsfw-warning p {
+    font-size: 0.85em;
+    margin-bottom: calc(var(--space-1) * 0.7);
+}
+
+.compact-density .show-content-btn {
+    font-size: 0.8em;
+    padding: 2px var(--space-1);
+}
+
 .toggle-blur-btn {
     position: absolute;
     left: var(--space-1);
@@ -267,21 +363,24 @@
     text-decoration: none;
 }
 
+/* Updated model name to fix text cutoff issues */
 .model-name {
     font-weight: bold;
     text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.5);
     font-size: 0.95em;
     word-break: break-word;
     display: block;
-    max-height: 2.8em;
+    max-height: 3em; /* Increased to ensure two full lines */
     overflow: hidden;
+    /* Add line height for consistency */
+    line-height: 1.4;
 }
 
 .model-info {
     flex: 1;
     min-width: 0;
     overflow: hidden;
-    padding-bottom: 4px;
+    padding-bottom: 6px; /* Increased from 4px to give more room for text */
 }
 
 .base-model {
@@ -313,6 +412,25 @@
     font-size: 0.85em;
 }
 
+/* Prevent text selection on cards and interactive elements */
+.lora-card,
+.lora-card *,
+.card-actions,
+.card-actions i,
+.toggle-blur-btn,
+.show-content-btn,
+.card-preview img,
+.card-preview video,
+.card-footer,
+.card-header,
+.model-name,
+.base-model-label {
+    -webkit-user-select: none;
+    -moz-user-select: none;
+    -ms-user-select: none;
+    user-select: none;
+}
+
 /* Recipe specific elements - migrated from recipe-card.css */
 .recipe-indicator {
     position: absolute;
@@ -362,4 +480,84 @@
     padding: 2rem;
     background: var(--lora-surface-alt);
     border-radius: var(--border-radius-base);
-}
+}
+
+/* Virtual scrolling specific styles - updated */
+.virtual-scroll-item {
+    position: absolute;
+    box-sizing: border-box;
+    transition: transform 160ms ease-out;
+    margin: 0; /* Remove margins, positioning is handled by VirtualScroller */
+    width: 100%; /* Allow width to be set by the VirtualScroller */
+}
+
+.virtual-scroll-item:hover {
+    transform: translateY(-2px); /* Keep hover effect */
+    z-index: 1; /* Ensure hovered items appear above others */
+}
+
+/* When using virtual scroll, adjust container */
+.card-grid.virtual-scroll {
+    display: block;
+    position: relative;
+    margin: 0 auto;
+    padding: 4px 0; /* Add top/bottom padding equivalent to card padding */
+    height: auto;
+    width: 100%;
+    max-width: 1400px; /* Keep the max-width from original grid */
+    box-sizing: border-box; /* Include padding in width calculation */
+    overflow-x: hidden; /* Prevent horizontal overflow */
+}
+
+/* For larger screens, allow more space for the cards */
+@media (min-width: 2000px) {
+    .card-grid.virtual-scroll {
+        max-width: 1800px;
+    }
+}
+
+@media (min-width: 3000px) {
+    .card-grid.virtual-scroll {
+        max-width: 2400px;
+    }
+}
+
+/* Add after the existing .lora-card:hover styles */
+
+@keyframes update-pulse {
+    0% { box-shadow: 0 0 0 0 var(--lora-accent-transparent); }
+    50% { box-shadow: 0 0 0 4px var(--lora-accent-transparent); }
+    100% { box-shadow: 0 0 0 0 var(--lora-accent-transparent); }
+  }
+  
+  /* Add semi-transparent version of accent color for animation */
+  :root {
+    --lora-accent-transparent: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.6);
+  }
+  
+  .lora-card.updated {
+    animation: update-pulse 1.2s ease-out;
+  }
+  
+  /* Add a subtle updated tag that fades in and out */
+  .update-indicator {
+    position: absolute;
+    top: 8px;
+    right: 8px;
+    background: var(--lora-accent);
+    color: white;
+    border-radius: var(--border-radius-xs);
+    padding: 3px 6px;
+    font-size: 0.75em;
+    opacity: 0;
+    transform: translateY(-5px);
+    z-index: 4;
+    animation: update-tag 1.8s ease-out forwards;
+  }
+  
+  @keyframes update-tag {
+    0% { opacity: 0; transform: translateY(-5px); }
+    15% { opacity: 1; transform: translateY(0); }
+    85% { opacity: 1; transform: translateY(0); }
+    100% { opacity: 0; transform: translateY(0); }
+  }

static/css/components/download-modal.css

@@ -95,7 +95,7 @@
     flex: 1;
 }
 
-.version-info {
+.version-content .version-info {
     display: flex;
     flex-wrap: wrap;
     flex-direction: row !important;
@@ -104,7 +104,7 @@
     font-size: 0.9em;
 }
 
-.version-info .base-model {
+.version-content .version-info .base-model {
     background: oklch(var(--lora-accent) / 0.1);
     color: var(--lora-accent);
     padding: 2px 8px;

static/css/components/duplicates.css

@@ -0,0 +1,608 @@
+/* Duplicates Management Styles */
+
+/* Duplicates banner */
+.duplicates-banner {
+    position: sticky; /* Keep the sticky position */
+    top: var(--space-1);
+    width: 100%;
+    background-color: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1); /* Use accent color with low opacity */
+    color: var(--text-color);
+    border-top: 1px solid oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.3); /* Add top border with accent color */
+    border-bottom: 1px solid oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.4); /* Make bottom border stronger */
+    z-index: var(--z-overlay);
+    padding: 12px 0;
+    box-shadow: 0 3px 10px rgba(0, 0, 0, 0.2); /* Stronger shadow */
+    transition: all 0.3s ease;
+    margin-bottom: 20px;
+}
+
+.duplicates-banner .banner-content {
+    position: relative;
+    max-width: 1400px;
+    margin: 0 auto;
+    display: flex;
+    align-items: center;
+    gap: 12px;
+    padding: 0 16px;
+}
+
+/* Responsive container for larger screens - match container in layout.css */
+@media (min-width: 2000px) {
+    .duplicates-banner .banner-content {
+        max-width: 1800px;
+    }
+}
+
+@media (min-width: 3000px) {
+    .duplicates-banner .banner-content {
+        max-width: 2400px;
+    }
+}
+
+.duplicates-banner i.fa-exclamation-triangle {
+    font-size: 18px;
+    color: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+}
+
+.duplicates-banner .banner-actions {
+    margin-left: auto;
+    display: flex;
+    gap: 8px;
+    align-items: center;
+}
+
+/* Improved exit button in banner */
+.duplicates-banner button.btn-exit-mode {
+    min-width: 120px;
+    background-color: var(--card-bg);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+    padding: 6px 12px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.85em;
+    cursor: pointer;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 6px;
+    transition: all 0.2s ease;
+}
+
+.duplicates-banner button.btn-exit-mode:hover {
+    background-color: var(--bg-color);
+    border-color: var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h);
+    transform: translateY(-1px);
+}
+
+.duplicates-banner button {
+    min-width: 100px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 4px;
+    border-radius: var(--border-radius-xs);
+    padding: 4px 10px;
+    border: 1px solid var(--border-color);
+    background: var(--card-bg);
+    color: var(--text-color);
+    font-size: 0.85em;
+    transition: all 0.2s ease;
+    cursor: pointer;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+}
+
+.duplicates-banner button:hover {
+    border-color: var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h);
+    background: var(--bg-color);
+    transform: translateY(-1px);
+    box-shadow: 0 3px 5px rgba(0, 0, 0, 0.08);
+}
+
+.duplicates-banner button.btn-exit {
+    min-width: unset;
+    width: 28px;
+    height: 28px;
+    padding: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    border-radius: 50%;
+}
+
+.duplicates-banner button.disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+/* Duplicate groups */
+.duplicate-group {
+    position: relative;
+    border: 2px solid oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    border-radius: var(--border-radius-base);
+    padding: 16px;
+    margin-bottom: 24px;
+    background: var(--card-bg);
+    box-shadow: 0 2px 6px rgba(0, 0, 0, 0.12); /* Add subtle shadow to groups */
+    /* Add responsive width settings to match banner */
+    max-width: 1400px;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+/* Add responsive container adjustments for duplicate groups - match container in banner */
+@media (min-width: 2000px) {
+    .duplicate-group {
+        max-width: 1800px;
+    }
+}
+
+@media (min-width: 3000px) {
+    .duplicate-group {
+        max-width: 2400px;
+    }
+}
+
+.duplicate-group-header {
+    background-color: var(--bg-color);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+    padding: 10px 16px; /* Slightly increased padding */
+    border-radius: var(--border-radius-xs);
+    margin-bottom: 16px;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    border-left: 4px solid oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h)); /* Add accent border on the left */
+}
+
+.duplicate-group-header span:last-child {
+    display: flex;
+    gap: 8px;
+    align-items: center;
+}
+
+.duplicate-group-header button {
+    min-width: 80px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 4px;
+    border-radius: var(--border-radius-xs);
+    padding: 4px 8px;
+    border: 1px solid var(--border-color);
+    background: var(--card-bg);
+    color: var(--text-color);
+    font-size: 0.85em;
+    transition: all 0.2s ease;
+    cursor: pointer;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+    margin-left: 8px;
+}
+
+.duplicate-group-header button:hover {
+    border-color: var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h);
+    background: var(--bg-color);
+    transform: translateY(-1px);
+    box-shadow: 0 3px 5px rgba(0, 0, 0, 0.08);
+}
+
+.card-group-container {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 16px;
+    justify-content: flex-start;
+    align-items: flex-start;
+}
+
+/* Make cards in duplicate groups have consistent width */
+.card-group-container .lora-card {
+    flex: 0 0 auto;
+    width: 240px;
+    margin: 0;
+    cursor: pointer; /* Indicate the card is clickable */
+}
+
+/* Ensure the grid layout is only applied to the main recipe grid, not duplicate groups */
+.duplicate-mode .card-grid {
+    display: block;
+}
+
+/* Scrollable container for large duplicate groups */
+.card-group-container.scrollable {
+    max-height: 450px;
+    overflow-y: auto;
+    padding-right: 8px;
+}
+
+/* Add a toggle button to expand/collapse large duplicate groups */
+.group-toggle-btn {
+    position: absolute;
+    right: 16px;
+    bottom: -12px;
+    background: var(--card-bg);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+    border-radius: 50%;
+    width: 24px;
+    height: 24px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    cursor: pointer;
+    z-index: 1;
+    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
+    transition: all 0.2s ease;
+}
+
+.group-toggle-btn:hover {
+    border-color: var(--lora-accent-l) var(--lora-accent-c) var (--lora-accent-h);
+    transform: translateY(-1px);
+    box-shadow: 0 3px 5px rgba(0, 0, 0, 0.08);
+}
+
+/* Duplicate card styling */
+.lora-card.duplicate {
+    position: relative;
+    transition: all 0.2s ease;
+}
+
+.lora-card.duplicate:hover {
+    border-color: var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h);
+}
+
+.lora-card.duplicate.latest {
+    border-style: solid;
+    border-color: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+}
+
+.lora-card.duplicate-selected {
+    border: 2px solid oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h));
+    box-shadow: 0 0 8px rgba(0, 0, 0, 0.2);
+}
+
+.lora-card .selector-checkbox {
+    position: absolute;
+    top: 10px;
+    right: 10px;
+    z-index: 10;
+    width: 20px;
+    height: 20px;
+    cursor: pointer;
+}
+
+/* Latest indicator */
+.lora-card.duplicate.latest::after {
+    content: "Latest";
+    position: absolute;
+    top: 10px;
+    left: 10px;
+    background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h));
+    color: white;
+    font-size: 12px;
+    padding: 2px 6px;
+    border-radius: var(--border-radius-xs);
+    z-index: 5;
+}
+
+/* Model tooltip for duplicates mode */
+.model-tooltip {
+    position: absolute;
+    background-color: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    box-shadow: 0 2px 10px rgba(0,0,0,0.2);
+    padding: 10px;
+    z-index: 1000;
+    max-width: 350px;
+    min-width: 250px;
+    color: var(--text-color);
+    font-size: 0.9em;
+    pointer-events: none; /* Don't block mouse events */
+}
+
+.model-tooltip .tooltip-header {
+    font-weight: bold;
+    font-size: 1.1em;
+    margin-bottom: 8px;
+    padding-bottom: 5px;
+    border-bottom: 1px solid var(--border-color);
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+
+.model-tooltip .tooltip-info div {
+    margin-bottom: 4px;
+    display: flex;
+    flex-wrap: wrap;
+    word-break: break-all; /* Ensure long hashes wrap properly */
+}
+
+.model-tooltip .tooltip-info div strong {
+    margin-right: 5px;
+    min-width: 70px;
+}
+
+/* Latest indicator */
+.hash-mismatch-info {
+    margin-top: 8px;
+    padding-top: 8px;
+    border-top: 1px dashed var(--border-color);
+    color: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    font-weight: bold;
+    word-break: break-all; /* Ensure long hashes wrap properly */
+}
+
+/* Verification Badge Styles */
+.verification-badge {
+    display: inline-flex;
+    align-items: center;
+    margin-left: 8px;
+    padding: 2px 6px;
+    font-size: 0.8em;
+    border-radius: var(--border-radius-xs);
+    font-weight: normal;
+}
+
+.verification-badge.metadata {
+    background-color: var(--bg-color);
+    border: 1px solid var(--border-color);
+    color: var(--text-color);
+}
+
+.verification-badge.verified {
+    background-color: oklch(70% 0.2 140); /* Green for verified */
+    color: white;
+}
+
+.verification-badge.mismatch {
+    background-color: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    color: white;
+}
+
+.verification-badge i {
+    margin-right: 4px;
+}
+
+/* Hash Mismatch Styling */
+.lora-card.duplicate.hash-mismatch {
+    border: 2px dashed oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    opacity: 0.85;
+    position: relative;
+}
+
+.lora-card.duplicate.hash-mismatch::before {
+    content: "";
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background: repeating-linear-gradient(
+        45deg,
+        oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h) / 0.05),
+        oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h) / 0.05) 10px,
+        transparent 10px,
+        transparent 20px
+    );
+    z-index: 1;
+    pointer-events: none;
+}
+
+.lora-card.duplicate.hash-mismatch .card-preview {
+    filter: grayscale(20%);
+}
+
+/* Mismatch Badge */
+.mismatch-badge {
+    position: absolute;
+    top: 10px;
+    left: 10px; /* Changed from right:10px to left:10px */
+    background: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    color: white;
+    font-size: 12px;
+    padding: 3px 8px;
+    border-radius: var(--border-radius-xs);
+    z-index: 5;
+}
+
+/* Disabled checkbox style */
+.lora-card.duplicate.hash-mismatch .selector-checkbox {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+/* Hash mismatch info in tooltip */
+.hash-mismatch-info {
+    margin-top: 8px;
+    padding-top: 8px;
+    border-top: 1px dashed var(--border-color);
+    color: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    font-weight: bold;
+}
+
+/* Verify hash button styling */
+.btn-verify-hashes {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    padding: 4px 10px;
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    font-size: 0.85em;
+    cursor: pointer;
+    transition: all 0.2s ease;
+}
+
+.btn-verify-hashes:hover {
+    background: var(--bg-color);
+    border-color: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h));
+    transform: translateY(-1px);
+}
+
+.btn-verify-hashes i {
+    font-size: 0.9em;
+}
+
+/* Badge Styles */
+.badge {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    min-width: 16px; /* Reduced from 20px */
+    height: 16px; /* Reduced from 20px */
+    border-radius: 8px; /* Adjusted for smaller size */
+    background-color: var(--lora-error);
+    color: white;
+    font-size: 10px; /* Smaller font size */
+    font-weight: bold;
+    padding: 0 4px; /* Reduced padding */
+    position: absolute;
+    top: -8px; /* Moved closer to button */
+    right: -8px; /* Moved closer to button */
+    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.15); /* Softer shadow */
+    transition: transform 0.2s ease, opacity 0.2s ease;
+}
+
+.badge:empty {
+    display: none;
+}
+
+/* Make the pulse animation more subtle */
+.badge.pulse {
+    animation: badge-pulse 2s infinite; /* Slower animation */
+}
+
+@keyframes badge-pulse {
+    0% {
+        transform: scale(1);
+    }
+    50% {
+        transform: scale(1.1); /* Less expansion */
+    }
+    100% {
+        transform: scale(1);
+    }
+}
+
+/* Help icon styling */
+.help-icon {
+    color: var(--text-color);
+    opacity: 0.7;
+    cursor: help;
+    font-size: 16px;
+    margin-left: 8px;
+    transition: all 0.2s ease;
+}
+
+.help-icon:hover {
+    opacity: 1;
+    color: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h));
+}
+
+/* Help tooltip */
+.help-tooltip {
+    display: none;
+    position: absolute;
+    max-width: 400px;
+    background: var(--card-bg);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    padding: 12px 16px;
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15);
+    z-index: var(--z-overlay);
+    font-size: 0.9em;
+    margin-top: 10px;
+    text-align: left;
+    pointer-events: none;
+}
+
+.help-tooltip:after {
+    content: "";
+    position: absolute;
+    top: -8px;
+    left: 10px; /* Position the arrow near the left instead of center */
+    border-width: 0 8px 8px 8px;
+    border-style: solid;
+    border-color: transparent transparent var(--card-bg) transparent;
+}
+
+/* Responsive adjustments */
+@media (max-width: 768px) {
+    .duplicates-banner .banner-content {
+        flex-direction: column;
+        align-items: flex-start;
+        gap: 8px;
+    }
+    
+    .duplicates-banner .banner-actions {
+        width: 100%;
+        margin-left: 0;
+        justify-content: space-between;
+    }
+    
+    .duplicate-group-header {
+        flex-direction: column;
+        gap: 8px;
+        align-items: flex-start;
+    }
+    
+    .duplicate-group-header span:last-child {
+        display: flex;
+        gap: 8px;
+        width: 100%;
+    }
+    
+    .duplicate-group-header button {
+        margin-left: 0;
+        flex: 1;
+    }
+
+    .help-tooltip {
+        max-width: calc(100% - 40px);
+    }
+    
+    /* Remove the fixed positioning adjustments for mobile since we're now using dynamic positioning */
+    .help-tooltip:after {
+        left: 10px;
+    }
+}
+
+/* In dark mode, add additional distinction */
+html[data-theme="dark"] .duplicates-banner {
+    box-shadow: 0 3px 12px rgba(0, 0, 0, 0.4); /* Stronger shadow in dark mode */
+    background-color: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.15); /* Slightly stronger background in dark mode */
+}
+
+html[data-theme="dark"] .duplicate-group {
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.25); /* Stronger shadow in dark mode */
+}
+
+html[data-theme="dark"] .help-tooltip {
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3);
+}
+
+/* Styles for disabled controls during duplicates mode */
+.disabled-during-duplicates {
+    opacity: 0.5 !important;
+    pointer-events: none !important;
+    cursor: not-allowed !important;
+    user-select: none !important;
+    filter: grayscale(50%) !important;
+}
+
+/* Make the active duplicates button more prominent */
+#findDuplicatesBtn.active {
+    background: var(--lora-accent);
+    color: white;
+    border-color: var(--lora-accent);
+    box-shadow: 0 0 0 2px oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.25);
+    position: relative;
+    z-index: 5;
+}
+
+#findDuplicatesBtn.active:hover {
+    background: oklch(calc(var(--lora-accent-l) - 5%) var(--lora-accent-c) var(--lora-accent-h));
+}

