chore: update version to 0.8.24 in pyproject.toml

Refactor

.gitignore

@@ -1,5 +1,6 @@
 __pycache__/
 settings.json
+path_mappings.yaml
 output/*
 py/run_test.py
 .vscode/

README.md

@@ -10,18 +10,43 @@ A comprehensive toolset that streamlines organizing, downloading, and applying L
 
 ![Interface Preview](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/static/images/screenshot.png)
 
-One-click Integration:
-![One-Click Integration](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/static/images/one-click-send.jpg)
-
 ## 📺 Tutorial: One-Click LoRA Integration
 Watch this quick tutorial to learn how to use the new one-click LoRA integration feature:
 
-[![One-Click LoRA Integration Tutorial](https://img.youtube.com/vi/hvKw31YpE-U/0.jpg)](https://youtu.be/hvKw31YpE-U)
+[![One-Click LoRA Integration Tutorial](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/static/images/video-thumbnails/getting-started.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>
+
+<div id="firefox-install" class="install-ok"><a href="https://github.com/willmiao/lm-civitai-extension-firefox/releases/latest/download/extension.xpi">📦 Install Firefox Extension (reviewed and verified by Mozilla)</a></div>
+
+📚 [Learn More: Complete Tutorial](https://github.com/willmiao/ComfyUI-Lora-Manager/wiki/LoRA-Manager-Civitai-Extension-(Chrome-Extension))
 
 ---
 
 ## Release Notes
 
+### v0.8.22
+* **Embeddings Management** - Added Embeddings page for comprehensive embedding model management.
+* **Advanced Sorting Options** - Introduced flexible sorting controls, allowing sorting by name, added date, or file size in both ascending and descending order.
+* **Custom Download Path Templates & Base Model Mapping** - Implemented UI settings for configuring download path templates and base model path mappings, allowing customized model organization and storage location when downloading models via LM Civitai Extension.
+* **LM Civitai Extension Enhancements** - Improved concurrent download performance and stability, with new support for canceling active downloads directly from the extension interface.
+* **Update Feature** - Added update functionality, allowing users to update LoRA Manager to the latest release version directly from the LoRA Manager UI.
+* **Bulk Operations: Refresh All** - Added bulk refresh functionality, allowing users to update Civitai metadata across multiple LoRAs.
+
+### v0.8.20
+* **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

__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

@@ -17,13 +17,17 @@ class Config:
     def __init__(self):
         self.templates_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'templates')
         self.static_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'static')
-        # 路径映射字典, target to link mapping
+        # Path mapping dictionary, target to link mapping
         self._path_mappings = {}
-        # 静态路由映射字典, target to route mapping
+        # Static route mapping dictionary, target to route mapping
         self._route_mappings = {}
         self.loras_roots = self._init_lora_paths()
-        self.checkpoints_roots = self._init_checkpoint_paths()
-        # 在初始化时扫描符号链接
+        self.checkpoints_roots = None
+        self.unet_roots = None
+        self.embeddings_roots = None
+        self.base_models_roots = self._init_checkpoint_paths()
+        self.embeddings_roots = self._init_embedding_paths()
+        # Scan symbolic links during initialization
         self._scan_symbolic_links()
         
         if not standalone_mode:
@@ -33,34 +37,37 @@ 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,
+                'embeddings': self.embeddings_roots,
+            }
+            
+            # Add default roots if there's only one item and key doesn't exist
+            if len(self.loras_roots) == 1 and "default_lora_root" not in settings:
+                settings["default_lora_root"] = self.loras_roots[0]
+            
+            if self.checkpoints_roots and len(self.checkpoints_roots) == 1 and "default_checkpoint_root" not in settings:
+                settings["default_checkpoint_root"] = self.checkpoints_roots[0]
+
+            if self.embeddings_roots and len(self.embeddings_roots) == 1 and "default_embedding_root" not in settings:
+                settings["default_embedding_root"] = self.embeddings_roots[0]
+            
+            # Save settings
+            with open(settings_path, 'w', encoding='utf-8') as f:
+                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}")
 
@@ -82,15 +89,18 @@ class Config:
             return False
 
     def _scan_symbolic_links(self):
-        """扫描所有 LoRA 和 Checkpoint 根目录中的符号链接"""
+        """Scan all symbolic links in LoRA, Checkpoint, and Embedding root directories"""
         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)
+            
+        for root in self.embeddings_roots:
             self._scan_directory_links(root)
 
     def _scan_directory_links(self, root: str):
-        """递归扫描目录中的符号链接"""
+        """Recursively scan symbolic links in a directory"""
         try:
             with os.scandir(root) as it:
                 for entry in it:
@@ -105,40 +115,40 @@ class Config:
             logger.error(f"Error scanning links in {root}: {e}")
 
     def add_path_mapping(self, link_path: str, target_path: str):
-        """添加符号链接路径映射
-        target_path: 实际目标路径
-        link_path: 符号链接路径
+        """Add a symbolic link path mapping
+        target_path: actual target path
+        link_path: symbolic link path
         """
         normalized_link = os.path.normpath(link_path).replace(os.sep, '/')
         normalized_target = os.path.normpath(target_path).replace(os.sep, '/')
-        # 保持原有的映射关系：目标路径 -> 链接路径
+        # Keep the original mapping: target path -> link path
         self._path_mappings[normalized_target] = normalized_link
         logger.info(f"Added path mapping: {normalized_target} -> {normalized_link}")
 
     def add_route_mapping(self, path: str, route: str):
-        """添加静态路由映射"""
+        """Add a static route mapping"""
         normalized_path = os.path.normpath(path).replace(os.sep, '/')
         self._route_mappings[normalized_path] = route
         # logger.info(f"Added route mapping: {normalized_path} -> {route}")
 
     def map_path_to_link(self, path: str) -> str:
-        """将目标路径映射回符号链接路径"""
+        """Map a target path back to its symbolic link path"""
         normalized_path = os.path.normpath(path).replace(os.sep, '/')
-        # 检查路径是否包含在任何映射的目标路径中
+        # Check if the path is contained in any mapped target path
         for target_path, link_path in self._path_mappings.items():
             if normalized_path.startswith(target_path):
-                # 如果路径以目标路径开头，则替换为链接路径
+                # If the path starts with the target path, replace with link path
                 mapped_path = normalized_path.replace(target_path, link_path, 1)
                 return mapped_path
         return path
     
     def map_link_to_path(self, link_path: str) -> str:
-        """将符号链接路径映射回实际路径"""
+        """Map a symbolic link path back to the actual path"""
         normalized_link = os.path.normpath(link_path).replace(os.sep, '/')
-        # 检查路径是否包含在任何映射的目标路径中
+        # Check if the path is contained in any mapped target path
         for target_path, link_path in self._path_mappings.items():
             if normalized_link.startswith(target_path):
-                # 如果路径以目标路径开头，则替换为实际路径
+                # If the path starts with the target path, replace with actual path
                 mapped_path = normalized_link.replace(target_path, link_path, 1)
                 return mapped_path
         return link_path
@@ -177,35 +187,81 @@ class Config:
         """Initialize and validate checkpoint paths from ComfyUI settings"""
         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")
+            raw_checkpoint_paths = folder_paths.get_folder_paths("checkpoints")
+            raw_unet_paths = folder_paths.get_folder_paths("unet")
             
-            # Combine all checkpoint-related paths
-            all_paths = checkpoint_paths + diffusion_paths + unet_paths
+            # Normalize and resolve symlinks for checkpoints, store mapping from resolved -> original
+            checkpoint_map = {}
+            for path in raw_checkpoint_paths:
+                if os.path.exists(path):
+                    real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
+                    checkpoint_map[real_path] = checkpoint_map.get(real_path, path.replace(os.sep, "/"))  # preserve first seen
             
-            # Filter and normalize paths
-            paths = sorted(set(path.replace(os.sep, "/") 
-                    for path in all_paths 
-                    if os.path.exists(path)), key=lambda p: p.lower())
+            # Normalize and resolve symlinks for unet, store mapping from resolved -> original
+            unet_map = {}
+            for path in raw_unet_paths:
+                if os.path.exists(path):
+                    real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
+                    unet_map[real_path] = unet_map.get(real_path, path.replace(os.sep, "/"))  # preserve first seen
             
-            logger.info("Found checkpoint roots:" + ("\n - " + "\n - ".join(paths) if paths else "[]"))
+            # Now sort and use only the deduplicated real paths
+            unique_checkpoint_paths = sorted(checkpoint_map.values(), key=lambda p: p.lower())
+            unique_unet_paths = sorted(unet_map.values(), key=lambda p: p.lower())
             
-            if not paths:
+            # Store individual paths in class properties
+            self.checkpoints_roots = unique_checkpoint_paths
+            self.unet_roots = unique_unet_paths
+            
+            # Combine all checkpoint-related paths for return value
+            all_paths = unique_checkpoint_paths + unique_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:
-                real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
-                if real_path != path:
-                    self.add_path_mapping(path, real_path)
+            # Initialize path mappings
+            for original_path in all_paths:
+                real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
+                if real_path != original_path:
+                    self.add_path_mapping(original_path, real_path)
             
-            return paths
+            return all_paths
         except Exception as e:
             logger.warning(f"Error initializing checkpoint paths: {e}")
             return []
 
+    def _init_embedding_paths(self) -> List[str]:
+        """Initialize and validate embedding paths from ComfyUI settings"""
+        try:
+            raw_paths = folder_paths.get_folder_paths("embeddings")
+            
+            # Normalize and resolve symlinks, store mapping from resolved -> original
+            path_map = {}
+            for path in raw_paths:
+                if os.path.exists(path):
+                    real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
+                    path_map[real_path] = path_map.get(real_path, path.replace(os.sep, "/"))  # preserve first seen
+            
+            # Now sort and use only the deduplicated real paths
+            unique_paths = sorted(path_map.values(), key=lambda p: p.lower())
+            logger.info("Found embedding roots:" + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]"))
+            
+            if not unique_paths:
+                logger.warning("No valid embeddings folders found in ComfyUI configuration")
+                return []
+            
+            for original_path in unique_paths:
+                real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
+                if real_path != original_path:
+                    self.add_path_mapping(original_path, real_path)
+            
+            return unique_paths
+        except Exception as e:
+            logger.warning(f"Error initializing embedding paths: {e}")
+            return []
+
     def get_preview_static_url(self, preview_path: str) -> str:
         """Convert local preview path to static URL"""
         if not preview_path:

py/lora_manager.py

@@ -6,10 +6,8 @@ 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 .services.model_service_factory import ModelServiceFactory, register_default_model_types
 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
@@ -17,6 +15,7 @@ from .routes.example_images_routes import ExampleImagesRoutes
 from .services.service_registry import ServiceRegistry
 from .services.settings_manager import settings
 from .utils.example_images_migration import ExampleImagesMigration
+from .services.websocket_manager import ws_manager
 
 logger = logging.getLogger(__name__)
 
@@ -28,12 +27,28 @@ class LoraManager:
     
     @classmethod
     def add_routes(cls):
-        """Initialize and register all routes"""
+        """Initialize and register all routes using the new refactored architecture"""
         app = PromptServer.instance.app
 
         # Configure aiohttp access logger to be less verbose
         logging.getLogger('aiohttp.access').setLevel(logging.WARNING)
 
+        # Add specific suppression for connection reset errors
+        class ConnectionResetFilter(logging.Filter):
+            def filter(self, record):
+                # Filter out connection reset errors that are not critical
+                if "ConnectionResetError" in str(record.getMessage()):
+                    return False
+                if "_call_connection_lost" in str(record.getMessage()):
+                    return False
+                if "WinError 10054" in str(record.getMessage()):
+                    return False
+                return True
+
+        # Apply the filter to asyncio logger
+        asyncio_logger = logging.getLogger("asyncio")
+        asyncio_logger.addFilter(ConnectionResetFilter())
+
         added_targets = set()  # Track already added target paths
         
         # Add static route for example images if the path exists in settings
@@ -62,7 +77,7 @@ 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
@@ -79,21 +94,45 @@ class LoraManager:
             config.add_route_mapping(real_root, preview_path)
             added_targets.add(real_root)
         
+        # Add static routes for each embedding root
+        for idx, root in enumerate(config.embeddings_roots, start=1):
+            preview_path = f'/embeddings_static/root{idx}/preview'
+            
+            real_root = root
+            if root in config._path_mappings.values():
+                for target, link in config._path_mappings.items():
+                    if link == root:
+                        real_root = target
+                        break
+            # Add static route for original path
+            app.router.add_static(preview_path, real_root)
+            logger.info(f"Added static route {preview_path} -> {real_root}")
+            
+            # Record route mapping
+            config.add_route_mapping(real_root, preview_path)
+            added_targets.add(real_root)
+        
         # Add static routes for symlink target paths
         link_idx = {
             'lora': 1,
-            'checkpoint': 1
+            'checkpoint': 1,
+            'embedding': 1
         }
         
         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)
+                # Determine if this is a checkpoint, lora, or embedding link based on path
+                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)
+                is_embedding = any(emb_root in link_path for emb_root in config.embeddings_roots)
+                is_embedding = is_embedding or any(emb_root in target_path for emb_root in config.embeddings_roots)
                 
                 if is_checkpoint:
                     route_path = f'/checkpoints_static/link_{link_idx["checkpoint"]}/preview'
                     link_idx["checkpoint"] += 1
+                elif is_embedding:
+                    route_path = f'/embeddings_static/link_{link_idx["embedding"]}/preview'
+                    link_idx["embedding"] += 1
                 else:
                     route_path = f'/loras_static/link_{link_idx["lora"]}/preview'
                     link_idx["lora"] += 1
@@ -110,35 +149,37 @@ class LoraManager:
         # Add static route for plugin assets
         app.router.add_static('/loras_static', config.static_path)
         
-        # Setup feature routes
-        lora_routes = LoraRoutes()
-        checkpoints_routes = CheckpointsRoutes()
-        stats_routes = StatsRoutes()
+        # Register default model types with the factory
+        register_default_model_types()
         
-        # Initialize routes
-        lora_routes.setup_routes(app)
-        checkpoints_routes.setup_routes(app)
-        stats_routes.setup_routes(app)  # Add statistics routes
-        ApiRoutes.setup_routes(app)
+        # Setup all model routes using the factory
+        ModelServiceFactory.setup_all_routes(app)
+        
+        # Setup non-model-specific routes
+        stats_routes = StatsRoutes()
+        stats_routes.setup_routes(app)
         RecipeRoutes.setup_routes(app)
         UpdateRoutes.setup_routes(app)  
-        MiscRoutes.setup_routes(app)  # Register miscellaneous routes
-        ExampleImagesRoutes.setup_routes(app)  # Register example images routes
+        MiscRoutes.setup_routes(app)
+        ExampleImagesRoutes.setup_routes(app)
+        
+        # Setup WebSocket routes that are shared across all model types
+        app.router.add_get('/ws/fetch-progress', ws_manager.handle_connection)
+        app.router.add_get('/ws/download-progress', ws_manager.handle_download_connection)
+        app.router.add_get('/ws/init-progress', ws_manager.handle_init_connection)
         
         # Schedule service initialization 
         app.on_startup.append(lambda app: cls._initialize_services())
         
         # Add cleanup
         app.on_shutdown.append(cls._cleanup)
-        app.on_shutdown.append(ApiRoutes.cleanup)
+        
+        logger.info(f"LoRA Manager: Set up routes for {len(ModelServiceFactory.get_registered_types())} model types: {', '.join(ModelServiceFactory.get_registered_types())}")
     
     @classmethod
     async def _initialize_services(cls):
         """Initialize all services using the ServiceRegistry"""
         try:
-            # Ensure aiohttp access logger is configured with reduced verbosity
-            logging.getLogger('aiohttp.access').setLevel(logging.WARNING)
-            
             # Initialize CivitaiClient first to ensure it's ready for other services
             await ServiceRegistry.get_civitai_client()
 
@@ -151,19 +192,15 @@ class LoraManager:
             # Initialize scanners in background
             lora_scanner = await ServiceRegistry.get_lora_scanner()
             checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+            embedding_scanner = await ServiceRegistry.get_embedding_scanner()
             
             # Initialize recipe scanner if needed
             recipe_scanner = await ServiceRegistry.get_recipe_scanner()
             
-            # Initialize metadata collector if not in standalone mode
-            if not STANDALONE_MODE:
-                from .metadata_collector import init as init_metadata
-                init_metadata()
-                logger.debug("Metadata collector initialized")
-            
             # Create low-priority initialization tasks
             asyncio.create_task(lora_scanner.initialize_in_background(), name='lora_cache_init')
             asyncio.create_task(checkpoint_scanner.initialize_in_background(), name='checkpoint_cache_init')
+            asyncio.create_task(embedding_scanner.initialize_in_background(), name='embedding_cache_init')
             asyncio.create_task(recipe_scanner.initialize_in_background(), name='recipe_cache_init')
 
             await ExampleImagesMigration.check_and_run_migrations()

py/metadata_collector/metadata_hook.py

@@ -26,98 +26,179 @@ class MetadataHook:
                 print("Could not locate ComfyUI execution module, metadata collection disabled")
                 return
             
-            # Store the original _map_node_over_list function
-            original_map_node_over_list = execution._map_node_over_list
+            # Detect whether we're using the new async version of ComfyUI
+            is_async = False
+            map_node_func_name = '_map_node_over_list'
             
-            # Define the wrapped _map_node_over_list function
-            def map_node_over_list_with_metadata(obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None):
-                # Only collect metadata when calling the main function of nodes
-                if func == obj.FUNCTION and hasattr(obj, '__class__'):
-                    try:
-                        # Get the current prompt_id from the registry
-                        registry = MetadataRegistry()
-                        prompt_id = registry.current_prompt_id
-                        
-                        if prompt_id is not None:
-                            # Get node class type
-                            class_type = obj.__class__.__name__
-                            
-                            # Unique ID might be available through the obj if it has a unique_id field
-                            node_id = getattr(obj, 'unique_id', None)
-                            if node_id is None and pre_execute_cb:
-                                # Try to extract node_id through reflection on GraphBuilder.set_default_prefix
-                                frame = inspect.currentframe()
-                                while frame:
-                                    if 'unique_id' in frame.f_locals:
-                                        node_id = frame.f_locals['unique_id']
-                                        break
-                                    frame = frame.f_back
-                            
-                            # Record inputs before execution
-                            if node_id is not None:
-                                registry.record_node_execution(node_id, class_type, input_data_all, None)
-                    except Exception as e:
-                        print(f"Error collecting metadata (pre-execution): {str(e)}")
-                
-                # Execute the original function
-                results = original_map_node_over_list(obj, input_data_all, func, allow_interrupt, execution_block_cb, pre_execute_cb)
-                
-                # After execution, collect outputs for relevant nodes
-                if func == obj.FUNCTION and hasattr(obj, '__class__'):
-                    try:
-                        # Get the current prompt_id from the registry
-                        registry = MetadataRegistry()
-                        prompt_id = registry.current_prompt_id
-                        
-                        if prompt_id is not None:
-                            # Get node class type
-                            class_type = obj.__class__.__name__
-                            
-                            # Unique ID might be available through the obj if it has a unique_id field
-                            node_id = getattr(obj, 'unique_id', None)
-                            if node_id is None and pre_execute_cb:
-                                # Try to extract node_id through reflection
-                                frame = inspect.currentframe()
-                                while frame:
-                                    if 'unique_id' in frame.f_locals:
-                                        node_id = frame.f_locals['unique_id']
-                                        break
-                                    frame = frame.f_back
-                            
-                            # Record outputs after execution
-                            if node_id is not None:
-                                registry.update_node_execution(node_id, class_type, results)
-                    except Exception as e:
-                        print(f"Error collecting metadata (post-execution): {str(e)}")
-                
-                return results
-                
-            # Also hook the execute function to track the current prompt_id
-            original_execute = execution.execute
+            if hasattr(execution, '_async_map_node_over_list'):
+                is_async = inspect.iscoroutinefunction(execution._async_map_node_over_list)
+                map_node_func_name = '_async_map_node_over_list'
+            elif hasattr(execution, '_map_node_over_list'):
+                is_async = inspect.iscoroutinefunction(execution._map_node_over_list)
             
-            def execute_with_prompt_tracking(*args, **kwargs):
-                if len(args) >= 7:  # Check if we have enough arguments
-                    server, prompt, caches, node_id, extra_data, executed, prompt_id = args[:7]
-                    registry = MetadataRegistry()
-                    
-                    # Start collection if this is a new prompt
-                    if not registry.current_prompt_id or registry.current_prompt_id != prompt_id:
-                        registry.start_collection(prompt_id)
-                        
-                    # Store the dynprompt reference for node lookups
-                    if hasattr(prompt, 'original_prompt'):
-                        registry.set_current_prompt(prompt)
-                
-                # Execute the original function
-                return original_execute(*args, **kwargs)
-                
-            # Replace the functions
-            execution._map_node_over_list = map_node_over_list_with_metadata
-            execution.execute = execute_with_prompt_tracking
-            # Make map_node_over_list public to avoid it being hidden by hooks
-            execution.map_node_over_list = original_map_node_over_list
+            if is_async:
+                print("Detected async ComfyUI execution, installing async metadata hooks")
+                MetadataHook._install_async_hooks(execution, map_node_func_name)
+            else:
+                print("Detected sync ComfyUI execution, installing sync metadata hooks")
+                MetadataHook._install_sync_hooks(execution)
             
             print("Metadata collection hooks installed for runtime values")
             
         except Exception as e:
             print(f"Error installing metadata hooks: {str(e)}")
+    
+    @staticmethod
+    def _install_sync_hooks(execution):
+        """Install hooks for synchronous execution model"""
+        # Store the original _map_node_over_list function
+        original_map_node_over_list = execution._map_node_over_list
+        
+        # Define the wrapped _map_node_over_list function
+        def map_node_over_list_with_metadata(obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None):
+            # Only collect metadata when calling the main function of nodes
+            if func == obj.FUNCTION and hasattr(obj, '__class__'):
+                try:
+                    # Get the current prompt_id from the registry
+                    registry = MetadataRegistry()
+                    prompt_id = registry.current_prompt_id
+                    
+                    if prompt_id is not None:
+                        # Get node class type
+                        class_type = obj.__class__.__name__
+                        
+                        # Unique ID might be available through the obj if it has a unique_id field
+                        node_id = getattr(obj, 'unique_id', None)
+                        if node_id is None and pre_execute_cb:
+                            # Try to extract node_id through reflection on GraphBuilder.set_default_prefix
+                            frame = inspect.currentframe()
+                            while frame:
+                                if 'unique_id' in frame.f_locals:
+                                    node_id = frame.f_locals['unique_id']
+                                    break
+                                frame = frame.f_back
+                        
+                        # Record inputs before execution
+                        if node_id is not None:
+                            registry.record_node_execution(node_id, class_type, input_data_all, None)
+                except Exception as e:
+                    print(f"Error collecting metadata (pre-execution): {str(e)}")
+            
+            # Execute the original function
+            results = original_map_node_over_list(obj, input_data_all, func, allow_interrupt, execution_block_cb, pre_execute_cb)
+            
+            # After execution, collect outputs for relevant nodes
+            if func == obj.FUNCTION and hasattr(obj, '__class__'):
+                try:
+                    # Get the current prompt_id from the registry
+                    registry = MetadataRegistry()
+                    prompt_id = registry.current_prompt_id
+                    
+                    if prompt_id is not None:
+                        # Get node class type
+                        class_type = obj.__class__.__name__
+                        
+                        # Unique ID might be available through the obj if it has a unique_id field
+                        node_id = getattr(obj, 'unique_id', None)
+                        if node_id is None and pre_execute_cb:
+                            # Try to extract node_id through reflection
+                            frame = inspect.currentframe()
+                            while frame:
+                                if 'unique_id' in frame.f_locals:
+                                    node_id = frame.f_locals['unique_id']
+                                    break
+                                frame = frame.f_back
+                        
+                        # Record outputs after execution
+                        if node_id is not None:
+                            registry.update_node_execution(node_id, class_type, results)
+                except Exception as e:
+                    print(f"Error collecting metadata (post-execution): {str(e)}")
+            
+            return results
+            
+        # Also hook the execute function to track the current prompt_id
+        original_execute = execution.execute
+        
+        def execute_with_prompt_tracking(*args, **kwargs):
+            if len(args) >= 7:  # Check if we have enough arguments
+                server, prompt, caches, node_id, extra_data, executed, prompt_id = args[:7]
+                registry = MetadataRegistry()
+                
+                # Start collection if this is a new prompt
+                if not registry.current_prompt_id or registry.current_prompt_id != prompt_id:
+                    registry.start_collection(prompt_id)
+                    
+                # Store the dynprompt reference for node lookups
+                if hasattr(prompt, 'original_prompt'):
+                    registry.set_current_prompt(prompt)
+            
+            # Execute the original function
+            return original_execute(*args, **kwargs)
+            
+        # Replace the functions
+        execution._map_node_over_list = map_node_over_list_with_metadata
+        execution.execute = execute_with_prompt_tracking
+    
+    @staticmethod
+    def _install_async_hooks(execution, map_node_func_name='_async_map_node_over_list'):
+        """Install hooks for asynchronous execution model"""
+        # Store the original _async_map_node_over_list function
+        original_map_node_over_list = getattr(execution, map_node_func_name)
+        
+        # Wrapped async function, compatible with both stable and nightly
+        async def async_map_node_over_list_with_metadata(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, *args, **kwargs):
+            hidden_inputs = kwargs.get('hidden_inputs', None)
+            # Only collect metadata when calling the main function of nodes
+            if func == obj.FUNCTION and hasattr(obj, '__class__'):
+                try:
+                    registry = MetadataRegistry()
+                    if prompt_id is not None:
+                        class_type = obj.__class__.__name__
+                        node_id = unique_id
+                        if node_id is not None:
+                            registry.record_node_execution(node_id, class_type, input_data_all, None)
+                except Exception as e:
+                    print(f"Error collecting metadata (pre-execution): {str(e)}")
+            
+            # Call original function with all args/kwargs
+            results = await original_map_node_over_list(
+                prompt_id, unique_id, obj, input_data_all, func,
+                allow_interrupt, execution_block_cb, pre_execute_cb, *args, **kwargs
+            )
+            
+            if func == obj.FUNCTION and hasattr(obj, '__class__'):
+                try:
+                    registry = MetadataRegistry()
+                    if prompt_id is not None:
+                        class_type = obj.__class__.__name__
+                        node_id = unique_id
+                        if node_id is not None:
+                            registry.update_node_execution(node_id, class_type, results)
+                except Exception as e:
+                    print(f"Error collecting metadata (post-execution): {str(e)}")
+            
+            return results
+        
+        # Also hook the execute function to track the current prompt_id
+        original_execute = execution.execute
+        
+        async def async_execute_with_prompt_tracking(*args, **kwargs):
+            if len(args) >= 7:  # Check if we have enough arguments
+                server, prompt, caches, node_id, extra_data, executed, prompt_id = args[:7]
+                registry = MetadataRegistry()
+                
+                # Start collection if this is a new prompt
+                if not registry.current_prompt_id or registry.current_prompt_id != prompt_id:
+                    registry.start_collection(prompt_id)
+                    
+                # Store the dynprompt reference for node lookups
+                if hasattr(prompt, 'original_prompt'):
+                    registry.set_current_prompt(prompt)
+            
+            # Execute the original function
+            return await original_execute(*args, **kwargs)
+            
+        # Replace the functions with async versions
+        setattr(execution, map_node_func_name, async_map_node_over_list_with_metadata)
+        execution.execute = async_execute_with_prompt_tracking

py/metadata_collector/metadata_processor.py

@@ -238,25 +238,45 @@ class MetadataProcessor:
             
             pos_conditioning = metadata[PROMPTS][sampler_id].get("pos_conditioning")
             neg_conditioning = metadata[PROMPTS][sampler_id].get("neg_conditioning")
-                
-            # Try to match conditioning objects with those stored by CLIPTextEncodeExtractor
-            for prompt_node_id, prompt_data in metadata[PROMPTS].items():
-                # For nodes with single conditioning output
-                if "conditioning" in prompt_data:
-                    if pos_conditioning is not None and id(prompt_data["conditioning"]) == id(pos_conditioning):
-                        result["prompt"] = prompt_data.get("text", "")
+            
+            # Helper function to recursively find prompt text for a conditioning object
+            def find_prompt_text_for_conditioning(conditioning_obj, is_positive=True):
+                if conditioning_obj is None:
+                    return ""
                     
-                    if neg_conditioning is not None and id(prompt_data["conditioning"]) == id(neg_conditioning):
-                        result["negative_prompt"] = prompt_data.get("text", "")
+                # 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)
                 
-                # For nodes with separate pos_conditioning and neg_conditioning outputs (like TSC_EfficientLoader)
-                if "positive_encoded" in prompt_data:
-                    if pos_conditioning is not None and id(prompt_data["positive_encoded"]) == id(pos_conditioning):
-                        result["prompt"] = prompt_data.get("positive_text", "")
-                
-                if "negative_encoded" in prompt_data:
-                    if neg_conditioning is not None and id(prompt_data["negative_encoded"]) == id(neg_conditioning):
-                        result["negative_prompt"] = prompt_data.get("negative_text", "")
+                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
     

py/metadata_collector/node_extractors.py

@@ -117,15 +117,15 @@ class CLIPTextEncodeExtractor(NodeMetadataExtractor):
             if isinstance(outputs[0], tuple) and len(outputs[0]) > 0:
                 conditioning = outputs[0][0]
                 metadata[PROMPTS][node_id]["conditioning"] = conditioning
-        
-class SamplerExtractor(NodeMetadataExtractor):
+
+# Base Sampler Extractor to reduce code redundancy
+class BaseSamplerExtractor(NodeMetadataExtractor):
+    """Base extractor for sampler nodes with common functionality"""
     @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        if not inputs:
-            return
-            
+    def extract_sampling_params(node_id, inputs, metadata, param_keys):
+        """Extract sampling parameters from inputs"""
         sampling_params = {}
-        for key in ["seed", "steps", "cfg", "sampler_name", "scheduler", "denoise"]:
+        for key in param_keys:
             if key in inputs:
                 sampling_params[key] = inputs[key]
                 
@@ -134,7 +134,10 @@ class SamplerExtractor(NodeMetadataExtractor):
             "node_id": node_id,
             IS_SAMPLER: True  # Add sampler flag
         }
-
+    
+    @staticmethod
+    def extract_conditioning(node_id, inputs, metadata):
+        """Extract conditioning objects from inputs"""
         # Store the conditioning objects directly in metadata for later matching
         pos_conditioning = inputs.get("positive", None)
         neg_conditioning = inputs.get("negative", None)
@@ -146,7 +149,10 @@ class SamplerExtractor(NodeMetadataExtractor):
             
             metadata[PROMPTS][node_id]["pos_conditioning"] = pos_conditioning
             metadata[PROMPTS][node_id]["neg_conditioning"] = neg_conditioning
-        
+    
+    @staticmethod
+    def extract_latent_dimensions(node_id, inputs, metadata):
+        """Extract dimensions from latent image"""
         # Extract latent image dimensions if available
         if "latent_image" in inputs and inputs["latent_image"] is not None:
             latent = inputs["latent_image"]
@@ -167,59 +173,106 @@ class SamplerExtractor(NodeMetadataExtractor):
                         "height": height,
                         "node_id": node_id
                     }
-
-class KSamplerAdvancedExtractor(NodeMetadataExtractor):
+        
+class SamplerExtractor(BaseSamplerExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
         if not inputs:
             return
             
-        sampling_params = {}
-        for key in ["noise_seed", "steps", "cfg", "sampler_name", "scheduler", "add_noise"]:
-            if key in inputs:
-                sampling_params[key] = inputs[key]
-                
-        metadata[SAMPLING][node_id] = {
-            "parameters": sampling_params,
-            "node_id": node_id,
-            IS_SAMPLER: True  # Add sampler flag
-        }
+        # Extract common sampling parameters
+        BaseSamplerExtractor.extract_sampling_params(
+            node_id, inputs, metadata, 
+            ["seed", "steps", "cfg", "sampler_name", "scheduler", "denoise"]
+        )
         
-        # Store the conditioning objects directly in metadata for later matching
-        pos_conditioning = inputs.get("positive", None)
-        neg_conditioning = inputs.get("negative", None)
+        # Extract conditioning objects
+        BaseSamplerExtractor.extract_conditioning(node_id, inputs, metadata)
+        
+        # Extract latent dimensions
+        BaseSamplerExtractor.extract_latent_dimensions(node_id, inputs, metadata)
 
-        # 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}
+class KSamplerAdvancedExtractor(BaseSamplerExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
             
-            metadata[PROMPTS][node_id]["pos_conditioning"] = pos_conditioning
-            metadata[PROMPTS][node_id]["neg_conditioning"] = neg_conditioning
+        # Extract common sampling parameters
+        BaseSamplerExtractor.extract_sampling_params(
+            node_id, inputs, metadata, 
+            ["noise_seed", "steps", "cfg", "sampler_name", "scheduler", "add_noise"]
+        )
         
-        # Extract latent image dimensions if available
-        if "latent_image" in inputs and inputs["latent_image"] is not None:
-            latent = inputs["latent_image"]
-            if isinstance(latent, dict) and "samples" in latent:
-                # Extract dimensions from latent tensor
-                samples = latent["samples"]
-                if hasattr(samples, "shape") and len(samples.shape) >= 3:
-                    # Correct shape interpretation: [batch_size, channels, height/8, width/8]
-                    # Multiply by 8 to get actual pixel dimensions
-                    height = int(samples.shape[2] * 8)
-                    width = int(samples.shape[3] * 8)
-                    
-                    if SIZE not in metadata:
-                        metadata[SIZE] = {}
-                        
-                    metadata[SIZE][node_id] = {
-                        "width": width,
-                        "height": height,
-                        "node_id": node_id
-                    }
+        # Extract conditioning objects
+        BaseSamplerExtractor.extract_conditioning(node_id, inputs, metadata)
+        
+        # Extract latent dimensions
+        BaseSamplerExtractor.extract_latent_dimensions(node_id, inputs, metadata)
+
+class KSamplerBasicPipeExtractor(BaseSamplerExtractor):
+    """Extractor for KSamplerBasicPipe and KSampler_inspire_pipe nodes"""
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+            
+        # Extract common sampling parameters
+        BaseSamplerExtractor.extract_sampling_params(
+            node_id, inputs, metadata, 
+            ["seed", "steps", "cfg", "sampler_name", "scheduler", "denoise"]
+        )
+        
+        # Extract conditioning objects from basic_pipe
+        if "basic_pipe" in inputs and inputs["basic_pipe"] is not None:
+            basic_pipe = inputs["basic_pipe"]
+            # Typically, basic_pipe structure is (model, clip, vae, positive, negative)
+            if isinstance(basic_pipe, tuple) and len(basic_pipe) >= 5:
+                pos_conditioning = basic_pipe[3]  # positive is at index 3
+                neg_conditioning = basic_pipe[4]  # negative is at index 4
+                
+                # Save conditioning objects in metadata
+                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 dimensions
+        BaseSamplerExtractor.extract_latent_dimensions(node_id, inputs, metadata)
+
+class KSamplerAdvancedBasicPipeExtractor(BaseSamplerExtractor):
+    """Extractor for KSamplerAdvancedBasicPipe nodes"""
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+            
+        # Extract common sampling parameters
+        BaseSamplerExtractor.extract_sampling_params(
+            node_id, inputs, metadata, 
+            ["noise_seed", "steps", "cfg", "sampler_name", "scheduler", "add_noise"]
+        )
+        
+        # Extract conditioning objects from basic_pipe
+        if "basic_pipe" in inputs and inputs["basic_pipe"] is not None:
+            basic_pipe = inputs["basic_pipe"]
+            # Typically, basic_pipe structure is (model, clip, vae, positive, negative)
+            if isinstance(basic_pipe, tuple) and len(basic_pipe) >= 5:
+                pos_conditioning = basic_pipe[3]  # positive is at index 3
+                neg_conditioning = basic_pipe[4]  # negative is at index 4
+                
+                # Save conditioning objects in metadata
+                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 dimensions
+        BaseSamplerExtractor.extract_latent_dimensions(node_id, inputs, metadata)
 
 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
@@ -273,7 +326,6 @@ class TSCSamplerBaseExtractor(NodeMetadataExtractor):
                         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
@@ -284,11 +336,10 @@ class TSCKSamplerExtractor(SamplerExtractor, 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)
+        KSamplerAdvancedExtractor.extract(node_id, inputs, outputs, metadata)
         TSCSamplerBaseExtractor.extract(node_id, inputs, outputs, metadata)
 
     # Update method is inherited from TSCSamplerBaseExtractor
@@ -461,7 +512,7 @@ class BasicSchedulerExtractor(NodeMetadataExtractor):
             IS_SAMPLER: False  # Mark as non-primary sampler
         }
 
-class SamplerCustomAdvancedExtractor(NodeMetadataExtractor):
+class SamplerCustomAdvancedExtractor(BaseSamplerExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
         if not inputs:
@@ -480,26 +531,8 @@ class SamplerCustomAdvancedExtractor(NodeMetadataExtractor):
             IS_SAMPLER: True  # Add sampler flag
         }
         
-        # Extract latent image dimensions if available
-        if "latent_image" in inputs and inputs["latent_image"] is not None:
-            latent = inputs["latent_image"]
-            if isinstance(latent, dict) and "samples" in latent:
-                # Extract dimensions from latent tensor
-                samples = latent["samples"]
-                if hasattr(samples, "shape") and len(samples.shape) >= 3:
-                    # Correct shape interpretation: [batch_size, channels, height/8, width/8]
-                    # Multiply by 8 to get actual pixel dimensions
-                    height = int(samples.shape[2] * 8)
-                    width = int(samples.shape[3] * 8)
-                    
-                    if SIZE not in metadata:
-                        metadata[SIZE] = {}
-                        
-                    metadata[SIZE][node_id] = {
-                        "width": width,
-                        "height": height,
-                        "node_id": node_id
-                    }
+        # Extract latent dimensions
+        BaseSamplerExtractor.extract_latent_dimensions(node_id, inputs, metadata)
 
 import json
 
@@ -569,6 +602,40 @@ 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 = {
@@ -578,6 +645,10 @@ NODE_EXTRACTORS = {
     "SamplerCustomAdvanced": SamplerCustomAdvancedExtractor,
     "TSC_KSampler": TSCKSamplerExtractor,   # Efficient Nodes
     "TSC_KSamplerAdvanced": TSCKSamplerAdvancedExtractor,  # Efficient Nodes
+    "KSamplerBasicPipe": KSamplerBasicPipeExtractor,    # comfyui-impact-pack
+    "KSamplerAdvancedBasicPipe": KSamplerAdvancedBasicPipeExtractor,    # comfyui-impact-pack
+    "KSampler_inspire_pipe": KSamplerBasicPipeExtractor,    # comfyui-inspire-pack
+    "KSamplerAdvanced_inspire_pipe": KSamplerAdvancedBasicPipeExtractor,  # comfyui-inspire-pack
     # Sampling Selectors
     "KSamplerSelect": KSamplerSelectExtractor,  # Add KSamplerSelect
     "BasicScheduler": BasicSchedulerExtractor,  # Add BasicScheduler
@@ -594,6 +665,8 @@ NODE_EXTRACTORS = {
     "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/lora_loader.py

@@ -1,15 +1,15 @@
 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 +37,39 @@ 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))
+                _, trigger_words = 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}")
@@ -66,13 +86,19 @@ class LoraManagerLoader:
             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))
+            lora_path, trigger_words = 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 +1,8 @@
 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__)
@@ -43,7 +42,7 @@ class LoraStacker:
             # Get trigger words from existing stack entries
             for lora_path, _, _ in lora_stack:
                 lora_name = extract_lora_name(lora_path)
-                _, trigger_words = asyncio.run(get_lora_info(lora_name))
+                _, trigger_words = get_lora_info(lora_name)
                 all_trigger_words.extend(trigger_words)
         
         # Process loras from kwargs with support for both old and new formats
@@ -58,7 +57,7 @@ class LoraStacker:
             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))
+            lora_path, trigger_words = get_lora_info(lora_name)
             
             # Add to stack without loading
             # replace '/' with os.sep to avoid different OS path format

py/nodes/save_image.py

@@ -4,8 +4,7 @@ import asyncio
 import re
 import numpy as np
 import folder_paths # type: ignore
-from ..services.lora_scanner import LoraScanner
-from ..services.checkpoint_scanner import CheckpointScanner
+from ..services.service_registry import ServiceRegistry
 from ..metadata_collector.metadata_processor import MetadataProcessor
 from ..metadata_collector import get_metadata
 from PIL import Image, PngImagePlugin
@@ -71,25 +70,20 @@ class SaveImage:
     FUNCTION = "process_image"
     OUTPUT_NODE = True
 
-    async def get_lora_hash(self, lora_name):
+    def get_lora_hash(self, lora_name):
         """Get the lora hash from cache"""
-        scanner = await LoraScanner.get_instance()
+        scanner = ServiceRegistry.get_service_sync("lora_scanner")
         
         # Use the new direct filename lookup method
         hash_value = scanner.get_hash_by_filename(lora_name)
         if hash_value:
             return hash_value
             
-        # Fallback to old method for compatibility
-        cache = await scanner.get_cached_data()
-        for item in cache.raw_data:
-            if item.get('file_name') == lora_name:
-                return item.get('sha256')
         return None
 
-    async def get_checkpoint_hash(self, checkpoint_path):
+    def get_checkpoint_hash(self, checkpoint_path):
         """Get the checkpoint hash from cache"""
-        scanner = await CheckpointScanner.get_instance()
+        scanner = ServiceRegistry.get_service_sync("checkpoint_scanner")
         
         if not checkpoint_path:
             return None
@@ -102,18 +96,10 @@ class SaveImage:
         hash_value = scanner.get_hash_by_filename(checkpoint_name)
         if hash_value:
             return hash_value
-            
-        # Fallback to old method for compatibility
-        cache = await scanner.get_cached_data()
-        normalized_path = checkpoint_path.replace('\\', '/')
-        
-        for item in cache.raw_data:
-            if item.get('file_name') == checkpoint_name and item.get('file_path').endswith(normalized_path):
-                return item.get('sha256')
                 
         return None
 
-    async def format_metadata(self, metadata_dict):
+    def format_metadata(self, metadata_dict):
         """Format metadata in the requested format similar to userComment example"""
         if not metadata_dict:
             return ""
@@ -140,7 +126,7 @@ class SaveImage:
             
             # Get hash for each lora
             for lora_name, strength in lora_matches:
-                hash_value = await self.get_lora_hash(lora_name)
+                hash_value = self.get_lora_hash(lora_name)
                 if hash_value:
                     lora_hashes[lora_name] = hash_value
         else:
@@ -226,7 +212,7 @@ class SaveImage:
             checkpoint = metadata_dict.get('checkpoint')
             if checkpoint is not None:
                 # Get model hash
-                model_hash = await self.get_checkpoint_hash(checkpoint)
+                model_hash = self.get_checkpoint_hash(checkpoint)
                 
                 # Extract basename without path
                 checkpoint_name = os.path.basename(checkpoint)
@@ -329,8 +315,7 @@ class SaveImage:
         raw_metadata = get_metadata()
         metadata_dict = MetadataProcessor.to_dict(raw_metadata, id)
             
-        # Get or create metadata asynchronously
-        metadata = asyncio.run(self.format_metadata(metadata_dict))
+        metadata = self.format_metadata(metadata_dict)
         
         # Process filename_prefix with pattern substitution
         filename_prefix = self.format_filename(filename_prefix, metadata_dict)

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 +1,92 @@
+from comfy.comfy_types import IO # type: ignore
+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 = 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/base.py

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

py/recipes/parsers/automatic.py

@@ -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]+)'
     
@@ -181,13 +181,30 @@ class AutomaticMetadataParser(RecipeMetadataParser):
             # First use Civitai resources if available (more reliable source)
             if metadata.get("civitai_resources"):
                 for resource in metadata.get("civitai_resources", []):
+                    # --- Added: Parse 'air' field if present ---
+                    air = resource.get("air")
+                    if air:
+                        # Format: urn:air:sdxl:lora:civitai:1221007@1375651
+                        # Or: urn:air:sdxl:checkpoint:civitai:623891@2019115
+                        air_pattern = r"urn:air:[^:]+:(?P<type>[^:]+):civitai:(?P<modelId>\d+)@(?P<modelVersionId>\d+)"
+                        air_match = re.match(air_pattern, air)
+                        if air_match:
+                            air_type = air_match.group("type")
+                            air_modelId = int(air_match.group("modelId"))
+                            air_modelVersionId = int(air_match.group("modelVersionId"))
+                            # checkpoint/lycoris/lora/hypernet
+                            resource["type"] = air_type
+                            resource["modelId"] = air_modelId
+                            resource["modelVersionId"] = air_modelVersionId
+                    # --- End added ---
+
                     if resource.get("type") in ["lora", "lycoris", "hypernet"] and resource.get("modelVersionId"):
                         # Initialize lora entry
                         lora_entry = {
                             'id': resource.get("modelVersionId", 0),
                             'modelId': resource.get("modelId", 0),
                             'name': resource.get("modelName", "Unknown LoRA"),
-                            'version': resource.get("modelVersionName", ""),
+                            'version': resource.get("modelVersionName", resource.get("versionName", "")),
                             'type': resource.get("type", "lora"),
                             'weight': round(float(resource.get("weight", 1.0)), 2),
                             'existsLocally': False,

py/recipes/parsers/civitai_image.py

@@ -50,6 +50,9 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                 '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"]
@@ -96,11 +99,17 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                 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': resource.get("hash", ""),
+                            'hash': lora_hash,
                             'existsLocally': False,
                             'localPath': None,
                             'file_name': resource.get("name", "Unknown"),
@@ -114,7 +123,6 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                         # Try to get info from Civitai if hash is available
                         if lora_entry['hash'] and civitai_client:
                             try:
-                                lora_hash = lora_entry['hash']
                                 civitai_info = await civitai_client.get_model_by_hash(lora_hash)
                                 
                                 populated_entry = await self.populate_lora_from_civitai(
@@ -129,43 +137,124 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                                     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"]:
-                    # Modified to process resources without a type field as potential LoRAs
-                    if resource.get("type") in ["lora", "lycoris"] or "type" not in resource:
-                        # Initialize lora entry with the same structure as in automatic.py
-                        lora_entry = {
-                            'id': 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
-                        }
+                    # Skip resources that aren't LoRAs or LyCORIS
+                    if resource.get("type") not in ["lora", "lycoris"] and "type" not in resource:
+                        continue
                         
-                        # Try to get info from Civitai if modelVersionId is available
-                        if resource.get('modelVersionId') and civitai_client:
-                            try:
-                                version_id = str(resource.get('modelVersionId'))
-                                # Use get_model_version_info instead of get_model_version
-                                civitai_info, error = await civitai_client.get_model_version_info(version_id)
-                                
-                                if error:
-                                    logger.warning(f"Error getting model version info: {error}")
-                                    continue
+                    # 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,
@@ -177,65 +266,14 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                                     continue  # Skip invalid LoRA types
                                     
