chore: Update version to 0.8.20-beta in pyproject.toml

Update requirements.txt

.github/FUNDING.yml

@@ -1,4 +1,5 @@
 # These are supported funding model platforms
 
+patreon: PixelPawsAI
 ko_fi: pixelpawsai
 custom: ['paypal.me/pixelpawsai']

README.md

@@ -18,10 +18,51 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 
 [![One-Click LoRA Integration Tutorial](https://img.youtube.com/vi/hvKw31YpE-U/0.jpg)](https://youtu.be/hvKw31YpE-U)
 
+## 🌐 Browser Extension
+Enhance your Civitai browsing experience with our companion browser extension! See which models you already have, download new ones with a single click, and manage your downloads efficiently.
+
+![LM Civitai Extension Preview](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/wiki-images/civitai-models-page.png)
+
+<div>
+  <a href="https://chromewebstore.google.com/detail/lm-civitai-extension/capigligggeijgmocnaflanlbghnamgm?utm_source=item-share-cb" style="display: inline-block; background-color: #4285F4; color: white; padding: 8px 16px; text-decoration: none; border-radius: 4px; font-weight: bold; margin: 10px 0;">
+    <img src="https://www.google.com/chrome/static/images/chrome-logo.svg" width="20" style="vertical-align: middle; margin-right: 8px;"> Get Extension from Chrome Web Store
+  </a>
+</div>
+
+📚 [Learn More: Complete Tutorial](https://github.com/willmiao/ComfyUI-Lora-Manager/wiki/LoRA-Manager-Civitai-Extension-(Chrome-Extension))
+
 ---
 
 ## Release Notes
 
+### v0.8.20
+* **LM Civitai Extension** - Released [browser extension through Chrome Web Store](https://chromewebstore.google.com/detail/lm-civitai-extension/capigligggeijgmocnaflanlbghnamgm?utm_source=item-share-cb) that works seamlessly with LoRA Manager to enhance Civitai browsing experience, showing which models are already in your local library, enabling one-click downloads, and providing queue and parallel download support
+* **Enhanced Lora Loader** - Added support for nunchaku, improving convenience when working with ComfyUI-nunchaku workflows, plus new template workflows for quick onboarding
+* **WanVideo Integration** - Introduced WanVideo Lora Select (LoraManager) node compatible with ComfyUI-WanVideoWrapper for streamlined lora usage in video workflows, including a template workflow to help you get started quickly
+
+### v0.8.19
+* **Analytics Dashboard** - Added new Statistics page providing comprehensive visual analysis of model collection and usage patterns for better library insights
+* **Target Node Selection** - Enhanced workflow integration with intelligent target choosing when sending LoRAs/recipes to workflows with multiple loader/stacker nodes; a visual selector now appears showing node color, type, ID, and title for precise targeting
+* **Enhanced NSFW Controls** - Added support for setting NSFW levels on recipes with automatic content blurring based on user preferences
+* **Customizable Card Display** - New display settings allowing users to choose whether card information and action buttons are always visible or only revealed on hover
+* **Expanded Compatibility** - Added support for efficiency-nodes-comfyui in Save Recipe and Save Image nodes, plus fixed compatibility with ComfyUI_Custom_Nodes_AlekPet
+
+### 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
@@ -85,13 +126,6 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 - 🚀 **High Performance**
   - Fast model loading and browsing
   - Smooth scrolling through large collections
-  - Real-time updates when files change
-  
-- 📂 **Advanced Organization**
-  - Quick search with fuzzy matching
-  - Folder-based categorization
-  - Move LoRAs between folders
-  - Sort by name or date
   
 - 🌐 **Rich Model Integration**
   - Direct download from CivitAI
@@ -255,6 +289,8 @@ If you find this project helpful, consider supporting its development:
 
 [![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/pixelpawsai)
 
+[![Patreon](https://img.shields.io/badge/Become%20a%20Patron-F96854.svg?style=for-the-badge&logo=patreon&logoColor=white)](https://patreon.com/PixelPawsAI)
+
 WeChat: [Click to view QR code](https://raw.githubusercontent.com/willmiao/ComfyUI-Lora-Manager/main/static/images/wechat-qr.webp)
 
 ## 💬 Community

__init__.py

@@ -4,6 +4,7 @@ from .py.nodes.trigger_word_toggle import TriggerWordToggle
 from .py.nodes.lora_stacker import LoraStacker
 from .py.nodes.save_image import SaveImage
 from .py.nodes.debug_metadata import DebugMetadata
+from .py.nodes.wanvideo_lora_select import WanVideoLoraSelect
 # Import metadata collector to install hooks on startup
 from .py.metadata_collector import init as init_metadata_collector
 
@@ -12,7 +13,8 @@ NODE_CLASS_MAPPINGS = {
     TriggerWordToggle.NAME: TriggerWordToggle,
     LoraStacker.NAME: LoraStacker,
     SaveImage.NAME: SaveImage,
-    DebugMetadata.NAME: DebugMetadata
+    DebugMetadata.NAME: DebugMetadata,
+    WanVideoLoraSelect.NAME: WanVideoLoraSelect
 }
 
 WEB_DIRECTORY = "./web/comfyui"

py/config.py

@@ -22,7 +22,9 @@ class Config:
         # 静态路由映射字典, target to route mapping
         self._route_mappings = {}
         self.loras_roots = self._init_lora_paths()
-        self.checkpoints_roots = self._init_checkpoint_paths()
+        self.checkpoints_roots = None
+        self.unet_roots = None
+        self.base_models_roots = self._init_checkpoint_paths()
         # 在初始化时扫描符号链接
         self._scan_symbolic_links()
         
@@ -33,34 +35,26 @@ class Config:
     def save_folder_paths_to_settings(self):
         """Save folder paths to settings.json for standalone mode to use later"""
         try:
-            # Check if we're running in ComfyUI mode (not standalone)
-            if hasattr(folder_paths, "get_folder_paths") and not isinstance(folder_paths, type):
-                # Get all relevant paths
-                lora_paths = folder_paths.get_folder_paths("loras")
-                checkpoint_paths = folder_paths.get_folder_paths("checkpoints")
-                diffuser_paths = folder_paths.get_folder_paths("diffusers")
-                unet_paths = folder_paths.get_folder_paths("unet")
-                
-                # Load existing settings
-                settings_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'settings.json')
-                settings = {}
-                if os.path.exists(settings_path):
-                    with open(settings_path, 'r', encoding='utf-8') as f:
-                        settings = json.load(f)
-                
-                # Update settings with paths
-                settings['folder_paths'] = {
-                    'loras': lora_paths,
-                    'checkpoints': checkpoint_paths,
-                    'diffusers': diffuser_paths,
-                    'unet': unet_paths
-                }
-                
-                # Save settings
-                with open(settings_path, 'w', encoding='utf-8') as f:
-                    json.dump(settings, f, indent=2)
-                
-                logger.info("Saved folder paths to settings.json")
+            # Check if we're running in ComfyUI mode (not standalone)           
+            # Load existing settings
+            settings_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'settings.json')
+            settings = {}
+            if os.path.exists(settings_path):
+                with open(settings_path, 'r', encoding='utf-8') as f:
+                    settings = json.load(f)
+            
+            # Update settings with paths
+            settings['folder_paths'] = {
+                'loras': self.loras_roots,
+                'checkpoints': self.checkpoints_roots,
+                'unet': self.unet_roots,
+            }
+            
+            # Save settings
+            with open(settings_path, 'w', encoding='utf-8') as f:
+                json.dump(settings, f, indent=2)
+            
+            logger.info("Saved folder paths to settings.json")
         except Exception as e:
             logger.warning(f"Failed to save folder paths: {e}")
 
@@ -86,7 +80,7 @@ class Config:
         for root in self.loras_roots:
             self._scan_directory_links(root)
         
-        for root in self.checkpoints_roots:
+        for root in self.base_models_roots:
             self._scan_directory_links(root)
 
     def _scan_directory_links(self, root: str):
@@ -178,30 +172,36 @@ class Config:
         try:
             # Get checkpoint paths from folder_paths
             checkpoint_paths = folder_paths.get_folder_paths("checkpoints")
-            diffusion_paths = folder_paths.get_folder_paths("diffusers")
             unet_paths = folder_paths.get_folder_paths("unet")
             
-            # Combine all checkpoint-related paths
-            all_paths = checkpoint_paths + diffusion_paths + unet_paths
-            
-            # Filter and normalize paths
-            paths = sorted(set(path.replace(os.sep, "/") 
-                    for path in all_paths 
+            # Sort each list individually
+            checkpoint_paths = sorted(set(path.replace(os.sep, "/") 
+                    for path in checkpoint_paths 
                     if os.path.exists(path)), key=lambda p: p.lower())
             
-            logger.info("Found checkpoint roots:" + ("\n - " + "\n - ".join(paths) if paths else "[]"))
+            unet_paths = sorted(set(path.replace(os.sep, "/") 
+                    for path in unet_paths 
+                    if os.path.exists(path)), key=lambda p: p.lower())
             
-            if not paths:
+            # Combine all checkpoint-related paths, ensuring checkpoint_paths are first
+            all_paths = checkpoint_paths + unet_paths
+
+            self.checkpoints_roots = checkpoint_paths
+            self.unet_roots = unet_paths
+            
+            logger.info("Found checkpoint roots:" + ("\n - " + "\n - ".join(all_paths) if all_paths else "[]"))
+            
+            if not all_paths:
                 logger.warning("No valid checkpoint folders found in ComfyUI configuration")
                 return []
             
-            # 初始化路径映射，与 LoRA 路径处理方式相同
-            for path in paths:
+            # Initialize path mappings, similar to LoRA path handling
+            for path in all_paths:
                 real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
                 if real_path != path:
                     self.add_path_mapping(path, real_path)
             
-            return paths
+            return all_paths
         except Exception as e:
             logger.warning(f"Error initializing checkpoint paths: {e}")
             return []

py/lora_manager.py

@@ -1,17 +1,22 @@
 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
 from .routes.recipe_routes import RecipeRoutes
 from .routes.checkpoints_routes import CheckpointsRoutes
+from .routes.stats_routes import StatsRoutes
 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__)
 
@@ -57,7 +62,7 @@ class LoraManager:
             added_targets.add(real_root)
         
         # Add static routes for each checkpoint root
-        for idx, root in enumerate(config.checkpoints_roots, start=1):
+        for idx, root in enumerate(config.base_models_roots, start=1):
             preview_path = f'/checkpoints_static/root{idx}/preview'
             
             real_root = root
@@ -83,8 +88,8 @@ class LoraManager:
         for target_path, link_path in config._path_mappings.items():
             if target_path not in added_targets:
                 # Determine if this is a checkpoint or lora link based on path
-                is_checkpoint = any(cp_root in link_path for cp_root in config.checkpoints_roots)
-                is_checkpoint = is_checkpoint or any(cp_root in target_path for cp_root in config.checkpoints_roots)
+                is_checkpoint = any(cp_root in link_path for cp_root in config.base_models_roots)
+                is_checkpoint = is_checkpoint or any(cp_root in target_path for cp_root in config.base_models_roots)
                 
                 if is_checkpoint:
                     route_path = f'/checkpoints_static/link_{link_idx["checkpoint"]}/preview'
@@ -93,10 +98,14 @@ 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)
@@ -104,14 +113,17 @@ class LoraManager:
         # Setup feature routes
         lora_routes = LoraRoutes()
         checkpoints_routes = CheckpointsRoutes()
+        stats_routes = StatsRoutes()
         
         # Initialize routes
         lora_routes.setup_routes(app)
         checkpoints_routes.setup_routes(app)
+        stats_routes.setup_routes(app)  # Add statistics routes
         ApiRoutes.setup_routes(app)
         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 +140,13 @@ 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 +165,8 @@ 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 +178,6 @@ 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/metadata_processor.py

@@ -1,5 +1,6 @@
 import json
 import sys
+from .constants import IMAGES
 
 # Check if running in standalone mode
 standalone_mode = 'nodes' not in sys.modules
@@ -18,6 +19,10 @@ class MetadataProcessor:
         - metadata: The workflow metadata
         - downstream_id: Optional ID of a downstream node to help identify the specific primary sampler
         """
+        if downstream_id is None:
+            if IMAGES in metadata and "first_decode" in metadata[IMAGES]:
+                downstream_id = metadata[IMAGES]["first_decode"]["node_id"]
+                
         # 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"]
@@ -209,6 +214,72 @@ class MetadataProcessor:
                 
         return None
     
+    @staticmethod
+    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")
+            
+            # Helper function to recursively find prompt text for a conditioning object
+            def find_prompt_text_for_conditioning(conditioning_obj, is_positive=True):
+                if conditioning_obj is None:
+                    return ""
+                    
+                # Try to match conditioning objects with those stored by extractors
+                for prompt_node_id, prompt_data in metadata[PROMPTS].items():
+                    # For nodes with single conditioning output
+                    if "conditioning" in prompt_data:
+                        if id(prompt_data["conditioning"]) == id(conditioning_obj):
+                            return prompt_data.get("text", "")
+                    
+                    # For nodes with separate pos_conditioning and neg_conditioning outputs (like TSC_EfficientLoader)
+                    if is_positive and "positive_encoded" in prompt_data:
+                        if id(prompt_data["positive_encoded"]) == id(conditioning_obj):
+                            if "positive_text" in prompt_data:
+                                return prompt_data["positive_text"]
+                            else:
+                                orig_conditioning = prompt_data.get("orig_pos_cond", None)
+                                if orig_conditioning is not None:
+                                    # Recursively find the prompt text for the original conditioning
+                                    return find_prompt_text_for_conditioning(orig_conditioning, is_positive=True)
+                    
+                    if not is_positive and "negative_encoded" in prompt_data:
+                        if id(prompt_data["negative_encoded"]) == id(conditioning_obj):
+                            if "negative_text" in prompt_data:
+                                return prompt_data["negative_text"]
+                            else:
+                                orig_conditioning = prompt_data.get("orig_neg_cond", None)
+                                if orig_conditioning is not None:
+                                    # Recursively find the prompt text for the original conditioning
+                                    return find_prompt_text_for_conditioning(orig_conditioning, is_positive=False)
+                
+                return ""
+            
+            # Find prompt texts using the helper function
+            result["prompt"] = find_prompt_text_for_conditioning(pos_conditioning, is_positive=True)
+            result["negative_prompt"] = find_prompt_text_for_conditioning(neg_conditioning, is_positive=False)
+            
+        return result
+    
     @staticmethod
     def extract_generation_params(metadata, id=None):
         """
@@ -261,7 +332,6 @@ 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
@@ -309,29 +379,36 @@ class MetadataProcessor:
                                 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", 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", "")
+                    # 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 = []

py/metadata_collector/node_extractors.py

@@ -35,7 +35,70 @@ class CheckpointLoaderExtractor(NodeMetadataExtractor):
                 "type": "checkpoint",
                 "node_id": node_id
             }
+
+class TSCCheckpointLoaderExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs or "ckpt_name" not in inputs:
+            return
+            
+        model_name = inputs.get("ckpt_name")
+        if model_name:
+            metadata[MODELS][node_id] = {
+                "name": model_name,
+                "type": "checkpoint",
+                "node_id": node_id
+            }
+
+        # For loader node has lora_stack input, like Efficient Loader from Efficient Nodes
+        active_loras = []
         
+        # Process lora_stack if available
+        if "lora_stack" in inputs:
+            lora_stack = inputs.get("lora_stack", [])
+            for lora_path, model_strength, clip_strength in lora_stack:
+                # Extract lora name from path (following the format in lora_loader.py)
+                lora_name = os.path.splitext(os.path.basename(lora_path))[0]
+                active_loras.append({
+                    "name": lora_name,
+                    "strength": model_strength
+                })
+        
+        if active_loras:
+            metadata[LORAS][node_id] = {
+                "lora_list": active_loras,
+                "node_id": node_id
+            }
+        
+        # Extract positive and negative prompt text if available
+        positive_text = inputs.get("positive", "")
+        negative_text = inputs.get("negative", "")
+        
+        if positive_text or negative_text:
+            if node_id not in metadata[PROMPTS]:
+                metadata[PROMPTS][node_id] = {"node_id": node_id}
+            
+            # Store both positive and negative text
+            metadata[PROMPTS][node_id]["positive_text"] = positive_text
+            metadata[PROMPTS][node_id]["negative_text"] = negative_text
+            
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        # Handle conditioning outputs from TSC_EfficientLoader
+        # outputs is a list with [(model, positive_encoded, negative_encoded, {"samples":latent}, vae, clip, dependencies,)]
+        if outputs and isinstance(outputs, list) and len(outputs) > 0:
+            first_output = outputs[0]
+            if isinstance(first_output, tuple) and len(first_output) >= 3:
+                positive_conditioning = first_output[1]
+                negative_conditioning = first_output[2]
+                
+                # Save both conditioning objects in metadata
+                if node_id not in metadata[PROMPTS]:
+                    metadata[PROMPTS][node_id] = {"node_id": node_id}
+                    
+                metadata[PROMPTS][node_id]["positive_encoded"] = positive_conditioning
+                metadata[PROMPTS][node_id]["negative_encoded"] = negative_conditioning
+
 class CLIPTextEncodeExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -47,6 +110,13 @@ 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
@@ -64,6 +134,18 @@ class SamplerExtractor(NodeMetadataExtractor):
             "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:
@@ -103,6 +185,18 @@ class KSamplerAdvancedExtractor(NodeMetadataExtractor):
             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"]
@@ -124,6 +218,81 @@ class KSamplerAdvancedExtractor(NodeMetadataExtractor):
                         "node_id": node_id
                     }
 
+class TSCSamplerBaseExtractor(NodeMetadataExtractor):
+    """Base extractor for handling TSC sampler node outputs"""
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        # Store vae_decode setting for later use in update
+        if inputs and "vae_decode" in inputs:
+            if SAMPLING not in metadata:
+                metadata[SAMPLING] = {}
+                
+            if node_id not in metadata[SAMPLING]:
+                metadata[SAMPLING][node_id] = {"parameters": {}, "node_id": node_id}
+                
+            # Store the vae_decode setting
+            metadata[SAMPLING][node_id]["vae_decode"] = inputs["vae_decode"]
+
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        # Check if vae_decode was set to "true"
+        should_save_image = True
+        if SAMPLING in metadata and node_id in metadata[SAMPLING]:
+            vae_decode = metadata[SAMPLING][node_id].get("vae_decode")
+            if vae_decode is not None:
+                should_save_image = (vae_decode == "true")
+        
+        # Skip image saving if vae_decode isn't "true"
+        if not should_save_image:
+            return
+        
+        # Ensure IMAGES category exists
+        if IMAGES not in metadata:
+            metadata[IMAGES] = {}
+        
+        # Extract output_images from the TSC sampler format
+        # outputs = [{"ui": {"images": preview_images}, "result": result}]
+        # where result = (original_model, original_positive, original_negative, latent_list, optional_vae, output_images,)
+        if outputs and isinstance(outputs, list) and len(outputs) > 0:
+            # Get the first item in the list
+            output_item = outputs[0]
+            if isinstance(output_item, dict) and "result" in output_item:
+                result = output_item["result"]
+                if isinstance(result, tuple) and len(result) >= 6:
+                    # The output_images is the last element in the result tuple
+                    output_images = (result[5],)
+                    
+                    # Save image data under node ID index to be captured by caching mechanism
+                    metadata[IMAGES][node_id] = {
+                    "node_id": node_id,
+                    "image": output_images
+                    }
+                    
+                    # Only set first_decode if it hasn't been recorded yet
+                    if "first_decode" not in metadata[IMAGES]:
+                        metadata[IMAGES]["first_decode"] = metadata[IMAGES][node_id]
+
+class TSCKSamplerExtractor(SamplerExtractor, TSCSamplerBaseExtractor):
+    """Extractor for TSC_KSampler nodes"""
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        # Call parent extract methods
+        SamplerExtractor.extract(node_id, inputs, outputs, metadata)
+        TSCSamplerBaseExtractor.extract(node_id, inputs, outputs, metadata)
+
+    # Update method is inherited from TSCSamplerBaseExtractor
+
+
+class TSCKSamplerAdvancedExtractor(KSamplerAdvancedExtractor, TSCSamplerBaseExtractor):
+    """Extractor for TSC_KSamplerAdvanced nodes"""
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        # Call parent extract methods
+        SamplerExtractor.extract(node_id, inputs, outputs, metadata)
+        TSCSamplerBaseExtractor.extract(node_id, inputs, outputs, metadata)
+
+    # Update method is inherited from TSCSamplerBaseExtractor
+
 class LoraLoaderExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -376,6 +545,13 @@ 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):
@@ -393,17 +569,56 @@ class CFGGuiderExtractor(NodeMetadataExtractor):
             
         metadata[SAMPLING][node_id]["parameters"]["cfg"] = cfg_value
 
+class CR_ApplyControlNetStackExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+            
+        # Save the original conditioning inputs
+        base_positive = inputs.get("base_positive")
+        base_negative = inputs.get("base_negative")
+        
+        if base_positive is not None or base_negative is not None:
+            if node_id not in metadata[PROMPTS]:
+                metadata[PROMPTS][node_id] = {"node_id": node_id}
+            
+            metadata[PROMPTS][node_id]["orig_pos_cond"] = base_positive
+            metadata[PROMPTS][node_id]["orig_neg_cond"] = base_negative
+
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        # Extract transformed conditionings from outputs
+        # outputs structure: [(base_positive, base_negative, show_help, )]
+        if outputs and isinstance(outputs, list) and len(outputs) > 0:
+            first_output = outputs[0]
+            if isinstance(first_output, tuple) and len(first_output) >= 2:
+                transformed_positive = first_output[0]
+                transformed_negative = first_output[1]
+                
+                # Save transformed conditioning objects in metadata
+                if node_id not in metadata[PROMPTS]:
+                    metadata[PROMPTS][node_id] = {"node_id": node_id}
+                    
+                metadata[PROMPTS][node_id]["positive_encoded"] = transformed_positive
+                metadata[PROMPTS][node_id]["negative_encoded"] = transformed_negative
+
 # Registry of node-specific extractors
+# Keys are node class names
 NODE_EXTRACTORS = {
     # Sampling
     "KSampler": SamplerExtractor,
     "KSamplerAdvanced": KSamplerAdvancedExtractor,
-    "SamplerCustomAdvanced": SamplerCustomAdvancedExtractor,  # Updated to use dedicated extractor
+    "SamplerCustomAdvanced": SamplerCustomAdvancedExtractor,
+    "TSC_KSampler": TSCKSamplerExtractor,   # Efficient Nodes
+    "TSC_KSamplerAdvanced": TSCKSamplerAdvancedExtractor,  # Efficient Nodes
     # Sampling Selectors
     "KSamplerSelect": KSamplerSelectExtractor,  # Add KSamplerSelect
     "BasicScheduler": BasicSchedulerExtractor,  # Add BasicScheduler
     # Loaders
     "CheckpointLoaderSimple": CheckpointLoaderExtractor,
+    "comfyLoader": CheckpointLoaderExtractor,  # easy comfyLoader
+    "TSC_EfficientLoader": TSCCheckpointLoaderExtractor,  # Efficient Nodes
     "UNETLoader": UNETLoaderExtractor,          # Updated to use dedicated extractor
     "UnetLoaderGGUF": UNETLoaderExtractor,  # Updated to use dedicated extractor
     "LoraLoader": LoraLoaderExtractor,
@@ -412,6 +627,9 @@ NODE_EXTRACTORS = {
     "CLIPTextEncode": CLIPTextEncodeExtractor,
     "CLIPTextEncodeFlux": CLIPTextEncodeFluxExtractor,  # Add CLIPTextEncodeFlux
     "WAS_Text_to_Conditioning": CLIPTextEncodeExtractor,
+    "AdvancedCLIPTextEncode": CLIPTextEncodeExtractor,  # From https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb
+    "smZ_CLIPTextEncode": CLIPTextEncodeExtractor,  # From https://github.com/shiimizu/ComfyUI_smZNodes
+    "CR_ApplyControlNetStack": CR_ApplyControlNetStackExtractor,  # Add CR_ApplyControlNetStack
     # Latent
     "EmptyLatentImage": ImageSizeExtractor,
     # Flux

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):
@@ -19,8 +21,7 @@ class DebugMetadata:
             },
         }
 
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("metadata_json",)
+    RETURN_TYPES = ()
     FUNCTION = "process_metadata"
 
     def process_metadata(self, images, id):
@@ -32,7 +33,13 @@ class DebugMetadata:
             # Use the MetadataProcessor to convert it to JSON string
             metadata_json = MetadataProcessor.to_json(metadata, id)
             
-            return (metadata_json,)
+            # Send metadata to frontend for display
+            PromptServer.instance.send_sync("metadata_update", {
+                "id": id,
+                "metadata": 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

@@ -2,14 +2,15 @@ import logging
 from nodes import LoraLoader
 from comfy.comfy_types import IO # type: ignore
 import asyncio
-from .utils import FlexibleOptionalInputType, any_type, get_lora_info, extract_lora_name, get_loras_list
+from ..utils.utils import get_lora_info
+from .utils import FlexibleOptionalInputType, any_type, extract_lora_name, get_loras_list, nunchaku_load_lora
 
 logger = logging.getLogger(__name__)
 
 class LoraManagerLoader:
     NAME = "Lora Loader (LoraManager)"
     CATEGORY = "Lora Manager/loaders"
-
+    
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -37,19 +38,39 @@ class LoraManagerLoader:
         
         clip = kwargs.get('clip', None)
         lora_stack = kwargs.get('lora_stack', None)
+        
+        # Check if model is a Nunchaku Flux model - simplified approach
+        is_nunchaku_model = False
+        
+        try:
+            model_wrapper = model.model.diffusion_model
+            # Check if model is a Nunchaku Flux model using only class name
+            if model_wrapper.__class__.__name__ == "ComfyFluxWrapper":
+                is_nunchaku_model = True
+                logger.info("Detected Nunchaku Flux model")
+        except (AttributeError, TypeError):
+            # Not a model with the expected structure
+            pass
+        
         # First process lora_stack if available
         if lora_stack:
             for lora_path, model_strength, clip_strength in lora_stack:
-                # Apply the LoRA using the provided path and strengths
-                model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+                # Apply the LoRA using the appropriate loader
+                if is_nunchaku_model:
+                    # Use our custom function for Flux models
+                    model = nunchaku_load_lora(model, lora_path, model_strength)
+                    # clip remains unchanged for Nunchaku models
+                else:
+                    # Use default loader for standard models
+                    model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
                 
                 # Extract lora name for trigger words lookup
                 lora_name = extract_lora_name(lora_path)
                 _, trigger_words = asyncio.run(get_lora_info(lora_name))
                 
                 all_trigger_words.extend(trigger_words)
-                # Add clip strength to output if different from model strength
-                if abs(model_strength - clip_strength) > 0.001:
+                # Add clip strength to output if different from model strength (except for Nunchaku models)
+                if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
                     loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
                 else:
                     loaded_loras.append(f"{lora_name}: {model_strength}")
@@ -68,11 +89,17 @@ class LoraManagerLoader:
             # Get lora path and trigger words
             lora_path, trigger_words = asyncio.run(get_lora_info(lora_name))
             
-            # Apply the LoRA using the resolved path with separate strengths
-            model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+            # Apply the LoRA using the appropriate loader
+            if is_nunchaku_model:
+                # For Nunchaku models, use our custom function
+                model = nunchaku_load_lora(model, lora_path, model_strength)
+                # clip remains unchanged
+            else:
+                # Use default loader for standard models
+                model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
             
-            # Include clip strength in output if different from model strength
-            if abs(model_strength - clip_strength) > 0.001:
+            # Include clip strength in output if different from model strength and not a Nunchaku model
+            if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
                 loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
             else:
                 loaded_loras.append(f"{lora_name}: {model_strength}")

py/nodes/lora_stacker.py

@@ -1,9 +1,9 @@
 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
+from ..utils.utils import get_lora_info
+from .utils import FlexibleOptionalInputType, any_type, extract_lora_name, get_loras_list
+
 import logging
 
 logger = logging.getLogger(__name__)

py/nodes/save_image.py

@@ -31,14 +31,33 @@ 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",

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",
             },
         }
 
@@ -41,17 +48,11 @@ 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_data = self._get_toggle_data(kwargs, 'orinalMessage')
         trigger_words = trigger_words_data if isinstance(trigger_words_data, str) else ""
         
-        # Send trigger words to frontend
-        # PromptServer.instance.send_sync("trigger_word_update", {
-        #     "id": id,
-        #     "message": trigger_words
-        # })
-        
         filtered_triggers = trigger_words
         
         # Get toggle data with support for both formats

py/nodes/utils.py

@@ -35,31 +35,11 @@ any_type = AnyType("*")
 # Common methods extracted from lora_loader.py and lora_stacker.py
 import os
 import logging
-import asyncio
-from ..services.lora_scanner import LoraScanner
-from ..config import config
+import copy
+import folder_paths
 
 logger = logging.getLogger(__name__)
 