static/css/components/header.css

@@ -136,6 +136,30 @@
   opacity: 0;
 }
 
+/* Badge styling */
+.update-badge {
+  position: absolute;
+  top: -3px;
+  right: -3px;
+  width: 8px;
+  height: 8px;
+  background-color: var(--lora-error);
+  border-radius: 50%;
+  border: 2px solid var(--card-bg);
+  transition: all 0.2s ease;
+  pointer-events: none;
+  opacity: 0;
+}
+
+.update-badge.visible {
+  opacity: 1;
+}
+
+.update-badge.hidden,
+.update-badge:not(.visible) {
+  opacity: 0;
+}
+
 /* Mobile adjustments */
 @media (max-width: 768px) {
   .app-title {

static/css/components/import-modal.css

@@ -291,7 +291,7 @@
     gap: 8px;
     padding: var(--space-1);
     border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-sm);
+    border-radius: var (--border-radius-sm);
     background: var(--lora-surface);
 }
 
@@ -733,3 +733,150 @@
     font-size: 0.9em;
     line-height: 1.4;
 }
+
+/* Duplicate Recipes Styles */
+.duplicate-recipes-container {
+    margin-bottom: var(--space-3);
+    border-radius: var(--border-radius-sm);
+    overflow: hidden;
+    animation: fadeIn 0.3s ease-in-out;
+}
+
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(-10px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+
+.duplicate-warning {
+    display: flex;
+    align-items: flex-start;
+    gap: 12px;
+    padding: 12px 16px;
+    background: oklch(var(--lora-warning) / 0.1);
+    border: 1px solid var(--lora-warning);
+    border-radius: var(--border-radius-sm) var(--border-radius-sm) 0 0;
+    color: var(--text-color);
+}
+
+.duplicate-warning .warning-icon {
+    color: var(--lora-warning);
+    font-size: 1.2em;
+    padding-top: 2px;
+}
+
+.duplicate-warning .warning-content {
+    flex: 1;
+}
+
+.duplicate-warning .warning-title {
+    font-weight: 600;
+    margin-bottom: 4px;
+}
+
+.duplicate-warning .warning-text {
+    font-size: 0.9em;
+    line-height: 1.4;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    flex-wrap: wrap;
+    gap: 8px;
+}
+
+.toggle-duplicates-btn {
+    background: none;
+    border: none;
+    color: var(--lora-warning);
+    cursor: pointer;
+    font-size: 0.9em;
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+}
+
+.toggle-duplicates-btn:hover {
+    background: oklch(var(--lora-warning) / 0.1);
+}
+
+.duplicate-recipes-list {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(150px, 1fr));
+    gap: 12px;
+    padding: 16px;
+    border: 1px solid var(--border-color);
+    border-top: none;
+    border-radius: 0 0 var(--border-radius-sm) var(--border-radius-sm);
+    background: var(--bg-color);
+    max-height: 300px;
+    overflow-y: auto;
+    transition: max-height 0.3s ease, padding 0.3s ease;
+}
+
+.duplicate-recipes-list.collapsed {
+    max-height: 0;
+    padding: 0 16px;
+    overflow: hidden;
+}
+
+.duplicate-recipe-card {
+    position: relative;
+    border-radius: var(--border-radius-sm);
+    overflow: hidden;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+    transition: transform 0.2s ease;
+}
+
+.duplicate-recipe-card:hover {
+    transform: translateY(-2px);
+}
+
+.duplicate-recipe-preview {
+    width: 100%;
+    position: relative;
+    aspect-ratio: 2/3;
+    background: var(--bg-color);
+}
+
+.duplicate-recipe-preview img {
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+.duplicate-recipe-title {
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    right: 0;
+    padding: 8px;
+    background: rgba(0, 0, 0, 0.7);
+    color: white;
+    font-size: 0.85em;
+    line-height: 1.3;
+    max-height: 50%;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    display: -webkit-box;
+    -webkit-line-clamp: 2;
+    -webkit-box-orient: vertical;
+}
+
+.duplicate-recipe-details {
+    padding: 8px;
+    background: var(--bg-color);
+    font-size: 0.75em;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    color: var(--text-color);
+    opacity: 0.8;
+}
+
+.duplicate-recipe-date, 
+.duplicate-recipe-lora-count {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+}

static/css/components/keyboard-nav.css

@@ -0,0 +1,96 @@
+/* Keyboard navigation indicator and help */
+.keyboard-nav-hint {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    position: relative;
+    width: 32px;
+    height: 32px;
+    border-radius: 50%;
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    color: var(--text-color);
+    cursor: help;
+    transition: all 0.2s ease;
+    margin-left: 8px;
+}
+
+.keyboard-nav-hint:hover {
+    background: var(--lora-accent);
+    color: white;
+    transform: translateY(-2px);
+    box-shadow: 0 3px 5px rgba(0, 0, 0, 0.08);
+}
+
+.keyboard-nav-hint i {
+    font-size: 14px;
+}
+
+/* Tooltip styling */
+.tooltip {
+    position: relative;
+}
+
+.tooltip .tooltiptext {
+    visibility: hidden;
+    width: 240px;
+    background-color: var(--lora-surface);
+    color: var(--text-color);
+    text-align: center;
+    border-radius: var(--border-radius-xs);
+    padding: 8px;
+    position: absolute;
+    z-index: 9999; /* 确保在卡片上方显示 */
+    left: 120%; /* 将tooltip显示在图标右侧 */
+    top: 50%; /* 垂直居中 */
+    transform: translateY(-50%); /* 垂直居中 */
+    opacity: 0;
+    transition: opacity 0.3s;
+    box-shadow: 0 3px 8px rgba(0, 0, 0, 0.15);
+    border: 1px solid var(--lora-border);
+    font-size: 0.85em;
+    line-height: 1.4;
+}
+
+.tooltip .tooltiptext::after {
+    content: "";
+    position: absolute;
+    top: 50%; /* 箭头垂直居中 */
+    right: 100%; /* 箭头在左侧 */
+    margin-top: -5px;
+    border-width: 5px;
+    border-style: solid;
+    border-color: transparent var(--lora-border) transparent transparent; /* 箭头指向左侧 */
+}
+
+.tooltip:hover .tooltiptext {
+    visibility: visible;
+    opacity: 1;
+}
+
+/* Keyboard shortcuts table */
+.keyboard-shortcuts {
+    width: 100%;
+    border-collapse: collapse;
+    margin-top: 5px;
+}
+
+.keyboard-shortcuts td {
+    padding: 4px;
+    text-align: left;
+}
+
+.keyboard-shortcuts td:first-child {
+    font-weight: bold;
+    width: 40%;
+}
+
+.key {
+    display: inline-block;
+    background: var(--bg-color);
+    border: 1px solid var(--border-color);
+    border-radius: 3px;
+    padding: 1px 5px;
+    font-size: 0.8em;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.08);
+}