                                 lora_entry = populated_entry
-                            except Exception as e:
-                                logger.error(f"Error fetching Civitai info for model version {resource.get('modelVersionId')}: {e}")
-                        
-                        result["loras"].append(lora_entry)
-            
-            # Process additionalResources array
-            if "additionalResources" in metadata and isinstance(metadata["additionalResources"], list):
-                for resource in metadata["additionalResources"]:
-                    # Modified to process resources without a type field as potential LoRAs
-                    if resource.get("type") in ["lora", "lycoris"] or "type" not in resource:
-                        lora_type = resource.get("type", "lora")
-                        name = resource.get("name", "")
-                        
-                        # Extract ID from URN format if available
-                        model_id = None
-                        if name and "civitai:" in name:
-                            parts = name.split("@")
-                            if len(parts) > 1:
-                                model_id = parts[1]
-                        
-                        lora_entry = {
-                            'name': name,
-                            'type': lora_type,
-                            'weight': float(resource.get("strength", 1.0)),
-                            'hash': "",
-                            'existsLocally': False,
-                            'localPath': None,
-                            'file_name': name,
-                            'thumbnailUrl': '/loras_static/images/no-preview.png',
-                            'baseModel': '',
-                            'size': 0,
-                            'downloadUrl': '',
-                            'isDeleted': False
-                        }
-                        
-                        # If we have a model ID and civitai client, try to get more info
-                        if model_id and civitai_client:
-                            try:
-                                # Use get_model_version_info with the model ID
-                                civitai_info, error = await civitai_client.get_model_version_info(model_id)
                                 
-                                if error:
-                                    logger.warning(f"Error getting model version info: {error}")
-                                else:
-                                    populated_entry = await self.populate_lora_from_civitai(
-                                        lora_entry,
-                                        civitai_info,
-                                        recipe_scanner,
-                                        base_model_counts
-                                    )
-                                    
-                                    if populated_entry is None:
-                                        continue  # Skip invalid LoRA types
-                                        
-                                    lora_entry = populated_entry
-                            except Exception as e:
-                                logger.error(f"Error fetching Civitai info for model ID {model_id}: {e}")
-                        
-                            result["loras"].append(lora_entry)
+                                # 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:

py/recipes/parsers/recipe_format.py

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

py/routes/base_model_routes.py

@@ -0,0 +1,619 @@
+from abc import ABC, abstractmethod
+import asyncio
+import json
+import logging
+from aiohttp import web
+from typing import Dict
+
+import jinja2
+
+from ..utils.routes_common import ModelRouteUtils
+from ..services.websocket_manager import ws_manager
+from ..services.settings_manager import settings
+from ..config import config
+
+logger = logging.getLogger(__name__)
+
+class BaseModelRoutes(ABC):
+    """Base route controller for all model types"""
+    
+    def __init__(self, service):
+        """Initialize the route controller
+        
+        Args:
+            service: Model service instance (LoraService, CheckpointService, etc.)
+        """
+        self.service = service
+        self.model_type = service.model_type
+        self.template_env = jinja2.Environment(
+            loader=jinja2.FileSystemLoader(config.templates_path),
+            autoescape=True
+        )
+    
+    def setup_routes(self, app: web.Application, prefix: str):
+        """Setup common routes for the model type
+        
+        Args:
+            app: aiohttp application
+            prefix: URL prefix (e.g., 'loras', 'checkpoints')
+        """
+        # Common model management routes
+        app.router.add_get(f'/api/{prefix}', self.get_models)
+        app.router.add_post(f'/api/{prefix}/delete', self.delete_model)
+        app.router.add_post(f'/api/{prefix}/exclude', self.exclude_model)
+        app.router.add_post(f'/api/{prefix}/fetch-civitai', self.fetch_civitai)
+        app.router.add_post(f'/api/{prefix}/relink-civitai', self.relink_civitai)
+        app.router.add_post(f'/api/{prefix}/replace-preview', self.replace_preview)
+        app.router.add_post(f'/api/{prefix}/save-metadata', self.save_metadata)
+        app.router.add_post(f'/api/{prefix}/rename', self.rename_model)
+        app.router.add_post(f'/api/{prefix}/bulk-delete', self.bulk_delete_models)
+        app.router.add_post(f'/api/{prefix}/verify-duplicates', self.verify_duplicates)
+        
+        # Common query routes
+        app.router.add_get(f'/api/{prefix}/top-tags', self.get_top_tags)
+        app.router.add_get(f'/api/{prefix}/base-models', self.get_base_models)
+        app.router.add_get(f'/api/{prefix}/scan', self.scan_models)
+        app.router.add_get(f'/api/{prefix}/roots', self.get_model_roots)
+        app.router.add_get(f'/api/{prefix}/folders', self.get_folders)
+        app.router.add_get(f'/api/{prefix}/find-duplicates', self.find_duplicate_models)
+        app.router.add_get(f'/api/{prefix}/find-filename-conflicts', self.find_filename_conflicts)
+
+        # Common Download management
+        app.router.add_post(f'/api/download-model', self.download_model)
+        app.router.add_get(f'/api/download-model-get', self.download_model_get)
+        app.router.add_get(f'/api/cancel-download-get', self.cancel_download_get)
+        app.router.add_get(f'/api/download-progress/{{download_id}}', self.get_download_progress)
+        
+        # CivitAI integration routes
+        app.router.add_post(f'/api/{prefix}/fetch-all-civitai', self.fetch_all_civitai)
+        # app.router.add_get(f'/api/civitai/versions/{{model_id}}', self.get_civitai_versions)
+        
+        # Add generic page route
+        app.router.add_get(f'/{prefix}', self.handle_models_page)
+        
+        # Setup model-specific routes
+        self.setup_specific_routes(app, prefix)
+    
+    @abstractmethod
+    def setup_specific_routes(self, app: web.Application, prefix: str):
+        """Setup model-specific routes - to be implemented by subclasses"""
+        pass
+    
+    async def handle_models_page(self, request: web.Request) -> web.Response:
+        """
+        Generic handler for model pages (e.g., /loras, /checkpoints).
+        Subclasses should set self.template_env and template_name.
+        """
+        try:
+            # Check if the scanner is initializing
+            is_initializing = (
+                self.service.scanner._cache is None or
+                (hasattr(self.service.scanner, 'is_initializing') and callable(self.service.scanner.is_initializing) and self.service.scanner.is_initializing()) or
+                (hasattr(self.service.scanner, '_is_initializing') and self.service.scanner._is_initializing)
+            )
+
+            template_name = getattr(self, "template_name", None)
+            if not self.template_env or not template_name:
+                return web.Response(text="Template environment or template name not set", status=500)
+
+            if is_initializing:
+                rendered = self.template_env.get_template(template_name).render(
+                    folders=[],
+                    is_initializing=True,
+                    settings=settings,
+                    request=request
+                )
+            else:
+                try:
+                    cache = await self.service.scanner.get_cached_data(force_refresh=False)
+                    rendered = self.template_env.get_template(template_name).render(
+                        folders=getattr(cache, "folders", []),
+                        is_initializing=False,
+                        settings=settings,
+                        request=request
+                    )
+                except Exception as cache_error:
+                    logger.error(f"Error loading cache data: {cache_error}")
+                    rendered = self.template_env.get_template(template_name).render(
+                        folders=[],
+                        is_initializing=True,
+                        settings=settings,
+                        request=request
+                    )
+            return web.Response(
+                text=rendered,
+                content_type='text/html'
+            )
+        except Exception as e:
+            logger.error(f"Error handling models page: {e}", exc_info=True)
+            return web.Response(
+                text="Error loading models page",
+                status=500
+            )
+    
+    async def get_models(self, request: web.Request) -> web.Response:
+        """Get paginated model data"""
+        try:
+            # Parse common query parameters
+            params = self._parse_common_params(request)
+            
+            # Get data from service
+            result = await self.service.get_paginated_data(**params)
+            
+            # Format response items
+            formatted_result = {
+                'items': [await self.service.format_response(item) for item in result['items']],
+                'total': result['total'],
+                'page': result['page'],
+                'page_size': result['page_size'],
+                'total_pages': result['total_pages']
+            }
+            
+            return web.json_response(formatted_result)
+            
+        except Exception as e:
+            logger.error(f"Error in get_{self.model_type}s: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)
+    
+    def _parse_common_params(self, request: web.Request) -> Dict:
+        """Parse common query parameters"""
+        # Parse basic pagination and sorting
+        page = int(request.query.get('page', '1'))
+        page_size = min(int(request.query.get('page_size', '20')), 100)
+        sort_by = request.query.get('sort_by', 'name')
+        folder = request.query.get('folder', None)
+        search = request.query.get('search', None)
+        fuzzy_search = request.query.get('fuzzy_search', 'false').lower() == 'true'
+        
+        # Parse filter arrays
+        base_models = request.query.getall('base_model', [])
+        tags = request.query.getall('tag', [])
+        favorites_only = request.query.get('favorites_only', 'false').lower() == 'true'
+        
+        # Parse search options
+        search_options = {
+            'filename': request.query.get('search_filename', 'true').lower() == 'true',
+            'modelname': request.query.get('search_modelname', 'true').lower() == 'true',
+            'tags': request.query.get('search_tags', 'false').lower() == 'true',
+            'recursive': request.query.get('recursive', 'false').lower() == 'true',
+        }
+        
+        # Parse hash filters if provided
+        hash_filters = {}
+        if 'hash' in request.query:
+            hash_filters['single_hash'] = request.query['hash']
+        elif 'hashes' in request.query:
+            try:
+                hash_list = json.loads(request.query['hashes'])
+                if isinstance(hash_list, list):
+                    hash_filters['multiple_hashes'] = hash_list
+            except (json.JSONDecodeError, TypeError):
+                pass
+        
+        return {
+            'page': page,
+            'page_size': page_size,
+            'sort_by': sort_by,
+            'folder': folder,
+            'search': search,
+            'fuzzy_search': fuzzy_search,
+            'base_models': base_models,
+            'tags': tags,
+            'search_options': search_options,
+            'hash_filters': hash_filters,
+            'favorites_only': favorites_only,
+            # Add model-specific parameters
+            **self._parse_specific_params(request)
+        }
+    
+    def _parse_specific_params(self, request: web.Request) -> Dict:
+        """Parse model-specific parameters - to be overridden by subclasses"""
+        return {}
+    
+    # Common route handlers
+    async def delete_model(self, request: web.Request) -> web.Response:
+        """Handle model deletion request"""
+        return await ModelRouteUtils.handle_delete_model(request, self.service.scanner)
+    
+    async def exclude_model(self, request: web.Request) -> web.Response:
+        """Handle model exclusion request"""
+        return await ModelRouteUtils.handle_exclude_model(request, self.service.scanner)
+    
+    async def fetch_civitai(self, request: web.Request) -> web.Response:
+        """Handle CivitAI metadata fetch request"""
+        response = await ModelRouteUtils.handle_fetch_civitai(request, self.service.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 = await self.service.format_response(data["metadata"])
+                return web.json_response({
+                    "success": True,
+                    "metadata": formatted_metadata
+                })
+        
+        return response
+    
+    async def relink_civitai(self, request: web.Request) -> web.Response:
+        """Handle CivitAI metadata re-linking request"""
+        return await ModelRouteUtils.handle_relink_civitai(request, self.service.scanner)
+    
+    async def replace_preview(self, request: web.Request) -> web.Response:
+        """Handle preview image replacement"""
+        return await ModelRouteUtils.handle_replace_preview(request, self.service.scanner)
+    
+    async def save_metadata(self, request: web.Request) -> web.Response:
+        """Handle saving metadata updates"""
+        return await ModelRouteUtils.handle_save_metadata(request, self.service.scanner)
+    
+    async def rename_model(self, request: web.Request) -> web.Response:
+        """Handle renaming a model file and its associated files"""
+        return await ModelRouteUtils.handle_rename_model(request, self.service.scanner)
+    
+    async def bulk_delete_models(self, request: web.Request) -> web.Response:
+        """Handle bulk deletion of models"""
+        return await ModelRouteUtils.handle_bulk_delete_models(request, self.service.scanner)
+    
+    async def verify_duplicates(self, request: web.Request) -> web.Response:
+        """Handle verification of duplicate model hashes"""
+        return await ModelRouteUtils.handle_verify_duplicates(request, self.service.scanner)
+    
+    async def get_top_tags(self, request: web.Request) -> web.Response:
+        """Handle request for top tags sorted by frequency"""
+        try:
+            limit = int(request.query.get('limit', '20'))
+            if limit < 1 or limit > 100:
+                limit = 20
+                
+            top_tags = await self.service.get_top_tags(limit)
+            
+            return web.json_response({
+                'success': True,
+                'tags': top_tags
+            })
+            
+        except Exception as e:
+            logger.error(f"Error getting top tags: {str(e)}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': 'Internal server error'
+            }, status=500)
+    
+    async def get_base_models(self, request: web.Request) -> web.Response:
+        """Get base models used in models"""
+        try:
+            limit = int(request.query.get('limit', '20'))
+            if limit < 1 or limit > 100:
+                limit = 20
+                
+            base_models = await self.service.get_base_models(limit)
+            
+            return web.json_response({
+                'success': True,
+                'base_models': base_models
+            })
+        except Exception as e:
+            logger.error(f"Error retrieving base models: {e}")
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def scan_models(self, request: web.Request) -> web.Response:
+        """Force a rescan of model files"""
+        try:
+            full_rebuild = request.query.get('full_rebuild', 'false').lower() == 'true'
+            
+            await self.service.scan_models(force_refresh=True, rebuild_cache=full_rebuild)
+            return web.json_response({
+                "status": "success", 
+                "message": f"{self.model_type.capitalize()} scan completed"
+            })
+        except Exception as e:
+            logger.error(f"Error in scan_{self.model_type}s: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)
+    
+    async def get_model_roots(self, request: web.Request) -> web.Response:
+        """Return the model root directories"""
+        try:
+            roots = self.service.get_model_roots()
+            return web.json_response({
+                "success": True,
+                "roots": roots
+            })
+        except Exception as e:
+            logger.error(f"Error getting {self.model_type} roots: {e}", exc_info=True)
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)
+        
+    async def get_folders(self, request: web.Request) -> web.Response:
+        """Get all folders in the cache"""
+        try:
+            cache = await self.service.scanner.get_cached_data()
+            return web.json_response({
+                'folders': cache.folders
+            })
+        except Exception as e:
+            logger.error(f"Error getting folders: {e}")
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def find_duplicate_models(self, request: web.Request) -> web.Response:
+        """Find models with duplicate SHA256 hashes"""
+        try:
+            # Get duplicate hashes from service
+            duplicates = self.service.find_duplicate_hashes()
+            
+            # Format the response
+            result = []
+            cache = await self.service.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(await self.service.format_response(model))
+                
+                # Add the primary model too
+                primary_path = self.service.get_path_by_hash(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, await self.service.format_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 {self.model_type}s: {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 models with conflicting filenames"""
+        try:
+            # Get duplicate filenames from service
+            duplicates = self.service.find_duplicate_filenames()
+            
+            # Format the response
+            result = []
+            cache = await self.service.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(await self.service.format_response(model))
+                
+                # Find the model from the main index too
+                hash_val = self.service.scanner.get_hash_by_filename(filename)
+                if hash_val:
+                    main_path = self.service.get_path_by_hash(hash_val)
+                    if main_path and main_path not in paths:
+                        main_model = next((m for m in cache.raw_data if m['file_path'] == main_path), None)
+                        if main_model:
+                            group["models"].insert(0, await self.service.format_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 for {self.model_type}s: {e}", exc_info=True)
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)
+        
+    # Download management methods
+    async def download_model(self, request: web.Request) -> web.Response:
+        """Handle model download request"""
+        return await ModelRouteUtils.handle_download_model(request)
+    
+    async def download_model_get(self, request: web.Request) -> web.Response:
+        """Handle model download request via GET method"""
+        try:
+            # Extract query parameters
+            model_id = request.query.get('model_id')
+            if not model_id:
+                return web.Response(
+                    status=400, 
+                    text="Missing required parameter: Please provide 'model_id'"
+                )
+            
+            # Get optional parameters
+            model_version_id = request.query.get('model_version_id')
+            download_id = request.query.get('download_id')
+            use_default_paths = request.query.get('use_default_paths', 'false').lower() == 'true'
+            
+            # Create a data dictionary that mimics what would be received from a POST request
+            data = {
+                'model_id': model_id
+            }
+            
+            # Add optional parameters only if they are provided
+            if model_version_id:
+                data['model_version_id'] = model_version_id
+                
+            if download_id:
+                data['download_id'] = download_id
+                
+            data['use_default_paths'] = use_default_paths
+            
+            # Create a mock request object with the data
+            future = asyncio.get_event_loop().create_future()
+            future.set_result(data)
+            
+            mock_request = type('MockRequest', (), {
+                'json': lambda self=None: future
+            })()
+            
+            # Call the existing download handler
+            return await ModelRouteUtils.handle_download_model(mock_request)
+            
+        except Exception as e:
+            error_message = str(e)
+            logger.error(f"Error downloading model via GET: {error_message}", exc_info=True)
+            return web.Response(status=500, text=error_message)
+    
+    async def cancel_download_get(self, request: web.Request) -> web.Response:
+        """Handle GET request for cancelling a download by download_id"""
+        try:
+            download_id = request.query.get('download_id')
+            if not download_id:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Download ID is required'
+                }, status=400)
+            
+            # Create a mock request with match_info for compatibility
+            mock_request = type('MockRequest', (), {
+                'match_info': {'download_id': download_id}
+            })()
+            return await ModelRouteUtils.handle_cancel_download(mock_request)
+        except Exception as e:
+            logger.error(f"Error cancelling download via GET: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_download_progress(self, request: web.Request) -> web.Response:
+        """Handle request for download progress by download_id"""
+        try:
+            # Get download_id from URL path
+            download_id = request.match_info.get('download_id')
+            if not download_id:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Download ID is required'
+                }, status=400)
+            
+            progress_data = ws_manager.get_download_progress(download_id)
+            
+            if progress_data is None:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Download ID not found'
+                }, status=404)
+            
+            return web.json_response({
+                'success': True,
+                'progress': progress_data.get('progress', 0)
+            })
+        except Exception as e:
+            logger.error(f"Error getting download progress: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def fetch_all_civitai(self, request: web.Request) -> web.Response:
+        """Fetch CivitAI metadata for all models in the background"""
+        try:
+            cache = await self.service.scanner.get_cached_data()
+            total = len(cache.raw_data)
+            processed = 0
+            success = 0
+            needs_resort = False
+            
+            # Prepare models to process
+            to_process = [
+                model for model in cache.raw_data 
+                if model.get('sha256') and (not model.get('civitai') or 'id' not in model.get('civitai')) and model.get('from_civitai', True)
+            ]
+            total_to_process = len(to_process)
+            
+            # Send initial progress
+            await ws_manager.broadcast({
+                'status': 'started',
+                'total': total_to_process,
+                'processed': 0,
+                'success': 0
+            })
+            
+            # Process each model
+            for model in to_process:
+                try:
+                    original_name = model.get('model_name')
+                    if await ModelRouteUtils.fetch_and_update_model(
+                        sha256=model['sha256'],
+                        file_path=model['file_path'],
+                        model_data=model,
+                        update_cache_func=self.service.scanner.update_single_model_cache
+                    ):
+                        success += 1
+                        if original_name != model.get('model_name'):
+                            needs_resort = True
+                    
+                    processed += 1
+                    
+                    # Send progress update
+                    await ws_manager.broadcast({
+                        'status': 'processing',
+                        'total': total_to_process,
+                        'processed': processed,
+                        'success': success,
+                        'current_name': model.get('model_name', 'Unknown')
+                    })
+                    
+                except Exception as e:
+                    logger.error(f"Error fetching CivitAI data for {model['file_path']}: {e}")
+            
+            if needs_resort:
+                await cache.resort()
+            
+            # Send completion message
+            await ws_manager.broadcast({
+                'status': 'completed',
+                'total': total_to_process,
+                'processed': processed,
+                'success': success
+            })
+                    
+            return web.json_response({
+                "success": True,
+                "message": f"Successfully updated {success} of {processed} processed {self.model_type}s (total: {total})"
+            })
+            
+        except Exception as e:
+            # Send error message
+            await ws_manager.broadcast({
+                'status': 'error',
+                'error': str(e)
+            })
+            logger.error(f"Error in fetch_all_civitai for {self.model_type}s: {e}")
+            return web.Response(text=str(e), status=500)
+    
+    async def get_civitai_versions(self, request: web.Request) -> web.Response:
+        """Get available versions for a Civitai model with local availability info"""
+        # This will be implemented by subclasses as they need CivitAI client access
+        return web.json_response({
+            "error": "Not implemented in base class"
+        }, status=501)

py/routes/checkpoint_routes.py

@@ -0,0 +1,105 @@
+import logging
+from aiohttp import web
+
+from .base_model_routes import BaseModelRoutes
+from ..services.checkpoint_service import CheckpointService
+from ..services.service_registry import ServiceRegistry
+
+logger = logging.getLogger(__name__)
+
+class CheckpointRoutes(BaseModelRoutes):
+    """Checkpoint-specific route controller"""
+    
+    def __init__(self):
+        """Initialize Checkpoint routes with Checkpoint service"""
+        # Service will be initialized later via setup_routes
+        self.service = None
+        self.civitai_client = None
+        self.template_name = "checkpoints.html"
+    
+    async def initialize_services(self):
+        """Initialize services from ServiceRegistry"""
+        checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+        self.service = CheckpointService(checkpoint_scanner)
+        self.civitai_client = await ServiceRegistry.get_civitai_client()
+        
+        # Initialize parent with the service
+        super().__init__(self.service)
+    
+    def setup_routes(self, app: web.Application):
+        """Setup Checkpoint routes"""
+        # Schedule service initialization on app startup
+        app.on_startup.append(lambda _: self.initialize_services())
+        
+        # Setup common routes with 'checkpoints' prefix (includes page route)
+        super().setup_routes(app, 'checkpoints')
+    
+    def setup_specific_routes(self, app: web.Application, prefix: str):
+        """Setup Checkpoint-specific routes"""
+        # Checkpoint-specific CivitAI integration
+        app.router.add_get(f'/api/{prefix}/civitai/versions/{{model_id}}', self.get_civitai_versions_checkpoint)
+        
+        # Checkpoint info by name
+        app.router.add_get(f'/api/{prefix}/info/{{name}}', self.get_checkpoint_info)
+    
+    async def get_checkpoint_info(self, request: web.Request) -> web.Response:
+        """Get detailed information for a specific checkpoint by name"""
+        try:
+            name = request.match_info.get('name', '')
+            checkpoint_info = await self.service.get_model_info_by_name(name)
+            
+            if checkpoint_info:
+                return web.json_response(checkpoint_info)
+            else:
+                return web.json_response({"error": "Checkpoint not found"}, status=404)
+                
+        except Exception as e:
+            logger.error(f"Error in get_checkpoint_info: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)
+    
+    async def get_civitai_versions_checkpoint(self, request: web.Request) -> web.Response:
+        """Get available versions for a Civitai checkpoint model with local availability info"""
+        try:
+            model_id = request.match_info['model_id']
+            response = await self.civitai_client.get_model_versions(model_id)
+            if not response or not response.get('modelVersions'):
+                return web.Response(status=404, text="Model not found")
+            
+            versions = response.get('modelVersions', [])
+            model_type = response.get('type', '')
+            
+            # Check model type - should be Checkpoint
+            if model_type.lower() != 'checkpoint':
+                return web.json_response({
+                    'error': f"Model type mismatch. Expected Checkpoint, got {model_type}"
+                }, status=400)
+            
+            # Check local availability for each version
+            for version in versions:
+                # Find the primary model file (type="Model" and primary=true) in the files list
+                model_file = next((file for file in version.get('files', []) 
+                                  if file.get('type') == 'Model' and file.get('primary') == True), None)
+                
+                # If no primary file found, try to find any model file
+                if not model_file:
+                    model_file = next((file for file in version.get('files', []) 
+                                      if file.get('type') == 'Model'), None)
+                
+                if model_file:
+                    sha256 = model_file.get('hashes', {}).get('SHA256')
+                    if sha256:
+                        # Set existsLocally and localPath at the version level
+                        version['existsLocally'] = self.service.has_hash(sha256)
+                        if version['existsLocally']:
+                            version['localPath'] = self.service.get_path_by_hash(sha256)
+                        
+                        # Also set the model file size at the version level for easier access
+                        version['modelSizeKB'] = model_file.get('sizeKB')
+                else:
+                    # No model file found in this version
+                    version['existsLocally'] = False
+                    
+            return web.json_response(versions)
+        except Exception as e:
+            logger.error(f"Error fetching checkpoint model versions: {e}")
+            return web.Response(status=500, text=str(e))

py/routes/checkpoints_routes.py

@@ -1,843 +0,0 @@
-import os
-import json
-import jinja2
-from aiohttp import web
-import logging
-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
-from ..services.settings_manager import settings
-from ..utils.utils import fuzzy_match
-
-logger = logging.getLogger(__name__)
-
-class CheckpointsRoutes:
-    """API routes for checkpoint management"""
-    
-    def __init__(self):
-        self.scanner = None  # Will be initialized in setup_routes
-        self.template_env = jinja2.Environment(
-            loader=jinja2.FileSystemLoader(config.templates_path),
-            autoescape=True
-        )
-        self.download_manager = None  # Will be initialized in setup_routes
-        self._download_lock = asyncio.Lock()
-        
-    async def initialize_services(self):
-        """Initialize services from ServiceRegistry"""
-        self.scanner = await ServiceRegistry.get_checkpoint_scanner()
-        self.download_manager = await ServiceRegistry.get_download_manager()
-        
-    def setup_routes(self, app):
-        """Register routes with the aiohttp app"""
-        # Schedule service initialization on app startup
-        app.on_startup.append(lambda _: self.initialize_services())
-        
-        app.router.add_get('/checkpoints', self.handle_checkpoints_page)
-        app.router.add_get('/api/checkpoints', self.get_checkpoints)
-        app.router.add_post('/api/checkpoints/fetch-all-civitai', self.fetch_all_civitai)
-        app.router.add_get('/api/checkpoints/base-models', self.get_base_models)
-        app.router.add_get('/api/checkpoints/top-tags', self.get_top_tags)
-        app.router.add_get('/api/checkpoints/scan', self.scan_checkpoints)
-        app.router.add_get('/api/checkpoints/info/{name}', self.get_checkpoint_info)
-        app.router.add_get('/api/checkpoints/roots', self.get_checkpoint_roots)
-        app.router.add_get('/api/checkpoints/civitai/versions/{model_id}', self.get_civitai_versions)  # Add new route
-        
-        # Add new routes for model management similar to LoRA routes
-        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
-        app.router.add_post('/api/checkpoints/rename', self.rename_checkpoint)  # Add new rename endpoint
-        
-        # Add new WebSocket endpoint for checkpoint progress
-        app.router.add_get('/ws/checkpoint-progress', ws_manager.handle_checkpoint_connection)
-
-        # Add new routes for finding duplicates and filename conflicts
-        app.router.add_get('/api/checkpoints/find-duplicates', self.find_duplicate_checkpoints)
-        app.router.add_get('/api/checkpoints/find-filename-conflicts', self.find_filename_conflicts)
-
-        # Add new endpoint for bulk deleting checkpoints
-        app.router.add_post('/api/checkpoints/bulk-delete', self.bulk_delete_checkpoints)
-
-        # Add new endpoint for verifying duplicates
-        app.router.add_post('/api/checkpoints/verify-duplicates', self.verify_duplicates)
-
-    async def get_checkpoints(self, request):
-        """Get paginated checkpoint data"""
-        try:
-            # Parse query parameters
-            page = int(request.query.get('page', '1'))
-            page_size = min(int(request.query.get('page_size', '20')), 100)
-            sort_by = request.query.get('sort_by', 'name')
-            folder = request.query.get('folder', None)
-            search = request.query.get('search', None)
-            fuzzy_search = request.query.get('fuzzy_search', 'false').lower() == 'true'
-            base_models = request.query.getall('base_model', [])
-            tags = request.query.getall('tag', [])
-            favorites_only = request.query.get('favorites_only', 'false').lower() == 'true'  # Add favorites_only parameter
-            
-            # Process search options
-            search_options = {
-                'filename': request.query.get('search_filename', 'true').lower() == 'true',
-                'modelname': request.query.get('search_modelname', 'true').lower() == 'true',
-                'tags': request.query.get('search_tags', 'false').lower() == 'true',
-                'recursive': request.query.get('recursive', 'false').lower() == 'true',
-            }
-            
-            # Process hash filters if provided
-            hash_filters = {}
-            if 'hash' in request.query:
-                hash_filters['single_hash'] = request.query['hash']
-            elif 'hashes' in request.query:
-                try:
-                    hash_list = json.loads(request.query['hashes'])
-                    if isinstance(hash_list, list):
-                        hash_filters['multiple_hashes'] = hash_list
-                except (json.JSONDecodeError, TypeError):
-                    pass
-            
-            # Get data from scanner
-            result = await self.get_paginated_data(
-                page=page,
-                page_size=page_size,
-                sort_by=sort_by,
-                folder=folder,
-                search=search,
-                fuzzy_search=fuzzy_search,
-                base_models=base_models,
-                tags=tags,
-                search_options=search_options,
-                hash_filters=hash_filters,
-                favorites_only=favorites_only  # Pass favorites_only parameter
-            )
-            
-            # Format response items
-            formatted_result = {
-                'items': [self._format_checkpoint_response(cp) for cp in result['items']],
-                'total': result['total'],
-                'page': result['page'],
-                'page_size': result['page_size'],
-                'total_pages': result['total_pages']
-            }
-            
-            # Return as JSON
-            return web.json_response(formatted_result)
-            
-        except Exception as e:
-            logger.error(f"Error in get_checkpoints: {e}", exc_info=True)
-            return web.json_response({"error": str(e)}, status=500)
-
-    async def get_paginated_data(self, page, page_size, sort_by='name', 
-                               folder=None, search=None, fuzzy_search=False,
-                               base_models=None, tags=None,
-                               search_options=None, hash_filters=None,
-                               favorites_only=False):  # Add favorites_only parameter with default False
-        """Get paginated and filtered checkpoint data"""
-        cache = await self.scanner.get_cached_data()
-
-        # Get default search options if not provided
-        if search_options is None:
-            search_options = {
-                'filename': True,
-                'modelname': True,
-                'tags': False,
-                'recursive': False,
-            }
-
-        # Get the base data set
-        filtered_data = cache.sorted_by_date if sort_by == 'date' else cache.sorted_by_name
-        
-        # Apply hash filtering if provided (highest priority)
-        if hash_filters:
-            single_hash = hash_filters.get('single_hash')
-            multiple_hashes = hash_filters.get('multiple_hashes')
-            
-            if single_hash:
-                # Filter by single hash
-                single_hash = single_hash.lower()  # Ensure lowercase for matching
-                filtered_data = [
-                    cp for cp in filtered_data
-                    if cp.get('sha256', '').lower() == single_hash
-                ]
-            elif multiple_hashes:
-                # Filter by multiple hashes
-                hash_set = set(hash.lower() for hash in multiple_hashes)  # Convert to set for faster lookup
-                filtered_data = [
-                    cp for cp in filtered_data
-                    if cp.get('sha256', '').lower() in hash_set
-                ]
-            
-            # Jump to pagination
-            total_items = len(filtered_data)
-            start_idx = (page - 1) * page_size
-            end_idx = min(start_idx + page_size, total_items)
-            
-            result = {
-                'items': filtered_data[start_idx:end_idx],
-                'total': total_items,
-                'page': page,
-                'page_size': page_size,
-                'total_pages': (total_items + page_size - 1) // page_size
-            }
-            
-            return result
-        
-        # Apply SFW filtering if enabled in settings
-        if settings.get('show_only_sfw', False):
-            filtered_data = [
-                cp for cp in filtered_data
-                if not cp.get('preview_nsfw_level') or cp.get('preview_nsfw_level') < NSFW_LEVELS['R']
-            ]
-        
-        # Apply favorites filtering if enabled
-        if favorites_only:
-            filtered_data = [
-                cp for cp in filtered_data
-                if cp.get('favorite', False) is True
-            ]
-        
-        # Apply folder filtering
-        if folder is not None:
-            if search_options.get('recursive', False):
-                # Recursive folder filtering - include all subfolders
-                filtered_data = [
-                    cp for cp in filtered_data
-                    if cp['folder'].startswith(folder)
-                ]
-            else:
-                # Exact folder filtering
-                filtered_data = [
-                    cp for cp in filtered_data
-                    if cp['folder'] == folder
-                ]
-        
-        # Apply base model filtering
-        if base_models and len(base_models) > 0:
-            filtered_data = [
-                cp for cp in filtered_data
-                if cp.get('base_model') in base_models
-            ]
-        
-        # Apply tag filtering
-        if tags and len(tags) > 0:
-            filtered_data = [
-                cp for cp in filtered_data
-                if any(tag in cp.get('tags', []) for tag in tags)
-            ]
-        
-        # Apply search filtering
-        if search:
-            search_results = []
-            
-            for cp in filtered_data:
-                # Search by file name
-                if search_options.get('filename', True):
-                    if fuzzy_search:
-                        if fuzzy_match(cp.get('file_name', ''), search):
-                            search_results.append(cp)
-                            continue
-                    elif search.lower() in cp.get('file_name', '').lower():
-                        search_results.append(cp)
-                        continue
-                
-                # Search by model name
-                if search_options.get('modelname', True):
-                    if fuzzy_search:
-                        if fuzzy_match(cp.get('model_name', ''), search):
-                            search_results.append(cp)
-                            continue
-                    elif search.lower() in cp.get('model_name', '').lower():
-                        search_results.append(cp)
-                        continue
-                
-                # Search by tags
-                if search_options.get('tags', False) and 'tags' in cp:
-                    if any((fuzzy_match(tag, search) if fuzzy_search else search.lower() in tag.lower()) for tag in cp['tags']):
-                        search_results.append(cp)
-                        continue
-            
-            filtered_data = search_results
-
-        # Calculate pagination
-        total_items = len(filtered_data)
-        start_idx = (page - 1) * page_size
-        end_idx = min(start_idx + page_size, total_items)
-        
-        result = {
-            'items': filtered_data[start_idx:end_idx],
-            'total': total_items,
-            'page': page,
-            'page_size': page_size,
-            'total_pages': (total_items + page_size - 1) // page_size
-        }
-        
-        return result
-
-    def _format_checkpoint_response(self, checkpoint):
-        """Format checkpoint data for API response"""
-        return {
-            "model_name": checkpoint["model_name"],
-            "file_name": checkpoint["file_name"],
-            "preview_url": config.get_preview_static_url(checkpoint.get("preview_url", "")),
-            "preview_nsfw_level": checkpoint.get("preview_nsfw_level", 0),
-            "base_model": checkpoint.get("base_model", ""),
-            "folder": checkpoint["folder"],
-            "sha256": checkpoint.get("sha256", ""),
-            "file_path": checkpoint["file_path"].replace(os.sep, "/"),
-            "file_size": checkpoint.get("size", 0),
-            "modified": checkpoint.get("modified", ""),
-            "tags": checkpoint.get("tags", []),
-            "modelDescription": checkpoint.get("modelDescription", ""),
-            "from_civitai": checkpoint.get("from_civitai", True),
-            "notes": checkpoint.get("notes", ""),
-            "model_type": checkpoint.get("model_type", "checkpoint"),
-            "favorite": checkpoint.get("favorite", False),
-            "civitai": ModelRouteUtils.filter_civitai_data(checkpoint.get("civitai", {}))
-        }
-    
-    async def fetch_all_civitai(self, request: web.Request) -> web.Response:
-        """Fetch CivitAI metadata for all checkpoints in the background"""
-        try:
-            cache = await self.scanner.get_cached_data()
-            total = len(cache.raw_data)
-            processed = 0
-            success = 0
-            needs_resort = False
-            
-            # Prepare checkpoints to process
-            to_process = [
-                cp for cp in cache.raw_data 
-                if cp.get('sha256') and (not cp.get('civitai') or 'id' not in cp.get('civitai')) and cp.get('from_civitai', True)
-            ]
-            total_to_process = len(to_process)
-            
-            # Send initial progress
-            await ws_manager.broadcast({
-                'status': 'started',
-                'total': total_to_process,
-                'processed': 0,
-                'success': 0
-            })
-            
-            # Process each checkpoint
-            for cp in to_process:
-                try:
-                    original_name = cp.get('model_name')
-                    if await ModelRouteUtils.fetch_and_update_model(
-                        sha256=cp['sha256'],
-                        file_path=cp['file_path'],
-                        model_data=cp,
-                        update_cache_func=self.scanner.update_single_model_cache
-                    ):
-                        success += 1
-                        if original_name != cp.get('model_name'):
-                            needs_resort = True
-                    
-                    processed += 1
-                    
-                    # Send progress update
-                    await ws_manager.broadcast({
-                        'status': 'processing',
-                        'total': total_to_process,
-                        'processed': processed,
-                        'success': success,
-                        'current_name': cp.get('model_name', 'Unknown')
-                    })
-                    
-                except Exception as e:
-                    logger.error(f"Error fetching CivitAI data for {cp['file_path']}: {e}")
-            
-            if needs_resort:
-                await cache.resort(name_only=True)
-            
-            # Send completion message
-            await ws_manager.broadcast({
-                'status': 'completed',
-                'total': total_to_process,
-                'processed': processed,
-                'success': success
-            })
-                    
-            return web.json_response({
-                "success": True,
-                "message": f"Successfully updated {success} of {processed} processed checkpoints (total: {total})"
-            })
-            
-        except Exception as e:
-            # Send error message
-            await ws_manager.broadcast({
-                'status': 'error',
-                'error': str(e)
-            })
-            logger.error(f"Error in fetch_all_civitai for checkpoints: {e}")
-            return web.Response(text=str(e), status=500)
-        
-    async def get_top_tags(self, request: web.Request) -> web.Response:
-        """Handle request for top tags sorted by frequency"""
-        try:
-            # Parse query parameters
-            limit = int(request.query.get('limit', '20'))
-            
-            # Validate limit
-            if limit < 1 or limit > 100:
-                limit = 20  # Default to a reasonable limit
-                
-            # Get top tags
-            top_tags = await self.scanner.get_top_tags(limit)
-            
-            return web.json_response({
-                'success': True,
-                'tags': top_tags
-            })
-            
-        except Exception as e:
-            logger.error(f"Error getting top tags: {str(e)}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': 'Internal server error'
-            }, status=500)
-        
-    async def get_base_models(self, request: web.Request) -> web.Response:
-        """Get base models used in loras"""
-        try:
-            # Parse query parameters
-            limit = int(request.query.get('limit', '20'))
-            
-            # Validate limit
-            if limit < 1 or limit > 100:
-                limit = 20  # Default to a reasonable limit
-                
-            # Get base models
-            base_models = await self.scanner.get_base_models(limit)
-            
-            return web.json_response({
-                'success': True,
-                'base_models': base_models
-            })
-        except Exception as e:
-            logger.error(f"Error retrieving base models: {e}")
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-
-    async def scan_checkpoints(self, request):
-        """Force a rescan of checkpoint files"""
-        try:
-            # Get the full_rebuild parameter and convert to bool, default to False
-            full_rebuild = request.query.get('full_rebuild', 'false').lower() == 'true'
-            
-            await self.scanner.get_cached_data(force_refresh=True, rebuild_cache=full_rebuild)
-            return web.json_response({"status": "success", "message": "Checkpoint scan completed"})
-        except Exception as e:
-            logger.error(f"Error in scan_checkpoints: {e}", exc_info=True)
-            return web.json_response({"error": str(e)}, status=500)
-
-    async def get_checkpoint_info(self, request):
-        """Get detailed information for a specific checkpoint by name"""
-        try:
-            name = request.match_info.get('name', '')
-            checkpoint_info = await self.scanner.get_model_info_by_name(name)
-            
-            if checkpoint_info:
-                return web.json_response(checkpoint_info)
-            else:
-                return web.json_response({"error": "Checkpoint not found"}, status=404)
-                
-        except Exception as e:
-            logger.error(f"Error in get_checkpoint_info: {e}", exc_info=True)
-            return web.json_response({"error": str(e)}, status=500)
-
-    async def handle_checkpoints_page(self, request: web.Request) -> web.Response:
-        """Handle GET /checkpoints request"""
-        try:
-            # Check if the CheckpointScanner is initializing
-            # 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)
-            )
-
-            if is_initializing:
-                # If still initializing, return loading page
-                template = self.template_env.get_template('checkpoints.html')
-                rendered = template.render(
-                    folders=[],  # 空文件夹列表
-                    is_initializing=True,  # 新增标志
-                    settings=settings,  # Pass settings to template
-                    request=request  # Pass the request object to the template
-                )
-                
-                logger.info("Checkpoints page is initializing, returning loading page")
-            else:
-                # 正常流程 - 获取已经初始化好的缓存数据
-                try:
-                    cache = await self.scanner.get_cached_data(force_refresh=False)
-                    template = self.template_env.get_template('checkpoints.html')
-                    rendered = template.render(
-                        folders=cache.folders,
-                        is_initializing=False,
-                        settings=settings,  # Pass settings to template
-                        request=request  # Pass the request object to the template
-                    )
-                except Exception as cache_error:
-                    logger.error(f"Error loading checkpoints cache data: {cache_error}")
-                    # 如果获取缓存失败，也显示初始化页面
-                    template = self.template_env.get_template('checkpoints.html')
-                    rendered = template.render(
-                        folders=[],
-                        is_initializing=True,
-                        settings=settings,
-                        request=request
-                    )
-                    logger.info("Checkpoints cache error, returning initialization page")
-            
-            return web.Response(
-                text=rendered,
-                content_type='text/html'
-            )
-        except Exception as e:
-            logger.error(f"Error handling checkpoints request: {e}", exc_info=True)
-            return web.Response(
-                text="Error loading checkpoints page",
-                status=500
-            )
-
-    async def delete_model(self, request: web.Request) -> web.Response:
-        """Handle checkpoint model deletion request"""
-        return await ModelRouteUtils.handle_delete_model(request, self.scanner)
-
-    async def exclude_model(self, request: web.Request) -> web.Response:
-        """Handle checkpoint model exclusion request"""
-        return await ModelRouteUtils.handle_exclude_model(request, self.scanner)
-    
-    async def fetch_civitai(self, request: web.Request) -> web.Response:
-        """Handle CivitAI metadata fetch request for checkpoints"""
-        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:
-            if self.scanner is None:
-                self.scanner = await ServiceRegistry.get_checkpoint_scanner()
-                
-            roots = self.scanner.get_model_roots()
-            return web.json_response({
-                "success": True,
-                "roots": roots
-            })
-        except Exception as e:
-            logger.error(f"Error getting checkpoint roots: {e}", exc_info=True)
-            return web.json_response({
-                "success": False,
-                "error": str(e)
-            }, status=500)
-
-    async def save_metadata(self, request: web.Request) -> web.Response:
-        """Handle saving metadata updates for checkpoints"""
-        try:
-            if self.scanner is None:
-                self.scanner = await ServiceRegistry.get_checkpoint_scanner()
-                
-            data = await request.json()
-            file_path = data.get('file_path')
-            if not file_path:
-                return web.Response(text='File path is required', status=400)
-
-            # Remove file path from data to avoid saving it
-            metadata_updates = {k: v for k, v in data.items() if k != 'file_path'}
-            
-            # Get metadata file path
-            metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
-            
-            # Load existing metadata
-            metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
-
-            # Update metadata
-            metadata.update(metadata_updates)
-
-            # Save updated metadata
-            await MetadataManager.save_metadata(file_path, metadata)
-
-            # Update cache
-            await self.scanner.update_single_model_cache(file_path, file_path, metadata)
-
-            # If model_name was updated, resort the cache
-            if 'model_name' in metadata_updates:
-                cache = await self.scanner.get_cached_data()
-                await cache.resort(name_only=True)
-
-            return web.json_response({'success': True})
-
-        except Exception as e:
-            logger.error(f"Error saving checkpoint metadata: {e}", exc_info=True)
-            return web.Response(text=str(e), status=500)
-
-    async def get_civitai_versions(self, request: web.Request) -> web.Response:
-        """Get available versions for a Civitai checkpoint model with local availability info"""
-        try:
-            if self.scanner is None:
-                self.scanner = await ServiceRegistry.get_checkpoint_scanner()
-                
-            # Get the civitai client from service registry
-            civitai_client = await ServiceRegistry.get_civitai_client()
-                
-            model_id = request.match_info['model_id']
-            response = await civitai_client.get_model_versions(model_id)
-            if not response or not response.get('modelVersions'):
-                return web.Response(status=404, text="Model not found")
-            
-            versions = response.get('modelVersions', [])
-            model_type = response.get('type', '')
-            
-            # Check model type - should be Checkpoint
-            if (model_type.lower() != 'checkpoint'):
-                return web.json_response({
-                    'error': f"Model type mismatch. Expected Checkpoint, got {model_type}"
-                }, status=400)
-            
-            # Check local availability for each version
-            for version in versions:
-                # Find the primary model file (type="Model" and primary=true) in the files list
-                model_file = next((file for file in version.get('files', []) 
-                                  if file.get('type') == 'Model' and file.get('primary') == True), None)
-                
-                # If no primary file found, try to find any model file
-                if not model_file:
-                    model_file = next((file for file in version.get('files', []) 
-                                      if file.get('type') == 'Model'), None)
-                
-                if model_file:
-                    sha256 = model_file.get('hashes', {}).get('SHA256')
-                    if sha256:
-                        # Set existsLocally and localPath at the version level
-                        version['existsLocally'] = self.scanner.has_hash(sha256)
-                        if version['existsLocally']:
-                            version['localPath'] = self.scanner.get_path_by_hash(sha256)
-                        
-                        # Also set the model file size at the version level for easier access
-                        version['modelSizeKB'] = model_file.get('sizeKB')
-                else:
-                    # No model file found in this version
-                    version['existsLocally'] = False
-                    
-            return web.json_response(versions)
-        except Exception as e:
-            logger.error(f"Error fetching checkpoint model versions: {e}")
-            return web.Response(status=500, text=str(e))
-
-    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/embedding_routes.py