-async def get_lora_info(lora_name):
-    """Get the lora path and trigger words from cache"""
-    scanner = await LoraScanner.get_instance()
-    cache = await scanner.get_cached_data()
-    
-    for item in cache.raw_data:
-        if item.get('file_name') == lora_name:
-            file_path = item.get('file_path')
-            if file_path:
-                for root in config.loras_roots:
-                    root = root.replace(os.sep, '/')
-                    if file_path.startswith(root):
-                        relative_path = os.path.relpath(file_path, root).replace(os.sep, '/')
-                        # Get trigger words from civitai metadata
-                        civitai = item.get('civitai', {})
-                        trigger_words = civitai.get('trainedWords', []) if civitai else []
-                        return relative_path, trigger_words
-    return lora_name, []  # Fallback if not found
-
 def extract_lora_name(lora_path):
     """Extract the lora name from a lora path (e.g., 'IL\\aorunIllstrious.safetensors' -> 'aorunIllstrious')"""
     # Get the basename without extension
@@ -81,4 +61,70 @@ def get_loras_list(kwargs):
     # Unexpected format
     else:
         logger.warning(f"Unexpected loras format: {type(loras_data)}")
-        return []
+        return []
+
+def load_state_dict_in_safetensors(path, device="cpu", filter_prefix=""):
+    """Simplified version of load_state_dict_in_safetensors that just loads from a local path"""  
+    import safetensors.torch
+    
+    state_dict = {}
+    with safetensors.torch.safe_open(path, framework="pt", device=device) as f:
+        for k in f.keys():
+            if filter_prefix and not k.startswith(filter_prefix):
+                continue
+            state_dict[k.removeprefix(filter_prefix)] = f.get_tensor(k)
+    return state_dict
+
+def to_diffusers(input_lora):
+    """Simplified version of to_diffusers for Flux LoRA conversion"""
+    import torch
+    from diffusers.utils.state_dict_utils import convert_unet_state_dict_to_peft
+    from diffusers.loaders import FluxLoraLoaderMixin
+    
+    if isinstance(input_lora, str):
+        tensors = load_state_dict_in_safetensors(input_lora, device="cpu")
+    else:
+        tensors = {k: v for k, v in input_lora.items()}
+
+    # Convert FP8 tensors to BF16
+    for k, v in tensors.items():
+        if v.dtype not in [torch.float64, torch.float32, torch.bfloat16, torch.float16]:
+            tensors[k] = v.to(torch.bfloat16)
+    
+    new_tensors = FluxLoraLoaderMixin.lora_state_dict(tensors)
+    new_tensors = convert_unet_state_dict_to_peft(new_tensors)
+
+    return new_tensors
+
+def nunchaku_load_lora(model, lora_name, lora_strength):
+    """Load a Flux LoRA for Nunchaku model"""   
+    model_wrapper = model.model.diffusion_model
+    transformer = model_wrapper.model
+    
+    # Save the transformer temporarily
+    model_wrapper.model = None
+    ret_model = copy.deepcopy(model)  # copy everything except the model
+    ret_model_wrapper = ret_model.model.diffusion_model
+    
+    # Restore the model and set it for the copy
+    model_wrapper.model = transformer
+    ret_model_wrapper.model = transformer
+    
+    # Get full path to the LoRA file
+    lora_path = folder_paths.get_full_path("loras", lora_name)
+    ret_model_wrapper.loras.append((lora_path, lora_strength))
+    
+    # Convert the LoRA to diffusers format
+    sd = to_diffusers(lora_path)
+    
+    # Handle embedding adjustment if needed
+    if "transformer.x_embedder.lora_A.weight" in sd:
+        new_in_channels = sd["transformer.x_embedder.lora_A.weight"].shape[1]
+        assert new_in_channels % 4 == 0
+        new_in_channels = new_in_channels // 4
+        
+        old_in_channels = ret_model.model.model_config.unet_config["in_channels"]
+        if old_in_channels < new_in_channels:
+            ret_model.model.model_config.unet_config["in_channels"] = new_in_channels
+    
+    return ret_model

py/nodes/wanvideo_lora_select.py

@@ -0,0 +1,93 @@
+from comfy.comfy_types import IO # type: ignore
+import asyncio
+import folder_paths # type: ignore
+from ..utils.utils import get_lora_info
+from .utils import FlexibleOptionalInputType, any_type, get_loras_list
+import logging
+
+logger = logging.getLogger(__name__)
+
+class WanVideoLoraSelect:
+    NAME = "WanVideo Lora Select (LoraManager)"
+    CATEGORY = "Lora Manager/stackers"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "low_mem_load": ("BOOLEAN", {"default": False, "tooltip": "Load the LORA model with less VRAM usage, slower loading"}),
+                "text": (IO.STRING, {
+                    "multiline": True, 
+                    "dynamicPrompts": True, 
+                    "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
+                    "placeholder": "LoRA syntax input: <lora:name:strength>"
+                }),
+            },
+            "optional": FlexibleOptionalInputType(any_type),
+        }
+
+    RETURN_TYPES = ("WANVIDLORA", IO.STRING, IO.STRING)
+    RETURN_NAMES = ("lora", "trigger_words", "active_loras")
+    FUNCTION = "process_loras"
+    
+    def process_loras(self, text, low_mem_load=False, **kwargs):
+        loras_list = []
+        all_trigger_words = []
+        active_loras = []
+        
+        # Process existing prev_lora if available
+        prev_lora = kwargs.get('prev_lora', None)
+        if prev_lora is not None:
+            loras_list.extend(prev_lora)
+        
+        # Get blocks if available
+        blocks = kwargs.get('blocks', {})
+        selected_blocks = blocks.get("selected_blocks", {})
+        layer_filter = blocks.get("layer_filter", "")
+        
+        # Process loras from kwargs with support for both old and new formats
+        loras_from_widget = get_loras_list(kwargs)
+        for lora in loras_from_widget:
+            if not lora.get('active', False):
+                continue
+                
+            lora_name = lora['name']
+            model_strength = float(lora['strength'])
+            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))
+            
+            # Create lora item for WanVideo format
+            lora_item = {
+                "path": folder_paths.get_full_path("loras", lora_path),
+                "strength": model_strength,
+                "name": lora_path.split(".")[0],
+                "blocks": selected_blocks,
+                "layer_filter": layer_filter,
+                "low_mem_load": low_mem_load,
+            }
+            
+            # Add to list and collect active loras
+            loras_list.append(lora_item)
+            active_loras.append((lora_name, model_strength, clip_strength))
+            
+            # Add trigger words to collection
+            all_trigger_words.extend(trigger_words)
+        
+        # Format trigger_words for output
+        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+        
+        # Format active_loras for output
+        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 (loras_list, trigger_words_text, active_loras_text)

py/recipes/__init__.py

@@ -7,7 +7,8 @@ from .parsers import (
     RecipeFormatParser,
     ComfyMetadataParser,
     MetaFormatParser,
-    AutomaticMetadataParser
+    AutomaticMetadataParser,
+    CivitaiApiMetadataParser
 )
 
 __all__ = [
@@ -18,5 +19,6 @@ __all__ = [
     'RecipeFormatParser',
     'ComfyMetadataParser',
     'MetaFormatParser',
-    'AutomaticMetadataParser'
+    'AutomaticMetadataParser',
+    'CivitaiApiMetadataParser'
 ]

py/recipes/base.py

@@ -7,7 +7,7 @@ import re
 from typing import Dict, List, Any, Optional, Tuple
 from abc import ABC, abstractmethod
 from ..config import config
-from .constants import VALID_LORA_TYPES
+from ..utils.constants import VALID_LORA_TYPES
 
 logger = logging.getLogger(__name__)
 
@@ -79,6 +79,9 @@ class RecipeMetadataParser(ABC):
             if 'model' in civitai_info and 'name' in civitai_info['model']:
                 lora_entry['name'] = civitai_info['model']['name']
             
+            lora_entry['id'] = civitai_info.get('id')
+            lora_entry['modelId'] = civitai_info.get('modelId')
+            
             # Update version if available
             if 'name' in civitai_info:
                 lora_entry['version'] = civitai_info.get('name', '')

py/recipes/constants.py

@@ -1,5 +1,8 @@
 """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',
@@ -11,6 +14,3 @@ GEN_PARAM_KEYS = [
     'size',
     'clip_skip',
 ]
-
-# Valid Lora types
-VALID_LORA_TYPES = ['lora', 'locon']

py/recipes/factory.py

@@ -5,7 +5,8 @@ from .parsers import (
     RecipeFormatParser,
     ComfyMetadataParser,
     MetaFormatParser,
-    AutomaticMetadataParser
+    AutomaticMetadataParser,
+    CivitaiApiMetadataParser
 )
 from .base import RecipeMetadataParser
 
@@ -15,29 +16,49 @@ class RecipeParserFactory:
     """Factory for creating recipe metadata parsers"""
     
     @staticmethod
-    def create_parser(user_comment: str) -> RecipeMetadataParser:
+    def create_parser(metadata) -> RecipeMetadataParser:
         """
-        Create appropriate parser based on the user comment content
+        Create appropriate parser based on the metadata content
         
         Args:
-            user_comment: The EXIF UserComment string from the image
+            metadata: The metadata from the image (dict or str)
             
         Returns:
             Appropriate RecipeMetadataParser implementation
         """
-        # Try ComfyMetadataParser first since it requires valid JSON
+        # 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(user_comment):
+            if ComfyMetadataParser().is_metadata_matching(metadata_str):
                 return ComfyMetadataParser()
         except Exception:
             # If JSON parsing fails, move on to other parsers
             pass
-            
-        if RecipeFormatParser().is_metadata_matching(user_comment):
+        
+        # Check other parsers that expect string input
+        if RecipeFormatParser().is_metadata_matching(metadata_str):
             return RecipeFormatParser()
-        elif AutomaticMetadataParser().is_metadata_matching(user_comment):
+        elif AutomaticMetadataParser().is_metadata_matching(metadata_str):
             return AutomaticMetadataParser()
-        elif MetaFormatParser().is_metadata_matching(user_comment):
+        elif MetaFormatParser().is_metadata_matching(metadata_str):
             return MetaFormatParser()
         else:
             return None

py/recipes/parsers/__init__.py

@@ -4,10 +4,12 @@ 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

@@ -19,7 +19,7 @@ class AutomaticMetadataParser(RecipeMetadataParser):
     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.]+)>'
+    EXTRANETS_REGEX = r'<(lora|hypernet):([^:]+):(-?[0-9.]+)>'
     MODEL_HASH_PATTERN = r'Model hash: ([a-zA-Z0-9]+)'
     VAE_HASH_PATTERN = r'VAE hash: ([a-zA-Z0-9]+)'
     
@@ -184,8 +184,8 @@ class AutomaticMetadataParser(RecipeMetadataParser):
                     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,
+                            'id': resource.get("modelVersionId", 0),
+                            'modelId': resource.get("modelId", 0),
                             'name': resource.get("modelName", "Unknown LoRA"),
                             'version': resource.get("modelVersionName", ""),
                             'type': resource.get("type", "lora"),

py/recipes/parsers/civitai_image.py

@@ -0,0 +1,286 @@
+"""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
+            }
+            
+            # Track already added LoRAs to prevent duplicates
+            added_loras = {}  # key: model_version_id or hash, value: index in result["loras"]
+            
+            # 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_hash = resource.get("hash", "")
+                        
+                        # Skip if we've already added this LoRA by hash
+                        if lora_hash and lora_hash in added_loras:
+                            continue
+                        
+                        lora_entry = {
+                            'name': resource.get("name", "Unknown LoRA"),
+                            'type': "lora",
+                            'weight': float(resource.get("weight", 1.0)),
+                            'hash': lora_hash,
+                            'existsLocally': False,
+                            'localPath': None,
+                            'file_name': resource.get("name", "Unknown"),
+                            'thumbnailUrl': '/loras_static/images/no-preview.png',
+                            'baseModel': '',
+                            'size': 0,
+                            'downloadUrl': '',
+                            'isDeleted': False
+                        }
+                        
+                        # Try to get info from Civitai if hash is available
+                        if lora_entry['hash'] and civitai_client:
+                            try:
+                                civitai_info = await civitai_client.get_model_by_hash(lora_hash)
+                                
+                                populated_entry = await self.populate_lora_from_civitai(
+                                    lora_entry,
+                                    civitai_info,
+                                    recipe_scanner,
+                                    base_model_counts,
+                                    lora_hash
+                                )
+                                
+                                if populated_entry is None:
+                                    continue  # Skip invalid LoRA types
+                                    
+                                lora_entry = populated_entry
+                                
+                                # If we have a version ID from Civitai, track it for deduplication
+                                if 'id' in lora_entry and lora_entry['id']:
+                                    added_loras[str(lora_entry['id'])] = len(result["loras"])
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}")
+                        
+                        # Track by hash if we have it
+                        if lora_hash:
+                            added_loras[lora_hash] = len(result["loras"])
+                            
+                        result["loras"].append(lora_entry)
+            
+            # Process civitaiResources array
+            if "civitaiResources" in metadata and isinstance(metadata["civitaiResources"], list):
+                for resource in metadata["civitaiResources"]:
+                    # Skip resources that aren't LoRAs or LyCORIS
+                    if resource.get("type") not in ["lora", "lycoris"] and "type" not in resource:
+                        continue
+                        
+                    # Get unique identifier for deduplication
+                    version_id = str(resource.get("modelVersionId", ""))
+                    
+                    # Skip if we've already added this LoRA
+                    if version_id and version_id in added_loras:
+                        continue
+                    
+                    # Initialize lora entry
+                    lora_entry = {
+                        'id': resource.get("modelVersionId", 0),
+                        'modelId': resource.get("modelId", 0),
+                        'name': resource.get("modelName", "Unknown LoRA"),
+                        'version': resource.get("modelVersionName", ""),
+                        'type': resource.get("type", "lora"),
+                        'weight': round(float(resource.get("weight", 1.0)), 2),
+                        'existsLocally': False,
+                        'thumbnailUrl': '/loras_static/images/no-preview.png',
+                        'baseModel': '',
+                        'size': 0,
+                        'downloadUrl': '',
+                        'isDeleted': False
+                    }
+                    
+                    # Try to get info from Civitai if modelVersionId is available
+                    if version_id and civitai_client:
+                        try:
+                            # 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 {version_id}: {e}")
+                    
+                    # Track this LoRA in our deduplication dict
+                    if version_id:
+                        added_loras[version_id] = len(result["loras"])
+                        
+                    result["loras"].append(lora_entry)
+            
+            # Process additionalResources array
+            if "additionalResources" in metadata and isinstance(metadata["additionalResources"], list):
+                for resource in metadata["additionalResources"]:
+                    # Skip resources that aren't LoRAs or LyCORIS
+                    if resource.get("type") not in ["lora", "lycoris"] and "type" not in resource:
+                        continue
+                        
+                    lora_type = resource.get("type", "lora")
+                    name = resource.get("name", "")
+                    
+                    # Extract ID from URN format if available
+                    version_id = None
+                    if name and "civitai:" in name:
+                        parts = name.split("@")
+                        if len(parts) > 1:
+                            version_id = parts[1]
+                            
+                            # Skip if we've already added this LoRA
+                            if version_id in added_loras:
+                                continue
+                    
+                    lora_entry = {
+                        'name': name,
+                        'type': lora_type,
+                        'weight': float(resource.get("strength", 1.0)),
+                        'hash': "",
+                        'existsLocally': False,
+                        'localPath': None,
+                        'file_name': name,
+                        'thumbnailUrl': '/loras_static/images/no-preview.png',
+                        'baseModel': '',
+                        'size': 0,
+                        'downloadUrl': '',
+                        'isDeleted': False
+                    }
+                    
+                    # If we have a version ID and civitai client, try to get more info
+                    if version_id and civitai_client:
+                        try:
+                            # Use get_model_version_info with the version ID
+                            civitai_info, error = await civitai_client.get_model_version_info(version_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
+                                
+                                # Track this LoRA for deduplication
+                                if version_id:
+                                    added_loras[version_id] = len(result["loras"])
+                        except Exception as e:
+                            logger.error(f"Error fetching Civitai info for model ID {version_id}: {e}")
+                    
+                        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/recipe_format.py

@@ -43,7 +43,7 @@ class RecipeFormatParser(RecipeMetadataParser):
             for lora in recipe_metadata.get('loras', []):
                 # Convert recipe lora format to frontend format
                 lora_entry = {
-                    'id': lora.get('modelVersionId', ''),
+                    'id': int(lora.get('modelVersionId', 0)),
                     'name': lora.get('modelName', ''),
                     'version': lora.get('modelVersionName', ''),
                     'type': 'lora',

py/routes/api_routes.py

@@ -6,15 +6,15 @@ from typing import Dict
 from server import PromptServer # type: ignore
 
 from ..utils.routes_common import ModelRouteUtils
-from ..nodes.utils import get_lora_info
+from ..utils.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__)
@@ -45,17 +45,19 @@ 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)
         app.router.add_get('/ws/fetch-progress', ws_manager.handle_connection)
+        app.router.add_get('/ws/download-progress', ws_manager.handle_download_connection)  # Add new WebSocket route for download progress
         app.router.add_get('/ws/init-progress', ws_manager.handle_init_connection)  # Add new WebSocket route
         app.router.add_get('/api/lora-roots', routes.get_lora_roots)
         app.router.add_get('/api/folders', routes.get_folders)
         app.router.add_get('/api/civitai/versions/{model_id}', routes.get_civitai_versions)
         app.router.add_get('/api/civitai/model/version/{modelVersionId}', routes.get_civitai_model_by_version)
         app.router.add_get('/api/civitai/model/hash/{hash}', routes.get_civitai_model_by_hash)
-        app.router.add_post('/api/download-lora', routes.download_lora)
+        app.router.add_post('/api/download-model', routes.download_model)
         app.router.add_post('/api/move_model', routes.move_model)
         app.router.add_get('/api/lora-model-description', routes.get_lora_model_description)  # Add new route
         app.router.add_post('/api/loras/save-metadata', routes.save_metadata)
@@ -64,7 +66,7 @@ class ApiRoutes:
         app.router.add_get('/api/loras/top-tags', routes.get_top_tags)  # Add new route for top tags
         app.router.add_get('/api/loras/base-models', routes.get_base_models)  # Add new route for base models
         app.router.add_get('/api/lora-civitai-url', routes.get_lora_civitai_url)  # Add new route for Civitai URL
-        app.router.add_post('/api/rename_lora', routes.rename_lora)  # Add new route for renaming LoRA files
+        app.router.add_post('/api/loras/rename', routes.rename_lora)  # Add new route for renaming LoRA files
         app.router.add_get('/api/loras/scan', routes.scan_loras)  # Add new route for scanning LoRA files
         
         # Add the new trigger words route
@@ -80,6 +82,16 @@ class ApiRoutes:
         # 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:
@@ -96,7 +108,21 @@ 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"""
@@ -221,66 +247,6 @@ 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:
@@ -393,8 +359,8 @@ class ApiRoutes:
             versions = response.get('modelVersions', [])
             model_type = response.get('type', '')
             
-            # Check model type - should be LORA or LoCon
-            if model_type.lower() not in ['lora', 'locon']:
+            # 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 or LoCon, got {model_type}"
                 }, status=400)
@@ -471,69 +437,8 @@ class ApiRoutes:
                 "error": str(e)
             }, status=500)
 
-    async def download_lora(self, request: web.Request) -> web.Response:
-        async with self._download_lock:
-            try:
-                if self.download_manager is None:
-                    self.download_manager = await ServiceRegistry.get_download_manager()
-                
-                data = await request.json()
-                
-                # Create progress callback
-                async def progress_callback(progress):
-                    await ws_manager.broadcast({
-                        'status': 'progress',
-                        'progress': progress
-                    })
-                
-                # Check which identifier is provided
-                download_url = data.get('download_url')
-                model_hash = data.get('model_hash')
-                model_version_id = data.get('model_version_id')
-                
-                # Validate that at least one identifier is provided
-                if not any([download_url, model_hash, model_version_id]):
-                    return web.Response(
-                        status=400, 
-                        text="Missing required parameter: Please provide either 'download_url', 'hash', or 'modelVersionId'"
-                    )
-                
-                result = await self.download_manager.download_from_civitai(
-                    download_url=download_url,
-                    model_hash=model_hash,
-                    model_version_id=model_version_id,
-                    save_dir=data.get('lora_root'),
-                    relative_path=data.get('relative_path'),
-                    progress_callback=progress_callback
-                )
-                
-                if not result.get('success', False):
-                    error_message = result.get('error', 'Unknown error')
-                    
-                    # Return 401 for early access errors
-                    if 'early access' in error_message.lower():
-                        logger.warning(f"Early access download failed: {error_message}")
-                        return web.Response(
-                            status=401,  # Use 401 status code to match Civitai's response
-                            text=error_message
-                        )
-                    
-                    return web.Response(status=500, text=error_message)
-                
-                return web.json_response(result)
-            except Exception as e:
-                error_message = str(e)
-                
-                # Check if this might be an early access error
-                if '401' in error_message:
-                    logger.warning(f"Early access error (401): {error_message}")
-                    return web.Response(
-                        status=401,
-                        text="Early Access Restriction: This LoRA requires purchase. Please buy early access on Civitai.com."
-                    )
-                
-                logger.error(f"Error downloading LoRA: {error_message}")
-                return web.Response(status=500, text=error_message)
+    async def download_model(self, request: web.Request) -> web.Response:
+        return await ModelRouteUtils.handle_download_model(request, self.download_manager)
 
 
     async def move_model(self, request: web.Request) -> web.Response:
@@ -616,8 +521,7 @@ 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)
@@ -828,11 +732,13 @@ class ApiRoutes:
                             
                             metadata['modelDescription'] = description
                             metadata['tags'] = tags
-                            metadata['creator'] = creator
+                            # Ensure the civitai dict exists
+                            if 'civitai' not in metadata:
+                                metadata['civitai'] = {}
+                            # Store creator in the civitai nested structure
+                            metadata['civitai']['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, True)
                         except Exception as e:
                             logger.error(f"Error saving model metadata: {e}")
             
@@ -907,139 +813,10 @@ class ApiRoutes:
 
     async def rename_lora(self, request: web.Request) -> web.Response:
         """Handle renaming a LoRA file and its associated files"""
-        try:
-            if self.scanner is None:
-                self.scanner = await ServiceRegistry.get_lora_scanner()
-                
-            if self.download_manager is None:
-                self.download_manager = await ServiceRegistry.get_download_manager()
-                
-            data = await request.json()
-            file_path = data.get('file_path')
-            new_file_name = data.get('new_file_name')
+        if self.scanner is None:
+            self.scanner = await ServiceRegistry.get_lora_scanner()
             
-            if not file_path or not new_file_name:
-                return web.json_response({
-                    'success': False,
-                    'error': 'File path and new file name are required'
-                }, status=400)
-            
-            # Validate the new file name (no path separators or invalid characters)
-            invalid_chars = ['/', '\\', ':', '*', '?', '"', '<', '>', '|']
-            if any(char in new_file_name for char in invalid_chars):
-                return web.json_response({
-                    'success': False,
-                    'error': 'Invalid characters in file name'
-                }, status=400)
-            
-            # Get the directory and current file name
-            target_dir = os.path.dirname(file_path)
-            old_file_name = os.path.splitext(os.path.basename(file_path))[0]
-            
-            # Check if the target file already exists
-            new_file_path = os.path.join(target_dir, f"{new_file_name}.safetensors").replace(os.sep, '/')
-            if os.path.exists(new_file_path):
-                return web.json_response({
-                    'success': False,
-                    'error': 'A file with this name already exists'
-                }, status=400)
-            
-            # Define the patterns for associated files
-            patterns = [
-                f"{old_file_name}.safetensors",  # Required
-                f"{old_file_name}.metadata.json",
-            ]
-            
-            # Add all preview file extensions
-            for ext in PREVIEW_EXTENSIONS:
-                patterns.append(f"{old_file_name}{ext}")
-            
-            # Find all matching files
-            existing_files = []
-            for pattern in patterns:
-                path = os.path.join(target_dir, pattern)
-                if os.path.exists(path):
-                    existing_files.append((path, pattern))
-            
-            # Get the hash from the main file to update hash index
-            hash_value = None
-            metadata = None
-            metadata_path = os.path.join(target_dir, f"{old_file_name}.metadata.json")
-            
-            if os.path.exists(metadata_path):
-                metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
-                hash_value = metadata.get('sha256')
-            
-            # Rename all files
-            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)
-
-                # Create the new path
-                new_path = os.path.join(target_dir, f"{new_file_name}{ext}").replace(os.sep, '/')
-                
-                # Rename the file
-                os.rename(old_path, new_path)
-                renamed_files.append(new_path)
-                
-                # Keep track of metadata path for later update
-                if ext == '.metadata.json':
-                    new_metadata_path = new_path
-            
-            # Update the metadata file with new file name and paths
-            if new_metadata_path and metadata:
-                # Update file_name, file_path and preview_url in metadata
-                metadata['file_name'] = new_file_name
-                metadata['file_path'] = new_file_path
-                
-                # Update preview_url if it exists
-                if 'preview_url' in metadata and metadata['preview_url']:
-                    old_preview = metadata['preview_url']
-                    ext = ModelRouteUtils.get_multipart_ext(old_preview)
-                    new_preview = os.path.join(target_dir, f"{new_file_name}{ext}").replace(os.sep, '/')
-                    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)
-            
-            # Update the scanner cache
-            if metadata:
-                await self.scanner.update_single_model_cache(file_path, new_file_path, metadata)
-                
-                # Update recipe files and cache if hash is available
-                if hash_value:
-                    recipe_scanner = await ServiceRegistry.get_recipe_scanner()
-                    recipes_updated, cache_updated = await recipe_scanner.update_lora_filename_by_hash(hash_value, new_file_name)
-                    logger.info(f"Updated {recipes_updated} recipe files and {cache_updated} cache entries for renamed LoRA")
-            
-            return web.json_response({
-                'success': True,
-                'new_file_path': new_file_path,
-                'renamed_files': renamed_files,
-                'reload_required': False
-            })
-            
-        except Exception as e:
-            logger.error(f"Error renaming LoRA: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
+        return await ModelRouteUtils.handle_rename_model(request, self.scanner)
 
     async def get_trigger_words(self, request: web.Request) -> web.Response:
         """Get trigger words for specified LoRA models"""
@@ -1169,3 +946,124 @@ class ApiRoutes:
                 '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
@@ -51,12 +52,20 @@ 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
-        
-        # Add new WebSocket endpoint for checkpoint progress
-        app.router.add_get('/ws/checkpoint-progress', ws_manager.handle_checkpoint_connection)
+        app.router.add_post('/api/checkpoints/rename', self.rename_checkpoint)  # Add new rename endpoint
+
+        # 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"""
@@ -510,80 +519,25 @@ 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"""
         return await ModelRouteUtils.handle_replace_preview(request, self.scanner)
 
-    async def download_checkpoint(self, request: web.Request) -> web.Response:
-        """Handle checkpoint download request"""
-        async with self._download_lock:
-            # Get the download manager from service registry if not already initialized
-            if self.download_manager is None:
-                self.download_manager = await ServiceRegistry.get_download_manager()
-            
-            try:
-                data = await request.json()
-                
-                # Create progress callback that uses checkpoint-specific WebSocket
-                async def progress_callback(progress):
-                    await ws_manager.broadcast_checkpoint_progress({
-                        'status': 'progress',
-                        'progress': progress
-                    })
-                
-                # Check which identifier is provided
-                download_url = data.get('download_url')
-                model_hash = data.get('model_hash')
-                model_version_id = data.get('model_version_id')
-                
-                # Validate that at least one identifier is provided
-                if not any([download_url, model_hash, model_version_id]):
-                    return web.Response(
-                        status=400, 
-                        text="Missing required parameter: Please provide either 'download_url', 'hash', or 'modelVersionId'"
-                    )
-                
-                result = await self.download_manager.download_from_civitai(
-                    download_url=download_url,
-                    model_hash=model_hash,
-                    model_version_id=model_version_id,
-                    save_dir=data.get('checkpoint_root'),
-                    relative_path=data.get('relative_path', ''),
-                    progress_callback=progress_callback,
-                    model_type="checkpoint"
-                )
-                
-                if not result.get('success', False):
-                    error_message = result.get('error', 'Unknown error')
-                    
-                    # Return 401 for early access errors
-                    if 'early access' in error_message.lower():
-                        logger.warning(f"Early access download failed: {error_message}")
-                        return web.Response(
-                            status=401,
-                            text=f"Early Access Restriction: {error_message}"
-                        )
-                    
-                    return web.Response(status=500, text=error_message)
-                
-                return web.json_response(result)
-                
-            except Exception as e:
-                error_message = str(e)
-                
-                # Check if this might be an early access error
-                if '401' in error_message:
-                    logger.warning(f"Early access error (401): {error_message}")
-                    return web.Response(
-                        status=401,
-                        text="Early Access Restriction: This model requires purchase. Please ensure you have purchased early access and are logged in to Civitai."
-                    )
-                
-                logger.error(f"Error downloading checkpoint: {error_message}")
-                return web.Response(status=500, text=error_message)
-
     async def get_checkpoint_roots(self, request):
         """Return the checkpoint root directories"""
         try:
@@ -626,8 +580,7 @@ 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)
@@ -695,3 +648,124 @@ 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)
+
+    async def rename_checkpoint(self, request: web.Request) -> web.Response:
+        """Handle renaming a checkpoint file and its associated files"""
+        return await ModelRouteUtils.handle_rename_model(request, self.scanner)

py/routes/example_images_routes.py

@@ -0,0 +1,67 @@
+import logging
+from ..utils.example_images_download_manager import DownloadManager
+from ..utils.example_images_processor import ExampleImagesProcessor
+from ..utils.example_images_file_manager import ExampleImagesFileManager
+
+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/recipe_routes.py