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

@@ -0,0 +1,100 @@
+/* Model Description Styling */
+.model-description-container {
+  background: var(--lora-surface);
+  border-radius: var(--border-radius-sm);
+  overflow: hidden;
+  min-height: 200px;
+  position: relative;
+  /* Remove the max-height and overflow-y to allow content to expand naturally */
+}
+
+.model-description-loading {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  padding: var(--space-3);
+  color: var(--text-color);
+  opacity: 0.7;
+  font-size: 0.9em;
+}
+
+.model-description-loading .fa-spinner {
+  margin-right: var(--space-1);
+}
+
+.model-description-content {
+  padding: var(--space-2);
+  line-height: 1.5;
+  overflow-wrap: break-word;
+  font-size: 0.95em;
+}
+
+.model-description-content h1,
+.model-description-content h2,
+.model-description-content h3,
+.model-description-content h4,
+.model-description-content h5,
+.model-description-content h6 {
+  margin-top: 1em;
+  margin-bottom: 0.5em;
+  font-weight: 600;
+}
+
+.model-description-content p {
+  margin-bottom: 1em;
+}
+
+.model-description-content img {
+  max-width: 100%;
+  height: auto;
+  border-radius: var(--border-radius-xs);
+  display: block;
+  margin: 1em 0;
+}
+
+.model-description-content pre {
+  background: rgba(0, 0, 0, 0.05);
+  border-radius: var(--border-radius-xs);
+  padding: var(--space-1);
+  white-space: pre-wrap;
+  margin: 1em 0;
+  overflow-x: auto;
+}
+
+.model-description-content code {
+  font-family: monospace;
+  font-size: 0.9em;
+  background: rgba(0, 0, 0, 0.05);
+  padding: 0.1em 0.3em;
+  border-radius: 3px;
+}
+
+.model-description-content pre code {
+  background: transparent;
+  padding: 0;
+}
+
+.model-description-content ul,
+.model-description-content ol {
+  margin-left: 1.5em;
+  margin-bottom: 1em;
+}
+
+.model-description-content li {
+  margin-bottom: 0.5em;
+}
+
+.model-description-content blockquote {
+  border-left: 3px solid var (--lora-accent);
+  padding-left: 1em;
+  margin-left: 0;
+  margin-right: 0;
+  font-style: italic;
+  opacity: 0.8;
+}
+
+/* Adjust dark mode for model description */
+[data-theme="dark"] .model-description-content pre,
+[data-theme="dark"] .model-description-content code {
+  background: rgba(255, 255, 255, 0.05);
+}

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

@@ -0,0 +1,501 @@
+/* Lora Modal Header */
+.modal-header {
+    display: flex;
+    flex-direction: column;
+    justify-content: flex-start;
+    align-items: flex-start;
+    margin-bottom: var(--space-3);
+    padding-bottom: var(--space-2);
+    border-bottom: 1px solid var(--lora-border);
+}
+
+/* Info Grid */
+.info-grid {
+    display: grid;
+    grid-template-columns: repeat(2, 1fr);
+    gap: var(--space-2);
+    margin-bottom: var(--space-3);
+}
+
+.info-item {
+    padding: var(--space-2);
+    background: rgba(0, 0, 0, 0.03);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: var(--border-radius-sm);
+}
+
+/* 调整深色主题下的样式 */
+[data-theme="dark"] .info-item {
+    background: rgba(255, 255, 255, 0.03);
+    border: 1px solid var(--lora-border);
+}
+
+.info-item.full-width {
+    grid-column: 1 / -1;
+}
+
+.info-item label {
+    display: block;
+    font-size: 0.85em;
+    color: var(--text-color);
+    opacity: 0.8;
+    margin-bottom: 4px;
+}
+
+.info-item span {
+    color: var(--text-color);
+    word-break: break-word;
+}
+
+.info-item.usage-tips,
+.info-item.notes {
+    grid-column: 1 / -1 !important; /* Make notes section full width */
+}
+
+/* Add specific styles for notes content */
+.info-item.notes .editable-field [contenteditable] {
+    min-height: 60px; /* Increase height for multiple lines */
+    max-height: 150px; /* Limit maximum height */
+    overflow-y: auto; /* Add scrolling for long content */
+    white-space: pre-wrap; /* Preserve line breaks */
+    line-height: 1.5; /* Improve readability */
+    padding: 8px 12px; /* Slightly increase padding */
+}
+
+.file-path {
+    font-family: monospace;
+    font-size: 0.9em;
+}
+
+.description-text {
+    line-height: 1.5;
+    max-height: 100px;
+    overflow-y: auto;
+}
+
+/* Editable Fields */
+.editable-field {
+    position: relative;
+    display: flex;
+    gap: 8px;
+    align-items: flex-start;
+}
+
+.editable-field [contenteditable] {
+    flex: 1;
+    min-height: 24px;
+    padding: 4px 8px;
+    background: var(--bg-color);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    font-size: 0.9em;
+    line-height: 1.4;
+    color: var(--text-color);
+    transition: border-color 0.2s;
+    word-break: break-word;
+}
+
+.editable-field [contenteditable]:focus {
+    outline: none;
+    border-color: var(--lora-accent);
+    background: var(--bg-color);
+}
+
+.editable-field [contenteditable]:empty::before {
+    content: attr(data-placeholder);
+    color: var(--text-color);
+    opacity: 0.5;
+}
+
+.save-btn {
+    padding: 4px 8px;
+    background: var(--lora-accent);
+    border: none;
+    border-radius: var(--border-radius-xs);
+    color: white;
+    cursor: pointer;
+    transition: opacity 0.2s;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.save-btn:hover {
+    opacity: 0.9;
+}
+
+.save-btn i {
+    font-size: 0.9em;
+}
+
+@media (max-width: 640px) {
+    .info-item.usage-tips,
+    .info-item.notes {
+        grid-column: 1 / -1;
+    }
+}
+
+/* 修改 back-to-top 按钮样式，使其固定在 modal 内部 */
+.modal-content .back-to-top {
+    position: sticky; /* 改用 sticky 定位 */
+    float: right; /* 使用 float 确保按钮在右侧 */
+    bottom: 20px; /* 距离底部的距离 */
+    margin-right: 20px; /* 右侧间距 */
+    margin-top: -56px; /* 负边距确保不占用额外空间 */
+    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;
+    opacity: 0;
+    visibility: hidden;
+    transform: translateY(10px);
+    transition: all 0.3s ease;
+    z-index: 10;
+}
+
+.modal-content .back-to-top.visible {
+    opacity: 1;
+    visibility: visible;
+    transform: translateY(0);
+}
+
+.modal-content .back-to-top:hover {
+    background: var(--lora-accent);
+    color: white;
+    transform: translateY(-2px);
+}
+
+/* File name copy styles */
+.file-name-wrapper {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 4px;
+    border-radius: var(--border-radius-xs);
+    transition: background-color 0.2s;
+    position: relative;
+}
+
+.file-name-content {
+    padding: 2px 4px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid transparent;
+    flex: 1;
+}
+
+.file-name-wrapper.editing .file-name-content {
+    border: 1px solid var(--lora-accent);
+    background: var(--bg-color);
+    outline: none;
+}
+
+.edit-file-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-file-name-btn.visible,
+.file-name-wrapper:hover .edit-file-name-btn {
+    opacity: 0.5;
+}
+
+.edit-file-name-btn:hover {
+    opacity: 0.8 !important;
+    background: rgba(0, 0, 0, 0.05);
+}
+
+[data-theme="dark"] .edit-file-name-btn:hover {
+    background: rgba(255, 255, 255, 0.05);
+}
+
+/* Base Model and Size combined styles */
+.info-item.base-size {
+    display: flex;
+    gap: var(--space-3);
+}
+
+.base-wrapper {
+    flex: 2;  /* 分配更多空间给base model */
+}
+
+/* Base model display and editing styles */
+.base-model-display {
+    display: flex;
+    align-items: center;
+    position: relative;
+}
+
+.base-model-content {
+    padding: 2px 4px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid transparent;
+    color: var(--text-color);
+    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;
+    background: var(--bg-color);
+    border: 1px solid var(--lora-accent);
+    border-radius: var(--border-radius-xs);
+    color: var(--text-color);
+    font-size: 0.9em;
+    outline: none;
+    margin-right: var(--space-1);
+}
+
+.size-wrapper {
+    flex: 1;
+    border-left: 1px solid var(--lora-border);
+    padding-left: var(--space-3);
+}
+
+.base-wrapper label,
+.size-wrapper label {
+    display: block;
+    margin-bottom: 4px;
+}
+
+.size-wrapper span {
+    font-family: monospace;
+    font-size: 0.9em;
+    opacity: 0.9;
+}
+
+/* New Model Name Header Styles */
+.model-name-header {
+    display: flex;
+    align-items: center;
+    width: calc(100% - 40px); /* Avoid overlap with close button */
+    position: relative;
+}
+
+.model-name-content {
+    margin: 0;
+    padding: var(--space-1);
+    border-radius: var(--border-radius-xs);
+    font-size: 1.5em !important;
+    font-weight: 600;
+    line-height: 1.2;
+    color: var(--text-color);
+    border: 1px solid transparent;
+    outline: none;
+    flex: 1;
+}
+
+.model-name-content:focus {
+    border: 1px solid var(--lora-accent);
+    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;
+    border-bottom: 1px solid var(--lora-border);
+    margin-bottom: var(--space-2);
+    position: relative;
+    z-index: 2;
+}
+
+.tab-btn {
+    padding: var(--space-1) var(--space-2);
+    background: transparent;
+    border: none;
+    border-bottom: 2px solid transparent;
+    color: var(--text-color);
+    cursor: pointer;
+    font-size: 0.95em;
+    transition: all 0.2s;
+    opacity: 0.7;
+    position: relative;
+}
+
+.tab-btn:hover {
+    opacity: 1;
+    background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.05);
+}
+
+.tab-btn.active {
+    border-bottom: 2px solid var(--lora-accent);
+    opacity: 1;
+    font-weight: 600;
+}
+
+.tab-content {
+    position: relative;
+    min-height: 100px;
+}
+
+.tab-pane {
+    display: none;
+}
+
+.tab-pane.active {
+    display: block;
+}
+
+.view-all-btn {
+    display: flex;
+    align-items: center;
+    gap: 5px;
+    padding: 6px 12px;
+    background-color: var(--lora-accent);
+    color: var(--lora-text);
+    border: none;
+    border-radius: var(--border-radius-sm);
+    cursor: pointer;
+    transition: background-color 0.2s;
+    font-size: 13px;
+}
+
+.view-all-btn:hover {
+    opacity: 0.9;
+}
+
+/* Loading, error and empty states */
+.recipes-loading,
+.recipes-error,
+.recipes-empty {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: center;
+    padding: 40px;
+    text-align: center;
+    min-height: 200px;
+}
+
+.recipes-loading i,
+.recipes-error i,
+.recipes-empty i {
+    font-size: 32px;
+    margin-bottom: 15px;
+    color: var(--lora-accent);
+}
+
+.recipes-error i {
+    color: var(--lora-error);
+}
+
+/* Creator Information Styles */
+.creator-info {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    margin-bottom: var(--space-1);
+    padding: 6px 10px;
+    background: rgba(0, 0, 0, 0.03);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: var(--border-radius-sm);
+    max-width: fit-content;
+}
+
+[data-theme="dark"] .creator-info {
+    background: rgba(255, 255, 255, 0.03);
+    border: 1px solid var(--lora-border);
+}
+
+.creator-avatar {
+    width: 28px;
+    height: 28px;
+    border-radius: 50%;
+    overflow: hidden;
+    flex-shrink: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    background: var(--lora-surface);
+    border: 1px solid var(--lora-border);
+}
+
+.creator-avatar img {
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+.creator-placeholder {
+    background: var(--lora-accent);
+    color: white;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.creator-username {
+    font-size: 0.9em;
+    font-weight: 500;
+    color: var(--text-color);
+}
+
+/* Optional: add hover effect for creator info */
+.creator-info:hover {
+    background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+    border-color: var(--lora-accent);
+}

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

@@ -0,0 +1,68 @@
+/* Update Preset Controls styles */
+.preset-controls {
+  display: flex;
+  gap: var(--space-2);
+  margin-bottom: var(--space-2);
+}
+
+.preset-controls select,
+.preset-controls input {
+  padding: var(--space-1);
+  background: var(--bg-color);
+  border: 1px solid var(--lora-border);
+  border-radius: var(--border-radius-xs);
+  color: var(--text-color);
+}
+
+.preset-tags {
+  display: flex;
+  flex-wrap: wrap;
+  gap: var(--space-1);
+}
+
+.preset-tag {
+  display: flex;
+  align-items: center;
+  background: var(--lora-surface);
+  border: 1px solid var(--lora-border);
+  border-radius: var(--border-radius-xs);
+  padding: calc(var(--space-1) * 0.5) var(--space-1);
+  gap: var(--space-1);
+  transition: all 0.2s ease;
+}
+
+.preset-tag span {
+  color: var(--lora-accent);
+  font-size: 0.9em;
+}
+
+.preset-tag i {
+  color: var(--text-color);
+  opacity: 0.5;
+  cursor: pointer;
+  transition: all 0.2s ease;
+}
+
+.preset-tag:hover {
+  background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+  border-color: var(--lora-accent);
+}
+
+.preset-tag i:hover {
+  color: var(--lora-error);
+  opacity: 1;
+}
+
+.add-preset-btn {
+  padding: calc(var(--space-1) * 0.5) var(--space-2);
+  background: var(--lora-accent);
+  color: var(--lora-text);
+  border: none;
+  border-radius: var(--border-radius-xs);
+  cursor: pointer;
+  transition: opacity 0.2s;
+}
+
+.add-preset-btn:hover {
+  opacity: 0.9;
+}

