feat: update README and pyproject.toml for v0.8.10 release; add standalone mode and portable edition features

Dev

README.md

@@ -20,6 +20,32 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 
 ## Release Notes
 
+### v0.8.10
+* **Standalone Mode** - Run LoRA Manager independently from ComfyUI for a lightweight experience that works even with other stable diffusion interfaces
+* **Portable Edition** - New one-click portable version for easy startup and updates in standalone mode
+* **Enhanced Metadata Collection** - Added support for SamplerCustomAdvanced node in the metadata collector module
+* **Improved UI Organization** - Optimized Lora Loader node height to display up to 5 LoRAs at once with scrolling capability for larger collections
+
+### v0.8.9
+* **Favorites System** - New functionality to bookmark your favorite LoRAs and checkpoints for quick access and better organization
+* **Enhanced UI Controls** - Increased model card button sizes for improved usability and easier interaction
+* **Smoother Page Transitions** - Optimized interface switching between pages, eliminating flash issues particularly noticeable in dark theme
+* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
+
+### v0.8.8
+* **Real-time TriggerWord Updates** - Enhanced TriggerWord Toggle node to instantly update when connected Lora Loader or Lora Stacker nodes change, without requiring workflow execution
+* **Optimized Metadata Recovery** - Improved utilization of existing .civitai.info files for faster initialization and preservation of metadata from models deleted from CivitAI
+* **Migration Acceleration** - Further speed improvements for users transitioning from A1111/Forge environments
+* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
+
+### v0.8.7
+* **Enhanced Context Menu** - Added comprehensive context menu functionality to Recipes and Checkpoints pages for improved workflow
+* **Interactive LoRA Strength Control** - Implemented drag functionality in LoRA Loader for intuitive strength adjustment
+* **Metadata Collector Overhaul** - Rebuilt metadata collection system with optimized architecture for better performance
+* **Improved Save Image Node** - Enhanced metadata capture and image saving performance with the new metadata collector
+* **Streamlined Recipe Saving** - Optimized Save Recipe functionality to work independently without requiring Preview Image nodes
+* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
+
 ### v0.8.6 Major Update
 * **Checkpoint Management** - Added comprehensive management for model checkpoints including scanning, searching, filtering, and deletion
 * **Enhanced Metadata Support** - New capabilities for retrieving and managing checkpoint metadata with improved operations
@@ -132,7 +158,7 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 ```bash
 git clone https://github.com/willmiao/ComfyUI-Lora-Manager.git
 cd ComfyUI-Lora-Manager
-pip install requirements.txt
+pip install -r requirements.txt
 ```
 
 ## Usage
@@ -153,23 +179,92 @@ pip install requirements.txt
    - Paste into the Lora Loader node's text input
    - The node will automatically apply preset strength and trigger words
 
+### Filename Format Patterns for Save Image Node
+
+The Save Image Node supports dynamic filename generation using pattern codes. You can customize how your images are named using the following format patterns:
+
+#### Available Pattern Codes
+
+- `%seed%` - Inserts the generation seed number
+- `%width%` - Inserts the image width
+- `%height%` - Inserts the image height
+- `%pprompt:N%` - Inserts the positive prompt (limited to N characters)
+- `%nprompt:N%` - Inserts the negative prompt (limited to N characters)
+- `%model:N%` - Inserts the model/checkpoint name (limited to N characters)
+- `%date%` - Inserts current date/time as "yyyyMMddhhmmss"
+- `%date:FORMAT%` - Inserts date using custom format with:
+  - `yyyy` - 4-digit year
+  - `yy` - 2-digit year
+  - `MM` - 2-digit month
+  - `dd` - 2-digit day
+  - `hh` - 2-digit hour
+  - `mm` - 2-digit minute
+  - `ss` - 2-digit second
+
+#### Examples
+
+- `image_%seed%` → `image_1234567890`
+- `gen_%width%x%height%` → `gen_512x768`
+- `%model:10%_%seed%` → `dreamshape_1234567890`
+- `%date:yyyy-MM-dd%` → `2025-04-28`
+- `%pprompt:20%_%seed%` → `beautiful landscape_1234567890`
+- `%model%_%date:yyMMdd%_%seed%` → `dreamshaper_v8_250428_1234567890`
+
+You can combine multiple patterns to create detailed, organized filenames for your generated images.
+
+### Standalone Mode
+
+You can now run LoRA Manager independently from ComfyUI:
+
+1. **For ComfyUI users**:
+   - Launch ComfyUI with LoRA Manager at least once to initialize the necessary path information in the `settings.json` file.
+   - Make sure dependencies are installed: `pip install -r requirements.txt`
+   - From your ComfyUI root directory, run:
+     ```bash
+     python custom_nodes\comfyui-lora-manager\standalone.py
+     ```
+   - Access the interface at: `http://localhost:8188/loras`
+   - You can specify a different host or port with arguments:
+     ```bash
+     python custom_nodes\comfyui-lora-manager\standalone.py --host 127.0.0.1 --port 9000
+     ```
+
+2. **For non-ComfyUI users**:
+   - Copy the provided `settings.json.example` file to create a new file named `settings.json`
+   - Edit `settings.json` to include your correct model folder paths and CivitAI API key
+   - Install required dependencies: `pip install -r requirements.txt`
+   - Run standalone mode:
+     ```bash
+     python standalone.py
+     ```
+   - Access the interface through your browser at: `http://localhost:8188/loras`
+
+This standalone mode provides a lightweight option for managing your model and recipe collection without needing to run the full ComfyUI environment, making it useful even for users who primarily use other stable diffusion interfaces.
+
+---
+
+## Contributing
+
+Thank you for your interest in contributing to ComfyUI LoRA Manager! As this project is currently in its early stages and undergoing rapid development and refactoring, we are temporarily not accepting pull requests.
+
+However, your feedback and ideas are extremely valuable to us:
+- Please feel free to open issues for any bugs you encounter
+- Submit feature requests through GitHub issues
+- Share your suggestions for improvements
+
+We appreciate your understanding and look forward to potentially accepting code contributions once the project architecture stabilizes.
+
 ---
 
 ## Credits
 
 This project has been inspired by and benefited from other excellent ComfyUI extensions:
 
-- [ComfyUI-SaveImageWithMetaData](https://github.com/Comfy-Community/ComfyUI-SaveImageWithMetaData) - For the image metadata functionality
+- [ComfyUI-SaveImageWithMetaData](https://github.com/nkchocoai/ComfyUI-SaveImageWithMetaData) - For the image metadata functionality
 - [rgthree-comfy](https://github.com/rgthree/rgthree-comfy) - For the lora loader functionality
 
 ---
 
-## Contributing
-
-If you have suggestions, bug reports, or improvements, feel free to open an issue or contribute directly to the codebase. Pull requests are always welcome!