@@ -3,7 +3,6 @@ import time
 import base64
 import numpy as np
 from PIL import Image
-import torch
 import io
 import logging
 from aiohttp import web
@@ -254,6 +253,7 @@ 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
@@ -287,17 +287,63 @@ 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:
@@ -601,7 +647,7 @@ class RecipeRoutes:
                     "file_name": lora.get("file_name", "") or os.path.splitext(os.path.basename(lora.get("localPath", "")))[0] if lora.get("localPath") else "",
                     "hash": lora.get("hash", "").lower() if lora.get("hash") else "",
                     "strength": float(lora.get("weight", 1.0)),
-                    "modelVersionId": lora.get("id", ""),
+                    "modelVersionId": lora.get("id", 0),
                     "modelName": lora.get("name", ""),
                     "modelVersionName": lora.get("version", ""),
                     "isDeleted": lora.get("isDeleted", False),  # Preserve deletion status in saved recipe
@@ -949,7 +995,7 @@ class RecipeRoutes:
             else:
                 latest_image = None
             
-            if not latest_image:
+            if latest_image is None:
                 return web.json_response({"error": "No recent images found to use for recipe. Try generating an image first."}, status=400)
             
             # Convert the image data to bytes - handle tuple and tensor cases
@@ -971,6 +1017,8 @@ class RecipeRoutes:
                     shape_info = tensor_image.shape
                     logger.debug(f"Tensor shape: {shape_info}, dtype: {tensor_image.dtype}")
                 
+                import torch
+                
                 # Convert tensor to numpy array
                 if isinstance(tensor_image, torch.Tensor):
                     image_np = tensor_image.cpu().numpy()
@@ -1060,7 +1108,7 @@ class RecipeRoutes:
                         "file_name": lora_name,
                         "hash": lora_info.get("sha256", "").lower() if lora_info else "",
                         "strength": float(lora_strength),
-                        "modelVersionId": lora_info.get("civitai", {}).get("id", "") if lora_info else "",
+                        "modelVersionId": lora_info.get("civitai", {}).get("id", 0) if lora_info else 0,
                         "modelName": lora_info.get("civitai", {}).get("model", {}).get("name", "") if lora_info else lora_name,
                         "modelVersionName": lora_info.get("civitai", {}).get("name", "") if lora_info else "",
                         "isDeleted": False
@@ -1219,9 +1267,9 @@ class RecipeRoutes:
             data = await request.json()
             
             # Validate required fields
-            if 'title' not in data and 'tags' not in data and 'source_path' not in data:
+            if 'title' not in data and 'tags' not in data and 'source_path' not in data and 'preview_nsfw_level' not in data:
                 return web.json_response({
-                    "error": "At least one field to update must be provided (title or tags or source_path)"
+                    "error": "At least one field to update must be provided (title or tags or source_path or preview_nsfw_level)"
                 }, status=400)
             
             # Use the recipe scanner's update method
@@ -1249,7 +1297,7 @@ class RecipeRoutes:
             data = await request.json()
             
             # Validate required fields
-            required_fields = ['recipe_id', 'lora_data', 'target_name']
+            required_fields = ['recipe_id', 'lora_index', 'target_name']
             for field in required_fields:
                 if field not in data:
                     return web.json_response({
@@ -1257,7 +1305,7 @@ class RecipeRoutes:
                     }, status=400)
             
             recipe_id = data['recipe_id']
-            lora_data = data['lora_data']
+            lora_index = int(data['lora_index'])
             target_name = data['target_name']
             
             # Get recipe scanner
@@ -1277,46 +1325,27 @@ class RecipeRoutes:
             # Load recipe data
             with open(recipe_path, 'r', encoding='utf-8') as f:
                 recipe_data = json.load(f)
-                
-            # Find the deleted LoRA in the recipe
-            found = False
-            updated_lora = None
+
+            lora = recipe_data.get("loras", [])[lora_index] if lora_index < len(recipe_data.get('loras', [])) else None
+
+            if lora is None:
+                return web.json_response({"error": "LoRA index out of range in recipe"}, status=404)
+
+            # Update LoRA data
+            lora['isDeleted'] = False
+            lora['exclude'] = False
+            lora['file_name'] = target_name
             
-            # Identification can be by hash, modelVersionId, or modelName
-            for i, lora in enumerate(recipe_data.get('loras', [])):
-                match_found = False
-                
-                # Try to match by available identifiers
-                if 'hash' in lora and 'hash' in lora_data and lora['hash'] == lora_data['hash']:
-                    match_found = True
-                elif 'modelVersionId' in lora and 'modelVersionId' in lora_data and lora['modelVersionId'] == lora_data['modelVersionId']:
-                    match_found = True
-                elif 'modelName' in lora and 'modelName' in lora_data and lora['modelName'] == lora_data['modelName']:
-                    match_found = True
-                    
-                if match_found:
-                    # Update LoRA data
-                    lora['isDeleted'] = False
-                    lora['file_name'] = target_name
-                    
-                    # Update with information from the target LoRA
-                    if 'sha256' in target_lora:
-                        lora['hash'] = target_lora['sha256'].lower()
-                    if target_lora.get("civitai"):
-                        lora['modelName'] = target_lora['civitai']['model']['name']
-                        lora['modelVersionName'] = target_lora['civitai']['name']
-                        lora['modelVersionId'] = target_lora['civitai']['id']
-                        
-                    # Keep original fields for identification
-                    
-                    # Mark as found and store updated lora
-                    found = True
-                    updated_lora = dict(lora)  # Make a copy for response
-                    break
-                    
-            if not found:
-                return web.json_response({"error": "Could not find matching deleted LoRA in recipe"}, status=404)
+            # Update with information from the target LoRA
+            if 'sha256' in target_lora:
+                lora['hash'] = target_lora['sha256'].lower()
+            if target_lora.get("civitai"):
+                lora['modelName'] = target_lora['civitai']['model']['name']
+                lora['modelVersionName'] = target_lora['civitai']['name']
+                lora['modelVersionId'] = target_lora['civitai']['id']
             
+            updated_lora = dict(lora)  # Make a copy for response
+
             # Recalculate recipe fingerprint after updating LoRA
             from ..utils.utils import calculate_recipe_fingerprint
             recipe_data['fingerprint'] = calculate_recipe_fingerprint(recipe_data.get('loras', []))
@@ -1326,7 +1355,7 @@ class RecipeRoutes:
                 json.dump(recipe_data, f, indent=4, ensure_ascii=False)
 
             updated_lora['inLibrary'] = True
-            updated_lora['preview_url'] = target_lora['preview_url']
+            updated_lora['preview_url'] = config.get_preview_static_url(target_lora['preview_url'])
             updated_lora['localPath'] = target_lora['file_path']
                 
             # Update in cache if it exists

py/routes/stats_routes.py

@@ -0,0 +1,438 @@
+import os
+import json
+import jinja2
+from aiohttp import web
+import logging
+from datetime import datetime, timedelta
+from collections import defaultdict, Counter
+from typing import Dict, List, Any
+
+from ..config import config
+from ..services.settings_manager import settings
+from ..services.service_registry import ServiceRegistry
+from ..utils.usage_stats import UsageStats
+
+logger = logging.getLogger(__name__)
+
+class StatsRoutes:
+    """Route handlers for Statistics page and API endpoints"""
+    
+    def __init__(self):
+        self.lora_scanner = None
+        self.checkpoint_scanner = None
+        self.usage_stats = None
+        self.template_env = jinja2.Environment(
+            loader=jinja2.FileSystemLoader(config.templates_path),
+            autoescape=True
+        )
+
+    async def init_services(self):
+        """Initialize services from ServiceRegistry"""
+        self.lora_scanner = await ServiceRegistry.get_lora_scanner()
+        self.checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+        self.usage_stats = UsageStats()
+
+    async def handle_stats_page(self, request: web.Request) -> web.Response:
+        """Handle GET /statistics request"""
+        try:
+            # Ensure services are initialized
+            await self.init_services()
+            
+            # Check if scanners are initializing
+            lora_initializing = (
+                self.lora_scanner._cache is None or 
+                (hasattr(self.lora_scanner, 'is_initializing') and self.lora_scanner.is_initializing())
+            )
+            
+            checkpoint_initializing = (
+                self.checkpoint_scanner._cache is None or
+                (hasattr(self.checkpoint_scanner, '_is_initializing') and self.checkpoint_scanner._is_initializing)
+            )
+            
+            is_initializing = lora_initializing or checkpoint_initializing
+
+            template = self.template_env.get_template('statistics.html')
+            rendered = template.render(
+                is_initializing=is_initializing,
+                settings=settings,
+                request=request
+            )
+            
+            return web.Response(
+                text=rendered,
+                content_type='text/html'
+            )
+            
+        except Exception as e:
+            logger.error(f"Error handling statistics request: {e}", exc_info=True)
+            return web.Response(
+                text="Error loading statistics page",
+                status=500
+            )
+
+    async def get_collection_overview(self, request: web.Request) -> web.Response:
+        """Get collection overview statistics"""
+        try:
+            await self.init_services()
+            
+            # Get LoRA statistics
+            lora_cache = await self.lora_scanner.get_cached_data()
+            lora_count = len(lora_cache.raw_data)
+            lora_size = sum(lora.get('size', 0) for lora in lora_cache.raw_data)
+            
+            # Get Checkpoint statistics
+            checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
+            checkpoint_count = len(checkpoint_cache.raw_data)
+            checkpoint_size = sum(cp.get('size', 0) for cp in checkpoint_cache.raw_data)
+            
+            # Get usage statistics
+            usage_data = await self.usage_stats.get_stats()
+            
+            return web.json_response({
+                'success': True,
+                'data': {
+                    'total_models': lora_count + checkpoint_count,
+                    'lora_count': lora_count,
+                    'checkpoint_count': checkpoint_count,
+                    'total_size': lora_size + checkpoint_size,
+                    'lora_size': lora_size,
+                    'checkpoint_size': checkpoint_size,
+                    'total_generations': usage_data.get('total_executions', 0),
+                    'unused_loras': self._count_unused_models(lora_cache.raw_data, usage_data.get('loras', {})),
+                    'unused_checkpoints': self._count_unused_models(checkpoint_cache.raw_data, usage_data.get('checkpoints', {}))
+                }
+            })
+            
+        except Exception as e:
+            logger.error(f"Error getting collection overview: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    async def get_usage_analytics(self, request: web.Request) -> web.Response:
+        """Get usage analytics data"""
+        try:
+            await self.init_services()
+            
+            # Get usage statistics
+            usage_data = await self.usage_stats.get_stats()
+            
+            # Get model data for enrichment
+            lora_cache = await self.lora_scanner.get_cached_data()
+            checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
+            
+            # Create hash to model mapping
+            lora_map = {lora['sha256']: lora for lora in lora_cache.raw_data}
+            checkpoint_map = {cp['sha256']: cp for cp in checkpoint_cache.raw_data}
+            
+            # Prepare top used models
+            top_loras = self._get_top_used_models(usage_data.get('loras', {}), lora_map, 10)
+            top_checkpoints = self._get_top_used_models(usage_data.get('checkpoints', {}), checkpoint_map, 10)
+            
+            # Prepare usage timeline (last 30 days)
+            timeline = self._get_usage_timeline(usage_data, 30)
+            
+            return web.json_response({
+                'success': True,
+                'data': {
+                    'top_loras': top_loras,
+                    'top_checkpoints': top_checkpoints,
+                    'usage_timeline': timeline,
+                    'total_executions': usage_data.get('total_executions', 0)
+                }
+            })
+            
+        except Exception as e:
+            logger.error(f"Error getting usage analytics: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    async def get_base_model_distribution(self, request: web.Request) -> web.Response:
+        """Get base model distribution statistics"""
+        try:
+            await self.init_services()
+            
+            # Get model data
+            lora_cache = await self.lora_scanner.get_cached_data()
+            checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
+            
+            # Count by base model
+            lora_base_models = Counter(lora.get('base_model', 'Unknown') for lora in lora_cache.raw_data)
+            checkpoint_base_models = Counter(cp.get('base_model', 'Unknown') for cp in checkpoint_cache.raw_data)
+            
+            return web.json_response({
+                'success': True,
+                'data': {
+                    'loras': dict(lora_base_models),
+                    'checkpoints': dict(checkpoint_base_models)
+                }
+            })
+            
+        except Exception as e:
+            logger.error(f"Error getting base model distribution: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    async def get_tag_analytics(self, request: web.Request) -> web.Response:
+        """Get tag usage analytics"""
+        try:
+            await self.init_services()
+            
+            # Get model data
+            lora_cache = await self.lora_scanner.get_cached_data()
+            checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
+            
+            # Count tag frequencies
+            all_tags = []
+            for lora in lora_cache.raw_data:
+                all_tags.extend(lora.get('tags', []))
+            for cp in checkpoint_cache.raw_data:
+                all_tags.extend(cp.get('tags', []))
+            
+            tag_counts = Counter(all_tags)
+            
+            # Get top 50 tags
+            top_tags = [{'tag': tag, 'count': count} for tag, count in tag_counts.most_common(50)]
+            
+            return web.json_response({
+                'success': True,
+                'data': {
+                    'top_tags': top_tags,
+                    'total_unique_tags': len(tag_counts)
+                }
+            })
+            
+        except Exception as e:
+            logger.error(f"Error getting tag analytics: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    async def get_storage_analytics(self, request: web.Request) -> web.Response:
+        """Get storage usage analytics"""
+        try:
+            await self.init_services()
+            
+            # Get usage statistics
+            usage_data = await self.usage_stats.get_stats()
+            
+            # Get model data
+            lora_cache = await self.lora_scanner.get_cached_data()
+            checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
+            
+            # Create models with usage data
+            lora_storage = []
+            for lora in lora_cache.raw_data:
+                usage_count = 0
+                if lora['sha256'] in usage_data.get('loras', {}):
+                    usage_count = usage_data['loras'][lora['sha256']].get('total', 0)
+                
+                lora_storage.append({
+                    'name': lora['model_name'],
+                    'size': lora.get('size', 0),
+                    'usage_count': usage_count,
+                    'folder': lora.get('folder', ''),
+                    'base_model': lora.get('base_model', 'Unknown')
+                })
+            
+            checkpoint_storage = []
+            for cp in checkpoint_cache.raw_data:
+                usage_count = 0
+                if cp['sha256'] in usage_data.get('checkpoints', {}):
+                    usage_count = usage_data['checkpoints'][cp['sha256']].get('total', 0)
+                
+                checkpoint_storage.append({
+                    'name': cp['model_name'],
+                    'size': cp.get('size', 0),
+                    'usage_count': usage_count,
+                    'folder': cp.get('folder', ''),
+                    'base_model': cp.get('base_model', 'Unknown')
+                })
+            
+            # Sort by size
+            lora_storage.sort(key=lambda x: x['size'], reverse=True)
+            checkpoint_storage.sort(key=lambda x: x['size'], reverse=True)
+            
+            return web.json_response({
+                'success': True,
+                'data': {
+                    'loras': lora_storage[:20],  # Top 20 by size
+                    'checkpoints': checkpoint_storage[:20]
+                }
+            })
+            
+        except Exception as e:
+            logger.error(f"Error getting storage analytics: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    async def get_insights(self, request: web.Request) -> web.Response:
+        """Get smart insights about the collection"""
+        try:
+            await self.init_services()
+            
+            # Get usage statistics
+            usage_data = await self.usage_stats.get_stats()
+            
+            # Get model data
+            lora_cache = await self.lora_scanner.get_cached_data()
+            checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
+            
+            insights = []
+            
+            # Calculate unused models
+            unused_loras = self._count_unused_models(lora_cache.raw_data, usage_data.get('loras', {}))
+            unused_checkpoints = self._count_unused_models(checkpoint_cache.raw_data, usage_data.get('checkpoints', {}))
+            
+            total_loras = len(lora_cache.raw_data)
+            total_checkpoints = len(checkpoint_cache.raw_data)
+            
+            if total_loras > 0:
+                unused_lora_percent = (unused_loras / total_loras) * 100
+                if unused_lora_percent > 50:
+                    insights.append({
+                        'type': 'warning',
+                        'title': 'High Number of Unused LoRAs',
+                        'description': f'{unused_lora_percent:.1f}% of your LoRAs ({unused_loras}/{total_loras}) have never been used.',
+                        'suggestion': 'Consider organizing or archiving unused models to free up storage space.'
+                    })
+            
+            if total_checkpoints > 0:
+                unused_checkpoint_percent = (unused_checkpoints / total_checkpoints) * 100
+                if unused_checkpoint_percent > 30:
+                    insights.append({
+                        'type': 'warning',
+                        'title': 'Unused Checkpoints Detected',
+                        'description': f'{unused_checkpoint_percent:.1f}% of your checkpoints ({unused_checkpoints}/{total_checkpoints}) have never been used.',
+                        'suggestion': 'Review and consider removing checkpoints you no longer need.'
+                    })
+            
+            # Storage insights
+            total_size = sum(lora.get('size', 0) for lora in lora_cache.raw_data) + \
+                        sum(cp.get('size', 0) for cp in checkpoint_cache.raw_data)
+            
+            if total_size > 100 * 1024 * 1024 * 1024:  # 100GB
+                insights.append({
+                    'type': 'info',
+                    'title': 'Large Collection Detected',
+                    'description': f'Your model collection is using {self._format_size(total_size)} of storage.',
+                    'suggestion': 'Consider using external storage or cloud solutions for better organization.'
+                })
+            
+            # Recent activity insight
+            if usage_data.get('total_executions', 0) > 100:
+                insights.append({
+                    'type': 'success',
+                    'title': 'Active User',
+                    'description': f'You\'ve completed {usage_data["total_executions"]} generations so far!',
+                    'suggestion': 'Keep exploring and creating amazing content with your models.'
+                })
+            
+            return web.json_response({
+                'success': True,
+                'data': {
+                    'insights': insights
+                }
+            })
+            
+        except Exception as e:
+            logger.error(f"Error getting insights: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    def _count_unused_models(self, models: List[Dict], usage_data: Dict) -> int:
+        """Count models that have never been used"""
+        used_hashes = set(usage_data.keys())
+        unused_count = 0
+        
+        for model in models:
+            if model.get('sha256') not in used_hashes:
+                unused_count += 1
+                
+        return unused_count
+
+    def _get_top_used_models(self, usage_data: Dict, model_map: Dict, limit: int) -> List[Dict]:
+        """Get top used models with their metadata"""
+        sorted_usage = sorted(usage_data.items(), key=lambda x: x[1].get('total', 0), reverse=True)
+        
+        top_models = []
+        for sha256, usage_info in sorted_usage[:limit]:
+            if sha256 in model_map:
+                model = model_map[sha256]
+                top_models.append({
+                    'name': model['model_name'],
+                    'usage_count': usage_info.get('total', 0),
+                    'base_model': model.get('base_model', 'Unknown'),
+                    'preview_url': config.get_preview_static_url(model.get('preview_url', '')),
+                    'folder': model.get('folder', '')
+                })
+        
+        return top_models
+
+    def _get_usage_timeline(self, usage_data: Dict, days: int) -> List[Dict]:
+        """Get usage timeline for the past N days"""
+        timeline = []
+        today = datetime.now()
+        
+        for i in range(days):
+            date = today - timedelta(days=i)
+            date_str = date.strftime('%Y-%m-%d')
+            
+            lora_usage = 0
+            checkpoint_usage = 0
+            
+            # Count usage for this date
+            for model_usage in usage_data.get('loras', {}).values():
+                if isinstance(model_usage, dict) and 'history' in model_usage:
+                    lora_usage += model_usage['history'].get(date_str, 0)
+            
+            for model_usage in usage_data.get('checkpoints', {}).values():
+                if isinstance(model_usage, dict) and 'history' in model_usage:
+                    checkpoint_usage += model_usage['history'].get(date_str, 0)
+            
+            timeline.append({
+                'date': date_str,
+                'lora_usage': lora_usage,
+                'checkpoint_usage': checkpoint_usage,
+                'total_usage': lora_usage + checkpoint_usage
+            })
+        
+        return list(reversed(timeline))  # Oldest to newest
+
+    def _format_size(self, size_bytes: int) -> str:
+        """Format file size in human readable format"""
+        for unit in ['B', 'KB', 'MB', 'GB', 'TB']:
+            if size_bytes < 1024.0:
+                return f"{size_bytes:.1f} {unit}"
+            size_bytes /= 1024.0
+        return f"{size_bytes:.1f} PB"
+
+    def setup_routes(self, app: web.Application):
+        """Register routes with the application"""
+        # Add an app startup handler to initialize services
+        app.on_startup.append(self._on_startup)
+        
+        # Register page route
+        app.router.add_get('/statistics', self.handle_stats_page)
+        
+        # Register API routes
+        app.router.add_get('/api/stats/collection-overview', self.get_collection_overview)
+        app.router.add_get('/api/stats/usage-analytics', self.get_usage_analytics)
+        app.router.add_get('/api/stats/base-model-distribution', self.get_base_model_distribution)
+        app.router.add_get('/api/stats/tag-analytics', self.get_tag_analytics)
+        app.router.add_get('/api/stats/storage-analytics', self.get_storage_analytics)
+        app.router.add_get('/api/stats/insights', self.get_insights)
+        
+    async def _on_startup(self, app):
+        """Initialize services when the app starts"""
+        await self.init_services()

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/services/checkpoint_scanner.py

@@ -33,7 +33,6 @@ class CheckpointScanner(ModelScanner):
                 file_extensions=file_extensions,
                 hash_index=ModelHashIndex()
             )
-            self._checkpoint_roots = self._init_checkpoint_roots()
             self._initialized = True
 
     @classmethod
@@ -44,27 +43,9 @@ class CheckpointScanner(ModelScanner):
                 cls._instance = cls()
             return cls._instance
     
-    def _init_checkpoint_roots(self) -> List[str]:
-        """Initialize checkpoint roots from ComfyUI settings"""
-        # Get both checkpoint and diffusion_models paths
-        checkpoint_paths = folder_paths.get_folder_paths("checkpoints")
-        diffusion_paths = folder_paths.get_folder_paths("diffusion_models")
-        
-        # Combine, normalize and deduplicate paths
-        all_paths = set()
-        for path in checkpoint_paths + diffusion_paths:
-            if os.path.exists(path):
-                norm_path = path.replace(os.sep, "/")
-                all_paths.add(norm_path)
-        
-        # Sort for consistent order
-        sorted_paths = sorted(all_paths, key=lambda p: p.lower())
-        
-        return sorted_paths
-    
     def get_model_roots(self) -> List[str]:
         """Get checkpoint root directories"""
-        return self._checkpoint_roots
+        return config.base_models_roots
         
     async def scan_all_models(self) -> List[Dict]:
         """Scan all checkpoint directories and return metadata"""
@@ -72,7 +53,7 @@ class CheckpointScanner(ModelScanner):
         
         # Create scan tasks for each directory
         scan_tasks = []
-        for root in self._checkpoint_roots:
+        for root in self.get_model_roots():
             task = asyncio.create_task(self._scan_directory(root))
             scan_tasks.append(task)
             

py/services/civitai_client.py

@@ -224,6 +224,54 @@ class CivitaiClient:
         except Exception as e:
             logger.error(f"Error fetching model versions: {e}")
             return None
+            
+    async def get_model_version(self, model_id: int, version_id: int = None) -> Optional[Dict]:
+        """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()
+            
+            # Step 1: Get model data to find version_id if not provided and get additional metadata
+            async with session.get(f"{self.base_url}/models/{model_id}") as response:
+                if response.status != 200:
+                    return None
+                    
+                data = await response.json()
+                model_versions = data.get('modelVersions', [])
+                
+                # Step 2: Determine the version_id to use
+                target_version_id = version_id
+                if target_version_id is None:
+                    target_version_id = model_versions[0].get('id')
+            
+            # Step 3: Get detailed version info using the version_id
+            headers = self._get_request_headers()
+            async with session.get(f"{self.base_url}/model-versions/{target_version_id}", headers=headers) as response:
+                if response.status != 200:
+                    return None
+                
+                version = await response.json()
+                
+                # Step 4: Enrich version_info with model data
+                # Add description and tags from model data
+                version['model']['description'] = data.get("description")
+                version['model']['tags'] = data.get("tags", [])
+                
+                # Add creator from model data
+                version['creator'] = data.get("creator")
+                
+                return version
+                
+        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 +394,34 @@ 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

@@ -1,12 +1,13 @@
 import logging
 import os
-import json
 import asyncio
 from typing import Dict
 from ..utils.models import LoraMetadata, CheckpointMetadata
-from ..utils.constants import CARD_PREVIEW_WIDTH
+from ..utils.constants import CARD_PREVIEW_WIDTH, VALID_LORA_TYPES
 from ..utils.exif_utils import ExifUtils
+from ..utils.metadata_manager import MetadataManager
 from .service_registry import ServiceRegistry
+from .settings_manager import settings
 
 # Download to temporary file first
 import tempfile
@@ -38,14 +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"""
@@ -55,54 +48,98 @@ class DownloadManager:
         """Get the checkpoint scanner from registry"""
         return await ServiceRegistry.get_checkpoint_scanner()
 
-    async def download_from_civitai(self, download_url: str = None, model_hash: str = None, 
-                                  model_version_id: str = None, save_dir: str = None, 
-                                  relative_path: str = '', progress_callback=None, 
-                                  model_type: str = "lora") -> Dict:
+    async def download_from_civitai(self, model_id: int, 
+                                  model_version_id: int, save_dir: str = None, 
+                                  relative_path: str = '', progress_callback=None, use_default_paths: bool = False) -> Dict:
         """Download model from Civitai
         
         Args:
-            download_url: Direct download URL for the model
-            model_hash: SHA256 hash of the model
-            model_version_id: Civitai model version ID
+            model_id: Civitai model ID
+            model_version_id: Civitai model version ID (optional, if not provided, will download the latest version)
             save_dir: Directory to save the model to
             relative_path: Relative path within save_dir
             progress_callback: Callback function for progress updates
-            model_type: Type of model ('lora' or 'checkpoint')
+            use_default_paths: Flag to indicate whether to use default paths
             
         Returns:
             Dict with download result
         """
         try:
-            # Update save directory with relative path if provided
-            if relative_path:
-                save_dir = os.path.join(save_dir, relative_path)
-                # Create directory if it doesn't exist
-                os.makedirs(save_dir, exist_ok=True)
+            # Check if model version already exists in library
+            if model_version_id is not None:
+                # Case 1: model_version_id is provided, check both scanners
+                lora_scanner = await self._get_lora_scanner()
+                checkpoint_scanner = await self._get_checkpoint_scanner()
+                
+                # Check lora scanner first
+                if await lora_scanner.check_model_version_exists(model_id, model_version_id):
+                    return {'success': False, 'error': 'Model version already exists in lora library'}
+                
+                # Check checkpoint scanner
+                if await checkpoint_scanner.check_model_version_exists(model_id, model_version_id):
+                    return {'success': False, 'error': 'Model version already exists in checkpoint library'}
 