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

@@ -0,0 +1,478 @@
+/* Showcase Section */
+.showcase-section {
+  position: relative;
+  margin-top: var(--space-4);
+}
+
+.carousel {
+  transition: max-height 0.3s ease-in-out;
+  overflow: hidden;
+}
+
+.carousel.collapsed {
+  max-height: 0;
+}
+
+.carousel-container {
+  display: flex;
+  flex-direction: column;
+  gap: var(--space-2);
+}
+
+.media-wrapper {
+  position: relative;
+  width: 100%;
+  background: var(--lora-surface);
+  margin-bottom: var(--space-2);
+  overflow: hidden; /* Ensure metadata panel is contained */
+}
+
+.media-wrapper:last-child {
+  margin-bottom: 0;
+}
+
+.media-wrapper img,
+.media-wrapper video {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  object-fit: contain;
+}
+
+.no-examples {
+  text-align: center;
+  padding: var(--space-3);
+  color: var(--text-color);
+  opacity: 0.7;
+}
+
+/* Adjust the media wrapper for tab system */
+#showcase-tab .carousel-container {
+  margin-top: var(--space-2);
+}
+
+/* Add styles for blurred showcase content */
+.nsfw-media-wrapper {
+  position: relative;
+}
+
+.media-wrapper img.blurred,
+.media-wrapper video.blurred {
+  filter: blur(25px);
+}
+
+.media-wrapper .nsfw-overlay {
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  z-index: 2;
+  pointer-events: none;
+}
+
+/* Position the toggle button at the top left of showcase media */
+.showcase-toggle-btn {
+  position: absolute;
+  z-index: 3;
+}
+
+/* Add styles for showcase media controls */
+.media-controls {
+  position: absolute;
+  display: flex;
+  gap: 6px;
+  z-index: 4;
+  opacity: 0;
+  transform: translateY(-5px);
+  transition: opacity 0.2s ease, transform 0.2s ease;
+  pointer-events: none;
+}
+
+.media-controls.visible {
+  opacity: 1;
+  transform: translateY(0);
+  pointer-events: auto;
+}
+
+.media-control-btn {
+  width: 28px;
+  height: 28px;
+  border-radius: 50%;
+  background: var(--bg-color);
+  border: 1px solid var(--border-color);
+  color: var(--text-color);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  cursor: pointer;
+  transition: all 0.2s ease;
+  box-shadow: 0 2px 5px rgba(0, 0, 0, 0.15);
+  padding: 0;
+  position: relative;
+  overflow: hidden;
+}
+
+.media-control-btn:hover {
+  transform: translateY(-2px);
+  box-shadow: 0 3px 7px rgba(0, 0, 0, 0.2);
+}
+
+.media-control-btn.set-preview-btn:hover {
+  background: var(--lora-accent);
+  color: white;
+  border-color: var(--lora-accent);
+}
+
+.media-control-btn.example-delete-btn:hover:not(.disabled) {
+  background: var(--lora-error);
+  color: white;
+  border-color: var(--lora-error);
+}
+
+/* Disabled state for delete button */
+.media-control-btn.example-delete-btn.disabled {
+  opacity: 0.5;
+  cursor: not-allowed;
+}
+
+/* Two-step confirmation for delete button */
+.media-control-btn.example-delete-btn .confirm-icon {
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  background: var(--lora-error);
+  color: white;
+  font-size: 1em;
+  opacity: 0;
+  transition: opacity 0.2s ease;
+}
+
+.media-control-btn.example-delete-btn.confirm .fa-trash-alt {
+  opacity: 0;
+}
+
+.media-control-btn.example-delete-btn.confirm .confirm-icon {
+  opacity: 1;
+}
+
+.media-control-btn.example-delete-btn.confirm {
+  background: var(--lora-error);
+  color: white;
+  border-color: var(--lora-error);
+}
+
+@keyframes pulse {
+  0% {
+    box-shadow: 0 0 0 0 rgba(220, 53, 69, 0.7);
+  }
+  70% {
+    box-shadow: 0 0 0 5px rgba(220, 53, 69, 0);
+  }
+  100% {
+    box-shadow: 0 0 0 0 rgba(220, 53, 69, 0);
+  }
+}
+
+/* Image Metadata Panel Styles */
+.image-metadata-panel {
+  position: absolute;
+  bottom: 0;
+  left: 0;
+  right: 0;
+  background: var(--bg-color);
+  border-top: 1px solid var(--border-color);
+  padding: var(--space-2);
+  transform: translateY(100%);
+  transition: transform 0.3s cubic-bezier(0.175, 0.885, 0.32, 1.275), opacity 0.25s ease;
+  z-index: 5;
+  max-height: 50%; /* Reduced to take less space */
+  overflow-y: auto;
+  box-shadow: 0 -2px 8px rgba(0, 0, 0, 0.1);
+  opacity: 0;
+  pointer-events: none;
+}
+
+/* Show metadata panel only when the 'visible' class is added */
+.media-wrapper .image-metadata-panel.visible {
+  transform: translateY(0);
+  opacity: 0.98;
+  pointer-events: auto;
+}
+
+/* Adjust to dark theme */
+[data-theme="dark"] .image-metadata-panel {
+  background: var(--card-bg);
+  box-shadow: 0 -2px 8px rgba(0, 0, 0, 0.3);
+}
+
+.metadata-content {
+  display: flex;
+  flex-direction: column;
+  gap: 10px;
+}
+
+/* Styling for parameters tags */
+.params-tags {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 6px;
+  margin-bottom: var(--space-1);
+  padding-bottom: var(--space-1);
+  border-bottom: 1px solid var(--lora-border);
+}
+
+.param-tag {
+  display: inline-flex;
+  align-items: center;
+  background: var(--lora-surface);
+  border: 1px solid var(--lora-border);
+  border-radius: var(--border-radius-xs);
+  padding: 2px 6px;
+  font-size: 0.8em;
+  line-height: 1.2;
+  white-space: nowrap;
+}
+
+.param-tag .param-name {
+  font-weight: 600;
+  color: var(--text-color);
+  margin-right: 4px;
+  opacity: 0.8;
+}
+
+.param-tag .param-value {
+  color: var(--lora-accent);
+}
+
+/* Special styling for prompt row */
+.metadata-row.prompt-row {
+  flex-direction: column;
+  padding-top: 0;
+}
+
+.metadata-row.prompt-row + .metadata-row.prompt-row {
+  margin-top: var(--space-2);
+}
+
+.metadata-label {
+  font-weight: 600;
+  color: var(--text-color);
+  opacity: 0.8;
+  font-size: 0.85em;
+  display: block;
+  margin-bottom: 4px;
+}
+
+.metadata-prompt-wrapper {
+  position: relative;
+  background: var(--lora-surface);
+  border: 1px solid var(--lora-border);
+  border-radius: var(--border-radius-xs);
+  padding: 6px 30px 6px 8px;
+  margin-top: 2px;
+  max-height: 80px; /* Reduced from 120px */
+  overflow-y: auto;
+  word-break: break-word;
+  width: 100%;
+  box-sizing: border-box;
+}
+
+.metadata-prompt {
+  color: var(--text-color);
+  font-family: monospace;
+  font-size: 0.85em;
+  white-space: pre-wrap;
+}
+
+.copy-prompt-btn {
+  position: absolute;
+  top: 6px;
+  right: 6px;
+  background: transparent;
+  border: none;
+  color: var(--text-color);
+  opacity: 0.6;
+  cursor: pointer;
+  padding: 3px;
+  transition: all 0.2s ease;
+}
+
+.copy-prompt-btn:hover {
+  opacity: 1;
+  color: var(--lora-accent);
+}
+
+/* Scrollbar styling for metadata panel */
+.image-metadata-panel::-webkit-scrollbar {
+  width: 6px;
+}
+
+.image-metadata-panel::-webkit-scrollbar-track {
+  background: transparent;
+}
+
+.image-metadata-panel::-webkit-scrollbar-thumb {
+  background-color: var(--border-color);
+  border-radius: 3px;
+}
+
+/* For Firefox */
+.image-metadata-panel {
+  scrollbar-width: thin;
+  scrollbar-color: var(--border-color) transparent;
+}
+
+/* No metadata message styling */
+.no-metadata-message {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  padding: var(--space-2);
+  color: var(--text-color);
+  opacity: 0.7;
+  text-align: center;
+  font-style: italic;
+  gap: 8px;
+}
+
+.no-metadata-message i {
+  font-size: 1.1em;
+  color: var(--lora-accent);
+  opacity: 0.8;
+}
+
+/* Scroll Indicator */
+.scroll-indicator {
+  cursor: pointer;
+  padding: var(--space-2);
+  background: var(--lora-surface);
+  border: 1px solid var(--lora-border);
+  border-radius: var(--border-radius-sm);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 8px;
+  margin-bottom: var(--space-2);
+  transition: background-color 0.2s, transform 0.2s;
+}
+
+.scroll-indicator:hover {
+  background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+  transform: translateY(-1px);
+}
+
+.scroll-indicator span {
+  font-size: 0.9em;
+  color: var(--text-color);
+}
+
+.lazy {
+  opacity: 0;
+  transition: opacity 0.3s;
+}
+
+.lazy[src] {
+  opacity: 1;
+}
+
+/* Example Import Area */
+.example-import-area {
+  margin-top: var(--space-4);
+  padding: var(--space-2);
+}
+
+.example-import-area.empty {
+  margin-top: var(--space-2);
+  padding: var(--space-4) var(--space-2);
+}
+
+.import-container {
+  border: 2px dashed var(--border-color);
+  border-radius: var(--border-radius-sm);
+  padding: var(--space-4);
+  text-align: center;
+  transition: all 0.3s ease;
+  background: var(--lora-surface);
+  cursor: pointer;
+}
+
+.import-container.highlight {
+  border-color: var(--lora-accent);
+  background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+  transform: scale(1.01);
+}
+
+.import-placeholder {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: var(--space-1);
+  padding-top: var(--space-1);
+}
+
+.import-placeholder i {
+  font-size: 2.5rem;
+  /* color: var(--lora-accent); */
+  opacity: 0.8;
+  margin-bottom: var(--space-1);
+}
+
+.import-placeholder h3 {
+  margin: 0 0 var(--space-1);
+  font-size: 1.2rem;
+  font-weight: 500;
+  color: var(--text-color);
+}
+
+.import-placeholder p {
+  margin: var(--space-1) 0;
+  color: var(--text-color);
+  opacity: 0.8;
+}
+
+.import-placeholder .sub-text {
+  font-size: 0.9em;
+  opacity: 0.6;
+  margin: var(--space-1) 0;
+}
+
+.import-formats {
+  font-size: 0.8em !important;
+  opacity: 0.6 !important;
+  margin-top: var(--space-2) !important;
+}
+
+.select-files-btn {
+  background: var(--lora-accent);
+  color: var(--lora-text);
+  border: none;
+  border-radius: var(--border-radius-xs);
+  padding: var(--space-2) var(--space-3);
+  cursor: pointer;
+  font-size: 0.9em;
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  transition: all 0.2s;
+}
+
+.select-files-btn:hover {
+  opacity: 0.9;
+  transform: translateY(-1px);
+}
+
+/* For dark theme */
+[data-theme="dark"] .import-container {
+  background: rgba(255, 255, 255, 0.03);
+}