@@ -0,0 +1,105 @@
+import logging
+from aiohttp import web
+
+from .base_model_routes import BaseModelRoutes
+from ..services.embedding_service import EmbeddingService
+from ..services.service_registry import ServiceRegistry
+
+logger = logging.getLogger(__name__)
+
+class EmbeddingRoutes(BaseModelRoutes):
+    """Embedding-specific route controller"""
+    
+    def __init__(self):
+        """Initialize Embedding routes with Embedding service"""
+        # Service will be initialized later via setup_routes
+        self.service = None
+        self.civitai_client = None
+        self.template_name = "embeddings.html"
+    
+    async def initialize_services(self):
+        """Initialize services from ServiceRegistry"""
+        embedding_scanner = await ServiceRegistry.get_embedding_scanner()
+        self.service = EmbeddingService(embedding_scanner)
+        self.civitai_client = await ServiceRegistry.get_civitai_client()
+        
+        # Initialize parent with the service
+        super().__init__(self.service)
+    
+    def setup_routes(self, app: web.Application):
+        """Setup Embedding routes"""
+        # Schedule service initialization on app startup
+        app.on_startup.append(lambda _: self.initialize_services())
+        
+        # Setup common routes with 'embeddings' prefix (includes page route)
+        super().setup_routes(app, 'embeddings')
+    
+    def setup_specific_routes(self, app: web.Application, prefix: str):
+        """Setup Embedding-specific routes"""
+        # Embedding-specific CivitAI integration
+        app.router.add_get(f'/api/{prefix}/civitai/versions/{{model_id}}', self.get_civitai_versions_embedding)
+        
+        # Embedding info by name
+        app.router.add_get(f'/api/{prefix}/info/{{name}}', self.get_embedding_info)
+    
+    async def get_embedding_info(self, request: web.Request) -> web.Response:
+        """Get detailed information for a specific embedding by name"""
+        try:
+            name = request.match_info.get('name', '')
+            embedding_info = await self.service.get_model_info_by_name(name)
+            
+            if embedding_info:
+                return web.json_response(embedding_info)
+            else:
+                return web.json_response({"error": "Embedding not found"}, status=404)
+                
+        except Exception as e:
+            logger.error(f"Error in get_embedding_info: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)
+    
+    async def get_civitai_versions_embedding(self, request: web.Request) -> web.Response:
+        """Get available versions for a Civitai embedding model with local availability info"""
+        try:
+            model_id = request.match_info['model_id']
+            response = await self.civitai_client.get_model_versions(model_id)
+            if not response or not response.get('modelVersions'):
+                return web.Response(status=404, text="Model not found")
+            
+            versions = response.get('modelVersions', [])
+            model_type = response.get('type', '')
+            
+            # Check model type - should be TextualInversion (Embedding)
+            if model_type.lower() not in ['textualinversion', 'embedding']:
+                return web.json_response({
+                    'error': f"Model type mismatch. Expected TextualInversion/Embedding, got {model_type}"
+                }, status=400)
+            
+            # Check local availability for each version
+            for version in versions:
+                # Find the primary model file (type="Model" and primary=true) in the files list
+                model_file = next((file for file in version.get('files', []) 
+                                  if file.get('type') == 'Model' and file.get('primary') == True), None)
+                
+                # If no primary file found, try to find any model file
+                if not model_file:
+                    model_file = next((file for file in version.get('files', []) 
+                                      if file.get('type') == 'Model'), None)
+                
+                if model_file:
+                    sha256 = model_file.get('hashes', {}).get('SHA256')
+                    if sha256:
+                        # Set existsLocally and localPath at the version level
+                        version['existsLocally'] = self.service.has_hash(sha256)
+                        if version['existsLocally']:
+                            version['localPath'] = self.service.get_path_by_hash(sha256)
+                        
+                        # Also set the model file size at the version level for easier access
+                        version['modelSizeKB'] = model_file.get('sizeKB')
+                else:
+                    # No model file found in this version
+                    version['existsLocally'] = False
+                    
+            return web.json_response(versions)
+        except Exception as e:
+            logger.error(f"Error fetching embedding model versions: {e}")
+            return web.Response(status=500, text=str(e))

py/routes/example_images_routes.py

@@ -1,7 +1,6 @@
 import logging
 from ..utils.example_images_download_manager import DownloadManager
 from ..utils.example_images_processor import ExampleImagesProcessor
-from ..utils.example_images_metadata import MetadataUpdater
 from ..utils.example_images_file_manager import ExampleImagesFileManager
 
 logger = logging.getLogger(__name__)

py/routes/lora_routes.py

@@ -1,188 +1,483 @@
-import os
-from aiohttp import web
-import jinja2
-from typing import Dict
+import asyncio
 import logging
-from ..config import config
-from ..services.settings_manager import settings
-from ..services.service_registry import ServiceRegistry  # Add ServiceRegistry import
+from aiohttp import web
+from typing import Dict
+from server import PromptServer  # type: ignore
+
+from .base_model_routes import BaseModelRoutes
+from ..services.lora_service import LoraService
+from ..services.service_registry import ServiceRegistry
+from ..utils.routes_common import ModelRouteUtils
+from ..utils.utils import get_lora_info
 
 logger = logging.getLogger(__name__)
-logging.getLogger('asyncio').setLevel(logging.CRITICAL)
 
-class LoraRoutes:
-    """Route handlers for LoRA management endpoints"""
+class LoraRoutes(BaseModelRoutes):
+    """LoRA-specific route controller"""
     
     def __init__(self):
-        # Initialize service references as None, will be set during async init
-        self.scanner = None
-        self.recipe_scanner = None
-        self.template_env = jinja2.Environment(
-            loader=jinja2.FileSystemLoader(config.templates_path),
-            autoescape=True
-        )
-
-    async def init_services(self):
-        """Initialize services from ServiceRegistry"""
-        self.scanner = await ServiceRegistry.get_lora_scanner()
-        self.recipe_scanner = await ServiceRegistry.get_recipe_scanner()
+        """Initialize LoRA routes with LoRA service"""
+        # Service will be initialized later via setup_routes
+        self.service = None
+        self.civitai_client = None
+        self.template_name = "loras.html"
+    
+    async def initialize_services(self):
+        """Initialize services from ServiceRegistry"""
+        lora_scanner = await ServiceRegistry.get_lora_scanner()
+        self.service = LoraService(lora_scanner)
+        self.civitai_client = await ServiceRegistry.get_civitai_client()
+        
+        # Initialize parent with the service
+        super().__init__(self.service)
     
-    def format_lora_data(self, lora: Dict) -> Dict:
-        """Format LoRA data for template rendering"""
-        return {
-            "model_name": lora["model_name"],
-            "file_name": lora["file_name"],   
-            "preview_url": config.get_preview_static_url(lora["preview_url"]),
-            "preview_nsfw_level": lora.get("preview_nsfw_level", 0),
-            "base_model": lora["base_model"],
-            "folder": lora["folder"],
-            "sha256": lora["sha256"],
-            "file_path": lora["file_path"].replace(os.sep, "/"),
-            "size": lora["size"],
-            "tags": lora["tags"],
-            "modelDescription": lora["modelDescription"],
-            "usage_tips": lora["usage_tips"],
-            "notes": lora["notes"],
-            "modified": lora["modified"],
-            "from_civitai": lora.get("from_civitai", True),
-            "civitai": self._filter_civitai_data(lora.get("civitai", {}))
-        }
-
-    def _filter_civitai_data(self, data: Dict) -> Dict:
-        """Filter relevant fields from CivitAI data"""
-        if not data:
-            return {}
-            
-        fields = [
-            "id", "modelId", "name", "createdAt", "updatedAt", 
-            "publishedAt", "trainedWords", "baseModel", "description",
-            "model", "images"
-        ]
-        return {k: data[k] for k in fields if k in data}
-
-    async def handle_loras_page(self, request: web.Request) -> web.Response:
-        """Handle GET /loras request"""
-        try:
-            # Ensure services are initialized
-            await self.init_services()
-            
-            # Check if the LoraScanner is initializing
-            # 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 self.scanner.is_initializing()
-            )
-
-            if is_initializing:
-                # If still initializing, return loading page
-                template = self.template_env.get_template('loras.html')
-                rendered = template.render(
-                    folders=[],
-                    is_initializing=True,
-                    settings=settings,
-                    request=request
-                )
-                
-                logger.info("Loras page is initializing, returning loading page")
-            else:
-                # Normal flow - get data from initialized cache
-                try:
-                    cache = await self.scanner.get_cached_data(force_refresh=False)
-                    template = self.template_env.get_template('loras.html')
-                    rendered = template.render(
-                        folders=cache.folders,
-                        is_initializing=False,
-                        settings=settings,
-                        request=request
-                    )
-                except Exception as cache_error:
-                    logger.error(f"Error loading cache data: {cache_error}")
-                    template = self.template_env.get_template('loras.html')
-                    rendered = template.render(
-                        folders=[],
-                        is_initializing=True,
-                        settings=settings,
-                        request=request
-                    )
-                    logger.info("Cache error, returning initialization page")
-            
-            return web.Response(
-                text=rendered,
-                content_type='text/html'
-            )
-            
-        except Exception as e:
-            logger.error(f"Error handling loras request: {e}", exc_info=True)
-            return web.Response(
-                text="Error loading loras page",
-                status=500
-            )
-
-    async def handle_recipes_page(self, request: web.Request) -> web.Response:
-        """Handle GET /loras/recipes request"""
-        try:
-            # Ensure services are initialized
-            await self.init_services()
-            
-            # Skip initialization check and directly try to get cached data
-            try:
-                # Recipe scanner will initialize cache if needed
-                await self.recipe_scanner.get_cached_data(force_refresh=False)
-                template = self.template_env.get_template('recipes.html')
-                rendered = template.render(
-                    recipes=[],  # Frontend will load recipes via API
-                    is_initializing=False,
-                    settings=settings,
-                    request=request
-                )
-            except Exception as cache_error:
-                logger.error(f"Error loading recipe cache data: {cache_error}")
-                # Still keep error handling - show initializing page on error
-                template = self.template_env.get_template('recipes.html')
-                rendered = template.render(
-                    is_initializing=True,
-                    settings=settings,
-                    request=request
-                )
-                logger.info("Recipe cache error, returning initialization page")
-            
-            return web.Response(
-                text=rendered,
-                content_type='text/html'
-            )
-            
-        except Exception as e:
-            logger.error(f"Error handling recipes request: {e}", exc_info=True)
-            return web.Response(
-                text="Error loading recipes page",
-                status=500
-            )
-
-    def _format_recipe_file_url(self, file_path: str) -> str:
-        """Format file path for recipe image as a URL - same as in recipe_routes"""
-        try:
-            # Return the file URL directly for the first lora root's preview
-            recipes_dir = os.path.join(config.loras_roots[0], "recipes").replace(os.sep, '/')
-            if file_path.replace(os.sep, '/').startswith(recipes_dir):
-                relative_path = os.path.relpath(file_path, config.loras_roots[0]).replace(os.sep, '/')
-                return f"/loras_static/root1/preview/{relative_path}" 
-            
-            # If not in recipes dir, try to create a valid URL from the file path
-            file_name = os.path.basename(file_path)
-            return f"/loras_static/root1/preview/recipes/{file_name}"
-        except Exception as e:
-            logger.error(f"Error formatting recipe file URL: {e}", exc_info=True)
-            return '/loras_static/images/no-preview.png'  # Return default image on error
-
     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)
+        """Setup LoRA routes"""
+        # Schedule service initialization on app startup
+        app.on_startup.append(lambda _: self.initialize_services())
         
-        # Register routes
-        app.router.add_get('/loras', self.handle_loras_page)
-        app.router.add_get('/loras/recipes', self.handle_recipes_page)
+        # Setup common routes with 'loras' prefix (includes page route)
+        super().setup_routes(app, 'loras')
+    
+    def setup_specific_routes(self, app: web.Application, prefix: str):
+        """Setup LoRA-specific routes"""
+        # LoRA-specific query routes
+        app.router.add_get(f'/api/{prefix}/letter-counts', self.get_letter_counts)
+        app.router.add_get(f'/api/{prefix}/get-notes', self.get_lora_notes)
+        app.router.add_get(f'/api/{prefix}/get-trigger-words', self.get_lora_trigger_words)
+        app.router.add_get(f'/api/{prefix}/preview-url', self.get_lora_preview_url)
+        app.router.add_get(f'/api/{prefix}/civitai-url', self.get_lora_civitai_url)
+        app.router.add_get(f'/api/{prefix}/model-description', self.get_lora_model_description)
         
-    async def _on_startup(self, app):
-        """Initialize services when the app starts"""
-        await self.init_services()
+        # LoRA-specific management routes
+        app.router.add_post(f'/api/{prefix}/move_model', self.move_model)
+        app.router.add_post(f'/api/{prefix}/move_models_bulk', self.move_models_bulk)
+        
+        # CivitAI integration with LoRA-specific validation
+        app.router.add_get(f'/api/{prefix}/civitai/versions/{{model_id}}', self.get_civitai_versions_lora)
+        app.router.add_get(f'/api/{prefix}/civitai/model/version/{{modelVersionId}}', self.get_civitai_model_by_version)
+        app.router.add_get(f'/api/{prefix}/civitai/model/hash/{{hash}}', self.get_civitai_model_by_hash)
+        
+        # ComfyUI integration
+        app.router.add_post(f'/api/{prefix}/get_trigger_words', self.get_trigger_words)
+    
+    def _parse_specific_params(self, request: web.Request) -> Dict:
+        """Parse LoRA-specific parameters"""
+        params = {}
+        
+        # LoRA-specific parameters
+        if 'first_letter' in request.query:
+            params['first_letter'] = request.query.get('first_letter')
+        
+        # Handle fuzzy search parameter name variation
+        if request.query.get('fuzzy') == 'true':
+            params['fuzzy_search'] = True
+        
+        # Handle additional filter parameters for LoRAs
+        if 'lora_hash' in request.query:
+            if not params.get('hash_filters'):
+                params['hash_filters'] = {}
+            params['hash_filters']['single_hash'] = request.query['lora_hash'].lower()
+        elif 'lora_hashes' in request.query:
+            if not params.get('hash_filters'):
+                params['hash_filters'] = {}
+            params['hash_filters']['multiple_hashes'] = [h.lower() for h in request.query['lora_hashes'].split(',')]
+        
+        return params
+    
+    # LoRA-specific route handlers
+    async def get_letter_counts(self, request: web.Request) -> web.Response:
+        """Get count of LoRAs for each letter of the alphabet"""
+        try:
+            letter_counts = await self.service.get_letter_counts()
+            return web.json_response({
+                'success': True,
+                'letter_counts': letter_counts
+            })
+        except Exception as e:
+            logger.error(f"Error getting letter counts: {e}")
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_lora_notes(self, request: web.Request) -> web.Response:
+        """Get notes for a specific LoRA file"""
+        try:
+            lora_name = request.query.get('name')
+            if not lora_name:
+                return web.Response(text='Lora file name is required', status=400)
+            
+            notes = await self.service.get_lora_notes(lora_name)
+            if notes is not None:
+                return web.json_response({
+                    'success': True,
+                    'notes': notes
+                })
+            else:
+                return web.json_response({
+                    'success': False,
+                    'error': 'LoRA not found in cache'
+                }, status=404)
+                
+        except Exception as e:
+            logger.error(f"Error getting lora notes: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_lora_trigger_words(self, request: web.Request) -> web.Response:
+        """Get trigger words for a specific LoRA file"""
+        try:
+            lora_name = request.query.get('name')
+            if not lora_name:
+                return web.Response(text='Lora file name is required', status=400)
+            
+            trigger_words = await self.service.get_lora_trigger_words(lora_name)
+            return web.json_response({
+                'success': True,
+                'trigger_words': trigger_words
+            })
+            
+        except Exception as e:
+            logger.error(f"Error getting lora trigger words: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_lora_preview_url(self, request: web.Request) -> web.Response:
+        """Get the static preview URL for a LoRA file"""
+        try:
+            lora_name = request.query.get('name')
+            if not lora_name:
+                return web.Response(text='Lora file name is required', status=400)
+            
+            preview_url = await self.service.get_lora_preview_url(lora_name)
+            if preview_url:
+                return web.json_response({
+                    'success': True,
+                    'preview_url': preview_url
+                })
+            else:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No preview URL found for the specified lora'
+                }, status=404)
+                
+        except Exception as e:
+            logger.error(f"Error getting lora preview URL: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_lora_civitai_url(self, request: web.Request) -> web.Response:
+        """Get the Civitai URL for a LoRA file"""
+        try:
+            lora_name = request.query.get('name')
+            if not lora_name:
+                return web.Response(text='Lora file name is required', status=400)
+            
+            result = await self.service.get_lora_civitai_url(lora_name)
+            if result['civitai_url']:
+                return web.json_response({
+                    'success': True,
+                    **result
+                })
+            else:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No Civitai data found for the specified lora'
+                }, status=404)
+                
+        except Exception as e:
+            logger.error(f"Error getting lora Civitai URL: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    # CivitAI integration methods
+    async def get_civitai_versions_lora(self, request: web.Request) -> web.Response:
+        """Get available versions for a Civitai LoRA model with local availability info"""
+        try:
+            model_id = request.match_info['model_id']
+            response = await self.civitai_client.get_model_versions(model_id)
+            if not response or not response.get('modelVersions'):
+                return web.Response(status=404, text="Model not found")
+            
+            versions = response.get('modelVersions', [])
+            model_type = response.get('type', '')
+            
+            # Check model type - should be LORA, LoCon, or DORA
+            from ..utils.constants import VALID_LORA_TYPES
+            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)
+            
+            # Check local availability for each version
+            for version in versions:
+                # Find the model file (type="Model") in the files list
+                model_file = next((file for file in version.get('files', []) 
+                                  if file.get('type') == 'Model'), None)
+                
+                if model_file:
+                    sha256 = model_file.get('hashes', {}).get('SHA256')
+                    if sha256:
+                        # Set existsLocally and localPath at the version level
+                        version['existsLocally'] = self.service.has_hash(sha256)
+                        if version['existsLocally']:
+                            version['localPath'] = self.service.get_path_by_hash(sha256)
+                        
+                        # Also set the model file size at the version level for easier access
+                        version['modelSizeKB'] = model_file.get('sizeKB')
+                else:
+                    # No model file found in this version
+                    version['existsLocally'] = False
+                    
+            return web.json_response(versions)
+        except Exception as e:
+            logger.error(f"Error fetching LoRA model versions: {e}")
+            return web.Response(status=500, text=str(e))
+    
+    async def get_civitai_model_by_version(self, request: web.Request) -> web.Response:
+        """Get CivitAI model details by model version ID"""
+        try:
+            model_version_id = request.match_info.get('modelVersionId')
+            
+            # Get model details from Civitai API    
+            model, error_msg = await self.civitai_client.get_model_version_info(model_version_id)
+            
+            if not model:
+                # Log warning for failed model retrieval
+                logger.warning(f"Failed to fetch model version {model_version_id}: {error_msg}")
+                
+                # Determine status code based on error message
+                status_code = 404 if error_msg and "not found" in error_msg.lower() else 500
+                
+                return web.json_response({
+                    "success": False,
+                    "error": error_msg or "Failed to fetch model information"
+                }, status=status_code)
+                
+            return web.json_response(model)
+        except Exception as e:
+            logger.error(f"Error fetching model details: {e}")
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)
+    
+    async def get_civitai_model_by_hash(self, request: web.Request) -> web.Response:
+        """Get CivitAI model details by hash"""
+        try:
+            hash = request.match_info.get('hash')
+            model = await self.civitai_client.get_model_by_hash(hash)
+            return web.json_response(model)
+        except Exception as e:
+            logger.error(f"Error fetching model details by hash: {e}")
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)
+    
+    # Model management methods
+    async def move_model(self, request: web.Request) -> web.Response:
+        """Handle model move request"""
+        try:
+            data = await request.json()
+            file_path = data.get('file_path')  # full path of the model file
+            target_path = data.get('target_path')  # folder path to move the model to
+            
+            if not file_path or not target_path:
+                return web.Response(text='File path and target path are required', status=400)
+
+            # Check if source and destination are the same
+            import os
+            source_dir = os.path.dirname(file_path)
+            if os.path.normpath(source_dir) == os.path.normpath(target_path):
+                logger.info(f"Source and target directories are the same: {source_dir}")
+                return web.json_response({'success': True, 'message': 'Source and target directories are the same'})
+
+            # Check if target file already exists
+            file_name = os.path.basename(file_path)
+            target_file_path = os.path.join(target_path, file_name).replace(os.sep, '/')
+            
+            if os.path.exists(target_file_path):
+                return web.json_response({
+                    'success': False, 
+                    'error': f"Target file already exists: {target_file_path}"
+                }, status=409)  # 409 Conflict
+
+            # Call scanner to handle the move operation
+            success = await self.service.scanner.move_model(file_path, target_path)
+            
+            if success:
+                return web.json_response({'success': True, 'new_file_path': target_file_path})
+            else:
+                return web.Response(text='Failed to move model', status=500)
+                
+        except Exception as e:
+            logger.error(f"Error moving model: {e}", exc_info=True)
+            return web.Response(text=str(e), status=500)
+    
+    async def move_models_bulk(self, request: web.Request) -> web.Response:
+        """Handle bulk model move request"""
+        try:
+            data = await request.json()
+            file_paths = data.get('file_paths', [])  # list of full paths of the model files
+            target_path = data.get('target_path')  # folder path to move the models to
+            
+            if not file_paths or not target_path:
+                return web.Response(text='File paths and target path are required', status=400)
+
+            results = []
+            import os
+            for file_path in file_paths:
+                # Check if source and destination are the same
+                source_dir = os.path.dirname(file_path)
+                if os.path.normpath(source_dir) == os.path.normpath(target_path):
+                    results.append({
+                        "path": file_path, 
+                        "success": True, 
+                        "message": "Source and target directories are the same"
+                    })
+                    continue
+                
+                # Check if target file already exists
+                file_name = os.path.basename(file_path)
+                target_file_path = os.path.join(target_path, file_name).replace(os.sep, '/')
+                
+                if os.path.exists(target_file_path):
+                    results.append({
+                        "path": file_path, 
+                        "success": False, 
+                        "message": f"Target file already exists: {target_file_path}"
+                    })
+                    continue
+                
+                # Try to move the model
+                success = await self.service.scanner.move_model(file_path, target_path)
+                results.append({
+                    "path": file_path, 
+                    "success": success,
+                    "message": "Success" if success else "Failed to move model"
+                })
+            
+            # Count successes and failures
+            success_count = sum(1 for r in results if r["success"])
+            failure_count = len(results) - success_count
+            
+            return web.json_response({
+                'success': True,
+                'message': f'Moved {success_count} of {len(file_paths)} models',
+                'results': results,
+                'success_count': success_count,
+                'failure_count': failure_count
+            })
+                
+        except Exception as e:
+            logger.error(f"Error moving models in bulk: {e}", exc_info=True)
+            return web.Response(text=str(e), status=500)
+    
+    async def get_lora_model_description(self, request: web.Request) -> web.Response:
+        """Get model description for a Lora model"""
+        try:
+            # Get parameters
+            model_id = request.query.get('model_id')
+            file_path = request.query.get('file_path')
+            
+            if not model_id:
+                return web.json_response({
+                    'success': False, 
+                    'error': 'Model ID is required'
+                }, status=400)
+            
+            # Check if we already have the description stored in metadata
+            description = None
+            tags = []
+            creator = {}
+            if file_path:
+                import os
+                from ..utils.metadata_manager import MetadataManager
+                metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+                metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
+                description = metadata.get('modelDescription')
+                tags = metadata.get('tags', [])
+                creator = metadata.get('creator', {})
+            
+            # If description is not in metadata, fetch from CivitAI
+            if not description:
+                logger.info(f"Fetching model metadata for model ID: {model_id}")
+                model_metadata, _ = await self.civitai_client.get_model_metadata(model_id)
+                
+                if model_metadata:
+                    description = model_metadata.get('description')
+                    tags = model_metadata.get('tags', [])
+                    creator = model_metadata.get('creator', {})
+                
+                    # Save the metadata to file if we have a file path and got metadata
+                    if file_path:
+                        try:
+                            metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+                            metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
+                            
+                            metadata['modelDescription'] = description
+                            metadata['tags'] = tags
+                            # Ensure the civitai dict exists
+                            if 'civitai' not in metadata:
+                                metadata['civitai'] = {}
+                            # Store creator in the civitai nested structure
+                            metadata['civitai']['creator'] = creator
+                            
+                            await MetadataManager.save_metadata(file_path, metadata, True)
+                        except Exception as e:
+                            logger.error(f"Error saving model metadata: {e}")
+            
+            return web.json_response({
+                'success': True,
+                'description': description or "<p>No model description available.</p>",
+                'tags': tags,
+                'creator': creator
+            })
+            
+        except Exception as e:
+            logger.error(f"Error getting model metadata: {e}")
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_trigger_words(self, request: web.Request) -> web.Response:
+        """Get trigger words for specified LoRA models"""
+        try:
+            json_data = await request.json()
+            lora_names = json_data.get("lora_names", [])
+            node_ids = json_data.get("node_ids", [])
+            
+            all_trigger_words = []
+            for lora_name in lora_names:
+                _, trigger_words = get_lora_info(lora_name)
+                all_trigger_words.extend(trigger_words)
+            
+            # Format the trigger words
+            trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+            
+            # Send update to all connected trigger word toggle nodes
+            for node_id in node_ids:
+                PromptServer.instance.send_sync("trigger_word_update", {
+                    "id": node_id,
+                    "message": trigger_words_text
+                })
+            
+            return web.json_response({"success": True})
+
+        except Exception as e:
+            logger.error(f"Error getting trigger words: {e}")
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)

py/routes/misc_routes.py

@@ -1,3 +1,4 @@
+import json
 import logging
 import os
 import sys
@@ -10,6 +11,7 @@ from ..utils.usage_stats import UsageStats
 from ..utils.lora_metadata import extract_trained_words
 from ..config import config
 from ..utils.constants import SUPPORTED_MEDIA_EXTENSIONS, NODE_TYPES, DEFAULT_NODE_COLOR
+from ..services.service_registry import ServiceRegistry
 import re
 
 logger = logging.getLogger(__name__)
@@ -90,6 +92,8 @@ class MiscRoutes:
         # Add new route for clearing cache
         app.router.add_post('/api/clear-cache', MiscRoutes.clear_cache)
 
+        app.router.add_get('/api/health-check', lambda request: web.json_response({'status': 'ok'}))
+
         # Usage stats routes
         app.router.add_post('/api/update-usage-stats', MiscRoutes.update_usage_stats)
         app.router.add_get('/api/get-usage-stats', MiscRoutes.get_usage_stats)
@@ -106,6 +110,9 @@ class MiscRoutes:
         # Node registry endpoints
         app.router.add_post('/api/register-nodes', MiscRoutes.register_nodes)
         app.router.add_get('/api/get-registry', MiscRoutes.get_registry)
+        
+        # Add new route for checking if a model exists in the library
+        app.router.add_get('/api/check-model-exists', MiscRoutes.check_model_exists)
 
     @staticmethod
     async def clear_cache(request):
@@ -160,6 +167,9 @@ class MiscRoutes:
             
             # Validate and update settings
             for key, value in data.items():
+                if value == settings.get(key):
+                    # No change, skip
+                    continue
                 # Special handling for example_images_path - verify path exists
                 if key == 'example_images_path' and value:
                     if not os.path.exists(value):
@@ -173,6 +183,16 @@ class MiscRoutes:
                     if old_path != value:
                         logger.info(f"Example images path changed to {value} - server restart required")
                 
+                # Special handling for base_model_path_mappings - parse JSON string
+                if key == 'base_model_path_mappings' and value:
+                    try:
+                        value = json.loads(value)
+                    except json.JSONDecodeError:
+                        return web.json_response({
+                            'success': False,
+                            'error': f"Invalid JSON format for base_model_path_mappings: {value}"
+                        })
+                
                 # Save to settings
                 settings.set(key, value)
             
@@ -580,3 +600,111 @@ class MiscRoutes:
                 'error': 'Internal Error',
                 'message': str(e)
             }, status=500)
+
+    @staticmethod
+    async def check_model_exists(request):
+        """
+        Check if a model with specified modelId and optionally modelVersionId exists in the library
+        
+        Expects query parameters:
+        - modelId: int - Civitai model ID (required)
+        - modelVersionId: int - Civitai model version ID (optional)
+        
+        Returns:
+        - If modelVersionId is provided: JSON with a boolean 'exists' field
+        - If modelVersionId is not provided: JSON with a list of modelVersionIds that exist in the library
+        """
+        try:
+            # Get the modelId and modelVersionId from query parameters
+            model_id_str = request.query.get('modelId')
+            model_version_id_str = request.query.get('modelVersionId')
+            
+            # Validate modelId parameter (required)
+            if not model_id_str:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing required parameter: modelId'
+                }, status=400)
+                
+            try:
+                # Convert modelId to integer
+                model_id = int(model_id_str)
+            except ValueError:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Parameter modelId must be an integer'
+                }, status=400)
+            
+            # Get all scanners
+            lora_scanner = await ServiceRegistry.get_lora_scanner()
+            checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+            embedding_scanner = await ServiceRegistry.get_embedding_scanner()
+            
+            # If modelVersionId is provided, check for specific version
+            if model_version_id_str:
+                try:
+                    model_version_id = int(model_version_id_str)
+                except ValueError:
+                    return web.json_response({
+                        'success': False,
+                        'error': 'Parameter modelVersionId must be an integer'
+                    }, status=400)
+                
+                # Check lora scanner first
+                exists = False
+                model_type = None
+
+                if await lora_scanner.check_model_version_exists(model_id, model_version_id):
+                    exists = True
+                    model_type = 'lora'
+                elif checkpoint_scanner and await checkpoint_scanner.check_model_version_exists(model_id, model_version_id):
+                    exists = True
+                    model_type = 'checkpoint'
+                elif embedding_scanner and await embedding_scanner.check_model_version_exists(model_id, model_version_id):
+                    exists = True
+                    model_type = 'embedding'
+                
+                return web.json_response({
+                    'success': True,
+                    'exists': exists,
+                    'modelType': model_type if exists else None
+                })
+            
+            # If modelVersionId is not provided, return all version IDs for the model
+            else:
+                lora_versions = await lora_scanner.get_model_versions_by_id(model_id)
+                checkpoint_versions = []
+                embedding_versions = []
+
+                # 优先lora，其次checkpoint，最后embedding
+                if not lora_versions:
+                    checkpoint_versions = await checkpoint_scanner.get_model_versions_by_id(model_id)
+                if not lora_versions and not checkpoint_versions:
+                    embedding_versions = await embedding_scanner.get_model_versions_by_id(model_id)
+
+                model_type = None
+                versions = []
+
+                if lora_versions:
+                    model_type = 'lora'
+                    versions = lora_versions
+                elif checkpoint_versions:
+                    model_type = 'checkpoint'
+                    versions = checkpoint_versions
+                elif embedding_versions:
+                    model_type = 'embedding'
+                    versions = embedding_versions
+
+                return web.json_response({
+                    'success': True,
+                    'modelId': model_id,
+                    'modelType': model_type,
+                    'versions': versions
+                })
+            
+        except Exception as e:
+            logger.error(f"Failed to check model existence: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/routes/recipe_routes.py

@@ -1,9 +1,9 @@
 import os
 import time
 import base64
+import jinja2
 import numpy as np
 from PIL import Image
-import torch
 import io
 import logging
 from aiohttp import web
@@ -16,6 +16,7 @@ from ..utils.exif_utils import ExifUtils
 from ..recipes import RecipeParserFactory
 from ..utils.constants import CARD_PREVIEW_WIDTH
 
+from ..services.settings_manager import settings
 from ..config import config
 
 # Check if running in standalone mode
@@ -40,7 +41,10 @@ class RecipeRoutes:
         # Initialize service references as None, will be set during async init
         self.recipe_scanner = None
         self.civitai_client = None
-        # Remove WorkflowParser instance
+        self.template_env = jinja2.Environment(
+            loader=jinja2.FileSystemLoader(config.templates_path),
+            autoescape=True
+        )
         
         # Pre-warm the cache
         self._init_cache_task = None
@@ -54,6 +58,8 @@ class RecipeRoutes:
     def setup_routes(cls, app: web.Application):
         """Register API routes"""
         routes = cls()
+        app.router.add_get('/loras/recipes', routes.handle_recipes_page)
+
         app.router.add_get('/api/recipes', routes.get_recipes)
         app.router.add_get('/api/recipe/{recipe_id}', routes.get_recipe_detail)
         app.router.add_post('/api/recipes/analyze-image', routes.analyze_recipe_image)
@@ -115,6 +121,46 @@ class RecipeRoutes:
             await self.recipe_scanner.get_cached_data(force_refresh=True)
         except Exception as e:
             logger.error(f"Error pre-warming recipe cache: {e}", exc_info=True)
+
+    async def handle_recipes_page(self, request: web.Request) -> web.Response:
+        """Handle GET /loras/recipes request"""
+        try:
+            # Ensure services are initialized
+            await self.init_services()
+            
+            # Skip initialization check and directly try to get cached data
+            try:
+                # Recipe scanner will initialize cache if needed
+                await self.recipe_scanner.get_cached_data(force_refresh=False)
+                template = self.template_env.get_template('recipes.html')
+                rendered = template.render(
+                    recipes=[],  # Frontend will load recipes via API
+                    is_initializing=False,
+                    settings=settings,
+                    request=request
+                )
+            except Exception as cache_error:
+                logger.error(f"Error loading recipe cache data: {cache_error}")
+                # Still keep error handling - show initializing page on error
+                template = self.template_env.get_template('recipes.html')
+                rendered = template.render(
+                    is_initializing=True,
+                    settings=settings,
+                    request=request
+                )
+                logger.info("Recipe cache error, returning initialization page")
+            
+            return web.Response(
+                text=rendered,
+                content_type='text/html'
+            )
+            
+        except Exception as e:
+            logger.error(f"Error handling recipes request: {e}", exc_info=True)
+            return web.Response(
+                text="Error loading recipes page",
+                status=500
+            )
     
     async def get_recipes(self, request: web.Request) -> web.Response:
         """API endpoint for getting paginated recipes"""
@@ -1018,6 +1064,8 @@ 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()
@@ -1100,7 +1148,7 @@ class RecipeRoutes:
             for lora_name, lora_strength in lora_matches:
                 try:
                     # Get lora info from scanner
-                    lora_info = await self.recipe_scanner._lora_scanner.get_lora_info_by_name(lora_name)
+                    lora_info = await self.recipe_scanner._lora_scanner.get_model_info_by_name(lora_name)
                     
                     # Create lora entry
                     lora_entry = {
@@ -1119,7 +1167,7 @@ class RecipeRoutes:
             # Get base model from lora scanner for the available loras
             base_model_counts = {}
             for lora in loras_data:
-                lora_info = await self.recipe_scanner._lora_scanner.get_lora_info_by_name(lora.get("file_name", ""))
+                lora_info = await self.recipe_scanner._lora_scanner.get_model_info_by_name(lora.get("file_name", ""))
                 if lora_info and "base_model" in lora_info:
                     base_model = lora_info["base_model"]
                     base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
@@ -1209,7 +1257,7 @@ class RecipeRoutes:
                 if lora.get("isDeleted", False):
                     continue
 
-                if not self.recipe_scanner._lora_scanner.has_lora_hash(lora.get("hash", "")):
+                if not self.recipe_scanner._lora_scanner.has_hash(lora.get("hash", "")):
                     continue
                 
                 # Get the strength
@@ -1317,7 +1365,7 @@ class RecipeRoutes:
                 return web.json_response({"error": "Recipe not found"}, status=404)
                 
             # Find target LoRA by name
-            target_lora = await lora_scanner.get_lora_info_by_name(target_name)
+            target_lora = await lora_scanner.get_model_info_by_name(target_name)
             if not target_lora:
                 return web.json_response({"error": f"Local LoRA not found with name: {target_name}"}, status=404)
                 
@@ -1429,9 +1477,9 @@ class RecipeRoutes:
                 if 'loras' in recipe:
                     for lora in recipe['loras']:
                         if 'hash' in lora and lora['hash']:
-                            lora['inLibrary'] = self.recipe_scanner._lora_scanner.has_lora_hash(lora['hash'].lower())
+                            lora['inLibrary'] = self.recipe_scanner._lora_scanner.has_hash(lora['hash'].lower())
                             lora['preview_url'] = self.recipe_scanner._lora_scanner.get_preview_url_by_hash(lora['hash'].lower())
-                            lora['localPath'] = self.recipe_scanner._lora_scanner.get_lora_path_by_hash(lora['hash'].lower())
+                            lora['localPath'] = self.recipe_scanner._lora_scanner.get_path_by_hash(lora['hash'].lower())
                 
                 # Ensure file_url is set (needed by frontend)
                 if 'file_path' in recipe:

py/routes/stats_routes.py

@@ -20,6 +20,7 @@ class StatsRoutes:
     def __init__(self):
         self.lora_scanner = None
         self.checkpoint_scanner = None
+        self.embedding_scanner = None
         self.usage_stats = None
         self.template_env = jinja2.Environment(
             loader=jinja2.FileSystemLoader(config.templates_path),
@@ -30,6 +31,7 @@ class StatsRoutes:
         """Initialize services from ServiceRegistry"""
         self.lora_scanner = await ServiceRegistry.get_lora_scanner()
         self.checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+        self.embedding_scanner = await ServiceRegistry.get_embedding_scanner()
         self.usage_stats = UsageStats()
 
     async def handle_stats_page(self, request: web.Request) -> web.Response:
@@ -49,7 +51,12 @@ class StatsRoutes:
                 (hasattr(self.checkpoint_scanner, '_is_initializing') and self.checkpoint_scanner._is_initializing)
             )
             
-            is_initializing = lora_initializing or checkpoint_initializing
+            embedding_initializing = (
+                self.embedding_scanner._cache is None or
+                (hasattr(self.embedding_scanner, 'is_initializing') and self.embedding_scanner.is_initializing())
+            )
+            
+            is_initializing = lora_initializing or checkpoint_initializing or embedding_initializing
 
             template = self.template_env.get_template('statistics.html')
             rendered = template.render(
@@ -85,21 +92,29 @@ class StatsRoutes:
             checkpoint_count = len(checkpoint_cache.raw_data)
             checkpoint_size = sum(cp.get('size', 0) for cp in checkpoint_cache.raw_data)
             
+            # Get Embedding statistics
+            embedding_cache = await self.embedding_scanner.get_cached_data()
+            embedding_count = len(embedding_cache.raw_data)
+            embedding_size = sum(emb.get('size', 0) for emb in embedding_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,
+                    'total_models': lora_count + checkpoint_count + embedding_count,
                     'lora_count': lora_count,
                     'checkpoint_count': checkpoint_count,
-                    'total_size': lora_size + checkpoint_size,
+                    'embedding_count': embedding_count,
+                    'total_size': lora_size + checkpoint_size + embedding_size,
                     'lora_size': lora_size,
                     'checkpoint_size': checkpoint_size,
+                    'embedding_size': embedding_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', {}))
+                    'unused_checkpoints': self._count_unused_models(checkpoint_cache.raw_data, usage_data.get('checkpoints', {})),
+                    'unused_embeddings': self._count_unused_models(embedding_cache.raw_data, usage_data.get('embeddings', {}))
                 }
             })
             
@@ -121,14 +136,17 @@ class StatsRoutes:
             # Get model data for enrichment
             lora_cache = await self.lora_scanner.get_cached_data()
             checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
+            embedding_cache = await self.embedding_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}
+            embedding_map = {emb['sha256']: emb for emb in embedding_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)
+            top_embeddings = self._get_top_used_models(usage_data.get('embeddings', {}), embedding_map, 10)
             
             # Prepare usage timeline (last 30 days)
             timeline = self._get_usage_timeline(usage_data, 30)
@@ -138,6 +156,7 @@ class StatsRoutes:
                 'data': {
                     'top_loras': top_loras,
                     'top_checkpoints': top_checkpoints,
+                    'top_embeddings': top_embeddings,
                     'usage_timeline': timeline,
                     'total_executions': usage_data.get('total_executions', 0)
                 }
@@ -158,16 +177,19 @@ class StatsRoutes:
             # Get model data
             lora_cache = await self.lora_scanner.get_cached_data()
             checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
+            embedding_cache = await self.embedding_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)
+            embedding_base_models = Counter(emb.get('base_model', 'Unknown') for emb in embedding_cache.raw_data)
             
             return web.json_response({
                 'success': True,
                 'data': {
                     'loras': dict(lora_base_models),
-                    'checkpoints': dict(checkpoint_base_models)
+                    'checkpoints': dict(checkpoint_base_models),
+                    'embeddings': dict(embedding_base_models)
                 }
             })
             
@@ -186,6 +208,7 @@ class StatsRoutes:
             # Get model data
             lora_cache = await self.lora_scanner.get_cached_data()
             checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
+            embedding_cache = await self.embedding_scanner.get_cached_data()
             
             # Count tag frequencies
             all_tags = []
@@ -193,6 +216,8 @@ class StatsRoutes:
                 all_tags.extend(lora.get('tags', []))
             for cp in checkpoint_cache.raw_data:
                 all_tags.extend(cp.get('tags', []))
+            for emb in embedding_cache.raw_data:
+                all_tags.extend(emb.get('tags', []))
             
             tag_counts = Counter(all_tags)
             
@@ -225,6 +250,7 @@ class StatsRoutes:
             # Get model data
             lora_cache = await self.lora_scanner.get_cached_data()
             checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
+            embedding_cache = await self.embedding_scanner.get_cached_data()
             
             # Create models with usage data
             lora_storage = []
@@ -255,15 +281,31 @@ class StatsRoutes:
                     'base_model': cp.get('base_model', 'Unknown')
                 })
             
+            embedding_storage = []
+            for emb in embedding_cache.raw_data:
+                usage_count = 0
+                if emb['sha256'] in usage_data.get('embeddings', {}):
+                    usage_count = usage_data['embeddings'][emb['sha256']].get('total', 0)
+                
+                embedding_storage.append({
+                    'name': emb['model_name'],
+                    'size': emb.get('size', 0),
+                    'usage_count': usage_count,
+                    'folder': emb.get('folder', ''),
+                    'base_model': emb.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)
+            embedding_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]
+                    'checkpoints': checkpoint_storage[:20],
+                    'embeddings': embedding_storage[:20]
                 }
             })
             
@@ -285,15 +327,18 @@ class StatsRoutes:
             # Get model data
             lora_cache = await self.lora_scanner.get_cached_data()
             checkpoint_cache = await self.checkpoint_scanner.get_cached_data()
+            embedding_cache = await self.embedding_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', {}))
+            unused_embeddings = self._count_unused_models(embedding_cache.raw_data, usage_data.get('embeddings', {}))
             
             total_loras = len(lora_cache.raw_data)
             total_checkpoints = len(checkpoint_cache.raw_data)
+            total_embeddings = len(embedding_cache.raw_data)
             
             if total_loras > 0:
                 unused_lora_percent = (unused_loras / total_loras) * 100
@@ -315,9 +360,20 @@ class StatsRoutes:
                         'suggestion': 'Review and consider removing checkpoints you no longer need.'
                     })
             
+            if total_embeddings > 0:
+                unused_embedding_percent = (unused_embeddings / total_embeddings) * 100
+                if unused_embedding_percent > 50:
+                    insights.append({
+                        'type': 'warning',
+                        'title': 'High Number of Unused Embeddings',
+                        'description': f'{unused_embedding_percent:.1f}% of your embeddings ({unused_embeddings}/{total_embeddings}) have never been used.',
+                        'suggestion': 'Consider organizing or archiving unused embeddings to optimize your collection.'
+                    })
+            
             # 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)
+                        sum(cp.get('size', 0) for cp in checkpoint_cache.raw_data) + \
+                        sum(emb.get('size', 0) for emb in embedding_cache.raw_data)
             
             if total_size > 100 * 1024 * 1024 * 1024:  # 100GB
                 insights.append({
@@ -390,6 +446,7 @@ class StatsRoutes:
             
             lora_usage = 0
             checkpoint_usage = 0
+            embedding_usage = 0
             
             # Count usage for this date
             for model_usage in usage_data.get('loras', {}).values():
@@ -400,11 +457,16 @@ class StatsRoutes:
                 if isinstance(model_usage, dict) and 'history' in model_usage:
                     checkpoint_usage += model_usage['history'].get(date_str, 0)
             
+            for model_usage in usage_data.get('embeddings', {}).values():
+                if isinstance(model_usage, dict) and 'history' in model_usage:
+                    embedding_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
+                'embedding_usage': embedding_usage,
+                'total_usage': lora_usage + checkpoint_usage + embedding_usage
             })
         
         return list(reversed(timeline))  # Oldest to newest

py/routes/update_routes.py

@@ -1,11 +1,16 @@
 import os
+import subprocess
 import aiohttp
 import logging
 import toml
-import subprocess
+import git
+import zipfile
+import shutil
+import tempfile
 from datetime import datetime
 from aiohttp import web
-from typing import Dict, Any, List
+from typing import Dict, List
+
 
 logger = logging.getLogger(__name__)
 
@@ -17,6 +22,7 @@ class UpdateRoutes:
         """Register update check routes"""
         app.router.add_get('/api/check-updates', UpdateRoutes.check_updates)
         app.router.add_get('/api/version-info', UpdateRoutes.get_version_info)
+        app.router.add_post('/api/perform-update', UpdateRoutes.perform_update)
     
     @staticmethod
     async def check_updates(request):
@@ -25,6 +31,8 @@ class UpdateRoutes:
         Returns update status and version information
         """
         try:
+            nightly = request.query.get('nightly', 'false').lower() == 'true'
+            
             # Read local version from pyproject.toml
             local_version = UpdateRoutes._get_local_version()
             
@@ -32,13 +40,21 @@ class UpdateRoutes:
             git_info = UpdateRoutes._get_git_info()
 
             # Fetch remote version from GitHub
-            remote_version, changelog = await UpdateRoutes._get_remote_version()
+            if nightly:
+                remote_version, changelog = await UpdateRoutes._get_nightly_version()
+            else:
+                remote_version, changelog = await UpdateRoutes._get_remote_version()
             
             # Compare versions
-            update_available = UpdateRoutes._compare_versions(
-                local_version.replace('v', ''), 
-                remote_version.replace('v', '')
-            )
+            if nightly:
+                # For nightly, compare commit hashes
+                update_available = UpdateRoutes._compare_nightly_versions(git_info, remote_version)
+            else:
+                # For stable, compare semantic versions
+                update_available = UpdateRoutes._compare_versions(
+                    local_version.replace('v', ''), 
+                    remote_version.replace('v', '')
+                )
             
             return web.json_response({
                 'success': True,
@@ -46,7 +62,8 @@ class UpdateRoutes:
                 'latest_version': remote_version,
                 'update_available': update_available,
                 'changelog': changelog,
-                'git_info': git_info
+                'git_info': git_info,
+                'nightly': nightly
             })
             
         except Exception as e:
@@ -55,7 +72,7 @@ class UpdateRoutes:
                 'success': False,
                 'error': str(e)
             })
-    
+        
     @staticmethod
     async def get_version_info(request):
         """
@@ -84,6 +101,241 @@ class UpdateRoutes:
                 'error': str(e)
             })
     