             # Get civitai client
             civitai_client = await self._get_civitai_client()
 
             # Get version info based on the provided identifier
-            version_info = None
-            error_msg = None
-            
-            if model_hash:
-                # Get model by hash
-                version_info = await civitai_client.get_model_by_hash(model_hash)
-            elif model_version_id:
-                # Use model version ID directly
-                version_info, error_msg = await civitai_client.get_model_version_info(model_version_id)
-            elif download_url:
-                # Extract version ID from download URL
-                version_id = download_url.split('/')[-1]
-                version_info, error_msg = await civitai_client.get_model_version_info(version_id)
-
+            version_info = await civitai_client.get_model_version(model_id, model_version_id)
             
             if not version_info:
-                if error_msg and "model not found" in error_msg.lower():
-                    return {'success': False, 'error': f'Model not found on Civitai: {error_msg}'}
-                return {'success': False, 'error': error_msg or 'Failed to fetch model metadata'}
+                return {'success': False, 'error': 'Failed to fetch model metadata'}
+
+            model_type_from_info = version_info.get('model', {}).get('type', '').lower()
+            if model_type_from_info == 'checkpoint':
+                model_type = 'checkpoint'
+            elif model_type_from_info in VALID_LORA_TYPES:
+                model_type = 'lora'
+            else:
+                return {'success': False, 'error': f'Model type "{model_type_from_info}" is not supported for download'}
+            
+            # Case 2: model_version_id was None, check after getting version_info
+            if model_version_id is None:
+                version_model_id = version_info.get('modelId')
+                version_id = version_info.get('id')
+                
+                if model_type == 'lora':
+                    # Check lora scanner
+                    lora_scanner = await self._get_lora_scanner()
+                    if await lora_scanner.check_model_version_exists(version_model_id, version_id):
+                        return {'success': False, 'error': 'Model version already exists in lora library'}
+                elif model_type == 'checkpoint':
+                    # Check checkpoint scanner
+                    checkpoint_scanner = await self._get_checkpoint_scanner()
+                    if await checkpoint_scanner.check_model_version_exists(version_model_id, version_id):
+                        return {'success': False, 'error': 'Model version already exists in checkpoint library'}
+            
+            # Handle use_default_paths
+            if use_default_paths:
+                # Set save_dir based on model type
+                if model_type == 'checkpoint':
+                    default_path = settings.get('default_checkpoint_root')
+                    if not default_path:
+                        return {'success': False, 'error': 'Default checkpoint root path not set in settings'}
+                    save_dir = default_path
+                else:  # model_type == 'lora'
+                    default_path = settings.get('default_lora_root')
+                    if not default_path:
+                        return {'success': False, 'error': 'Default lora root path not set in settings'}
+                    save_dir = default_path
+                    
+                # Set relative_path to version_info.baseModel/first_tag if available
+                base_model = version_info.get('baseModel', '')
+                model_tags = version_info.get('model', {}).get('tags', [])
+                if base_model:
+                    if model_tags:
+                        relative_path = os.path.join(base_model, model_tags[0])
+                    else:
+                        relative_path = base_model
+
+            # Update save directory with relative path if provided
+            if relative_path:
+                save_dir = os.path.join(save_dir, relative_path)
+                # Create directory if it doesn't exist
+                os.makedirs(save_dir, exist_ok=True)
 
             # Check if this is an early access model
             if version_info.get('earlyAccessEndsAt'):
@@ -136,9 +173,6 @@ class DownloadManager:
             file_name = file_info['name']
             save_path = os.path.join(save_dir, file_name)
 
-            # 4. Notify file monitor - use normalized path and file size
-            # file monitor is despreted, so we don't need to use it
-
             # 5. Prepare metadata based on model type
             if model_type == "checkpoint":
                 metadata = CheckpointMetadata.from_civitai_info(version_info, file_info, save_path)
@@ -147,18 +181,6 @@ class DownloadManager:
                 metadata = LoraMetadata.from_civitai_info(version_info, file_info, save_path)
                 logger.info(f"Creating LoraMetadata for {file_name}")
             
-            # 5.1 Get and update model tags, description and creator info
-            model_id = version_info.get('modelId')
-            if model_id:
-                model_metadata, _ = await civitai_client.get_model_metadata(str(model_id))
-                if model_metadata:
-                    if model_metadata.get("tags"):
-                        metadata.tags = model_metadata.get("tags", [])
-                    if model_metadata.get("description"):
-                        metadata.modelDescription = model_metadata.get("description", "")
-                    if model_metadata.get("creator"):
-                        metadata.civitai["creator"] = model_metadata.get("creator")
-            
             # 6. Start download process
             result = await self._execute_download(
                 download_url=file_info.get('downloadUrl', ''),
@@ -209,8 +231,6 @@ 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:
@@ -237,8 +257,6 @@ 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:
@@ -269,8 +287,7 @@ 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, True)
 
             # 6. Update cache based on model type
             if model_type == "checkpoint":

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_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

@@ -1,11 +1,7 @@
-import json
 import os
 import logging
 import asyncio
-import shutil
-import time
-import re
-from typing import List, Dict, Optional, Set
+from typing import List, Dict, Optional
 
 from ..utils.models import LoraMetadata
 from ..config import config
@@ -14,7 +10,6 @@ from .model_hash_index import ModelHashIndex  # Changed from LoraHashIndex to Mo
 from .settings_manager import settings
 from ..utils.constants import NSFW_LEVELS
 from ..utils.utils import fuzzy_match
-from .service_registry import ServiceRegistry
 import sys
 
 logger = logging.getLogger(__name__)
@@ -374,32 +369,6 @@ 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

@@ -32,12 +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
@@ -47,19 +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

@@ -9,7 +9,8 @@ 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
@@ -19,7 +20,11 @@ from .websocket_manager import ws_manager
 logger = logging.getLogger(__name__)
 
 # Define cache version to handle future format changes
-CACHE_VERSION = 1
+# 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"""
@@ -44,10 +49,25 @@ class ModelScanner:
         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"
@@ -89,6 +109,10 @@ class ModelScanner:
 
     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
@@ -107,10 +131,13 @@ class ModelScanner:
                 "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
+                    "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()
+                "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
@@ -128,6 +155,7 @@ class ModelScanner:
                 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}")
@@ -158,27 +186,21 @@ class ModelScanner:
     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
-            
-        # Check if directories have changed
-        stored_dirs = cache_data.get("dirs_last_modified", {})
-        current_dirs = self._get_dirs_last_modified()
-        
-        # If directory structure has changed, cache is invalid
-        if set(stored_dirs.keys()) != set(current_dirs.keys()):
-            return False
-            
-        # Remove the modification time check to make cache validation less strict
-        # This allows the cache to be valid even when files have changed
-        # Users can explicitly refresh the cache when needed
                 
         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):
@@ -205,10 +227,15 @@ class ModelScanner:
             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()
             
@@ -643,26 +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
@@ -715,36 +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"""
@@ -757,7 +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"
@@ -773,7 +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, True)
                         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}")
@@ -800,13 +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, True)
                         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()
         
@@ -856,9 +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}")
@@ -871,9 +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, True)
         except Exception as e:
             logger.error(f"Failed to update metadata from Civitai for {file_path}: {e}")
 
@@ -979,26 +990,6 @@ 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)
             
             # Move all associated files with the same base name
@@ -1052,15 +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
                 
@@ -1194,12 +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
@@ -1207,8 +1198,223 @@ class ModelScanner:
         if self._cache is None:
             return False
 
-        updated = 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]
+
+    async def check_model_version_exists(self, model_id: int, model_version_id: int) -> bool:
+        """Check if a specific model version exists in the cache
+        
+        Args:
+            model_id: Civitai model ID
+            model_version_id: Civitai model version ID
+            
+        Returns:
+            bool: True if the model version exists, False otherwise
+        """
+        try:
+            cache = await self.get_cached_data()
+            if not cache or not cache.raw_data:
+                return False
+                
+            for item in cache.raw_data:
+                if (item.get('civitai') and 
+                    item['civitai'].get('modelId') == model_id and 
+                    item['civitai'].get('id') == model_version_id):
+                    return True
+                    
+            return False
+        except Exception as e:
+            logger.error(f"Error checking model version existence: {e}")
+            return False
+
+    async def get_model_versions_by_id(self, model_id: int) -> List[Dict]:
+        """Get all versions of a model by its ID
+        
+        Args:
+            model_id: Civitai model ID
+            
+        Returns:
+            List[Dict]: List of version information dictionaries
+        """
+        try:
+            cache = await self.get_cached_data()
+            if not cache or not cache.raw_data:
+                return []
+                
+            versions = []
+            for item in cache.raw_data:
+                if (item.get('civitai') and 
+                    item['civitai'].get('modelId') == model_id and 
+                    item['civitai'].get('id')):
+                    versions.append({
+                        'versionId': item['civitai'].get('id'),
+                        'name': item['civitai'].get('name'),
+                        'fileName': item.get('file_name', '')
+                    })
+                    
+            return versions
+        except Exception as e:
+            logger.error(f"Error getting model versions: {e}")
+            return []

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/services/websocket_manager.py

@@ -1,6 +1,7 @@
 import logging
 from aiohttp import web
 from typing import Set, Dict, Optional
+from uuid import uuid4
 
 logger = logging.getLogger(__name__)
 
@@ -10,7 +11,7 @@ class WebSocketManager:
     def __init__(self):
         self._websockets: Set[web.WebSocketResponse] = set()
         self._init_websockets: Set[web.WebSocketResponse] = set()  # New set for initialization progress clients
-        self._checkpoint_websockets: Set[web.WebSocketResponse] = set()  # New set for checkpoint download progress
+        self._download_websockets: Dict[str, web.WebSocketResponse] = {}  # New dict for download-specific clients
         
     async def handle_connection(self, request: web.Request) -> web.WebSocketResponse:
         """Handle new WebSocket connection"""
@@ -39,19 +40,35 @@ class WebSocketManager:
         finally:
             self._init_websockets.discard(ws)
         return ws
-        
-    async def handle_checkpoint_connection(self, request: web.Request) -> web.WebSocketResponse:
-        """Handle new WebSocket connection for checkpoint download progress"""
+    
+    async def handle_download_connection(self, request: web.Request) -> web.WebSocketResponse:
+        """Handle new WebSocket connection for download progress"""
         ws = web.WebSocketResponse()
         await ws.prepare(request)
-        self._checkpoint_websockets.add(ws)
+        
+        # Get download_id from query parameters
+        download_id = request.query.get('id')
+        
+        if not download_id:
+            # Generate a new download ID if not provided
+            download_id = str(uuid4())
+        
+        # Store the websocket with its download ID
+        self._download_websockets[download_id] = ws
         
         try:
+            # Send the download ID back to the client
+            await ws.send_json({
+                'type': 'download_id',
+                'download_id': download_id
+            })
+            
             async for msg in ws:
                 if msg.type == web.WSMsgType.ERROR:
-                    logger.error(f'Checkpoint WebSocket error: {ws.exception()}')
+                    logger.error(f'Download WebSocket error: {ws.exception()}')
         finally:
-            self._checkpoint_websockets.discard(ws)
+            if download_id in self._download_websockets:
+                del self._download_websockets[download_id]
         return ws
         
     async def broadcast(self, data: Dict):
@@ -84,17 +101,18 @@ class WebSocketManager:
             except Exception as e:
                 logger.error(f"Error sending initialization progress: {e}")
     
-    async def broadcast_checkpoint_progress(self, data: Dict):
-        """Broadcast checkpoint download progress to connected clients"""
-        if not self._checkpoint_websockets:
+    async def broadcast_download_progress(self, download_id: str, data: Dict):
+        """Send progress update to specific download client"""
+        if download_id not in self._download_websockets:
+            logger.debug(f"No WebSocket found for download ID: {download_id}")
             return
             
-        for ws in self._checkpoint_websockets:
-            try:
-                await ws.send_json(data)
-            except Exception as e:
-                logger.error(f"Error sending checkpoint progress: {e}")
-                
+        ws = self._download_websockets[download_id]
+        try:
+            await ws.send_json(data)
+        except Exception as e:
+            logger.error(f"Error sending download progress: {e}")
+            
     def get_connected_clients_count(self) -> int:
         """Get number of connected clients"""
         return len(self._websockets)
@@ -102,10 +120,14 @@ class WebSocketManager:
     def get_init_clients_count(self) -> int:
         """Get number of initialization progress clients"""
         return len(self._init_websockets)
-
-    def get_checkpoint_clients_count(self) -> int:
-        """Get number of checkpoint progress clients"""
-        return len(self._checkpoint_websockets)
+        
+    def get_download_clients_count(self) -> int:
+        """Get number of download progress clients"""
+        return len(self._download_websockets)
+        
+    def generate_download_id(self) -> str:
+        """Generate a unique download ID"""
+        return str(uuid4())
 
 # Global instance
 ws_manager = WebSocketManager()

py/utils/constants.py

@@ -7,6 +7,16 @@ NSFW_LEVELS = {
     "Blocked": 32, # Probably not actually visible through the API without being logged in on model owner account?
 }
 
+# Node type constants
+NODE_TYPES = {
+    "Lora Loader (LoraManager)": 1,
+    "Lora Stacker (LoraManager)": 2,
+    "WanVideo Lora Select (LoraManager)": 3
+}
+
+# Default ComfyUI node color when bgcolor is null
+DEFAULT_NODE_COLOR = "#353535"
+
 # preview extensions
 PREVIEW_EXTENSIONS = [
     '.webp',
@@ -18,7 +28,9 @@ PREVIEW_EXTENSIONS = [
     '.png',
     '.jpeg',
     '.jpg',
-    '.mp4'
+    '.mp4',
+    '.gif',
+    '.webm'
 ]
 
 # Card preview image width
@@ -31,4 +43,7 @@ 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 +1,404 @@
+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 AND directory exists with files
+            if model_hash in download_progress['processed_models']:
+                model_dir = os.path.join(output_dir, model_hash)
+                has_files = os.path.exists(model_dir) and any(os.listdir(model_dir))
+                if has_files:
+                    logger.debug(f"Skipping already processed model: {model_name}")
+                    return False
+                else:
+                    logger.info(f"Model {model_name} marked as processed but folder empty or missing, reprocessing")
+            
+            # 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):
@@ -147,7 +147,7 @@ class ExifUtils:
                     "file_name": lora.get("file_name", ""),
                     "hash": lora.get("hash", "").lower() if lora.get("hash") else "",
                     "strength": float(lora.get("strength", 1.0)),
-                    "modelVersionId": lora.get("modelVersionId", ""),
+                    "modelVersionId": lora.get("modelVersionId", 0),
                     "modelName": lora.get("modelName", ""),
                     "modelVersionName": lora.get("modelVersionName", ""),
                 }

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
@@ -63,199 +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
-                
-                # Update preview_nsfw_level if needed
-                civitai_data = data.get('civitai', {})
-                civitai_images = civitai_data.get('images', []) if civitai_data else []
-                if (data.get('preview_url') and 
-                    data.get('preview_nsfw_level', 0) == 0 and 
-                    civitai_images and 
-                    civitai_images[0].get('nsfwLevel', 0) != 0):
-                    data['preview_nsfw_level'] = civitai_images[0]['nsfwLevel']
-                    # TODO: write to metadata file
-                    # 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

@@ -1,8 +1,9 @@
 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__)
 
@@ -80,4 +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 +1,292 @@
+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 = False) -> 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 new backup of existing file if a backup doesn't already exist
+          
+        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 file exists and either:
+            # 1. create_backup is True, OR
+            # 2. backup file doesn't already exist
+            if os.path.exists(metadata_path) and (create_backup or not os.path.exists(backup_path)):
+                try:
+                    shutil.copy2(metadata_path, backup_path)
+                    logger.debug(f"Created metadata backup at: {backup_path}")
+                except Exception as e:
+                    logger.warning(f"Failed to create metadata backup: {str(e)}")
+            
+            # Convert to dict if needed
+            if isinstance(metadata, BaseModelMetadata):
+                metadata_dict = metadata.to_dict()
+                # 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
+        """
+        need_update = False
+        
+        # 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)
+            need_update = True
+        
+        # Check if preview exists at the current location
+        preview_url = metadata.preview_url
+        if preview_url:
+            # Get directory parts of both paths
+            file_dir = os.path.dirname(file_path)
+            preview_dir = os.path.dirname(preview_url)
+            
+            # Update preview if it doesn't exist OR if model and preview are in different directories
+            if not os.path.exists(preview_url) or file_dir != preview_dir:
+                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)
+                    need_update = True
+        
+        # If path attributes were changed, save the metadata back to disk
+        if need_update:
+            await MetadataManager.save_metadata(file_path, metadata, create_backup=False)

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

@@ -8,8 +8,11 @@ from .model_utils import determine_base_model
 from .constants import PREVIEW_EXTENSIONS, CARD_PREVIEW_WIDTH
 from ..config import config
 from ..services.civitai_client import CivitaiClient
+from ..services.service_registry import ServiceRegistry
 from ..utils.exif_utils import ExifUtils
+from ..utils.metadata_manager import MetadataManager
 from ..services.download_manager import DownloadManager
+from ..services.websocket_manager import ws_manager
 
 logger = logging.getLogger(__name__)
 
@@ -32,14 +35,29 @@ 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
@@ -47,13 +65,30 @@ class ModelRouteUtils:
             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
@@ -62,7 +97,7 @@ 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'
                 
@@ -121,8 +156,7 @@ 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, True)
 
     @staticmethod
     async def fetch_and_update_model(
@@ -160,8 +194,7 @@ 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
@@ -204,18 +237,17 @@ class ModelRouteUtils:
         fields = [
             "id", "modelId", "name", "createdAt", "updatedAt", 
             "publishedAt", "trainedWords", "baseModel", "description",
-            "model", "images", "creator"
+            "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
@@ -233,11 +265,7 @@ 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)
@@ -258,10 +286,12 @@ 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
@@ -286,13 +316,9 @@ 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
@@ -303,6 +329,8 @@ 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,
@@ -322,7 +350,7 @@ 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()
@@ -347,7 +375,8 @@ 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()
 
@@ -357,15 +386,7 @@ 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()
             
@@ -374,6 +395,15 @@ 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
@@ -382,17 +412,47 @@ 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,
@@ -400,35 +460,45 @@ 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:
@@ -458,8 +528,7 @@ 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()
@@ -484,6 +553,8 @@ 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,
@@ -495,13 +566,12 @@ class ModelRouteUtils:
             return web.Response(text=str(e), status=500)
 
     @staticmethod
-    async def handle_download_model(request: web.Request, download_manager: DownloadManager, model_type="lora") -> web.Response:
+    async def handle_download_model(request: web.Request, download_manager: DownloadManager) -> web.Response:
         """Handle model download request
         
         Args:
             request: The aiohttp request
             download_manager: Instance of DownloadManager
-            model_type: Type of model ('lora' or 'checkpoint')
             
         Returns:
             web.Response: The HTTP response
@@ -509,40 +579,58 @@ class ModelRouteUtils:
         try:
             data = await request.json()
             
-            # Create progress callback
+            # Get or generate a download ID
+            download_id = data.get('download_id', ws_manager.generate_download_id())
+            
+            # Create progress callback with download ID
             async def progress_callback(progress):
-                from ..services.websocket_manager import ws_manager
-                await ws_manager.broadcast({
+                await ws_manager.broadcast_download_progress(download_id, {
                     'status': 'progress',
-                    'progress': progress
+                    'progress': progress,
+                    'download_id': download_id
                 })
             
-            # Check which identifier is provided
-            download_url = data.get('download_url')
-            model_hash = data.get('model_hash')
-            model_version_id = data.get('model_version_id')
+            # Check which identifier is provided and convert to int
+            try:
+                model_id = int(data.get('model_id'))
+            except (TypeError, ValueError):
+                return web.Response(
+                    status=400,
+                    text="Invalid model_id: Must be an integer"
+                )
+
+            # Convert model_version_id to int if provided
+            model_version_id = None 
+            if data.get('model_version_id'):
+                try:
+                    model_version_id = int(data.get('model_version_id'))
+                except (TypeError, ValueError):
+                    return web.Response(
+                        status=400,
+                        text="Invalid model_version_id: Must be an integer"
+                    )
             
-            # Validate that at least one identifier is provided
-            if not any([download_url, model_hash, model_version_id]):
+            # Only model_id is required, model_version_id is optional
+            if not model_id:
                 return web.Response(
                     status=400, 
-                    text="Missing required parameter: Please provide either 'download_url', 'hash', or 'modelVersionId'"
+                    text="Missing required parameter: Please provide 'model_id'"
                 )
             
-            # Use the correct root directory based on model type
-            root_key = 'checkpoint_root' if model_type == 'checkpoint' else 'lora_root'
-            save_dir = data.get(root_key)
+            use_default_paths = data.get('use_default_paths', False)
             
             result = await download_manager.download_from_civitai(
-                download_url=download_url,
-                model_hash=model_hash,
+                model_id=model_id,
                 model_version_id=model_version_id,
-                save_dir=save_dir,
+                save_dir=data.get('model_root'),
                 relative_path=data.get('relative_path', ''),
-                progress_callback=progress_callback,
-                model_type=model_type
+                use_default_paths=use_default_paths,
+                progress_callback=progress_callback
             )
             
+            # Include download_id in the response
+            result['download_id'] = download_id
+            
             if not result.get('success', False):
                 error_message = result.get('error', 'Unknown error')
                 
@@ -569,5 +657,332 @@ class ModelRouteUtils:
                     text="Early Access Restriction: This model requires purchase. Please buy early access on Civitai.com."
                 )
             
-            logger.error(f"Error downloading {model_type}: {error_message}")
+            logger.error(f"Error downloading model: {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 = int(data.get('model_id'))
+            model_version_id = None 
+            if data.get('model_version_id'):
+                model_version_id = int(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)
+
+    @staticmethod
+    async def handle_rename_model(request: web.Request, scanner) -> web.Response:
+        """Handle renaming a model file and its associated files
+        
+        Args:
+            request: The aiohttp request
+            scanner: The model scanner instance
+            
+        Returns:
+            web.Response: The HTTP response
+        """
+        try:
+            data = await request.json()
+            file_path = data.get('file_path')
+            new_file_name = data.get('new_file_name')
+            
+            if not file_path or not new_file_name:
+                return web.json_response({
+                    'success': False,
+                    'error': 'File path and new file name are required'
+                }, status=400)
+            
+            # Validate the new file name (no path separators or invalid characters)
+            invalid_chars = ['/', '\\', ':', '*', '?', '"', '<', '>', '|']
+            if any(char in new_file_name for char in invalid_chars):
+                return web.json_response({
+                    'success': False,
+                    'error': 'Invalid characters in file name'
+                }, status=400)
+            
+            # Get the directory and current file name
+            target_dir = os.path.dirname(file_path)
+            old_file_name = os.path.splitext(os.path.basename(file_path))[0]
+            
+            # Check if the target file already exists
+            new_file_path = os.path.join(target_dir, f"{new_file_name}.safetensors").replace(os.sep, '/')
+            if os.path.exists(new_file_path):
+                return web.json_response({
+                    'success': False,
+                    'error': 'A file with this name already exists'
+                }, status=400)
+            
+            # Define the patterns for associated files
+            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
+            for ext in PREVIEW_EXTENSIONS:
+                patterns.append(f"{old_file_name}{ext}")
+            
+            # Find all matching files
+            existing_files = []
+            for pattern in patterns:
+                path = os.path.join(target_dir, pattern)
+                if os.path.exists(path):
+                    existing_files.append((path, pattern))
+            
+            # Get the hash from the main file to update hash index
+            hash_value = None
+            metadata = None
+            metadata_path = os.path.join(target_dir, f"{old_file_name}.metadata.json")
+            
+            if os.path.exists(metadata_path):
+                metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
+                hash_value = metadata.get('sha256')
+            
+            # Rename all files
+            renamed_files = []
+            new_metadata_path = None
+            
+            for old_path, pattern in existing_files:
+                # Get the file extension like .safetensors or .metadata.json
+                ext = ModelRouteUtils.get_multipart_ext(pattern)
+
+                # Create the new path
+                new_path = os.path.join(target_dir, f"{new_file_name}{ext}").replace(os.sep, '/')
+                
+                # Rename the file
+                os.rename(old_path, new_path)
+                renamed_files.append(new_path)
+                
+                # Keep track of metadata path for later update
+                if ext == '.metadata.json':
+                    new_metadata_path = new_path
+            
+            # Update the metadata file with new file name and paths
+            if new_metadata_path and metadata:
+                # Update file_name, file_path and preview_url in metadata
+                metadata['file_name'] = new_file_name
+                metadata['file_path'] = new_file_path
+                
+                # Update preview_url if it exists
+                if 'preview_url' in metadata and metadata['preview_url']:
+                    old_preview = metadata['preview_url']
+                    ext = ModelRouteUtils.get_multipart_ext(old_preview)
+                    new_preview = os.path.join(target_dir, f"{new_file_name}{ext}").replace(os.sep, '/')
+                    metadata['preview_url'] = new_preview
+                
+                # Save updated metadata
+                await MetadataManager.save_metadata(new_file_path, metadata)
+            
+            # Update the scanner cache
+            if metadata:
+                await scanner.update_single_model_cache(file_path, new_file_path, metadata)
+                
+                # Update recipe files and cache if hash is available and recipe_scanner exists
+                if hash_value and hasattr(scanner, 'update_lora_filename_by_hash'):
+                    recipe_scanner = await ServiceRegistry.get_recipe_scanner()
+                    if recipe_scanner:
+                        recipes_updated, cache_updated = await recipe_scanner.update_lora_filename_by_hash(hash_value, new_file_name)
+                        logger.info(f"Updated {recipes_updated} recipe files and {cache_updated} cache entries for renamed model")
+            
+            return web.json_response({
+                'success': True,
+                'new_file_path': new_file_path,
+                'renamed_files': renamed_files,
+                'reload_required': False
+            })
+            
+        except Exception as e:
+            logger.error(f"Error renaming model: {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

@@ -1,8 +1,29 @@
 from difflib import SequenceMatcher
 import requests
 import tempfile
-import re
+import os
 from bs4 import BeautifulSoup
+from ..services.service_registry import ServiceRegistry
+from ..config import config
+
+async def get_lora_info(lora_name):
+    """Get the lora path and trigger words from cache"""
+    scanner = await ServiceRegistry.get_lora_scanner()
+    cache = await scanner.get_cached_data()
+    
+    for item in cache.raw_data:
+        if item.get('file_name') == lora_name:
+            file_path = item.get('file_path')
+            if file_path:
+                for root in config.loras_roots:
+                    root = root.replace(os.sep, '/')
+                    if file_path.startswith(root):
+                        relative_path = os.path.relpath(file_path, root).replace(os.sep, '/')
+                        # Get trigger words from civitai metadata
+                        civitai = item.get('civitai', {})
+                        trigger_words = civitai.get('trainedWords', []) if civitai else []
+                        return relative_path, trigger_words
+    return lora_name, []
 
 def download_twitter_image(url):
     """Download image from a URL containing twitter:image meta tag
@@ -142,7 +163,7 @@ def calculate_recipe_fingerprint(loras):
         # 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")
+            hash_value = str(lora.get("modelVersionId"))
             
         # Skip entries without a valid hash
         if not hash_value:

pyproject.toml

@@ -1,13 +1,12 @@
 [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 = "0.8.16"
+description = "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!"
+version = "0.8.20-beta"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",
     "jinja2", 
     "safetensors",
-    "watchdog",
     "beautifulsoup4",
     "piexif",
     "Pillow",
@@ -25,4 +24,4 @@ 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

@@ -1,7 +1,6 @@
 aiohttp
 jinja2
 safetensors
-watchdog
 beautifulsoup4
 piexif
 Pillow
@@ -9,6 +8,5 @@ olefile
 requests
 toml
 numpy
-torch
 natsort
-msgpack
+msgpack

standalone.py

@@ -1,7 +1,28 @@
+from pathlib import Path
 import os
 import sys
 import json
 
+# Create mock modules for py/nodes directory - add this before any other imports
+def mock_nodes_directory():
+    """Create mock modules for all Python files in the py/nodes directory"""
+    nodes_dir = os.path.join(os.path.dirname(__file__), 'py', 'nodes')
+    if os.path.exists(nodes_dir):
+        # Create a mock module for the nodes package itself
+        sys.modules['py.nodes'] = type('MockNodesModule', (), {})
+        
+        # Create mock modules for all Python files in the nodes directory
+        for file in os.listdir(nodes_dir):
+            if file.endswith('.py') and file != '__init__.py':
+                module_name = file[:-3]  # Remove .py extension
+                full_module_name = f'py.nodes.{module_name}'
+                # Create empty module object
+                sys.modules[full_module_name] = type(f'Mock{module_name.capitalize()}Module', (), {})
+                print(f"Created mock module for: {full_module_name}")
+
+# Run the mocking function before any other imports
+mock_nodes_directory()
+
 # Create mock folder_paths module BEFORE any other imports
 class MockFolderPaths:
     @staticmethod
@@ -231,7 +252,7 @@ class StandaloneLoraManager(LoraManager):
             added_targets.add(os.path.normpath(real_root))
         