-
----
-
 ## ☕ Support
 
 If you find this project helpful, consider supporting its development:
@@ -182,3 +277,4 @@ Join our Discord community for support, discussions, and updates:
 [Discord Server](https://discord.gg/vcqNrWVFvM)
 
 ---
+````

__init__.py

@@ -3,16 +3,23 @@ from .py.nodes.lora_loader import LoraManagerLoader
 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
+# Import metadata collector to install hooks on startup
+from .py.metadata_collector import init as init_metadata_collector
 
 NODE_CLASS_MAPPINGS = {
     LoraManagerLoader.NAME: LoraManagerLoader,
     TriggerWordToggle.NAME: TriggerWordToggle,
     LoraStacker.NAME: LoraStacker,
-    SaveImage.NAME: SaveImage
+    SaveImage.NAME: SaveImage,
+    DebugMetadata.NAME: DebugMetadata
 }
 
 WEB_DIRECTORY = "./web/comfyui"
 
+# Initialize metadata collector
+init_metadata_collector()
+
 # Register routes on import
 LoraManager.add_routes()
 __all__ = ['NODE_CLASS_MAPPINGS', 'WEB_DIRECTORY']

py/config.py

@@ -3,6 +3,11 @@ import platform
 import folder_paths # type: ignore
 from typing import List
 import logging
+import sys
+import json
+
+# Check if running in standalone mode
+standalone_mode = 'nodes' not in sys.modules
 
 logger = logging.getLogger(__name__)
 
@@ -18,9 +23,46 @@ class Config:
         self._route_mappings = {}
         self.loras_roots = self._init_lora_paths()
         self.checkpoints_roots = self._init_checkpoint_paths()
-        self.temp_directory = folder_paths.get_temp_directory()
         # 在初始化时扫描符号链接
         self._scan_symbolic_links()
+        
+        if not standalone_mode:
+            # Save the paths to settings.json when running in ComfyUI mode
+            self.save_folder_paths_to_settings()
+
+    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")
+        except Exception as e:
+            logger.warning(f"Failed to save folder paths: {e}")
 
     def _is_link(self, path: str) -> bool:
         try:
@@ -103,50 +145,66 @@ class Config:
 
     def _init_lora_paths(self) -> List[str]:
         """Initialize and validate LoRA paths from ComfyUI settings"""
-        paths = sorted(set(path.replace(os.sep, "/") 
-                for path in folder_paths.get_folder_paths("loras") 
-                if os.path.exists(path)), key=lambda p: p.lower())
-        print("Found LoRA roots:", "\n - " + "\n - ".join(paths))
-        
-        if not paths:
-            raise ValueError("No valid loras folders found in ComfyUI configuration")
-        
-        # 初始化路径映射
-        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)
-        
-        return paths
+        try:
+            raw_paths = folder_paths.get_folder_paths("loras")
+            
+            # 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 LoRA roots:" + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]"))
+            
+            if not unique_paths:
+                logger.warning("No valid loras folders found in ComfyUI configuration")
+                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 LoRA paths: {e}")
+            return []
 
     def _init_checkpoint_paths(self) -> List[str]:
         """Initialize and validate checkpoint paths from ComfyUI settings"""
-        # Get checkpoint paths from folder_paths
-        checkpoint_paths = folder_paths.get_folder_paths("checkpoints")
-        diffusion_paths = folder_paths.get_folder_paths("diffusers")
-        unet_paths = folder_paths.get_folder_paths("unet")
-        
-        # Combine all checkpoint-related paths
-        all_paths = checkpoint_paths + diffusion_paths + unet_paths
-        
-        # Filter and normalize paths
-        paths = sorted(set(path.replace(os.sep, "/") 
-                for path in all_paths 
-                if os.path.exists(path)), key=lambda p: p.lower())
-        
-        print("Found checkpoint roots:", paths)
-        
-        if not paths:
-            logger.warning("No valid checkpoint folders found in ComfyUI configuration")
+        try:
+            # Get checkpoint paths from folder_paths
+            checkpoint_paths = folder_paths.get_folder_paths("checkpoints")
+            diffusion_paths = folder_paths.get_folder_paths("diffusers")
+            unet_paths = folder_paths.get_folder_paths("unet")
+            
+            # Combine all checkpoint-related paths
+            all_paths = checkpoint_paths + diffusion_paths + unet_paths
+            
+            # Filter and normalize paths
+            paths = sorted(set(path.replace(os.sep, "/") 
+                    for path in all_paths 
+                    if os.path.exists(path)), key=lambda p: p.lower())
+            
+            logger.info("Found checkpoint roots:" + ("\n - " + "\n - ".join(paths) if paths else "[]"))
+            
+            if not 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)
+            
+            return paths
+        except Exception as e:
+            logger.warning(f"Error initializing checkpoint paths: {e}")
             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)
-        
-        return paths
 
     def get_preview_static_url(self, preview_path: str) -> str:
         """Convert local preview path to static URL"""

py/lora_manager.py

@@ -5,11 +5,17 @@ from .routes.lora_routes import LoraRoutes
 from .routes.api_routes import ApiRoutes
 from .routes.recipe_routes import RecipeRoutes
 from .routes.checkpoints_routes import CheckpointsRoutes
+from .routes.update_routes import UpdateRoutes
+from .routes.usage_stats_routes import UsageStatsRoutes
 from .services.service_registry import ServiceRegistry
 import logging
+import sys
 
 logger = logging.getLogger(__name__)
 
+# Check if we're in standalone mode
+STANDALONE_MODE = 'nodes' not in sys.modules
+
 class LoraManager:
     """Main entry point for LoRA Manager plugin"""
     
@@ -18,6 +24,9 @@ class LoraManager:
         """Initialize and register all routes"""
         app = PromptServer.instance.app
 
+        # Configure aiohttp access logger to be less verbose
+        logging.getLogger('aiohttp.access').setLevel(logging.WARNING)
+
         added_targets = set()  # Track already added target paths
         
         # Add static routes for each lora root
@@ -92,6 +101,8 @@ class LoraManager:
         checkpoints_routes.setup_routes(app)
         ApiRoutes.setup_routes(app)
         RecipeRoutes.setup_routes(app)
+        UpdateRoutes.setup_routes(app)  
+        UsageStatsRoutes.setup_routes(app)  # Register usage stats routes
         
         # Schedule service initialization 
         app.on_startup.append(lambda app: cls._initialize_services())
@@ -104,7 +115,8 @@ class LoraManager:
     async def _initialize_services(cls):
         """Initialize all services using the ServiceRegistry"""
         try:
-            logger.info("LoRA Manager: Initializing services via ServiceRegistry")
+            # 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
             civitai_client = await ServiceRegistry.get_civitai_client()
@@ -115,12 +127,12 @@ class LoraManager:
             
             # Start monitors
             lora_monitor.start()
-            logger.info("Lora monitor started")
+            logger.debug("Lora monitor started")
             
             # Make sure checkpoint monitor has paths before starting
             await checkpoint_monitor.initialize_paths()
             checkpoint_monitor.start()
-            logger.info("Checkpoint monitor started")
+            logger.debug("Checkpoint monitor started")
 
             # Register DownloadManager with ServiceRegistry
             download_manager = await ServiceRegistry.get_download_manager()
@@ -135,6 +147,12 @@ class LoraManager:
             # 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')

py/metadata_collector/__init__.py

@@ -0,0 +1,32 @@
+import os
+import importlib
+import sys
+
+# Check if running in standalone mode
+standalone_mode = 'nodes' not in sys.modules
+
+if not standalone_mode:
+    from .metadata_hook import MetadataHook
+    from .metadata_registry import MetadataRegistry
+
+    def init():
+        # Install hooks to collect metadata during execution
+        MetadataHook.install()
+        
+        # Initialize registry
+        registry = MetadataRegistry()
+        
+        print("ComfyUI Metadata Collector initialized")
+        
+    def get_metadata(prompt_id=None):
+        """Helper function to get metadata from the registry"""
+        registry = MetadataRegistry()
+        return registry.get_metadata(prompt_id)
+else:
+    # Standalone mode - provide dummy implementations
+    def init():
+        print("ComfyUI Metadata Collector disabled in standalone mode")
+        
+    def get_metadata(prompt_id=None):
+        """Dummy implementation for standalone mode"""
+        return {}

py/metadata_collector/constants.py

@@ -0,0 +1,14 @@
+"""Constants used by the metadata collector"""
+
+# Metadata collection constants
+
+# Metadata categories
+MODELS = "models"
+PROMPTS = "prompts"
+SAMPLING = "sampling"
+LORAS = "loras"
+SIZE = "size"
+IMAGES = "images"
+
+# Complete list of categories to track
+METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES]

py/metadata_collector/metadata_hook.py

@@ -0,0 +1,123 @@
+import sys
+import inspect
+from .metadata_registry import MetadataRegistry
+
+class MetadataHook:
+    """Install hooks for metadata collection"""
+    
+    @staticmethod
+    def install():
+        """Install hooks to collect metadata during execution"""
+        try:
+            # Import ComfyUI's execution module
+            execution = None
+            try:
+                # Try direct import first
+                import execution # type: ignore
+            except ImportError:
+                # Try to locate from system modules
+                for module_name in sys.modules:
+                    if module_name.endswith('.execution'):
+                        execution = sys.modules[module_name]