+    @staticmethod
+    async def perform_update(request):
+        """
+        Perform Git-based update to latest release tag or main branch.
+        If .git is missing, fallback to ZIP download.
+        """
+        try:
+            body = await request.json() if request.has_body else {}
+            nightly = body.get('nightly', False)
+
+            current_dir = os.path.dirname(os.path.abspath(__file__))
+            plugin_root = os.path.dirname(os.path.dirname(current_dir))
+
+            settings_path = os.path.join(plugin_root, 'settings.json')
+            settings_backup = None
+            if os.path.exists(settings_path):
+                with open(settings_path, 'r', encoding='utf-8') as f:
+                    settings_backup = f.read()
+                logger.info("Backed up settings.json")
+
+            git_folder = os.path.join(plugin_root, '.git')
+            if os.path.exists(git_folder):
+                # Git update
+                success, new_version = await UpdateRoutes._perform_git_update(plugin_root, nightly)
+            else:
+                # Fallback: Download ZIP and replace files
+                success, new_version = await UpdateRoutes._download_and_replace_zip(plugin_root)
+
+            if settings_backup and success:
+                with open(settings_path, 'w', encoding='utf-8') as f:
+                    f.write(settings_backup)
+                logger.info("Restored settings.json")
+
+            if success:
+                return web.json_response({
+                    'success': True,
+                    'message': f'Successfully updated to {new_version}',
+                    'new_version': new_version
+                })
+            else:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Failed to complete update'
+                })
+
+        except Exception as e:
+            logger.error(f"Failed to perform update: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            })
+
+    @staticmethod
+    async def _download_and_replace_zip(plugin_root: str) -> tuple[bool, str]:
+        """
+        Download latest release ZIP from GitHub and replace plugin files.
+        Skips settings.json.
+        """
+        repo_owner = "willmiao"
+        repo_name = "ComfyUI-Lora-Manager"
+        github_api = f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/latest"
+
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(github_api) as resp:
+                    if resp.status != 200:
+                        logger.error(f"Failed to fetch release info: {resp.status}")
+                        return False, ""
+                    data = await resp.json()
+                    zip_url = data.get("zipball_url")
+                    version = data.get("tag_name", "unknown")
+
+                # Download ZIP
+                async with session.get(zip_url) as zip_resp:
+                    if zip_resp.status != 200:
+                        logger.error(f"Failed to download ZIP: {zip_resp.status}")
+                        return False, ""
+                    with tempfile.NamedTemporaryFile(delete=False, suffix=".zip") as tmp_zip:
+                        tmp_zip.write(await zip_resp.read())
+                        zip_path = tmp_zip.name
+
+                UpdateRoutes._clean_plugin_folder(plugin_root, skip_files=['settings.json'])
+
+                # Extract ZIP to temp dir
+                with tempfile.TemporaryDirectory() as tmp_dir:
+                    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+                        zip_ref.extractall(tmp_dir)
+                    # Find extracted folder (GitHub ZIP contains a root folder)
+                    extracted_root = next(os.scandir(tmp_dir)).path
+
+                    # Copy files, skipping settings.json
+                    for item in os.listdir(extracted_root):
+                        src = os.path.join(extracted_root, item)
+                        dst = os.path.join(plugin_root, item)
+                        if os.path.isdir(src):
+                            # Remove old folder, then copy
+                            if os.path.exists(dst):
+                                shutil.rmtree(dst)
+                            shutil.copytree(src, dst, ignore=shutil.ignore_patterns('settings.json'))
+                        else:
+                            if item == 'settings.json':
+                                continue
+                            shutil.copy2(src, dst)
+
+                os.remove(zip_path)
+                logger.info(f"Updated plugin via ZIP to {version}")
+                return True, version
+
+        except Exception as e:
+            logger.error(f"ZIP update failed: {e}", exc_info=True)
+            return False, ""
+        
+    def _clean_plugin_folder(plugin_root, skip_files=None):
+        skip_files = skip_files or []
+        for item in os.listdir(plugin_root):
+            if item in skip_files:
+                continue
+            path = os.path.join(plugin_root, item)
+            if os.path.isdir(path):
+                shutil.rmtree(path)
+            else:
+                os.remove(path)
+    
+    @staticmethod
+    async def _get_nightly_version() -> tuple[str, List[str]]:
+        """
+        Fetch latest commit from main branch
+        """
+        repo_owner = "willmiao"
+        repo_name = "ComfyUI-Lora-Manager"
+        
+        # Use GitHub API to fetch the latest commit from main branch
+        github_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/commits/main"
+        
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(github_url, headers={'Accept': 'application/vnd.github+json'}) as response:
+                    if response.status != 200:
+                        logger.warning(f"Failed to fetch GitHub commit: {response.status}")
+                        return "main", []
+                    
+                    data = await response.json()
+                    commit_sha = data.get('sha', '')[:7]  # Short hash
+                    commit_message = data.get('commit', {}).get('message', '')
+                    
+                    # Format as "main-{short_hash}"
+                    version = f"main-{commit_sha}"
+                    
+                    # Use commit message as changelog
+                    changelog = [commit_message] if commit_message else []
+                    
+                    return version, changelog
+        
+        except Exception as e:
+            logger.error(f"Error fetching nightly version: {e}", exc_info=True)
+            return "main", []
+    
+    @staticmethod
+    def _compare_nightly_versions(local_git_info: Dict[str, str], remote_version: str) -> bool:
+        """
+        Compare local commit hash with remote main branch
+        """
+        try:
+            local_hash = local_git_info.get('short_hash', 'unknown')
+            if local_hash == 'unknown':
+                return True  # Assume update available if we can't get local hash
+            
+            # Extract remote hash from version string (format: "main-{hash}")
+            if '-' in remote_version:
+                remote_hash = remote_version.split('-')[-1]
+                return local_hash != remote_hash
+            
+            return True  # Default to update available
+            
+        except Exception as e:
+            logger.error(f"Error comparing nightly versions: {e}")
+            return False
+    
+    @staticmethod
+    async def _perform_git_update(plugin_root: str, nightly: bool = False) -> tuple[bool, str]:
+        """
+        Perform Git-based update using GitPython
+        
+        Args:
+            plugin_root: Path to the plugin root directory
+            nightly: Whether to update to main branch or latest release
+            
+        Returns:
+            tuple: (success, new_version)
+        """
+        try:
+            # Open the Git repository
+            repo = git.Repo(plugin_root)
+            
+            # Fetch latest changes
+            origin = repo.remotes.origin
+            origin.fetch()
+            
+            if nightly:
+                # Switch to main branch and pull latest
+                main_branch = 'main'
+                if main_branch not in [branch.name for branch in repo.branches]:
+                    # Create local main branch if it doesn't exist
+                    repo.create_head(main_branch, origin.refs.main)
+                
+                repo.heads[main_branch].checkout()
+                origin.pull(main_branch)
+                
+                # Get new commit hash
+                new_version = f"main-{repo.head.commit.hexsha[:7]}"
+                
+            else:
+                # Get latest release tag
+                tags = sorted(repo.tags, key=lambda t: t.commit.committed_datetime, reverse=True)
+                if not tags:
+                    logger.error("No tags found in repository")
+                    return False, ""
+                
+                latest_tag = tags[0]
+                
+                # Checkout to latest tag
+                repo.git.checkout(latest_tag.name)
+                
+                new_version = latest_tag.name
+            
+            logger.info(f"Successfully updated to {new_version}")
+            return True, new_version
+            
+        except git.exc.GitError as e:
+            logger.error(f"Git error during update: {e}")
+            return False, ""
+        except Exception as e:
+            logger.error(f"Error during Git update: {e}")
+            return False, ""
+    
     @staticmethod
     def _get_local_version() -> str:
         """Get local plugin version from pyproject.toml"""
@@ -115,7 +367,7 @@ class UpdateRoutes:
         
         git_info = {
             'commit_hash': 'unknown',
-            'short_hash': 'unknown',
+            'short_hash': 'stable',
             'branch': 'unknown',
             'commit_date': 'unknown'
         }

py/services/base_model_service.py

@@ -0,0 +1,259 @@
+from abc import ABC, abstractmethod
+from typing import Dict, List, Optional, Type
+import logging
+
+from ..utils.models import BaseModelMetadata
+from ..utils.constants import NSFW_LEVELS
+from .settings_manager import settings
+from ..utils.utils import fuzzy_match
+
+logger = logging.getLogger(__name__)
+
+class BaseModelService(ABC):
+    """Base service class for all model types"""
+    
+    def __init__(self, model_type: str, scanner, metadata_class: Type[BaseModelMetadata]):
+        """Initialize the service
+        
+        Args:
+            model_type: Type of model (lora, checkpoint, etc.)
+            scanner: Model scanner instance
+            metadata_class: Metadata class for this model type
+        """
+        self.model_type = model_type
+        self.scanner = scanner
+        self.metadata_class = metadata_class
+    
+    async def get_paginated_data(self, page: int, page_size: int, sort_by: str = 'name', 
+                               folder: str = None, search: str = None, fuzzy_search: bool = False,
+                               base_models: list = None, tags: list = None,
+                               search_options: dict = None, hash_filters: dict = None,
+                               favorites_only: bool = False, **kwargs) -> Dict:
+        """Get paginated and filtered model data
+        
+        Args:
+            page: Page number (1-based)
+            page_size: Number of items per page
+            sort_by: Sort criteria, e.g. 'name', 'name:asc', 'name:desc', 'date', 'date:asc', 'date:desc'
+            folder: Folder filter
+            search: Search term
+            fuzzy_search: Whether to use fuzzy search
+            base_models: List of base models to filter by
+            tags: List of tags to filter by
+            search_options: Search options dict
+            hash_filters: Hash filtering options
+            favorites_only: Filter for favorites only
+            **kwargs: Additional model-specific filters
+            
+        Returns:
+            Dict containing paginated results
+        """
+        cache = await self.scanner.get_cached_data()
+
+        # Parse sort_by into sort_key and order
+        if ':' in sort_by:
+            sort_key, order = sort_by.split(':', 1)
+            sort_key = sort_key.strip()
+            order = order.strip().lower()
+            if order not in ('asc', 'desc'):
+                order = 'asc'
+        else:
+            sort_key = sort_by.strip()
+            order = 'asc'
+
+        # Get default search options if not provided
+        if search_options is None:
+            search_options = {
+                'filename': True,
+                'modelname': True,
+                'tags': False,
+                'recursive': False,
+            }
+
+        # Get the base data set using new sort logic
+        filtered_data = await cache.get_sorted_data(sort_key, order)
+        
+        # Apply hash filtering if provided (highest priority)
+        if hash_filters:
+            filtered_data = await self._apply_hash_filters(filtered_data, hash_filters)
+            
+            # Jump to pagination for hash filters
+            return self._paginate(filtered_data, page, page_size)
+        
+        # Apply common filters
+        filtered_data = await self._apply_common_filters(
+            filtered_data, folder, base_models, tags, favorites_only, search_options
+        )
+        
+        # Apply search filtering
+        if search:
+            filtered_data = await self._apply_search_filters(
+                filtered_data, search, fuzzy_search, search_options
+            )
+        
+        # Apply model-specific filters
+        filtered_data = await self._apply_specific_filters(filtered_data, **kwargs)
+        
+        return self._paginate(filtered_data, page, page_size)
+    
+    async def _apply_hash_filters(self, data: List[Dict], hash_filters: Dict) -> List[Dict]:
+        """Apply hash-based filtering"""
+        single_hash = hash_filters.get('single_hash')
+        multiple_hashes = hash_filters.get('multiple_hashes')
+        
+        if single_hash:
+            # Filter by single hash
+            single_hash = single_hash.lower()
+            return [
+                item for item in data
+                if item.get('sha256', '').lower() == single_hash
+            ]
+        elif multiple_hashes:
+            # Filter by multiple hashes
+            hash_set = set(hash.lower() for hash in multiple_hashes)
+            return [
+                item for item in data
+                if item.get('sha256', '').lower() in hash_set
+            ]
+        
+        return data
+    
+    async def _apply_common_filters(self, data: List[Dict], folder: str = None, 
+                                  base_models: list = None, tags: list = None,
+                                  favorites_only: bool = False, search_options: dict = None) -> List[Dict]:
+        """Apply common filters that work across all model types"""
+        # Apply SFW filtering if enabled in settings
+        if settings.get('show_only_sfw', False):
+            data = [
+                item for item in data
+                if not item.get('preview_nsfw_level') or item.get('preview_nsfw_level') < NSFW_LEVELS['R']
+            ]
+        
+        # Apply favorites filtering if enabled
+        if favorites_only:
+            data = [
+                item for item in data
+                if item.get('favorite', False) is True
+            ]
+        
+        # Apply folder filtering
+        if folder is not None:
+            if search_options and search_options.get('recursive', False):
+                # Recursive folder filtering - include all subfolders
+                data = [
+                    item for item in data
+                    if item['folder'].startswith(folder)
+                ]
+            else:
+                # Exact folder filtering
+                data = [
+                    item for item in data
+                    if item['folder'] == folder
+                ]
+        
+        # Apply base model filtering
+        if base_models and len(base_models) > 0:
+            data = [
+                item for item in data
+                if item.get('base_model') in base_models
+            ]
+        
+        # Apply tag filtering
+        if tags and len(tags) > 0:
+            data = [
+                item for item in data
+                if any(tag in item.get('tags', []) for tag in tags)
+            ]
+        
+        return data
+    
+    async def _apply_search_filters(self, data: List[Dict], search: str, 
+                                  fuzzy_search: bool, search_options: dict) -> List[Dict]:
+        """Apply search filtering"""
+        search_results = []
+        
+        for item in data:
+            # Search by file name
+            if search_options.get('filename', True):
+                if fuzzy_search:
+                    if fuzzy_match(item.get('file_name', ''), search):
+                        search_results.append(item)
+                        continue
+                elif search.lower() in item.get('file_name', '').lower():
+                    search_results.append(item)
+                    continue
+            
+            # Search by model name
+            if search_options.get('modelname', True):
+                if fuzzy_search:
+                    if fuzzy_match(item.get('model_name', ''), search):
+                        search_results.append(item)
+                        continue
+                elif search.lower() in item.get('model_name', '').lower():
+                    search_results.append(item)
+                    continue
+            
+            # Search by tags
+            if search_options.get('tags', False) and 'tags' in item:
+                if any((fuzzy_match(tag, search) if fuzzy_search else search.lower() in tag.lower()) 
+                       for tag in item['tags']):
+                    search_results.append(item)
+                    continue
+        
+        return search_results
+    
+    async def _apply_specific_filters(self, data: List[Dict], **kwargs) -> List[Dict]:
+        """Apply model-specific filters - to be overridden by subclasses if needed"""
+        return data
+    
+    def _paginate(self, data: List[Dict], page: int, page_size: int) -> Dict:
+        """Apply pagination to filtered data"""
+        total_items = len(data)
+        start_idx = (page - 1) * page_size
+        end_idx = min(start_idx + page_size, total_items)
+        
+        return {
+            'items': data[start_idx:end_idx],
+            'total': total_items,
+            'page': page,
+            'page_size': page_size,
+            'total_pages': (total_items + page_size - 1) // page_size
+        }
+    
+    @abstractmethod
+    async def format_response(self, model_data: Dict) -> Dict:
+        """Format model data for API response - must be implemented by subclasses"""
+        pass
+    
+    # Common service methods that delegate to scanner
+    async def get_top_tags(self, limit: int = 20) -> List[Dict]:
+        """Get top tags sorted by frequency"""
+        return await self.scanner.get_top_tags(limit)
+    
+    async def get_base_models(self, limit: int = 20) -> List[Dict]:
+        """Get base models sorted by frequency"""
+        return await self.scanner.get_base_models(limit)
+    
+    def has_hash(self, sha256: str) -> bool:
+        """Check if a model with given hash exists"""
+        return self.scanner.has_hash(sha256)
+    
+    def get_path_by_hash(self, sha256: str) -> Optional[str]:
+        """Get file path for a model by its hash"""
+        return self.scanner.get_path_by_hash(sha256)
+    
+    def get_hash_by_path(self, file_path: str) -> Optional[str]:
+        """Get hash for a model by its file path"""
+        return self.scanner.get_hash_by_path(file_path)
+    
+    async def scan_models(self, force_refresh: bool = False, rebuild_cache: bool = False):
+        """Trigger model scanning"""
+        return await self.scanner.get_cached_data(force_refresh=force_refresh, rebuild_cache=rebuild_cache)
+    
+    async def get_model_info_by_name(self, name: str):
+        """Get model information by name"""
+        return await self.scanner.get_model_info_by_name(name)
+    
+    def get_model_roots(self) -> List[str]:
+        """Get model root directories"""
+        return self.scanner.get_model_roots()

py/services/checkpoint_scanner.py

@@ -1,131 +1,26 @@
-import os
 import logging
-import asyncio
-from typing import List, Dict, Optional, Set
-import folder_paths # type: ignore
+from typing import List
 
 from ..utils.models import CheckpointMetadata
 from ..config import config
 from .model_scanner import ModelScanner
 from .model_hash_index import ModelHashIndex
-from .service_registry import ServiceRegistry
 
 logger = logging.getLogger(__name__)
 
 class CheckpointScanner(ModelScanner):
     """Service for scanning and managing checkpoint files"""
     
-    _instance = None
-    _lock = asyncio.Lock()
-    
-    def __new__(cls):
-        if cls._instance is None:
-            cls._instance = super().__new__(cls)
-        return cls._instance
-    
     def __init__(self):
-        if not hasattr(self, '_initialized'):
-            # Define supported file extensions
-            file_extensions = {'.safetensors', '.ckpt', '.pt', '.pth', '.sft', '.gguf'}
-            super().__init__(
-                model_type="checkpoint",
-                model_class=CheckpointMetadata,
-                file_extensions=file_extensions,
-                hash_index=ModelHashIndex()
-            )
-            self._checkpoint_roots = self._init_checkpoint_roots()
-            self._initialized = True
+        # Define supported file extensions
+        file_extensions = {'.safetensors', '.ckpt', '.pt', '.pth', '.sft', '.gguf'}
+        super().__init__(
+            model_type="checkpoint",
+            model_class=CheckpointMetadata,
+            file_extensions=file_extensions,
+            hash_index=ModelHashIndex()
+        )
 
-    @classmethod
-    async def get_instance(cls):
-        """Get singleton instance with async support"""
-        async with cls._lock:
-            if cls._instance is None:
-                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
-        
-    async def scan_all_models(self) -> List[Dict]:
-        """Scan all checkpoint directories and return metadata"""
-        all_checkpoints = []
-        
-        # Create scan tasks for each directory
-        scan_tasks = []
-        for root in self._checkpoint_roots:
-            task = asyncio.create_task(self._scan_directory(root))
-            scan_tasks.append(task)
-            
-        # Wait for all tasks to complete
-        for task in scan_tasks:
-            try:
-                checkpoints = await task
-                all_checkpoints.extend(checkpoints)
-            except Exception as e:
-                logger.error(f"Error scanning checkpoint directory: {e}")
-                
-        return all_checkpoints
-    
-    async def _scan_directory(self, root_path: str) -> List[Dict]:
-        """Scan a directory for checkpoint files"""
-        checkpoints = []
-        original_root = root_path
-
-        async def scan_recursive(path: str, visited_paths: set):
-            try:
-                real_path = os.path.realpath(path)
-                if real_path in visited_paths:
-                    logger.debug(f"Skipping already visited path: {path}")
-                    return
-                visited_paths.add(real_path)
-                
-                with os.scandir(path) as it:
-                    entries = list(it)
-                    for entry in entries:
-                        try:
-                            if entry.is_file(follow_symlinks=True):
-                                # Check if file has supported extension
-                                ext = os.path.splitext(entry.name)[1].lower()
-                                if ext in self.file_extensions:
-                                    file_path = entry.path.replace(os.sep, "/")
-                                    await self._process_single_file(file_path, original_root, checkpoints)
-                                    await asyncio.sleep(0)
-                            elif entry.is_dir(follow_symlinks=True):
-                                # For directories, continue scanning with original path
-                                await scan_recursive(entry.path, visited_paths)
-                        except Exception as e:
-                            logger.error(f"Error processing entry {entry.path}: {e}")
-            except Exception as e:
-                logger.error(f"Error scanning {path}: {e}")
-        
-        await scan_recursive(root_path, set())
-        return checkpoints
-
-    async def _process_single_file(self, file_path: str, root_path: str, checkpoints: list):
-        """Process a single checkpoint file and add to results"""
-        try:
-            result = await self._process_model_file(file_path, root_path)
-            if result:
-                checkpoints.append(result)
-        except Exception as e:
-            logger.error(f"Error processing {file_path}: {e}")
+        return config.base_models_roots

py/services/checkpoint_service.py

@@ -0,0 +1,51 @@
+import os
+import logging
+from typing import Dict, List, Optional
+
+from .base_model_service import BaseModelService
+from ..utils.models import CheckpointMetadata
+from ..config import config
+from ..utils.routes_common import ModelRouteUtils
+
+logger = logging.getLogger(__name__)
+
+class CheckpointService(BaseModelService):
+    """Checkpoint-specific service implementation"""
+    
+    def __init__(self, scanner):
+        """Initialize Checkpoint service
+        
+        Args:
+            scanner: Checkpoint scanner instance
+        """
+        super().__init__("checkpoint", scanner, CheckpointMetadata)
+    
+    async def format_response(self, checkpoint_data: Dict) -> Dict:
+        """Format Checkpoint data for API response"""
+        return {
+            "model_name": checkpoint_data["model_name"],
+            "file_name": checkpoint_data["file_name"],
+            "preview_url": config.get_preview_static_url(checkpoint_data.get("preview_url", "")),
+            "preview_nsfw_level": checkpoint_data.get("preview_nsfw_level", 0),
+            "base_model": checkpoint_data.get("base_model", ""),
+            "folder": checkpoint_data["folder"],
+            "sha256": checkpoint_data.get("sha256", ""),
+            "file_path": checkpoint_data["file_path"].replace(os.sep, "/"),
+            "file_size": checkpoint_data.get("size", 0),
+            "modified": checkpoint_data.get("modified", ""),
+            "tags": checkpoint_data.get("tags", []),
+            "modelDescription": checkpoint_data.get("modelDescription", ""),
+            "from_civitai": checkpoint_data.get("from_civitai", True),
+            "notes": checkpoint_data.get("notes", ""),
+            "model_type": checkpoint_data.get("model_type", "checkpoint"),
+            "favorite": checkpoint_data.get("favorite", False),
+            "civitai": ModelRouteUtils.filter_civitai_data(checkpoint_data.get("civitai", {}))
+        }
+    
+    def find_duplicate_hashes(self) -> Dict:
+        """Find Checkpoints with duplicate SHA256 hashes"""
+        return self.scanner._hash_index.get_duplicate_hashes()
+    
+    def find_duplicate_filenames(self) -> Dict:
+        """Find Checkpoints with conflicting filenames"""
+        return self.scanner._hash_index.get_duplicate_filenames()

py/services/civitai_client.py

@@ -1,13 +1,11 @@
 from datetime import datetime
 import aiohttp
 import os
-import json
 import logging
 import asyncio
 from email.parser import Parser
 from typing import Optional, Dict, Tuple, List
 from urllib.parse import unquote
-from ..utils.models import LoraMetadata
 
 logger = logging.getLogger(__name__)
 
@@ -45,14 +43,14 @@ class CivitaiClient:
             # Optimize TCP connection parameters
             connector = aiohttp.TCPConnector(
                 ssl=True,
-                limit=3,  # Further reduced from 5 to 3
-                ttl_dns_cache=0,  # Disabled DNS caching completely
+                limit=8,  # Increase from 3 to 8 for better parallelism
+                ttl_dns_cache=300,  # Enable DNS caching with reasonable timeout
                 force_close=False,  # Keep connections for reuse
                 enable_cleanup_closed=True
             )
             trust_env = True  # Allow using system environment proxy settings
-            # Configure timeout parameters
-            timeout = aiohttp.ClientTimeout(total=None, connect=60, sock_read=60)
+            # Configure timeout parameters - increase read timeout for large files
+            timeout = aiohttp.ClientTimeout(total=None, connect=60, sock_read=120)
             self._session = aiohttp.ClientSession(
                 connector=connector, 
                 trust_env=trust_env,
@@ -165,7 +163,7 @@ class CivitaiClient:
                             now = datetime.now()
                             time_diff = (now - last_progress_report_time).total_seconds()
                             
-                            if progress_callback and total_size and time_diff >= 0.5:
+                            if progress_callback and total_size and time_diff >= 1.0:
                                 progress = (current_size / total_size) * 100
                                 await progress_callback(progress)
                                 last_progress_report_time = now
@@ -225,7 +223,7 @@ class CivitaiClient:
             logger.error(f"Error fetching model versions: {e}")
             return None
             
-    async def get_model_version(self, model_id: str, version_id: str = "") -> Optional[Dict]:
+    async def get_model_version(self, model_id: int, version_id: int = None) -> Optional[Dict]:
         """Get specific model version with additional metadata
         
         Args:
@@ -237,6 +235,8 @@ class CivitaiClient:
         """
         try:
             session = await self._ensure_fresh_session()
+            
+            # Step 1: Get model data to find version_id if not provided and get additional metadata
             async with session.get(f"{self.base_url}/models/{model_id}") as response:
                 if response.status != 200:
                     return None
@@ -244,45 +244,28 @@ class CivitaiClient:
                 data = await response.json()
                 model_versions = data.get('modelVersions', [])
                 
-                # Find matching version
-                matched_version = None
-                
-                if version_id:
-                    # If version_id provided, find exact match
-                    for version in model_versions:
-                        if str(version.get('id')) == str(version_id):
-                            matched_version = version
-                            break
-                else:
-                    # If no version_id then use the first version
-                    matched_version = model_versions[0] if model_versions else None
-                
-                # If no match found, return None
-                if not matched_version:
+                # 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
-                    
-                # Build result with modified fields
-                result = matched_version.copy()  # Copy to avoid modifying original
                 
-                # Replace index with modelId
-                if 'index' in result:
-                    del result['index']
-                result['modelId'] = model_id
+                version = await response.json()
                 
-                # Add model field with metadata from top level
-                result['model'] = {
-                    "name": data.get("name"),
-                    "type": data.get("type"),
-                    "nsfw": data.get("nsfw", False),
-                    "poi": data.get("poi", False),
-                    "description": data.get("description"),
-                    "tags": data.get("tags", [])
-                }
+                # 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 field from top level
-                result['creator'] = data.get("creator")
+                # Add creator from model data
+                version['creator'] = data.get("creator")
                 
-                return result
+                return version
                 
         except Exception as e:
             logger.error(f"Error fetching model version: {e}")

py/services/download_manager.py

@@ -1,13 +1,15 @@
 import logging
 import os
-import json
 import asyncio
+from collections import OrderedDict
+import uuid
 from typing import Dict
-from ..utils.models import LoraMetadata, CheckpointMetadata
-from ..utils.constants import CARD_PREVIEW_WIDTH
+from ..utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata
+from ..utils.constants import CARD_PREVIEW_WIDTH, VALID_LORA_TYPES, CIVITAI_MODEL_TAGS
 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
@@ -33,6 +35,10 @@ class DownloadManager:
         self._initialized = True
         
         self._civitai_client = None  # Will be lazily initialized
+        # Add download management
+        self._active_downloads = OrderedDict()  # download_id -> download_info
+        self._download_semaphore = asyncio.Semaphore(5)  # Limit concurrent downloads
+        self._download_tasks = {}  # download_id -> asyncio.Task
 
     async def _get_civitai_client(self):
         """Lazily initialize CivitaiClient from registry"""
@@ -47,55 +53,210 @@ class DownloadManager:
     async def _get_checkpoint_scanner(self):
         """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:
-        """Download model from Civitai
+    
+    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,
+                                  download_id: str = None) -> Dict:
+        """Download model from Civitai with task tracking and concurrency control
         
         Args:
-            download_url: Direct download URL for the model
-            model_hash: SHA256 hash of the model
+            model_id: Civitai model ID
             model_version_id: Civitai model version ID
-            save_dir: Directory to save the model to
+            save_dir: Directory to save the model
             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 use default paths
+            download_id: Unique identifier for this download task
             
         Returns:
             Dict with download result
         """
+        # Use provided download_id or generate new one
+        task_id = download_id or str(uuid.uuid4())
+        
+        # Register download task in tracking dict
+        self._active_downloads[task_id] = {
+            'model_id': model_id,
+            'model_version_id': model_version_id,
+            'progress': 0,
+            'status': 'queued'
+        }
+        
+        # Create tracking task
+        download_task = asyncio.create_task(
+            self._download_with_semaphore(
+                task_id, model_id, model_version_id, save_dir, 
+                relative_path, progress_callback, use_default_paths
+            )
+        )
+        
+        # Store task for tracking and cancellation
+        self._download_tasks[task_id] = download_task
+        
         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)
+            # Wait for download to complete
+            result = await download_task
+            result['download_id'] = task_id  # Include download_id in result
+            return result
+        except asyncio.CancelledError:
+            return {'success': False, 'error': 'Download was cancelled', 'download_id': task_id}
+        finally:
+            # Clean up task reference
+            if task_id in self._download_tasks:
+                del self._download_tasks[task_id]
+
+    async def _download_with_semaphore(self, task_id: str, model_id: int, model_version_id: int,
+                                     save_dir: str, relative_path: str, 
+                                     progress_callback=None, use_default_paths: bool = False):
+        """Execute download with semaphore to limit concurrency"""
+        # Update status to waiting
+        if task_id in self._active_downloads:
+            self._active_downloads[task_id]['status'] = 'waiting'
+        
+        # Wrap progress callback to track progress in active_downloads
+        original_callback = progress_callback
+        async def tracking_callback(progress):
+            if task_id in self._active_downloads:
+                self._active_downloads[task_id]['progress'] = progress
+            if original_callback:
+                await original_callback(progress)
+        
+        # Acquire semaphore to limit concurrent downloads
+        try:
+            async with self._download_semaphore:
+                # Update status to downloading
+                if task_id in self._active_downloads:
+                    self._active_downloads[task_id]['status'] = 'downloading'
+                
+                # Use original download implementation
+                try:
+                    # Check for cancellation before starting
+                    if asyncio.current_task().cancelled():
+                        raise asyncio.CancelledError()
+                    
+                    result = await self._execute_original_download(
+                        model_id, model_version_id, save_dir,
+                        relative_path, tracking_callback, use_default_paths,
+                        task_id
+                    )
+                    
+                    # Update status based on result
+                    if task_id in self._active_downloads:
+                        self._active_downloads[task_id]['status'] = 'completed' if result['success'] else 'failed'
+                        if not result['success']:
+                            self._active_downloads[task_id]['error'] = result.get('error', 'Unknown error')
+                    
+                    return result
+                except asyncio.CancelledError:
+                    # Handle cancellation
+                    if task_id in self._active_downloads:
+                        self._active_downloads[task_id]['status'] = 'cancelled'
+                    logger.info(f"Download cancelled for task {task_id}")
+                    raise
+                except Exception as e:
+                    # Handle other errors
+                    logger.error(f"Download error for task {task_id}: {str(e)}", exc_info=True)
+                    if task_id in self._active_downloads:
+                        self._active_downloads[task_id]['status'] = 'failed'
+                        self._active_downloads[task_id]['error'] = str(e)
+                    return {'success': False, 'error': str(e)}
+        finally:
+            # Schedule cleanup of download record after delay
+            asyncio.create_task(self._cleanup_download_record(task_id))
+
+    async def _cleanup_download_record(self, task_id: str):
+        """Keep completed downloads in history for a short time"""
+        await asyncio.sleep(600)  # Keep for 10 minutes
+        if task_id in self._active_downloads:
+            del self._active_downloads[task_id]
+
+    async def _execute_original_download(self, model_id, model_version_id, save_dir, 
+                                     relative_path, progress_callback, use_default_paths,
+                                     download_id=None):
+        """Wrapper for original download_from_civitai implementation"""
+        try:
+            # Check if model version already exists in library
+            if model_version_id is not None:
+                # 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'
+            elif model_type_from_info == 'textualinversion':
+                model_type = 'embedding'
+            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'}
+                elif model_type == 'embedding':
+                    # Embeddings are not checked in scanners, but we can still check if it exists
+                    embedding_scanner = await ServiceRegistry.get_embedding_scanner()
+                    if await embedding_scanner.check_model_version_exists(version_model_id, version_id):
+                        return {'success': False, 'error': 'Model version already exists in embedding 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
+                elif 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
+                elif model_type == 'embedding':
+                    default_path = settings.get('default_embedding_root')
+                    if not default_path:
+                        return {'success': False, 'error': 'Default embedding root path not set in settings'}
+                    save_dir = default_path
+                    
+                # Calculate relative path using template
+                relative_path = self._calculate_relative_path(version_info)
+
+            # 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'):
@@ -133,21 +294,12 @@ class DownloadManager:
             if model_type == "checkpoint":
                 metadata = CheckpointMetadata.from_civitai_info(version_info, file_info, save_path)
                 logger.info(f"Creating CheckpointMetadata for {file_name}")
-            else:
+            elif model_type == "lora":
                 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")
+            elif model_type == "embedding":
+                metadata = EmbeddingMetadata.from_civitai_info(version_info, file_info, save_path)
+                logger.info(f"Creating EmbeddingMetadata for {file_name}")
             
             # 6. Start download process
             result = await self._execute_download(
@@ -157,7 +309,8 @@ class DownloadManager:
                 version_info=version_info,
                 relative_path=relative_path,
                 progress_callback=progress_callback,
-                model_type=model_type
+                model_type=model_type,
+                download_id=download_id
             )
 
             return result
@@ -170,15 +323,63 @@ class DownloadManager:
                 return {'success': False, 'error': f"Early access restriction: {str(e)}. Please ensure you have purchased early access and are logged in to Civitai."}
             return {'success': False, 'error': str(e)}
 
+    def _calculate_relative_path(self, version_info: Dict) -> str:
+        """Calculate relative path using template from settings
+        
+        Args:
+            version_info: Version info from Civitai API
+            
+        Returns:
+            Relative path string
+        """
+        # Get path template from settings, default to '{base_model}/{first_tag}'
+        path_template = settings.get('download_path_template', '{base_model}/{first_tag}')
+        
+        # If template is empty, return empty path (flat structure)
+        if not path_template:
+            return ''
+        
+        # Get base model name
+        base_model = version_info.get('baseModel', '')
+        
+        # Apply mapping if available
+        base_model_mappings = settings.get('base_model_path_mappings', {})
+        mapped_base_model = base_model_mappings.get(base_model, base_model)
+        
+        # Get model tags
+        model_tags = version_info.get('model', {}).get('tags', [])
+        
+        # Find the first Civitai model tag that exists in model_tags
+        first_tag = ''
+        for civitai_tag in CIVITAI_MODEL_TAGS:
+            if civitai_tag in model_tags:
+                first_tag = civitai_tag
+                break
+        
+        # If no Civitai model tag found, fallback to first tag
+        if not first_tag and model_tags:
+            first_tag = model_tags[0]
+        
+        # Format the template with available data
+        formatted_path = path_template
+        formatted_path = formatted_path.replace('{base_model}', mapped_base_model)
+        formatted_path = formatted_path.replace('{first_tag}', first_tag)
+        
+        return formatted_path
+
     async def _execute_download(self, download_url: str, save_dir: str, 
                               metadata, version_info: Dict, 
                               relative_path: str, progress_callback=None,
-                              model_type: str = "lora") -> Dict:
+                              model_type: str = "lora", download_id: str = None) -> Dict:
         """Execute the actual download process including preview images and model files"""
         try:
             civitai_client = await self._get_civitai_client()
             save_path = metadata.file_path
             metadata_path = os.path.splitext(save_path)[0] + '.metadata.json'
+            
+            # Store file path in active_downloads for potential cleanup
+            if download_id and download_id in self._active_downloads:
+                self._active_downloads[download_id]['file_path'] = save_path
 
             # Download preview image if available
             images = version_info.get('images', [])
@@ -261,9 +462,12 @@ class DownloadManager:
             if model_type == "checkpoint":
                 scanner = await self._get_checkpoint_scanner()
                 logger.info(f"Updating checkpoint cache for {save_path}")
-            else:
+            elif model_type == "lora":
                 scanner = await self._get_lora_scanner()
                 logger.info(f"Updating lora cache for {save_path}")
+            elif model_type == "embedding":
+                scanner = await ServiceRegistry.get_embedding_scanner()
+                logger.info(f"Updating embedding cache for {save_path}")
                 
             # Convert metadata to dictionary
             metadata_dict = metadata.to_dict()
@@ -297,4 +501,86 @@ class DownloadManager:
         if progress_callback:
             # Scale file progress to 3-100 range (after preview download)
             overall_progress = 3 + (file_progress * 0.97)  # 97% of progress for file download
-            await progress_callback(round(overall_progress))
+            await progress_callback(round(overall_progress))
+            
+    async def cancel_download(self, download_id: str) -> Dict:
+        """Cancel an active download by download_id
+        
+        Args:
+            download_id: The unique identifier of the download task
+            
+        Returns:
+            Dict: Status of the cancellation operation
+        """
+        if download_id not in self._download_tasks:
+            return {'success': False, 'error': 'Download task not found'}
+        
+        try:
+            # Get the task and cancel it
+            task = self._download_tasks[download_id]
+            task.cancel()
+            
+            # Update status in active downloads
+            if download_id in self._active_downloads:
+                self._active_downloads[download_id]['status'] = 'cancelling'
+            
+            # Wait briefly for the task to acknowledge cancellation
+            try:
+                await asyncio.wait_for(asyncio.shield(task), timeout=2.0)
+            except (asyncio.CancelledError, asyncio.TimeoutError):
+                pass
+            
+            # Clean up partial downloads
+            download_info = self._active_downloads.get(download_id)
+            if download_info and 'file_path' in download_info:
+                # Delete the partial file
+                file_path = download_info['file_path']
+                if os.path.exists(file_path):
+                    try:
+                        os.unlink(file_path)
+                        logger.debug(f"Deleted partial download: {file_path}")
+                    except Exception as e:
+                        logger.error(f"Error deleting partial file: {e}")
+                
+                # Delete metadata file if exists
+                metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+                if os.path.exists(metadata_path):
+                    try:
+                        os.unlink(metadata_path)
+                    except Exception as e:
+                        logger.error(f"Error deleting metadata file: {e}")
+
+                # Delete preview file if exists (.webp or .mp4)
+                for preview_ext in ['.webp', '.mp4']:
+                    preview_path = os.path.splitext(file_path)[0] + preview_ext
+                    if os.path.exists(preview_path):
+                        try:
+                            os.unlink(preview_path)
+                            logger.debug(f"Deleted preview file: {preview_path}")
+                        except Exception as e:
+                            logger.error(f"Error deleting preview file: {e}")
+            
+            return {'success': True, 'message': 'Download cancelled successfully'}
+        except Exception as e:
+            logger.error(f"Error cancelling download: {e}", exc_info=True)
+            return {'success': False, 'error': str(e)}
+    
+    async def get_active_downloads(self) -> Dict:
+        """Get information about all active downloads
+        
+        Returns:
+            Dict: List of active downloads and their status
+        """
+        return {
+            'downloads': [
+                {
+                    'download_id': task_id,
+                    'model_id': info.get('model_id'),
+                    'model_version_id': info.get('model_version_id'),
+                    'progress': info.get('progress', 0),
+                    'status': info.get('status', 'unknown'),
+                    'error': info.get('error', None)
+                }
+                for task_id, info in self._active_downloads.items()
+            ]
+        }

py/services/embedding_scanner.py

@@ -0,0 +1,26 @@
+import logging
+from typing import List
+
+from ..utils.models import EmbeddingMetadata
+from ..config import config
+from .model_scanner import ModelScanner
+from .model_hash_index import ModelHashIndex
+
+logger = logging.getLogger(__name__)
+
+class EmbeddingScanner(ModelScanner):
+    """Service for scanning and managing embedding files"""
+    
+    def __init__(self):
+        # Define supported file extensions
+        file_extensions = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
+        super().__init__(
+            model_type="embedding",
+            model_class=EmbeddingMetadata,
+            file_extensions=file_extensions,
+            hash_index=ModelHashIndex()
+        )
+
+    def get_model_roots(self) -> List[str]:
+        """Get embedding root directories"""
+        return config.embeddings_roots

py/services/embedding_service.py

@@ -0,0 +1,51 @@
+import os
+import logging
+from typing import Dict, List, Optional
+
+from .base_model_service import BaseModelService
+from ..utils.models import EmbeddingMetadata
+from ..config import config
+from ..utils.routes_common import ModelRouteUtils
+
+logger = logging.getLogger(__name__)
+
+class EmbeddingService(BaseModelService):
+    """Embedding-specific service implementation"""
+    
+    def __init__(self, scanner):
+        """Initialize Embedding service
+        
+        Args:
+            scanner: Embedding scanner instance
+        """
+        super().__init__("embedding", scanner, EmbeddingMetadata)
+    
+    async def format_response(self, embedding_data: Dict) -> Dict:
+        """Format Embedding data for API response"""
+        return {
+            "model_name": embedding_data["model_name"],
+            "file_name": embedding_data["file_name"],
+            "preview_url": config.get_preview_static_url(embedding_data.get("preview_url", "")),
+            "preview_nsfw_level": embedding_data.get("preview_nsfw_level", 0),
+            "base_model": embedding_data.get("base_model", ""),
+            "folder": embedding_data["folder"],
+            "sha256": embedding_data.get("sha256", ""),
+            "file_path": embedding_data["file_path"].replace(os.sep, "/"),
+            "file_size": embedding_data.get("size", 0),
+            "modified": embedding_data.get("modified", ""),
+            "tags": embedding_data.get("tags", []),
+            "modelDescription": embedding_data.get("modelDescription", ""),
+            "from_civitai": embedding_data.get("from_civitai", True),
+            "notes": embedding_data.get("notes", ""),
+            "model_type": embedding_data.get("model_type", "embedding"),
+            "favorite": embedding_data.get("favorite", False),
+            "civitai": ModelRouteUtils.filter_civitai_data(embedding_data.get("civitai", {}))
+        }
+    
+    def find_duplicate_hashes(self) -> Dict:
+        """Find Embeddings with duplicate SHA256 hashes"""
+        return self.scanner._hash_index.get_duplicate_hashes()
+    
+    def find_duplicate_filenames(self) -> Dict:
+        """Find Embeddings with conflicting filenames"""
+        return self.scanner._hash_index.get_duplicate_filenames()

py/services/lora_cache.py

@@ -1,65 +0,0 @@
-import asyncio
-from typing import List, Dict
-from dataclasses import dataclass
-from operator import itemgetter
-from natsort import natsorted
-
-@dataclass
-class LoraCache:
-    """Cache structure for LoRA data"""
-    raw_data: List[Dict]
-    sorted_by_name: List[Dict]
-    sorted_by_date: List[Dict]
-    folders: List[str]
-    
-    def __post_init__(self):
-        self._lock = asyncio.Lock()
-
-    async def resort(self, name_only: bool = False):
-        """Resort all cached data views"""
-        async with self._lock:
-            self.sorted_by_name = natsorted(
-                self.raw_data, 
-                key=lambda x: x['model_name'].lower()  # Case-insensitive sort
-            )
-            if not name_only:
-                self.sorted_by_date = sorted(
-                    self.raw_data, 
-                    key=itemgetter('modified'), 
-                    reverse=True
-                )
-            # Update folder list
-            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:
-        """Update preview_url for a specific lora in all cached data
-        
-        Args:
-            file_path: The file path of the lora to update
-            preview_url: The new preview URL
-            
-        Returns:
-            bool: True if the update was successful, False if the lora wasn't found
-        """
-        async with self._lock:
-            # Update in raw_data
-            for item in self.raw_data:
-                if item['file_path'] == file_path:
-                    item['preview_url'] = preview_url
-                    break
-            else:
-                return False  # Lora 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/lora_scanner.py

@@ -1,20 +1,10 @@
-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
 
 from ..utils.models import LoraMetadata
 from ..config import config
 from .model_scanner import ModelScanner
 from .model_hash_index import ModelHashIndex  # Changed from LoraHashIndex to ModelHashIndex
-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__)
@@ -22,404 +12,21 @@ logger = logging.getLogger(__name__)
 class LoraScanner(ModelScanner):
     """Service for scanning and managing LoRA files"""
     