         # Add static routes for each checkpoint root
-        for idx, root in enumerate(config.checkpoints_roots, start=1):
+        for idx, root in enumerate(config.base_models_roots, start=1):
             if not os.path.exists(root):
                 logger.warning(f"Checkpoint root path does not exist: {root}")
                 continue
@@ -267,8 +288,8 @@ class StandaloneLoraManager(LoraManager):
             norm_target = os.path.normpath(target_path)
             if norm_target not in added_targets:
                 # Determine if this is a checkpoint or lora link based on path
-                is_checkpoint = any(os.path.normpath(cp_root) in os.path.normpath(link_path) for cp_root in config.checkpoints_roots)
-                is_checkpoint = is_checkpoint or any(os.path.normpath(cp_root) in norm_target for cp_root in config.checkpoints_roots)
+                is_checkpoint = any(os.path.normpath(cp_root) in os.path.normpath(link_path) for cp_root in config.base_models_roots)
+                is_checkpoint = is_checkpoint or any(os.path.normpath(cp_root) in norm_target for cp_root in config.base_models_roots)
                 
                 if is_checkpoint:
                     route_path = f'/checkpoints_static/link_{link_idx["checkpoint"]}/preview'
@@ -280,10 +301,14 @@ 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)
@@ -295,17 +320,22 @@ 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
+        from py.routes.stats_routes import StatsRoutes
         
         lora_routes = LoraRoutes()
         checkpoints_routes = CheckpointsRoutes()
+        stats_routes = StatsRoutes()
         
         # Initialize routes
         lora_routes.setup_routes(app)
         checkpoints_routes.setup_routes(app)
+        stats_routes.setup_routes(app)
         ApiRoutes.setup_routes(app)
         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

@@ -29,16 +29,29 @@ html, body {
 :root {
     --bg-color: #ffffff;
     --text-color: #333333;
+    --text-muted: #6c757d;
     --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); /* Modified to be used with oklch() */
+    --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);
@@ -72,6 +85,7 @@ html[data-theme="light"] {
 [data-theme="dark"] {
     --bg-color: #1a1a1a;
     --text-color: #e0e0e0;
+    --text-muted: #a0a0a0;
     --card-bg: #2d2d2d;
     --border-color: #404040;
 

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

@@ -89,7 +89,7 @@
 /* Smaller text for medium density */
 .medium-density .model-name {
     font-size: 0.95em;
-    max-height: 2.6em;
+    max-height: 3em; /* Increased from 2.6em */
 }
 
 .medium-density .base-model-label {
@@ -105,7 +105,7 @@
 /* Smaller text for compact mode */
 .compact-density .model-name {
     font-size: 0.9em;
-    max-height: 2.4em;
+    max-height: 2.8em; /* Increased from 2.4em */
 }
 
 .compact-density .base-model-label {
@@ -167,6 +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);
@@ -220,6 +252,18 @@
     z-index: 3;
 }
 
+/* New styles for hover reveal mode */
+.hover-reveal .card-header,
+.hover-reveal .card-footer {
+    opacity: 0;
+    transition: opacity 0.2s ease;
+}
+
+.hover-reveal .lora-card:hover .card-header,
+.hover-reveal .lora-card:hover .card-footer {
+    opacity: 1;
+}
+
 .card-footer {
     position: absolute;
     bottom: 0;
@@ -331,21 +375,24 @@
     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 {
@@ -396,30 +443,6 @@
     user-select: none;
 }
 
-/* Recipe specific elements - migrated from recipe-card.css */
-.recipe-indicator {
-    position: absolute;
-    top: 6px;
-    left: 8px;
-    width: 24px;
-    height: 24px;
-    background: var(--lora-primary);
-    border-radius: 50%;
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    color: white;
-    font-weight: bold;
-    z-index: 2;
-}
-
-.base-model-wrapper {
-    display: flex;
-    align-items: center;
-    gap: 8px;
-    margin-left: 32px; /* For accommodating the recipe indicator */
-}
-
 .lora-count {
     display: flex;
     align-items: center;
@@ -485,4 +508,44 @@
     .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

@@ -2,25 +2,28 @@
 
 /* Duplicates banner */
 .duplicates-banner {
-    position: relative; /* Changed from sticky to relative */
+    position: sticky; /* Keep the sticky position */
+    top: var(--space-1);
     width: 100%;
-    background-color: var(--card-bg);
+    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-bottom: 1px solid var(--border-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; /* Removed horizontal padding */
-    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.15);
+    padding: 12px 0;
+    box-shadow: 0 3px 10px rgba(0, 0, 0, 0.2); /* Stronger shadow */
     transition: all 0.3s ease;
-    margin-bottom: 20px; /* Add margin to create space below the banner */
+    margin-bottom: 20px;
 }
 
 .duplicates-banner .banner-content {
-    max-width: 1400px; /* Match the container max-width */
+    position: relative;
+    max-width: 1400px;
     margin: 0 auto;
     display: flex;
     align-items: center;
     gap: 12px;
-    padding: 0 16px; /* Move horizontal padding to the content */
+    padding: 0 16px;
 }
 
 /* Responsive container for larger screens - match container in layout.css */
@@ -38,7 +41,7 @@
 
 .duplicates-banner i.fa-exclamation-triangle {
     font-size: 18px;
-    color: oklch(var(--lora-warning));
+    color: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
 }
 
 .duplicates-banner .banner-actions {
@@ -48,6 +51,29 @@
     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;
@@ -66,7 +92,7 @@
 }
 
 .duplicates-banner button:hover {
-    border-color: var(--lora-accent);
+    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);
@@ -91,23 +117,42 @@
 /* Duplicate groups */
 .duplicate-group {
     position: relative;
-    border: 2px solid oklch(var(--lora-warning));
+    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: 8px 16px;
+    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 {
@@ -135,7 +180,7 @@
 }
 
 .duplicate-group-header button:hover {
-    border-color: var(--lora-accent);
+    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);
@@ -190,7 +235,7 @@
 }
 
 .group-toggle-btn:hover {
-    border-color: var(--lora-accent);
+    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);
 }
@@ -202,16 +247,16 @@
 }
 
 .lora-card.duplicate:hover {
-    border-color: var(--lora-accent);
+    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));
+    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));
+    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);
 }
 
@@ -231,7 +276,7 @@
     position: absolute;
     top: 10px;
     left: 10px;
-    background: oklch(var(--lora-accent));
+    background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h));
     color: white;
     font-size: 12px;
     padding: 2px 6px;
@@ -239,6 +284,251 @@
     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 {
@@ -269,4 +559,50 @@
         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

@@ -79,6 +79,50 @@
   flex: 1;
   max-width: 400px;
   margin: 0 1rem;
+  transition: opacity 0.2s ease;
+}
+
+/* Disabled state for header search */
+.header-search.disabled {
+  opacity: 0.5;
+  pointer-events: none;
+}
+
+.header-search.disabled input {
+  background-color: var(--input-disabled-bg, #f5f5f5);
+  color: var(--text-muted);
+  cursor: not-allowed;
+}
+
+.header-search.disabled button {
+  background-color: var(--button-disabled-bg, #e0e0e0);
+  color: var(--text-muted);
+  cursor: not-allowed;
+}
+
+.header-search.disabled .search-icon {
+  color: var(--text-muted);
+}
+
+/* Dark theme specific styles for disabled header search */
+[data-theme="dark"] .header-search.disabled input {
+  background-color: #3a3a3a;
+  color: #888888;
+  border-color: #555555;
+}
+
+[data-theme="dark"] .header-search.disabled button {
+  background-color: #3a3a3a;
+  color: #888888;
+  border-color: #555555;
+}
+
+[data-theme="dark"] .header-search.disabled .search-icon {
+  color: #888888;
+}
+
+[data-theme="dark"] .header-search.disabled .fas {
+  color: #888888;
 }
 
 /* Header controls (formerly corner controls) */
@@ -115,7 +159,8 @@
 }
 
 .theme-toggle .light-icon,
-.theme-toggle .dark-icon {
+.theme-toggle .dark-icon,
+.theme-toggle .auto-icon {
   position: absolute;
   top: 50%;
   left: 50%;
@@ -124,15 +169,62 @@
   transition: opacity 0.3s ease;
 }
 
+/* Default state shows dark icon */
 .theme-toggle .dark-icon {
   opacity: 1;
 }
 
-[data-theme="light"] .theme-toggle .light-icon {
+/* Light theme shows light icon */
+.theme-toggle.theme-light .light-icon {
   opacity: 1;
 }
 
-[data-theme="light"] .theme-toggle .dark-icon {
+.theme-toggle.theme-light .dark-icon,
+.theme-toggle.theme-light .auto-icon {
+  opacity: 0;
+}
+
+/* Dark theme shows dark icon */
+.theme-toggle.theme-dark .dark-icon {
+  opacity: 1;
+}
+
+.theme-toggle.theme-dark .light-icon,
+.theme-toggle.theme-dark .auto-icon {
+  opacity: 0;
+}
+
+/* Auto theme shows auto icon */
+.theme-toggle.theme-auto .auto-icon {
+  opacity: 1;
+}
+
+.theme-toggle.theme-auto .light-icon,
+.theme-toggle.theme-auto .dark-icon {
+  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;
 }
 

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 +1,489 @@
+/* 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;
+}
+
+.notes-hint {
+    font-size: 0.8em;
+    color: var(--text-color);
+    opacity: 0.7;
+    margin-left: 5px;
+    cursor: help;
+    position: relative; /* Add positioning context */
+}
+
+@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 +39,182 @@
 .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);
+}
+
+/* Node Selector */
+.node-selector {
+    position: fixed;
+    background: var(--lora-surface);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    padding: 4px 0;
+    min-width: 200px;
+    max-width: 350px;
+    max-height: 400px;
+    overflow-y: auto;
+    box-shadow: 0 2px 10px rgba(0, 0, 0, 0.2);
+    z-index: 1000;
+    display: none;
+    backdrop-filter: blur(10px);
+}
+
+.node-item {
+    padding: 10px 15px;
+    cursor: pointer;
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    color: var(--text-color);
+    background: var(--lora-surface);
+    transition: background-color 0.2s;
+    border-bottom: 1px solid var(--border-color);
+}
+
+.node-item:last-child {
+    border-bottom: none;
+}
+
+.node-item:hover {
+    background-color: var(--lora-accent);
+    color: var(--lora-text);
+}
+
+.node-icon-indicator {
+    width: 24px;
+    height: 24px;
+    border-radius: 4px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    flex-shrink: 0;
+}
+
+.node-icon-indicator i {
+    color: white;
+    font-size: 12px;
+    text-shadow: 0 1px 2px rgba(0, 0, 0, 0.3);
+}
+
+.node-icon-indicator.all-nodes {
+    background: linear-gradient(45deg, #4a90e2, #357abd);
+}
+
+/* Remove old node-color-indicator styles */
+.node-color-indicator {
+    display: none;
+}
+
+.send-all-item {
+    border-top: 1px solid var(--border-color);
+    font-weight: 500;
+    background: var(--card-bg);
+}
+
+.send-all-item:hover {
+    background-color: var(--lora-accent);
+    color: var(--lora-text);
+}
+
+.send-all-item i {
+    width: 16px;
+    text-align: center;
+}
+
+/* Node Selector Header */
+.node-selector-header {
+    padding: 10px 15px;
+    border-bottom: 1px solid var(--border-color);
+    background: var(--card-bg);
+    display: flex;
+    flex-direction: column;
+    gap: 4px;
+}
+
+.selector-action-type {
+    font-weight: 600;
+    font-size: 14px;
+    color: var(--lora-accent);
+}
+
+.selector-instruction {
+    font-size: 12px;
+    color: var(--text-muted);
+    font-style: italic;
 }

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;
 }
@@ -144,7 +144,7 @@ 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%);
 }
@@ -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%);
@@ -693,4 +700,439 @@ input:checked + .toggle-slider:before {
 
 .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;
+}
+
+/* Add styles for tab with new content indicator */
+.tab-btn.has-new-content {
+  position: relative;
+}
+
+.tab-btn.has-new-content::after {
+  content: "";
+  position: absolute;
+  top: 4px;
+  right: 4px;
+  width: 8px;
+  height: 8px;
+  background-color: var(--lora-accent);
+  border-radius: 50%;
+  animation: pulse 2s infinite;
+}
+
+@keyframes pulse {
+  0% { opacity: 1; transform: scale(1); }
+  50% { opacity: 0.7; transform: scale(1.1); }
+  100% { opacity: 1; transform: scale(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;
+}
+
+/* New content badge styles */
+.new-content-badge {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  font-size: 0.7em;
+  font-weight: 600;
+  background-color: var(--lora-accent);
+  color: var(--lora-text);
+  padding: 2px 6px;
+  border-radius: 10px;
+  margin-left: 8px;
+  vertical-align: middle;
+  animation: fadeIn 0.5s ease-in-out;
+  box-shadow: 0 1px 3px rgba(0, 0, 0, 0.2);
+  text-transform: uppercase;
+  letter-spacing: 0.5px;
+}
+
+.new-content-badge.inline {
+  font-size: 0.65em;
+  padding: 1px 4px;
+  margin-left: 6px;
+  border-radius: 8px;
+}
+
+/* Dark theme adjustments for new content badge */
+[data-theme="dark"] .new-content-badge {
+  box-shadow: 0 1px 3px rgba(0, 0, 0, 0.4);
+}
+
+/* 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/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/statistics.css

@@ -0,0 +1,520 @@
+/* Statistics Page Styles */
+.metrics-panel {
+    margin-bottom: var(--space-3);
+}
+
+.metrics-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+    gap: var(--space-2);
+    margin-bottom: var(--space-3);
+}
+
+.metric-card {
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-base);
+    padding: var(--space-2);
+    text-align: center;
+    transition: all 0.3s ease;
+    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
+}
+
+.metric-card:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15);
+}
+
+.metric-card .metric-icon {
+    font-size: 2rem;
+    color: var(--lora-accent);
+    margin-bottom: var(--space-1);
+}
+
+.metric-card .metric-value {
+    font-size: 1.8rem;
+    font-weight: bold;
+    color: var(--text-color);
+    margin-bottom: 4px;
+}
+
+.metric-card .metric-label {
+    font-size: 0.9rem;
+    color: oklch(var(--text-color) / 0.7);
+}
+
+.metric-card .metric-change {
+    font-size: 0.8rem;
+    margin-top: 4px;
+}
+
+.metric-change.positive {
+    color: var(--lora-success);
+}
+
+.metric-change.negative {
+    color: var(--lora-error);
+}
+
+/* Dashboard Content */
+.dashboard-content {
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-base);
+    overflow: hidden;
+}
+
+.dashboard-tabs {
+    display: flex;
+    background: var(--bg-color);
+    border-bottom: 1px solid var(--border-color);
+    overflow-x: auto;
+}
+
+.tab-button {
+    background: none;
+    border: none;
+    padding: var(--space-2) var(--space-3);
+    cursor: pointer;
+    transition: all 0.3s ease;
+    color: var(--text-color);
+    border-bottom: 3px solid transparent;
+    white-space: nowrap;
+    font-size: 0.9rem;
+    display: flex;
+    align-items: center;
+    gap: 8px;
+}
+
+.tab-button:hover {
+    background: oklch(var(--lora-accent) / 0.1);
+}
+
+.tab-button.active {
+    color: var(--lora-accent);
+    border-bottom-color: var(--lora-accent);
+    background: oklch(var(--lora-accent) / 0.05);
+}
+
+.tab-content {
+    padding: var(--space-3);
+}
+
+.tab-panel {
+    display: none;
+}
+
+.tab-panel.active {
+    display: block;
+}
+
+/* Panel Grid Layout */
+.panel-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(350px, 1fr));
+    gap: var(--space-3);
+    align-items: start;
+}
+
+.panel-grid .full-width {
+    grid-column: 1 / -1;
+}
+
+/* Chart Containers */
+.chart-container {
+    background: var(--bg-color);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    padding: var(--space-2);
+    min-height: 300px;
+}
+
+.chart-container h3 {
+    margin: 0 0 var(--space-2) 0;
+    color: var(--text-color);
+    font-size: 1.1rem;
+    display: flex;
+    align-items: center;
+    gap: 8px;
+}
+
+.chart-container h3 i {
+    color: var(--lora-accent);
+}
+
+.chart-wrapper {
+    position: relative;
+    height: 250px;
+}
+
+.chart-wrapper canvas {
+    max-height: 100%;
+}
+
+/* List Containers */
+.list-container {
+    background: var(--bg-color);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    padding: var(--space-2);
+    min-height: 300px;
+}
+
+.list-container h3 {
+    margin: 0 0 var(--space-2) 0;
+    color: var(--text-color);
+    font-size: 1.1rem;
+    display: flex;
+    align-items: center;
+    gap: 8px;
+}
+
+.list-container h3 i {
+    color: var(--lora-accent);
+}
+
+.model-list {
+    display: flex;
+    flex-direction: column;
+    gap: 8px;
+}
+
+.model-item {
+    display: flex;
+    align-items: center;
+    padding: 8px;
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    transition: all 0.2s ease;
+}
+
+.model-item:hover {
+    border-color: var(--lora-accent);
+    transform: translateX(2px);
+}
+
+.model-item .model-preview {
+    width: 40px;
+    height: 40px;
+    border-radius: var(--border-radius-xs);
+    margin-right: 12px;
+    object-fit: cover;
+    background: var(--border-color);
+}
+
+.model-item .model-info {
+    flex: 1;
+    min-width: 0;
+}
+
+.model-item .model-name {
+    font-weight: 600;
+    text-shadow: none;
+    color: var(--text-color);
+    font-size: 0.9rem;
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+
+.model-item .model-meta {
+    font-size: 0.8rem;
+    color: oklch(var(--text-color) / 0.7);
+    margin-top: 2px;
+}
+
+.model-item .model-usage {
+    text-align: right;
+    color: var(--lora-accent);
+    font-weight: 600;
+    font-size: 0.9rem;
+}
+
+/* Tag Cloud */
+.tag-cloud {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 8px;
+    padding: var(--space-2) 0;
+    max-height: 250px;
+    overflow-y: auto;
+}
+
+.tag-cloud-item {
+    padding: 4px 8px;
+    background: oklch(var(--lora-accent) / 0.1);
+    color: var(--lora-accent);
+    border-radius: var(--border-radius-xs);
+    font-size: 0.8rem;
+    border: 1px solid oklch(var(--lora-accent) / 0.2);
+    transition: all 0.2s ease;
+    cursor: pointer;
+}
+
+.tag-cloud-item:hover {
+    background: oklch(var(--lora-accent) / 0.2);
+    transform: scale(1.05);
+}
+
+.tag-cloud-item.size-1 { font-size: 0.7rem; }
+.tag-cloud-item.size-2 { font-size: 0.8rem; }
+.tag-cloud-item.size-3 { font-size: 0.9rem; }
+.tag-cloud-item.size-4 { font-size: 1.0rem; }
+.tag-cloud-item.size-5 { font-size: 1.1rem; font-weight: 600; }
+
+/* Analysis Cards */
+.analysis-cards {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+    gap: var(--space-2);
+}
+
+.analysis-card {
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    padding: var(--space-2);
+    text-align: center;
+}
+
+.analysis-card .card-icon {
+    font-size: 1.5rem;
+    color: var(--lora-accent);
+    margin-bottom: 8px;
+}
+
+.analysis-card .card-value {
+    font-size: 1.4rem;
+    font-weight: bold;
+    color: var(--text-color);
+    margin-bottom: 4px;
+}
+
+.analysis-card .card-label {
+    font-size: 0.85rem;
+    color: oklch(var(--text-color) / 0.7);
+}
+
+/* Insights */
+.insights-container {
+    background: var(--bg-color);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    padding: var(--space-3);
+}
+
+.insights-container h3 {
+    margin: 0 0 var(--space-2) 0;
+    color: var(--text-color);
+    font-size: 1.2rem;
+    display: flex;
+    align-items: center;
+    gap: 8px;
+}
+
+.insights-container h3 i {
+    color: var(--lora-accent);
+}
+
+.insights-list {
+    display: flex;
+    flex-direction: column;
+    gap: var(--space-2);
+}
+
+.insight-card {
+    padding: var(--space-2);
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    transition: all 0.3s ease;
+}
+
+.insight-card:hover {
+    transform: translateY(-1px);
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+}
+
+.insight-card.type-success {
+    border-left: 4px solid var(--lora-success);
+    background: oklch(var(--lora-success) / 0.05);
+}
+
+.insight-card.type-warning {
+    border-left: 4px solid var(--lora-warning);
+    background: oklch(var(--lora-warning) / 0.05);
+}
+
+.insight-card.type-info {
+    border-left: 4px solid var(--lora-accent);
+    background: oklch(var(--lora-accent) / 0.05);
+}
+
+.insight-card.type-error {
+    border-left: 4px solid var(--lora-error);
+    background: oklch(var(--lora-error) / 0.05);
+}
+
+.insight-title {
+    font-weight: 600;
+    color: var(--text-color);
+    margin-bottom: 8px;
+    font-size: 1rem;
+}
+
+.insight-description {
+    color: oklch(var(--text-color) / 0.8);
+    margin-bottom: 8px;
+    font-size: 0.9rem;
+    line-height: 1.4;
+}
+
+.insight-suggestion {
+    color: oklch(var(--text-color) / 0.7);
+    font-size: 0.85rem;
+    font-style: italic;
+}
+
+/* Recommendations Section */
+.recommendations-section {
+    margin-top: var(--space-3);
+    padding-top: var(--space-3);
+    border-top: 1px solid var(--border-color);
+}
+
+.recommendations-section h4 {
+    margin: 0 0 var(--space-2) 0;
+    color: var(--text-color);
+    font-size: 1.1rem;
+    display: flex;
+    align-items: center;
+    gap: 8px;
+}
+
+.recommendations-section h4 i {
+    color: var(--lora-accent);
+}
+
+.recommendations-list {
+    display: flex;
+    flex-direction: column;
+    gap: 12px;
+}
+
+.recommendation-item {
+    padding: 12px;
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    transition: all 0.2s ease;
+}
+
+.recommendation-item:hover {
+    border-color: var(--lora-accent);
+}
+
+.recommendation-title {
+    font-weight: 600;
+    color: var(--text-color);
+    margin-bottom: 6px;
+    font-size: 0.9rem;
+}
+
+.recommendation-description {
+    color: oklch(var(--text-color) / 0.8);
+    font-size: 0.85rem;
+    line-height: 1.4;
+}
+
+/* Loading States */
+.loading-placeholder {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    height: 200px;
+    color: oklch(var(--text-color) / 0.6);
+    font-size: 0.9rem;
+}
+
+.loading-placeholder i {
+    margin-right: 8px;
+    animation: spin 1s linear infinite;
+}
+
+@keyframes spin {
+    from { transform: rotate(0deg); }
+    to { transform: rotate(360deg); }
+}
+
+/* Responsive Design */
+@media (max-width: 1200px) {
+    .panel-grid {
+        grid-template-columns: 1fr;
+    }
+    
+    .metrics-grid {
+        grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
+    }
+}
+
+@media (max-width: 768px) {
+    .dashboard-tabs {
+        flex-wrap: wrap;
+    }
+    
+    .tab-button {
+        flex: 1;
+        min-width: 0;
+        font-size: 0.8rem;
+        padding: 12px 8px;
+    }
+    
+    .tab-button i {
+        display: none;
+    }
+    
+    .tab-content {
+        padding: var(--space-2);
+    }
+    
+    .metrics-grid {
+        grid-template-columns: repeat(2, 1fr);
+        gap: var(--space-1);
+    }
+    
+    .metric-card {
+        padding: var(--space-1);
+    }
+    
+    .metric-card .metric-icon {
+        font-size: 1.5rem;
+    }
+    
+    .metric-card .metric-value {
+        font-size: 1.4rem;
+    }
+    
+    .chart-wrapper {
+        height: 200px;
+    }
+    
+    .model-item .model-preview {
+        width: 32px;
+        height: 32px;
+    }
+}
+
+/* Dark mode adjustments */
+[data-theme="dark"] .chart-container,
+[data-theme="dark"] .list-container,
+[data-theme="dark"] .insights-container {
+    border-color: oklch(var(--border-color) / 0.3);
+}
+
+[data-theme="dark"] .metric-card {
+    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.3);
+}
+
+[data-theme="dark"] .metric-card:hover {
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.4);
+}

static/css/components/support-modal.css

@@ -7,9 +7,6 @@
     display: flex;
     align-items: center;
     gap: var(--space-2);
-    margin-bottom: var(--space-3);
-    padding-bottom: var(--space-2);
-    border-bottom: 1px solid var(--lora-border);
 }
 
 .support-icon {
@@ -33,13 +30,11 @@
 .support-content {
     display: flex;
     flex-direction: column;
-    gap: var(--space-2);
 }
 
 .support-content > p {
     font-size: 1.1em;
     text-align: center;
-    margin-bottom: var(--space-1);
 }
 
 .support-section {
@@ -117,6 +112,28 @@
     box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
 }
 
+/* Patreon button style */
+.patreon-button {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 10px;
+    padding: 10px 20px;
+    background: #F96854;
+    color: white;
+    border-radius: var(--border-radius-sm);
+    text-decoration: none;
+    font-weight: 500;
+    transition: all 0.2s ease;
+    margin-top: var(--space-1);
+}
+
+.patreon-button:hover {
+    background: #E04946;
+    transform: translateY(-2px);
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+}
+
 /* QR Code section styles */
 .qrcode-toggle {
     width: 100%;

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

@@ -4,7 +4,7 @@
     width: 100%;
     position: relative;
     overflow-x: hidden; /* Prevent horizontal scrolling */
-    overflow-y: auto; /* Enable vertical scrolling */
+    overflow-y: scroll; /* Enable vertical scrolling */
 }
 
 .container {

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';
@@ -24,6 +30,7 @@
 @import 'components/alphabet-bar.css'; /* Add alphabet bar component */
 @import 'components/duplicates.css'; /* Add duplicates component */
 @import 'components/keyboard-nav.css'; /* Add keyboard navigation component */
+@import 'components/statistics.css'; /* Add statistics component */
 
 .initialization-notice {
     display: flex;

static/js/api/baseModelApi.js

@@ -1,164 +1,7 @@
-// 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 { 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 = {}) {
-    const {
-        resetPage = false,
-        updateFolders = false,
-        modelType = 'lora', // 'lora' or 'checkpoint'
-        createCardFunction,
-        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,
-            sort_by: pageState.sortBy
-        });
-        
-        if (pageState.activeFolder !== null) {
-            params.append('folder', pageState.activeFolder);
-        }
-
-        // Add favorites filter parameter if enabled
-        if (pageState.showFavoritesOnly) {
-            params.append('favorites_only', 'true');
-        }
-        
-        // Add active letter filter if set
-        if (pageState.activeLetterFilter) {
-            params.append('first_letter', pageState.activeLetterFilter);
-        }
-
-        // Add search parameters if there's a search term
-        if (pageState.filters?.search) {
-            params.append('search', pageState.filters.search);
-            params.append('fuzzy', 'true');
-            
-            // Add search option parameters if available
-            if (pageState.searchOptions) {
-                params.append('search_filename', pageState.searchOptions.filename.toString());
-                params.append('search_modelname', pageState.searchOptions.modelname.toString());
-                if (pageState.searchOptions.tags !== undefined) {
-                    params.append('search_tags', pageState.searchOptions.tags.toString());
-                }
-                params.append('recursive', (pageState.searchOptions?.recursive ?? false).toString());
-            }
-        }
-        
-        // Add filter parameters if active
-        if (pageState.filters) {
-            // Handle tags filters
-            if (pageState.filters.tags && pageState.filters.tags.length > 0) {
-                // Checkpoints API expects individual 'tag' parameters, Loras API expects comma-separated 'tags'
-                if (modelType === 'checkpoint') {
-                    pageState.filters.tags.forEach(tag => {
-                        params.append('tag', tag);
-                    });
-                } else {
-                    params.append('tags', pageState.filters.tags.join(','));
-                }
-            }
-            
-            // Handle base model filters
-            if (pageState.filters.baseModel && pageState.filters.baseModel.length > 0) {
-                if (modelType === 'checkpoint') {
-                    pageState.filters.baseModel.forEach(model => {
-                        params.append('base_model', model);
-                    });
-                } else {
-                    params.append('base_models', pageState.filters.baseModel.join(','));
-                }
-            }
-        }
-
-        // Add model-specific parameters
-        if (modelType === 'lora') {
-            // Check for recipe-based filtering parameters from session storage
-            const filterLoraHash = getSessionItem('recipe_to_lora_filterLoraHash');
-            const filterLoraHashes = getSessionItem('recipe_to_lora_filterLoraHashes');
-
-            // Add hash filter parameter if present
-            if (filterLoraHash) {
-                params.append('lora_hash', filterLoraHash);
-            } 
-            // Add multiple hashes filter if present
-            else if (filterLoraHashes) {
-                try {
-                    if (Array.isArray(filterLoraHashes) && filterLoraHashes.length > 0) {
-                        params.append('lora_hashes', filterLoraHashes.join(','));
-                    }
-                } catch (error) {
-                    console.error('Error parsing lora hashes from session storage:', error);
-                }
-            }
-        }
-
-        const response = await fetch(`${endpoint}?${params}`);
-        if (!response.ok) {
-            throw new Error(`Failed to fetch models: ${response.statusText}`);
-        }
-        
-        const data = await response.json();
-        
-        const gridId = modelType === 'checkpoint' ? 'checkpointGrid' : 'loraGrid';
-        const grid = document.getElementById(gridId);
-        
-        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;
-        }
-
-        if (updateFolders && data.folders) {
-            updateFolderTags(data.folders);
-        }
-        
-    } catch (error) {
-        console.error(`Error loading ${modelType}s:`, error);
-        showToast(`Failed to load ${modelType}s: ${error.message}`, 'error');
-    } finally {
-        pageState.isLoading = false;
-        document.body.classList.remove('loading');
-    }
-}
-
 // New method for virtual scrolling fetch
 export async function fetchModelsPage(options = {}) {
     const {
@@ -294,7 +137,6 @@ export async function resetAndReloadWithVirtualScroll(options = {}) {
     
     try {
         pageState.isLoading = true;
-        document.body.classList.add('loading');
         