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

@@ -0,0 +1,148 @@
+/* Model Tags styles */
+.model-tags {
+  display: none;
+}
+
+.model-tag {
+  display: none;
+}
+
+/* Updated Model Tags styles - improved visibility in light theme */
+.model-tags-container {
+  position: relative;
+}
+
+.model-tags-compact {
+  display: flex;
+  flex-wrap: nowrap;
+  gap: 6px;
+  align-items: center;
+}
+
+.model-tag-compact {
+  /* Updated styles to match info-item appearance */
+  background: rgba(0, 0, 0, 0.03);
+  border: 1px solid rgba(0, 0, 0, 0.1);
+  border-radius: var(--border-radius-xs);
+  padding: 2px 8px;
+  font-size: 0.75em;
+  color: var(--text-color);
+  white-space: nowrap;
+}
+
+/* Style for empty tags placeholder */
+.model-tag-empty {
+  background: rgba(0, 0, 0, 0.02);
+  border: 1px dashed rgba(0, 0, 0, 0.1);
+  border-radius: var(--border-radius-xs);
+  padding: 2px 8px;
+  font-size: 0.75em;
+  color: var(--text-color);
+  white-space: nowrap;
+  opacity: 0.7;
+  font-style: italic;
+}
+
+/* Adjust dark theme tag styles */
+[data-theme="dark"] .model-tag-compact {
+  background: rgba(255, 255, 255, 0.03);
+  border: 1px solid var(--lora-border);
+}
+
+/* Dark theme for empty tags */
+[data-theme="dark"] .model-tag-empty {
+  background: rgba(255, 255, 255, 0.02);
+  border: 1px dashed var(--lora-border);
+}
+
+.model-tag-more {
+  background: var(--lora-accent);
+  color: var(--lora-text);
+  border-radius: var(--border-radius-xs);
+  padding: 2px 8px;
+  font-size: 0.75em;
+  cursor: pointer;
+  white-space: nowrap;
+  font-weight: 500;
+}
+
+.model-tags-tooltip {
+  position: absolute;
+  top: calc(100% + 8px);
+  left: 0;
+  background: var(--card-bg);
+  border: 1px solid var(--border-color);
+  border-radius: var(--border-radius-sm);
+  box-shadow: 0 3px 8px rgba(0, 0, 0, 0.15);
+  padding: 10px 14px;
+  max-width: 400px;
+  z-index: 10;
+  opacity: 0;
+  visibility: hidden;
+  transform: translateY(-4px);
+  transition: all 0.2s ease;
+  pointer-events: none;
+}
+
+.model-tags-tooltip.visible {
+  opacity: 1;
+  visibility: visible;
+  transform: translateY(0);
+  pointer-events: auto;
+}
+
+.tooltip-content {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 6px;
+  max-height: 200px;
+  overflow-y: auto;
+}
+
+.tooltip-tag {
+  /* Updated styles to match info-item appearance */
+  background: rgba(0, 0, 0, 0.03);
+  border: 1px solid rgba(0, 0, 0, 0.1);
+  border-radius: var(--border-radius-xs);
+  padding: 3px 8px;
+  font-size: 0.75em;
+  color: var(--text-color);
+}
+
+/* Adjust dark theme tooltip tag styles */
+[data-theme="dark"] .tooltip-tag {
+  background: rgba(255, 255, 255, 0.03);
+  border: 1px solid var(--lora-border);
+}
+
+/* Model Tags Edit Mode */
+.model-tags-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+}
+
+.edit-tags-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-tags-btn.visible,
+.model-tags-container:hover .edit-tags-btn {
+  opacity: 0.5;
+}
+
+/* Edit mode active state */
+.model-tags-container.edit-mode {
+  width: 100%;
+  display: block;
+  flex-basis: 100%;
+  grid-column: 1 / -1;
+}

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

@@ -0,0 +1,112 @@
+/* Update Trigger Words styles */
+.info-item.trigger-words {
+  padding: var(--space-2);
+  background: rgba(0, 0, 0, 0.03);
+  border: 1px solid rgba(0, 0, 0, 0.1);
+  border-radius: var(--border-radius-sm);
+}
+
+/* 调整 trigger words 样式 */
+[data-theme="dark"] .info-item.trigger-words {
+  background: rgba(255, 255, 255, 0.03);
+  border: 1px solid var(--lora-border);
+}
+
+/* New header style for trigger words */
+.trigger-words-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 6px;
+}
+
+.trigger-words-content {
+  margin-bottom: var(--space-1);
+}
+
+.trigger-words-tags {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 8px;
+  align-items: flex-start;
+}
+
+/* No trigger words message */
+.no-trigger-words {
+  color: var(--text-color);
+  opacity: 0.7;
+  font-style: italic;
+  font-size: 0.9em;
+}
+
+/* Trigger word tags in display mode */
+.trigger-word-tag {
+  display: inline-flex;
+  align-items: center;
+  background: var(--bg-color);
+  border: 1px solid var(--border-color);
+  border-radius: var(--border-radius-xs);
+  padding: 4px 8px;
+  cursor: pointer;
+  transition: all 0.2s ease;
+  gap: 6px;
+  position: relative;
+}
+
+.trigger-word-content {
+  color: var(--lora-accent) !important;
+  font-size: 0.85em;
+  line-height: 1.4;
+  word-break: break-word;
+}
+
+.trigger-word-tag:hover {
+  background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+  border-color: var(--lora-accent);
+}
+
+.trigger-word-copy {
+  display: flex;
+  align-items: center;
+  color: var(--text-color);
+  opacity: 0.5;
+  flex-shrink: 0;
+  transition: opacity 0.2s;
+}
+
+.trained-word-freq {
+  color: var(--text-color);
+  font-size: 0.75em;
+  background: rgba(0, 0, 0, 0.05);
+  border-radius: 10px;
+  min-width: 20px;
+  padding: 1px 5px;
+  text-align: center;
+  line-height: 1.2;
+}
+
+[data-theme="dark"] .trained-word-freq {
+  background: rgba(255, 255, 255, 0.05);
+}
+
+/* Class tokens styling */
+.class-tokens-container {
+  padding: 10px;
+  display: flex;
+  flex-wrap: wrap;
+  gap: 8px;
+}
+
+.class-token-item {
+  background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1) !important;
+  border: 1px solid var(--lora-accent) !important;
+}
+
+.token-badge {
+  background: var(--lora-accent);
+  color: white;
+  font-size: 0.7em;
+  padding: 2px 5px;
+  border-radius: 8px;
+  white-space: nowrap;
+}

static/css/components/menu.css

@@ -39,4 +39,81 @@
 .context-menu-item i {
     width: 16px;
     text-align: center;
+}
+
+/* NSFW Level Selector */
+.nsfw-level-selector {
+    position: fixed;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-base);
+    padding: 16px;
+    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.2);
+    z-index: var(--z-modal);
+    width: 300px;
+    display: none;
+}
+
+.nsfw-level-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 16px;
+}
+
+.nsfw-level-header h3 {
+    margin: 0;
+    font-size: 16px;
+    font-weight: 500;
+}
+
+.close-nsfw-selector {
+    background: transparent;
+    border: none;
+    color: var(--text-color);
+    cursor: pointer;
+    padding: 4px;
+    border-radius: var(--border-radius-xs);
+}
+
+.close-nsfw-selector:hover {
+    background: var(--border-color);
+}
+
+.current-level {
+    margin-bottom: 12px;
+    padding: 8px;
+    background: var(--bg-color);
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+}
+
+.nsfw-level-options {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 8px;
+}
+
+.nsfw-level-btn {
+    flex: 1 0 calc(33% - 8px);
+    padding: 8px;
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    border: 1px solid var(--border-color);
+    color: var(--text-color);
+    cursor: pointer;
+    transition: all 0.2s ease;
+}
+
+.nsfw-level-btn:hover {
+    background: var(--lora-border);
+}
+
+.nsfw-level-btn.active {
+    background: var(--lora-accent);
+    color: white;
+    border-color: var(--lora-accent);
 }

static/css/components/modal.css

@@ -110,7 +110,7 @@ body.modal-open {
     margin-top: var(--space-3);
 }
 
-.cancel-btn, .delete-btn, .exclude-btn {
+.cancel-btn, .delete-btn, .exclude-btn, .confirm-btn {
     padding: 8px var(--space-2);
     border-radius: 6px;
     border: none;
@@ -131,7 +131,7 @@ body.modal-open {
 }
 
 /* Style for exclude button - different from delete button */
-.exclude-btn {
+.exclude-btn, .confirm-btn {
     background: var(--lora-accent, #4f46e5);
     color: white;
 }
@@ -144,14 +144,14 @@ body.modal-open {
     opacity: 0.9;
 }
 
-.exclude-btn:hover {
+.exclude-btn:hover, .confirm-btn:hover {
     opacity: 0.9;
     background: oklch(from var(--lora-accent, #4f46e5) l c h / 85%);
 }
 
 .modal-content h2 {
     color: var(--text-color);
-    margin-bottom: var(--space-2);
+    margin-bottom: var(--space-1);
     font-size: 1.5em;
 }
 
@@ -526,7 +526,7 @@ input:checked + .toggle-slider:before {
     gap: 8px;
     padding: 8px 16px;
     background-color: var(--card-bg);
-    color: var(--text-color);
+    color: var (--text-color);
     border: 1px solid var(--border-color);
     border-radius: var(--border-radius-sm);
     cursor: pointer;
@@ -554,6 +554,13 @@ input:checked + .toggle-slider:before {
     pointer-events: none;
 }
 
+.restart-required-icon {
+    color: var(--lora-warning);
+    margin-left: 5px;
+    font-size: 0.85em;
+    vertical-align: text-bottom;
+}
+
 /* Dark theme specific button adjustments */
 [data-theme="dark"] .primary-btn:hover {
     background-color: oklch(from var(--lora-accent) l c h / 75%);
@@ -672,4 +679,406 @@ input:checked + .toggle-slider:before {
 
 .changelog-item a:hover {
     text-decoration: underline;
+}
+
+/* Add warning text style for settings */
+.warning-text {
+    color: var(--lora-warning, #e67e22);
+    font-weight: 500;
+}
+
+[data-theme="dark"] .warning-text {
+    color: var(--lora-warning, #f39c12);
+}
+
+/* Add styles for density description list */
+.density-description {
+    margin: 8px 0;
+    padding-left: 20px;
+    font-size: 0.9em;
+}
+
+.density-description li {
+    margin-bottom: 4px;
+}
+
+/* Help Modal styles */
+.help-modal {
+  max-width: 850px;
+}
+
+.help-header {
+  display: flex;
+  align-items: center;
+  margin-bottom: var(--space-2);
+}
+
+.modal-help-icon {
+  font-size: 24px;
+  color: var(--lora-accent);
+  margin-right: var(--space-2);
+  vertical-align: text-bottom;
+}
+
+/* Tab navigation styles */
+.help-tabs {
+  display: flex;
+  border-bottom: 1px solid var(--lora-border);
+  margin-bottom: var(--space-2);
+  gap: 8px;
+}
+
+.tab-btn {
+  padding: 8px 16px;
+  background: transparent;
+  border: none;
+  border-bottom: 2px solid transparent;
+  color: var(--text-color);
+  cursor: pointer;
+  font-weight: 500;
+  transition: all 0.2s;
+  opacity: 0.7;
+}
+
+.tab-btn:hover {
+  background-color: rgba(0, 0, 0, 0.05);
+  opacity: 0.9;
+}
+
+.tab-btn.active {
+  color: var(--lora-accent);
+  border-bottom: 2px solid var(--lora-accent);
+  opacity: 1;
+}
+
+/* Tab content styles */
+.help-content {
+  padding: var(--space-1) 0;
+  overflow-y: auto;
+}
+
+.tab-pane {
+  display: none;
+}
+
+.tab-pane.active {
+  display: block;
+}
+
+.help-text {
+  margin: var(--space-2) 0;
+}
+
+.help-text ul {
+  padding-left: 20px;
+  margin-top: 8px;
+}
+
+.help-text li {
+  margin-bottom: 8px;
+}
+
+/* Documentation link styles */
+.docs-section {
+  margin-bottom: var(--space-3);
+}
+
+.docs-section h4 {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  margin-bottom: var(--space-1);
+}
+
+.docs-links {
+  list-style-type: none;
+  padding-left: var(--space-3);
+}
+
+.docs-links li {
+  margin-bottom: var(--space-1);
+  position: relative;
+}
+
+.docs-links li:before {
+  content: "•";
+  position: absolute;
+  left: -15px;
+  color: var(--lora-accent);
+}
+
+.docs-links a {
+  color: var(--lora-accent);
+  text-decoration: none;
+  transition: color 0.2s;
+}
+
+.docs-links a:hover {
+  text-decoration: underline;
+}
+
+/* Update video list styles */
+.video-list {
+  display: flex;
+  flex-direction: column;
+  gap: var(--space-3);
+}
+
+.video-item {
+  display: flex;
+  flex-direction: column;
+}
+
+.video-info {
+  padding: var(--space-1);
+}
+
+.video-info h4 {
+  margin-bottom: var(--space-1);
+}
+
+.video-info p {
+  font-size: 0.9em;
+  opacity: 0.8;
+}
+
+/* Dark theme adjustments */
+[data-theme="dark"] .tab-btn:hover {
+  background-color: rgba(255, 255, 255, 0.05);
+}
+
+/* Update date badge styles */
+.update-date-badge {
+  display: inline-flex;
+  align-items: center;
+  font-size: 0.75em;
+  font-weight: 500;
+  background-color: var(--lora-accent);
+  color: var(--lora-text);
+  padding: 4px 8px;
+  border-radius: 12px;
+  margin-left: 10px;
+  vertical-align: middle;
+  animation: fadeIn 0.5s ease-in-out;
+  box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+}
+
+.update-date-badge i {
+  margin-right: 5px;
+  font-size: 0.9em;
+}
+
+@keyframes fadeIn {
+  from { opacity: 0; transform: translateY(-5px); }
+  to { opacity: 1; transform: translateY(0); }
+}
+
+/* Dark theme adjustments */
+[data-theme="dark"] .update-date-badge {
+  box-shadow: 0 2px 4px rgba(0, 0, 0, 0.3);
+}
+
+/* Re-link to Civitai Modal styles */
+.warning-box {
+  background-color: rgba(255, 193, 7, 0.1);
+  border: 1px solid rgba(255, 193, 7, 0.5);
+  border-radius: var(--border-radius-sm);
+  padding: var(--space-2);
+  margin-bottom: var(--space-3);
+}
+
+.warning-box i {
+  color: var(--lora-warning);
+  margin-right: var(--space-1);
+}
+
+.warning-box ul {
+  padding-left: 20px;
+  margin: var(--space-1) 0;
+}
+
+.warning-box li {
+  margin-bottom: 4px;
+}
+
+.input-group {
+  display: flex;
+  flex-direction: column;
+  margin-bottom: var(--space-2);
+}
+
+.input-group label {
+  margin-bottom: var(--space-1);
+  font-weight: 500;
+}
+
+.input-group input {
+  padding: 8px 12px;
+  border-radius: var(--border-radius-xs);
+  border: 1px solid var(--border-color);
+  background-color: var(--lora-surface);
+  color: var(--text-color);
+}
+
+.input-error {
+  color: var(--lora-error);
+  font-size: 0.9em;
+  min-height: 20px;
+  margin-top: 4px;
+}
+
+[data-theme="dark"] .warning-box {
+  background-color: rgba(255, 193, 7, 0.05);
+  border-color: rgba(255, 193, 7, 0.3);
+}
+
+/* Privacy-friendly video embed styles */
+.video-container {
+  position: relative;
+  width: 100%;
+  padding-bottom: 56.25%; /* 16:9 aspect ratio */
+  height: 0;
+  margin-bottom: var(--space-2);
+  border-radius: var(--border-radius-sm);
+  overflow: hidden;
+  background-color: rgba(0, 0, 0, 0.05);
+}
+
+.video-thumbnail {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  display: flex;
+  justify-content: center;
+  align-items: center;
+}
+
+.video-thumbnail img {
+  width: 100%;
+  height: 100%;
+  object-fit: cover;
+  transition: filter 0.2s ease;
+}
+
+.video-play-overlay {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  background-color: rgba(0, 0, 0, 0.5);
+  display: flex;
+  flex-direction: column;
+  justify-content: center;
+  align-items: center;
+  transition: opacity 0.2s ease;
+}
+
+/* External link button styles */
+.external-link-btn {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  padding: 10px 20px;
+  border-radius: var(--border-radius-sm);
+  font-weight: 500;
+  cursor: pointer;
+  transition: all 0.2s ease;
+  background-color: var(--lora-accent);
+  color: white;
+  text-decoration: none;
+  border: none;
+}
+
+.external-link-btn:hover {
+  background-color: oklch(from var(--lora-accent) l c h / 85%);
+}
+
+.video-thumbnail i {
+  font-size: 1.2em;
+}
+
+/* Smaller video container for the updates tab */
+.video-item .video-container {
+  padding-bottom: 40%; /* Shorter height for the playlist */
+}
+
+/* Dark theme adjustments */
+[data-theme="dark"] .video-container {
+  background-color: rgba(255, 255, 255, 0.03);
+}
+
+/* Example Access Modal */
+.example-access-modal {
+    max-width: 550px;
+    text-align: center;
+}
+
+.example-access-options {
+    display: flex;
+    flex-direction: column;
+    gap: var(--space-2);
+    margin: var(--space-3) 0;
+}
+
+.example-option-btn {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    padding: var(--space-2);
+    border-radius: var(--border-radius-sm);
+    border: 1px solid var(--lora-border);
+    background-color: var(--lora-surface);
+    cursor: pointer;
+    transition: all 0.2s;
+}
+
+.example-option-btn:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    border-color: var(--lora-accent);
+}
+
+.example-option-btn i {
+    font-size: 2em;
+    margin-bottom: var(--space-1);
+    color: var(--lora-accent);
+}
+
+.option-title {
+    font-weight: 500;
+    margin-bottom: 4px;
+    font-size: 1.1em;
+}
+
+.option-desc {
+    font-size: 0.9em;
+    opacity: 0.8;
+}
+
+.example-option-btn.disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+.example-option-btn.disabled i {
+    color: var(--text-color);
+    opacity: 0.5;
+}
+
+.modal-footer-note {
+    font-size: 0.9em;
+    opacity: 0.7;
+    margin-top: var(--space-2);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 8px;
+}
+
+/* Dark theme adjustments */
+[data-theme="dark"] .example-option-btn:hover {
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.25);
 }