+                        break
+                    
+            # If we can't find the execution module, we can't install hooks
+            if execution is None:
+                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
+            
+            # 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
+            # Make map_node_over_list public to avoid it being hidden by hooks
+            execution.map_node_over_list = original_map_node_over_list
+            
+            print("Metadata collection hooks installed for runtime values")
+            
+        except Exception as e:
+            print(f"Error installing metadata hooks: {str(e)}")

py/metadata_collector/metadata_processor.py

@@ -0,0 +1,300 @@
+import json
+import sys
+
+# Check if running in standalone mode
+standalone_mode = 'nodes' not in sys.modules
+
+from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE
+
+class MetadataProcessor:
+    """Process and format collected metadata"""
+    
+    @staticmethod
+    def find_primary_sampler(metadata):
+        """Find the primary KSampler node (with denoise=1)"""
+        primary_sampler = None
+        primary_sampler_id = None
+        
+        # First, check for SamplerCustomAdvanced
+        prompt = metadata.get("current_prompt")
+        if prompt and prompt.original_prompt:
+            for node_id, node_info in prompt.original_prompt.items():
+                if node_info.get("class_type") == "SamplerCustomAdvanced":
+                    # Found a SamplerCustomAdvanced node
+                    if node_id in metadata.get(SAMPLING, {}):
+                        return node_id, metadata[SAMPLING][node_id]
+        
+        # Next, check for KSamplerAdvanced with add_noise="enable"
+        for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
+            parameters = sampler_info.get("parameters", {})
+            add_noise = parameters.get("add_noise")
+            
+            # If add_noise is "enable", this is likely the primary sampler for KSamplerAdvanced
+            if add_noise == "enable":
+                primary_sampler = sampler_info
+                primary_sampler_id = node_id
+                break
+        
+        # If no specialized sampler found, fall back to traditional KSampler with denoise=1
+        if primary_sampler is None:
+            for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
+                parameters = sampler_info.get("parameters", {})
+                denoise = parameters.get("denoise")
+                
+                # If denoise is 1.0, this is likely the primary sampler
+                if denoise == 1.0 or denoise == 1:
+                    primary_sampler = sampler_info
+                    primary_sampler_id = node_id
+                    break
+                
+        return primary_sampler_id, primary_sampler
+    
+    @staticmethod
+    def trace_node_input(prompt, node_id, input_name, target_class=None, max_depth=10):
+        """
+        Trace an input connection from a node to find the source node
+        
+        Parameters:
+        - prompt: The prompt object containing node connections
+        - node_id: ID of the starting node
+        - input_name: Name of the input to trace
+        - target_class: Optional class name to search for (e.g., "CLIPTextEncode")
+        - max_depth: Maximum depth to follow the node chain to prevent infinite loops
+        
+        Returns:
+        - node_id of the found node, or None if not found
+        """
+        if not prompt or not prompt.original_prompt or node_id not in prompt.original_prompt:
+            return None
+            
+        # For depth tracking
+        current_depth = 0
+        
+        current_node_id = node_id
+        current_input = input_name
+        
+        while current_depth < max_depth:
+            if current_node_id not in prompt.original_prompt:
+                return None
+                
+            node_inputs = prompt.original_prompt[current_node_id].get("inputs", {})
+            if current_input not in node_inputs:
+                return None
+                
+            input_value = node_inputs[current_input]
+            # Input connections are formatted as [node_id, output_index]
+            if isinstance(input_value, list) and len(input_value) >= 2:
+                found_node_id = input_value[0]  # Connected node_id
+                
+                # If we're looking for a specific node class
+                if target_class and prompt.original_prompt[found_node_id].get("class_type") == target_class:
+                    return found_node_id
+                
+                # If we're not looking for a specific class or haven't found it yet
+                if not target_class:
+                    return found_node_id
+                
+                # Continue tracing through intermediate nodes
+                current_node_id = found_node_id
+                # For most conditioning nodes, the input we want to follow is named "conditioning"
+                if "conditioning" in prompt.original_prompt[current_node_id].get("inputs", {}):
+                    current_input = "conditioning"
+                else:
+                    # If there's no "conditioning" input, we can't trace further
+                    return found_node_id if not target_class else None
+            else:
+                # We've reached a node with no further connections
+                return None
+            
+            current_depth += 1
+            
+        # If we've reached max depth without finding target_class
+        return None
+    
+    @staticmethod
+    def find_primary_checkpoint(metadata):
+        """Find the primary checkpoint model in the workflow"""
+        if not metadata.get(MODELS):
+            return None
+            
+        # In most workflows, there's only one checkpoint, so we can just take the first one
+        for node_id, model_info in metadata.get(MODELS, {}).items():
+            if model_info.get("type") == "checkpoint":
+                return model_info.get("name")
+                
+        return None
+    
+    @staticmethod
+    def extract_generation_params(metadata):
+        """Extract generation parameters from metadata using node relationships"""
+        params = {
+            "prompt": "",
+            "negative_prompt": "",
+            "seed": None,
+            "steps": None,
+            "cfg_scale": None,
+            "guidance": None,  # Add guidance parameter
+            "sampler": None,
+            "scheduler": None,
+            "checkpoint": None,
+            "loras": "",
+            "size": None,
+            "clip_skip": None
+        }
+        
+        # Get the prompt object for node relationship tracing
+        prompt = metadata.get("current_prompt")
+        
+        # Find the primary KSampler node
+        primary_sampler_id, primary_sampler = MetadataProcessor.find_primary_sampler(metadata)
+        
+        # Directly get checkpoint from metadata instead of tracing
+        checkpoint = MetadataProcessor.find_primary_checkpoint(metadata)
+        if checkpoint:
+            params["checkpoint"] = checkpoint
+        
+        if primary_sampler:
+            # Extract sampling parameters
+            sampling_params = primary_sampler.get("parameters", {})
+            # Handle both seed and noise_seed
+            params["seed"] = sampling_params.get("seed") if sampling_params.get("seed") is not None else sampling_params.get("noise_seed")
+            params["steps"] = sampling_params.get("steps")
+            params["cfg_scale"] = sampling_params.get("cfg")
+            params["sampler"] = sampling_params.get("sampler_name")
+            params["scheduler"] = sampling_params.get("scheduler")
+            
+            # Trace connections from the primary sampler
+            if prompt and primary_sampler_id:
+                # Check if this is a SamplerCustomAdvanced node
+                is_custom_advanced = False
+                if prompt.original_prompt and primary_sampler_id in prompt.original_prompt:
+                    is_custom_advanced = prompt.original_prompt[primary_sampler_id].get("class_type") == "SamplerCustomAdvanced"
+                
+                if is_custom_advanced:
+                    # For SamplerCustomAdvanced, trace specific inputs
+                    
+                    # 1. Trace sigmas input to find BasicScheduler
+                    scheduler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sigmas", "BasicScheduler", max_depth=5)
+                    if scheduler_node_id and scheduler_node_id in metadata.get(SAMPLING, {}):
+                        scheduler_params = metadata[SAMPLING][scheduler_node_id].get("parameters", {})
+                        params["steps"] = scheduler_params.get("steps")
+                        params["scheduler"] = scheduler_params.get("scheduler")
+                    
+                    # 2. Trace sampler input to find KSamplerSelect
+                    sampler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sampler", "KSamplerSelect", max_depth=5)
+                    if sampler_node_id and sampler_node_id in metadata.get(SAMPLING, {}):
+                        sampler_params = metadata[SAMPLING][sampler_node_id].get("parameters", {})
+                        params["sampler"] = sampler_params.get("sampler_name")
+                    
+                    # 3. Trace guider input for FluxGuidance and CLIPTextEncode
+                    guider_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "guider", max_depth=5)
+                    if guider_node_id:
+                        # Look for FluxGuidance along the guider path
+                        flux_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", "FluxGuidance", max_depth=5)
+                        if flux_node_id and flux_node_id in metadata.get(SAMPLING, {}):