         // Reset page counter
         pageState.currentPage = 1;
@@ -325,7 +167,6 @@ export async function resetAndReloadWithVirtualScroll(options = {}) {
         throw error;
     } finally {
         pageState.isLoading = false;
-        document.body.classList.remove('loading');
     }
 }
 
@@ -347,7 +188,6 @@ export async function loadMoreWithVirtualScroll(options = {}) {
     try {
         // Start loading state
         pageState.isLoading = true;
-        document.body.classList.add('loading');
         
         // Reset to first page if requested
         if (resetPage) {
@@ -380,7 +220,6 @@ export async function loadMoreWithVirtualScroll(options = {}) {
         throw error;
     } finally {
         pageState.isLoading = false;
-        document.body.classList.remove('loading');
     }
 }
 
@@ -434,6 +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';
@@ -475,22 +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();
     }
 }
 
@@ -644,6 +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 {
@@ -662,6 +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';
@@ -703,26 +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' 
@@ -739,56 +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

@@ -1,8 +1,5 @@
-import { createCheckpointCard } from '../components/CheckpointCard.js';
 import {
-    loadMoreModels,
     fetchModelsPage,
-    resetAndReload as baseResetAndReload,
     resetAndReloadWithVirtualScroll,
     loadMoreWithVirtualScroll,
     refreshModels as baseRefreshModels,
@@ -36,43 +33,21 @@ export async function fetchCheckpointsPage(page = 1, pageSize = 100) {
  * @returns {Promise<void>}
  */
 export async function loadMoreCheckpoints(resetPage = false, updateFolders = false) {
-    // Check if virtual scroller is available
-    if (state.virtualScroller) {
-        return loadMoreWithVirtualScroll({
-            modelType: 'checkpoint',
-            resetPage,
-            updateFolders,
-            fetchPageFunction: fetchCheckpointsPage
-        });
-    } else {
-        // Fall back to the original implementation if virtual scroller isn't available
-        return loadMoreModels({
-            resetPage,
-            updateFolders,
-            modelType: 'checkpoint',
-            createCardFunction: createCheckpointCard,
-            endpoint: '/api/checkpoints'
-        });
-    }
+    return loadMoreWithVirtualScroll({
+        modelType: 'checkpoint',
+        resetPage,
+        updateFolders,
+        fetchPageFunction: fetchCheckpointsPage
+    });
 }
 
 // Reset and reload checkpoints
 export async function resetAndReload(updateFolders = false) {
-    // Check if virtual scroller is available
-    if (state.virtualScroller) {
-        return resetAndReloadWithVirtualScroll({
-            modelType: 'checkpoint',
-            updateFolders,
-            fetchPageFunction: fetchCheckpointsPage
-        });
-    } else {
-        // Fall back to original implementation
-        return baseResetAndReload({
-            updateFolders,
-            modelType: 'checkpoint',
-            loadMoreFunction: loadMoreCheckpoints
-        });
-    }
+    return resetAndReloadWithVirtualScroll({
+        modelType: 'checkpoint',
+        updateFolders,
+        fetchPageFunction: fetchCheckpointsPage
+    });
 }
 
 // Refresh checkpoints
@@ -106,11 +81,7 @@ export async function fetchCivitai() {
 
 // Refresh single checkpoint metadata
 export async function refreshSingleCheckpointMetadata(filePath) {
-    const success = await refreshSingleModelMetadata(filePath, 'checkpoint');
-    if (success) {
-        // Reload the current view to show updated data
-        await resetAndReload();
-    }
+    await refreshSingleModelMetadata(filePath, 'checkpoint');
 }
 
 /**
@@ -138,6 +109,9 @@ export async function saveModelMetadata(filePath, data) {
         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 {
@@ -166,7 +140,7 @@ export async function renameCheckpointFile(filePath, newFileName) {
         // Show loading indicator
         state.loadingManager.showSimpleLoading('Renaming checkpoint file...');
         
-        const response = await fetch('/api/rename_checkpoint', {
+        const response = await fetch('/api/checkpoints/rename', {
             method: 'POST',
             headers: {
                 'Content-Type': 'application/json',
@@ -186,7 +160,6 @@ export async function renameCheckpointFile(filePath, newFileName) {
         console.error('Error renaming checkpoint file:', error);
         throw error;
     } finally {
-        // Hide loading indicator
         state.loadingManager.hide();
     }
 }

static/js/api/loraApi.js

@@ -1,8 +1,5 @@
-import { createLoraCard } from '../components/LoraCard.js';
 import {
-    loadMoreModels,
     fetchModelsPage,
-    resetAndReload as baseResetAndReload,
     resetAndReloadWithVirtualScroll,
     loadMoreWithVirtualScroll,
     refreshModels as baseRefreshModels,
@@ -12,7 +9,7 @@ import {
     refreshSingleModelMetadata,
     excludeModel as baseExcludeModel
 } from './baseModelApi.js';
-import { state, getCurrentPageState } from '../state/index.js';
+import { state } from '../state/index.js';
 
 /**
  * Save model metadata to the server
@@ -39,6 +36,9 @@ export async function saveModelMetadata(filePath, data) {
         if (!response.ok) {
             throw new Error('Failed to save metadata');
         }
+
+        // Update the virtual scroller with the new data
+        state.virtualScroller.updateSingleItem(filePath, data);
         
         return response.json();
     } finally {
@@ -63,26 +63,12 @@ export async function excludeLora(filePath) {
  * @returns {Promise<void>}
  */
 export async function loadMoreLoras(resetPage = false, updateFolders = false) {
-    const pageState = getCurrentPageState();
-    
-    // Check if virtual scroller is available
-    if (state.virtualScroller) {
-        return loadMoreWithVirtualScroll({
-            modelType: 'lora',
-            resetPage,
-            updateFolders,
-            fetchPageFunction: fetchLorasPage
-        });
-    } else {
-        // Fall back to the original implementation if virtual scroller isn't available
-        return loadMoreModels({
-            resetPage,
-            updateFolders,
-            modelType: 'lora',
-            createCardFunction: createLoraCard,
-            endpoint: '/api/loras'
-        });
-    }
+    return loadMoreWithVirtualScroll({
+        modelType: 'lora',
+        resetPage,
+        updateFolders,
+        fetchPageFunction: fetchLorasPage
+    });
 }
 
 /**
@@ -116,39 +102,12 @@ export async function replacePreview(filePath) {
     return replaceModelPreview(filePath, 'lora');
 }
 
-export function appendLoraCards(loras) {
-    // This function is no longer needed with virtual scrolling
-    // but kept for compatibility
-    if (state.virtualScroller) {
-        console.warn('appendLoraCards is deprecated when using virtual scrolling');
-    } else {
-        const grid = document.getElementById('loraGrid');
-        
-        loras.forEach(lora => {
-            const card = createLoraCard(lora);
-            grid.appendChild(card);
-        });
-    }
-}
-
 export async function resetAndReload(updateFolders = false) {
-    const pageState = getCurrentPageState();
-    
-    // Check if virtual scroller is available
-    if (state.virtualScroller) {
-        return resetAndReloadWithVirtualScroll({
-            modelType: 'lora',
-            updateFolders,
-            fetchPageFunction: fetchLorasPage
-        });
-    } else {
-        // Fall back to original implementation
-        return baseResetAndReload({
-            updateFolders,
-            modelType: 'lora',
-            loadMoreFunction: loadMoreLoras
-        });
-    }
+    return resetAndReloadWithVirtualScroll({
+        modelType: 'lora',
+        updateFolders,
+        fetchPageFunction: fetchLorasPage
+    });
 }
 
 export async function refreshLoras(fullRebuild = false) {
@@ -161,11 +120,7 @@ export async function refreshLoras(fullRebuild = false) {
 }
 
 export async function refreshSingleLoraMetadata(filePath) {
-    const success = await refreshSingleModelMetadata(filePath, 'lora');
-    if (success) {
-        // Reload the current view to show updated data
-        await resetAndReload();
-    }
+    await refreshSingleModelMetadata(filePath, 'lora');
 }
 
 export async function fetchModelDescription(modelId, filePath) {
@@ -194,7 +149,7 @@ export async function renameLoraFile(filePath, newFileName) {
         // Show loading indicator
         state.loadingManager.showSimpleLoading('Renaming LoRA file...');
         
-        const response = await fetch('/api/rename_lora', {
+        const response = await fetch('/api/loras/rename', {
             method: 'POST',
             headers: {
                 'Content-Type': 'application/json',

static/js/api/recipeApi.js

@@ -1,6 +1,5 @@
 import { RecipeCard } from '../components/RecipeCard.js';
 import {
-    fetchModelsPage,
     resetAndReloadWithVirtualScroll,
     loadMoreWithVirtualScroll
 } from './baseModelApi.js';
@@ -172,3 +171,44 @@ export function createRecipeCard(recipe) {
     });
     return recipeCard.element;
 }
+
+/**
+ * Update recipe metadata on the server
+ * @param {string} filePath - The file path of the recipe (e.g. D:/Workspace/ComfyUI/models/loras/recipes/86b4c335-ecfc-4791-89d2-3746e55a7614.webp)
+ * @param {Object} updates - The metadata updates to apply
+ * @returns {Promise<Object>} The updated recipe data
+ */
+export async function updateRecipeMetadata(filePath, updates) {
+    try {
+        state.loadingManager.showSimpleLoading('Saving metadata...');
+
+        // Extract recipeId from filePath (basename without extension)
+        const basename = filePath.split('/').pop().split('\\').pop();
+        const recipeId = basename.substring(0, basename.lastIndexOf('.'));
+        
+        const response = await fetch(`/api/recipe/${recipeId}/update`, {
+            method: 'PUT',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify(updates)
+        });
+
+        const data = await response.json();
+
+        if (!data.success) {
+            showToast(`Failed to update recipe: ${data.error}`, 'error');
+            throw new Error(data.error || 'Failed to update recipe');
+        }
+
+        state.virtualScroller.updateSingleItem(filePath, updates);
+        
+        return data;
+    } catch (error) {
+        console.error('Error updating recipe:', error);
+        showToast(`Error updating recipe: ${error.message}`, 'error');
+        throw error;
+    } finally {
+        state.loadingManager.hide();
+    }
+}

static/js/checkpoints.js

@@ -4,6 +4,7 @@ import { createPageControls } from './components/controls/index.js';
 import { loadMoreCheckpoints } from './api/checkpointApi.js';
 import { CheckpointDownloadManager } from './managers/CheckpointDownloadManager.js';
 import { CheckpointContextMenu } from './components/ContextMenu/index.js';
+import { ModelDuplicatesManager } from './components/ModelDuplicatesManager.js';
 
 // Initialize the Checkpoints page
 class CheckpointsPageManager {
@@ -14,6 +15,9 @@ class CheckpointsPageManager {
         // Initialize checkpoint download manager
         window.checkpointDownloadManager = new CheckpointDownloadManager();
         
+        // Initialize the ModelDuplicatesManager
+        this.duplicatesManager = new ModelDuplicatesManager(this, 'checkpoints');
+        
         // Expose only necessary functions to global scope
         this._exposeRequiredGlobalFunctions();
     }
@@ -29,6 +33,9 @@ class CheckpointsPageManager {
         window.checkpointManager = {
             loadCheckpoints: (reset) => loadMoreCheckpoints(reset)
         };
+        
+        // Expose duplicates manager
+        window.modelDuplicatesManager = this.duplicatesManager;
     }
     
     async initialize() {

static/js/components/CheckpointCard.js

@@ -1,10 +1,203 @@
-import { showToast, copyToClipboard, openExampleImagesFolder } from '../utils/uiHelpers.js';
+import { showToast, copyToClipboard, openExampleImagesFolder, openCivitai } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { showCheckpointModal } from './checkpointModal/index.js';
 import { NSFW_LEVELS } from '../utils/constants.js';
 import { replaceCheckpointPreview as apiReplaceCheckpointPreview, saveModelMetadata } from '../api/checkpointApi.js';
 import { showDeleteModal } from '../utils/modalUtils.js';
 
+// Add a global event delegation handler
+export function setupCheckpointCardEventDelegation() {
+    const gridElement = document.getElementById('checkpointGrid');
+    if (!gridElement) return;
+    
+    // Remove any existing event listener to prevent duplication
+    gridElement.removeEventListener('click', handleCheckpointCardEvent);
+    
+    // Add the event delegation handler
+    gridElement.addEventListener('click', handleCheckpointCardEvent);
+}
+
+// Event delegation handler for all checkpoint card events
+function handleCheckpointCardEvent(event) {
+    // Find the closest card element
+    const card = event.target.closest('.lora-card');
+    if (!card) return;
+    
+    // Handle specific elements within the card
+    if (event.target.closest('.toggle-blur-btn')) {
+        event.stopPropagation();
+        toggleBlurContent(card);
+        return;
+    }
+    
+    if (event.target.closest('.show-content-btn')) {
+        event.stopPropagation();
+        showBlurredContent(card);
+        return;
+    }
+    
+    if (event.target.closest('.fa-star')) {
+        event.stopPropagation();
+        toggleFavorite(card);
+        return;
+    }
+    
+    if (event.target.closest('.fa-globe')) {
+        event.stopPropagation();
+        if (card.dataset.from_civitai === 'true') {
+            openCivitai(card.dataset.filepath);
+        }
+        return;
+    }
+    
+    if (event.target.closest('.fa-copy')) {
+        event.stopPropagation();
+        copyCheckpointName(card);
+        return;
+    }
+    
+    if (event.target.closest('.fa-trash')) {
+        event.stopPropagation();
+        showDeleteModal(card.dataset.filepath);
+        return;
+    }
+    
+    if (event.target.closest('.fa-image')) {
+        event.stopPropagation();
+        replaceCheckpointPreview(card.dataset.filepath);
+        return;
+    }
+    
+    if (event.target.closest('.fa-folder-open')) {
+        event.stopPropagation();
+        openExampleImagesFolder(card.dataset.sha256);
+        return;
+    }
+    
+    // If no specific element was clicked, handle the card click (show modal)
+    showCheckpointModalFromCard(card);
+}
+
+// Helper functions for event handling
+function toggleBlurContent(card) {
+    const preview = card.querySelector('.card-preview');
+    const isBlurred = preview.classList.toggle('blurred');
+    const icon = card.querySelector('.toggle-blur-btn i');
+    
+    // Update the icon based on blur state
+    if (isBlurred) {
+        icon.className = 'fas fa-eye';
+    } else {
+        icon.className = 'fas fa-eye-slash';
+    }
+    
+    // Toggle the overlay visibility
+    const overlay = card.querySelector('.nsfw-overlay');
+    if (overlay) {
+        overlay.style.display = isBlurred ? 'flex' : 'none';
+    }
+}
+
+function showBlurredContent(card) {
+    const preview = card.querySelector('.card-preview');
+    preview.classList.remove('blurred');
+    
+    // Update the toggle button icon
+    const toggleBtn = card.querySelector('.toggle-blur-btn');
+    if (toggleBtn) {
+        toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
+    }
+    
+    // Hide the overlay
+    const overlay = card.querySelector('.nsfw-overlay');
+    if (overlay) {
+        overlay.style.display = 'none';
+    }
+}
+
+async function toggleFavorite(card) {
+    const starIcon = card.querySelector('.fa-star');
+    const isFavorite = starIcon.classList.contains('fas');
+    const newFavoriteState = !isFavorite;
+    
+    try {
+        // Save the new favorite state to the server
+        await saveModelMetadata(card.dataset.filepath, { 
+            favorite: newFavoriteState 
+        });
+
+        if (newFavoriteState) {
+            showToast('Added to favorites', 'success');
+        } else {
+            showToast('Removed from favorites', 'success');
+        }
+    } catch (error) {
+        console.error('Failed to update favorite status:', error);
+        showToast('Failed to update favorite status', 'error');
+    }
+}
+
+async function copyCheckpointName(card) {
+    const checkpointName = card.dataset.file_name;
+    
+    try {
+        await copyToClipboard(checkpointName, 'Checkpoint name copied');
+    } catch (err) {
+        console.error('Copy failed:', err);
+        showToast('Copy failed', 'error');
+    }
+}
+
+function showCheckpointModalFromCard(card) {
+    // Get the page-specific previewVersions map
+    const previewVersions = state.pages.checkpoints.previewVersions || new Map();
+    const version = previewVersions.get(card.dataset.filepath);
+    const previewUrl = card.dataset.preview_url || '/loras_static/images/no-preview.png';
+    const versionedPreviewUrl = version ? `${previewUrl}?t=${version}` : previewUrl;
+    
+    // Show checkpoint details modal
+    const checkpointMeta = {
+        sha256: card.dataset.sha256,
+        file_path: card.dataset.filepath,
+        model_name: card.dataset.name,
+        file_name: card.dataset.file_name,
+        folder: card.dataset.folder,
+        modified: card.dataset.modified,
+        file_size: parseInt(card.dataset.file_size || '0'),
+        from_civitai: card.dataset.from_civitai === 'true',
+        base_model: card.dataset.base_model,
+        notes: card.dataset.notes || '',
+        preview_url: versionedPreviewUrl,
+        // Parse civitai metadata from the card's dataset
+        civitai: (() => {
+            try {
+                return JSON.parse(card.dataset.meta || '{}');
+            } catch (e) {
+                console.error('Failed to parse civitai metadata:', e);
+                return {}; // Return empty object on error
+            }
+        })(),
+        tags: (() => {
+            try {
+                return JSON.parse(card.dataset.tags || '[]');
+            } catch (e) {
+                console.error('Failed to parse tags:', e);
+                return []; // Return empty array on error
+            }
+        })(),
+        modelDescription: card.dataset.modelDescription || ''
+    };
+    showCheckpointModal(checkpointMeta);
+}
+
+function replaceCheckpointPreview(filePath) {
+    if (window.replaceCheckpointPreview) {
+        window.replaceCheckpointPreview(filePath);
+    } else {
+        apiReplaceCheckpointPreview(filePath);
+    }
+}
+
 export function createCheckpointCard(checkpoint) {
     const card = document.createElement('div');
     card.className = 'lora-card';  // Reuse the same class for styling
@@ -123,162 +316,7 @@ export function createCheckpointCard(checkpoint) {
         </div>
     `;
 
-    // Main card click event
-    card.addEventListener('click', () => {
-        // Show checkpoint details modal
-        const checkpointMeta = {
-            sha256: card.dataset.sha256,
-            file_path: card.dataset.filepath,
-            model_name: card.dataset.name,
-            file_name: card.dataset.file_name,
-            folder: card.dataset.folder,
-            modified: card.dataset.modified,
-            file_size: parseInt(card.dataset.file_size || '0'),
-            from_civitai: card.dataset.from_civitai === 'true',
-            base_model: card.dataset.base_model,
-            notes: card.dataset.notes || '',
-            preview_url: versionedPreviewUrl,
-            // Parse civitai metadata from the card's dataset
-            civitai: (() => {
-                try {
-                    return JSON.parse(card.dataset.meta || '{}');
-                } catch (e) {
-                    console.error('Failed to parse civitai metadata:', e);
-                    return {}; // Return empty object on error
-                }
-            })(),
-            tags: (() => {
-                try {
-                    return JSON.parse(card.dataset.tags || '[]');
-                } catch (e) {
-                    console.error('Failed to parse tags:', e);
-                    return []; // Return empty array on error
-                }
-            })(),
-            modelDescription: card.dataset.modelDescription || ''
-        };
-        showCheckpointModal(checkpointMeta);
-    });
-
-    // Toggle blur button functionality
-    const toggleBlurBtn = card.querySelector('.toggle-blur-btn');
-    if (toggleBlurBtn) {
-        toggleBlurBtn.addEventListener('click', (e) => {
-            e.stopPropagation();
-            const preview = card.querySelector('.card-preview');
-            const isBlurred = preview.classList.toggle('blurred');
-            const icon = toggleBlurBtn.querySelector('i');
-            
-            // Update the icon based on blur state
-            if (isBlurred) {
-                icon.className = 'fas fa-eye';
-            } else {
-                icon.className = 'fas fa-eye-slash';
-            }
-            
-            // Toggle the overlay visibility
-            const overlay = card.querySelector('.nsfw-overlay');
-            if (overlay) {
-                overlay.style.display = isBlurred ? 'flex' : 'none';
-            }
-        });
-    }
-
-    // Show content button functionality
-    const showContentBtn = card.querySelector('.show-content-btn');
-    if (showContentBtn) {
-        showContentBtn.addEventListener('click', (e) => {
-            e.stopPropagation();
-            const preview = card.querySelector('.card-preview');
-            preview.classList.remove('blurred');
-            
-            // Update the toggle button icon
-            const toggleBtn = card.querySelector('.toggle-blur-btn');
-            if (toggleBtn) {
-                toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
-            }
-            
-            // Hide the overlay
-            const overlay = card.querySelector('.nsfw-overlay');
-            if (overlay) {
-                overlay.style.display = 'none';
-            }
-        });
-    }
-
-    // Favorite button click event
-    card.querySelector('.fa-star')?.addEventListener('click', async e => {
-        e.stopPropagation();
-        const starIcon = e.currentTarget;
-        const isFavorite = starIcon.classList.contains('fas');
-        const newFavoriteState = !isFavorite;
-        
-        try {
-            // Save the new favorite state to the server
-            await saveModelMetadata(card.dataset.filepath, { 
-                favorite: newFavoriteState 
-            });
-
-            // Update the UI
-            if (newFavoriteState) {
-                starIcon.classList.remove('far');
-                starIcon.classList.add('fas', 'favorite-active');
-                starIcon.title = 'Remove from favorites';
-                card.dataset.favorite = 'true';
-                showToast('Added to favorites', 'success');
-            } else {
-                starIcon.classList.remove('fas', 'favorite-active');
-                starIcon.classList.add('far');
-                starIcon.title = 'Add to favorites';
-                card.dataset.favorite = 'false';
-                showToast('Removed from favorites', 'success');
-            }
-        } catch (error) {
-            console.error('Failed to update favorite status:', error);
-            showToast('Failed to update favorite status', 'error');
-        }
-    });
-
-    // Copy button click event
-    card.querySelector('.fa-copy')?.addEventListener('click', async e => {
-        e.stopPropagation();
-        const checkpointName = card.dataset.file_name;
-        
-        try {
-            await copyToClipboard(checkpointName, 'Checkpoint name copied');
-        } catch (err) {
-            console.error('Copy failed:', err);
-            showToast('Copy failed', 'error');
-        }
-    });
-
-    // Civitai button click event
-    if (checkpoint.from_civitai) {
-        card.querySelector('.fa-globe')?.addEventListener('click', e => {
-            e.stopPropagation();
-            openCivitai(checkpoint.model_name);
-        });
-    }
-
-    // Delete button click event
-    card.querySelector('.fa-trash')?.addEventListener('click', e => {
-        e.stopPropagation();
-        showDeleteModal(checkpoint.file_path);
-    });
-
-    // Replace preview button click event
-    card.querySelector('.fa-image')?.addEventListener('click', e => {
-        e.stopPropagation();
-        replaceCheckpointPreview(checkpoint.file_path);
-    });
-
-    // Open example images folder button click event
-    card.querySelector('.fa-folder-open')?.addEventListener('click', e => {
-        e.stopPropagation();
-        openExampleImagesFolder(checkpoint.sha256);
-    });
-
-    // Add autoplayOnHover handlers for video elements if needed
+    // Add video auto-play on hover functionality if needed
     const videoElement = card.querySelector('video');
     if (videoElement && autoplayOnHover) {
         const cardPreview = card.querySelector('.card-preview');
@@ -287,52 +325,10 @@ export function createCheckpointCard(checkpoint) {
         videoElement.removeAttribute('autoplay');
         videoElement.pause();
         
-        // Add mouse events to trigger play/pause
-        cardPreview.addEventListener('mouseenter', () => {
-            videoElement.play();
-        });
-        
-        cardPreview.addEventListener('mouseleave', () => {
-            videoElement.pause();
-            videoElement.currentTime = 0;
-        });
+        // Add mouse events to trigger play/pause using event attributes
+        cardPreview.setAttribute('onmouseenter', 'this.querySelector("video")?.play()');
+        cardPreview.setAttribute('onmouseleave', 'const v=this.querySelector("video"); if(v){v.pause();v.currentTime=0;}');
     }
 
     return card;
-}
-
-// These functions will be implemented in checkpointApi.js
-function openCivitai(modelName) {
-    // Check if the global function exists (registered by PageControls)
-    if (window.openCivitai) {
-        window.openCivitai(modelName);
-    } else {
-        // Fallback implementation
-        const card = document.querySelector(`.lora-card[data-name="${modelName}"]`);
-        if (!card) return;
-        
-        const metaData = JSON.parse(card.dataset.meta || '{}');
-        const civitaiId = metaData.modelId;
-        const versionId = metaData.id;
-        
-        // Build URL
-        if (civitaiId) {
-            let url = `https://civitai.com/models/${civitaiId}`;
-            if (versionId) {
-                url += `?modelVersionId=${versionId}`;
-            }
-            window.open(url, '_blank');
-        } else {
-            // If no ID, try searching by name
-            window.open(`https://civitai.com/models?query=${encodeURIComponent(modelName)}`, '_blank');
-        }
-    }
-}
-
-function replaceCheckpointPreview(filePath) {
-    if (window.replaceCheckpointPreview) {
-        window.replaceCheckpointPreview(filePath);
-    } else {
-        apiReplaceCheckpointPreview(filePath);
-    }
 }