-    _instance = None
-    _lock = asyncio.Lock()
-    
-    def __new__(cls):
-        if cls._instance is None:
-            cls._instance = super().__new__(cls)
-        return cls._instance
-    
     def __init__(self):
-        # Ensure initialization happens only once
-        if not hasattr(self, '_initialized'):
-            # Define supported file extensions
-            file_extensions = {'.safetensors'}
-            
-            # Initialize parent class with ModelHashIndex
-            super().__init__(
-                model_type="lora",
-                model_class=LoraMetadata, 
-                file_extensions=file_extensions,
-                hash_index=ModelHashIndex()  # Changed from LoraHashIndex to ModelHashIndex
-            )
-            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()
-            return cls._instance
+        # Define supported file extensions
+        file_extensions = {'.safetensors'}
+        
+        # Initialize parent class with ModelHashIndex
+        super().__init__(
+            model_type="lora",
+            model_class=LoraMetadata, 
+            file_extensions=file_extensions,
+            hash_index=ModelHashIndex()  # Changed from LoraHashIndex to ModelHashIndex
+        )
     
     def get_model_roots(self) -> List[str]:
         """Get lora root directories"""
         return config.loras_roots
-        
-    async def scan_all_models(self) -> List[Dict]:
-        """Scan all LoRA directories and return metadata"""
-        all_loras = []
-        
-        # Create scan tasks for each directory
-        scan_tasks = []
-        for lora_root in self.get_model_roots():
-            task = asyncio.create_task(self._scan_directory(lora_root))
-            scan_tasks.append(task)
-            
-        # Wait for all tasks to complete
-        for task in scan_tasks:
-            try:
-                loras = await task
-                all_loras.extend(loras)
-            except Exception as e:
-                logger.error(f"Error scanning directory: {e}")
-                
-        return all_loras
-    
-    async def _scan_directory(self, root_path: str) -> List[Dict]:
-        """Scan a single directory for LoRA files"""
-        loras = []
-        original_root = root_path  # Save original root path
-        
-        async def scan_recursive(path: str, visited_paths: set):
-            """Recursively scan directory, avoiding circular symlinks"""
-            try:
-                real_path = os.path.realpath(path)
-                if real_path in visited_paths:
-                    logger.debug(f"Skipping already visited path: {path}")
-                    return
-                visited_paths.add(real_path)
-                
-                with os.scandir(path) as it:
-                    entries = list(it)
-                    for entry in entries:
-                        try:
-                            if entry.is_file(follow_symlinks=True) and any(entry.name.endswith(ext) for ext in self.file_extensions):
-                                # Use original path instead of real path
-                                file_path = entry.path.replace(os.sep, "/")
-                                await self._process_single_file(file_path, original_root, loras)
-                                await asyncio.sleep(0)
-                            elif entry.is_dir(follow_symlinks=True):
-                                # For directories, continue scanning with original path
-                                await scan_recursive(entry.path, visited_paths)
-                        except Exception as e:
-                            logger.error(f"Error processing entry {entry.path}: {e}")
-            except Exception as e:
-                logger.error(f"Error scanning {path}: {e}")
-
-        await scan_recursive(root_path, set())
-        return loras
-
-    async def _process_single_file(self, file_path: str, root_path: str, loras: list):
-        """Process a single file and add to results list"""
-        try:
-            result = await self._process_model_file(file_path, root_path)
-            if result:
-                loras.append(result)
-        except Exception as e:
-            logger.error(f"Error processing {file_path}: {e}")
-    
-    async def get_paginated_data(self, page: int, page_size: int, sort_by: str = 'name', 
-                               folder: str = None, search: str = None, fuzzy_search: bool = False,
-                               base_models: list = None, tags: list = None,
-                               search_options: dict = None, hash_filters: dict = None,
-                               favorites_only: bool = False, first_letter: str = None) -> Dict:
-        """Get paginated and filtered lora data
-        
-        Args:
-            page: Current page number (1-based)
-            page_size: Number of items per page
-            sort_by: Sort method ('name' or 'date')
-            folder: Filter by folder path
-            search: Search term
-            fuzzy_search: Use fuzzy matching for search
-            base_models: List of base models to filter by
-            tags: List of tags to filter by
-            search_options: Dictionary with search options (filename, modelname, tags, recursive)
-            hash_filters: Dictionary with hash filtering options (single_hash or multiple_hashes)
-            favorites_only: Filter for favorite models only
-            first_letter: Filter by first letter of model name
-        """
-        cache = await self.get_cached_data()
-
-        # Get default search options if not provided
-        if search_options is None:
-            search_options = {
-                'filename': True,
-                'modelname': True,
-                'tags': False,
-                'recursive': False,
-            }
-
-        # Get the base data set
-        filtered_data = cache.sorted_by_date if sort_by == 'date' else cache.sorted_by_name
-        
-        # Apply hash filtering if provided (highest priority)
-        if hash_filters:
-            single_hash = hash_filters.get('single_hash')
-            multiple_hashes = hash_filters.get('multiple_hashes')
-            
-            if single_hash:
-                # Filter by single hash
-                single_hash = single_hash.lower()  # Ensure lowercase for matching
-                filtered_data = [
-                    lora for lora in filtered_data
-                    if lora.get('sha256', '').lower() == single_hash
-                ]
-            elif multiple_hashes:
-                # Filter by multiple hashes
-                hash_set = set(hash.lower() for hash in multiple_hashes)  # Convert to set for faster lookup
-                filtered_data = [
-                    lora for lora in filtered_data
-                    if lora.get('sha256', '').lower() in hash_set
-                ]
-            
-
-            # Jump to pagination
-            total_items = len(filtered_data)
-            start_idx = (page - 1) * page_size
-            end_idx = min(start_idx + page_size, total_items)
-            
-            result = {
-                'items': filtered_data[start_idx:end_idx],
-                'total': total_items,
-                'page': page,
-                'page_size': page_size,
-                'total_pages': (total_items + page_size - 1) // page_size
-            }
-            
-            return result
-        
-        # Apply SFW filtering if enabled
-        if settings.get('show_only_sfw', False):
-            filtered_data = [
-                lora for lora in filtered_data
-                if not lora.get('preview_nsfw_level') or lora.get('preview_nsfw_level') < NSFW_LEVELS['R']
-            ]
-        
-        # Apply favorites filtering if enabled
-        if favorites_only:
-            filtered_data = [
-                lora for lora in filtered_data
-                if lora.get('favorite', False) is True
-            ]
-            
-        # Apply first letter filtering
-        if first_letter:
-            filtered_data = self._filter_by_first_letter(filtered_data, first_letter)
-        
-        # Apply folder filtering
-        if folder is not None:
-            if search_options.get('recursive', False):
-                # Recursive folder filtering - include all subfolders
-                filtered_data = [
-                    lora for lora in filtered_data
-                    if lora['folder'].startswith(folder)
-                ]
-            else:
-                # Exact folder filtering
-                filtered_data = [
-                    lora for lora in filtered_data
-                    if lora['folder'] == folder
-                ]
-        
-        # Apply base model filtering
-        if base_models and len(base_models) > 0:
-            filtered_data = [
-                lora for lora in filtered_data
-                if lora.get('base_model') in base_models
-            ]
-        
-        # Apply tag filtering
-        if tags and len(tags) > 0:
-            filtered_data = [
-                lora for lora in filtered_data
-                if any(tag in lora.get('tags', []) for tag in tags)
-            ]
-        
-        # Apply search filtering
-        if search:
-            search_results = []
-            search_opts = search_options or {}
-            
-            for lora in filtered_data:
-                # Search by file name
-                if search_opts.get('filename', True):
-                    if fuzzy_match(lora.get('file_name', ''), search):
-                        search_results.append(lora)
-                        continue
-                        
-                # Search by model name
-                if search_opts.get('modelname', True):
-                    if fuzzy_match(lora.get('model_name', ''), search):
-                        search_results.append(lora)
-                        continue
-                        
-                # Search by tags
-                if search_opts.get('tags', False) and 'tags' in lora:
-                    if any(fuzzy_match(tag, search) for tag in lora['tags']):
-                        search_results.append(lora)
-                        continue
-            
-            filtered_data = search_results
-
-        # Calculate pagination
-        total_items = len(filtered_data)
-        start_idx = (page - 1) * page_size
-        end_idx = min(start_idx + page_size, total_items)
-        
-        result = {
-            'items': filtered_data[start_idx:end_idx],
-            'total': total_items,
-            'page': page,
-            'page_size': page_size,
-            'total_pages': (total_items + page_size - 1) // page_size
-        }
-        
-        return result
-
-    def _filter_by_first_letter(self, data, letter):
-        """Filter data by first letter of model name
-        
-        Special handling:
-        - '#': Numbers (0-9)
-        - '@': Special characters (not alphanumeric)
-        - '漢': CJK characters
-        """
-        filtered_data = []
-        
-        for lora in data:
-            model_name = lora.get('model_name', '')
-            if not model_name:
-                continue
-                
-            first_char = model_name[0].upper()
-            
-            if letter == '#' and first_char.isdigit():
-                filtered_data.append(lora)
-            elif letter == '@' and not first_char.isalnum():
-                # Special characters (not alphanumeric)
-                filtered_data.append(lora)
-            elif letter == '漢' and self._is_cjk_character(first_char):
-                # CJK characters
-                filtered_data.append(lora)
-            elif letter.upper() == first_char:
-                # Regular alphabet matching
-                filtered_data.append(lora)
-                
-        return filtered_data
-    
-    def _is_cjk_character(self, char):
-        """Check if character is a CJK character"""
-        # Define Unicode ranges for CJK characters
-        cjk_ranges = [
-            (0x4E00, 0x9FFF),   # CJK Unified Ideographs
-            (0x3400, 0x4DBF),   # CJK Unified Ideographs Extension A
-            (0x20000, 0x2A6DF), # CJK Unified Ideographs Extension B
-            (0x2A700, 0x2B73F), # CJK Unified Ideographs Extension C
-            (0x2B740, 0x2B81F), # CJK Unified Ideographs Extension D
-            (0x2B820, 0x2CEAF), # CJK Unified Ideographs Extension E
-            (0x2CEB0, 0x2EBEF), # CJK Unified Ideographs Extension F
-            (0x30000, 0x3134F), # CJK Unified Ideographs Extension G
-            (0xF900, 0xFAFF),   # CJK Compatibility Ideographs
-            (0x3300, 0x33FF),   # CJK Compatibility
-            (0x3200, 0x32FF),   # Enclosed CJK Letters and Months
-            (0x3100, 0x312F),   # Bopomofo
-            (0x31A0, 0x31BF),   # Bopomofo Extended
-            (0x3040, 0x309F),   # Hiragana
-            (0x30A0, 0x30FF),   # Katakana
-            (0x31F0, 0x31FF),   # Katakana Phonetic Extensions
-            (0xAC00, 0xD7AF),   # Hangul Syllables
-            (0x1100, 0x11FF),   # Hangul Jamo
-            (0xA960, 0xA97F),   # Hangul Jamo Extended-A
-            (0xD7B0, 0xD7FF),   # Hangul Jamo Extended-B
-        ]
-        
-        code_point = ord(char)
-        return any(start <= code_point <= end for start, end in cjk_ranges)
-    
-    async def get_letter_counts(self):
-        """Get count of models for each letter of the alphabet"""
-        cache = await self.get_cached_data()
-        data = cache.sorted_by_name
-        
-        # Define letter categories
-        letters = {
-            '#': 0,  # Numbers
-            'A': 0, 'B': 0, 'C': 0, 'D': 0, 'E': 0, 'F': 0, 'G': 0, 'H': 0,
-            'I': 0, 'J': 0, 'K': 0, 'L': 0, 'M': 0, 'N': 0, 'O': 0, 'P': 0,
-            'Q': 0, 'R': 0, 'S': 0, 'T': 0, 'U': 0, 'V': 0, 'W': 0, 'X': 0,
-            'Y': 0, 'Z': 0,
-            '@': 0,  # Special characters
-            '漢': 0   # CJK characters
-        }
-        
-        # Count models for each letter
-        for lora in data:
-            model_name = lora.get('model_name', '')
-            if not model_name:
-                continue
-                
-            first_char = model_name[0].upper()
-            
-            if first_char.isdigit():
-                letters['#'] += 1
-            elif first_char in letters:
-                letters[first_char] += 1
-            elif self._is_cjk_character(first_char):
-                letters['漢'] += 1
-            elif not first_char.isalnum():
-                letters['@'] += 1
-                
-        return letters
-
-    # Lora-specific hash index functionality
-    def has_lora_hash(self, sha256: str) -> bool:
-        """Check if a LoRA with given hash exists"""
-        return self.has_hash(sha256)
-        
-    def get_lora_path_by_hash(self, sha256: str) -> Optional[str]:
-        """Get file path for a LoRA by its hash"""
-        return self.get_path_by_hash(sha256)
-        
-    def get_lora_hash_by_path(self, file_path: str) -> Optional[str]:
-        """Get hash for a LoRA by its file path"""
-        return self.get_hash_by_path(file_path)
-
-    async def get_top_tags(self, limit: int = 20) -> List[Dict[str, any]]:
-        """Get top tags sorted by count"""
-        # Make sure cache is initialized
-        await self.get_cached_data()
-        
-        # Sort tags by count in descending order
-        sorted_tags = sorted(
-            [{"tag": tag, "count": count} for tag, count in self._tags_count.items()],
-            key=lambda x: x['count'],
-            reverse=True
-        )
-        
-        # Return limited number
-        return sorted_tags[:limit]
-        
-    async def get_base_models(self, limit: int = 20) -> List[Dict[str, any]]:
-        """Get base models used in loras sorted by frequency"""
-        # Make sure cache is initialized
-        cache = await self.get_cached_data()
-        
-        # Count base model occurrences
-        base_model_counts = {}
-        for lora in cache.raw_data:
-            if 'base_model' in lora and lora['base_model']:
-                base_model = lora['base_model']
-                base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
-        
-        # Sort base models by count
-        sorted_models = [{'name': model, 'count': count} for model, count in base_model_counts.items()]
-        sorted_models.sort(key=lambda x: x['count'], reverse=True)
-        
-        # Return limited number
-        return sorted_models[:limit]
 
     async def diagnose_hash_index(self):
         """Diagnostic method to verify hash index functionality"""
@@ -456,19 +63,3 @@ class LoraScanner(ModelScanner):
         test_hash_result = self._hash_index.get_hash(test_path)
         print(f"Test reverse lookup: {test_path} -> {test_hash_result[:8]}...\n\n", file=sys.stderr)
 
-    async def get_lora_info_by_name(self, name):
-        """Get LoRA information by name"""
-        try:
-            # Get cached data
-            cache = await self.get_cached_data()
-            
-            # Find the LoRA by name
-            for lora in cache.raw_data:
-                if lora.get("file_name") == name:
-                    return lora
-                    
-            return None
-        except Exception as e:
-            logger.error(f"Error getting LoRA info by name: {e}", exc_info=True)
-            return None
-

py/services/lora_service.py

@@ -0,0 +1,212 @@
+import os
+import logging
+from typing import Dict, List, Optional
+
+from .base_model_service import BaseModelService
+from ..utils.models import LoraMetadata
+from ..config import config
+from ..utils.routes_common import ModelRouteUtils
+
+logger = logging.getLogger(__name__)
+
+class LoraService(BaseModelService):
+    """LoRA-specific service implementation"""
+    
+    def __init__(self, scanner):
+        """Initialize LoRA service
+        
+        Args:
+            scanner: LoRA scanner instance
+        """
+        super().__init__("lora", scanner, LoraMetadata)
+    
+    async def format_response(self, lora_data: Dict) -> Dict:
+        """Format LoRA data for API response"""
+        return {
+            "model_name": lora_data["model_name"],
+            "file_name": lora_data["file_name"],
+            "preview_url": config.get_preview_static_url(lora_data.get("preview_url", "")),
+            "preview_nsfw_level": lora_data.get("preview_nsfw_level", 0),
+            "base_model": lora_data.get("base_model", ""),
+            "folder": lora_data["folder"],
+            "sha256": lora_data.get("sha256", ""),
+            "file_path": lora_data["file_path"].replace(os.sep, "/"),
+            "file_size": lora_data.get("size", 0),
+            "modified": lora_data.get("modified", ""),
+            "tags": lora_data.get("tags", []),
+            "modelDescription": lora_data.get("modelDescription", ""),
+            "from_civitai": lora_data.get("from_civitai", True),
+            "usage_tips": lora_data.get("usage_tips", ""),
+            "notes": lora_data.get("notes", ""),
+            "favorite": lora_data.get("favorite", False),
+            "civitai": ModelRouteUtils.filter_civitai_data(lora_data.get("civitai", {}))
+        }
+    
+    async def _apply_specific_filters(self, data: List[Dict], **kwargs) -> List[Dict]:
+        """Apply LoRA-specific filters"""
+        # Handle first_letter filter for LoRAs
+        first_letter = kwargs.get('first_letter')
+        if first_letter:
+            data = self._filter_by_first_letter(data, first_letter)
+        
+        return data
+    
+    def _filter_by_first_letter(self, data: List[Dict], letter: str) -> List[Dict]:
+        """Filter data by first letter of model name
+        
+        Special handling:
+        - '#': Numbers (0-9)
+        - '@': Special characters (not alphanumeric)
+        - '漢': CJK characters
+        """
+        filtered_data = []
+        
+        for lora in data:
+            model_name = lora.get('model_name', '')
+            if not model_name:
+                continue
+                
+            first_char = model_name[0].upper()
+            
+            if letter == '#' and first_char.isdigit():
+                filtered_data.append(lora)
+            elif letter == '@' and not first_char.isalnum():
+                # Special characters (not alphanumeric)
+                filtered_data.append(lora)
+            elif letter == '漢' and self._is_cjk_character(first_char):
+                # CJK characters
+                filtered_data.append(lora)
+            elif letter.upper() == first_char:
+                # Regular alphabet matching
+                filtered_data.append(lora)
+                
+        return filtered_data
+    
+    def _is_cjk_character(self, char: str) -> bool:
+        """Check if character is a CJK character"""
+        # Define Unicode ranges for CJK characters
+        cjk_ranges = [
+            (0x4E00, 0x9FFF),   # CJK Unified Ideographs
+            (0x3400, 0x4DBF),   # CJK Unified Ideographs Extension A
+            (0x20000, 0x2A6DF), # CJK Unified Ideographs Extension B
+            (0x2A700, 0x2B73F), # CJK Unified Ideographs Extension C
+            (0x2B740, 0x2B81F), # CJK Unified Ideographs Extension D
+            (0x2B820, 0x2CEAF), # CJK Unified Ideographs Extension E
+            (0x2CEB0, 0x2EBEF), # CJK Unified Ideographs Extension F
+            (0x30000, 0x3134F), # CJK Unified Ideographs Extension G
+            (0xF900, 0xFAFF),   # CJK Compatibility Ideographs
+            (0x3300, 0x33FF),   # CJK Compatibility
+            (0x3200, 0x32FF),   # Enclosed CJK Letters and Months
+            (0x3100, 0x312F),   # Bopomofo
+            (0x31A0, 0x31BF),   # Bopomofo Extended
+            (0x3040, 0x309F),   # Hiragana
+            (0x30A0, 0x30FF),   # Katakana
+            (0x31F0, 0x31FF),   # Katakana Phonetic Extensions
+            (0xAC00, 0xD7AF),   # Hangul Syllables
+            (0x1100, 0x11FF),   # Hangul Jamo
+            (0xA960, 0xA97F),   # Hangul Jamo Extended-A
+            (0xD7B0, 0xD7FF),   # Hangul Jamo Extended-B
+        ]
+        
+        code_point = ord(char)
+        return any(start <= code_point <= end for start, end in cjk_ranges)
+    
+    # LoRA-specific methods
+    async def get_letter_counts(self) -> Dict[str, int]:
+        """Get count of LoRAs for each letter of the alphabet"""
+        cache = await self.scanner.get_cached_data()
+        data = cache.raw_data
+        
+        # Define letter categories
+        letters = {
+            '#': 0,  # Numbers
+            'A': 0, 'B': 0, 'C': 0, 'D': 0, 'E': 0, 'F': 0, 'G': 0, 'H': 0,
+            'I': 0, 'J': 0, 'K': 0, 'L': 0, 'M': 0, 'N': 0, 'O': 0, 'P': 0,
+            'Q': 0, 'R': 0, 'S': 0, 'T': 0, 'U': 0, 'V': 0, 'W': 0, 'X': 0,
+            'Y': 0, 'Z': 0,
+            '@': 0,  # Special characters
+            '漢': 0   # CJK characters
+        }
+        
+        # Count models for each letter
+        for lora in data:
+            model_name = lora.get('model_name', '')
+            if not model_name:
+                continue
+                
+            first_char = model_name[0].upper()
+            
+            if first_char.isdigit():
+                letters['#'] += 1
+            elif first_char in letters:
+                letters[first_char] += 1
+            elif self._is_cjk_character(first_char):
+                letters['漢'] += 1
+            elif not first_char.isalnum():
+                letters['@'] += 1
+                
+        return letters
+    
+    async def get_lora_notes(self, lora_name: str) -> Optional[str]:
+        """Get notes for a specific LoRA file"""
+        cache = await self.scanner.get_cached_data()
+        
+        for lora in cache.raw_data:
+            if lora['file_name'] == lora_name:
+                return lora.get('notes', '')
+        
+        return None
+    
+    async def get_lora_trigger_words(self, lora_name: str) -> List[str]:
+        """Get trigger words for a specific LoRA file"""
+        cache = await self.scanner.get_cached_data()
+        
+        for lora in cache.raw_data:
+            if lora['file_name'] == lora_name:
+                civitai_data = lora.get('civitai', {})
+                return civitai_data.get('trainedWords', [])
+        
+        return []
+    
+    async def get_lora_preview_url(self, lora_name: str) -> Optional[str]:
+        """Get the static preview URL for a LoRA file"""
+        cache = await self.scanner.get_cached_data()
+        
+        for lora in cache.raw_data:
+            if lora['file_name'] == lora_name:
+                preview_url = lora.get('preview_url')
+                if preview_url:
+                    return config.get_preview_static_url(preview_url)
+        
+        return None
+    
+    async def get_lora_civitai_url(self, lora_name: str) -> Dict[str, Optional[str]]:
+        """Get the Civitai URL for a LoRA file"""
+        cache = await self.scanner.get_cached_data()
+        
+        for lora in cache.raw_data:
+            if lora['file_name'] == lora_name:
+                civitai_data = lora.get('civitai', {})
+                model_id = civitai_data.get('modelId')
+                version_id = civitai_data.get('id')
+                
+                if model_id:
+                    civitai_url = f"https://civitai.com/models/{model_id}"
+                    if version_id:
+                        civitai_url += f"?modelVersionId={version_id}"
+                    
+                    return {
+                        'civitai_url': civitai_url,
+                        'model_id': str(model_id),
+                        'version_id': str(version_id) if version_id else None
+                    }
+        
+        return {'civitai_url': None, 'model_id': None, 'version_id': None}
+    
+    def find_duplicate_hashes(self) -> Dict:
+        """Find LoRAs with duplicate SHA256 hashes"""
+        return self.scanner._hash_index.get_duplicate_hashes()
+    
+    def find_duplicate_filenames(self) -> Dict:
+        """Find LoRAs with conflicting filenames"""
+        return self.scanner._hash_index.get_duplicate_filenames()

py/services/model_cache.py

@@ -1,37 +1,85 @@
 import asyncio
-from typing import List, Dict
+from typing import List, Dict, Tuple
 from dataclasses import dataclass
 from operator import itemgetter
 from natsort import natsorted
 
+# Supported sort modes: (sort_key, order)
+# order: 'asc' for ascending, 'desc' for descending
+SUPPORTED_SORT_MODES = [
+    ('name', 'asc'),
+    ('name', 'desc'),
+    ('date', 'asc'),
+    ('date', 'desc'),
+    ('size', 'asc'),
+    ('size', 'desc'),
+]
+
 @dataclass
 class ModelCache:
-    """Cache structure for model data"""
+    """Cache structure for model data with extensible sorting"""
     raw_data: List[Dict]
-    sorted_by_name: List[Dict]
-    sorted_by_date: List[Dict]
     folders: List[str]
     
     def __post_init__(self):
         self._lock = asyncio.Lock()
+        # Cache for last sort: (sort_key, order) -> sorted list
+        self._last_sort: Tuple[str, str] = (None, None)
+        self._last_sorted_data: List[Dict] = []
+        # Default sort on init
+        asyncio.create_task(self.resort())
 
-    async def resort(self, name_only: bool = False):
-        """Resort all cached data views"""
+    async def resort(self):
+        """Resort cached data according to last sort mode if set"""
         async with self._lock:
-            self.sorted_by_name = natsorted(
-                self.raw_data, 
-                key=lambda x: x['model_name'].lower()  # Case-insensitive sort
-            )
-            if not name_only:
-                self.sorted_by_date = sorted(
-                    self.raw_data, 
-                    key=itemgetter('modified'), 
-                    reverse=True
-                )
-            # Update folder list
+            if self._last_sort != (None, None):
+                sort_key, order = self._last_sort
+                sorted_data = self._sort_data(self.raw_data, sort_key, order)
+                self._last_sorted_data = sorted_data
+                # Update folder list
+            # else: do nothing
+
             all_folders = set(l['folder'] for l in self.raw_data)
             self.folders = sorted(list(all_folders), key=lambda x: x.lower())
 
+    def _sort_data(self, data: List[Dict], sort_key: str, order: str) -> List[Dict]:
+        """Sort data by sort_key and order"""
+        reverse = (order == 'desc')
+        if sort_key == 'name':
+            # Natural sort by model_name, case-insensitive
+            return natsorted(
+                data,
+                key=lambda x: x['model_name'].lower(),
+                reverse=reverse
+            )
+        elif sort_key == 'date':
+            # Sort by modified timestamp
+            return sorted(
+                data,
+                key=itemgetter('modified'),
+                reverse=reverse
+            )
+        elif sort_key == 'size':
+            # Sort by file size
+            return sorted(
+                data,
+                key=itemgetter('size'),
+                reverse=reverse
+            )
+        else:
+            # Fallback: no sort
+            return list(data)
+
+    async def get_sorted_data(self, sort_key: str = 'name', order: str = 'asc') -> List[Dict]:
+        """Get sorted data by sort_key and order, using cache if possible"""
+        async with self._lock:
+            if (sort_key, order) == self._last_sort:
+                return self._last_sorted_data
+            sorted_data = self._sort_data(self.raw_data, sort_key, order)
+            self._last_sort = (sort_key, order)
+            self._last_sorted_data = sorted_data
+            return sorted_data
+
     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
         

py/services/model_scanner.py

@@ -5,7 +5,6 @@ import asyncio
 import time
 import shutil
 from typing import List, Dict, Optional, Type, Set
-import msgpack  # Add MessagePack import for efficient serialization
 
 from ..utils.models import BaseModelMetadata
 from ..config import config
@@ -19,17 +18,33 @@ from .websocket_manager import ws_manager
 
 logger = logging.getLogger(__name__)
 
-# Define cache version to handle future format changes
-# Version history:
-# 1 - Initial version
-# 2 - Added duplicate_filenames and duplicate_hashes tracking
-# 3 - Added _excluded_models list to cache
-CACHE_VERSION = 3
-
 class ModelScanner:
     """Base service for scanning and managing model files"""
     
-    _lock = asyncio.Lock()
+    _instances = {}  # Dictionary to store instances by class
+    _locks = {}  # Dictionary to store locks by class
+    
+    def __new__(cls, *args, **kwargs):
+        """Implement singleton pattern for each subclass"""
+        if cls not in cls._instances:
+            cls._instances[cls] = super().__new__(cls)
+        return cls._instances[cls]
+    
+    @classmethod
+    def _get_lock(cls):
+        """Get or create a lock for this class"""
+        if cls not in cls._locks:
+            cls._locks[cls] = asyncio.Lock()
+        return cls._locks[cls]
+    
+    @classmethod
+    async def get_instance(cls):
+        """Get singleton instance with async support"""
+        lock = cls._get_lock()
+        async with lock:
+            if cls not in cls._instances:
+                cls._instances[cls] = cls()
+            return cls._instances[cls]
     
     def __init__(self, model_type: str, model_class: Type[BaseModelMetadata], file_extensions: Set[str], hash_index: Optional[ModelHashIndex] = None):
         """Initialize the scanner
@@ -40,6 +55,10 @@ class ModelScanner:
             file_extensions: Set of supported file extensions including the dot (e.g. {'.safetensors'})
             hash_index: Hash index instance (optional)
         """
+        # Ensure initialization happens only once per instance
+        if hasattr(self, '_initialized'):
+            return
+            
         self.model_type = model_type
         self.model_class = model_class
         self.file_extensions = file_extensions
@@ -48,202 +67,15 @@ class ModelScanner:
         self._tags_count = {}  # Dictionary to store tag counts
         self._is_initializing = False  # Flag to track initialization state
         self._excluded_models = []  # List to track excluded models
-        self._dirs_last_modified = {}  # Track directory modification times
-        self._use_cache_files = False  # Flag to control cache file usage, default to disabled
-        
-        # Clear cache files if disabled
-        if not self._use_cache_files:
-            self._clear_cache_files()
+        self._initialized = True
         
         # Register this service
         asyncio.create_task(self._register_service())
     
-    def _clear_cache_files(self):
-        """Clear existing cache files if they exist"""
-        try:
-            cache_path = self._get_cache_file_path()
-            if cache_path and os.path.exists(cache_path):
-                os.remove(cache_path)
-                logger.info(f"Cleared {self.model_type} cache file: {cache_path}")
-        except Exception as e:
-            logger.error(f"Error clearing {self.model_type} cache file: {e}")
-    
     async def _register_service(self):
         """Register this instance with the ServiceRegistry"""
         service_name = f"{self.model_type}_scanner"
         await ServiceRegistry.register_service(service_name, self)
-    
-    def _get_cache_file_path(self) -> Optional[str]:
-        """Get the path to the cache file"""
-        # Get the directory where this module is located
-        current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
-        
-        # Create a cache directory within the project if it doesn't exist
-        cache_dir = os.path.join(current_dir, "cache")
-        os.makedirs(cache_dir, exist_ok=True)
-        
-        # Create filename based on model type
-        cache_filename = f"lm_{self.model_type}_cache.msgpack"
-        return os.path.join(cache_dir, cache_filename)
-
-    def _prepare_for_msgpack(self, data):
-        """Preprocess data to accommodate MessagePack serialization limitations
-        
-        Converts integers exceeding safe range to strings
-        
-        Args:
-            data: Any type of data structure
-            
-        Returns:
-            Preprocessed data structure with large integers converted to strings
-        """
-        if isinstance(data, dict):
-            return {k: self._prepare_for_msgpack(v) for k, v in data.items()}
-        elif isinstance(data, list):
-            return [self._prepare_for_msgpack(item) for item in data]
-        elif isinstance(data, int) and (data > 9007199254740991 or data < -9007199254740991):
-            # Convert integers exceeding JavaScript's safe integer range (2^53-1) to strings
-            return str(data)
-        else:
-            return data
-
-    async def _save_cache_to_disk(self) -> bool:
-        """Save cache data to disk using MessagePack"""
-        if not self._use_cache_files:
-            logger.debug(f"Cache files disabled for {self.model_type}, skipping save")
-            return False
-            
-        if self._cache is None or not self._cache.raw_data:
-            logger.debug(f"No {self.model_type} cache data to save")
-            return False
-            
-        cache_path = self._get_cache_file_path()
-        if not cache_path:
-            logger.warning(f"Cannot determine {self.model_type} cache file location")
-            return False
-            
-        try:
-            # Create cache data structure
-            cache_data = {
-                "version": CACHE_VERSION,
-                "timestamp": time.time(),
-                "model_type": self.model_type,
-                "raw_data": self._cache.raw_data,
-                "hash_index": {
-                    "hash_to_path": self._hash_index._hash_to_path,
-                    "filename_to_hash": self._hash_index._filename_to_hash,  # Fix: changed from path_to_hash to filename_to_hash
-                    "duplicate_hashes": self._hash_index._duplicate_hashes,
-                    "duplicate_filenames": self._hash_index._duplicate_filenames
-                },
-                "tags_count": self._tags_count,
-                "dirs_last_modified": self._get_dirs_last_modified(),
-                "excluded_models": self._excluded_models  # Add excluded_models to cache data
-            }
-            
-            # Preprocess data to handle large integers
-            processed_cache_data = self._prepare_for_msgpack(cache_data)
-            
-            # Write to temporary file first (atomic operation)
-            temp_path = f"{cache_path}.tmp"
-            with open(temp_path, 'wb') as f:
-                msgpack.pack(processed_cache_data, f)
-                
-            # Replace the old file with the new one
-            if os.path.exists(cache_path):
-                os.replace(temp_path, cache_path)
-            else:
-                os.rename(temp_path, cache_path)
-                
-            logger.info(f"Saved {self.model_type} cache with {len(self._cache.raw_data)} models to {cache_path}")
-            logger.debug(f"Hash index stats - hash_to_path: {len(self._hash_index._hash_to_path)}, filename_to_hash: {len(self._hash_index._filename_to_hash)}, duplicate_hashes: {len(self._hash_index._duplicate_hashes)}, duplicate_filenames: {len(self._hash_index._duplicate_filenames)}")
-            return True
-        except Exception as e:
-            logger.error(f"Error saving {self.model_type} cache to disk: {e}")
-            # Try to clean up temp file if it exists
-            if 'temp_path' in locals() and os.path.exists(temp_path):
-                try:
-                    os.remove(temp_path)
-                except:
-                    pass
-            return False
-    
-    def _get_dirs_last_modified(self) -> Dict[str, float]:
-        """Get last modified time for all model directories"""
-        dirs_info = {}
-        for root in self.get_model_roots():
-            if os.path.exists(root):
-                dirs_info[root] = os.path.getmtime(root)
-                # Also check immediate subdirectories for changes
-                try:
-                    with os.scandir(root) as it:
-                        for entry in it:
-                            if entry.is_dir(follow_symlinks=True):
-                                dirs_info[entry.path] = entry.stat().st_mtime
-                except Exception as e:
-                    logger.error(f"Error getting directory info for {root}: {e}")
-        return dirs_info
-                
-    def _is_cache_valid(self, cache_data: Dict) -> bool:
-        """Validate if the loaded cache is still valid"""
-        if not cache_data or cache_data.get("version") != CACHE_VERSION:
-            logger.info(f"Cache invalid - version mismatch. Got: {cache_data.get('version')}, Expected: {CACHE_VERSION}")
-            return False
-            
-        if cache_data.get("model_type") != self.model_type:
-            logger.info(f"Cache invalid - model type mismatch. Got: {cache_data.get('model_type')}, Expected: {self.model_type}")
-            return False
-                
-        return True
-        
-    async def _load_cache_from_disk(self) -> bool:
-        """Load cache data from disk using MessagePack"""
-        if not self._use_cache_files:
-            logger.info(f"Cache files disabled for {self.model_type}, skipping load")
-            return False
-            
-        start_time = time.time()
-        cache_path = self._get_cache_file_path()
-        if not cache_path or not os.path.exists(cache_path):
-            return False
-            
-        try:
-            with open(cache_path, 'rb') as f:
-                cache_data = msgpack.unpack(f)
-                
-            # Validate cache data
-            if not self._is_cache_valid(cache_data):
-                logger.info(f"{self.model_type.capitalize()} cache file found but invalid or outdated")
-                return False
-                
-            # Load data into memory
-            self._cache = ModelCache(
-                raw_data=cache_data["raw_data"],
-                sorted_by_name=[],
-                sorted_by_date=[],
-                folders=[]
-            )
-            
-            # Load hash index
-            hash_index_data = cache_data.get("hash_index", {})
-            self._hash_index._hash_to_path = hash_index_data.get("hash_to_path", {})
-            self._hash_index._filename_to_hash = hash_index_data.get("filename_to_hash", {})  # Fix: changed from path_to_hash to filename_to_hash
-            self._hash_index._duplicate_hashes = hash_index_data.get("duplicate_hashes", {})
-            self._hash_index._duplicate_filenames = hash_index_data.get("duplicate_filenames", {})
-            
-            # Load tags count
-            self._tags_count = cache_data.get("tags_count", {})
-            
-            # Load excluded models
-            self._excluded_models = cache_data.get("excluded_models", [])
-            
-            # Resort the cache
-            await self._cache.resort()
-            
-            logger.info(f"Loaded {self.model_type} cache from disk with {len(self._cache.raw_data)} models in {time.time() - start_time:.2f} seconds")
-            return True
-        except Exception as e:
-            logger.error(f"Error loading {self.model_type} cache from disk: {e}")
-            return False
 
     async def initialize_in_background(self) -> None:
         """Initialize cache in background using thread pool"""
@@ -252,8 +84,6 @@ class ModelScanner:
             if self._cache is None:
                 self._cache = ModelCache(
                     raw_data=[],
-                    sorted_by_name=[],
-                    sorted_by_date=[],
                     folders=[]
                 )
             
@@ -271,21 +101,6 @@ class ModelScanner:
                 'scanner_type': self.model_type,
                 'pageType': page_type
             })
-            
-            cache_loaded = await self._load_cache_from_disk()
-            
-            if cache_loaded:
-                # Cache loaded successfully, broadcast complete message
-                await ws_manager.broadcast_init_progress({
-                    'stage': 'finalizing',
-                    'progress': 100,
-                    'status': 'complete',
-                    'details': f"Loaded {len(self._cache.raw_data)} {self.model_type} files from cache.",
-                    'scanner_type': self.model_type,
-                    'pageType': page_type
-                })
-                self._is_initializing = False
-                return
                 
             # If cache loading failed, proceed with full scan
             await ws_manager.broadcast_init_progress({
@@ -332,9 +147,6 @@ class ModelScanner:
             
             logger.info(f"{self.model_type.capitalize()} cache initialized in {time.time() - start_time:.2f} seconds. Found {len(self._cache.raw_data)} models")
             
-            # Save the cache to disk after initialization
-            await self._save_cache_to_disk()
-            
             # Send completion message
             await asyncio.sleep(0.5)  # Small delay to ensure final progress message is sent
             await ws_manager.broadcast_init_progress({
@@ -509,40 +321,21 @@ class ModelScanner:
         
         Args:
             force_refresh: Whether to refresh the cache
-            rebuild_cache: Whether to completely rebuild the cache by reloading from disk first
+            rebuild_cache: Whether to completely rebuild the cache
         """
         # If cache is not initialized, return an empty cache
         # Actual initialization should be done via initialize_in_background
         if self._cache is None and not force_refresh:
             return ModelCache(
                 raw_data=[],
-                sorted_by_name=[],
-                sorted_by_date=[],
                 folders=[]
             )
 
         # If force refresh is requested, initialize the cache directly
         if force_refresh:
-            # If rebuild_cache is True, try to reload from disk before reconciliation
             if rebuild_cache:
-                logger.info(f"{self.model_type.capitalize()} Scanner: Attempting to rebuild cache from disk...")
-                cache_loaded = await self._load_cache_from_disk()
-                if cache_loaded:
-                    logger.info(f"{self.model_type.capitalize()} Scanner: Successfully reloaded cache from disk")
-                else:
-                    logger.info(f"{self.model_type.capitalize()} Scanner: Could not reload cache from disk, proceeding with complete rebuild")
-                    # If loading from disk failed, do a complete rebuild and save to disk
-                    await self._initialize_cache()
-                    await self._save_cache_to_disk()
-                    return self._cache
-            
-            if self._cache is None:
-                # For initial creation, do a full initialization
                 await self._initialize_cache()
-                # Save the newly built cache
-                await self._save_cache_to_disk()
             else:
-                # For subsequent refreshes, use fast reconciliation
                 await self._reconcile_cache()
         
         return self._cache
@@ -577,8 +370,6 @@ class ModelScanner:
             # Update cache
             self._cache = ModelCache(
                 raw_data=raw_data,
-                sorted_by_name=[],
-                sorted_by_date=[],
                 folders=[]
             )
             
@@ -592,8 +383,6 @@ class ModelScanner:
             if self._cache is None:
                 self._cache = ModelCache(
                     raw_data=[],
-                    sorted_by_name=[],
-                    sorted_by_date=[],
                     folders=[]
                 )
         finally:
@@ -735,19 +524,74 @@ class ModelScanner:
                 # Resort cache
                 await self._cache.resort()
                 
-                # Save updated cache to disk
-                await self._save_cache_to_disk()
-                
             logger.info(f"{self.model_type.capitalize()} Scanner: Cache reconciliation completed in {time.time() - start_time:.2f} seconds. Added {total_added}, removed {total_removed} models.")
         except Exception as e:
             logger.error(f"{self.model_type.capitalize()} Scanner: Error reconciling cache: {e}", exc_info=True)
         finally:
             self._is_initializing = False # Unset flag
 
-    # These methods should be implemented in child classes
     async def scan_all_models(self) -> List[Dict]:
         """Scan all model directories and return metadata"""
-        raise NotImplementedError("Subclasses must implement scan_all_models")
+        all_models = []
+        
+        # Create scan tasks for each directory
+        scan_tasks = []
+        for model_root in self.get_model_roots():
+            task = asyncio.create_task(self._scan_directory(model_root))
+            scan_tasks.append(task)
+            
+        # Wait for all tasks to complete
+        for task in scan_tasks:
+            try:
+                models = await task
+                all_models.extend(models)
+            except Exception as e:
+                logger.error(f"Error scanning directory: {e}")
+                
+        return all_models
+    
+    async def _scan_directory(self, root_path: str) -> List[Dict]:
+        """Scan a single directory for model files"""
+        models = []
+        original_root = root_path  # Save original root path
+
+        async def scan_recursive(path: str, visited_paths: set):
+            """Recursively scan directory, avoiding circular symlinks"""
+            try:
+                real_path = os.path.realpath(path)
+                if real_path in visited_paths:
+                    logger.debug(f"Skipping already visited path: {path}")
+                    return
+                visited_paths.add(real_path)
+
+                with os.scandir(path) as it:
+                    entries = list(it)
+                    for entry in entries:
+                        try:
+                            if entry.is_file(follow_symlinks=True) and any(entry.name.endswith(ext) for ext in self.file_extensions):
+                                # Use original path instead of real path
+                                file_path = entry.path.replace(os.sep, "/")
+                                await self._process_single_file(file_path, original_root, models)
+                                await asyncio.sleep(0)
+                            elif entry.is_dir(follow_symlinks=True):
+                                # For directories, continue scanning with original path
+                                await scan_recursive(entry.path, visited_paths)
+                        except Exception as e:
+                            logger.error(f"Error processing entry {entry.path}: {e}")
+            except Exception as e:
+                logger.error(f"Error scanning {path}: {e}")
+
+        await scan_recursive(root_path, set())
+        return models
+
+    async def _process_single_file(self, file_path: str, root_path: str, models: list):
+        """Process a single file and add to results list"""
+        try:
+            result = await self._process_model_file(file_path, root_path)
+            if result:
+                models.append(result)
+        except Exception as e:
+            logger.error(f"Error processing {file_path}: {e}")
     
     def is_initializing(self) -> bool:
         """Check if the scanner is currently initializing"""
@@ -931,7 +775,7 @@ class ModelScanner:
             logger.error(f"Error processing {file_path}: {e}")
 
     async def add_model_to_cache(self, metadata_dict: Dict, folder: str = '') -> bool:
-        """Add a model to the cache and save to disk
+        """Add a model to the cache
         
         Args:
             metadata_dict: The model metadata dictionary
@@ -960,9 +804,6 @@ class ModelScanner:
             
             # Update the hash index
             self._hash_index.add_entry(metadata_dict['sha256'], metadata_dict['file_path'])
-                
-            # Save to disk
-            await self._save_cache_to_disk()
             return True
         except Exception as e:
             logger.error(f"Error adding model to cache: {e}")
@@ -1102,9 +943,6 @@ class ModelScanner:
         
         await cache.resort()
         
-        # Save the updated cache
-        await self._save_cache_to_disk()
-        
         return True
         
     def has_hash(self, sha256: str) -> bool:
@@ -1198,11 +1036,7 @@ class ModelScanner:
         if self._cache is None:
             return False
 
-        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
+        return await self._cache.update_preview_url(file_path, preview_url, preview_nsfw_level)
 
     async def bulk_delete_models(self, file_paths: List[str]) -> Dict:
         """Delete multiple models and update cache in a batch operation
@@ -1334,9 +1168,6 @@ class ModelScanner:
             # Resort cache
             await self._cache.resort()
             
-            # Save updated cache to disk
-            await self._save_cache_to_disk()
-            
             return True
             
         except Exception as e:
@@ -1362,3 +1193,59 @@ class ModelScanner:
         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/model_service_factory.py

@@ -0,0 +1,142 @@
+from typing import Dict, Type, Any
+import logging
+
+logger = logging.getLogger(__name__)
+
+class ModelServiceFactory:
+    """Factory for managing model services and routes"""
+    
+    _services: Dict[str, Type] = {}
+    _routes: Dict[str, Type] = {}
+    _initialized_services: Dict[str, Any] = {}
+    _initialized_routes: Dict[str, Any] = {}
+    
+    @classmethod
+    def register_model_type(cls, model_type: str, service_class: Type, route_class: Type):
+        """Register a new model type with its service and route classes
+        
+        Args:
+            model_type: The model type identifier (e.g., 'lora', 'checkpoint')
+            service_class: The service class for this model type
+            route_class: The route class for this model type
+        """
+        cls._services[model_type] = service_class
+        cls._routes[model_type] = route_class
+        logger.info(f"Registered model type '{model_type}' with service {service_class.__name__} and routes {route_class.__name__}")
+    
+    @classmethod
+    def get_service_class(cls, model_type: str) -> Type:
+        """Get service class for a model type
+        
+        Args:
+            model_type: The model type identifier
+            
+        Returns:
+            The service class for the model type
+            
+        Raises:
+            ValueError: If model type is not registered
+        """
+        if model_type not in cls._services:
+            raise ValueError(f"Unknown model type: {model_type}")
+        return cls._services[model_type]
+    
+    @classmethod
+    def get_route_class(cls, model_type: str) -> Type:
+        """Get route class for a model type
+        
+        Args:
+            model_type: The model type identifier
+            
+        Returns:
+            The route class for the model type
+            
+        Raises:
+            ValueError: If model type is not registered
+        """
+        if model_type not in cls._routes:
+            raise ValueError(f"Unknown model type: {model_type}")
+        return cls._routes[model_type]
+    
+    @classmethod
+    def get_route_instance(cls, model_type: str):
+        """Get or create route instance for a model type
+        
+        Args:
+            model_type: The model type identifier
+            
+        Returns:
+            The route instance for the model type
+        """
+        if model_type not in cls._initialized_routes:
+            route_class = cls.get_route_class(model_type)
+            cls._initialized_routes[model_type] = route_class()
+        return cls._initialized_routes[model_type]
+    
+    @classmethod
+    def setup_all_routes(cls, app):
+        """Setup routes for all registered model types
+        
+        Args:
+            app: The aiohttp application instance
+        """
+        logger.info(f"Setting up routes for {len(cls._services)} registered model types")
+        
+        for model_type in cls._services.keys():
+            try:
+                routes_instance = cls.get_route_instance(model_type)
+                routes_instance.setup_routes(app)
+                logger.info(f"Successfully set up routes for {model_type}")
+            except Exception as e:
+                logger.error(f"Failed to setup routes for {model_type}: {e}", exc_info=True)
+    
+    @classmethod
+    def get_registered_types(cls) -> list:
+        """Get list of all registered model types
+        
+        Returns:
+            List of registered model type identifiers
+        """
+        return list(cls._services.keys())
+    
+    @classmethod
+    def is_registered(cls, model_type: str) -> bool:
+        """Check if a model type is registered
+        
+        Args:
+            model_type: The model type identifier
+            
+        Returns:
+            True if the model type is registered, False otherwise
+        """
+        return model_type in cls._services
+    
+    @classmethod
+    def clear_registrations(cls):
+        """Clear all registrations - mainly for testing purposes"""
+        cls._services.clear()
+        cls._routes.clear()
+        cls._initialized_services.clear()
+        cls._initialized_routes.clear()
+        logger.info("Cleared all model type registrations")
+
+
+def register_default_model_types():
+    """Register the default model types (LoRA, Checkpoint, and Embedding)"""
+    from ..services.lora_service import LoraService
+    from ..services.checkpoint_service import CheckpointService
+    from ..services.embedding_service import EmbeddingService
+    from ..routes.lora_routes import LoraRoutes
+    from ..routes.checkpoint_routes import CheckpointRoutes
+    from ..routes.embedding_routes import EmbeddingRoutes
+    
+    # Register LoRA model type
+    ModelServiceFactory.register_model_type('lora', LoraService, LoraRoutes)
+    
+    # Register Checkpoint model type
+    ModelServiceFactory.register_model_type('checkpoint', CheckpointService, CheckpointRoutes)
+    
+    # Register Embedding model type
+    ModelServiceFactory.register_model_type('embedding', EmbeddingService, EmbeddingRoutes)
+    
+    logger.info("Registered default model types: lora, checkpoint, embedding")

py/services/recipe_scanner.py

@@ -393,8 +393,8 @@ class RecipeScanner:
             if 'hash' in lora and (not lora.get('file_name') or not lora['file_name']):
                 hash_value = lora['hash']
                 
-                if self._lora_scanner.has_lora_hash(hash_value):
-                    lora_path = self._lora_scanner.get_lora_path_by_hash(hash_value)
+                if self._lora_scanner.has_hash(hash_value):
+                    lora_path = self._lora_scanner.get_path_by_hash(hash_value)
                     if lora_path:
                         file_name = os.path.splitext(os.path.basename(lora_path))[0]
                         lora['file_name'] = file_name
@@ -465,7 +465,7 @@ class RecipeScanner:
         # Count occurrences of each base model
         for lora in loras:
             if 'hash' in lora:
-                lora_path = self._lora_scanner.get_lora_path_by_hash(lora['hash'])
+                lora_path = self._lora_scanner.get_path_by_hash(lora['hash'])
                 if lora_path:
                     base_model = await self._get_base_model_for_lora(lora_path)
                     if base_model:
@@ -603,9 +603,9 @@ class RecipeScanner:
             if 'loras' in item:
                 for lora in item['loras']:
                     if 'hash' in lora and lora['hash']:
-                        lora['inLibrary'] = self._lora_scanner.has_lora_hash(lora['hash'].lower())
+                        lora['inLibrary'] = self._lora_scanner.has_hash(lora['hash'].lower())
                         lora['preview_url'] = self._lora_scanner.get_preview_url_by_hash(lora['hash'].lower())
-                        lora['localPath'] = self._lora_scanner.get_lora_path_by_hash(lora['hash'].lower())
+                        lora['localPath'] = self._lora_scanner.get_path_by_hash(lora['hash'].lower())
         
         result = {
             'items': paginated_items,
@@ -655,9 +655,9 @@ class RecipeScanner:
             for lora in formatted_recipe['loras']:
                 if 'hash' in lora and lora['hash']:
                     lora_hash = lora['hash'].lower()
-                    lora['inLibrary'] = self._lora_scanner.has_lora_hash(lora_hash)
+                    lora['inLibrary'] = self._lora_scanner.has_hash(lora_hash)
                     lora['preview_url'] = self._lora_scanner.get_preview_url_by_hash(lora_hash)
-                    lora['localPath'] = self._lora_scanner.get_lora_path_by_hash(lora_hash)
+                    lora['localPath'] = self._lora_scanner.get_path_by_hash(lora_hash)
                     
         return formatted_recipe
         

py/services/service_registry.py

@@ -7,97 +7,209 @@ logger = logging.getLogger(__name__)
 T = TypeVar('T')  # Define a type variable for service types
 
 class ServiceRegistry:
-    """Centralized registry for service singletons"""
+    """Central registry for managing singleton services"""
     
-    _instance = None
     _services: Dict[str, Any] = {}
-    _lock = asyncio.Lock()
+    _locks: Dict[str, asyncio.Lock] = {}
     
     @classmethod
-    def get_instance(cls):
-        """Get singleton instance of the registry"""
-        if cls._instance is None:
-            cls._instance = cls()
-        return cls._instance
+    async def register_service(cls, name: str, service: Any) -> None:
+        """Register a service instance with the registry
+        
+        Args:
+            name: Service name identifier
+            service: Service instance to register
+        """
+        cls._services[name] = service
+        logger.debug(f"Registered service: {name}")
     