+                            flux_params = metadata[SAMPLING][flux_node_id].get("parameters", {})
+                            params["guidance"] = flux_params.get("guidance")
+                        
+                        # Find CLIPTextEncode for positive prompt (through conditioning)
+                        positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", "CLIPTextEncode", max_depth=10)
+                        if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
+                            params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
+                
+                else:
+                    # Original tracing for standard samplers
+                    # Trace positive prompt - look specifically for CLIPTextEncode
+                    positive_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "CLIPTextEncode", max_depth=10)
+                    if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
+                        params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
+                    
+                    # Find any FluxGuidance nodes in the positive conditioning path
+                    flux_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "FluxGuidance", max_depth=5)
+                    if flux_node_id and flux_node_id in metadata.get(SAMPLING, {}):
+                        flux_params = metadata[SAMPLING][flux_node_id].get("parameters", {})
+                        params["guidance"] = flux_params.get("guidance")
+                    
+                    # Trace negative prompt - look specifically for CLIPTextEncode
+                    negative_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "negative", "CLIPTextEncode", max_depth=10)
+                    if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
+                        params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
+                
+                # Size extraction is same for all sampler types
+                # Check if the sampler itself has size information (from latent_image)
+                if primary_sampler_id in metadata.get(SIZE, {}):
+                    width = metadata[SIZE][primary_sampler_id].get("width")
+                    height = metadata[SIZE][primary_sampler_id].get("height")
+                    if width and height:
+                        params["size"] = f"{width}x{height}"
+                else:
+                    # Fallback to the previous trace method if needed
+                    latent_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "latent_image")
+                    if latent_node_id:
+                        # Follow chain to find EmptyLatentImage node
+                        size_found = False
+                        current_node_id = latent_node_id
+                        
+                        # Limit depth to avoid infinite loops in complex workflows
+                        max_depth = 10
+                        for _ in range(max_depth):
+                            if current_node_id in metadata.get(SIZE, {}):
+                                width = metadata[SIZE][current_node_id].get("width")
+                                height = metadata[SIZE][current_node_id].get("height")
+                                if width and height:
+                                    params["size"] = f"{width}x{height}"
+                                    size_found = True
+                                    break
+                            
+                            # Try to follow the chain
+                            if prompt and prompt.original_prompt and current_node_id in prompt.original_prompt:
+                                node_info = prompt.original_prompt[current_node_id]
+                                if "inputs" in node_info:
+                                    # Look for a connection that might lead to size information
+                                    for input_name, input_value in node_info["inputs"].items():
+                                        if isinstance(input_value, list) and len(input_value) >= 2:
+                                            current_node_id = input_value[0]
+                                            break
+                                    else:
+                                        break  # No connections to follow
+                                else:
+                                    break  # No inputs to follow
+                            else:
+                                break  # Can't follow further
+        
+        # Extract LoRAs using the standardized format
+        lora_parts = []
+        for node_id, lora_info in metadata.get(LORAS, {}).items():
+            # Access the lora_list from the standardized format
+            lora_list = lora_info.get("lora_list", [])
+            for lora in lora_list:
+                name = lora.get("name", "unknown")
+                strength = lora.get("strength", 1.0)
+                lora_parts.append(f"<lora:{name}:{strength}>")
+        
+        params["loras"] = " ".join(lora_parts)
+        
+        # Set default clip_skip value
+        params["clip_skip"] = "1"  # Common default
+        
+        return params
+    
+    @staticmethod
+    def to_dict(metadata):
+        """Convert extracted metadata to the ComfyUI output.json format"""           
+        if standalone_mode:
+            # Return empty dictionary in standalone mode
+            return {}
+        
+        params = MetadataProcessor.extract_generation_params(metadata)
+        
+        # Convert all values to strings to match output.json format
+        for key in params:
+            if params[key] is not None:
+                params[key] = str(params[key])
+        
+        return params
+    
+    @staticmethod
+    def to_json(metadata):
+        """Convert metadata to JSON string"""
+        params = MetadataProcessor.to_dict(metadata)
+        return json.dumps(params, indent=4)

py/metadata_collector/metadata_registry.py

@@ -0,0 +1,275 @@
+import time
+from nodes import NODE_CLASS_MAPPINGS
+from .node_extractors import NODE_EXTRACTORS, GenericNodeExtractor
+from .constants import METADATA_CATEGORIES, IMAGES
+
+class MetadataRegistry:
+    """A singleton registry to store and retrieve workflow metadata"""
+    _instance = None
+    
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._reset()
+        return cls._instance
+    
+    def _reset(self):
+        self.current_prompt_id = None
+        self.current_prompt = None
+        self.metadata = {}
+        self.prompt_metadata = {}
+        self.executed_nodes = set()
+        
+        # Node-level cache for metadata
+        self.node_cache = {}
+        
+        # Limit the number of stored prompts
+        self.max_prompt_history = 3
+        
+        # Categories we want to track and retrieve from cache
+        self.metadata_categories = METADATA_CATEGORIES
+    
+    def _clean_old_prompts(self):
+        """Clean up old prompt metadata, keeping only recent ones"""
+        if len(self.prompt_metadata) <= self.max_prompt_history:
+            return
+            
+        # Sort all prompt_ids by timestamp
+        sorted_prompts = sorted(
+            self.prompt_metadata.keys(),
+            key=lambda pid: self.prompt_metadata[pid].get("timestamp", 0)
+        )
+        
+        # Remove oldest records
+        prompts_to_remove = sorted_prompts[:len(sorted_prompts) - self.max_prompt_history]
+        for pid in prompts_to_remove:
+            del self.prompt_metadata[pid]
+    
+    def start_collection(self, prompt_id):
+        """Begin metadata collection for a new prompt"""
+        self.current_prompt_id = prompt_id
+        self.executed_nodes = set()
+        self.prompt_metadata[prompt_id] = {
+            category: {} for category in METADATA_CATEGORIES
+        }
+        # Add additional metadata fields
+        self.prompt_metadata[prompt_id].update({
+            "execution_order": [],
+            "current_prompt": None,  # Will store the prompt object
+            "timestamp": time.time()
+        })
+        
+        # Clean up old prompt data
+        self._clean_old_prompts()
+    
+    def set_current_prompt(self, prompt):
+        """Set the current prompt object reference"""
+        self.current_prompt = prompt
+        if self.current_prompt_id and self.current_prompt_id in self.prompt_metadata:
+            # Store the prompt in the metadata for later relationship tracing
+            self.prompt_metadata[self.current_prompt_id]["current_prompt"] = prompt
+        
+    def get_metadata(self, prompt_id=None):
+        """Get collected metadata for a prompt"""
+        key = prompt_id if prompt_id is not None else self.current_prompt_id
+        if key not in self.prompt_metadata:
+            return {}
+            
+        metadata = self.prompt_metadata[key]
+        
+        # If we have a current prompt object, check for non-executed nodes
+        prompt_obj = metadata.get("current_prompt")
+        if prompt_obj and hasattr(prompt_obj, "original_prompt"):
+            original_prompt = prompt_obj.original_prompt
+            
+            # Fill in missing metadata from cache for nodes that weren't executed
+            self._fill_missing_metadata(key, original_prompt)
+            
+        return self.prompt_metadata.get(key, {})
+    
+    def _fill_missing_metadata(self, prompt_id, original_prompt):
+        """Fill missing metadata from cache for non-executed nodes"""
+        if not original_prompt:
+            return
+            
+        executed_nodes = self.executed_nodes
+        metadata = self.prompt_metadata[prompt_id]
+        
+        # Iterate through nodes in the original prompt
+        for node_id, node_data in original_prompt.items():
+            # Skip if already executed in this run