static/js/components/ContextMenu.js

@@ -1,372 +0,0 @@
-import { refreshSingleLoraMetadata } from '../api/loraApi.js';
-import { showToast, getNSFWLevelName } from '../utils/uiHelpers.js';
-import { NSFW_LEVELS } from '../utils/constants.js';
-import { getStorageItem } from '../utils/storageHelpers.js';
-
-export class LoraContextMenu {
-    constructor() {
-        this.menu = document.getElementById('loraContextMenu');
-        this.currentCard = null;
-        this.nsfwSelector = document.getElementById('nsfwLevelSelector');
-        this.init();
-    }
-
-    init() {
-        document.addEventListener('click', () => this.hideMenu());
-        document.addEventListener('contextmenu', (e) => {
-            const card = e.target.closest('.lora-card');
-            if (!card) {
-                this.hideMenu();
-                return;
-            }
-            e.preventDefault();
-            this.showMenu(e.clientX, e.clientY, card);
-        });
-
-        this.menu.addEventListener('click', (e) => {
-            const menuItem = e.target.closest('.context-menu-item');
-            if (!menuItem || !this.currentCard) return;
-
-            const action = menuItem.dataset.action;
-            if (!action) return;
-            
-            switch(action) {
-                case 'detail':
-                    // Trigger the main card click which shows the modal
-                    this.currentCard.click();
-                    break;
-                case 'civitai':
-                    // Only trigger if the card is from civitai
-                    if (this.currentCard.dataset.from_civitai === 'true') {
-                        if (this.currentCard.dataset.meta === '{}') {
-                            showToast('Please fetch metadata from CivitAI first', 'info');
-                        } else {
-                            this.currentCard.querySelector('.fa-globe')?.click();
-                        }
-                    } else {
-                        showToast('No CivitAI information available', 'info');
-                    }
-                    break;
-                case 'copyname':
-                    this.currentCard.querySelector('.fa-copy')?.click();
-                    break;
-                case 'preview':
-                    this.currentCard.querySelector('.fa-image')?.click();
-                    break;
-                case 'delete':
-                    this.currentCard.querySelector('.fa-trash')?.click();
-                    break;
-                case 'move':
-                    moveManager.showMoveModal(this.currentCard.dataset.filepath);
-                    break;
-                case 'refresh-metadata':
-                    refreshSingleLoraMetadata(this.currentCard.dataset.filepath);
-                    break;
-                case 'set-nsfw':
-                    this.showNSFWLevelSelector(null, null, this.currentCard);
-                    break;
-            }
-            
-            this.hideMenu();
-        });
-
-        // Initialize NSFW Level Selector events
-        this.initNSFWSelector();
-    }
-
-    initNSFWSelector() {
-        // Close button
-        const closeBtn = this.nsfwSelector.querySelector('.close-nsfw-selector');
-        closeBtn.addEventListener('click', () => {
-            this.nsfwSelector.style.display = 'none';
-        });
-
-        // Level buttons
-        const levelButtons = this.nsfwSelector.querySelectorAll('.nsfw-level-btn');
-        levelButtons.forEach(btn => {
-            btn.addEventListener('click', async () => {
-                const level = parseInt(btn.dataset.level);
-                const filePath = this.nsfwSelector.dataset.cardPath;
-                
-                if (!filePath) return;
-                
-                try {
-                    await this.saveModelMetadata(filePath, { preview_nsfw_level: level });
-                    
-                    // Update card data
-                    const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
-                    if (card) {
-                        let metaData = {};
-                        try {
-                            metaData = JSON.parse(card.dataset.meta || '{}');
-                        } catch (err) {
-                            console.error('Error parsing metadata:', err);
-                        }
-                        
-                        metaData.preview_nsfw_level = level;
-                        card.dataset.meta = JSON.stringify(metaData);
-                        card.dataset.nsfwLevel = level.toString();
-                        
-                        // Apply blur effect immediately
-                        this.updateCardBlurEffect(card, level);
-                    }
-                    
-                    showToast(`Content rating set to ${getNSFWLevelName(level)}`, 'success');
-                    this.nsfwSelector.style.display = 'none';
-                } catch (error) {
-                    showToast(`Failed to set content rating: ${error.message}`, 'error');
-                }
-            });
-        });
-        
-        // Close when clicking outside
-        document.addEventListener('click', (e) => {
-            if (this.nsfwSelector.style.display === 'block' && 
-                !this.nsfwSelector.contains(e.target) && 
-                !e.target.closest('.context-menu-item[data-action="set-nsfw"]')) {
-                this.nsfwSelector.style.display = 'none';
-            }
-        });
-    }
-
-    async saveModelMetadata(filePath, data) {
-        const response = await fetch('/api/loras/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');
-        }
-        
-        return await response.json();
-    }
-
-    updateCardBlurEffect(card, level) {
-        // Get user settings for blur threshold
-        const blurThreshold = parseInt(getStorageItem('nsfwBlurLevel') || '4');
-        
-        // Get card preview container
-        const previewContainer = card.querySelector('.card-preview');
-        if (!previewContainer) return;
-        
-        // Get preview media element
-        const previewMedia = previewContainer.querySelector('img') || previewContainer.querySelector('video');
-        if (!previewMedia) return;
-        
-        // Check if blur should be applied
-        if (level >= blurThreshold) {
-            // Add blur class to the preview container
-            previewContainer.classList.add('blurred');
-            
-            // Get or create the NSFW overlay
-            let nsfwOverlay = previewContainer.querySelector('.nsfw-overlay');
-            if (!nsfwOverlay) {
-                // Create new overlay
-                nsfwOverlay = document.createElement('div');
-                nsfwOverlay.className = 'nsfw-overlay';
-                
-                // Create and configure the warning content
-                const warningContent = document.createElement('div');
-                warningContent.className = 'nsfw-warning';
-                
-                // Determine NSFW warning text based on level
-                let nsfwText = "Mature Content";
-                if (level >= NSFW_LEVELS.XXX) {
-                    nsfwText = "XXX-rated Content";
-                } else if (level >= NSFW_LEVELS.X) {
-                    nsfwText = "X-rated Content";
-                } else if (level >= NSFW_LEVELS.R) {
-                    nsfwText = "R-rated Content";
-                }
-                
-                // Add warning text and show button
-                warningContent.innerHTML = `
-                    <p>${nsfwText}</p>
-                    <button class="show-content-btn">Show</button>
-                `;
-                
-                // Add click event to the show button
-                const showBtn = warningContent.querySelector('.show-content-btn');
-                showBtn.addEventListener('click', (e) => {
-                    e.stopPropagation();
-                    previewContainer.classList.remove('blurred');
-                    nsfwOverlay.style.display = 'none';
-                    
-                    // Update toggle button icon if it exists
-                    const toggleBtn = card.querySelector('.toggle-blur-btn');
-                    if (toggleBtn) {
-                        toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
-                    }
-                });
-                
-                nsfwOverlay.appendChild(warningContent);
-                previewContainer.appendChild(nsfwOverlay);
-            } else {
-                // Update existing overlay
-                const warningText = nsfwOverlay.querySelector('p');
-                if (warningText) {
-                    let nsfwText = "Mature Content";
-                    if (level >= NSFW_LEVELS.XXX) {
-                        nsfwText = "XXX-rated Content";
-                    } else if (level >= NSFW_LEVELS.X) {
-                        nsfwText = "X-rated Content";
-                    } else if (level >= NSFW_LEVELS.R) {
-                        nsfwText = "R-rated Content";
-                    }
-                    warningText.textContent = nsfwText;
-                }
-                nsfwOverlay.style.display = 'flex';
-            }
-            
-            // Get or create the toggle button in the header
-            const cardHeader = previewContainer.querySelector('.card-header');
-            if (cardHeader) {
-                let toggleBtn = cardHeader.querySelector('.toggle-blur-btn');
-                
-                if (!toggleBtn) {
-                    toggleBtn = document.createElement('button');
-                    toggleBtn.className = 'toggle-blur-btn';
-                    toggleBtn.title = 'Toggle blur';
-                    toggleBtn.innerHTML = '<i class="fas fa-eye"></i>';
-                    
-                    // Add click event to toggle button
-                    toggleBtn.addEventListener('click', (e) => {
-                        e.stopPropagation();
-                        const isBlurred = previewContainer.classList.toggle('blurred');
-                        const icon = toggleBtn.querySelector('i');
-                        
-                        // Update icon and overlay visibility
-                        if (isBlurred) {
-                            icon.className = 'fas fa-eye';
-                            nsfwOverlay.style.display = 'flex';
-                        } else {
-                            icon.className = 'fas fa-eye-slash';
-                            nsfwOverlay.style.display = 'none';
-                        }
-                    });
-                    
-                    // Add to the beginning of header
-                    cardHeader.insertBefore(toggleBtn, cardHeader.firstChild);
-                    
-                    // Update base model label class
-                    const baseModelLabel = cardHeader.querySelector('.base-model-label');
-                    if (baseModelLabel && !baseModelLabel.classList.contains('with-toggle')) {
-                        baseModelLabel.classList.add('with-toggle');
-                    }
-                } else {
-                    // Update existing toggle button
-                    toggleBtn.querySelector('i').className = 'fas fa-eye';
-                }
-            }
-        } else {
-            // Remove blur
-            previewContainer.classList.remove('blurred');
-            
-            // Hide overlay if it exists
-            const overlay = previewContainer.querySelector('.nsfw-overlay');
-            if (overlay) overlay.style.display = 'none';
-            
-            // Update or remove toggle button
-            const toggleBtn = card.querySelector('.toggle-blur-btn');
-            if (toggleBtn) {
-                // We'll leave the button but update the icon
-                toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
-            }
-        }
-    }
-
-    showNSFWLevelSelector(x, y, card) {
-        const selector = document.getElementById('nsfwLevelSelector');
-        const currentLevelEl = document.getElementById('currentNSFWLevel');
-        
-        // Get current NSFW level
-        let currentLevel = 0;
-        try {
-            const metaData = JSON.parse(card.dataset.meta || '{}');
-            currentLevel = metaData.preview_nsfw_level || 0;
-            
-            // Update if we have no recorded level but have a dataset attribute
-            if (!currentLevel && card.dataset.nsfwLevel) {
-                currentLevel = parseInt(card.dataset.nsfwLevel) || 0;
-            }
-        } catch (err) {
-            console.error('Error parsing metadata:', err);
-        }
-        
-        currentLevelEl.textContent = getNSFWLevelName(currentLevel);
-        
-        // Position the selector
-        if (x && y) {
-            const viewportWidth = document.documentElement.clientWidth;
-            const viewportHeight = document.documentElement.clientHeight;
-            const selectorRect = selector.getBoundingClientRect();
-            
-            // Center the selector if no coordinates provided
-            let finalX = (viewportWidth - selectorRect.width) / 2;
-            let finalY = (viewportHeight - selectorRect.height) / 2;
-            
-            selector.style.left = `${finalX}px`;
-            selector.style.top = `${finalY}px`;
-        }
-        
-        // Highlight current level button
-        document.querySelectorAll('.nsfw-level-btn').forEach(btn => {
-            if (parseInt(btn.dataset.level) === currentLevel) {
-                btn.classList.add('active');
-            } else {
-                btn.classList.remove('active');
-            }
-        });
-        
-        // Store reference to current card
-        selector.dataset.cardPath = card.dataset.filepath;
-        
-        // Show selector
-        selector.style.display = 'block';
-    }
-
-    showMenu(x, y, card) {
-        this.currentCard = card;
-        this.menu.style.display = 'block';
-
-        // 获取菜单尺寸
-        const menuRect = this.menu.getBoundingClientRect();
-        
-        // 获取视口尺寸
-        const viewportWidth = document.documentElement.clientWidth;
-        const viewportHeight = document.documentElement.clientHeight;
-        
-        // 计算最终位置 - 使用 clientX/Y，不需要考虑滚动偏移
-        let finalX = x;
-        let finalY = y;
-        
-        // 确保菜单不会超出右侧边界
-        if (x + menuRect.width > viewportWidth) {
-            finalX = x - menuRect.width;
-        }
-        
-        // 确保菜单不会超出底部边界
-        if (y + menuRect.height > viewportHeight) {
-            finalY = y - menuRect.height;
-        }
-        
-        // 直接设置位置，因为 position: fixed 是相对于视口定位的
-        this.menu.style.left = `${finalX}px`;
-        this.menu.style.top = `${finalY}px`;
-    }
-
-    hideMenu() {
-        this.menu.style.display = 'none';
-        this.currentCard = null;
-    }
-}
-
-// For backward compatibility, re-export the LoraContextMenu class
-// export { LoraContextMenu } from './ContextMenu/LoraContextMenu.js';

static/js/components/ContextMenu/CheckpointContextMenu.js

@@ -1,14 +1,15 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
-import { refreshSingleCheckpointMetadata, saveModelMetadata, replaceCheckpointPreview } from '../../api/checkpointApi.js';
-import { showToast, getNSFWLevelName, openExampleImagesFolder } from '../../utils/uiHelpers.js';
-import { NSFW_LEVELS } from '../../utils/constants.js';
-import { getStorageItem } from '../../utils/storageHelpers.js';
+import { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
+import { refreshSingleCheckpointMetadata, saveModelMetadata, replaceCheckpointPreview, resetAndReload } from '../../api/checkpointApi.js';
+import { showToast } from '../../utils/uiHelpers.js';
 import { showExcludeModal } from '../../utils/modalUtils.js';
 
 export class CheckpointContextMenu extends BaseContextMenu {
     constructor() {
         super('checkpointContextMenu', '.lora-card');
         this.nsfwSelector = document.getElementById('nsfwLevelSelector');
+        this.modelType = 'checkpoint';
+        this.resetAndReload = resetAndReload;
         
         // Initialize NSFW Level Selector events
         if (this.nsfwSelector) {
@@ -16,30 +17,27 @@ export class CheckpointContextMenu extends BaseContextMenu {
         }
     }
     
+    // Implementation needed by the mixin
+    async saveModelMetadata(filePath, data) {
+        return saveModelMetadata(filePath, data);
+    }
+    
     handleMenuAction(action) {
+        // First try to handle with common actions
+        if (ModelContextMenuMixin.handleCommonMenuActions.call(this, action)) {
+            return;
+        }
+
+        // Otherwise handle checkpoint-specific actions
         switch(action) {
             case 'details':
                 // Show checkpoint details
                 this.currentCard.click();
                 break;
-            case 'preview':
-                // Open example images folder instead of replacing preview
-                openExampleImagesFolder(this.currentCard.dataset.sha256);
-                break;
             case 'replace-preview':
                 // Add new action for replacing preview images
                 replaceCheckpointPreview(this.currentCard.dataset.filepath);
                 break;
-            case 'civitai':
-                // Open civitai page
-                if (this.currentCard.dataset.from_civitai === 'true') {
-                    if (this.currentCard.querySelector('.fa-globe')) {
-                        this.currentCard.querySelector('.fa-globe').click();
-                    }
-                } else {
-                    showToast('No CivitAI information available', 'info');
-                }
-                break;
             case 'delete':
                 // Delete checkpoint
                 if (this.currentCard.querySelector('.fa-trash')) {
@@ -56,10 +54,6 @@ export class CheckpointContextMenu extends BaseContextMenu {
                 // Refresh metadata from CivitAI
                 refreshSingleCheckpointMetadata(this.currentCard.dataset.filepath);
                 break;
-            case 'set-nsfw':
-                // Set NSFW level
-                this.showNSFWLevelSelector(null, null, this.currentCard);
-                break;
             case 'move':
                 // Move to folder (placeholder)
                 showToast('Move to folder feature coming soon', 'info');
@@ -67,256 +61,9 @@ export class CheckpointContextMenu extends BaseContextMenu {
             case 'exclude':
                 showExcludeModal(this.currentCard.dataset.filepath, 'checkpoint');
                 break;
-
         }
     }
+}
 
-    // NSFW Selector methods
-    initNSFWSelector() {
-        // Close button
-        const closeBtn = this.nsfwSelector.querySelector('.close-nsfw-selector');
-        closeBtn.addEventListener('click', () => {
-            this.nsfwSelector.style.display = 'none';
-        });
-
-        // Level buttons
-        const levelButtons = this.nsfwSelector.querySelectorAll('.nsfw-level-btn');
-        levelButtons.forEach(btn => {
-            btn.addEventListener('click', async () => {
-                const level = parseInt(btn.dataset.level);
-                const filePath = this.nsfwSelector.dataset.cardPath;
-                
-                if (!filePath) return;
-                
-                try {
-                    await saveModelMetadata(filePath, { preview_nsfw_level: level });
-                    
-                    // Update card data
-                    const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
-                    if (card) {
-                        let metaData = {};
-                        try {
-                            metaData = JSON.parse(card.dataset.meta || '{}');
-                        } catch (err) {
-                            console.error('Error parsing metadata:', err);
-                        }
-                        
-                        metaData.preview_nsfw_level = level;
-                        card.dataset.meta = JSON.stringify(metaData);
-                        card.dataset.nsfwLevel = level.toString();
-                        
-                        // Apply blur effect immediately
-                        this.updateCardBlurEffect(card, level);
-                    }
-                    
-                    showToast(`Content rating set to ${getNSFWLevelName(level)}`, 'success');
-                    this.nsfwSelector.style.display = 'none';
-                } catch (error) {
-                    showToast(`Failed to set content rating: ${error.message}`, 'error');
-                }
-            });
-        });
-        
-        // Close when clicking outside
-        document.addEventListener('click', (e) => {
-            if (this.nsfwSelector.style.display === 'block' && 
-                !this.nsfwSelector.contains(e.target) && 
-                !e.target.closest('.context-menu-item[data-action="set-nsfw"]')) {
-                this.nsfwSelector.style.display = 'none';
-            }
-        });
-    }
-
-    updateCardBlurEffect(card, level) {
-        // Get user settings for blur threshold
-        const blurThreshold = parseInt(getStorageItem('nsfwBlurLevel') || '4');
-        
-        // Get card preview container
-        const previewContainer = card.querySelector('.card-preview');
-        if (!previewContainer) return;
-        
-        // Get preview media element
-        const previewMedia = previewContainer.querySelector('img') || previewContainer.querySelector('video');
-        if (!previewMedia) return;
-        
-        // Check if blur should be applied
-        if (level >= blurThreshold) {
-            // Add blur class to the preview container
-            previewContainer.classList.add('blurred');
-            
-            // Get or create the NSFW overlay
-            let nsfwOverlay = previewContainer.querySelector('.nsfw-overlay');
-            if (!nsfwOverlay) {
-                // Create new overlay
-                nsfwOverlay = document.createElement('div');
-                nsfwOverlay.className = 'nsfw-overlay';
-                
-                // Create and configure the warning content
-                const warningContent = document.createElement('div');
-                warningContent.className = 'nsfw-warning';
-                
-                // Determine NSFW warning text based on level
-                let nsfwText = "Mature Content";
-                if (level >= NSFW_LEVELS.XXX) {
-                    nsfwText = "XXX-rated Content";
-                } else if (level >= NSFW_LEVELS.X) {
-                    nsfwText = "X-rated Content";
-                } else if (level >= NSFW_LEVELS.R) {
-                    nsfwText = "R-rated Content";
-                }
-                
-                // Add warning text and show button
-                warningContent.innerHTML = `
-                    <p>${nsfwText}</p>
-                    <button class="show-content-btn">Show</button>
-                `;
-                
-                // Add click event to the show button
-                const showBtn = warningContent.querySelector('.show-content-btn');
-                showBtn.addEventListener('click', (e) => {
-                    e.stopPropagation();
-                    previewContainer.classList.remove('blurred');
-                    nsfwOverlay.style.display = 'none';
-                    
-                    // Update toggle button icon if it exists
-                    const toggleBtn = card.querySelector('.toggle-blur-btn');
-                    if (toggleBtn) {
-                        toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
-                    }
-                });
-                
-                nsfwOverlay.appendChild(warningContent);
-                previewContainer.appendChild(nsfwOverlay);
-            } else {
-                // Update existing overlay
-                const warningText = nsfwOverlay.querySelector('p');
-                if (warningText) {
-                    let nsfwText = "Mature Content";
-                    if (level >= NSFW_LEVELS.XXX) {
-                        nsfwText = "XXX-rated Content";
-                    } else if (level >= NSFW_LEVELS.X) {
-                        nsfwText = "X-rated Content";
-                    } else if (level >= NSFW_LEVELS.R) {
-                        nsfwText = "R-rated Content";
-                    }
-                    warningText.textContent = nsfwText;
-                }
-                nsfwOverlay.style.display = 'flex';
-            }
-            
-            // Get or create the toggle button in the header
-            const cardHeader = previewContainer.querySelector('.card-header');
-            if (cardHeader) {
-                let toggleBtn = cardHeader.querySelector('.toggle-blur-btn');
-                
-                if (!toggleBtn) {
-                    toggleBtn = document.createElement('button');
-                    toggleBtn.className = 'toggle-blur-btn';
-                    toggleBtn.title = 'Toggle blur';
-                    toggleBtn.innerHTML = '<i class="fas fa-eye"></i>';
-                    
-                    // Add click event to toggle button
-                    toggleBtn.addEventListener('click', (e) => {
-                        e.stopPropagation();
-                        const isBlurred = previewContainer.classList.toggle('blurred');
-                        const icon = toggleBtn.querySelector('i');
-                        
-                        // Update icon and overlay visibility
-                        if (isBlurred) {
-                            icon.className = 'fas fa-eye';
-                            nsfwOverlay.style.display = 'flex';
-                        } else {
-                            icon.className = 'fas fa-eye-slash';
-                            nsfwOverlay.style.display = 'none';
-                        }
-                    });
-                    
-                    // Add to the beginning of header
-                    cardHeader.insertBefore(toggleBtn, cardHeader.firstChild);
-                    
-                    // Update base model label class
-                    const baseModelLabel = cardHeader.querySelector('.base-model-label');
-                    if (baseModelLabel && !baseModelLabel.classList.contains('with-toggle')) {
-                        baseModelLabel.classList.add('with-toggle');
-                    }
-                } else {
-                    // Update existing toggle button
-                    toggleBtn.querySelector('i').className = 'fas fa-eye';
-                }
-            }
-        } else {
-            // Remove blur
-            previewContainer.classList.remove('blurred');
-            
-            // Hide overlay if it exists
-            const overlay = previewContainer.querySelector('.nsfw-overlay');
-            if (overlay) overlay.style.display = 'none';
-            
-            // Remove toggle button when content is set to PG or PG13
-            const cardHeader = previewContainer.querySelector('.card-header');
-            if (cardHeader) {
-                const toggleBtn = cardHeader.querySelector('.toggle-blur-btn');
-                if (toggleBtn) {
-                    // Remove the toggle button completely
-                    toggleBtn.remove();
-                    
-                    // Update base model label class if it exists
-                    const baseModelLabel = cardHeader.querySelector('.base-model-label');
-                    if (baseModelLabel && baseModelLabel.classList.contains('with-toggle')) {
-                        baseModelLabel.classList.remove('with-toggle');
-                    }
-                }
-            }
-        }
-    }
-
-    showNSFWLevelSelector(x, y, card) {
-        const selector = document.getElementById('nsfwLevelSelector');
-        const currentLevelEl = document.getElementById('currentNSFWLevel');
-        
-        // Get current NSFW level
-        let currentLevel = 0;
-        try {
-            const metaData = JSON.parse(card.dataset.meta || '{}');
-            currentLevel = metaData.preview_nsfw_level || 0;
-            
-            // Update if we have no recorded level but have a dataset attribute
-            if (!currentLevel && card.dataset.nsfwLevel) {
-                currentLevel = parseInt(card.dataset.nsfwLevel) || 0;
-            }
-        } catch (err) {
-            console.error('Error parsing metadata:', err);
-        }
-        
-        currentLevelEl.textContent = getNSFWLevelName(currentLevel);
-        
-        // Position the selector
-        if (x && y) {
-            const viewportWidth = document.documentElement.clientWidth;
-            const viewportHeight = document.documentElement.clientHeight;
-            const selectorRect = selector.getBoundingClientRect();
-            
-            // Center the selector if no coordinates provided
-            let finalX = (viewportWidth - selectorRect.width) / 2;
-            let finalY = (viewportHeight - selectorRect.height) / 2;
-            
-            selector.style.left = `${finalX}px`;
-            selector.style.top = `${finalY}px`;
-        }
-        
-        // Highlight current level button
-        document.querySelectorAll('.nsfw-level-btn').forEach(btn => {
-            if (parseInt(btn.dataset.level) === currentLevel) {
-                btn.classList.add('active');
-            } else {
-                btn.classList.remove('active');
-            }
-        });
-        
-        // Store reference to current card
-        selector.dataset.cardPath = card.dataset.filepath;
-        
-        // Show selector
-        selector.style.display = 'block';
-    }
-}
+// Mix in shared methods
+Object.assign(CheckpointContextMenu.prototype, ModelContextMenuMixin);

static/js/components/ContextMenu/LoraContextMenu.js

@@ -1,14 +1,15 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
-import { refreshSingleLoraMetadata, saveModelMetadata, replacePreview } from '../../api/loraApi.js';
-import { showToast, getNSFWLevelName, copyToClipboard, sendLoraToWorkflow, openExampleImagesFolder } from '../../utils/uiHelpers.js';
-import { NSFW_LEVELS } from '../../utils/constants.js';
-import { getStorageItem } from '../../utils/storageHelpers.js';
+import { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
+import { refreshSingleLoraMetadata, saveModelMetadata, replacePreview, resetAndReload } from '../../api/loraApi.js';
+import { copyToClipboard, sendLoraToWorkflow } from '../../utils/uiHelpers.js';
 import { showExcludeModal, showDeleteModal } from '../../utils/modalUtils.js';
 
 export class LoraContextMenu extends BaseContextMenu {
     constructor() {
         super('loraContextMenu', '.lora-card');
         this.nsfwSelector = document.getElementById('nsfwLevelSelector');
+        this.modelType = 'lora';
+        this.resetAndReload = resetAndReload;
         
         // Initialize NSFW Level Selector events
         if (this.nsfwSelector) {
@@ -16,24 +17,23 @@ export class LoraContextMenu extends BaseContextMenu {
         }
     }
 
+    // Use the saveModelMetadata implementation from loraApi
+    async saveModelMetadata(filePath, data) {
+        return saveModelMetadata(filePath, data);
+    }
+
     handleMenuAction(action, menuItem) {
+        // First try to handle with common actions
+        if (ModelContextMenuMixin.handleCommonMenuActions.call(this, action)) {
+            return;
+        }
+
+        // Otherwise handle lora-specific actions
         switch(action) {
             case 'detail':
                 // Trigger the main card click which shows the modal
                 this.currentCard.click();
                 break;
-            case 'civitai':
-                // Only trigger if the card is from civitai
-                if (this.currentCard.dataset.from_civitai === 'true') {
-                    if (this.currentCard.dataset.meta === '{}') {
-                        showToast('Please fetch metadata from CivitAI first', 'info');
-                    } else {
-                        this.currentCard.querySelector('.fa-globe')?.click();
-                    }
-                } else {
-                    showToast('No CivitAI information available', 'info');
-                }
-                break;
             case 'copyname':
                 // Generate and copy LoRA syntax
                 this.copyLoraSyntax();
@@ -46,10 +46,6 @@ export class LoraContextMenu extends BaseContextMenu {
                 // Send LoRA to workflow (replace mode)
                 this.sendLoraToWorkflow(true);
                 break;
-            case 'preview':
-                // Open example images folder instead of showing preview image dialog
-                openExampleImagesFolder(this.currentCard.dataset.sha256);
-                break;
             case 'replace-preview':
                 // Add a new action for replacing preview images
                 replacePreview(this.currentCard.dataset.filepath);
@@ -64,16 +60,13 @@ export class LoraContextMenu extends BaseContextMenu {
             case 'refresh-metadata':
                 refreshSingleLoraMetadata(this.currentCard.dataset.filepath);
                 break;
-            case 'set-nsfw':
-                this.showNSFWLevelSelector(null, null, this.currentCard);
-                break;
             case 'exclude':
                 showExcludeModal(this.currentCard.dataset.filepath);
                 break;
         }
     }
 
-    // New method to handle copy syntax functionality
+    // Specific LoRA methods
     copyLoraSyntax() {
         const card = this.currentCard;
         const usageTips = JSON.parse(card.dataset.usage_tips || '{}');
@@ -83,7 +76,6 @@ export class LoraContextMenu extends BaseContextMenu {
         copyToClipboard(loraSyntax, 'LoRA syntax copied to clipboard');
     }
 