static/css/components/progress-panel.css

@@ -12,11 +12,13 @@
     transition: transform 0.3s ease, opacity 0.3s ease;
     opacity: 0;
     transform: translateY(20px);
+    pointer-events: none; /* Ignore mouse events when invisible */
 }
 
 .progress-panel.visible {
     opacity: 1;
     transform: translateY(0);
+    pointer-events: auto; /* Capture mouse events when visible */
 }
 
 .progress-panel.collapsed .progress-panel-content {

static/css/components/recipe-modal.css

@@ -229,8 +229,10 @@
     background: var(--lora-surface);
     border: 1px solid var(--border-color);
     display: flex;
+    flex-direction: column;
     align-items: center;
     justify-content: center;
+    position: relative;
 }
 
 .recipe-preview-container img,
@@ -246,6 +248,133 @@
     object-fit: contain;
 }
 
+/* Source URL container */
+.source-url-container {
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    right: 0;
+    background: rgba(0, 0, 0, 0.5);
+    padding: 8px 12px;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    transition: transform 0.3s ease;
+    transform: translateY(100%);
+}
+
+.recipe-preview-container:hover .source-url-container {
+    transform: translateY(0);
+}
+
+.source-url-container.active {
+    transform: translateY(0);
+}
+
+.source-url-content {
+    display: flex;
+    align-items: center;
+    color: #fff;
+    flex: 1;
+    overflow: hidden;
+    font-size: 0.85em;
+}
+
+.source-url-icon {
+    margin-right: 8px;
+    flex-shrink: 0;
+}
+
+.source-url-text {
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    cursor: pointer;
+    flex: 1;
+}
+
+.source-url-edit-btn {
+    background: none;
+    border: none;
+    color: #fff;
+    cursor: pointer;
+    padding: 4px;
+    margin-left: 8px;
+    border-radius: var(--border-radius-xs);
+    opacity: 0.7;
+    transition: opacity 0.2s ease;
+    flex-shrink: 0;
+}
+
+.source-url-edit-btn:hover {
+    opacity: 1;
+    background: rgba(255, 255, 255, 0.1);
+}
+
+/* Source URL editor */
+.source-url-editor {
+    display: none;
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    right: 0;
+    background: var(--bg-color);
+    border-top: 1px solid var(--border-color);
+    padding: 12px;
+    flex-direction: column;
+    gap: 10px;
+    z-index: 5;
+}
+
+.source-url-editor.active {
+    display: flex;
+}
+
+.source-url-input {
+    width: 100%;
+    padding: 8px 10px;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    color: var(--text-color);
+    font-size: 0.9em;
+}
+
+.source-url-actions {
+    display: flex;
+    justify-content: flex-end;
+    gap: 8px;
+}
+
+.source-url-cancel-btn, 
+.source-url-save-btn {
+    padding: 6px 12px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.85em;
+    cursor: pointer;
+    border: none;
+    transition: all 0.2s;
+}
+
+.source-url-cancel-btn {
+    background: var(--bg-color);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+}
+
+.source-url-save-btn {
+    background: var(--lora-accent);
+    color: white;
+}
+
+.source-url-cancel-btn:hover {
+    background: var(--lora-surface);
+}
+
+.source-url-save-btn:hover {
+    background: color-mix(in oklch, var(--lora-accent), black 10%);
+}
+
 /* Generation Parameters */
 .recipe-gen-params {
     height: 360px;

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

@@ -0,0 +1,321 @@
+/* Common Metadata Edit UI Components */
+/* Used by both tag editing and trigger words editing interfaces */
+
+/* Edit Button */
+.metadata-edit-btn {
+  background: transparent;
+  border: none;
+  color: var(--text-color);
+  opacity: 0.5;
+  cursor: pointer;
+  padding: 2px 5px;
+  border-radius: var(--border-radius-xs);
+  transition: all 0.2s ease;
+}
+
+.metadata-edit-btn:hover {
+  opacity: 0.8;
+  background: rgba(0, 0, 0, 0.05);
+}
+
+[data-theme="dark"] .metadata-edit-btn:hover {
+  background: rgba(255, 255, 255, 0.05);
+}
+
+/* Edit mode active state */
+.edit-mode .metadata-edit-btn {
+  opacity: 0.8;
+  color: var(--lora-accent);
+}
+
+/* Edit Container */
+.metadata-edit-container {
+  padding: var(--space-2);
+  background: rgba(0, 0, 0, 0.03);
+  border: 1px solid rgba(0, 0, 0, 0.1);
+  border-radius: var(--border-radius-sm);
+  margin-top: var(--space-2);
+  width: 100%;
+  box-sizing: border-box;
+  position: relative;
+  display: block;
+}
+
+[data-theme="dark"] .metadata-edit-container {
+  background: rgba(255, 255, 255, 0.03);
+  border: 1px solid var(--lora-border);
+}
+
+/* Edit Header */
+.metadata-edit-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 10px;
+  padding-bottom: 8px;
+  border-bottom: 1px solid var(--lora-border);
+  width: 100%;
+}
+
+/* Style for the edit button when positioned in the header */
+.metadata-header-btn {
+  display: inline-flex !important;
+  opacity: 0.8 !important;
+  color: var(--lora-accent) !important;
+  margin-left: auto;
+}
+
+/* Edit Content */
+.metadata-edit-content {
+  margin-bottom: var(--space-1);
+  width: 100%;
+  display: block;
+}
+
+/* Items Container */
+.metadata-items {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 8px;
+  align-items: flex-start;
+  margin-bottom: var(--space-2);
+  width: 100%;
+}
+
+/* Individual Item */
+.metadata-item {
+  display: inline-flex;
+  align-items: center;
+  background: var(--bg-color);
+  border: 1px solid var(--border-color);
+  border-radius: var(--border-radius-xs);
+  padding: 4px 8px;
+  position: relative;
+}
+
+.metadata-item-content {
+  color: var(--lora-accent) !important;
+  font-size: 0.85em;
+  line-height: 1.4;
+  word-break: break-word;
+}
+
+/* Delete Button */
+.metadata-delete-btn {
+  position: absolute;
+  top: -5px;
+  right: -5px;
+  width: 16px;
+  height: 16px;
+  background: var(--lora-error);
+  color: white;
+  border: none;
+  border-radius: 50%;
+  cursor: pointer;
+  padding: 0;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  font-size: 9px;
+  transition: transform 0.2s ease;
+}
+
+.metadata-delete-btn:hover {
+  transform: scale(1.1);
+}
+
+/* Edit Controls */
+.metadata-edit-controls {
+  display: flex;
+  justify-content: flex-end;
+  gap: var(--space-2);
+  margin-top: var(--space-2);
+  margin-bottom: var(--space-2);
+}
+
+.metadata-edit-controls button {
+  padding: 3px 8px;
+  border-radius: var(--border-radius-xs);
+  border: 1px solid var(--border-color);
+  background: var(--bg-color);
+  color: var(--text-color);
+  font-size: 0.85em;
+  cursor: pointer;
+  display: flex;
+  align-items: center;
+  gap: 4px;
+  transition: all 0.2s ease;
+}
+
+.metadata-edit-controls button:hover {
+  background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+  border-color: var(--lora-accent);
+}
+
+.metadata-save-btn,
+.save-tags-btn {
+  background: var(--lora-accent) !important;
+  color: white !important;
+  border-color: var(--lora-accent) !important;
+}
+
+.metadata-save-btn:hover,
+.save-tags-btn:hover {
+  opacity: 0.9;
+}
+
+/* Add Form */
+.metadata-add-form {
+  display: flex;
+  gap: var(--space-1);
+  position: relative;
+  width: 100%;
+}
+
+.metadata-input {
+  flex: 1;
+  padding: 4px 8px;
+  border-radius: var(--border-radius-xs);
+  border: 1px solid var(--border-color);
+  background: var(--bg-color);
+  color: var(--text-color);
+  font-size: 0.9em;
+}
+
+.metadata-input:focus {
+  border-color: var(--lora-accent);
+  outline: none;
+}
+
+/* Suggestions Dropdown */
+.metadata-suggestions-dropdown {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  right: 0;
+  background: var(--bg-color);
+  border: 1px solid var(--border-color);
+  border-radius: var(--border-radius-sm);
+  margin-top: 4px;
+  z-index: 100;
+  box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15);
+  overflow: hidden;
+  display: flex;
+  flex-direction: column;
+}
+
+.metadata-suggestions-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  padding: 8px 12px;
+  background: var(--card-bg);
+  border-bottom: 1px solid var(--border-color);
+}
+
+.metadata-suggestions-header span {
+  font-size: 0.9em;
+  font-weight: 500;
+  color: var(--text-color);
+}
+
+.metadata-suggestions-header small {
+  font-size: 0.8em;
+  opacity: 0.7;
+}
+
+.metadata-suggestions-container {
+  max-height: 200px;
+  overflow-y: auto;
+  padding: 10px;
+  display: flex;
+  flex-wrap: wrap;
+  gap: 8px;
+  align-content: flex-start;
+}
+
+.metadata-suggestion-item {
+  display: inline-flex;
+  align-items: center;
+  justify-content: space-between;
+  padding: 5px 10px;
+  cursor: pointer;
+  transition: all 0.2s ease;
+  border-radius: var(--border-radius-xs);
+  background: var(--lora-surface);
+  border: 1px solid var(--lora-border);
+  max-width: 150px;
+}
+
+.metadata-suggestion-item:hover {
+  background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
+  border-color: var(--lora-accent);
+}
+
+.metadata-suggestion-item.already-added {
+  opacity: 0.7;
+  cursor: default;
+}
+
+.metadata-suggestion-item.already-added:hover {
+  background: var(--lora-surface);
+  border-color: var(--lora-border);
+}
+
+.metadata-suggestion-text {
+  color: var(--lora-accent) !important;
+  font-size: 0.9em;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  margin-right: 4px;
+  max-width: 100px;
+}
+
+.metadata-suggestion-meta {
+  display: flex;
+  align-items: center;
+  gap: 4px;
+  flex-shrink: 0;
+}
+
+.added-indicator {
+  color: var(--lora-accent);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  font-size: 0.75em;
+}
+
+/* No suggestions message */
+.no-suggestions {
+  padding: 16px 12px;
+  text-align: center;
+  color: var(--text-color);
+  opacity: 0.7;
+  font-style: italic;
+  font-size: 0.9em;
+}
+
+/* Loading indicator */
+.metadata-loading {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  margin: var(--space-1) 0;
+  color: var(--text-color);
+  opacity: 0.7;
+  font-size: 0.9em;
+  gap: 8px;
+}
+
+.metadata-loading i {
+  color: var(--lora-accent);
+}
+
+/* Dropdown separator */
+.dropdown-separator {
+  height: 1px;
+  background: var(--lora-border);
+  margin: 5px 10px;
+}

static/css/components/update-modal.css

@@ -37,13 +37,11 @@
     display: flex;
     justify-content: space-between;
     align-items: center;
-    background: rgba(0, 0, 0, 0.02); /* 轻微的灰色背景 */
-    border: 1px solid rgba(0, 0, 0, 0.08); /* 更明显的边框 */
     border-radius: var(--border-radius-sm);
     padding: var(--space-3);
 }
 