+            if node_id in executed_nodes:
+                continue
+                
+            # Get the node type from the prompt (this is the key in NODE_CLASS_MAPPINGS)
+            prompt_class_type = node_data.get("class_type")
+            if not prompt_class_type:
+                continue
+                
+            # Convert to actual class name (which is what we use in our cache)
+            class_type = prompt_class_type
+            if prompt_class_type in NODE_CLASS_MAPPINGS:
+                class_obj = NODE_CLASS_MAPPINGS[prompt_class_type]
+                class_type = class_obj.__name__
+            
+            # Create cache key using the actual class name
+            cache_key = f"{node_id}:{class_type}"
+            
+            # Check if this node type is relevant for metadata collection
+            if class_type in NODE_EXTRACTORS:
+                # Check if we have cached metadata for this node
+                if cache_key in self.node_cache:
+                    cached_data = self.node_cache[cache_key]
+                    
+                    # Apply cached metadata to the current metadata
+                    for category in self.metadata_categories:
+                        if category in cached_data and node_id in cached_data[category]:
+                            if node_id not in metadata[category]:
+                                metadata[category][node_id] = cached_data[category][node_id]
+    
+    def record_node_execution(self, node_id, class_type, inputs, outputs):
+        """Record information about a node's execution"""
+        if not self.current_prompt_id:
+            return
+            
+        # Add to execution order and mark as executed
+        if node_id not in self.executed_nodes:
+            self.executed_nodes.add(node_id)
+            self.prompt_metadata[self.current_prompt_id]["execution_order"].append(node_id)
+        
+        # Process inputs to simplify working with them
+        processed_inputs = {}
+        for input_name, input_values in inputs.items():
+            if isinstance(input_values, list) and len(input_values) > 0:
+                # For single values, just use the first one (most common case)
+                processed_inputs[input_name] = input_values[0]
+            else:
+                processed_inputs[input_name] = input_values
+            
+        # Extract node-specific metadata
+        extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
+        extractor.extract(
+            node_id, 
+            processed_inputs, 
+            outputs, 
+            self.prompt_metadata[self.current_prompt_id]
+        )
+        
+        # Cache this node's metadata
+        self._cache_node_metadata(node_id, class_type)
+    
+    def update_node_execution(self, node_id, class_type, outputs):
+        """Update node metadata with output information"""
+        if not self.current_prompt_id:
+            return
+        
+        # Process outputs to make them more usable
+        processed_outputs = outputs
+            
+        # Use the same extractor to update with outputs
+        extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
+        if hasattr(extractor, 'update'):
+            extractor.update(
+                node_id, 
+                processed_outputs, 
+                self.prompt_metadata[self.current_prompt_id]
+            )
+            
+        # Update the cached metadata for this node
+        self._cache_node_metadata(node_id, class_type)
+            
+    def _cache_node_metadata(self, node_id, class_type):
+        """Cache the metadata for a specific node"""
+        if not self.current_prompt_id or not node_id or not class_type:
+            return
+            
+        # Create a cache key combining node_id and class_type
+        cache_key = f"{node_id}:{class_type}"
+        
+        # Create a shallow copy of the node's metadata
+        node_metadata = {}
+        current_metadata = self.prompt_metadata[self.current_prompt_id]
+        
+        for category in self.metadata_categories:
+            if category in current_metadata and node_id in current_metadata[category]:
+                if category not in node_metadata:
+                    node_metadata[category] = {}
+                node_metadata[category][node_id] = current_metadata[category][node_id]
+        
+        # Save to cache if we have any metadata for this node
+        if any(node_metadata.values()):
+            self.node_cache[cache_key] = node_metadata
+    
+    def clear_unused_cache(self):
+        """Clean up node_cache entries that are no longer in use"""
+        # Collect all node_ids currently in prompt_metadata
+        active_node_ids = set()
+        for prompt_data in self.prompt_metadata.values():
+            for category in self.metadata_categories:
+                if category in prompt_data:
+                    active_node_ids.update(prompt_data[category].keys())
+        
+        # Find cache keys that are no longer needed
+        keys_to_remove = []
+        for cache_key in self.node_cache:
+            node_id = cache_key.split(':')[0]
+            if node_id not in active_node_ids:
+                keys_to_remove.append(cache_key)
+        
+        # Remove cache entries that are no longer needed
+        for key in keys_to_remove:
+            del self.node_cache[key]
+    
+    def clear_metadata(self, prompt_id=None):
+        """Clear metadata for a specific prompt or reset all data"""
+        if prompt_id is not None:
+            if prompt_id in self.prompt_metadata:
+                del self.prompt_metadata[prompt_id]
+                # Clean up cache after removing prompt
+                self.clear_unused_cache()
+        else:
+            # Reset all data
+            self._reset()
+    
+    def get_first_decoded_image(self, prompt_id=None):
+        """Get the first decoded image result"""
+        key = prompt_id if prompt_id is not None else self.current_prompt_id
+        if key not in self.prompt_metadata:
+            return None
+            
+        metadata = self.prompt_metadata[key]
+        if IMAGES in metadata and "first_decode" in metadata[IMAGES]:
+            image_data = metadata[IMAGES]["first_decode"]["image"]
+            
+            # If it's an image batch or tuple, handle various formats
+            if isinstance(image_data, (list, tuple)) and len(image_data) > 0:
+                # Return first element of list/tuple
+                return image_data[0]
+            
+            # If it's a tensor, return as is for processing in the route handler
+            return image_data
+        
+        # If no image is found in the current metadata, try to find it in the cache
+        # This handles the case where VAEDecode was cached by ComfyUI and not executed
+        prompt_obj = metadata.get("current_prompt")
+        if prompt_obj and hasattr(prompt_obj, "original_prompt"):
+            original_prompt = prompt_obj.original_prompt
+            for node_id, node_data in original_prompt.items():
+                class_type = node_data.get("class_type")
+                if class_type and class_type in NODE_CLASS_MAPPINGS:
+                    class_obj = NODE_CLASS_MAPPINGS[class_type]
+                    class_name = class_obj.__name__
+                    # Check if this is a VAEDecode node
+                    if class_name == "VAEDecode":
+                        # Try to find this node in the cache
+                        cache_key = f"{node_id}:{class_name}"
+                        if cache_key in self.node_cache:
+                            cached_data = self.node_cache[cache_key]
+                            if IMAGES in cached_data and node_id in cached_data[IMAGES]:
+                                image_data = cached_data[IMAGES][node_id]["image"]
+                                # Handle different image formats
+                                if isinstance(image_data, (list, tuple)) and len(image_data) > 0:
+                                    return image_data[0]
+                                return image_data
+        
+        return None

py/metadata_collector/node_extractors.py

@@ -0,0 +1,353 @@
+import os
+
+from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES
+
+
+class NodeMetadataExtractor:
+    """Base class for node-specific metadata extraction"""
+    
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        """Extract metadata from node inputs/outputs"""
+        pass
+        
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        """Update metadata with node outputs after execution"""
+        pass
+        
+class GenericNodeExtractor(NodeMetadataExtractor):
+    """Default extractor for nodes without specific handling"""
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        pass
+        
+class CheckpointLoaderExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs or "ckpt_name" not in inputs:
+            return
+            
+        model_name = inputs.get("ckpt_name")
+        if model_name:
+            metadata[MODELS][node_id] = {
+                "name": model_name,
+                "type": "checkpoint",
+                "node_id": node_id
+            }
+        
+class CLIPTextEncodeExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs or "text" not in inputs:
+            return
+            
+        text = inputs.get("text", "")
+        metadata[PROMPTS][node_id] = {
+            "text": text,
+            "node_id": node_id
+        }
+        
+class SamplerExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+            
+        sampling_params = {}
+        for key in ["seed", "steps", "cfg", "sampler_name", "scheduler", "denoise"]:
+            if key in inputs:
+                sampling_params[key] = inputs[key]
+                
+        metadata[SAMPLING][node_id] = {
+            "parameters": sampling_params,