     @classmethod
-    async def register_service(cls, service_name: str, service_instance: Any) -> None:
-        """Register a service instance with the registry"""
-        registry = cls.get_instance()
-        async with cls._lock:
-            registry._services[service_name] = service_instance
-            logger.debug(f"Registered service: {service_name}")
+    async def get_service(cls, name: str) -> Optional[Any]:
+        """Get a service instance by name
+        
+        Args:
+            name: Service name identifier
+            
+        Returns:
+            Service instance or None if not found
+        """
+        return cls._services.get(name)
     
     @classmethod
-    async def get_service(cls, service_name: str) -> Any:
-        """Get a service instance by name"""
-        registry = cls.get_instance()
-        async with cls._lock:
-            if service_name not in registry._services:
-                logger.debug(f"Service {service_name} not found in registry")
-                return None
-            return registry._services[service_name]
+    def get_service_sync(cls, name: str) -> Optional[Any]:
+        """Synchronously get a service instance by name
+
+        Args:
+            name: Service name identifier
+
+        Returns:
+            Service instance or None if not found
+        """
+        return cls._services.get(name)
+    
+    @classmethod
+    def _get_lock(cls, name: str) -> asyncio.Lock:
+        """Get or create a lock for a service
+        
+        Args:
+            name: Service name identifier
+            
+        Returns:
+            AsyncIO lock for the service
+        """
+        if name not in cls._locks:
+            cls._locks[name] = asyncio.Lock()
+        return cls._locks[name]
     
-    # Convenience methods for common services
     @classmethod
     async def get_lora_scanner(cls):
-        """Get the LoraScanner instance"""
-        from .lora_scanner import LoraScanner
-        scanner = await cls.get_service("lora_scanner")
-        if scanner is None:
-            scanner = await LoraScanner.get_instance()
-            await cls.register_service("lora_scanner", scanner)
-        return scanner
+        """Get or create LoRA scanner instance"""
+        service_name = "lora_scanner"
         
+        if service_name in cls._services:
+            return cls._services[service_name]
+        
+        async with cls._get_lock(service_name):
+            # Double-check after acquiring lock
+            if service_name in cls._services:
+                return cls._services[service_name]
+            
+            # Import here to avoid circular imports
+            from .lora_scanner import LoraScanner
+            
+            scanner = await LoraScanner.get_instance()
+            cls._services[service_name] = scanner
+            logger.debug(f"Created and registered {service_name}")
+            return scanner
+    
     @classmethod
     async def get_checkpoint_scanner(cls):
-        """Get the CheckpointScanner instance"""
-        from .checkpoint_scanner import CheckpointScanner
-        scanner = await cls.get_service("checkpoint_scanner")
-        if scanner is None:
+        """Get or create Checkpoint scanner instance"""
+        service_name = "checkpoint_scanner"
+        
+        if service_name in cls._services:
+            return cls._services[service_name]
+        
+        async with cls._get_lock(service_name):
+            # Double-check after acquiring lock
+            if service_name in cls._services:
+                return cls._services[service_name]
+            
+            # Import here to avoid circular imports
+            from .checkpoint_scanner import CheckpointScanner
+            
             scanner = await CheckpointScanner.get_instance()
-            await cls.register_service("checkpoint_scanner", scanner)
-        return scanner
-
-    @classmethod
-    async def get_civitai_client(cls):
-        """Get the CivitaiClient instance"""
-        from .civitai_client import CivitaiClient
-        client = await cls.get_service("civitai_client")
-        if client is None:
-            client = await CivitaiClient.get_instance()
-            await cls.register_service("civitai_client", client)
-        return client
-
-    @classmethod
-    async def get_download_manager(cls):
-        """Get the DownloadManager instance"""
-        from .download_manager import DownloadManager
-        manager = await cls.get_service("download_manager")
-        if manager is None:
-            manager = await DownloadManager.get_instance()
-            await cls.register_service("download_manager", manager)
-        return manager
-
+            cls._services[service_name] = scanner
+            logger.debug(f"Created and registered {service_name}")
+            return scanner
+    
     @classmethod
     async def get_recipe_scanner(cls):
-        """Get the RecipeScanner instance"""
-        from .recipe_scanner import RecipeScanner
-        scanner = await cls.get_service("recipe_scanner")
-        if scanner is None:
-            lora_scanner = await cls.get_lora_scanner()
-            scanner = RecipeScanner(lora_scanner)
-            await cls.register_service("recipe_scanner", scanner)
-        return scanner
-
+        """Get or create Recipe scanner instance"""
+        service_name = "recipe_scanner"
+        
+        if service_name in cls._services:
+            return cls._services[service_name]
+        
+        async with cls._get_lock(service_name):
+            # Double-check after acquiring lock
+            if service_name in cls._services:
+                return cls._services[service_name]
+            
+            # Import here to avoid circular imports
+            from .recipe_scanner import RecipeScanner
+            
+            scanner = await RecipeScanner.get_instance()
+            cls._services[service_name] = scanner
+            logger.debug(f"Created and registered {service_name}")
+            return scanner
+    
+    @classmethod
+    async def get_civitai_client(cls):
+        """Get or create CivitAI client instance"""
+        service_name = "civitai_client"
+        
+        if service_name in cls._services:
+            return cls._services[service_name]
+        
+        async with cls._get_lock(service_name):
+            # Double-check after acquiring lock
+            if service_name in cls._services:
+                return cls._services[service_name]
+            
+            # Import here to avoid circular imports
+            from .civitai_client import CivitaiClient
+            
+            client = await CivitaiClient.get_instance()
+            cls._services[service_name] = client
+            logger.debug(f"Created and registered {service_name}")
+            return client
+    
+    @classmethod
+    async def get_download_manager(cls):
+        """Get or create Download manager instance"""
+        service_name = "download_manager"
+        
+        if service_name in cls._services:
+            return cls._services[service_name]
+        
+        async with cls._get_lock(service_name):
+            # Double-check after acquiring lock
+            if service_name in cls._services:
+                return cls._services[service_name]
+            
+            # Import here to avoid circular imports
+            from .download_manager import DownloadManager
+            
+            manager = DownloadManager()
+            cls._services[service_name] = manager
+            logger.debug(f"Created and registered {service_name}")
+            return manager
+    
     @classmethod
     async def get_websocket_manager(cls):
-        """Get the WebSocketManager instance"""
-        from .websocket_manager import ws_manager
-        manager = await cls.get_service("websocket_manager")
-        if manager is None:
-            # ws_manager is already a global instance in websocket_manager.py
+        """Get or create WebSocket manager instance"""
+        service_name = "websocket_manager"
+        
+        if service_name in cls._services:
+            return cls._services[service_name]
+        
+        async with cls._get_lock(service_name):
+            # Double-check after acquiring lock
+            if service_name in cls._services:
+                return cls._services[service_name]
+            
+            # Import here to avoid circular imports
             from .websocket_manager import ws_manager
-            await cls.register_service("websocket_manager", ws_manager)
-            manager = ws_manager
-        return manager
+            
+            cls._services[service_name] = ws_manager
+            logger.debug(f"Registered {service_name}")
+            return ws_manager
+    
+    @classmethod
+    async def get_embedding_scanner(cls):
+        """Get or create Embedding scanner instance"""
+        service_name = "embedding_scanner"
+        
+        if service_name in cls._services:
+            return cls._services[service_name]
+        
+        async with cls._get_lock(service_name):
+            # Double-check after acquiring lock
+            if service_name in cls._services:
+                return cls._services[service_name]
+            
+            # Import here to avoid circular imports
+            from .embedding_scanner import EmbeddingScanner
+            
+            scanner = await EmbeddingScanner.get_instance()
+            cls._services[service_name] = scanner
+            logger.debug(f"Created and registered {service_name}")
+            return scanner
+    
+    @classmethod
+    def clear_services(cls):
+        """Clear all registered services - mainly for testing"""
+        cls._services.clear()
+        cls._locks.clear()
+        logger.info("Cleared all registered services")

py/services/settings_manager.py

@@ -9,6 +9,7 @@ class SettingsManager:
     def __init__(self):
         self.settings_file = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'settings.json')
         self.settings = self._load_settings()
+        self._auto_set_default_roots()
         self._check_environment_variables()
 
     def _load_settings(self) -> Dict[str, Any]:
@@ -21,6 +22,28 @@ class SettingsManager:
                 logger.error(f"Error loading settings: {e}")
         return self._get_default_settings()
 
+    def _auto_set_default_roots(self):
+        """Auto set default root paths if only one folder is present and default is empty."""
+        folder_paths = self.settings.get('folder_paths', {})
+        updated = False
+        # loras
+        loras = folder_paths.get('loras', [])
+        if isinstance(loras, list) and len(loras) == 1 and not self.settings.get('default_lora_root'):
+            self.settings['default_lora_root'] = loras[0]
+            updated = True
+        # checkpoints
+        checkpoints = folder_paths.get('checkpoints', [])
+        if isinstance(checkpoints, list) and len(checkpoints) == 1 and not self.settings.get('default_checkpoint_root'):
+            self.settings['default_checkpoint_root'] = checkpoints[0]
+            updated = True
+        # embeddings
+        embeddings = folder_paths.get('embeddings', [])
+        if isinstance(embeddings, list) and len(embeddings) == 1 and not self.settings.get('default_embedding_root'):
+            self.settings['default_embedding_root'] = embeddings[0]
+            updated = True
+        if updated:
+            self._save_settings()
+
     def _check_environment_variables(self) -> None:
         """Check for environment variables and update settings if needed"""
         env_api_key = os.environ.get('CIVITAI_API_KEY')

py/services/websocket_manager.py

@@ -1,6 +1,9 @@
 import logging
 from aiohttp import web
 from typing import Set, Dict, Optional
+from uuid import uuid4
+import asyncio
+from datetime import datetime, timedelta
 
 logger = logging.getLogger(__name__)
 
@@ -10,7 +13,9 @@ 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
+        # Add progress tracking dictionary
+        self._download_progress: Dict[str, Dict] = {}
         
     async def handle_connection(self, request: web.Request) -> web.WebSocketResponse:
         """Handle new WebSocket connection"""
@@ -39,21 +44,48 @@ 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]
+            
+            # Schedule cleanup of completed downloads after WebSocket disconnection
+            asyncio.create_task(self._delayed_cleanup(download_id))
         return ws
-        
+    
+    async def _delayed_cleanup(self, download_id: str, delay_seconds: int = 300):
+        """Clean up download progress after a delay (5 minutes by default)"""
+        await asyncio.sleep(delay_seconds)
+        progress_data = self._download_progress.get(download_id)
+        if progress_data and progress_data.get('progress', 0) >= 100:
+            self.cleanup_download_progress(download_id)
+            logger.debug(f"Delayed cleanup completed for download {download_id}")
+    
     async def broadcast(self, data: Dict):
         """Broadcast message to all connected clients"""
         if not self._websockets:
@@ -84,17 +116,45 @@ 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"""
+        # Store simplified progress data in memory (only progress percentage)
+        self._download_progress[download_id] = {
+            'progress': data.get('progress', 0),
+            'timestamp': datetime.now()
+        }
+        
+        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_download_progress(self, download_id: str) -> Optional[Dict]:
+        """Get progress information for a specific download"""
+        return self._download_progress.get(download_id)
+    
+    def cleanup_download_progress(self, download_id: str):
+        """Remove progress info for a specific download"""
+        self._download_progress.pop(download_id, None)
+    
+    def cleanup_old_downloads(self, max_age_hours: int = 24):
+        """Clean up old download progress entries"""
+        cutoff_time = datetime.now() - timedelta(hours=max_age_hours)
+        to_remove = []
+        
+        for download_id, progress_data in self._download_progress.items():
+            if progress_data.get('timestamp', datetime.now()) < cutoff_time:
+                to_remove.append(download_id)
+        
+        for download_id in to_remove:
+            self._download_progress.pop(download_id, None)
+            logger.debug(f"Cleaned up old download progress for {download_id}")
+            
     def get_connected_clients_count(self) -> int:
         """Get number of connected clients"""
         return len(self._websockets)
@@ -102,10 +162,14 @@ 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

@@ -10,7 +10,8 @@ NSFW_LEVELS = {
 # Node type constants
 NODE_TYPES = {
     "Lora Loader (LoraManager)": 1,
-    "Lora Stacker (LoraManager)": 2
+    "Lora Stacker (LoraManager)": 2,
+    "WanVideo Lora Select (LoraManager)": 3
 }
 
 # Default ComfyUI node color when bgcolor is null
@@ -45,4 +46,11 @@ SUPPORTED_MEDIA_EXTENSIONS = {
 }
 
 # Valid Lora types
-VALID_LORA_TYPES = ['lora', 'locon', 'dora']
+VALID_LORA_TYPES = ['lora', 'locon', 'dora']
+
+# Civitai model tags in priority order for subfolder organization
+CIVITAI_MODEL_TAGS = [
+    'character', 'style', 'concept', 'clothing', 'base model', 
+    'poses', 'background', 'tool', 'vehicle', 'buildings', 
+    'objects', 'assets', 'animal', 'action'
+]

py/utils/example_images_download_manager.py

@@ -214,6 +214,10 @@ class DownloadManager:
             if 'checkpoint' in model_types:
                 checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
                 scanners.append(('checkpoint', checkpoint_scanner))
+
+            if 'embedding' in model_types:
+                embedding_scanner = await ServiceRegistry.get_embedding_scanner()
+                scanners.append(('embedding', embedding_scanner))
             
             # Get all models
             all_models = []

py/utils/example_images_processor.py

@@ -251,12 +251,13 @@ class ExampleImagesProcessor:
             # Find the model and get current metadata
             lora_scanner = await ServiceRegistry.get_lora_scanner()
             checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+            embedding_scanner = await ServiceRegistry.get_embedding_scanner()
             
             model_data = None
             scanner = None
             
             # Check both scanners to find the model
-            for scan_obj in [lora_scanner, checkpoint_scanner]:
+            for scan_obj in [lora_scanner, checkpoint_scanner, embedding_scanner]:
                 cache = await scan_obj.get_cached_data()
                 for item in cache.raw_data:
                     if item.get('sha256') == model_hash:
@@ -384,12 +385,13 @@ class ExampleImagesProcessor:
             # Find the model and get current metadata
             lora_scanner = await ServiceRegistry.get_lora_scanner()
             checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+            embedding_scanner = await ServiceRegistry.get_embedding_scanner()
             
             model_data = None
             scanner = None
             
             # Check both scanners to find the model
-            for scan_obj in [lora_scanner, checkpoint_scanner]:
+            for scan_obj in [lora_scanner, checkpoint_scanner, embedding_scanner]:
                 if scan_obj.has_hash(model_hash):
                     cache = await scan_obj.get_cached_data()
                     for item in cache.raw_data:

py/utils/metadata_manager.py

@@ -1,3 +1,4 @@
+from datetime import datetime
 import os
 import json
 import shutil
@@ -196,7 +197,7 @@ class MetadataManager:
                     model_name=base_name,
                     file_path=normalize_path(file_path),
                     size=os.path.getsize(real_path),
-                    modified=os.path.getmtime(real_path),
+                    modified=datetime.now().timestamp(),
                     sha256=sha256,
                     base_model="Unknown",
                     preview_url=normalize_path(preview_url),
@@ -205,13 +206,27 @@ class MetadataManager:
                     model_type="checkpoint",
                     from_civitai=True
                 )
+            elif model_class.__name__ == "EmbeddingMetadata":
+                metadata = model_class(
+                    file_name=base_name,
+                    model_name=base_name,
+                    file_path=normalize_path(file_path),
+                    size=os.path.getsize(real_path),
+                    modified=datetime.now().timestamp(),
+                    sha256=sha256,
+                    base_model="Unknown",
+                    preview_url=normalize_path(preview_url),
+                    tags=[],
+                    modelDescription="",
+                    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),
+                    modified=datetime.now().timestamp(),
                     sha256=sha256,
                     base_model="Unknown",
                     preview_url=normalize_path(preview_url),
@@ -222,7 +237,7 @@ class MetadataManager:
                 )
             
             # Try to extract model-specific metadata
-            await MetadataManager._enrich_metadata(metadata, real_path)
+            # await MetadataManager._enrich_metadata(metadata, real_path)
             
             # Save the created metadata
             await MetadataManager.save_metadata(file_path, metadata, create_backup=False)
@@ -266,6 +281,12 @@ class MetadataManager:
         """
         need_update = False
         
+        # Check if file_name matches the actual file name
+        base_name = os.path.splitext(os.path.basename(file_path))[0]
+        if metadata.file_name != base_name:
+            metadata.file_name = base_name
+            need_update = True
+        
         # 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)

py/utils/models.py

@@ -11,7 +11,7 @@ class BaseModelMetadata:
     model_name: str             # The model's name defined by the creator
     file_path: str              # Full path to the model file
     size: int                   # File size in bytes
-    modified: float             # Last modified timestamp
+    modified: float             # Timestamp when the model was added to the management system
     sha256: str                 # SHA256 hash of the file
     base_model: str             # Base model type (SD1.5/SD2.1/SDXL/etc.)
     preview_url: str            # Preview image URL
@@ -73,11 +73,6 @@ class BaseModelMetadata:
             
         return result
 
-    @property
-    def modified_datetime(self) -> datetime:
-        """Convert modified timestamp to datetime object"""
-        return datetime.fromtimestamp(self.modified)
-
     def update_civitai_info(self, civitai_data: Dict) -> None:
         """Update Civitai information"""
         self.civitai = civitai_data
@@ -128,7 +123,7 @@ class LoraMetadata(BaseModelMetadata):
 @dataclass
 class CheckpointMetadata(BaseModelMetadata):
     """Represents the metadata structure for a Checkpoint model"""
-    model_type: str = "checkpoint"  # Model type (checkpoint, inpainting, etc.)
+    model_type: str = "checkpoint"  # Model type (checkpoint, diffusion_model, etc.)
 
     @classmethod
     def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'CheckpointMetadata':
@@ -163,3 +158,41 @@ class CheckpointMetadata(BaseModelMetadata):
             modelDescription=description
         )
 
+@dataclass
+class EmbeddingMetadata(BaseModelMetadata):
+    """Represents the metadata structure for an Embedding model"""
+    model_type: str = "embedding"  # Model type (embedding, textual_inversion, etc.)
+
+    @classmethod
+    def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'EmbeddingMetadata':
+        """Create EmbeddingMetadata instance from Civitai version info"""
+        file_name = file_info['name']
+        base_model = determine_base_model(version_info.get('baseModel', ''))
+        model_type = version_info.get('type', 'embedding')
+        
+        # Extract tags and description if available
+        tags = []
+        description = ""
+        if 'model' in version_info:
+            if 'tags' in version_info['model']:
+                tags = version_info['model']['tags']
+            if 'description' in version_info['model']:
+                description = version_info['model']['description']
+        
+        return cls(
+            file_name=os.path.splitext(file_name)[0],
+            model_name=version_info.get('model').get('name', os.path.splitext(file_name)[0]),
+            file_path=save_path.replace(os.sep, '/'),
+            size=file_info.get('sizeKB', 0) * 1024,
+            modified=datetime.now().timestamp(),
+            sha256=file_info['hashes'].get('SHA256', '').lower(),
+            base_model=base_model,
+            preview_url=None,  # Will be updated after preview download
+            preview_nsfw_level=0,
+            from_civitai=True,
+            civitai=version_info,
+            model_type=model_type,
+            tags=tags,
+            modelDescription=description
+        )
+

py/utils/routes_common.py

@@ -8,9 +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__)
 
@@ -327,8 +329,6 @@ 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,
@@ -551,8 +551,6 @@ 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,
@@ -564,66 +562,83 @@ 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:
-        """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
-        """
+    async def handle_download_model(request: web.Request) -> web.Response:
+        """Handle model download request"""
         try:
+            download_manager = await ServiceRegistry.get_download_manager()
             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.json_response({
+                    'success': False,
+                    'error': "Invalid model_id: Must be an integer"
+                }, status=400)
+
+            # Convert model_version_id to int if provided
+            model_version_id = None 
+            if data.get('model_version_id'):
+                try:
+                    model_version_id = int(data.get('model_version_id'))
+                except (TypeError, ValueError):
+                    return web.json_response({
+                        'success': False,
+                        'error': "Invalid model_version_id: Must be an integer"
+                    }, status=400)
             
-            # 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'"
-                )
+            # Only model_id is required, model_version_id is optional
+            if not model_id:
+                return web.json_response({
+                    'success': False,
+                    'error': "Missing required parameter: Please provide 'model_id'"
+                }, status=400)
             
-            # 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)
             
+            # Pass the download_id to download_from_civitai
             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', ''),
+                use_default_paths=use_default_paths,
                 progress_callback=progress_callback,
-                model_type=model_type
+                download_id=download_id  # Pass download_id explicitly
             )
             
+            # Include download_id in the response
+            result['download_id'] = download_id
+            
             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=f"Early Access Restriction: {error_message}"
-                    )
+                    return web.json_response({
+                        'success': False,
+                        'error': f"Early Access Restriction: {error_message}",
+                        'download_id': download_id
+                    }, status=401)
                 
-                return web.Response(status=500, text=error_message)
+                return web.json_response({
+                    'success': False,
+                    'error': error_message,
+                    'download_id': download_id
+                }, status=500)
             
             return web.json_response(result)
             
@@ -633,13 +648,75 @@ class ModelRouteUtils:
             # 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 buy early access on Civitai.com."
-                )
+                return web.json_response({
+                    'success': False,
+                    'error': "Early Access Restriction: This model requires purchase. Please buy early access on Civitai.com."
+                }, status=401)
             
-            logger.error(f"Error downloading {model_type}: {error_message}")
-            return web.Response(status=500, text=error_message)
+            logger.error(f"Error downloading model: {error_message}")
+            return web.json_response({
+                'success': False,
+                'error': error_message
+            }, status=500)
+
+    @staticmethod
+    async def handle_cancel_download(request: web.Request) -> web.Response:
+        """Handle cancellation of a download task
+        
+        Args:
+            request: The aiohttp request
+            
+        Returns:
+            web.Response: The HTTP response
+        """
+        try:
+            download_manager = await ServiceRegistry.get_download_manager()
+            download_id = request.match_info.get('download_id')
+            if not download_id:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Download ID is required'
+                }, status=400)
+            
+            result = await download_manager.cancel_download(download_id)
+            
+            # Notify clients about cancellation via WebSocket
+            await ws_manager.broadcast_download_progress(download_id, {
+                'status': 'cancelled',
+                'progress': 0,
+                'download_id': download_id,
+                'message': 'Download cancelled by user'
+            })
+            
+            return web.json_response(result)
+            
+        except Exception as e:
+            logger.error(f"Error cancelling download: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+            
+    @staticmethod
+    async def handle_list_downloads(request: web.Request) -> web.Response:
+        """Get list of active downloads
+        
+        Args:
+            request: The aiohttp request
+            
+        Returns:
+            web.Response: The HTTP response with list of downloads
+        """
+        try:
+            download_manager = await ServiceRegistry.get_download_manager()
+            result = await download_manager.get_active_downloads()
+            return web.json_response(result)
+        except Exception as e:
+            logger.error(f"Error listing downloads: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
 
     @staticmethod
     async def handle_bulk_delete_models(request: web.Request, scanner) -> web.Response:
@@ -693,8 +770,10 @@ class ModelRouteUtils:
         try:
             data = await request.json()
             file_path = data.get('file_path')
-            model_id = data.get('model_id')
-            model_version_id = data.get('model_version_id')
+            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)
@@ -955,6 +1034,7 @@ class ModelRouteUtils:
             return web.json_response({
                 'success': True,
                 'new_file_path': new_file_path,
+                'new_preview_path': config.get_preview_static_url(new_preview),
                 'renamed_files': renamed_files,
                 'reload_required': False
             })
@@ -965,3 +1045,56 @@ class ModelRouteUtils:
                 'success': False,
                 'error': str(e)
             }, status=500)
+
+    @staticmethod
+    async def handle_save_metadata(request: web.Request, scanner) -> web.Response:
+        """Handle saving metadata updates
+        
+        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')
+            if not file_path:
+                return web.Response(text='File path is required', status=400)
+
+            # Remove file path from data to avoid saving it
+            metadata_updates = {k: v for k, v in data.items() if k != 'file_path'}
+            
+            # Get metadata file path
+            metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+            
+            # Load existing metadata
+            metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
+
+            # Handle nested updates (for civitai.trainedWords)
+            for key, value in metadata_updates.items():
+                if isinstance(value, dict) and key in metadata and isinstance(metadata[key], dict):
+                    # Deep update for nested dictionaries
+                    for nested_key, nested_value in value.items():
+                        metadata[key][nested_key] = nested_value
+                else:
+                    # Regular update for top-level keys
+                    metadata[key] = value
+
+            # Save updated metadata
+            await MetadataManager.save_metadata(file_path, metadata)
+
+            # Update cache
+            await scanner.update_single_model_cache(file_path, file_path, metadata)
+
+            # If model_name was updated, resort the cache
+            if 'model_name' in metadata_updates:
+                cache = await scanner.get_cached_data()
+                await cache.resort()
+
+            return web.json_response({'success': True})
+
+        except Exception as e:
+            logger.error(f"Error saving metadata: {e}", exc_info=True)
+            return web.Response(text=str(e), status=500)

py/utils/utils.py

@@ -1,8 +1,54 @@
 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
+import asyncio
+
+def get_lora_info(lora_name):
+    """Get the lora path and trigger words from cache"""
+    async def _get_lora_info_async():
+        scanner = await ServiceRegistry.get_lora_scanner()
+        cache = await scanner.get_cached_data()
+        
+        for item in cache.raw_data:
+            if item.get('file_name') == lora_name:
+                file_path = item.get('file_path')
+                if file_path:
+                    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, []
+    
+    try:
+        # Check if we're already in an event loop
+        loop = asyncio.get_running_loop()
+        # If we're in a running loop, we need to use a different approach
+        # Create a new thread to run the async code
+        import concurrent.futures
+        
+        def run_in_thread():
+            new_loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(new_loop)
+            try:
+                return new_loop.run_until_complete(_get_lora_info_async())
+            finally:
+                new_loop.close()
+        
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            future = executor.submit(run_in_thread)
+            return future.result()
+            
+    except RuntimeError:
+        # No event loop is running, we can use asyncio.run()
+        return asyncio.run(_get_lora_info_async())
 
 def download_twitter_image(url):
     """Download image from a URL containing twitter:image meta tag

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 = "0.8.19"
+description = "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!"
+version = "0.8.24"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",
     "jinja2", 
     "safetensors",
-    "watchdog",
     "beautifulsoup4",
     "piexif",
     "Pillow",
@@ -15,7 +14,7 @@ dependencies = [
     "requests",
     "toml",
     "natsort",
-    "msgpack"
+    "GitPython"
 ]
 
 [project.urls]

requirements.txt

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

standalone.py

@@ -3,6 +3,26 @@ 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
@@ -86,6 +106,22 @@ logger = logging.getLogger("lora-manager-standalone")
 # Configure aiohttp access logger to be less verbose
 logging.getLogger('aiohttp.access').setLevel(logging.WARNING)
 
+# Add specific suppression for connection reset errors
+class ConnectionResetFilter(logging.Filter):
+    def filter(self, record):
+        # Filter out connection reset errors that are not critical
+        if "ConnectionResetError" in str(record.getMessage()):
+            return False
+        if "_call_connection_lost" in str(record.getMessage()):
+            return False
+        if "WinError 10054" in str(record.getMessage()):
+            return False
+        return True
+
+# Apply the filter to asyncio logger
+asyncio_logger = logging.getLogger("asyncio")
+asyncio_logger.addFilter(ConnectionResetFilter())
+
 # Now we can import the global config from our local modules
 from py.config import config
 
@@ -98,17 +134,6 @@ class StandaloneServer:
         
         # Ensure the app's access logger is configured to reduce verbosity
         self.app._subapps = []  # Ensure this exists to avoid AttributeError
-        
-        # Configure access logging for the app
-        self.app.on_startup.append(self._configure_access_logger)
-    
-    async def _configure_access_logger(self, app):
-        """Configure access logger to reduce verbosity"""
-        logging.getLogger('aiohttp.access').setLevel(logging.WARNING)
-        
-        # If using aiohttp>=3.8.0, configure access logger through app directly
-        if hasattr(app, 'access_logger'):
-            app.access_logger.setLevel(logging.WARNING)
     
     async def setup(self):
         """Set up the standalone server"""
@@ -198,9 +223,6 @@ class StandaloneLoraManager(LoraManager):
         
         # Store app in a global-like location for compatibility
         sys.modules['server'].PromptServer.instance = server_instance
-
-        # Configure aiohttp access logger to be less verbose
-        logging.getLogger('aiohttp.access').setLevel(logging.WARNING)
         
         added_targets = set()  # Track already added target paths
         
@@ -232,7 +254,7 @@ 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
@@ -257,23 +279,50 @@ class StandaloneLoraManager(LoraManager):
             # Record route mapping
             config.add_route_mapping(real_root, preview_path)
             added_targets.add(os.path.normpath(real_root))
+
+        # Add static routes for each embedding root
+        for idx, root in enumerate(getattr(config, "embeddings_roots", []), start=1):
+            if not os.path.exists(root):
+                logger.warning(f"Embedding root path does not exist: {root}")
+                continue
+
+            preview_path = f'/embeddings_static/root{idx}/preview'
+
+            real_root = root
+            for target, link in config._path_mappings.items():
+                if os.path.normpath(link) == os.path.normpath(root):
+                    real_root = target
+                    break
+
+            display_root = real_root.replace('\\', '/')
+            app.router.add_static(preview_path, real_root)
+            logger.info(f"Added static route {preview_path} -> {display_root}")
+
+            config.add_route_mapping(real_root, preview_path)
+            added_targets.add(os.path.normpath(real_root))
         
         # Add static routes for symlink target paths that aren't already covered
         link_idx = {
             'lora': 1,
-            'checkpoint': 1
+            'checkpoint': 1,
+            'embedding': 1
         }
         
         for target_path, link_path in config._path_mappings.items():
             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)
-                
+                # Determine if this is a checkpoint, lora, or embedding link based on path
+                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)
+                is_embedding = any(os.path.normpath(emb_root) in os.path.normpath(link_path) for emb_root in getattr(config, "embeddings_roots", []))
+                is_embedding = is_embedding or any(os.path.normpath(emb_root) in norm_target for emb_root in getattr(config, "embeddings_roots", []))
+
                 if is_checkpoint:
                     route_path = f'/checkpoints_static/link_{link_idx["checkpoint"]}/preview'
                     link_idx["checkpoint"] += 1
+                elif is_embedding:
+                    route_path = f'/embeddings_static/link_{link_idx["embedding"]}/preview'
+                    link_idx["embedding"] += 1
                 else:
                     route_path = f'/loras_static/link_{link_idx["lora"]}/preview'
                     link_idx["lora"] += 1
@@ -294,35 +343,39 @@ class StandaloneLoraManager(LoraManager):
         app.router.add_static('/loras_static', config.static_path)
         
         # Setup feature routes
-        from py.routes.lora_routes import LoraRoutes
-        from py.routes.api_routes import ApiRoutes
+        from py.services.model_service_factory import ModelServiceFactory, register_default_model_types
         from py.routes.recipe_routes import RecipeRoutes
-        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
+        from py.services.websocket_manager import ws_manager
         
-        lora_routes = LoraRoutes()
-        checkpoints_routes = CheckpointsRoutes()
+
+        register_default_model_types()
+
+        # Setup all model routes using the factory
+        ModelServiceFactory.setup_all_routes(app)
+
         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)
+
+        # Setup WebSocket routes that are shared across all model types
+        app.router.add_get('/ws/fetch-progress', ws_manager.handle_connection)
+        app.router.add_get('/ws/download-progress', ws_manager.handle_download_connection)
+        app.router.add_get('/ws/init-progress', ws_manager.handle_init_connection)
         
         # Schedule service initialization
         app.on_startup.append(lambda app: cls._initialize_services())
         
         # Add cleanup
         app.on_shutdown.append(cls._cleanup)
-        app.on_shutdown.append(ApiRoutes.cleanup)
 
 def parse_args():
     """Parse command line arguments"""
@@ -347,9 +400,6 @@ async def main():
     # Set log level
     logging.getLogger().setLevel(getattr(logging, args.log_level))
     
-    # Explicitly configure aiohttp access logger regardless of selected log level
-    logging.getLogger('aiohttp.access').setLevel(logging.WARNING)
-    
     # Create the server instance
     server = StandaloneServer()
     

static/css/base.css

@@ -50,8 +50,8 @@ html, body {
     --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(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 */
+    --lora-warning: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
+    --lora-success: oklch(var(--lora-success-l) var(--lora-success-c) var(--lora-success-h));
     
     /* Spacing Scale */
     --space-1: calc(8px * 1);
@@ -70,6 +70,11 @@ html, body {
     --border-radius-xs: 4px;
 
     --scrollbar-width: 8px; /* 添加滚动条宽度变量 */
+    
+    /* Shortcut styles */
+    --shortcut-bg: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.12);
+    --shortcut-border: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.25);
+    --shortcut-text: var(--text-color);
 }
 
 html[data-theme="dark"] {

static/css/components/banner.css

@@ -0,0 +1,245 @@
+/* Banner Container */
+.banner-container {
+    position: relative;
+    width: 100%;
+    z-index: calc(var(--z-header) - 1);
+    border-bottom: 1px solid var(--border-color);
+    background: var(--card-bg);
+    margin-bottom: var(--space-2);
+}
+
+/* Individual Banner */
+.banner-item {
+    position: relative;
+    padding: var(--space-2) var(--space-3);
+    background: linear-gradient(135deg, 
+        oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.05),
+        oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.02)
+    );
+    border-left: 4px solid var(--lora-accent);
+    animation: banner-slide-down 0.3s ease-in-out;
+}
+
+/* Banner Content Layout */
+.banner-content {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    gap: var(--space-3);
+    max-width: 1400px;
+    margin: 0 auto;
+}
+
+/* Banner Text Section */
+.banner-text {
+    flex: 1;
+    min-width: 0;
+}
+
+.banner-title {
+    margin: 0 0 4px 0;
+    font-size: 1.1em;
+    font-weight: 600;
+    color: var(--text-color);
+    line-height: 1.3;
+}
+
+.banner-description {
+    margin: 0;
+    font-size: 0.9em;
+    color: var(--text-muted);
+    line-height: 1.4;
+}
+
+/* Banner Actions */
+.banner-actions {
+    display: flex;
+    align-items: center;
+    gap: var(--space-1);
+    flex-shrink: 0;
+}
+
+.banner-action {
+    display: inline-flex;
+    align-items: center;
+    gap: 6px;
+    padding: 6px 12px;
+    border-radius: var(--border-radius-xs);
+    text-decoration: none;
+    font-size: 0.85em;
+    font-weight: 500;
+    transition: all 0.2s ease;
+    white-space: nowrap;
+    border: 1px solid transparent;
+}
+
+.banner-action i {
+    font-size: 0.9em;
+}
+
+/* Primary Action Button */
+.banner-action-primary {
+    background: var(--lora-accent);
+    color: white;
+    border-color: var(--lora-accent);
+}
+
+.banner-action-primary:hover {
+    background: oklch(calc(var(--lora-accent-l) - 5%) var(--lora-accent-c) var(--lora-accent-h));
+    transform: translateY(-1px);
+    box-shadow: 0 3px 6px oklch(var(--lora-accent) / 0.3);
+}
+
+/* Secondary Action Button */
+.banner-action-secondary {
+    background: var(--card-bg);
+    color: var(--text-color);
+    border-color: var(--border-color);
+}
+
+.banner-action-secondary:hover {
+    background: var(--lora-accent);
+    color: white;
+    border-color: var(--lora-accent);
+    transform: translateY(-1px);
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+}
+
+/* Tertiary Action Button */
+.banner-action-tertiary {
+    background: transparent;
+    color: var(--lora-accent);
+    border-color: var(--lora-accent);
+}
+
+.banner-action-tertiary:hover {
+    background: var(--lora-accent);
+    color: white;
+    transform: translateY(-1px);
+}
+
+/* Dismiss Button */
+.banner-dismiss {
+    position: absolute;
+    top: 8px;
+    right: 8px;
+    width: 24px;
+    height: 24px;
+    border: none;
+    background: transparent;
+    color: var(--text-muted);
+    cursor: pointer;
+    border-radius: 50%;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    transition: all 0.2s ease;
+    font-size: 0.8em;
+}
+
+.banner-dismiss:hover {
+    background: oklch(var(--lora-accent) / 0.1);
+    color: var(--lora-accent);
+    transform: scale(1.1);
+}
+
+/* Animations */
+@keyframes banner-slide-down {
+    from {
+        opacity: 0;
+        transform: translateY(-100%);
+    }
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
+}
+
+@keyframes banner-slide-up {
+    from {
+        opacity: 1;
+        transform: translateY(0);
+        max-height: 200px;
+    }
+    to {
+        opacity: 0;
+        transform: translateY(-20px);
+        max-height: 0;
+        padding-top: 0;
+        padding-bottom: 0;
+    }
+}
+
+/* Responsive Design */
+@media (max-width: 768px) {
+    .banner-content {
+        flex-direction: column;
+        align-items: flex-start;
+        gap: var(--space-2);
+    }
+    
+    .banner-actions {
+        width: 100%;
+        flex-wrap: wrap;
+        justify-content: flex-start;
+    }
+    
+    .banner-action {
+        flex: 1;
+        min-width: 0;
+        justify-content: center;
+    }
+    
+    .banner-dismiss {
+        top: 6px;
+        right: 6px;
+    }
+    
+    .banner-item {
+        padding: var(--space-2);
+    }
+    
+    .banner-title {
+        font-size: 1em;
+    }
+    
+    .banner-description {
+        font-size: 0.85em;
+    }
+}
+
+@media (max-width: 480px) {
+    .banner-actions {
+        flex-direction: column;
+        width: 100%;
+    }
+    
+    .banner-action {
+        width: 100%;
+        justify-content: center;
+    }
+    
+    .banner-content {
+        gap: var(--space-1);
+    }
+}
+
+/* Dark theme adjustments */
+[data-theme="dark"] .banner-item {
+    background: linear-gradient(135deg, 
+        oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.08),
+        oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.03)
+    );
+}
+
+/* Prevent text selection */
+.banner-item,
+.banner-title,
+.banner-description,
+.banner-action,
+.banner-dismiss {
+    -webkit-user-select: none;
+    -moz-user-select: none;
+    -ms-user-select: none;
+    user-select: none;
+}

static/css/components/bulk.css

@@ -73,12 +73,12 @@
 }
 
 /* Style for selected cards */
-.lora-card.selected {
+.model-card.selected {
     box-shadow: 0 0 0 2px var(--lora-accent);
     position: relative;
 }
 
-.lora-card.selected::after {
+.model-card.selected::after {
     content: "✓";
     position: absolute;
     top: 10px;

static/css/components/card.css

@@ -14,7 +14,7 @@
     box-sizing: border-box; /* Include padding in width calculation */
 }
 
-.lora-card {
+.model-card {
     background: var(--lora-surface);
     border: 1px solid var(--lora-border);
     border-radius: var(--border-radius-base);
@@ -30,12 +30,12 @@
     overflow: hidden;
 }
 
-.lora-card:hover {
+.model-card:hover {
     transform: translateY(-2px);
     background: oklch(100% 0 0 / 0.6);
 }
 
-.lora-card:focus-visible {
+.model-card:focus-visible {
     outline: 2px solid var(--lora-accent);
     outline-offset: 2px;
 }
@@ -47,7 +47,7 @@
         grid-template-columns: repeat(auto-fill, minmax(270px, 1fr));
     }
     
-    .lora-card {
+    .model-card {
         max-width: 270px;
     }
 }
@@ -59,7 +59,7 @@
         grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
     }
     
-    .lora-card {
+    .model-card {
         max-width: 280px;
     }
 }
@@ -70,7 +70,7 @@
         grid-template-columns: repeat(auto-fill, minmax(240px, 1fr));
     }
     
-    .lora-card {
+    .model-card {
         max-width: 240px;
     }
 }
@@ -259,8 +259,8 @@
     transition: opacity 0.2s ease;
 }
 
-.hover-reveal .lora-card:hover .card-header,
-.hover-reveal .lora-card:hover .card-footer {
+.hover-reveal .model-card:hover .card-header,
+.hover-reveal .model-card:hover .card-footer {
     opacity: 1;
 }
 
@@ -345,7 +345,7 @@
         grid-template-columns: minmax(260px, 1fr); /* Adjusted minimum size for mobile */
     }
     
-    .lora-card {
+    .model-card {
         max-width: 100%; /* Allow cards to fill available space on mobile */
     }
 }
@@ -424,9 +424,36 @@
     font-size: 0.85em;
 }
 
+/* Style for version name */
+.version-name {
+    display: inline-block;
+    color: rgba(255,255,255,0.8); /* Muted white */
+    text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.5);
+    font-size: 0.85em;
+    word-break: break-word;
+    overflow: hidden;
+    line-height: 1.4;
+    margin-top: 2px;
+    opacity: 0.8; /* Slightly transparent for better readability */
+    border: 1px solid rgba(255,255,255,0.25); /* Subtle border */
+    border-radius: var(--border-radius-xs);
+    padding: 1px 6px;
+    background: rgba(0,0,0,0.18); /* Optional: subtle background for contrast */
+}
+
+/* Medium density adjustments for version name */
+.medium-density .version-name {
+    font-size: 0.8em;
+}
+
+/* Compact density adjustments for version name */
+.compact-density .version-name {
+    font-size: 0.75em;
+}
+
 /* Prevent text selection on cards and interactive elements */
-.lora-card,
-.lora-card *,
+.model-card,
+.model-card *,
 .card-actions,
 .card-actions i,
 .toggle-blur-btn,
@@ -510,7 +537,7 @@
     }
 }
 
-/* Add after the existing .lora-card:hover styles */
+/* Add after the existing .model-card:hover styles */
 
 @keyframes update-pulse {
     0% { box-shadow: 0 0 0 0 var(--lora-accent-transparent); }
@@ -523,7 +550,7 @@
     --lora-accent-transparent: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.6);
   }
   
-  .lora-card.updated {
+  .model-card.updated {
     animation: update-pulse 1.2s ease-out;
   }
   

static/css/components/duplicates.css

@@ -195,7 +195,7 @@
 }
 
 /* Make cards in duplicate groups have consistent width */
-.card-group-container .lora-card {
+.card-group-container .model-card {
     flex: 0 0 auto;
     width: 240px;
     margin: 0;
@@ -241,26 +241,26 @@
 }
 
 /* Duplicate card styling */
-.lora-card.duplicate {
+.model-card.duplicate {
     position: relative;
     transition: all 0.2s ease;
 }
 
-.lora-card.duplicate:hover {
+.model-card.duplicate:hover {
     border-color: var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h);
 }
 
-.lora-card.duplicate.latest {
+.model-card.duplicate.latest {
     border-style: solid;
     border-color: oklch(var(--lora-warning-l) var(--lora-warning-c) var(--lora-warning-h));
 }
 
-.lora-card.duplicate-selected {
+.model-card.duplicate-selected {
     border: 2px solid oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h));
     box-shadow: 0 0 8px rgba(0, 0, 0, 0.2);
 }
 
-.lora-card .selector-checkbox {
+.model-card .selector-checkbox {
     position: absolute;
     top: 10px;
     right: 10px;
@@ -271,7 +271,7 @@
 }
 
 /* Latest indicator */
-.lora-card.duplicate.latest::after {
+.model-card.duplicate.latest::after {
     content: "Latest";
     position: absolute;
     top: 10px;
@@ -365,13 +365,13 @@
 }
 
 /* Hash Mismatch Styling */
-.lora-card.duplicate.hash-mismatch {
+.model-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 {
+.model-card.duplicate.hash-mismatch::before {
     content: "";
     position: absolute;
     top: 0;
@@ -389,7 +389,7 @@
     pointer-events: none;
 }
 
-.lora-card.duplicate.hash-mismatch .card-preview {
+.model-card.duplicate.hash-mismatch .card-preview {
     filter: grayscale(20%);
 }
 
@@ -407,7 +407,7 @@
 }
 
 /* Disabled checkbox style */
-.lora-card.duplicate.hash-mismatch .selector-checkbox {
+.model-card.duplicate.hash-mismatch .selector-checkbox {
     opacity: 0.5;
     cursor: not-allowed;
 }

static/css/components/header.css

@@ -31,7 +31,7 @@
   align-items: center;
   text-decoration: none;
   color: var(--text-color);
-  gap: 8px;
+  gap: 2px;
 }
 
 .app-logo {
@@ -223,11 +223,6 @@
   opacity: 1;
 }
 
-.update-badge.hidden,
-.update-badge:not(.visible) {
-  opacity: 0;
-}
-
 /* Mobile adjustments */
 @media (max-width: 768px) {
   .app-title {

static/css/components/loading.css

@@ -109,7 +109,7 @@
 }
 
 @media (prefers-reduced-motion: reduce) {
-    .lora-card,
+    .model-card,
     .progress-bar,
     .current-item-bar {
         transition: none;

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

@@ -183,7 +183,11 @@
     outline: none;
 }
 
-.edit-file-name-btn {
+/* 合并编辑按钮样式 */
+.edit-model-name-btn,
+.edit-file-name-btn,
+.edit-base-model-btn,
+.edit-model-description-btn {
     background: transparent;
     border: none;
     color: var(--text-color);
@@ -195,17 +199,28 @@
     margin-left: var(--space-1);
 }
 
+.edit-model-name-btn.visible,
 .edit-file-name-btn.visible,
-.file-name-wrapper:hover .edit-file-name-btn {
+.edit-base-model-btn.visible,
+.edit-model-description-btn.visible,
+.model-name-header:hover .edit-model-name-btn,
+.file-name-wrapper:hover .edit-file-name-btn,
+.base-model-display:hover .edit-base-model-btn,
+.model-name-header:hover .edit-model-description-btn {
     opacity: 0.5;
 }
 
-.edit-file-name-btn:hover {
+.edit-model-name-btn:hover,
+.edit-file-name-btn:hover,
+.edit-base-model-btn:hover,
+.edit-model-description-btn:hover {
     opacity: 0.8 !important;
     background: rgba(0, 0, 0, 0.05);
 }
 
-[data-theme="dark"] .edit-file-name-btn:hover {
+[data-theme="dark"] .edit-model-name-btn:hover,
+[data-theme="dark"] .edit-file-name-btn:hover,
+[data-theme="dark"] .edit-base-model-btn:hover {
     background: rgba(255, 255, 255, 0.05);
 }
 
@@ -234,32 +249,6 @@
     flex: 1;
 }
 
-.edit-base-model-btn {
-    background: transparent;
-    border: none;
-    color: var(--text-color);
-    opacity: 0;
-    cursor: pointer;
-    padding: 2px 5px;
-    border-radius: var(--border-radius-xs);
-    transition: all 0.2s ease;
-    margin-left: var(--space-1);
-}
-
-.edit-base-model-btn.visible,
-.base-model-display:hover .edit-base-model-btn {
-    opacity: 0.5;
-}
-
-.edit-base-model-btn:hover {
-    opacity: 0.8 !important;
-    background: rgba(0, 0, 0, 0.05);
-}
-
-[data-theme="dark"] .edit-base-model-btn:hover {
-    background: rgba(255, 255, 255, 0.05);
-}
-
 .base-model-selector {
     width: 100%;
     padding: 3px 5px;
@@ -316,32 +305,6 @@
     background: var(--bg-color);
 }
 