-.version-info {
+.update-info .version-info {
     display: flex;
     flex-direction: column;
     gap: 8px;
@@ -70,6 +68,15 @@
     color: var(--lora-accent);
 }
 
+/* Add styling for git info display */
+.git-info {
+    font-size: 0.85em;
+    opacity: 0.7;
+    margin-top: 4px;
+    font-family: monospace;
+    color: var(--text-color);
+}
+
 .update-link {
     display: flex;
     align-items: center;

static/css/layout.css

@@ -1,10 +1,10 @@
 .page-content {
     height: calc(100vh - 48px); /* Full height minus header */
     margin-top: 48px; /* Push down below header */
-    overflow-y: auto; /* Enable scrolling here */
     width: 100%;
     position: relative;
-    overflow-y: scroll;
+    overflow-x: hidden; /* Prevent horizontal scrolling */
+    overflow-y: auto; /* Enable vertical scrolling */
 }
 
 .container {
@@ -15,6 +15,19 @@
     z-index: var(--z-base);
 }
 
+/* Responsive container for larger screens */
+@media (min-width: 2000px) {
+    .container {
+        max-width: 1800px;
+    }
+}
+
+@media (min-width: 3000px) {
+    .container {
+        max-width: 2400px;
+    }
+}
+
 .controls {
     display: flex;
     flex-direction: column;
@@ -22,6 +35,13 @@
     margin-bottom: var(--space-2);
 }
 
+.controls-right {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    margin-left: auto; /* Push to the right */
+}
+
 .actions {
     display: flex;
     align-items: center;
@@ -293,6 +313,86 @@
     box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15);
 }
 
+/* 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,
+.app-title,
+.main-nav,
+.nav-item,
+.header-actions button,
+.header-controls,
+.toggle-folders-container button {
+    -webkit-user-select: none;
+    -moz-user-select: none;
+    -ms-user-select: none;
+    user-select: none;
+}
+
+/* Dropdown Button Styling */
+.dropdown-group {
+    position: relative;
+    display: flex;
+}
+
+.dropdown-main {
+    border-top-right-radius: 0;
+    border-bottom-right-radius: 0;
+    border-right: 1px solid rgba(0, 0, 0, 0.1);
+}
+
+.dropdown-toggle {
+    width: 24px !important;
+    min-width: unset !important;
+    border-top-left-radius: 0;
+    border-bottom-left-radius: 0;
+    padding: 0 !important;
+}
+
+.dropdown-menu {
+    position: absolute;
+    top: 100%;
+    left: 0;
+    z-index: 1000;
+    display: none;
+    min-width: 230px;
+    padding: 5px 0;
+    margin: 2px 0 0;
+    font-size: 0.85em;
+    background-color: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+}
+
+.dropdown-group.active .dropdown-menu {
+    display: block;
+}
+
+.dropdown-item {
+    display: block;
+    padding: 6px 15px;
+    clear: both;
+    font-weight: 400;
+    color: var(--text-color);
+    cursor: pointer;
+    transition: background-color 0.2s ease;
+}
+
+.dropdown-item:hover {
+    background-color: oklch(var(--lora-accent) / 0.1);
+}
+
+.dropdown-item i {
+    margin-right: 8px;
+    width: 16px;
+    text-align: center;
+}
+
 @media (max-width: 768px) {
     .actions {
         flex-wrap: wrap;
@@ -305,11 +405,14 @@
         width: 100%;
     }
     
+    .controls-right {
+        width: 100%;
+        justify-content: flex-end;
+        margin-top: 8px;
+    }
+    
     .toggle-folders-container {
         margin-left: 0;
-        width: 100%;
-        display: flex;
-        justify-content: flex-end;
     }
     
     .folder-tags-container {
@@ -335,4 +438,9 @@
     .back-to-top {
         bottom: 60px; /* Give some extra space from bottom on mobile */
     }
+
+    .dropdown-menu {
+        left: auto;
+        right: 0; /* Align to right on mobile */
+    }
 }

static/css/style.css

@@ -13,7 +13,13 @@
 @import 'components/loading.css';
 @import 'components/menu.css';
 @import 'components/update-modal.css';
-@import 'components/lora-modal.css';
+@import 'components/lora-modal/lora-modal.css';
+@import 'components/lora-modal/description.css';
+@import 'components/lora-modal/tag.css';
+@import 'components/lora-modal/preset-tags.css';
+@import 'components/lora-modal/showcase.css';
+@import 'components/lora-modal/triggerwords.css';
+@import 'components/shared/edit-metadata.css';
 @import 'components/support-modal.css';
 @import 'components/search-filter.css';
 @import 'components/bulk.css';
@@ -22,6 +28,8 @@
 @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 */
 
 .initialization-notice {
     display: flex;

static/js/api/baseModelApi.js

@@ -1,43 +1,22 @@
-// filepath: d:\Workspace\ComfyUI\custom_nodes\ComfyUI-Lora-Manager\static\js\api\baseModelApi.js
 import { state, getCurrentPageState } from '../state/index.js';
 import { showToast } from '../utils/uiHelpers.js';
-import { showDeleteModal, confirmDelete } from '../utils/modalUtils.js';
 import { getSessionItem, saveMapToStorage } from '../utils/storageHelpers.js';
 
-/**
- * Shared functionality for handling models (loras and checkpoints)
- */
-
-// Generic function to load more models with pagination
-export async function loadMoreModels(options = {}) {
+// New method for virtual scrolling fetch
+export async function fetchModelsPage(options = {}) {
     const {
-        resetPage = false,
-        updateFolders = false,
-        modelType = 'lora', // 'lora' or 'checkpoint'
-        createCardFunction,
+        modelType = 'lora',
+        page = 1,
+        pageSize = 100,
         endpoint = '/api/loras'
     } = options;
 
     const pageState = getCurrentPageState();
     
-    if (pageState.isLoading || (!pageState.hasMore && !resetPage)) return;
-    
-    pageState.isLoading = true;
-    document.body.classList.add('loading');
-    
     try {
-        // Reset to first page if requested
-        if (resetPage) {
-            pageState.currentPage = 1;
-            // Clear grid if resetting
-            const gridId = modelType === 'checkpoint' ? 'checkpointGrid' : 'loraGrid';
-            const grid = document.getElementById(gridId);
-            if (grid) grid.innerHTML = '';
-        }
-        
         const params = new URLSearchParams({
-            page: pageState.currentPage,
-            page_size: pageState.pageSize || 20,
+            page: page,
+            page_size: pageSize || pageState.pageSize || 20,
             sort_by: pageState.sortBy
         });
         
@@ -126,37 +105,121 @@ export async function loadMoreModels(options = {}) {
         
         const data = await response.json();
         
-        const gridId = modelType === 'checkpoint' ? 'checkpointGrid' : 'loraGrid';
-        const grid = document.getElementById(gridId);
+        return {
+            items: data.items,
+            totalItems: data.total,
+            totalPages: data.total_pages,
+            currentPage: page,
+            hasMore: page < data.total_pages,
+            folders: data.folders
+        };
         
-        if (data.items.length === 0 && pageState.currentPage === 1) {
-            grid.innerHTML = `<div class="no-results">No ${modelType}s found in this folder</div>`;
-            pageState.hasMore = false;
-        } else if (data.items.length > 0) {
-            pageState.hasMore = pageState.currentPage < data.total_pages;
-            
-            // Append model cards using the provided card creation function
-            data.items.forEach(model => {
-                const card = createCardFunction(model);
-                grid.appendChild(card);
-            });
-            
-            // Increment the page number AFTER successful loading
-            pageState.currentPage++;
-        } else {
-            pageState.hasMore = false;
-        }
+    } catch (error) {
+        console.error(`Error fetching ${modelType}s:`, error);
+        showToast(`Failed to fetch ${modelType}s: ${error.message}`, 'error');
+        throw error;
+    }
+}
 
-        if (updateFolders && data.folders) {
-            updateFolderTags(data.folders);
+/**
+ * Reset and reload models using virtual scrolling
+ * @param {Object} options - Operation options
+ * @returns {Promise<Object>} The fetch result
+ */
+export async function resetAndReloadWithVirtualScroll(options = {}) {
+    const {
+        modelType = 'lora',
+        updateFolders = false,
+        fetchPageFunction
+    } = options;
+    
+    const pageState = getCurrentPageState();
+    
+    try {
+        pageState.isLoading = true;
+        
+        // Reset page counter
+        pageState.currentPage = 1;
+        
+        // Fetch the first page
+        const result = await fetchPageFunction(1, pageState.pageSize || 50);
+        
+        // Update the virtual scroller
+        state.virtualScroller.refreshWithData(
+            result.items,
+            result.totalItems,
+            result.hasMore
+        );
+        
+        // Update state
+        pageState.hasMore = result.hasMore;
+        pageState.currentPage = 2; // Next page will be 2
+        
+        // Update folders if needed
+        if (updateFolders && result.folders) {
+            updateFolderTags(result.folders);
         }
         
+        return result;
+    } catch (error) {
+        console.error(`Error reloading ${modelType}s:`, error);
+        showToast(`Failed to reload ${modelType}s: ${error.message}`, 'error');
+        throw error;
+    } finally {
+        pageState.isLoading = false;
+    }
+}
+
+/**
+ * Load more models using virtual scrolling
+ * @param {Object} options - Operation options
+ * @returns {Promise<Object>} The fetch result
+ */
+export async function loadMoreWithVirtualScroll(options = {}) {
+    const {
+        modelType = 'lora',
+        resetPage = false,
+        updateFolders = false,
+        fetchPageFunction
+    } = options;
+    
+    const pageState = getCurrentPageState();
+    
+    try {
+        // Start loading state
+        pageState.isLoading = true;
+        
+        // Reset to first page if requested
+        if (resetPage) {
+            pageState.currentPage = 1;
+        }
+        
+        // Fetch the first page of data
+        const result = await fetchPageFunction(pageState.currentPage, pageState.pageSize || 50);
+        
+        // Update virtual scroller with the new data
+        state.virtualScroller.refreshWithData(
+            result.items,
+            result.totalItems,
+            result.hasMore
+        );
+        
+        // Update state
+        pageState.hasMore = result.hasMore;
+        pageState.currentPage = 2; // Next page to load would be 2
+        
+        // Update folders if needed
+        if (updateFolders && result.folders) {
+            updateFolderTags(result.folders);
+        }
+        
+        return result;
     } catch (error) {
         console.error(`Error loading ${modelType}s:`, error);
         showToast(`Failed to load ${modelType}s: ${error.message}`, 'error');
+        throw error;
     } finally {
         pageState.isLoading = false;
-        document.body.classList.remove('loading');
     }
 }
 
@@ -210,6 +273,8 @@ export function replaceModelPreview(filePath, modelType = 'lora') {
 // Delete a model (generic)
 export async function deleteModel(filePath, modelType = 'lora') {
     try {
+        state.loadingManager.showSimpleLoading(`Deleting ${modelType}...`);
+
         const endpoint = modelType === 'checkpoint' 
             ? '/api/checkpoints/delete' 
             : '/api/delete_model';
@@ -231,10 +296,15 @@ export async function deleteModel(filePath, modelType = 'lora') {
         const data = await response.json();
         
         if (data.success) {
-            // Remove the card from UI
-            const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
-            if (card) {
-                card.remove();
+            // If virtual scroller exists, update its data
+            if (state.virtualScroller) {
+                state.virtualScroller.removeItemByFilePath(filePath);
+            } else {
+                // Legacy approach: remove the card from UI directly
+                const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
+                if (card) {
+                    card.remove();
+                }
             }
             
             showToast(`${modelType} deleted successfully`, 'success');
@@ -246,22 +316,8 @@ export async function deleteModel(filePath, modelType = 'lora') {
         console.error(`Error deleting ${modelType}:`, error);
         showToast(`Failed to delete ${modelType}: ${error.message}`, 'error');
         return false;
-    }
-}
-
-// Reset and reload models
-export async function resetAndReload(options = {}) {
-    const {
-        updateFolders = false,
-        modelType = 'lora', 
-        loadMoreFunction
-    } = options;
-    
-    const pageState = getCurrentPageState();
-    
-    // Reset pagination and load more models
-    if (typeof loadMoreFunction === 'function') {
-        await loadMoreFunction(true, updateFolders);
+    } finally {
+        state.loadingManager.hide();
     }
 }
 