+            "node_id": node_id
+        }
+        
+        # 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
+                    }
+
+class KSamplerAdvancedExtractor(NodeMetadataExtractor):
+    @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
+        }
+        
+        # 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
+                    }
+
+class LoraLoaderExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs or "lora_name" not in inputs:
+            return
+            
+        lora_name = inputs.get("lora_name")
+        # Extract base filename without extension from path
+        lora_name = os.path.splitext(os.path.basename(lora_name))[0]
+        strength_model = round(float(inputs.get("strength_model", 1.0)), 2)
+        
+        # Use the standardized format with lora_list
+        metadata[LORAS][node_id] = {
+            "lora_list": [
+                {
+                    "name": lora_name,
+                    "strength": strength_model
+                }
+            ],
+            "node_id": node_id
+        }
+
+class ImageSizeExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+        
+        width = inputs.get("width", 512)
+        height = inputs.get("height", 512)
+        
+        if SIZE not in metadata:
+            metadata[SIZE] = {}
+            
+        metadata[SIZE][node_id] = {
+            "width": width,
+            "height": height,
+            "node_id": node_id
+        }
+
+class LoraLoaderManagerExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+        
+        active_loras = []
+        
+        # Process lora_stack if available
+        if "lora_stack" in inputs:
+            lora_stack = inputs.get("lora_stack", [])
+            for lora_path, model_strength, clip_strength in lora_stack:
+                # Extract lora name from path (following the format in lora_loader.py)
+                lora_name = os.path.splitext(os.path.basename(lora_path))[0]
+                active_loras.append({
+                    "name": lora_name,
+                    "strength": model_strength
+                })
+        
+        # Process loras from inputs
+        if "loras" in inputs:
+            loras_data = inputs.get("loras", [])
+            
+            # Handle new format: {'loras': {'__value__': [...]}} 
+            if isinstance(loras_data, dict) and '__value__' in loras_data:
+                loras_list = loras_data['__value__']
+            # Handle old format: {'loras': [...]}
+            elif isinstance(loras_data, list):
+                loras_list = loras_data
+            else:
+                loras_list = []
+                
+            # Filter for active loras
+            for lora in loras_list:
+                if isinstance(lora, dict) and lora.get("active", True) and not lora.get("_isDummy", False):
+                    active_loras.append({
+                        "name": lora.get("name", ""),
+                        "strength": float(lora.get("strength", 1.0))
+                    })
+        
+        if active_loras:
+            metadata[LORAS][node_id] = {
+                "lora_list": active_loras,
+                "node_id": node_id
+            }
+
+class FluxGuidanceExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs or "guidance" not in inputs:
+            return
+            
+        guidance_value = inputs.get("guidance")
+        
+        # Store the guidance value in SAMPLING category
+        if node_id not in metadata[SAMPLING]:
+            metadata[SAMPLING][node_id] = {"parameters": {}, "node_id": node_id}
+            
+        metadata[SAMPLING][node_id]["parameters"]["guidance"] = guidance_value
+
+class UNETLoaderExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs or "unet_name" not in inputs:
+            return
+            
+        model_name = inputs.get("unet_name")
+        if model_name:
+            metadata[MODELS][node_id] = {
+                "name": model_name,
+                "type": "checkpoint",
+                "node_id": node_id
+            }
+
+class VAEDecodeExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        pass
+        
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        # Ensure IMAGES category exists
+        if IMAGES not in metadata:
+            metadata[IMAGES] = {}
+            
+        # Save image data under node ID index to be captured by caching mechanism
+        metadata[IMAGES][node_id] = {
+            "node_id": node_id,
+            "image": outputs
+        }
+        
+        # Only set first_decode if it hasn't been recorded yet
+        if "first_decode" not in metadata[IMAGES]:
+            metadata[IMAGES]["first_decode"] = metadata[IMAGES][node_id]
+
+class KSamplerSelectExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs or "sampler_name" not in inputs:
+            return
+            
+        sampling_params = {}
+        if "sampler_name" in inputs:
+            sampling_params["sampler_name"] = inputs["sampler_name"]
+                
+        metadata[SAMPLING][node_id] = {
+            "parameters": sampling_params,
+            "node_id": node_id
+        }
+
+class BasicSchedulerExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+            
+        sampling_params = {}
+        for key in ["scheduler", "steps", "denoise"]:
+            if key in inputs:
+                sampling_params[key] = inputs[key]
+                
+        metadata[SAMPLING][node_id] = {
+            "parameters": sampling_params,
+            "node_id": node_id
+        }
+
+class SamplerCustomAdvancedExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+            
+        sampling_params = {}
+
+        # Handle noise.seed as seed
+        if "noise" in inputs and inputs["noise"] is not None and hasattr(inputs["noise"], "seed"):
+            noise = inputs["noise"]
+            sampling_params["seed"] = noise.seed
+                
+        metadata[SAMPLING][node_id] = {
+            "parameters": sampling_params,
+            "node_id": node_id
+        }
+        
+        # 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
+                    }
+
+# Registry of node-specific extractors
+NODE_EXTRACTORS = {
+    # Sampling
+    "KSampler": SamplerExtractor,
+    "KSamplerAdvanced": KSamplerAdvancedExtractor,
+    "SamplerCustomAdvanced": SamplerCustomAdvancedExtractor,  # Updated to use dedicated extractor
+    # Sampling Selectors
+    "KSamplerSelect": KSamplerSelectExtractor,  # Add KSamplerSelect
+    "BasicScheduler": BasicSchedulerExtractor,  # Add BasicScheduler
+    # Loaders
+    "CheckpointLoaderSimple": CheckpointLoaderExtractor,
+    "UNETLoader": UNETLoaderExtractor,          # Updated to use dedicated extractor
+    "LoraLoader": LoraLoaderExtractor,
+    "LoraManagerLoader": LoraLoaderManagerExtractor,
+    # Conditioning
+    "CLIPTextEncode": CLIPTextEncodeExtractor,
+    # Latent
+    "EmptyLatentImage": ImageSizeExtractor,
+    # Flux
+    "FluxGuidance": FluxGuidanceExtractor,      # Add FluxGuidance
+    # Image
+    "VAEDecode": VAEDecodeExtractor,  # Added VAEDecode extractor
+    # Add other nodes as needed
+}

py/nodes/debug_metadata.py

@@ -0,0 +1,35 @@
+import logging
+from ..metadata_collector.metadata_processor import MetadataProcessor
+
+logger = logging.getLogger(__name__)
+
+class DebugMetadata:
+    NAME = "Debug Metadata (LoraManager)"
+    CATEGORY = "Lora Manager/utils"
+    DESCRIPTION = "Debug node to verify metadata_processor functionality"
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("metadata_json",)
+    FUNCTION = "process_metadata"
+
+    def process_metadata(self, images):
+        try:
+            # Get the current execution context's metadata
+            from ..metadata_collector import get_metadata
+            metadata = get_metadata()
+            
+            # Use the MetadataProcessor to convert it to JSON string
+            metadata_json = MetadataProcessor.to_json(metadata)
+            
+            return (metadata_json,)
+        except Exception as e:
+            logger.error(f"Error processing metadata: {e}")
+            return ("{}",)  # Return empty JSON object in case of error

py/nodes/lora_loader.py

@@ -5,7 +5,7 @@ from ..services.lora_scanner import LoraScanner
 from ..config import config
 import asyncio
 import os
-from .utils import FlexibleOptionalInputType, any_type
+from .utils import FlexibleOptionalInputType, any_type, get_lora_info, extract_lora_name, get_loras_list
 
 logger = logging.getLogger(__name__)
 
@@ -32,48 +32,6 @@ class LoraManagerLoader:
     RETURN_TYPES = ("MODEL", "CLIP", IO.STRING, IO.STRING)
     RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
     FUNCTION = "load_loras"
-
-    async def get_lora_info(self, 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(self, lora_path):
-        """Extract the lora name from a lora path (e.g., 'IL\\aorunIllstrious.safetensors' -> 'aorunIllstrious')"""
-        # Get the basename without extension
-        basename = os.path.basename(lora_path)
-        return os.path.splitext(basename)[0]
-    
-    def _get_loras_list(self, kwargs):
-        """Helper to extract loras list from either old or new kwargs format"""
-        if 'loras' not in kwargs:
-            return []
-            
-        loras_data = kwargs['loras']
-        # Handle new format: {'loras': {'__value__': [...]}}
-        if isinstance(loras_data, dict) and '__value__' in loras_data:
-            return loras_data['__value__']
-        # Handle old format: {'loras': [...]}
-        elif isinstance(loras_data, list):
-            return loras_data
-        # Unexpected format
-        else:
-            logger.warning(f"Unexpected loras format: {type(loras_data)}")
-            return []
     
     def load_loras(self, model, text, **kwargs):
         """Loads multiple LoRAs based on the kwargs input and lora_stack."""