-.edit-model-name-btn {
-    background: transparent;
-    border: none;
-    color: var(--text-color);
-    opacity: 0;
-    cursor: pointer;
-    padding: 2px 5px;
-    border-radius: var(--border-radius-xs);
-    transition: all 0.2s ease;
-    margin-left: var(--space-1);
-}
-
-.edit-model-name-btn.visible,
-.model-name-header:hover .edit-model-name-btn {
-    opacity: 0.5;
-}
-
-.edit-model-name-btn:hover {
-    opacity: 0.8 !important;
-    background: rgba(0, 0, 0, 0.05);
-}
-
-[data-theme="dark"] .edit-model-name-btn:hover {
-    background: rgba(255, 255, 255, 0.05);
-}
-
 /* Tab System Styling */
 .showcase-tabs {
     display: flex;
@@ -436,22 +399,24 @@
     display: flex;
     align-items: center;
     gap: 10px;
-    margin-bottom: var(--space-1);
-    padding: 6px 10px;
+    padding: 2px 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;
+    cursor: pointer;
+    transition: all 0.2s;
 }
 
-[data-theme="dark"] .creator-info {
+[data-theme="dark"] .creator-info,
+[data-theme="dark"] .civitai-view {
     background: rgba(255, 255, 255, 0.03);
     border: 1px solid var(--lora-border);
 }
 
 .creator-avatar {
-    width: 28px;
-    height: 28px;
+    width: 26px;
+    height: 26px;
     border-radius: 50%;
     overflow: hidden;
     flex-shrink: 0;
@@ -482,8 +447,40 @@
     color: var(--text-color);
 }
 
-/* Optional: add hover effect for creator info */
-.creator-info:hover {
+/* Add hover effect for creator info */
+.creator-info:hover,
+.civitai-view:hover {
     background: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.1);
     border-color: var(--lora-accent);
+    transform: translateY(-1px);
+}
+
+.creator-actions {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    margin-bottom: var(--space-1);
+    flex-wrap: wrap;
+}
+
+.civitai-view {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    padding: 6px 12px;
+    background: rgba(0, 0, 0, 0.03);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: var(--border-radius-sm);
+    color: var(--text-color);
+    cursor: pointer;
+    font-weight: 500;
+    font-size: 0.9em;
+    transition: all 0.2s;
+}
+
+.civitai-view i {
+    font-size: 20px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
 }

static/css/components/modal/_base.css

@@ -0,0 +1,274 @@
+/* modal 基础样式 */
+.modal {
+    display: none;
+    position: fixed;
+    top: 48px; /* Start below the header */
+    left: 0;
+    width: 100%;
+    height: calc(100% - 48px); /* Adjust height to exclude header */
+    background: rgba(0, 0, 0, 0.2); /* 调整为更淡的半透明黑色 */
+    z-index: var(--z-modal);
+    overflow: auto; /* Change from hidden to auto to allow scrolling */
+}
+
+/* 当模态窗口打开时，禁止body滚动 */
+body.modal-open {
+    position: fixed;
+    width: 100%;
+    padding-right: var(--scrollbar-width, 0px); /* 补偿滚动条消失导致的页面偏移 */
+}
+
+/* modal-content 样式 */
+.modal-content {
+    position: relative;
+    max-width: 800px;
+    height: auto;
+    max-height: calc(90vh - 48px); /* Adjust to account for header height */
+    margin: 1rem auto; /* Keep reduced top margin */
+    background: var(--lora-surface);
+    border-radius: var(--border-radius-base);
+    padding: var(--space-3);
+    border: 1px solid var(--lora-border);
+    box-shadow: 
+        0 4px 6px -1px rgba(0, 0, 0, 0.1),
+        0 2px 4px -1px rgba(0, 0, 0, 0.06),
+        0 10px 15px -3px rgba(0, 0, 0, 0.05);
+    overflow-y: auto;
+    overflow-x: hidden; /* 防止水平滚动条 */
+}
+
+/* 当 modal 打开时锁定 body */
+body.modal-open {
+    overflow: hidden !important; /* 覆盖 base.css 中的 scroll */
+    padding-right: var(--scrollbar-width, 8px); /* 使用滚动条宽度作为补偿 */
+}
+
+@keyframes modalFadeIn {
+    from {
+        opacity: 0;
+        transform: translateY(-20px);
+    }
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
+}
+
+.modal-actions {
+    display: flex;
+    gap: var(--space-2);
+    justify-content: center;
+    margin-top: var(--space-3);
+}
+
+.cancel-btn, .delete-btn, .exclude-btn, .confirm-btn {
+    padding: 8px var(--space-2);
+    border-radius: 6px;
+    border: none;
+    cursor: pointer;
+    font-weight: 500;
+    min-width: 100px;
+}
+
+.cancel-btn {
+    background: var(--lora-surface);
+    border: 1px solid var(--lora-border);
+    color: var(--text-color);
+}
+
+.delete-btn {
+    background: var(--lora-error);
+    color: white;
+}
+
+/* Style for exclude button - different from delete button */
+.exclude-btn, .confirm-btn {
+    background: var(--lora-accent, #4f46e5);
+    color: white;
+}
+
+.cancel-btn:hover {
+    background: var(--lora-border);
+}
+
+.delete-btn:hover {
+    opacity: 0.9;
+}
+
+.exclude-btn:hover, .confirm-btn:hover {
+    opacity: 0.9;
+    background: oklch(from var(--lora-accent, #4f46e5) l c h / 85%);
+}
+
+.modal-content h2 {
+    color: var(--text-color);
+    margin-bottom: var(--space-1);
+    font-size: 1.5em;
+}
+
+.close {
+    position: absolute;
+    top: var(--space-2);
+    right: var(--space-2);
+    background: transparent;
+    border: none;
+    color: var(--text-color);
+    font-size: 1.5em;
+    cursor: pointer;
+    opacity: 0.7;
+    transition: opacity 0.2s;
+}
+
+.close:hover {
+    opacity: 1;
+}
+
+/* 统一各个 section 的样式 */
+.support-section,
+.changelog-section,
+.update-info,
+.info-item,
+.path-preview {
+    background: rgba(0, 0, 0, 0.03);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: var(--border-radius-sm);
+    padding: var(--space-2);
+}
+
+/* 深色主题统一样式 */
+[data-theme="dark"] .modal-content {
+    background: var(--lora-surface);
+    border: 1px solid var(--lora-border);
+}
+
+[data-theme="dark"] .support-section,
+[data-theme="dark"] .changelog-section,
+[data-theme="dark"] .update-info,
+[data-theme="dark"] .info-item,
+[data-theme="dark"] .path-preview {
+    background: rgba(255, 255, 255, 0.03);
+    border: 1px solid var(--lora-border);
+}
+
+.primary-btn {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 8px 16px;
+    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: 0.95em;
+}
+
+.primary-btn:hover {
+    background-color: oklch(from var(--lora-accent) l c h / 85%);
+    color: var(--lora-text);
+}
+
+/* Secondary button styles */
+.secondary-btn {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 8px 16px;
+    background-color: var(--card-bg);
+    color: var (--text-color);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    cursor: pointer;
+    transition: all 0.2s;
+    font-size: 0.95em;
+}
+
+.secondary-btn:hover {
+    background-color: var(--border-color);
+    color: var(--text-color);
+}
+
+/* Disabled button styles */
+.primary-btn.disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+    background-color: var(--lora-accent);
+    color: var(--lora-text);
+    pointer-events: none;
+}
+
+.secondary-btn.disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+    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%);
+}
+
+[data-theme="dark"] .secondary-btn {
+    background-color: var(--lora-surface);
+}
+
+[data-theme="dark"] .secondary-btn:hover {
+    background-color: oklch(35% 0.02 256 / 0.98);
+}
+
+.primary-btn.disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+.primary-btn.disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+/* Add styles for delete preview image */
+.delete-preview {
+    max-width: 150px;
+    margin: 0 auto var(--space-2);
+    overflow: hidden;
+}
+
+.delete-preview img {
+    width: 100%;
+    height: auto;
+    max-height: 150px;
+    object-fit: contain;
+    border-radius: var(--border-radius-sm);
+}
+
+.delete-info {
+    text-align: center;
+}
+
+.delete-info h3 {
+    margin-bottom: var(--space-1);
+    word-break: break-word;
+}
+
+.delete-info p {
+    margin: var(--space-1) 0;
+    font-size: 0.9em;
+    opacity: 0.8;
+}
+
+.delete-note {
+    font-size: 0.85em;
+    color: var(--text-color);
+    opacity: 0.7;
+    font-style: italic;
+    margin-top: var(--space-1);
+    text-align: center;
+}

static/css/components/modal/delete-modal.css

@@ -0,0 +1,48 @@
+/* Delete Modal specific styles */
+
+.delete-message {
+    color: var(--text-color);
+    margin: var(--space-2) 0;
+}
+
+/* Update delete modal styles */
+.delete-modal {
+    display: none;  /* Set initial display to none */
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    background: rgba(0, 0, 0, 0.8);
+    z-index: var(--z-overlay);
+}
+
+/* Add new style for when modal is shown */
+.delete-modal.show {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.delete-modal-content {
+    max-width: 500px;
+    width: 90%;
+    text-align: center;
+    margin: 0 auto;
+    position: relative;
+    animation: modalFadeIn 0.2s ease-out;
+}
+
+.delete-model-info,
+.exclude-model-info {
+    /* Update info display styling */
+    background: var(--lora-surface);
+    border: 1px solid var(--lora-border);
+    border-radius: var(--border-radius-sm);
+    padding: var(--space-2);
+    margin: var(--space-2) 0;
+    color: var(--text-color);
+    word-break: break-all;
+    text-align: left;
+    line-height: 1.5;
+}

static/css/components/modal/example-access-modal.css

@@ -0,0 +1,72 @@
+/* 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/modal/help-modal.css

@@ -0,0 +1,307 @@
+/* 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);
+}
+
+/* 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);
+}

static/css/components/modal/relink-civitai-modal.css

@@ -0,0 +1,53 @@
+/* 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);
+}

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

@@ -0,0 +1,485 @@
+/* Settings styles */
+.settings-toggle {
+    width: 36px;
+    height: 36px;
+    border-radius: 50%;
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    color: var(--text-color);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    cursor: pointer;
+    transition: all 0.2s ease;
+}
+
+.settings-toggle:hover {
+    background: var(--lora-accent);
+    color: white;
+    transform: translateY(-2px);
+}
+
+.settings-modal {
+    max-width: 650px; /* Further increased from 600px for more space */
+}
+
+/* Settings Links */
+.settings-links {
+    margin-top: var(--space-3);
+    padding-top: var(--space-2);
+    border-top: 1px solid var(--lora-border);
+    display: flex;
+    gap: var(--space-2);
+    justify-content: center;
+}
+
+.settings-link {
+    width: 36px;
+    height: 36px;
+    border-radius: 50%;
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    color: var(--text-color);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    text-decoration: none;
+    position: relative;
+}
+
+.settings-link:hover {
+    background: var(--lora-accent);
+    color: white;
+    transform: translateY(-2px);
+}
+
+.settings-link i {
+    font-size: 1.1em;
+}
+
+/* Tooltip styles */
+.settings-link::after {
+    content: attr(title);
+    position: absolute;
+    bottom: calc(100% + 8px);
+    left: 50%;
+    transform: translateX(-50%);
+    background: rgba(0, 0, 0, 0.8);
+    color: white;
+    padding: 4px 8px;
+    border-radius: 4px;
+    font-size: 0.8em;
+    white-space: nowrap;
+    opacity: 0;
+    visibility: hidden;
+    transition: opacity 0.2s, visibility 0.2s;
+    pointer-events: none;
+}
+
+.settings-link:hover::after {
+    opacity: 1;
+    visibility: visible;
+}
+
+/* Responsive adjustment */
+@media (max-width: 480px) {
+    .settings-links {
+        flex-wrap: wrap;
+    }
+}
+
+/* API key input specific styles */
+.api-key-input {
+    width: 100%; /* Take full width of parent */
+    position: relative;
+    display: flex;
+    align-items: center;
+}
+
+.api-key-input input {
+    width: 100%;
+    padding: 6px 40px 6px 10px; /* Add left padding */
+    height: 32px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    background-color: var(--lora-surface);
+    color: var(--text-color);
+}
+
+.api-key-input .toggle-visibility {
+    position: absolute;
+    right: 8px;
+    background: none;
+    border: none;
+    color: var(--text-color);
+    opacity: 0.6;
+    cursor: pointer;
+    padding: 4px 8px;
+}
+
+.api-key-input .toggle-visibility:hover {
+    opacity: 1;
+}
+
+.input-help {
+    font-size: 0.85em;
+    color: var(--text-color);
+    opacity: 0.7;
+    margin-top: 8px; /* Space between control and help */
+    line-height: 1.4;
+    width: 100%; /* Full width */
+}
+
+/* Settings Styles */
+.settings-section {
+    margin-top: var(--space-3);
+    border-top: 1px solid var(--lora-border);
+    padding-top: var(--space-2);
+}
+
+.settings-section h3 {
+    font-size: 1.1em;
+    margin-bottom: var(--space-2);
+    color: var(--text-color);
+    opacity: 0.9;
+}
+
+.setting-item {
+    display: flex;
+    flex-direction: column; /* Changed to column for help text placement */
+    margin-bottom: var(--space-3); /* Increased to provide more spacing between items */
+    padding: var(--space-1);
+    border-radius: var(--border-radius-xs);
+}
+
+.setting-item:hover {
+    background: rgba(0, 0, 0, 0.02);
+}
+
+[data-theme="dark"] .setting-item:hover {
+    background: rgba(255, 255, 255, 0.05);
+}
+
+/* Control row with label and input together */
+.setting-row {
+    display: flex;
+    flex-direction: row;
+    justify-content: space-between;
+    align-items: center;
+    width: 100%;
+}
+
+.setting-info {
+    margin-bottom: 0;
+    width: 35%; /* Increased from 30% to prevent wrapping */
+    flex-shrink: 0; /* Prevent shrinking */
+}
+
+.setting-info label {
+    display: block;
+    font-weight: 500;
+    margin-bottom: 0;
+    white-space: nowrap; /* Prevent label wrapping */
+}
+
+.setting-control {
+    width: 60%; /* Decreased slightly from 65% */
+    margin-bottom: 0;
+    display: flex;
+    justify-content: flex-end; /* Right-align all controls */
+}
+
+/* Select Control Styles */
+.select-control {
+    width: 100%;
+    display: flex;
+    justify-content: flex-end;
+}
+
+.select-control select {
+    width: 100%;
+    max-width: 100%; /* Increased from 200px */
+    padding: 6px 10px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    background-color: var(--lora-surface);
+    color: var(--text-color);
+    font-size: 0.95em;
+    height: 32px;
+}
+
+/* Fix dark theme select dropdown text color */
+[data-theme="dark"] .select-control select {
+    background-color: rgba(30, 30, 30, 0.9);
+    color: var(--text-color);
+}
+
+[data-theme="dark"] .select-control select option {
+    background-color: #2d2d2d;
+    color: var(--text-color);
+}
+
+.select-control select:focus {
+    border-color: var(--lora-accent);
+    outline: none;
+}
+
+/* Toggle Switch */
+.toggle-switch {
+    position: relative;
+    display: inline-block;
+    width: 50px;
+    height: 24px;
+    cursor: pointer;
+    margin-left: auto; /* Push to right side */
+}
+
+.toggle-switch input {
+    opacity: 0;
+    width: 0;
+    height: 0;
+}
+
+.toggle-slider {
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background-color: var(--border-color);
+    transition: .3s;
+    border-radius: 24px;
+}
+
+.toggle-slider:before {
+    position: absolute;
+    content: "";
+    height: 18px;
+    width: 18px;
+    left: 3px;
+    bottom: 3px;
+    background-color: white;
+    transition: .3s;
+    border-radius: 50%;
+}
+
+input:checked + .toggle-slider {
+    background-color: var(--lora-accent);
+}
+
+input:checked + .toggle-slider:before {
+    transform: translateX(26px);
+}
+
+.toggle-label {
+    margin-left: 60px;
+    line-height: 24px;
+}
+
+/* Add small animation for the toggle */
+.toggle-slider:active:before {
+    width: 22px;
+}
+
+/* Blur effect for NSFW content */
+.nsfw-blur {
+    filter: blur(12px);
+    transition: filter 0.3s ease;
+}
+
+.nsfw-blur:hover {
+    filter: blur(8px);
+}
+
+/* Example Images Settings Styles */
+.download-buttons {
+    justify-content: flex-start;
+    gap: var(--space-2);
+}
+
+.path-control {
+    display: flex;
+    gap: 8px;
+    align-items: center;
+    width: 100%;
+}
+
+.path-control input[type="text"] {
+    flex: 1;
+    padding: 6px 10px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    background-color: var(--lora-surface);
+    color: var (--text-color);
+    font-size: 0.95em;
+    height: 32px;
+}
+
+/* Add warning text style for settings */
+.warning-text {
+    color: var(--lora-warning, #e67e22);
+    font-weight: 500;
+}
+
+[data-theme="dark"] .warning-text {
+    color: var(--lora-warning, #f39c12);
+}
+
+/* Add styles for list description */
+.list-description {
+    margin: 8px 0;
+    padding-left: 20px;
+    font-size: 0.9em;
+}
+
+.list-description li {
+    margin-bottom: 4px;
+}
+
+/* Path Template Settings Styles */
+.template-preview {
+    background: rgba(0, 0, 0, 0.03);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: var(--border-radius-xs);
+    padding: var(--space-1);
+    margin-top: 8px;
+    font-family: monospace;
+    font-size: 1.1em;
+    color: var(--lora-accent);
+    display: none;
+}
+
+[data-theme="dark"] .template-preview {
+    background: rgba(255, 255, 255, 0.03);
+    border: 1px solid var(--lora-border);
+}
+
+.template-preview:before {
+    content: "Preview: ";
+    opacity: 0.7;
+    color: var(--text-color);
+    font-family: inherit;
+}
+
+/* Base Model Mappings Styles - Updated to match other settings */
+.mappings-container {
+    border: 1px solid var(--lora-border);
+    border-radius: var(--border-radius-sm);
+    padding: var(--space-2);
+    background: rgba(0, 0, 0, 0.02);
+    margin-top: 8px; /* Add consistent spacing */
+}
+
+[data-theme="dark"] .mappings-container {
+    background: rgba(255, 255, 255, 0.02);
+}
+
+.add-mapping-btn {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    padding: 6px 12px;
+    background: var(--lora-accent);
+    color: white;
+    border: none;
+    border-radius: var(--border-radius-xs);
+    cursor: pointer;
+    font-size: 0.9em;
+    transition: all 0.2s;
+    height: 32px; /* Match other control heights */
+}
+
+.add-mapping-btn:hover {
+    background: oklch(from var(--lora-accent) l c h / 85%);
+}
+
+.mapping-row {
+    margin-bottom: var(--space-2);
+}
+
+.mapping-row:last-child {
+    margin-bottom: 0;
+}
+
+.mapping-controls {
+    display: grid;
+    grid-template-columns: 1fr 1fr auto;
+    gap: var(--space-1);
+    align-items: center;
+}
+
+.base-model-select,
+.path-value-input {
+    padding: 6px 10px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    background-color: var(--lora-surface);
+    color: var(--text-color);
+    font-size: 0.9em;
+    height: 32px;
+}
+
+.path-value-input {
+    height: 18px;
+}
+
+.base-model-select:focus,
+.path-value-input:focus {
+    border-color: var(--lora-accent);
+    outline: none;
+    box-shadow: 0 0 0 2px rgba(var(--lora-accent-rgb, 79, 70, 229), 0.1);
+}
+
+.remove-mapping-btn {
+    width: 32px;
+    height: 32px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--lora-error);
+    background: transparent;
+    color: var(--lora-error);
+    cursor: pointer;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    transition: all 0.2s;
+}
+
+.remove-mapping-btn:hover {
+    background: var(--lora-error);
+    color: white;
+}
+
+.mapping-empty-state {
+    text-align: center;
+    padding: var(--space-3);
+    color: var(--text-color);
+    opacity: 0.6;
+    font-style: italic;
+}
+
+/* Responsive adjustments for mapping controls */
+@media (max-width: 768px) {
+    .mapping-controls {
+        grid-template-columns: 1fr;
+        gap: 8px;
+    }
+    
+    .remove-mapping-btn {
+        width: 100%;
+        height: 36px;
+        justify-self: stretch;
+    }
+}
+
+/* Dark theme specific adjustments */
+[data-theme="dark"] .base-model-select,
+[data-theme="dark"] .path-value-input {
+    background-color: rgba(30, 30, 30, 0.9);
+}
+
+[data-theme="dark"] .base-model-select option {
+    background-color: #2d2d2d;
+    color: var(--text-color);
+}

static/css/components/modal/update-modal.css

@@ -0,0 +1,124 @@
+/* Update Modal specific styles */
+.update-actions {
+    display: flex;
+    flex-direction: column;
+    gap: var(--space-2);
+    align-items: stretch;
+    flex-wrap: nowrap;
+}
+
+.update-link {
+    color: var(--lora-accent);
+    text-decoration: none;
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    font-size: 0.95em;
+}
+
+.update-link:hover {
+    text-decoration: underline;
+}
+
+/* Update progress styles */
+.update-progress {
+    background: rgba(0, 0, 0, 0.03);
+    border: 1px solid var(--lora-border);
+    border-radius: var(--border-radius-sm);
+    padding: var(--space-2);
+    margin: var(--space-2) 0;
+}
+
+[data-theme="dark"] .update-progress {
+    background: rgba(255, 255, 255, 0.03);
+}
+
+.progress-info {
+    display: flex;
+    flex-direction: column;
+    gap: var(--space-1);
+}
+
+.progress-text {
+    font-size: 0.9em;
+    color: var(--text-color);
+    opacity: 0.8;
+}
+
+.update-progress-bar {
+    width: 100%;
+    height: 8px;
+    background-color: rgba(0, 0, 0, 0.1);
+    border-radius: 4px;
+    overflow: hidden;
+}
+
+[data-theme="dark"] .update-progress-bar {
+    background-color: rgba(255, 255, 255, 0.1);
+}
+
+.progress-fill {
+    height: 100%;
+    background-color: var(--lora-accent);
+    width: 0%;
+    transition: width 0.3s ease;
+    border-radius: 4px;
+}
+
+/* Update button states */
+#updateBtn {
+    min-width: 120px;
+}
+
+#updateBtn.updating {
+    background-color: var(--lora-warning);
+    cursor: not-allowed;
+}
+
+#updateBtn.success {
+    background-color: var(--lora-success);
+}
+
+#updateBtn.error {
+    background-color: var(--lora-error);
+}
+
+/* Add styles for markdown elements in changelog */
+.changelog-item ul {
+    padding-left: 20px;
+    margin-top: 8px;
+}
+
+.changelog-item li {
+    margin-bottom: 6px;
+    line-height: 1.4;
+}
+
+.changelog-item strong {
+    font-weight: 600;
+}
+
+.changelog-item em {
+    font-style: italic;
+}
+
+.changelog-item code {
+    background: rgba(0, 0, 0, 0.05);
+    padding: 2px 4px;
+    border-radius: 3px;
+    font-family: monospace;
+    font-size: 0.9em;
+}
+
+[data-theme="dark"] .changelog-item code {
+    background: rgba(255, 255, 255, 0.1);
+}
+
+.changelog-item a {
+    color: var(--lora-accent);
+    text-decoration: none;
+}
+
+.changelog-item a:hover {
+    text-decoration: underline;
+}

static/css/components/statistics.css

@@ -56,6 +56,24 @@
     color: var(--lora-error);
 }
 
+/* Update color scheme to include embeddings */
+:root {
+    --embedding-color: oklch(68% 0.28 120); /* Green for embeddings */
+}
+
+/* Update metric cards and chart colors to support embeddings */
+.metric-card.embedding .metric-icon {
+    color: var(--embedding-color);
+}
+
+.model-item.embedding {
+    border-left: 3px solid var(--embedding-color);
+}
+
+.model-item.embedding:hover {
+    border-color: var(--embedding-color);
+}
+
 /* Dashboard Content */
 .dashboard-content {
     background: var(--card-bg);

static/css/layout.css

@@ -124,6 +124,43 @@
     border-color: var(--lora-accent);
 }
 
+/* Keyboard shortcut indicator styling */
+.shortcut-key {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    margin-left: 6px;
+    min-width: 16px;
+    height: 16px;
+    padding: 0 3px;
+    font-size: 11px;
+    font-weight: 600;
+    line-height: 1;
+    border-radius: var(--border-radius-xs);
+    background-color: var(--shortcut-bg);
+    border: 1px solid var(--shortcut-border);
+    color: var(--shortcut-text);
+    vertical-align: middle;
+    opacity: 0.8;
+    transition: all 0.2s ease;
+}
+
+.control-group button:hover .shortcut-key {
+    opacity: 1;
+    background-color: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.2);
+}
+
+[data-theme="dark"] .shortcut-key {
+    --shortcut-bg: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.15);
+    --shortcut-border: oklch(var(--lora-accent-l) var(--lora-accent-c) var(--lora-accent-h) / 0.3);
+}
+
+/* Ensure correct vertical alignment for text+shortcut */
+.control-group button span {
+    display: inline-flex;
+    align-items: center;
+}
+
 /* Select dropdown styling */
 .control-group select {
     min-width: 100px;
@@ -145,6 +182,31 @@
     box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
 }
 
+/* Style for optgroups */
+.control-group select optgroup {
+    font-weight: 600;
+    font-style: normal;
+    color: var(--text-color);
+    background-color: var(--card-bg);
+}
+
+.control-group select option {
+    padding: 4px 8px;
+    background-color: var(--card-bg);
+    color: var(--text-color);
+}
+
+/* Dark theme optgroup styling */
+[data-theme="dark"] .control-group select optgroup {
+    background-color: var(--card-bg);
+    color: var(--text-color);
+}
+
+[data-theme="dark"] .control-group select option {
+    background-color: var(--card-bg);
+    color: var(--text-color);
+}
+
 .control-group select:hover {
     border-color: var(--lora-accent);
     background-color: var(--bg-color);

static/css/style.css

@@ -6,8 +6,16 @@
 
 /* Import Components */
 @import 'components/header.css';
+@import 'components/banner.css';
 @import 'components/card.css';
-@import 'components/modal.css';
+@import 'components/modal/_base.css';
+@import 'components/modal/delete-modal.css';
+@import 'components/modal/update-modal.css';
+@import 'components/modal/settings-modal.css';
+@import 'components/modal/help-modal.css';
+@import 'components/modal/relink-civitai-modal.css';
+@import 'components/modal/example-access-modal.css';
+@import 'components/modal/support-modal.css';
 @import 'components/download-modal.css';
 @import 'components/toast.css';
 @import 'components/loading.css';
@@ -20,7 +28,6 @@
 @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';
 @import 'components/shared.css';

static/js/api/apiConfig.js

@@ -0,0 +1,168 @@
+import { state } from '../state/index.js';
+
+/**
+ * API Configuration
+ * Centralized configuration for all model types and their endpoints
+ */
+
+// Model type definitions
+export const MODEL_TYPES = {
+    LORA: 'loras',
+    CHECKPOINT: 'checkpoints',
+    EMBEDDING: 'embeddings' // Future model type
+};
+
+// Base API configuration for each model type
+export const MODEL_CONFIG = {
+    [MODEL_TYPES.LORA]: {
+        displayName: 'LoRA',
+        singularName: 'lora',
+        defaultPageSize: 100,
+        supportsLetterFilter: true,
+        supportsBulkOperations: true,
+        supportsMove: true,
+        templateName: 'loras.html'
+    },
+    [MODEL_TYPES.CHECKPOINT]: {
+        displayName: 'Checkpoint',
+        singularName: 'checkpoint',
+        defaultPageSize: 100,
+        supportsLetterFilter: false,
+        supportsBulkOperations: true,
+        supportsMove: false,
+        templateName: 'checkpoints.html'
+    },
+    [MODEL_TYPES.EMBEDDING]: {
+        displayName: 'Embedding',
+        singularName: 'embedding',
+        defaultPageSize: 100,
+        supportsLetterFilter: true,
+        supportsBulkOperations: true,
+        supportsMove: true,
+        templateName: 'embeddings.html'
+    }
+};
+
+/**
+ * Generate API endpoints for a given model type
+ * @param {string} modelType - The model type (e.g., 'loras', 'checkpoints')
+ * @returns {Object} Object containing all API endpoints for the model type
+ */
+export function getApiEndpoints(modelType) {
+    if (!Object.values(MODEL_TYPES).includes(modelType)) {
+        throw new Error(`Invalid model type: ${modelType}`);
+    }
+    
+    return {
+        // Base CRUD operations
+        list: `/api/${modelType}`,
+        delete: `/api/${modelType}/delete`,
+        exclude: `/api/${modelType}/exclude`,
+        rename: `/api/${modelType}/rename`,
+        save: `/api/${modelType}/save-metadata`,
+        
+        // Bulk operations
+        bulkDelete: `/api/${modelType}/bulk-delete`,
+        
+        // CivitAI integration
+        fetchCivitai: `/api/${modelType}/fetch-civitai`,
+        fetchAllCivitai: `/api/${modelType}/fetch-all-civitai`,
+        relinkCivitai: `/api/${modelType}/relink-civitai`,
+        civitaiVersions: `/api/${modelType}/civitai/versions`,
+        
+        // Preview management
+        replacePreview: `/api/${modelType}/replace-preview`,
+        
+        // Query operations
+        scan: `/api/${modelType}/scan`,
+        topTags: `/api/${modelType}/top-tags`,
+        baseModels: `/api/${modelType}/base-models`,
+        roots: `/api/${modelType}/roots`,
+        folders: `/api/${modelType}/folders`,
+        duplicates: `/api/${modelType}/find-duplicates`,
+        conflicts: `/api/${modelType}/find-filename-conflicts`,
+        verify: `/api/${modelType}/verify-duplicates`,
+        
+        // Model-specific endpoints (will be merged with specific configs)
+        specific: {}
+    };
+}
+
+/**
+ * Model-specific endpoint configurations
+ */
+export const MODEL_SPECIFIC_ENDPOINTS = {
+    [MODEL_TYPES.LORA]: {
+        letterCounts: `/api/${MODEL_TYPES.LORA}/letter-counts`,
+        notes: `/api/${MODEL_TYPES.LORA}/get-notes`,
+        triggerWords: `/api/${MODEL_TYPES.LORA}/get-trigger-words`,
+        previewUrl: `/api/${MODEL_TYPES.LORA}/preview-url`,
+        civitaiUrl: `/api/${MODEL_TYPES.LORA}/civitai-url`,
+        modelDescription: `/api/${MODEL_TYPES.LORA}/model-description`,
+        moveModel: `/api/${MODEL_TYPES.LORA}/move_model`,
+        moveBulk: `/api/${MODEL_TYPES.LORA}/move_models_bulk`,
+        getTriggerWordsPost: `/api/${MODEL_TYPES.LORA}/get_trigger_words`,
+        civitaiModelByVersion: `/api/${MODEL_TYPES.LORA}/civitai/model/version`,
+        civitaiModelByHash: `/api/${MODEL_TYPES.LORA}/civitai/model/hash`,
+    },
+    [MODEL_TYPES.CHECKPOINT]: {
+        info: `/api/${MODEL_TYPES.CHECKPOINT}/info`,
+    },
+    [MODEL_TYPES.EMBEDDING]: {
+    }
+};
+
+/**
+ * Get complete API configuration for a model type
+ * @param {string} modelType - The model type
+ * @returns {Object} Complete API configuration
+ */
+export function getCompleteApiConfig(modelType) {
+    const baseEndpoints = getApiEndpoints(modelType);
+    const specificEndpoints = MODEL_SPECIFIC_ENDPOINTS[modelType] || {};
+    const config = MODEL_CONFIG[modelType];
+    
+    return {
+        modelType,
+        config,
+        endpoints: {
+            ...baseEndpoints,
+            specific: specificEndpoints
+        }
+    };
+}
+
+/**
+ * Validate if a model type is supported
+ * @param {string} modelType - The model type to validate
+ * @returns {boolean} True if valid, false otherwise
+ */
+export function isValidModelType(modelType) {
+    return Object.values(MODEL_TYPES).includes(modelType);
+}
+
+/**
+ * Get model type from current page or explicit parameter
+ * @param {string} [explicitType] - Explicitly provided model type
+ * @returns {string} The model type
+ */
+export function getCurrentModelType(explicitType = null) {
+    if (explicitType && isValidModelType(explicitType)) {
+        return explicitType;
+    }
+
+    return state.currentPageType || MODEL_TYPES.LORA;
+}
+
+// Download API endpoints (shared across all model types)
+export const DOWNLOAD_ENDPOINTS = {
+    download: '/api/download-model',
+    downloadGet: '/api/download-model-get',
+    cancelGet: '/api/cancel-download-get',
+    progress: '/api/download-progress'
+};
+
+// WebSocket endpoints
+export const WS_ENDPOINTS = {
+    fetchProgress: '/ws/fetch-progress'
+};

static/js/api/checkpointApi.js

@@ -1,165 +0,0 @@
-import {
-    fetchModelsPage,
-    resetAndReloadWithVirtualScroll,
-    loadMoreWithVirtualScroll,
-    refreshModels as baseRefreshModels,
-    deleteModel as baseDeleteModel,
-    replaceModelPreview,
-    fetchCivitaiMetadata,
-    refreshSingleModelMetadata,
-    excludeModel as baseExcludeModel
-} from './baseModelApi.js';
-import { state } from '../state/index.js';
-
-/**
- * Fetch checkpoints with pagination for virtual scrolling
- * @param {number} page - Page number to fetch
- * @param {number} pageSize - Number of items per page
- * @returns {Promise<Object>} Object containing items, total count, and pagination info
- */
-export async function fetchCheckpointsPage(page = 1, pageSize = 100) {
-    return fetchModelsPage({
-        modelType: 'checkpoint',
-        page,
-        pageSize,
-        endpoint: '/api/checkpoints'
-    });
-}
-
-/**
- * Load more checkpoints with pagination - updated to work with VirtualScroller
- * @param {boolean} resetPage - Whether to reset to the first page
- * @param {boolean} updateFolders - Whether to update folder tags
- * @returns {Promise<void>}
- */
-export async function loadMoreCheckpoints(resetPage = false, updateFolders = false) {
-    return loadMoreWithVirtualScroll({
-        modelType: 'checkpoint',
-        resetPage,
-        updateFolders,
-        fetchPageFunction: fetchCheckpointsPage
-    });
-}
-
-// Reset and reload checkpoints
-export async function resetAndReload(updateFolders = false) {
-    return resetAndReloadWithVirtualScroll({
-        modelType: 'checkpoint',
-        updateFolders,
-        fetchPageFunction: fetchCheckpointsPage
-    });
-}
-
-// Refresh checkpoints
-export async function refreshCheckpoints(fullRebuild = false) {
-    return baseRefreshModels({
-        modelType: 'checkpoint',
-        scanEndpoint: '/api/checkpoints/scan',
-        resetAndReloadFunction: resetAndReload,
-        fullRebuild: fullRebuild
-    });
-}
-
-// Delete a checkpoint
-export function deleteCheckpoint(filePath) {
-    return baseDeleteModel(filePath, 'checkpoint');
-}
-
-// Replace checkpoint preview
-export function replaceCheckpointPreview(filePath) {
-    return replaceModelPreview(filePath, 'checkpoint');
-}
-
-// Fetch metadata from Civitai for checkpoints
-export async function fetchCivitai() {
-    return fetchCivitaiMetadata({
-        modelType: 'checkpoint',
-        fetchEndpoint: '/api/checkpoints/fetch-all-civitai',
-        resetAndReloadFunction: resetAndReload
-    });
-}
-
-// Refresh single checkpoint metadata
-export async function refreshSingleCheckpointMetadata(filePath) {
-    await refreshSingleModelMetadata(filePath, 'checkpoint');
-}
-
-/**
- * Save model metadata to the server
- * @param {string} filePath - Path to the model file
- * @param {Object} data - Metadata to save
- * @returns {Promise} - Promise that resolves with the server response
- */
-export async function saveModelMetadata(filePath, data) {
-    try {
-        // Show loading indicator
-        state.loadingManager.showSimpleLoading('Saving metadata...');
-        
-        const response = await fetch('/api/checkpoints/save-metadata', {
-            method: 'POST',
-            headers: {
-                'Content-Type': 'application/json',
-            },
-            body: JSON.stringify({
-                file_path: filePath,
-                ...data
-            })
-        });
-
-        if (!response.ok) {
-            throw new Error('Failed to save metadata');
-        }
-
-        // Update the virtual scroller with the new metadata
-        state.virtualScroller.updateSingleItem(filePath, data);
-        
-        return response.json();
-    } finally {
-        // Always hide the loading indicator when done
-        state.loadingManager.hide();
-    }
-}
-
-/**
- * Exclude a checkpoint model from being shown in the UI
- * @param {string} filePath - File path of the checkpoint to exclude
- * @returns {Promise<boolean>} Promise resolving to success status
- */
-export function excludeCheckpoint(filePath) {
-    return baseExcludeModel(filePath, 'checkpoint');
-}
-
-/**
- * Rename a checkpoint file
- * @param {string} filePath - Current file path
- * @param {string} newFileName - New file name (without path)
- * @returns {Promise<Object>} - Promise that resolves with the server response
- */
-export async function renameCheckpointFile(filePath, newFileName) {
-    try {
-        // Show loading indicator
-        state.loadingManager.showSimpleLoading('Renaming checkpoint file...');
-        
-        const response = await fetch('/api/checkpoints/rename', {
-            method: 'POST',
-            headers: {
-                'Content-Type': 'application/json',
-            },
-            body: JSON.stringify({
-                file_path: filePath,
-                new_file_name: newFileName
-            })
-        });
-        
-        if (!response.ok) {
-            throw new Error(`Server returned ${response.status}: ${response.statusText}`);
-        }
-        
-        return await response.json();
-    } catch (error) {
-        console.error('Error renaming checkpoint file:', error);
-        throw error;
-    } finally {
-        state.loadingManager.hide();
-    }
-}

static/js/api/loraApi.js

@@ -1,175 +0,0 @@
-import {
-    fetchModelsPage,
-    resetAndReloadWithVirtualScroll,
-    loadMoreWithVirtualScroll,
-    refreshModels as baseRefreshModels,
-    deleteModel as baseDeleteModel,
-    replaceModelPreview,
-    fetchCivitaiMetadata,
-    refreshSingleModelMetadata,
-    excludeModel as baseExcludeModel
-} from './baseModelApi.js';
-import { state } from '../state/index.js';
-
-/**
- * Save model metadata to the server
- * @param {string} filePath - File path
- * @param {Object} data - Data to save
- * @returns {Promise} Promise of the save operation
- */
-export async function saveModelMetadata(filePath, data) {
-    try {
-        // Show loading indicator
-        state.loadingManager.showSimpleLoading('Saving metadata...');
-        
-        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');
-        }
-
-        // Update the virtual scroller with the new data
-        state.virtualScroller.updateSingleItem(filePath, data);
-        
-        return response.json();
-    } finally {
-        // Always hide the loading indicator when done
-        state.loadingManager.hide();
-    }
-}
-
-/**
- * Exclude a lora model from being shown in the UI
- * @param {string} filePath - File path of the model to exclude
- * @returns {Promise<boolean>} Promise resolving to success status
- */
-export async function excludeLora(filePath) {
-    return baseExcludeModel(filePath, 'lora');
-}
-
-/**
- * Load more loras with pagination - updated to work with VirtualScroller
- * @param {boolean} resetPage - Whether to reset to the first page
- * @param {boolean} updateFolders - Whether to update folder tags
- * @returns {Promise<void>}
- */
-export async function loadMoreLoras(resetPage = false, updateFolders = false) {
-    return loadMoreWithVirtualScroll({
-        modelType: 'lora',
-        resetPage,
-        updateFolders,
-        fetchPageFunction: fetchLorasPage
-    });
-}
-
-/**
- * Fetch loras with pagination for virtual scrolling
- * @param {number} page - Page number to fetch
- * @param {number} pageSize - Number of items per page
- * @returns {Promise<Object>} Object containing items, total count, and pagination info
- */
-export async function fetchLorasPage(page = 1, pageSize = 100) {
-    return fetchModelsPage({
-        modelType: 'lora',
-        page,
-        pageSize,
-        endpoint: '/api/loras'
-    });
-}
-
-export async function fetchCivitai() {
-    return fetchCivitaiMetadata({
-        modelType: 'lora',
-        fetchEndpoint: '/api/fetch-all-civitai',
-        resetAndReloadFunction: resetAndReload
-    });
-}
-
-export async function deleteModel(filePath) {
-    return baseDeleteModel(filePath, 'lora');
-}
-
-export async function replacePreview(filePath) {
-    return replaceModelPreview(filePath, 'lora');
-}
-
-export async function resetAndReload(updateFolders = false) {
-    return resetAndReloadWithVirtualScroll({
-        modelType: 'lora',
-        updateFolders,
-        fetchPageFunction: fetchLorasPage
-    });
-}
-
-export async function refreshLoras(fullRebuild = false) {
-    return baseRefreshModels({
-        modelType: 'lora',
-        scanEndpoint: '/api/loras/scan',
-        resetAndReloadFunction: resetAndReload,
-        fullRebuild: fullRebuild
-    });
-}
-
-export async function refreshSingleLoraMetadata(filePath) {
-    await refreshSingleModelMetadata(filePath, 'lora');
-}
-
-export async function fetchModelDescription(modelId, filePath) {
-    try {
-        const response = await fetch(`/api/lora-model-description?model_id=${modelId}&file_path=${encodeURIComponent(filePath)}`);
-        
-        if (!response.ok) {
-            throw new Error(`Failed to fetch model description: ${response.statusText}`);
-        }
-        
-        return await response.json();
-    } catch (error) {
-        console.error('Error fetching model description:', error);
-        throw error;
-    }
-}
-
-/**
- * Rename a LoRA file
- * @param {string} filePath - Current file path
- * @param {string} newFileName - New file name (without path)
- * @returns {Promise<Object>} - Promise that resolves with the server response
- */
-export async function renameLoraFile(filePath, newFileName) {
-    try {
-        // Show loading indicator
-        state.loadingManager.showSimpleLoading('Renaming LoRA file...');
-        
-        const response = await fetch('/api/loras/rename', {
-            method: 'POST',
-            headers: {
-                'Content-Type': 'application/json',
-            },
-            body: JSON.stringify({
-                file_path: filePath,
-                new_file_name: newFileName
-            })
-        });
-        
-        if (!response.ok) {
-            throw new Error(`Server returned ${response.status}: ${response.statusText}`);
-        }
-        
-        return await response.json();
-    } catch (error) {
-        console.error('Error renaming LoRA file:', error);
-        throw error;
-    } finally {
-        // Hide loading indicator
-        state.loadingManager.hide();
-    }
-}

static/js/api/recipeApi.js

@@ -1,8 +1,4 @@
 import { RecipeCard } from '../components/RecipeCard.js';
-import {
-    resetAndReloadWithVirtualScroll,
-    loadMoreWithVirtualScroll
-} from './baseModelApi.js';
 import { state, getCurrentPageState } from '../state/index.js';
 import { showToast } from '../utils/uiHelpers.js';
 
@@ -98,6 +94,98 @@ export async function fetchRecipesPage(page = 1, pageSize = 100) {
     }
 }
 
+/**
+ * Reset and reload models using virtual scrolling
+ * @param {Object} options - Operation options
+ * @returns {Promise<Object>} The fetch result
+ */
+export async function resetAndReloadWithVirtualScroll(options = {}) {
+    const {
+        modelType = 'lora',
+        updateFolders = false,
+        fetchPageFunction
+    } = options;
+    
+    const pageState = getCurrentPageState();
+    
+    try {
+        pageState.isLoading = true;
+        
+        // Reset page counter
+        pageState.currentPage = 1;
+        
+        // Fetch the first page
+        const result = await fetchPageFunction(1, pageState.pageSize || 50);
+        
+        // Update the virtual scroller
+        state.virtualScroller.refreshWithData(
+            result.items,
+            result.totalItems,
+            result.hasMore
+        );
+        
+        // Update state
+        pageState.hasMore = result.hasMore;
+        pageState.currentPage = 2; // Next page will be 2
+        
+        return result;
+    } catch (error) {
+        console.error(`Error reloading ${modelType}s:`, error);
+        showToast(`Failed to reload ${modelType}s: ${error.message}`, 'error');
+        throw error;
+    } finally {
+        pageState.isLoading = false;
+    }
+}
+
+/**
+ * Load more models using virtual scrolling
+ * @param {Object} options - Operation options
+ * @returns {Promise<Object>} The fetch result
+ */
+export async function loadMoreWithVirtualScroll(options = {}) {
+    const {
+        modelType = 'lora',
+        resetPage = false,
+        updateFolders = false,
+        fetchPageFunction
+    } = options;
+    
+    const pageState = getCurrentPageState();
+    
+    try {
+        // Start loading state
+        pageState.isLoading = true;
+        
+        // Reset to first page if requested
+        if (resetPage) {
+            pageState.currentPage = 1;
+        }
+        
+        // Fetch the first page of data
+        const result = await fetchPageFunction(pageState.currentPage, pageState.pageSize || 50);
+        
+        // Update virtual scroller with the new data
+        state.virtualScroller.refreshWithData(
+            result.items,
+            result.totalItems,
+            result.hasMore
+        );
+        
+        // Update state
+        pageState.hasMore = result.hasMore;
+        pageState.currentPage = 2; // Next page to load would be 2
+        
+        return result;
+    } catch (error) {
+        console.error(`Error loading ${modelType}s:`, error);
+        showToast(`Failed to load ${modelType}s: ${error.message}`, 'error');
+        throw error;
+    } finally {
+        pageState.isLoading = false;
+    }
+}
+
 /**
  * Reset and reload recipes using virtual scrolling
  * @param {boolean} updateFolders - Whether to update folder tags

static/js/checkpoints.js

@@ -1,22 +1,18 @@
 import { appCore } from './core.js';
 import { confirmDelete, closeDeleteModal, confirmExclude, closeExcludeModal } from './utils/modalUtils.js';
 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';
+import { MODEL_TYPES } from './api/apiConfig.js';
 
 // Initialize the Checkpoints page
 class CheckpointsPageManager {
     constructor() {
         // Initialize page controls
-        this.pageControls = createPageControls('checkpoints');
-        
-        // Initialize checkpoint download manager
-        window.checkpointDownloadManager = new CheckpointDownloadManager();
+        this.pageControls = createPageControls(MODEL_TYPES.CHECKPOINT);
         
         // Initialize the ModelDuplicatesManager
-        this.duplicatesManager = new ModelDuplicatesManager(this, 'checkpoints');
+        this.duplicatesManager = new ModelDuplicatesManager(this, MODEL_TYPES.CHECKPOINT);
         
         // Expose only necessary functions to global scope
         this._exposeRequiredGlobalFunctions();
@@ -29,11 +25,6 @@ class CheckpointsPageManager {
         window.confirmExclude = confirmExclude;
         window.closeExcludeModal = closeExcludeModal;
         