@@ -270,26 +326,31 @@ export async function refreshModels(options = {}) {
     const { 
         modelType = 'lora',
         scanEndpoint = '/api/loras/scan',
-        resetAndReloadFunction
+        resetAndReloadFunction,
+        fullRebuild = false // New parameter with default value false
     } = options;
     
     try {
-        state.loadingManager.showSimpleLoading(`Refreshing ${modelType}s...`);
+        state.loadingManager.showSimpleLoading(`${fullRebuild ? 'Full rebuild' : 'Refreshing'} ${modelType}s...`);
         
-        const response = await fetch(scanEndpoint);
+        // Add fullRebuild parameter to the request
+        const url = new URL(scanEndpoint, window.location.origin);
+        url.searchParams.append('full_rebuild', fullRebuild);
+        
+        const response = await fetch(url);
         
         if (!response.ok) {
             throw new Error(`Failed to refresh ${modelType}s: ${response.status} ${response.statusText}`);
         }
         
         if (typeof resetAndReloadFunction === 'function') {
-            await resetAndReloadFunction();
+            await resetAndReloadFunction(true); // update folders
         }
         
-        showToast(`Refresh complete`, 'success');
+        showToast(`${fullRebuild ? 'Full rebuild' : 'Refresh'} complete`, 'success');
     } catch (error) {
         console.error(`Refresh failed:`, error);
-        showToast(`Failed to refresh ${modelType}s`, 'error');
+        showToast(`Failed to ${fullRebuild ? 'rebuild' : 'refresh'} ${modelType}s`, 'error');
     } finally {
         state.loadingManager.hide();
         state.loadingManager.restoreProgressBar();
@@ -410,6 +471,11 @@ export async function refreshSingleModelMetadata(filePath, modelType = 'lora') {
         const data = await response.json();
         
         if (data.success) {
+            // Use the returned metadata to update just this single item
+            if (data.metadata && state.virtualScroller) {
+                state.virtualScroller.updateSingleItem(filePath, data.metadata);
+            }
+
             showToast('Metadata refreshed successfully', 'success');
             return true;
         } else {
@@ -428,6 +494,8 @@ export async function refreshSingleModelMetadata(filePath, modelType = 'lora') {
 // Generic function to exclude a model
 export async function excludeModel(filePath, modelType = 'lora') {
     try {
+        state.loadingManager.showSimpleLoading(`Excluding ${modelType}...`);
+
         const endpoint = modelType === 'checkpoint' 
             ? '/api/checkpoints/exclude' 
             : '/api/loras/exclude';
@@ -449,10 +517,15 @@ export async function excludeModel(filePath, modelType = 'lora') {
         const data = await response.json();
         
         if (data.success) {
-            // Remove the card from UI
-            const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
-            if (card) {
-                card.remove();
+            // If virtual scroller exists, update its data
+            if (state.virtualScroller) {
+                state.virtualScroller.removeItemByFilePath(filePath);
+            } else {
+                // Legacy approach: remove the card from UI directly
+                const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
+                if (card) {
+                    card.remove();
+                }
             }
             
             showToast(`${modelType} excluded successfully`, 'success');
@@ -464,26 +537,22 @@ export async function excludeModel(filePath, modelType = 'lora') {
         console.error(`Error excluding ${modelType}:`, error);
         showToast(`Failed to exclude ${modelType}: ${error.message}`, 'error');
         return false;
+    } finally {
+        state.loadingManager.hide();
     }
 }
 
-// Private methods
-
 // Upload a preview image
-async function uploadPreview(filePath, file, modelType = 'lora') {
-    const loadingOverlay = document.getElementById('loading-overlay');
-    const loadingStatus = document.querySelector('.loading-status');
-    
+export async function uploadPreview(filePath, file, modelType = 'lora', nsfwLevel = 0) {
     try {
-        if (loadingOverlay) loadingOverlay.style.display = 'flex';
-        if (loadingStatus) loadingStatus.textContent = 'Uploading preview...';
+        state.loadingManager.showSimpleLoading('Uploading preview...');
         
         const formData = new FormData();
         
-        // Use appropriate parameter names and endpoint based on model type
         // Prepare common form data
         formData.append('preview_file', file);
         formData.append('model_path', filePath);
+        formData.append('nsfw_level', nsfwLevel.toString());  // Add nsfw_level parameter
 
         // Set endpoint based on model type
         const endpoint = modelType === 'checkpoint' 
@@ -500,56 +569,39 @@ async function uploadPreview(filePath, file, modelType = 'lora') {
         }
 
         const data = await response.json();
+
+        // Get the current page's previewVersions Map based on model type
+        const pageType = modelType === 'checkpoint' ? 'checkpoints' : 'loras';
+        const previewVersions = state.pages[pageType].previewVersions;
         
-        // Update the card preview in UI
-        const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
-        if (card) {
-            const previewContainer = card.querySelector('.card-preview');
-            const oldPreview = previewContainer.querySelector('img, video');
+        // Update the version timestamp
+        const timestamp = Date.now();
+        if (previewVersions) {
+            previewVersions.set(filePath, timestamp);
             
-            // Get the current page's previewVersions Map based on model type
-            const pageType = modelType === 'checkpoint' ? 'checkpoints' : 'loras';
-            const previewVersions = state.pages[pageType].previewVersions;
-            
-            // Update the version timestamp
-            const timestamp = Date.now();
-            if (previewVersions) {
-                previewVersions.set(filePath, timestamp);
-                
-                // Save the updated Map to localStorage
-                const storageKey = modelType === 'checkpoint' ? 'checkpoint_preview_versions' : 'lora_preview_versions';
-                saveMapToStorage(storageKey, previewVersions);
-            }
-            
-            const previewUrl = data.preview_url ? 
-                `${data.preview_url}?t=${timestamp}` : 
-                `/api/model/preview_image?path=${encodeURIComponent(filePath)}&t=${timestamp}`;
-            
-            // Create appropriate element based on file type
-            if (file.type.startsWith('video/')) {
-                const video = document.createElement('video');
-                video.controls = true;
-                video.autoplay = true;
-                video.muted = true;
-                video.loop = true;
-                video.src = previewUrl;
-                oldPreview.replaceWith(video);
-            } else {
-                const img = document.createElement('img');
-                img.src = previewUrl;
-                oldPreview.replaceWith(img);
-            }
-            
-            showToast('Preview updated successfully', 'success');
+            // Save the updated Map to localStorage
+            const storageKey = modelType === 'checkpoint' ? 'checkpoint_preview_versions' : 'lora_preview_versions';
+            saveMapToStorage(storageKey, previewVersions);
         }
+
+        const updateData = {
+            preview_url: data.preview_url,
+            preview_nsfw_level: data.preview_nsfw_level // Include nsfw level in update data
+        };
+
+        state.virtualScroller.updateSingleItem(filePath, updateData);
+
+        showToast('Preview updated successfully', 'success');
     } catch (error) {
         console.error('Error uploading preview:', error);
         showToast('Failed to upload preview image', 'error');
     } finally {
-        if (loadingOverlay) loadingOverlay.style.display = 'none';
+        state.loadingManager.hide();
     }
 }
 
+// Private methods
+
 // Private function to perform the delete operation
 async function performDelete(filePath, modelType = 'lora') {
     try {

static/js/api/checkpointApi.js

@@ -1,7 +1,7 @@
-import { createCheckpointCard } from '../components/CheckpointCard.js';
 import {
-    loadMoreModels,
-    resetAndReload as baseResetAndReload,
+    fetchModelsPage,
+    resetAndReloadWithVirtualScroll,
+    loadMoreWithVirtualScroll,
     refreshModels as baseRefreshModels,
     deleteModel as baseDeleteModel,
     replaceModelPreview,
@@ -9,33 +9,54 @@ import {
     refreshSingleModelMetadata,
     excludeModel as baseExcludeModel
 } from './baseModelApi.js';
+import { state } from '../state/index.js';
 
-// Load more checkpoints with pagination
-export async function loadMoreCheckpoints(resetPagination = true) {
-    return loadMoreModels({
-        resetPage: resetPagination,
-        updateFolders: true,
+/**
+ * Fetch checkpoints with pagination for virtual scrolling
+ * @param {number} page - Page number to fetch
+ * @param {number} pageSize - Number of items per page
+ * @returns {Promise<Object>} Object containing items, total count, and pagination info
+ */
+export async function fetchCheckpointsPage(page = 1, pageSize = 100) {
+    return fetchModelsPage({
         modelType: 'checkpoint',
-        createCardFunction: createCheckpointCard,
+        page,
+        pageSize,
         endpoint: '/api/checkpoints'
     });
 }
 
-// Reset and reload checkpoints
-export async function resetAndReload() {
-    return baseResetAndReload({
-        updateFolders: true,
+/**
+ * Load more checkpoints with pagination - updated to work with VirtualScroller
+ * @param {boolean} resetPage - Whether to reset to the first page
+ * @param {boolean} updateFolders - Whether to update folder tags
+ * @returns {Promise<void>}
+ */
+export async function loadMoreCheckpoints(resetPage = false, updateFolders = false) {
+    return loadMoreWithVirtualScroll({
         modelType: 'checkpoint',
-        loadMoreFunction: loadMoreCheckpoints
+        resetPage,
+        updateFolders,
+        fetchPageFunction: fetchCheckpointsPage
+    });
+}
+
+// Reset and reload checkpoints
+export async function resetAndReload(updateFolders = false) {
+    return resetAndReloadWithVirtualScroll({
+        modelType: 'checkpoint',
+        updateFolders,
+        fetchPageFunction: fetchCheckpointsPage
     });
 }
 
 // Refresh checkpoints
-export async function refreshCheckpoints() {
+export async function refreshCheckpoints(fullRebuild = false) {
     return baseRefreshModels({
         modelType: 'checkpoint',
         scanEndpoint: '/api/checkpoints/scan',
-        resetAndReloadFunction: resetAndReload
+        resetAndReloadFunction: resetAndReload,
+        fullRebuild: fullRebuild
     });
 }
 
@@ -60,7 +81,7 @@ export async function fetchCivitai() {
 
 // Refresh single checkpoint metadata
 export async function refreshSingleCheckpointMetadata(filePath) {
-    return refreshSingleModelMetadata(filePath, 'checkpoint');
+    await refreshSingleModelMetadata(filePath, 'checkpoint');
 }
 
 /**
@@ -70,22 +91,33 @@ export async function refreshSingleCheckpointMetadata(filePath) {
  * @returns {Promise} - Promise that resolves with the server response
  */
 export async function saveModelMetadata(filePath, data) {
-    const response = await fetch('/api/checkpoints/save-metadata', {
-        method: 'POST',
-        headers: {
-            'Content-Type': 'application/json',
-        },
-        body: JSON.stringify({
-            file_path: filePath,
-            ...data
-        })
-    });
+    try {
+        // Show loading indicator
+        state.loadingManager.showSimpleLoading('Saving metadata...');
+        
+        const response = await fetch('/api/checkpoints/save-metadata', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_path: filePath,
+                ...data
+            })
+        });
 
-    if (!response.ok) {
-        throw new Error('Failed to save metadata');
+        if (!response.ok) {
+            throw new Error('Failed to save metadata');
+        }
+
+        // Update the virtual scroller with the new metadata
+        state.virtualScroller.updateSingleItem(filePath, data);
+        
+        return response.json();
+    } finally {
+        // Always hide the loading indicator when done
+        state.loadingManager.hide();
     }
-    
-    return response.json();
 }
 
 /**
@@ -95,4 +127,40 @@ export async function saveModelMetadata(filePath, data) {
  */
 export function excludeCheckpoint(filePath) {
     return baseExcludeModel(filePath, 'checkpoint');
+}
+
+/**
+ * Rename a checkpoint file
+ * @param {string} filePath - Current file path
+ * @param {string} newFileName - New file name (without path)
+ * @returns {Promise<Object>} - Promise that resolves with the server response
+ */
+export async function renameCheckpointFile(filePath, newFileName) {
+    try {
+        // Show loading indicator
+        state.loadingManager.showSimpleLoading('Renaming checkpoint file...');
+        
+        const response = await fetch('/api/rename_checkpoint', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_path: filePath,
+                new_file_name: newFileName
+            })
+        });
+        
+        if (!response.ok) {
+            throw new Error(`Server returned ${response.status}: ${response.statusText}`);
+        }
+        
+        return await response.json();
+    } catch (error) {
+        console.error('Error renaming checkpoint file:', error);
+        throw error;
+    } finally {
+        // Hide loading indicator
+        state.loadingManager.hide();
+    }
 }