-    // New method to handle send to workflow functionality
     sendLoraToWorkflow(replaceMode) {
         const card = this.currentCard;
         const usageTips = JSON.parse(card.dataset.usage_tips || '{}');
@@ -92,257 +84,7 @@ export class LoraContextMenu extends BaseContextMenu {
         
         sendLoraToWorkflow(loraSyntax, replaceMode, 'lora');
     }
+}
 
-    // NSFW Selector methods from the original context menu
-    initNSFWSelector() {
-        // Close button
-        const closeBtn = this.nsfwSelector.querySelector('.close-nsfw-selector');
-        closeBtn.addEventListener('click', () => {
-            this.nsfwSelector.style.display = 'none';
-        });
-
-        // Level buttons
-        const levelButtons = this.nsfwSelector.querySelectorAll('.nsfw-level-btn');
-        levelButtons.forEach(btn => {
-            btn.addEventListener('click', async () => {
-                const level = parseInt(btn.dataset.level);
-                const filePath = this.nsfwSelector.dataset.cardPath;
-                
-                if (!filePath) return;
-                
-                try {
-                    await this.saveModelMetadata(filePath, { preview_nsfw_level: level });
-                    
-                    // Update card data
-                    const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
-                    if (card) {
-                        let metaData = {};
-                        try {
-                            metaData = JSON.parse(card.dataset.meta || '{}');
-                        } catch (err) {
-                            console.error('Error parsing metadata:', err);
-                        }
-                        
-                        metaData.preview_nsfw_level = level;
-                        card.dataset.meta = JSON.stringify(metaData);
-                        card.dataset.nsfwLevel = level.toString();
-                        
-                        // Apply blur effect immediately
-                        this.updateCardBlurEffect(card, level);
-                    }
-                    
-                    showToast(`Content rating set to ${getNSFWLevelName(level)}`, 'success');
-                    this.nsfwSelector.style.display = 'none';
-                } catch (error) {
-                    showToast(`Failed to set content rating: ${error.message}`, 'error');
-                }
-            });
-        });
-        
-        // Close when clicking outside
-        document.addEventListener('click', (e) => {
-            if (this.nsfwSelector.style.display === 'block' && 
-                !this.nsfwSelector.contains(e.target) && 
-                !e.target.closest('.context-menu-item[data-action="set-nsfw"]')) {
-                this.nsfwSelector.style.display = 'none';
-            }
-        });
-    }
-
-    async saveModelMetadata(filePath, data) {
-        return saveModelMetadata(filePath, data);
-    }
-
-    updateCardBlurEffect(card, level) {
-        // Get user settings for blur threshold
-        const blurThreshold = parseInt(getStorageItem('nsfwBlurLevel') || '4');
-        
-        // Get card preview container
-        const previewContainer = card.querySelector('.card-preview');
-        if (!previewContainer) return;
-        
-        // Get preview media element
-        const previewMedia = previewContainer.querySelector('img') || previewContainer.querySelector('video');
-        if (!previewMedia) return;
-        
-        // Check if blur should be applied
-        if (level >= blurThreshold) {
-            // Add blur class to the preview container
-            previewContainer.classList.add('blurred');
-            
-            // Get or create the NSFW overlay
-            let nsfwOverlay = previewContainer.querySelector('.nsfw-overlay');
-            if (!nsfwOverlay) {
-                // Create new overlay
-                nsfwOverlay = document.createElement('div');
-                nsfwOverlay.className = 'nsfw-overlay';
-                
-                // Create and configure the warning content
-                const warningContent = document.createElement('div');
-                warningContent.className = 'nsfw-warning';
-                
-                // Determine NSFW warning text based on level
-                let nsfwText = "Mature Content";
-                if (level >= NSFW_LEVELS.XXX) {
-                    nsfwText = "XXX-rated Content";
-                } else if (level >= NSFW_LEVELS.X) {
-                    nsfwText = "X-rated Content";
-                } else if (level >= NSFW_LEVELS.R) {
-                    nsfwText = "R-rated Content";
-                }
-                
-                // Add warning text and show button
-                warningContent.innerHTML = `
-                    <p>${nsfwText}</p>
-                    <button class="show-content-btn">Show</button>
-                `;
-                
-                // Add click event to the show button
-                const showBtn = warningContent.querySelector('.show-content-btn');
-                showBtn.addEventListener('click', (e) => {
-                    e.stopPropagation();
-                    previewContainer.classList.remove('blurred');
-                    nsfwOverlay.style.display = 'none';
-                    
-                    // Update toggle button icon if it exists
-                    const toggleBtn = card.querySelector('.toggle-blur-btn');
-                    if (toggleBtn) {
-                        toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
-                    }
-                });
-                
-                nsfwOverlay.appendChild(warningContent);
-                previewContainer.appendChild(nsfwOverlay);
-            } else {
-                // Update existing overlay
-                const warningText = nsfwOverlay.querySelector('p');
-                if (warningText) {
-                    let nsfwText = "Mature Content";
-                    if (level >= NSFW_LEVELS.XXX) {
-                        nsfwText = "XXX-rated Content";
-                    } else if (level >= NSFW_LEVELS.X) {
-                        nsfwText = "X-rated Content";
-                    } else if (level >= NSFW_LEVELS.R) {
-                        nsfwText = "R-rated Content";
-                    }
-                    warningText.textContent = nsfwText;
-                }
-                nsfwOverlay.style.display = 'flex';
-            }
-            
-            // Get or create the toggle button in the header
-            const cardHeader = previewContainer.querySelector('.card-header');
-            if (cardHeader) {
-                let toggleBtn = cardHeader.querySelector('.toggle-blur-btn');
-                
-                if (!toggleBtn) {
-                    toggleBtn = document.createElement('button');
-                    toggleBtn.className = 'toggle-blur-btn';
-                    toggleBtn.title = 'Toggle blur';
-                    toggleBtn.innerHTML = '<i class="fas fa-eye"></i>';
-                    
-                    // Add click event to toggle button
-                    toggleBtn.addEventListener('click', (e) => {
-                        e.stopPropagation();
-                        const isBlurred = previewContainer.classList.toggle('blurred');
-                        const icon = toggleBtn.querySelector('i');
-                        
-                        // Update icon and overlay visibility
-                        if (isBlurred) {
-                            icon.className = 'fas fa-eye';
-                            nsfwOverlay.style.display = 'flex';
-                        } else {
-                            icon.className = 'fas fa-eye-slash';
-                            nsfwOverlay.style.display = 'none';
-                        }
-                    });
-                    
-                    // Add to the beginning of header
-                    cardHeader.insertBefore(toggleBtn, cardHeader.firstChild);
-                    
-                    // Update base model label class
-                    const baseModelLabel = cardHeader.querySelector('.base-model-label');
-                    if (baseModelLabel && !baseModelLabel.classList.contains('with-toggle')) {
-                        baseModelLabel.classList.add('with-toggle');
-                    }
-                } else {
-                    // Update existing toggle button
-                    toggleBtn.querySelector('i').className = 'fas fa-eye';
-                }
-            }
-        } else {
-            // Remove blur
-            previewContainer.classList.remove('blurred');
-            
-            // Hide overlay if it exists
-            const overlay = previewContainer.querySelector('.nsfw-overlay');
-            if (overlay) overlay.style.display = 'none';
-            
-            // Remove toggle button when content is set to PG or PG13
-            const cardHeader = previewContainer.querySelector('.card-header');
-            if (cardHeader) {
-                const toggleBtn = cardHeader.querySelector('.toggle-blur-btn');
-                if (toggleBtn) {
-                    // Remove the toggle button completely
-                    toggleBtn.remove();
-                    
-                    // Update base model label class if it exists
-                    const baseModelLabel = cardHeader.querySelector('.base-model-label');
-                    if (baseModelLabel && baseModelLabel.classList.contains('with-toggle')) {
-                        baseModelLabel.classList.remove('with-toggle');
-                    }
-                }
-            }
-        }
-    }
-
-    showNSFWLevelSelector(x, y, card) {
-        const selector = document.getElementById('nsfwLevelSelector');
-        const currentLevelEl = document.getElementById('currentNSFWLevel');
-        
-        // Get current NSFW level
-        let currentLevel = 0;
-        try {
-            const metaData = JSON.parse(card.dataset.meta || '{}');
-            currentLevel = metaData.preview_nsfw_level || 0;
-            
-            // Update if we have no recorded level but have a dataset attribute
-            if (!currentLevel && card.dataset.nsfwLevel) {
-                currentLevel = parseInt(card.dataset.nsfwLevel) || 0;
-            }
-        } catch (err) {
-            console.error('Error parsing metadata:', err);
-        }
-        
-        currentLevelEl.textContent = getNSFWLevelName(currentLevel);
-        
-        // Position the selector
-        if (x && y) {
-            const viewportWidth = document.documentElement.clientWidth;
-            const viewportHeight = document.documentElement.clientHeight;
-            const selectorRect = selector.getBoundingClientRect();
-            
-            // Center the selector if no coordinates provided
-            let finalX = (viewportWidth - selectorRect.width) / 2;
-            let finalY = (viewportHeight - selectorRect.height) / 2;
-            
-            selector.style.left = `${finalX}px`;
-            selector.style.top = `${finalY}px`;
-        }
-        
-        // Highlight current level button
-        document.querySelectorAll('.nsfw-level-btn').forEach(btn => {
-            if (parseInt(btn.dataset.level) === currentLevel) {
-                btn.classList.add('active');
-            } else {
-                btn.classList.remove('active');
-            }
-        });
-        
-        // Store reference to current card
-        selector.dataset.cardPath = card.dataset.filepath;
-        
-        // Show selector
-        selector.style.display = 'block';
-    }
-}
+// Mix in shared methods
+Object.assign(LoraContextMenu.prototype, ModelContextMenuMixin);

static/js/components/ContextMenu/ModelContextMenuMixin.js

@@ -0,0 +1,226 @@
+import { showToast, getNSFWLevelName, openExampleImagesFolder } from '../../utils/uiHelpers.js';
+import { modalManager } from '../../managers/ModalManager.js';
+import { state } from '../../state/index.js';
+
+// Mixin with shared functionality for LoraContextMenu and CheckpointContextMenu
+export const ModelContextMenuMixin = {
+    // NSFW Selector methods
+    initNSFWSelector() {
+        // Close button
+        const closeBtn = this.nsfwSelector.querySelector('.close-nsfw-selector');
+        closeBtn.addEventListener('click', () => {
+            this.nsfwSelector.style.display = 'none';
+        });
+
+        // Level buttons
+        const levelButtons = this.nsfwSelector.querySelectorAll('.nsfw-level-btn');
+        levelButtons.forEach(btn => {
+            btn.addEventListener('click', async () => {
+                const level = parseInt(btn.dataset.level);
+                const filePath = this.nsfwSelector.dataset.cardPath;
+                
+                if (!filePath) return;
+                
+                try {
+                    await this.saveModelMetadata(filePath, { preview_nsfw_level: level });
+                    
+                    showToast(`Content rating set to ${getNSFWLevelName(level)}`, 'success');
+                    this.nsfwSelector.style.display = 'none';
+                } catch (error) {
+                    showToast(`Failed to set content rating: ${error.message}`, 'error');
+                }
+            });
+        });
+        
+        // Close when clicking outside
+        document.addEventListener('click', (e) => {
+            if (this.nsfwSelector.style.display === 'block' && 
+                !this.nsfwSelector.contains(e.target) && 
+                !e.target.closest('.context-menu-item[data-action="set-nsfw"]')) {
+                this.nsfwSelector.style.display = 'none';
+            }
+        });
+    },
+
+    showNSFWLevelSelector(x, y, card) {
+        const selector = document.getElementById('nsfwLevelSelector');
+        const currentLevelEl = document.getElementById('currentNSFWLevel');
+        
+        // Get current NSFW level
+        let currentLevel = 0;
+        try {
+            const metaData = JSON.parse(card.dataset.meta || '{}');
+            currentLevel = metaData.preview_nsfw_level || 0;
+            
+            // Update if we have no recorded level but have a dataset attribute
+            if (!currentLevel && card.dataset.nsfwLevel) {
+                currentLevel = parseInt(card.dataset.nsfwLevel) || 0;
+            }
+        } catch (err) {
+            console.error('Error parsing metadata:', err);
+        }
+        
+        currentLevelEl.textContent = getNSFWLevelName(currentLevel);
+        
+        // Position the selector
+        if (x && y) {
+            const viewportWidth = document.documentElement.clientWidth;
+            const viewportHeight = document.documentElement.clientHeight;
+            const selectorRect = selector.getBoundingClientRect();
+            
+            // Center the selector if no coordinates provided
+            let finalX = (viewportWidth - selectorRect.width) / 2;
+            let finalY = (viewportHeight - selectorRect.height) / 2;
+            
+            selector.style.left = `${finalX}px`;
+            selector.style.top = `${finalY}px`;
+        }
+        
+        // Highlight current level button
+        document.querySelectorAll('.nsfw-level-btn').forEach(btn => {
+            if (parseInt(btn.dataset.level) === currentLevel) {
+                btn.classList.add('active');
+            } else {
+                btn.classList.remove('active');
+            }
+        });
+        
+        // Store reference to current card
+        selector.dataset.cardPath = card.dataset.filepath;
+        
+        // Show selector
+        selector.style.display = 'block';
+    },
+
+    // Civitai re-linking methods
+    showRelinkCivitaiModal() {
+        const filePath = this.currentCard.dataset.filepath;
+        if (!filePath) return;
+        
+        // Set up confirm button handler
+        const confirmBtn = document.getElementById('confirmRelinkBtn');
+        const urlInput = document.getElementById('civitaiModelUrl');
+        const errorDiv = document.getElementById('civitaiModelUrlError');
+        
+        // Remove previous event listener if exists
+        if (this._boundRelinkHandler) {
+            confirmBtn.removeEventListener('click', this._boundRelinkHandler);
+        }
+        
+        // Create new bound handler
+        this._boundRelinkHandler = async () => {
+            const url = urlInput.value.trim();
+            const { modelId, modelVersionId } = this.extractModelVersionId(url);
+            
+            if (!modelId) {
+                errorDiv.textContent = 'Invalid URL format. Must include model ID.';
+                return;
+            }
+            
+            errorDiv.textContent = '';
+            modalManager.closeModal('relinkCivitaiModal');
+            
+            try {
+                state.loadingManager.showSimpleLoading('Re-linking to Civitai...');
+                
+                const endpoint = this.modelType === 'checkpoint' ? 
+                    '/api/checkpoints/relink-civitai' : 
+                    '/api/relink-civitai';
+                
+                const response = await fetch(endpoint, {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json'
+                    },
+                    body: JSON.stringify({
+                        file_path: filePath,
+                        model_id: modelId,
+                        model_version_id: modelVersionId
+                    })
+                });
+                
+                if (!response.ok) {
+                    throw new Error(`Failed to re-link model: ${response.statusText}`);
+                }
+                
+                const data = await response.json();
+                
+                if (data.success) {
+                    showToast('Model successfully re-linked to Civitai', 'success');
+                    // Reload the current view to show updated data
+                    await this.resetAndReload();
+                } else {
+                    throw new Error(data.error || 'Failed to re-link model');
+                }
+            } catch (error) {
+                console.error('Error re-linking model:', error);
+                showToast(`Error: ${error.message}`, 'error');
+            } finally {
+                state.loadingManager.hide();
+            }
+        };
+        
+        // Set new event listener
+        confirmBtn.addEventListener('click', this._boundRelinkHandler);
+        
+        // Clear previous input
+        urlInput.value = '';
+        errorDiv.textContent = '';
+        
+        // Show modal
+        modalManager.showModal('relinkCivitaiModal');
+        
+        // Auto-focus the URL input field after modal is shown
+        setTimeout(() => urlInput.focus(), 50);
+    },
+
+    extractModelVersionId(url) {
+        try {
+            // Handle all three URL formats:
+            // 1. https://civitai.com/models/649516
+            // 2. https://civitai.com/models/649516?modelVersionId=726676
+            // 3. https://civitai.com/models/649516/cynthia-pokemon-diamond-and-pearl-pdxl-lora?modelVersionId=726676
+            
+            const parsedUrl = new URL(url);
+            
+            // Extract model ID from path
+            const pathMatch = parsedUrl.pathname.match(/\/models\/(\d+)/);
+            const modelId = pathMatch ? pathMatch[1] : null;
+            
+            // Extract model version ID from query parameters
+            const modelVersionId = parsedUrl.searchParams.get('modelVersionId');
+            
+            return { modelId, modelVersionId };
+        } catch (e) {
+            return { modelId: null, modelVersionId: null };
+        }
+    },
+    
+    // Common action handlers
+    handleCommonMenuActions(action) {
+        switch(action) {
+            case 'preview':
+                openExampleImagesFolder(this.currentCard.dataset.sha256);
+                return true;
+            case 'civitai':
+                if (this.currentCard.dataset.from_civitai === 'true') {
+                    if (this.currentCard.querySelector('.fa-globe')) {
+                        this.currentCard.querySelector('.fa-globe').click();
+                    } else {
+                        showToast('Please fetch metadata from CivitAI first', 'info');
+                    }
+                } else {
+                    showToast('No CivitAI information available', 'info');
+                }
+                return true;
+            case 'relink-civitai':
+                this.showRelinkCivitaiModal();
+                return true;
+            case 'set-nsfw':
+                this.showNSFWLevelSelector(null, null, this.currentCard);
+                return true;
+            default:
+                return false;
+        }
+    }
+};

static/js/components/ContextMenu/RecipeContextMenu.js

@@ -1,11 +1,31 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
+import { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
 import { showToast, copyToClipboard, sendLoraToWorkflow } from '../../utils/uiHelpers.js';
 import { setSessionItem, removeSessionItem } from '../../utils/storageHelpers.js';
+import { updateRecipeMetadata } from '../../api/recipeApi.js';
 import { state } from '../../state/index.js';
 
 export class RecipeContextMenu extends BaseContextMenu {
     constructor() {
         super('recipeContextMenu', '.lora-card');
+        this.nsfwSelector = document.getElementById('nsfwLevelSelector');
+        this.modelType = 'recipe';
+        
+        // Initialize NSFW Level Selector events
+        if (this.nsfwSelector) {
+            this.initNSFWSelector();
+        }
+    }
+
+    // Use the updateRecipeMetadata implementation from recipeApi
+    async saveModelMetadata(filePath, data) {
+        return updateRecipeMetadata(filePath, data);
+    }
+
+    // Override resetAndReload for recipe context
+    async resetAndReload() {
+        const { resetAndReload } = await import('../../api/recipeApi.js');
+        return resetAndReload();
     }
     
     showMenu(x, y, card) {
@@ -31,6 +51,12 @@ export class RecipeContextMenu extends BaseContextMenu {
     }
     
     handleMenuAction(action) {
+        // First try to handle with common actions from ModelContextMenuMixin
+        if (ModelContextMenuMixin.handleCommonMenuActions.call(this, action)) {
+            return;
+        }
+
+        // Handle recipe-specific actions
         const recipeId = this.currentCard.dataset.id;
         
         switch(action) {
@@ -256,4 +282,7 @@ export class RecipeContextMenu extends BaseContextMenu {
             }
         }
     }
-}
+}
+
+// Mix in shared methods from ModelContextMenuMixin
+Object.assign(RecipeContextMenu.prototype, ModelContextMenuMixin);

static/js/components/ContextMenu/index.js

@@ -1,3 +1,4 @@
 export { LoraContextMenu } from './LoraContextMenu.js';
 export { RecipeContextMenu } from './RecipeContextMenu.js';
-export { CheckpointContextMenu } from './CheckpointContextMenu.js';
+export { CheckpointContextMenu } from './CheckpointContextMenu.js';
+export { ModelContextMenuMixin } from './ModelContextMenuMixin.js';

static/js/components/DuplicatesManager.js

@@ -2,7 +2,6 @@
 import { showToast } from '../utils/uiHelpers.js';
 import { RecipeCard } from './RecipeCard.js';
 import { state, getCurrentPageState } from '../state/index.js';
-import { initializeInfiniteScroll } from '../utils/infiniteScroll.js';
 
 export class DuplicatesManager {
     constructor(recipeManager) {
@@ -14,8 +13,6 @@ export class DuplicatesManager {
     
     async findDuplicates() {
         try {
-            document.body.classList.add('loading');
-            
             const response = await fetch('/api/recipes/find-duplicates');
             if (!response.ok) {
                 throw new Error('Failed to find duplicates');
@@ -39,8 +36,6 @@ export class DuplicatesManager {
             console.error('Error finding duplicates:', error);
             showToast('Failed to find duplicates: ' + error.message, 'error');
             return false;
-        } finally {
-            document.body.classList.remove('loading');
         }
     }
     
@@ -100,14 +95,7 @@ export class DuplicatesManager {
         }
         
         // Re-enable virtual scrolling
-        if (state.virtualScroller) {
-            state.virtualScroller.enable();
-        } else {
-            // If virtual scroller doesn't exist, reinitialize it
-            setTimeout(() => {
-                initializeInfiniteScroll('recipes');
-            }, 100);
-        }
+        state.virtualScroller.enable();
     }
     
     renderDuplicateGroups() {
@@ -234,7 +222,7 @@ export class DuplicatesManager {
     }
     
     updateSelectedCount() {
-        const selectedCountEl = document.getElementById('selectedCount');
+        const selectedCountEl = document.getElementById('duplicatesSelectedCount');
         if (selectedCountEl) {
             selectedCountEl.textContent = this.selectedForDeletion.size;
         }
@@ -358,9 +346,7 @@ export class DuplicatesManager {
     
     // Add new method to execute deletion after confirmation
     async confirmDeleteDuplicates() {
-        try {
-            document.body.classList.add('loading');
-            
+        try {           
             // Close the modal
             modalManager.closeModal('duplicateDeleteModal');
             
@@ -395,8 +381,6 @@ export class DuplicatesManager {
         } catch (error) {
             console.error('Error deleting recipes:', error);
             showToast('Failed to delete recipes: ' + error.message, 'error');
-        } finally {
-            document.body.classList.remove('loading');
         }
     }
 }

static/js/components/Header.js

@@ -26,6 +26,7 @@ export class HeaderManager {
       const path = window.location.pathname;
       if (path.includes('/loras/recipes')) return 'recipes';
       if (path.includes('/checkpoints')) return 'checkpoints';
+      if (path.includes('/statistics')) return 'statistics';
       if (path.includes('/loras')) return 'loras';
       return 'unknown';
     }
@@ -46,9 +47,21 @@ export class HeaderManager {
       // Handle theme toggle
       const themeToggle = document.querySelector('.theme-toggle');
       if (themeToggle) {
+        // Set initial state based on current theme
+        const currentTheme = localStorage.getItem('lm_theme') || 'auto';
+        themeToggle.classList.add(`theme-${currentTheme}`);
+        
         themeToggle.addEventListener('click', () => {
           if (typeof toggleTheme === 'function') {
-            toggleTheme();
+            const newTheme = toggleTheme();
+            // Update tooltip based on next toggle action
+            if (newTheme === 'light') {
+              themeToggle.title = "Switch to dark theme";
+            } else if (newTheme === 'dark') {
+              themeToggle.title = "Switch to auto theme";
+            } else {
+              themeToggle.title = "Switch to light theme";
+            }
           }
         });
       }
@@ -75,7 +88,9 @@ export class HeaderManager {
       const supportToggle = document.getElementById('supportToggleBtn');
       if (supportToggle) {
         supportToggle.addEventListener('click', () => {
-          // Handle support panel logic
+          if (window.modalManager) {
+            window.modalManager.toggleModal('supportModal');
+          }
         });
       }
 
@@ -106,5 +121,33 @@ export class HeaderManager {
               }
           });
       }
+      
+      // Hide search functionality on Statistics page
+      this.updateHeaderForPage();
+    }
+    
+    updateHeaderForPage() {
+      const headerSearch = document.getElementById('headerSearch');
+      
+      if (this.currentPage === 'statistics' && headerSearch) {
+        headerSearch.classList.add('disabled');
+        // Disable search functionality
+        const searchInput = headerSearch.querySelector('#searchInput');
+        const searchButtons = headerSearch.querySelectorAll('button');
+        if (searchInput) {
+          searchInput.disabled = true;
+          searchInput.placeholder = 'Search not available on statistics page';
+        }
+        searchButtons.forEach(btn => btn.disabled = true);
+      } else if (headerSearch) {
+        headerSearch.classList.remove('disabled');
+        // Re-enable search functionality
+        const searchInput = headerSearch.querySelector('#searchInput');
+        const searchButtons = headerSearch.querySelectorAll('button');
+        if (searchInput) {
+          searchInput.disabled = false;
+        }
+        searchButtons.forEach(btn => btn.disabled = false);
+      }
     }
 }

static/js/components/LoraCard.js

@@ -1,10 +1,9 @@
 import { showToast, openCivitai, copyToClipboard, sendLoraToWorkflow, openExampleImagesFolder } from '../utils/uiHelpers.js';
-import { state } from '../state/index.js';
+import { state, getCurrentPageState } from '../state/index.js';
 import { showLoraModal } from './loraModal/index.js';
 import { bulkManager } from '../managers/BulkManager.js';
 import { NSFW_LEVELS } from '../utils/constants.js';
 import { replacePreview, saveModelMetadata } from '../api/loraApi.js'
-import { showDeleteModal } from '../utils/modalUtils.js';
 
 // Add a global event delegation handler
 export function setupLoraCardEventDelegation() {
@@ -46,7 +45,7 @@ function handleLoraCardEvent(event) {
     if (event.target.closest('.fa-globe')) {
         event.stopPropagation();
         if (card.dataset.from_civitai === 'true') {
-            openCivitai(card.dataset.name);
+            openCivitai(card.dataset.filepath);
         }
         return;
     }
@@ -71,14 +70,18 @@ function handleLoraCardEvent(event) {
     
     if (event.target.closest('.fa-folder-open')) {
         event.stopPropagation();
-        openExampleImagesFolder(card.dataset.sha256);
+        handleExampleImagesAccess(card);
         return;
     }
     
     // If no specific element was clicked, handle the card click (show modal or toggle selection)
+    const pageState = getCurrentPageState();
     if (state.bulkMode) {
         // Toggle selection using the bulk manager
         bulkManager.toggleCardSelection(card);
+    } else if (pageState && pageState.duplicatesMode) {
+        // In duplicates mode, don't open modal when clicking cards
+        return;
     } else {
         // Normal behavior - show modal
         const loraMeta = {
@@ -159,18 +162,9 @@ async function toggleFavorite(card) {
             favorite: newFavoriteState 
         });
 
-        // Update the UI
         if (newFavoriteState) {
-            starIcon.classList.remove('far');
-            starIcon.classList.add('fas', 'favorite-active');
-            starIcon.title = 'Remove from favorites';
-            card.dataset.favorite = 'true';
             showToast('Added to favorites', 'success');
         } else {
-            starIcon.classList.remove('fas', 'favorite-active');
-            starIcon.classList.add('far');
-            starIcon.title = 'Add to favorites';
-            card.dataset.favorite = 'false';
             showToast('Removed from favorites', 'success');
         }
     } catch (error) {
@@ -197,6 +191,142 @@ function copyLoraSyntax(card) {
     copyToClipboard(loraSyntax, 'LoRA syntax copied to clipboard');
 }
 
+// New function to handle example images access
+async function handleExampleImagesAccess(card) {
+    const modelHash = card.dataset.sha256;
+    
+    try {
+        // Check if example images exist
+        const response = await fetch(`/api/has-example-images?model_hash=${modelHash}`);
+        const data = await response.json();
+        
+        if (data.has_images) {
+            // If images exist, open the folder directly (existing behavior)
+            openExampleImagesFolder(modelHash);
+        } else {
+            // If no images exist, show the new modal
+            showExampleAccessModal(card);
+        }
+    } catch (error) {
+        console.error('Error checking for example images:', error);
+        showToast('Error checking for example images', 'error');
+    }
+}
+
+// Function to show the example access modal
+function showExampleAccessModal(card) {
+    const modal = document.getElementById('exampleAccessModal');
+    if (!modal) return;
+    
+    // Get download button and determine if download should be enabled
+    const downloadBtn = modal.querySelector('#downloadExamplesBtn');
+    let hasRemoteExamples = false;
+    
+    try {
+        const metaData = JSON.parse(card.dataset.meta || '{}');
+        hasRemoteExamples = metaData.images && 
+                            Array.isArray(metaData.images) && 
+                            metaData.images.length > 0 && 
+                            metaData.images[0].url;
+    } catch (e) {
+        console.error('Error parsing meta data:', e);
+    }
+    
+    // Enable or disable download button
+    if (downloadBtn) {
+        if (hasRemoteExamples) {
+            downloadBtn.classList.remove('disabled');
+            downloadBtn.removeAttribute('title'); // Remove any previous tooltip
+            downloadBtn.onclick = () => {
+                modalManager.closeModal('exampleAccessModal');
+                // Open settings modal and scroll to example images section
+                const settingsModal = document.getElementById('settingsModal');
+                if (settingsModal) {
+                    modalManager.showModal('settingsModal');
+                    // Scroll to example images section after modal is visible
+                    setTimeout(() => {
+                        const exampleSection = settingsModal.querySelector('.settings-section:nth-child(5)'); // Example Images section
+                        if (exampleSection) {
+                            exampleSection.scrollIntoView({ behavior: 'smooth' });
+                        }
+                    }, 300);
+                }
+            };
+        } else {
+            downloadBtn.classList.add('disabled');
+            downloadBtn.setAttribute('title', 'No remote example images available for this model on Civitai');
+            downloadBtn.onclick = null;
+        }
+    }
+    
+    // Set up import button
+    const importBtn = modal.querySelector('#importExamplesBtn');
+    if (importBtn) {
+        importBtn.onclick = () => {
+            modalManager.closeModal('exampleAccessModal');
+            
+            // Get the lora data from card dataset
+            const loraMeta = {
+                sha256: card.dataset.sha256,
+                file_path: card.dataset.filepath,
+                model_name: card.dataset.name,
+                file_name: card.dataset.file_name,
+                // Other properties needed for showLoraModal
+                folder: card.dataset.folder,
+                modified: card.dataset.modified,
+                file_size: card.dataset.file_size,
+                from_civitai: card.dataset.from_civitai === 'true',
+                base_model: card.dataset.base_model,
+                usage_tips: card.dataset.usage_tips,
+                notes: card.dataset.notes,
+                favorite: card.dataset.favorite === 'true',
+                civitai: (() => {
+                    try {
+                        return JSON.parse(card.dataset.meta || '{}');
+                    } catch (e) {
+                        return {};
+                    }
+                })(),
+                tags: JSON.parse(card.dataset.tags || '[]'),
+                modelDescription: card.dataset.modelDescription || ''
+            };
+            
+            // Show the lora modal
+            showLoraModal(loraMeta);
+            
+            // Scroll to import area after modal is visible
+            setTimeout(() => {
+                const importArea = document.querySelector('.example-import-area');
+                if (importArea) {
+                    const showcaseTab = document.getElementById('showcase-tab');
+                    if (showcaseTab) {
+                        // First make sure showcase tab is visible
+                        const tabBtn = document.querySelector('.tab-btn[data-tab="showcase"]');
+                        if (tabBtn && !tabBtn.classList.contains('active')) {
+                            tabBtn.click();
+                        }
+                        
+                        // Then toggle showcase if collapsed
+                        const carousel = showcaseTab.querySelector('.carousel');
+                        if (carousel && carousel.classList.contains('collapsed')) {
+                            const scrollIndicator = showcaseTab.querySelector('.scroll-indicator');
+                            if (scrollIndicator) {
+                                scrollIndicator.click();
+                            }
+                        }
+                        
+                        // Finally scroll to the import area
+                        importArea.scrollIntoView({ behavior: 'smooth' });
+                    }
+                }
+            }, 500);
+        };
+    }
+    
+    // Show the modal
+    modalManager.showModal('exampleAccessModal');
+}
+
 export function createLoraCard(lora) {
     const card = document.createElement('div');
     card.className = 'lora-card';