-        // Add loadCheckpoints function to window for FilterManager compatibility
-        window.checkpointManager = {
-            loadCheckpoints: (reset) => loadMoreCheckpoints(reset)
-        };
-        
         // Expose duplicates manager
         window.modelDuplicatesManager = this.duplicatesManager;
     }

static/js/components/CheckpointCard.js

@@ -1,334 +0,0 @@
-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
-    card.dataset.sha256 = checkpoint.sha256;
-    card.dataset.filepath = checkpoint.file_path;
-    card.dataset.name = checkpoint.model_name;
-    card.dataset.file_name = checkpoint.file_name;
-    card.dataset.folder = checkpoint.folder;
-    card.dataset.modified = checkpoint.modified;
-    card.dataset.file_size = checkpoint.file_size;
-    card.dataset.from_civitai = checkpoint.from_civitai;
-    card.dataset.notes = checkpoint.notes || '';
-    card.dataset.base_model = checkpoint.base_model || 'Unknown';
-    card.dataset.favorite = checkpoint.favorite ? 'true' : 'false';
-
-    // Store metadata if available
-    if (checkpoint.civitai) {
-        card.dataset.meta = JSON.stringify(checkpoint.civitai || {});
-    }
-    
-    // Store tags if available
-    if (checkpoint.tags && Array.isArray(checkpoint.tags)) {
-        card.dataset.tags = JSON.stringify(checkpoint.tags);
-    }
-
-    if (checkpoint.modelDescription) {
-        card.dataset.modelDescription = checkpoint.modelDescription;
-    }
-
-    // Store NSFW level if available
-    const nsfwLevel = checkpoint.preview_nsfw_level !== undefined ? checkpoint.preview_nsfw_level : 0;
-    card.dataset.nsfwLevel = nsfwLevel;
-    
-    // Determine if the preview should be blurred based on NSFW level and user settings
-    const shouldBlur = state.settings.blurMatureContent && nsfwLevel > NSFW_LEVELS.PG13;
-    if (shouldBlur) {
-        card.classList.add('nsfw-content');
-    }
-
-    // Determine preview URL
-    const previewUrl = checkpoint.preview_url || '/loras_static/images/no-preview.png';
-    
-    // Get the page-specific previewVersions map
-    const previewVersions = state.pages.checkpoints.previewVersions || new Map();
-    const version = previewVersions.get(checkpoint.file_path);
-    const versionedPreviewUrl = version ? `${previewUrl}?t=${version}` : previewUrl;
-
-    // Determine NSFW warning text based on level
-    let nsfwText = "Mature Content";
-    if (nsfwLevel >= NSFW_LEVELS.XXX) {
-        nsfwText = "XXX-rated Content";
-    } else if (nsfwLevel >= NSFW_LEVELS.X) {
-        nsfwText = "X-rated Content";
-    } else if (nsfwLevel >= NSFW_LEVELS.R) {
-        nsfwText = "R-rated Content";
-    }
-
-    // Check if autoplayOnHover is enabled for video previews
-    const autoplayOnHover = state.global?.settings?.autoplayOnHover || false;
-    const isVideo = previewUrl.endsWith('.mp4');
-    const videoAttrs = autoplayOnHover ? 'controls muted loop' : 'controls autoplay muted loop';
-
-    // Get favorite status from checkpoint data
-    const isFavorite = checkpoint.favorite === true;
-
-    card.innerHTML = `
-        <div class="card-preview ${shouldBlur ? 'blurred' : ''}">
-            ${isVideo ? 
-                `<video ${videoAttrs}>
-                    <source src="${versionedPreviewUrl}" type="video/mp4">
-                </video>` :
-                `<img src="${versionedPreviewUrl}" alt="${checkpoint.model_name}">`
-            }
-            <div class="card-header">
-                ${shouldBlur ? 
-                  `<button class="toggle-blur-btn" title="Toggle blur">
-                      <i class="fas fa-eye"></i>
-                  </button>` : ''}
-                <span class="base-model-label ${shouldBlur ? 'with-toggle' : ''}" title="${checkpoint.base_model}">
-                    ${checkpoint.base_model}
-                </span>
-                <div class="card-actions">
-                    <i class="${isFavorite ? 'fas fa-star favorite-active' : 'far fa-star'}" 
-                       title="${isFavorite ? 'Remove from favorites' : 'Add to favorites'}">
-                    </i>
-                    <i class="fas fa-globe" 
-                       title="${checkpoint.from_civitai ? 'View on Civitai' : 'Not available from Civitai'}"
-                       ${!checkpoint.from_civitai ? 'style="opacity: 0.5; cursor: not-allowed"' : ''}>
-                    </i>
-                    <i class="fas fa-copy" 
-                       title="Copy Checkpoint Name">
-                    </i>
-                    <i class="fas fa-trash" 
-                       title="Delete Model">
-                    </i>
-                </div>
-            </div>
-            ${shouldBlur ? `
-                <div class="nsfw-overlay">
-                    <div class="nsfw-warning">
-                        <p>${nsfwText}</p>
-                        <button class="show-content-btn">Show</button>
-                    </div>
-                </div>
-            ` : ''}
-            <div class="card-footer">
-                <div class="model-info">
-                    <span class="model-name">${checkpoint.model_name}</span>
-                </div>
-                <div class="card-actions">
-                    <i class="fas fa-folder-open" 
-                       title="Open Example Images Folder">
-                    </i>
-                </div>
-            </div>
-        </div>
-    `;
-
-    // Add video auto-play on hover functionality if needed
-    const videoElement = card.querySelector('video');
-    if (videoElement && autoplayOnHover) {
-        const cardPreview = card.querySelector('.card-preview');
-        
-        // Remove autoplay attribute and pause initially
-        videoElement.removeAttribute('autoplay');
-        videoElement.pause();
-        
-        // 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;
-}

static/js/components/ContextMenu/CheckpointContextMenu.js

@@ -1,12 +1,12 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
 import { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
-import { refreshSingleCheckpointMetadata, saveModelMetadata, replaceCheckpointPreview, resetAndReload } from '../../api/checkpointApi.js';
+import { getModelApiClient, resetAndReload } from '../../api/baseModelApi.js';
 import { showToast } from '../../utils/uiHelpers.js';
-import { showExcludeModal } from '../../utils/modalUtils.js';
+import { showDeleteModal, showExcludeModal } from '../../utils/modalUtils.js';
 
 export class CheckpointContextMenu extends BaseContextMenu {
     constructor() {
-        super('checkpointContextMenu', '.lora-card');
+        super('checkpointContextMenu', '.model-card');
         this.nsfwSelector = document.getElementById('nsfwLevelSelector');
         this.modelType = 'checkpoint';
         this.resetAndReload = resetAndReload;
@@ -19,7 +19,7 @@ export class CheckpointContextMenu extends BaseContextMenu {
     
     // Implementation needed by the mixin
     async saveModelMetadata(filePath, data) {
-        return saveModelMetadata(filePath, data);
+        return getModelApiClient().saveModelMetadata(filePath, data);
     }
     
     handleMenuAction(action) {
@@ -28,6 +28,8 @@ export class CheckpointContextMenu extends BaseContextMenu {
             return;
         }
 
+        const apiClient = getModelApiClient();
+
         // Otherwise handle checkpoint-specific actions
         switch(action) {
             case 'details':
@@ -36,13 +38,10 @@ export class CheckpointContextMenu extends BaseContextMenu {
                 break;
             case 'replace-preview':
                 // Add new action for replacing preview images
-                replaceCheckpointPreview(this.currentCard.dataset.filepath);
+                apiClient.replaceModelPreview(this.currentCard.dataset.filepath);
                 break;
             case 'delete':
-                // Delete checkpoint
-                if (this.currentCard.querySelector('.fa-trash')) {
-                    this.currentCard.querySelector('.fa-trash').click();
-                }
+                showDeleteModal(this.currentCard.dataset.filepath);
                 break;
             case 'copyname':
                 // Copy checkpoint name
@@ -52,14 +51,14 @@ export class CheckpointContextMenu extends BaseContextMenu {
                 break;
             case 'refresh-metadata':
                 // Refresh metadata from CivitAI
-                refreshSingleCheckpointMetadata(this.currentCard.dataset.filepath);
+                apiClient.refreshSingleModelMetadata(this.currentCard.dataset.filepath);
                 break;
             case 'move':
                 // Move to folder (placeholder)
                 showToast('Move to folder feature coming soon', 'info');
                 break;
             case 'exclude':
-                showExcludeModal(this.currentCard.dataset.filepath, 'checkpoint');
+                showExcludeModal(this.currentCard.dataset.filepath);
                 break;
         }
     }

static/js/components/ContextMenu/EmbeddingContextMenu.js

@@ -0,0 +1,68 @@
+import { BaseContextMenu } from './BaseContextMenu.js';
+import { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
+import { getModelApiClient, resetAndReload } from '../../api/baseModelApi.js';
+import { showToast } from '../../utils/uiHelpers.js';
+import { showDeleteModal, showExcludeModal } from '../../utils/modalUtils.js';
+
+export class EmbeddingContextMenu extends BaseContextMenu {
+    constructor() {
+        super('embeddingContextMenu', '.model-card');
+        this.nsfwSelector = document.getElementById('nsfwLevelSelector');
+        this.modelType = 'embedding';
+        this.resetAndReload = resetAndReload;
+        
+        // Initialize NSFW Level Selector events
+        if (this.nsfwSelector) {
+            this.initNSFWSelector();
+        }
+    }
+    
+    // Implementation needed by the mixin
+    async saveModelMetadata(filePath, data) {
+        return getModelApiClient().saveModelMetadata(filePath, data);
+    }
+    
+    handleMenuAction(action) {
+        // First try to handle with common actions
+        if (ModelContextMenuMixin.handleCommonMenuActions.call(this, action)) {
+            return;
+        }
+
+        const apiClient = getModelApiClient();
+
+        // Otherwise handle embedding-specific actions
+        switch(action) {
+            case 'details':
+                // Show embedding details
+                this.currentCard.click();
+                break;
+            case 'replace-preview':
+                // Add new action for replacing preview images
+                apiClient.replaceModelPreview(this.currentCard.dataset.filepath);
+                break;
+            case 'delete':
+                showDeleteModal(this.currentCard.dataset.filepath);
+                break;
+            case 'copyname':
+                // Copy embedding name
+                if (this.currentCard.querySelector('.fa-copy')) {
+                    this.currentCard.querySelector('.fa-copy').click();
+                }
+                break;
+            case 'refresh-metadata':
+                // Refresh metadata from CivitAI
+                apiClient.refreshSingleModelMetadata(this.currentCard.dataset.filepath);
+                break;
+            case 'move':
+                // Move to folder (placeholder)
+                showToast('Move to folder feature coming soon', 'info');
+                break;
+            case 'exclude':
+                showExcludeModal(this.currentCard.dataset.filepath);
+                break;
+        }
+    }
+}
+
+// Mix in shared methods
+Object.assign(EmbeddingContextMenu.prototype, ModelContextMenuMixin);

static/js/components/ContextMenu/LoraContextMenu.js

@@ -1,12 +1,12 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
 import { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
-import { refreshSingleLoraMetadata, saveModelMetadata, replacePreview, resetAndReload } from '../../api/loraApi.js';
+import { getModelApiClient, resetAndReload } from '../../api/baseModelApi.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');
+        super('loraContextMenu', '.model-card');
         this.nsfwSelector = document.getElementById('nsfwLevelSelector');
         this.modelType = 'lora';
         this.resetAndReload = resetAndReload;
@@ -19,7 +19,7 @@ export class LoraContextMenu extends BaseContextMenu {
 
     // Use the saveModelMetadata implementation from loraApi
     async saveModelMetadata(filePath, data) {
-        return saveModelMetadata(filePath, data);
+        return getModelApiClient().saveModelMetadata(filePath, data);
     }
 
     handleMenuAction(action, menuItem) {
@@ -48,7 +48,7 @@ export class LoraContextMenu extends BaseContextMenu {
                 break;
             case 'replace-preview':
                 // Add a new action for replacing preview images
-                replacePreview(this.currentCard.dataset.filepath);
+                getModelApiClient().replaceModelPreview(this.currentCard.dataset.filepath);
                 break;
             case 'delete':
                 // Call showDeleteModal directly instead of clicking the trash button
@@ -58,7 +58,7 @@ export class LoraContextMenu extends BaseContextMenu {
                 moveManager.showMoveModal(this.currentCard.dataset.filepath);
                 break;
             case 'refresh-metadata':
-                refreshSingleLoraMetadata(this.currentCard.dataset.filepath);
+                getModelApiClient().refreshSingleModelMetadata(this.currentCard.dataset.filepath);
                 break;
             case 'exclude':
                 showExcludeModal(this.currentCard.dataset.filepath);

static/js/components/ContextMenu/ModelContextMenuMixin.js

@@ -125,7 +125,7 @@ export const ModelContextMenuMixin = {
                 
                 const endpoint = this.modelType === 'checkpoint' ? 
                     '/api/checkpoints/relink-civitai' : 
-                    '/api/relink-civitai';
+                    '/api/loras/relink-civitai';
                 
                 const response = await fetch(endpoint, {
                     method: 'POST',

static/js/components/ContextMenu/RecipeContextMenu.js

@@ -7,7 +7,7 @@ import { state } from '../../state/index.js';
 
 export class RecipeContextMenu extends BaseContextMenu {
     constructor() {
-        super('recipeContextMenu', '.lora-card');
+        super('recipeContextMenu', '.model-card');
         this.nsfwSelector = document.getElementById('nsfwLevelSelector');
         this.modelType = 'recipe';
         
@@ -209,9 +209,9 @@ export class RecipeContextMenu extends BaseContextMenu {
                 
                 // Determine which endpoint to use based on available data
                 if (lora.modelVersionId) {
-                    endpoint = `/api/civitai/model/version/${lora.modelVersionId}`;
+                    endpoint = `/api/loras/civitai/model/version/${lora.modelVersionId}`;
                 } else if (lora.hash) {
-                    endpoint = `/api/civitai/model/hash/${lora.hash}`;
+                    endpoint = `/api/loras/civitai/model/hash/${lora.hash}`;
                 } else {
                     console.error("Missing both hash and modelVersionId for lora:", lora);
                     return null;

static/js/components/ContextMenu/index.js

@@ -1,4 +1,5 @@
 export { LoraContextMenu } from './LoraContextMenu.js';
 export { RecipeContextMenu } from './RecipeContextMenu.js';
 export { CheckpointContextMenu } from './CheckpointContextMenu.js';
+export { EmbeddingContextMenu } from './EmbeddingContextMenu.js';
 export { ModelContextMenuMixin } from './ModelContextMenuMixin.js';

static/js/components/DuplicatesManager.js

@@ -243,7 +243,7 @@ export class DuplicatesManager {
         checkboxes.forEach(checkbox => {
             checkbox.checked = !allSelected;
             const recipeId = checkbox.dataset.recipeId;
-            const card = checkbox.closest('.lora-card');
+            const card = checkbox.closest('.model-card');
             
             if (!allSelected) {
                 this.selectedForDeletion.add(recipeId);
@@ -268,7 +268,7 @@ export class DuplicatesManager {
         checkboxes.forEach(checkbox => {
             checkbox.checked = true;
             this.selectedForDeletion.add(checkbox.dataset.recipeId);
-            checkbox.closest('.lora-card').classList.add('duplicate-selected');
+            checkbox.closest('.model-card').classList.add('duplicate-selected');
         });
         
         // Update the button text
@@ -299,7 +299,7 @@ export class DuplicatesManager {
             if (checkbox) {
                 checkbox.checked = true;
                 this.selectedForDeletion.add(recipeId);
-                checkbox.closest('.lora-card').classList.add('duplicate-selected');
+                checkbox.closest('.model-card').classList.add('duplicate-selected');
             }
         }
         
@@ -310,7 +310,7 @@ export class DuplicatesManager {
         if (latestCheckbox) {
             latestCheckbox.checked = false;
             this.selectedForDeletion.delete(latestId);
-            latestCheckbox.closest('.lora-card').classList.remove('duplicate-selected');
+            latestCheckbox.closest('.model-card').classList.remove('duplicate-selected');
         }
         
         this.updateSelectedCount();

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('/embeddings')) return 'embeddings';
       if (path.includes('/statistics')) return 'statistics';
       if (path.includes('/loras')) return 'loras';
       return 'unknown';

static/js/components/ModelDuplicatesManager.js

@@ -2,8 +2,7 @@
 import { showToast } from '../utils/uiHelpers.js';
 import { state, getCurrentPageState } from '../state/index.js';
 import { formatDate } from '../utils/formatters.js';
-import { resetAndReload as resetAndReloadLoras } from '../api/loraApi.js';
-import { resetAndReload as resetAndReloadCheckpoints } from '../api/checkpointApi.js';
+import { resetAndReload} from '../api/baseModelApi.js';
 import { LoadingManager } from '../managers/LoadingManager.js';
 
 export class ModelDuplicatesManager {
@@ -184,7 +183,7 @@ export class ModelDuplicatesManager {
         document.body.classList.remove('duplicate-mode');
         
         // Clear the model grid first
-        const modelGrid = document.getElementById(this.modelType === 'loras' ? 'loraGrid' : 'checkpointGrid');
+        const modelGrid = document.getElementById('modelGrid');
         if (modelGrid) {
             modelGrid.innerHTML = '';
         }
@@ -241,7 +240,7 @@ export class ModelDuplicatesManager {
     }
     
     renderDuplicateGroups() {
-        const modelGrid = document.getElementById(this.modelType === 'loras' ? 'loraGrid' : 'checkpointGrid');
+        const modelGrid = document.getElementById('modelGrid');
         if (!modelGrid) return;
         
         // Clear existing content
@@ -331,7 +330,7 @@ export class ModelDuplicatesManager {
     renderModelCard(model, groupHash) {
         // Create basic card structure
         const card = document.createElement('div');
-        card.className = 'lora-card duplicate';
+        card.className = 'model-card duplicate';
         card.dataset.hash = model.sha256;
         card.dataset.filePath = model.file_path;
         
@@ -550,7 +549,7 @@ export class ModelDuplicatesManager {
         checkboxes.forEach(checkbox => {
             checkbox.checked = !allSelected;
             const filePath = checkbox.dataset.filePath;
-            const card = checkbox.closest('.lora-card');
+            const card = checkbox.closest('.model-card');
             
             if (!allSelected) {
                 this.selectedForDeletion.add(filePath);
@@ -622,12 +621,7 @@ export class ModelDuplicatesManager {
             
             // If models were successfully deleted
             if (data.total_deleted > 0) {
-                // Reload model data with updated folders
-                if (this.modelType === 'loras') {
-                    await resetAndReloadLoras(true);
-                } else {
-                    await resetAndReloadCheckpoints(true);
-                }
+                await resetAndReload(true);
                 
                 // Check if there are still duplicates
                 try {

static/js/components/RecipeCard.js

@@ -17,7 +17,7 @@ class RecipeCard {
     
     createCardElement() {
         const card = document.createElement('div');
-        card.className = 'lora-card';
+        card.className = 'model-card';
         card.dataset.filepath = this.recipe.file_path;
         card.dataset.title = this.recipe.title;
         card.dataset.nsfwLevel = this.recipe.preview_nsfw_level || 0;

static/js/components/RecipeModal.js

@@ -831,9 +831,9 @@ class RecipeModal {
                 
                 // Determine which endpoint to use based on available data
                 if (lora.modelVersionId) {
-                    endpoint = `/api/civitai/model/version/${lora.modelVersionId}`;
+                    endpoint = `/api/loras/civitai/model/version/${lora.modelVersionId}`;
                 } else if (lora.hash) {
-                    endpoint = `/api/civitai/model/hash/${lora.hash}`;
+                    endpoint = `/api/loras/civitai/model/hash/${lora.hash}`;
                 } else {
                     console.error("Missing both hash and modelVersionId for lora:", lora);
                     return null;

static/js/components/alphabet/AlphabetBar.js

@@ -1,7 +1,7 @@
 // AlphabetBar.js - Component for alphabet filtering
-import { getCurrentPageState, setCurrentPageType } from '../../state/index.js';
+import { getCurrentPageState } from '../../state/index.js';
 import { getStorageItem, setStorageItem } from '../../utils/storageHelpers.js';
-import { resetAndReload } from '../../api/loraApi.js';
+import { resetAndReload } from '../../api/baseModelApi.js';
 
 /**
  * AlphabetBar class - Handles the alphabet filtering UI and interactions
@@ -227,7 +227,7 @@ export class AlphabetBar {
         this.updateToggleIndicator();
         
         // Trigger a reload with the new filter
-        resetAndReload(true);
+        resetAndReload(false);
     }
     
     /**

static/js/components/checkpointModal/ModelDescription.js

@@ -1,102 +0,0 @@
-/**
- * ModelDescription.js
- * Handles checkpoint model descriptions
- */
-import { showToast } from '../../utils/uiHelpers.js';
-
-/**
- * Set up tab switching functionality
- */
-export function setupTabSwitching() {
-    const tabButtons = document.querySelectorAll('.showcase-tabs .tab-btn');
-    
-    tabButtons.forEach(button => {
-        button.addEventListener('click', () => {
-            // Remove active class from all tabs
-            document.querySelectorAll('.showcase-tabs .tab-btn').forEach(btn => 
-                btn.classList.remove('active')
-            );
-            document.querySelectorAll('.tab-content .tab-pane').forEach(tab => 
-                tab.classList.remove('active')
-            );
-            
-            // Add active class to clicked tab
-            button.classList.add('active');
-            const tabId = `${button.dataset.tab}-tab`;
-            document.getElementById(tabId).classList.add('active');
-            
-            // If switching to description tab, make sure content is properly loaded and displayed
-            if (button.dataset.tab === 'description') {
-                const descriptionContent = document.querySelector('.model-description-content');
-                if (descriptionContent) {
-                    const hasContent = descriptionContent.innerHTML.trim() !== '';
-                    document.querySelector('.model-description-loading')?.classList.add('hidden');
-                    
-                    // If no content, show a message
-                    if (!hasContent) {
-                        descriptionContent.innerHTML = '<div class="no-description">No model description available</div>';
-                        descriptionContent.classList.remove('hidden');
-                    }
-                }
-            }
-        });
-    });
-}
-
-/**
- * Load model description from API
- * @param {string} modelId - The Civitai model ID
- * @param {string} filePath - File path for the model
- */
-export async function loadModelDescription(modelId, filePath) {
-    try {
-        const descriptionContainer = document.querySelector('.model-description-content');
-        const loadingElement = document.querySelector('.model-description-loading');
-        
-        if (!descriptionContainer || !loadingElement) return;
-        
-        // Show loading indicator
-        loadingElement.classList.remove('hidden');
-        descriptionContainer.classList.add('hidden');
-        
-        // Try to get model description from API
-        const response = await fetch(`/api/checkpoint-model-description?model_id=${modelId}&file_path=${encodeURIComponent(filePath)}`);
-        
-        if (!response.ok) {
-            throw new Error(`Failed to fetch model description: ${response.statusText}`);
-        }
-        
-        const data = await response.json();
-        
-        if (data.success && data.description) {
-            // Update the description content
-            descriptionContainer.innerHTML = data.description;
-            
-            // Process any links in the description to open in new tab
-            const links = descriptionContainer.querySelectorAll('a');
-            links.forEach(link => {
-                link.setAttribute('target', '_blank');
-                link.setAttribute('rel', 'noopener noreferrer');
-            });
-            
-            // Show the description and hide loading indicator
-            descriptionContainer.classList.remove('hidden');
-            loadingElement.classList.add('hidden');
-        } else {
-            throw new Error(data.error || 'No description available');
-        }
-    } catch (error) {
-        console.error('Error loading model description:', error);
-        const loadingElement = document.querySelector('.model-description-loading');
-        if (loadingElement) {
-            loadingElement.innerHTML = `<div class="error-message">Failed to load model description. ${error.message}</div>`;
-        }
-        
-        // Show empty state message in the description container
-        const descriptionContainer = document.querySelector('.model-description-content');
-        if (descriptionContainer) {
-            descriptionContainer.innerHTML = '<div class="no-description">No model description available</div>';
-            descriptionContainer.classList.remove('hidden');
-        }
-    }
-}

static/js/components/checkpointModal/ModelTags.js

@@ -1,471 +0,0 @@
-/**
- * ModelTags.js
- * Module for handling checkpoint model tag editing functionality
- */
-import { showToast } from '../../utils/uiHelpers.js';
-import { saveModelMetadata } from '../../api/checkpointApi.js';
-
-// Preset tag suggestions
-const PRESET_TAGS = [
-  'character', 'style', 'concept', 'clothing', 'base model',
-  'poses', 'background', 'vehicle', 'buildings', 
-  'objects', 'animal'
-];
-
-// Create a named function so we can remove it later
-let saveTagsHandler = null;
-
-/**
- * Set up tag editing mode
- */
-export function setupTagEditMode() {
-    const editBtn = document.querySelector('.edit-tags-btn');
-    if (!editBtn) return;
-    
-    // Store original tags for restoring on cancel
-    let originalTags = [];
-    
-    // Remove any previously attached click handler
-    if (editBtn._hasClickHandler) {
-        editBtn.removeEventListener('click', editBtn._clickHandler);
-    }
-    
-    // Create new handler and store reference
-    const editBtnClickHandler = function() {
-        const tagsSection = document.querySelector('.model-tags-container');
-        const isEditMode = tagsSection.classList.toggle('edit-mode');
-        const filePath = this.dataset.filePath;
-        
-        // Toggle edit mode UI elements
-        const compactTagsDisplay = tagsSection.querySelector('.model-tags-compact');
-        const tagsEditContainer = tagsSection.querySelector('.metadata-edit-container');
-        
-        if (isEditMode) {
-            // Enter edit mode
-            this.innerHTML = '<i class="fas fa-times"></i>'; // Change to cancel icon
-            this.title = "Cancel editing";
-            
-            // Get all tags from tooltip, not just the visible ones in compact display
-            originalTags = Array.from(
-                tagsSection.querySelectorAll('.tooltip-tag')
-            ).map(tag => tag.textContent);
-            
-            // Hide compact display, show edit container
-            compactTagsDisplay.style.display = 'none';
-            
-            // If edit container doesn't exist yet, create it
-            if (!tagsEditContainer) {
-                const editContainer = document.createElement('div');
-                editContainer.className = 'metadata-edit-container';
-                
-                // Move the edit button inside the container header for better visibility
-                const editBtnClone = editBtn.cloneNode(true);
-                editBtnClone.classList.add('metadata-header-btn');
-                
-                // Create edit UI with edit button in the header
-                editContainer.innerHTML = createTagEditUI(originalTags, editBtnClone.outerHTML);
-                tagsSection.appendChild(editContainer);
-                
-                // Setup the tag input field behavior
-                setupTagInput();
-                
-                // Create and add preset suggestions dropdown
-                const tagForm = editContainer.querySelector('.metadata-add-form');
-                const suggestionsDropdown = createSuggestionsDropdown(originalTags);
-                tagForm.appendChild(suggestionsDropdown);
-                
-                // Setup delete buttons for existing tags
-                setupDeleteButtons();
-                
-                // Transfer click event from original button to the cloned one
-                const newEditBtn = editContainer.querySelector('.metadata-header-btn');
-                if (newEditBtn) {
-                    newEditBtn.addEventListener('click', function() {
-                        editBtn.click();
-                    });
-                }
-                
-                // Hide the original button when in edit mode
-                editBtn.style.display = 'none';
-            } else {
-                // Just show the existing edit container
-                tagsEditContainer.style.display = 'block';
-                editBtn.style.display = 'none';
-            }
-        } else {
-            // Exit edit mode
-            this.innerHTML = '<i class="fas fa-pencil-alt"></i>'; // Change back to edit icon
-            this.title = "Edit tags";
-            editBtn.style.display = 'block';
-            
-            // Show compact display, hide edit container
-            compactTagsDisplay.style.display = 'flex';
-            if (tagsEditContainer) tagsEditContainer.style.display = 'none';
-            
-            // Check if we're exiting edit mode due to "Save" or "Cancel"
-            if (!this.dataset.skipRestore) {
-                // If canceling, restore original tags
-                restoreOriginalTags(tagsSection, originalTags);
-            } else {
-                // Reset the skip restore flag
-                delete this.dataset.skipRestore;
-            }
-        }
-    };
-    
-    // Store the handler reference on the button itself
-    editBtn._clickHandler = editBtnClickHandler;
-    editBtn._hasClickHandler = true;
-    editBtn.addEventListener('click', editBtnClickHandler);
-    
-    // Clean up any previous document click handler
-    if (saveTagsHandler) {
-        document.removeEventListener('click', saveTagsHandler);
-    }
-    
-    // Create new save handler and store reference
-    saveTagsHandler = function(e) {
-        if (e.target.classList.contains('save-tags-btn') || 
-            e.target.closest('.save-tags-btn')) {
-            saveTags();
-        }
-    };
-    
-    // Add the new handler
-    document.addEventListener('click', saveTagsHandler);
-}
-
-/**
- * Create the tag editing UI
- * @param {Array} currentTags - Current tags
- * @param {string} editBtnHTML - HTML for the edit button to include in header
- * @returns {string} HTML markup for tag editing UI
- */
-function createTagEditUI(currentTags, editBtnHTML = '') {
-    return `
-        <div class="metadata-edit-content">
-            <div class="metadata-edit-header">
-                <label>Edit Tags</label>
-                ${editBtnHTML}
-            </div>
-            <div class="metadata-items">
-                ${currentTags.map(tag => `
-                    <div class="metadata-item" data-tag="${tag}">
-                        <span class="metadata-item-content">${tag}</span>
-                        <button class="metadata-delete-btn">
-                            <i class="fas fa-times"></i>
-                        </button>
-                    </div>
-                `).join('')}
-            </div>
-            <div class="metadata-edit-controls">
-                <button class="save-tags-btn" title="Save changes">
-                    <i class="fas fa-save"></i> Save
-                </button>
-            </div>
-            <div class="metadata-add-form">
-                <input type="text" class="metadata-input" placeholder="Type to add or click suggestions below">
-            </div>
-        </div>
-    `;
-}
-
-/**
- * Create suggestions dropdown with preset tags
- * @param {Array} existingTags - Already added tags
- * @returns {HTMLElement} - Dropdown element
- */
-function createSuggestionsDropdown(existingTags = []) {
-    const dropdown = document.createElement('div');
-    dropdown.className = 'metadata-suggestions-dropdown';
-    
-    // Create header
-    const header = document.createElement('div');
-    header.className = 'metadata-suggestions-header';
-    header.innerHTML = `
-        <span>Suggested Tags</span>
-        <small>Click to add</small>
-    `;
-    dropdown.appendChild(header);
-    
-    // Create tag container
-    const container = document.createElement('div');
-    container.className = 'metadata-suggestions-container';
-    
-    // Add each preset tag as a suggestion
-    PRESET_TAGS.forEach(tag => {
-        const isAdded = existingTags.includes(tag);
-        
-        const item = document.createElement('div');
-        item.className = `metadata-suggestion-item ${isAdded ? 'already-added' : ''}`;
-        item.title = tag;
-        item.innerHTML = `
-            <span class="metadata-suggestion-text">${tag}</span>
-            ${isAdded ? '<span class="added-indicator"><i class="fas fa-check"></i></span>' : ''}
-        `;
-        
-        if (!isAdded) {
-            item.addEventListener('click', () => {
-                addNewTag(tag);
-                
-                // Also populate the input field for potential editing
-                const input = document.querySelector('.metadata-input');
-                if (input) input.value = tag;
-                
-                // Focus on the input
-                if (input) input.focus();
-                
-                // Update dropdown without removing it
-                updateSuggestionsDropdown();
-            });
-        }
-        
-        container.appendChild(item);
-    });
-    
-    dropdown.appendChild(container);
-    return dropdown;
-}
-
-/**
- * Set up tag input behavior
- */
-function setupTagInput() {
-    const tagInput = document.querySelector('.metadata-input');
-    
-    if (tagInput) {
-        tagInput.addEventListener('keydown', function(e) {
-            if (e.key === 'Enter') {
-                e.preventDefault();
-                addNewTag(this.value);
-                this.value = ''; // Clear input after adding
-            }
-        });
-    }
-}
-
-/**
- * Set up delete buttons for tags
- */
-function setupDeleteButtons() {
-    document.querySelectorAll('.metadata-delete-btn').forEach(btn => {
-        btn.addEventListener('click', function(e) {
-            e.stopPropagation();
-            const tag = this.closest('.metadata-item');
-            tag.remove();
-            
-            // Update status of items in the suggestion dropdown
-            updateSuggestionsDropdown();
-        });
-    });
-}
-
-/**
- * Add a new tag
- * @param {string} tag - Tag to add
- */
-function addNewTag(tag) {
-    tag = tag.trim().toLowerCase();
-    if (!tag) return;
-    
-    const tagsContainer = document.querySelector('.metadata-items');
-    if (!tagsContainer) return;
-    
-    // Validation: Check length
-    if (tag.length > 30) {
-        showToast('Tag should not exceed 30 characters', 'error');
-        return;
-    }
-    
-    // Validation: Check total number
-    const currentTags = tagsContainer.querySelectorAll('.metadata-item');
-    if (currentTags.length >= 30) {
-        showToast('Maximum 30 tags allowed', 'error');
-        return;
-    }
-    
-    // Validation: Check for duplicates
-    const existingTags = Array.from(currentTags).map(tag => tag.dataset.tag);
-    if (existingTags.includes(tag)) {
-        showToast('This tag already exists', 'error');
-        return;
-    }
-    
-    // Create new tag
-    const newTag = document.createElement('div');
-    newTag.className = 'metadata-item';
-    newTag.dataset.tag = tag;
-    newTag.innerHTML = `
-        <span class="metadata-item-content">${tag}</span>
-        <button class="metadata-delete-btn">
-            <i class="fas fa-times"></i>
-        </button>
-    `;
-    
-    // Add event listener to delete button
-    const deleteBtn = newTag.querySelector('.metadata-delete-btn');
-    deleteBtn.addEventListener('click', function(e) {
-        e.stopPropagation();
-        newTag.remove();
-        
-        // Update status of items in the suggestion dropdown
-        updateSuggestionsDropdown();
-    });
-    
-    tagsContainer.appendChild(newTag);
-    
-    // Update status of items in the suggestions dropdown
-    updateSuggestionsDropdown();
-}
-
-/**
- * Update status of items in the suggestions dropdown
- */
-function updateSuggestionsDropdown() {
-    const dropdown = document.querySelector('.metadata-suggestions-dropdown');
-    if (!dropdown) return;
-    
-    // Get all current tags
-    const currentTags = document.querySelectorAll('.metadata-item');
-    const existingTags = Array.from(currentTags).map(tag => tag.dataset.tag);
-    
-    // Update status of each item in dropdown
-    dropdown.querySelectorAll('.metadata-suggestion-item').forEach(item => {
-        const tagText = item.querySelector('.metadata-suggestion-text').textContent;
-        const isAdded = existingTags.includes(tagText);
-        
-        if (isAdded) {
-            item.classList.add('already-added');
-            
-            // Add indicator if it doesn't exist
-            let indicator = item.querySelector('.added-indicator');
-            if (!indicator) {
-                indicator = document.createElement('span');
-                indicator.className = 'added-indicator';
-                indicator.innerHTML = '<i class="fas fa-check"></i>';
-                item.appendChild(indicator);
-            }
-            
-            // Remove click event
-            item.onclick = null;
-        } else {
-            // Re-enable items that are no longer in the list
-            item.classList.remove('already-added');
-            
-            // Remove indicator if it exists
-            const indicator = item.querySelector('.added-indicator');
-            if (indicator) indicator.remove();
-            
-            // Restore click event if not already set
-            if (!item.onclick) {
-                item.onclick = () => {
-                    const tag = item.querySelector('.metadata-suggestion-text').textContent;
-                    addNewTag(tag);
-                    
-                    // Also populate the input field
-                    const input = document.querySelector('.metadata-input');
-                    if (input) input.value = tag;
-                    
-                    // Focus the input
-                    if (input) input.focus();
-                };
-            }
-        }
-    });
-}
-
-/**
- * Restore original tags when canceling edit
- * @param {HTMLElement} section - The tags section
- * @param {Array} originalTags - Original tags array
- */
-function restoreOriginalTags(section, originalTags) {
-    // Nothing to do here as we're just hiding the edit UI
-    // and showing the original compact tags which weren't modified
-}
-
-/**
- * Save tags
- */
-async function saveTags() {
-    const editBtn = document.querySelector('.edit-tags-btn');
-    if (!editBtn) return;
-    
-    const filePath = editBtn.dataset.filePath;
-    const tagElements = document.querySelectorAll('.metadata-item');
-    const tags = Array.from(tagElements).map(tag => tag.dataset.tag);
-
-    // Get original tags to compare
-    const originalTagElements = document.querySelectorAll('.tooltip-tag');
-    const originalTags = Array.from(originalTagElements).map(tag => tag.textContent);
-    
-    // Check if tags have actually changed
-    const tagsChanged = JSON.stringify(tags) !== JSON.stringify(originalTags);
-    
-    if (!tagsChanged) {
-        // No changes made, just exit edit mode without API call
-        editBtn.dataset.skipRestore = "true";
-        editBtn.click();
-        return;
-    }
-    
-    try {
-        // Save tags metadata
-        await saveModelMetadata(filePath, { tags: tags });
-        
-        // Set flag to skip restoring original tags when exiting edit mode
-        editBtn.dataset.skipRestore = "true";
-        
-        // Update the compact tags display
-        const compactTagsContainer = document.querySelector('.model-tags-container');
-        if (compactTagsContainer) {
-            // Generate new compact tags HTML
-            const compactTagsDisplay = compactTagsContainer.querySelector('.model-tags-compact');
-            
-            if (compactTagsDisplay) {
-                // Clear current tags
-                compactTagsDisplay.innerHTML = '';
-                
-                // Add visible tags (up to 5)
-                const visibleTags = tags.slice(0, 5);
-                visibleTags.forEach(tag => {
-                    const span = document.createElement('span');
-                    span.className = 'model-tag-compact';
-                    span.textContent = tag;
-                    compactTagsDisplay.appendChild(span);
-                });
-                
-                // Add more indicator if needed
-                const remainingCount = Math.max(0, tags.length - 5);
-                if (remainingCount > 0) {
-                    const more = document.createElement('span');
-                    more.className = 'model-tag-more';
-                    more.dataset.count = remainingCount;
-                    more.textContent = `+${remainingCount}`;
-                    compactTagsDisplay.appendChild(more);
-                }
-            }
-            
-            // Update tooltip content
-            const tooltipContent = compactTagsContainer.querySelector('.tooltip-content');
-            if (tooltipContent) {
-                tooltipContent.innerHTML = '';
-                
-                tags.forEach(tag => {
-                    const span = document.createElement('span');
-                    span.className = 'tooltip-tag';
-                    span.textContent = tag;
-                    tooltipContent.appendChild(span);
-                });
-            }
-        }
-        
-        // Exit edit mode
-        editBtn.click();
-        
-        showToast('Tags updated successfully', 'success');
-    } catch (error) {
-        console.error('Error saving tags:', error);
-        showToast('Failed to update tags', 'error');
-    }
-}

static/js/components/checkpointModal/index.js

@@ -1,239 +0,0 @@
-/**
- * CheckpointModal - Main entry point
- * 
- * Modularized checkpoint modal component that handles checkpoint model details display
- */
-import { showToast } from '../../utils/uiHelpers.js';
-import { modalManager } from '../../managers/ModalManager.js';
-import { 
-    toggleShowcase,
-    setupShowcaseScroll, 
-    scrollToTop,
-    loadExampleImages
-} from '../shared/showcase/ShowcaseView.js';
-import { setupTabSwitching, loadModelDescription } from './ModelDescription.js';
-import { 
-    setupModelNameEditing, 
-    setupBaseModelEditing, 
-    setupFileNameEditing
-} from './ModelMetadata.js';
-import { setupTagEditMode } from './ModelTags.js'; // Add import for tag editing
-import { saveModelMetadata } from '../../api/checkpointApi.js';
-import { renderCompactTags, setupTagTooltip, formatFileSize } from './utils.js';
-
-/**
- * Display the checkpoint modal with the given checkpoint data
- * @param {Object} checkpoint - Checkpoint data object
- */
-export function showCheckpointModal(checkpoint) {
-    const content = `
-        <div class="modal-content">
-            <button class="close" onclick="modalManager.closeModal('checkpointModal')">&times;</button>
-            <header class="modal-header">
-                <div class="model-name-header">
-                    <h2 class="model-name-content">${checkpoint.model_name || 'Checkpoint Details'}</h2>
-                    <button class="edit-model-name-btn" title="Edit model name">
-                        <i class="fas fa-pencil-alt"></i>
-                    </button>
-                </div>
-
-                ${checkpoint.civitai?.creator ? `
-                <div class="creator-info">
-                    ${checkpoint.civitai.creator.image ? 
-                        `<div class="creator-avatar">
-                            <img src="${checkpoint.civitai.creator.image}" alt="${checkpoint.civitai.creator.username}" onerror="this.onerror=null; this.src='static/icons/user-placeholder.png';">
-                        </div>` : 
-                        `<div class="creator-avatar creator-placeholder">
-                            <i class="fas fa-user"></i>
-                        </div>`
-                    }
-                    <span class="creator-username">${checkpoint.civitai.creator.username}</span>
-                </div>` : ''}
-
-                ${renderCompactTags(checkpoint.tags || [], checkpoint.file_path)}
-            </header>
-
-            <div class="modal-body">
-                <div class="info-section">
-                    <div class="info-grid">
-                        <div class="info-item">
-                            <label>Version</label>
-                            <span>${checkpoint.civitai?.name || 'N/A'}</span>
-                        </div>
-                        <div class="info-item">
-                            <label>File Name</label>
-                            <div class="file-name-wrapper">
-                                <span id="file-name" class="file-name-content">${checkpoint.file_name || 'N/A'}</span>
-                                <button class="edit-file-name-btn" title="Edit file name">
-                                    <i class="fas fa-pencil-alt"></i>
-                                </button>
-                            </div>
-                        </div>
-                        <div class="info-item location-size">
-                            <div class="location-wrapper">
-                                <label>Location</label>
-                                <span class="file-path">${checkpoint.file_path.replace(/[^/]+$/, '')}</span>
-                            </div>
-                        </div>
-                        <div class="info-item base-size">
-                            <div class="base-wrapper">
-                                <label>Base Model</label>
-                                <div class="base-model-display">
-                                    <span class="base-model-content">${checkpoint.base_model || 'Unknown'}</span>
-                                    <button class="edit-base-model-btn" title="Edit base model">
-                                        <i class="fas fa-pencil-alt"></i>
-                                    </button>
-                                </div>
-                            </div>
-                            <div class="size-wrapper">
-                                <label>Size</label>
-                                <span>${formatFileSize(checkpoint.file_size)}</span>
-                            </div>
-                        </div>
-                        <div class="info-item notes">
-                            <label>Additional Notes</label>
-                            <div class="editable-field">
-                                <div class="notes-content" contenteditable="true" spellcheck="false">${checkpoint.notes || 'Add your notes here...'}</div>
-                                <button class="save-btn" onclick="saveCheckpointNotes('${checkpoint.file_path}')">
-                                    <i class="fas fa-save"></i>
-                                </button>
-                            </div>
-                        </div>
-                        <div class="info-item full-width">
-                            <label>About this version</label>
-                            <div class="description-text">${checkpoint.civitai?.description || 'N/A'}</div>
-                        </div>
-                    </div>
-                </div>
-
-                <div class="showcase-section" data-model-hash="${checkpoint.sha256 || ''}" data-filepath="${checkpoint.file_path}">
-                    <div class="showcase-tabs">
-                        <button class="tab-btn active" data-tab="showcase">Examples</button>
-                        <button class="tab-btn" data-tab="description">Model Description</button>
-                    </div>
-                    
-                    <div class="tab-content">
-                        <div id="showcase-tab" class="tab-pane active">
-                            <div class="recipes-loading">
-                                <i class="fas fa-spinner fa-spin"></i> Loading recipes...
-                            </div>
-                        </div>
-                        
-                        <div id="description-tab" class="tab-pane">
-                            <div class="model-description-container">
-                                <div class="model-description-loading">
-                                    <i class="fas fa-spinner fa-spin"></i> Loading model description...
-                                </div>
-                                <div class="model-description-content">
-                                    ${checkpoint.modelDescription || ''}
-                                </div>
-                            </div>
-                        </div>
-                    </div>
-                    
-                    <button class="back-to-top" onclick="scrollToTopCheckpoint(this)">
-                        <i class="fas fa-arrow-up"></i>
-                    </button>
-                </div>
-            </div>
-        </div>
-    `;
-    
-    modalManager.showModal('checkpointModal', content);
-    setupEditableFields(checkpoint.file_path);
-    setupShowcaseScroll('checkpointModal');
-    setupTabSwitching();
-    setupTagTooltip();
-    setupTagEditMode(); // Initialize tag editing functionality
-    setupModelNameEditing(checkpoint.file_path);
-    setupBaseModelEditing(checkpoint.file_path);
-    setupFileNameEditing(checkpoint.file_path);
-    
-    // If we have a model ID but no description, fetch it
-    if (checkpoint.civitai?.modelId && !checkpoint.modelDescription) {
-        loadModelDescription(checkpoint.civitai.modelId, checkpoint.file_path);
-    }
-    
-    // Load example images asynchronously - merge regular and custom images
-    const regularImages = checkpoint.civitai?.images || [];
-    const customImages = checkpoint.civitai?.customImages || [];
-    // Combine images - regular images first, then custom images
-    const allImages = [...regularImages, ...customImages];
-    loadExampleImages(allImages, checkpoint.sha256);
-}
-
-/**
- * Set up editable fields in the checkpoint modal
-* @param {string} filePath - The full file path of the model.
- */
-function setupEditableFields(filePath) {
-    const editableFields = document.querySelectorAll('.editable-field [contenteditable]');
-    
-    editableFields.forEach(field => {
-        field.addEventListener('focus', function() {
-            if (this.textContent === 'Add your notes here...') {
-                this.textContent = '';
-            }
-        });
-
-        field.addEventListener('blur', function() {
-            if (this.textContent.trim() === '') {
-                if (this.classList.contains('notes-content')) {
-                    this.textContent = 'Add your notes here...';
-                }
-            }
-        });
-    });
-
-    // Add keydown event listeners for notes
-    const notesContent = document.querySelector('.notes-content');
-    if (notesContent) {
-        notesContent.addEventListener('keydown', async function(e) {
-            if (e.key === 'Enter') {
-                if (e.shiftKey) {
-                    // Allow shift+enter for new line
-                    return;
-                }
-                e.preventDefault();
-                await saveNotes(filePath);
-            }
-        });
-    }
-}
-
-/**
- * Save checkpoint notes
- * @param {string} filePath - Path to the checkpoint file
- */
-async function saveNotes(filePath) {
-    const content = document.querySelector('.notes-content').textContent;
-    try {
-        await saveModelMetadata(filePath, { notes: content });
-
-        showToast('Notes saved successfully', 'success');
-    } catch (error) {
-        showToast('Failed to save notes', 'error');
-    }
-}
-
-// Export the checkpoint modal API
-const checkpointModal = {
-    show: showCheckpointModal,
-    toggleShowcase,
-    scrollToTop
-};
-
-export { checkpointModal };
-
-// Define global functions for use in HTML
-window.toggleShowcase = function(element) {
-    toggleShowcase(element);
-};
-
-window.scrollToTopCheckpoint = function(button) {
-    scrollToTop(button);
-};
-
-window.saveCheckpointNotes = function(filePath) {
-    saveNotes(filePath);
-};