@@ -89,14 +47,14 @@ class LoraManagerLoader:
                 model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
                 
                 # Extract lora name for trigger words lookup
-                lora_name = self.extract_lora_name(lora_path)
-                _, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+                lora_name = extract_lora_name(lora_path)
+                _, trigger_words = asyncio.run(get_lora_info(lora_name))
                 
                 all_trigger_words.extend(trigger_words)
                 loaded_loras.append(f"{lora_name}: {model_strength}")
         
         # Then process loras from kwargs with support for both old and new formats
-        loras_list = self._get_loras_list(kwargs)
+        loras_list = get_loras_list(kwargs)
         for lora in loras_list:
             if not lora.get('active', False):
                 continue
@@ -105,7 +63,7 @@ class LoraManagerLoader:
             strength = float(lora['strength'])
             
             # Get lora path and trigger words
-            lora_path, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+            lora_path, trigger_words = asyncio.run(get_lora_info(lora_name))
             
             # Apply the LoRA using the resolved path
             model, clip = LoraLoader().load_lora(model, clip, lora_path, strength, strength)

py/nodes/lora_stacker.py

@@ -3,7 +3,7 @@ from ..services.lora_scanner import LoraScanner
 from ..config import config
 import asyncio
 import os
-from .utils import FlexibleOptionalInputType, any_type
+from .utils import FlexibleOptionalInputType, any_type, get_lora_info, extract_lora_name, get_loras_list
 import logging
 
 logger = logging.getLogger(__name__)
@@ -29,48 +29,6 @@ class LoraStacker:
     RETURN_TYPES = ("LORA_STACK", IO.STRING, IO.STRING)
     RETURN_NAMES = ("LORA_STACK", "trigger_words", "active_loras")
     FUNCTION = "stack_loras"
-
-    async def get_lora_info(self, 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(self, lora_path):
-        """Extract the lora name from a lora path (e.g., 'IL\\aorunIllstrious.safetensors' -> 'aorunIllstrious')"""
-        # Get the basename without extension
-        basename = os.path.basename(lora_path)
-        return os.path.splitext(basename)[0]
-    
-    def _get_loras_list(self, kwargs):
-        """Helper to extract loras list from either old or new kwargs format"""
-        if 'loras' not in kwargs:
-            return []
-            
-        loras_data = kwargs['loras']
-        # Handle new format: {'loras': {'__value__': [...]}}
-        if isinstance(loras_data, dict) and '__value__' in loras_data:
-            return loras_data['__value__']
-        # Handle old format: {'loras': [...]}
-        elif isinstance(loras_data, list):
-            return loras_data
-        # Unexpected format
-        else:
-            logger.warning(f"Unexpected loras format: {type(loras_data)}")
-            return []
     
     def stack_loras(self, text, **kwargs):
         """Stacks multiple LoRAs based on the kwargs input without loading them."""
@@ -84,12 +42,12 @@ class LoraStacker:
             stack.extend(lora_stack)
             # Get trigger words from existing stack entries
             for lora_path, _, _ in lora_stack:
-                lora_name = self.extract_lora_name(lora_path)
-                _, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+                lora_name = extract_lora_name(lora_path)
+                _, trigger_words = asyncio.run(get_lora_info(lora_name))
                 all_trigger_words.extend(trigger_words)
         
         # Process loras from kwargs with support for both old and new formats
-        loras_list = self._get_loras_list(kwargs)
+        loras_list = get_loras_list(kwargs)
         for lora in loras_list:
             if not lora.get('active', False):
                 continue
@@ -99,7 +57,7 @@ class LoraStacker:
             clip_strength = model_strength  # Using same strength for both as in the original loader
             
             # Get lora path and trigger words
-            lora_path, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+            lora_path, trigger_words = asyncio.run(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

@@ -5,10 +5,11 @@ import re
 import numpy as np
 import folder_paths # type: ignore
 from ..services.lora_scanner import LoraScanner
-from ..workflow.parser import WorkflowParser
+from ..services.checkpoint_scanner import CheckpointScanner
+from ..metadata_collector.metadata_processor import MetadataProcessor
+from ..metadata_collector import get_metadata
 from PIL import Image, PngImagePlugin
 import piexif
-from io import BytesIO
 
 class SaveImage:
     NAME = "Save Image (LoraManager)"
@@ -34,8 +35,7 @@ class SaveImage:
                 "file_format": (["png", "jpeg", "webp"],),
             },
             "optional": {
-                "custom_prompt": ("STRING", {"default": "", "forceInput": True}),
-                "lossless_webp": ("BOOLEAN", {"default": True}),
+                "lossless_webp": ("BOOLEAN", {"default": False}),
                 "quality": ("INT", {"default": 100, "min": 1, "max": 100}),
                 "embed_workflow": ("BOOLEAN", {"default": False}),
                 "add_counter_to_filename": ("BOOLEAN", {"default": True}),
@@ -54,28 +54,61 @@ class SaveImage:
     async def get_lora_hash(self, lora_name):
         """Get the lora hash from cache"""
         scanner = await LoraScanner.get_instance()
-        cache = await scanner.get_cached_data()
         
+        # 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 format_metadata(self, parsed_workflow, custom_prompt=None):
+    async def get_checkpoint_hash(self, checkpoint_path):
+        """Get the checkpoint hash from cache"""
+        scanner = await CheckpointScanner.get_instance()
+        
+        if not checkpoint_path:
+            return None
+            
+        # Extract basename without extension
+        checkpoint_name = os.path.basename(checkpoint_path)
+        checkpoint_name = os.path.splitext(checkpoint_name)[0]
+        
+        # Try direct filename lookup first
+        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):
         """Format metadata in the requested format similar to userComment example"""
-        if not parsed_workflow:
+        if not metadata_dict:
             return ""
         
-        # Extract the prompt and negative prompt
-        prompt = parsed_workflow.get('prompt', '')
-        negative_prompt = parsed_workflow.get('negative_prompt', '')
+        # Helper function to only add parameter if value is not None
+        def add_param_if_not_none(param_list, label, value):
+            if value is not None:
+                param_list.append(f"{label}: {value}")
         
-        # Override prompt with custom_prompt if provided
-        if custom_prompt:
-            prompt = custom_prompt
+        # Extract the prompt and negative prompt
+        prompt = metadata_dict.get('prompt', '')
+        negative_prompt = metadata_dict.get('negative_prompt', '')
         
         # Extract loras from the prompt if present
-        loras_text = parsed_workflow.get('loras', '')
+        loras_text = metadata_dict.get('loras', '')
         lora_hashes = {}
         
         # If loras are found, add them on a new line after the prompt
@@ -104,11 +137,15 @@ class SaveImage:
         params = []
         
         # Add standard parameters in the correct order
-        if 'steps' in parsed_workflow:
-            params.append(f"Steps: {parsed_workflow.get('steps')}")
+        if 'steps' in metadata_dict:
+            add_param_if_not_none(params, "Steps", metadata_dict.get('steps'))
         
-        if 'sampler' in parsed_workflow:
-            sampler = parsed_workflow.get('sampler')
+        # Combine sampler and scheduler information
+        sampler_name = None
+        scheduler_name = None
+        
+        if 'sampler' in metadata_dict:
+            sampler = metadata_dict.get('sampler')
             # Convert ComfyUI sampler names to user-friendly names
             sampler_mapping = {
                 'euler': 'Euler',
@@ -128,10 +165,9 @@ class SaveImage:
                 'ddim': 'DDIM'
             }
             sampler_name = sampler_mapping.get(sampler, sampler)
-            params.append(f"Sampler: {sampler_name}")
         
-        if 'scheduler' in parsed_workflow:
-            scheduler = parsed_workflow.get('scheduler')
+        if 'scheduler' in metadata_dict:
+            scheduler = metadata_dict.get('scheduler')
             scheduler_mapping = {
                 'normal': 'Simple',
                 'karras': 'Karras',
@@ -140,29 +176,48 @@ class SaveImage:
                 'sgm_quadratic': 'SGM Quadratic'
             }
             scheduler_name = scheduler_mapping.get(scheduler, scheduler)
-            params.append(f"Schedule type: {scheduler_name}")
         
-        # CFG scale (cfg in parsed_workflow)
-        if 'cfg_scale' in parsed_workflow:
-            params.append(f"CFG scale: {parsed_workflow.get('cfg_scale')}")
-        elif 'cfg' in parsed_workflow:
-            params.append(f"CFG scale: {parsed_workflow.get('cfg')}")
+        # Add combined sampler and scheduler information
+        if sampler_name:
+            if scheduler_name:
+                params.append(f"Sampler: {sampler_name} {scheduler_name}")
+            else:
+                params.append(f"Sampler: {sampler_name}")
+        
+        # CFG scale (Use guidance if available, otherwise fall back to cfg_scale or cfg)
+        if 'guidance' in metadata_dict:
+            add_param_if_not_none(params, "CFG scale", metadata_dict.get('guidance'))
+        elif 'cfg_scale' in metadata_dict:
+            add_param_if_not_none(params, "CFG scale", metadata_dict.get('cfg_scale'))
+        elif 'cfg' in metadata_dict:
+            add_param_if_not_none(params, "CFG scale", metadata_dict.get('cfg'))
         
         # Seed
-        if 'seed' in parsed_workflow:
-            params.append(f"Seed: {parsed_workflow.get('seed')}")
+        if 'seed' in metadata_dict:
+            add_param_if_not_none(params, "Seed", metadata_dict.get('seed'))
         
         # Size
-        if 'size' in parsed_workflow:
-            params.append(f"Size: {parsed_workflow.get('size')}")
+        if 'size' in metadata_dict:
+            add_param_if_not_none(params, "Size", metadata_dict.get('size'))
         
         # Model info
-        if 'checkpoint' in parsed_workflow:
-            # Extract basename without path
-            checkpoint = os.path.basename(parsed_workflow.get('checkpoint', ''))
-            # Remove extension if present
-            checkpoint = os.path.splitext(checkpoint)[0]
-            params.append(f"Model: {checkpoint}")
+        if 'checkpoint' in metadata_dict:
+            # Ensure checkpoint is a string before processing
+            checkpoint = metadata_dict.get('checkpoint')
+            if checkpoint is not None:
+                # Get model hash
+                model_hash = await self.get_checkpoint_hash(checkpoint)
+                
+                # Extract basename without path
+                checkpoint_name = os.path.basename(checkpoint)
+                # Remove extension if present
+                checkpoint_name = os.path.splitext(checkpoint_name)[0]
+                
+                # Add model hash if available
+                if model_hash:
+                    params.append(f"Model hash: {model_hash[:10]}, Model: {checkpoint_name}")
+                else:
+                    params.append(f"Model: {checkpoint_name}")
         
         # Add LoRA hashes if available
         if lora_hashes:
@@ -181,9 +236,9 @@ class SaveImage:
 
     # credit to nkchocoai
     # Add format_filename method to handle pattern substitution
-    def format_filename(self, filename, parsed_workflow):
+    def format_filename(self, filename, metadata_dict):
         """Format filename with metadata values"""
-        if not parsed_workflow:
+        if not metadata_dict:
             return filename
             
         result = re.findall(self.pattern_format, filename)
@@ -191,30 +246,30 @@ class SaveImage:
             parts = segment.replace("%", "").split(":")
             key = parts[0]
             
-            if key == "seed" and 'seed' in parsed_workflow:
-                filename = filename.replace(segment, str(parsed_workflow.get('seed', '')))
-            elif key == "width" and 'size' in parsed_workflow:
-                size = parsed_workflow.get('size', 'x')
+            if key == "seed" and 'seed' in metadata_dict:
+                filename = filename.replace(segment, str(metadata_dict.get('seed', '')))
+            elif key == "width" and 'size' in metadata_dict:
+                size = metadata_dict.get('size', 'x')
                 w = size.split('x')[0] if isinstance(size, str) else size[0]
                 filename = filename.replace(segment, str(w))
-            elif key == "height" and 'size' in parsed_workflow:
-                size = parsed_workflow.get('size', 'x')
+            elif key == "height" and 'size' in metadata_dict:
+                size = metadata_dict.get('size', 'x')
                 h = size.split('x')[1] if isinstance(size, str) else size[1]
                 filename = filename.replace(segment, str(h))
-            elif key == "pprompt" and 'prompt' in parsed_workflow:
-                prompt = parsed_workflow.get('prompt', '').replace("\n", " ")
+            elif key == "pprompt" and 'prompt' in metadata_dict:
+                prompt = metadata_dict.get('prompt', '').replace("\n", " ")
                 if len(parts) >= 2:
                     length = int(parts[1])
                     prompt = prompt[:length]
                 filename = filename.replace(segment, prompt.strip())
-            elif key == "nprompt" and 'negative_prompt' in parsed_workflow:
-                prompt = parsed_workflow.get('negative_prompt', '').replace("\n", " ")
+            elif key == "nprompt" and 'negative_prompt' in metadata_dict:
+                prompt = metadata_dict.get('negative_prompt', '').replace("\n", " ")
                 if len(parts) >= 2:
                     length = int(parts[1])
                     prompt = prompt[:length]
                 filename = filename.replace(segment, prompt.strip())
-            elif key == "model" and 'checkpoint' in parsed_workflow:
-                model = parsed_workflow.get('checkpoint', '')
+            elif key == "model" and 'checkpoint' in metadata_dict:
+                model = metadata_dict.get('checkpoint', '')
                 model = os.path.splitext(os.path.basename(model))[0]
                 if len(parts) >= 2:
                     length = int(parts[1])
@@ -224,12 +279,13 @@ class SaveImage:
                 from datetime import datetime
                 now = datetime.now()
                 date_table = {
-                    "yyyy": str(now.year),
-                    "MM": str(now.month).zfill(2),
-                    "dd": str(now.day).zfill(2),
-                    "hh": str(now.hour).zfill(2),
-                    "mm": str(now.minute).zfill(2),
-                    "ss": str(now.second).zfill(2),
+                    "yyyy": f"{now.year:04d}",
+                    "yy": f"{now.year % 100:02d}",
+                    "MM": f"{now.month:02d}",
+                    "dd": f"{now.day:02d}",
+                    "hh": f"{now.hour:02d}",
+                    "mm": f"{now.minute:02d}",
+                    "ss": f"{now.second:02d}",
                 }
                 if len(parts) >= 2:
                     date_format = parts[1]
@@ -245,23 +301,19 @@ class SaveImage:
         return filename
 
     def save_images(self, images, filename_prefix, file_format, prompt=None, extra_pnginfo=None, 
-                   lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True,
-                   custom_prompt=None):
+                   lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
         """Save images with metadata"""
         results = []
         
-        # Parse the workflow using the WorkflowParser
-        parser = WorkflowParser()
-        if prompt:
-            parsed_workflow = parser.parse_workflow(prompt)
-        else:
-            parsed_workflow = {}
+        # Get metadata using the metadata collector
+        raw_metadata = get_metadata()
+        metadata_dict = MetadataProcessor.to_dict(raw_metadata)
             
         # Get or create metadata asynchronously
-        metadata = asyncio.run(self.format_metadata(parsed_workflow, custom_prompt))
+        metadata = asyncio.run(self.format_metadata(metadata_dict))
         
         # Process filename_prefix with pattern substitution
-        filename_prefix = self.format_filename(filename_prefix, parsed_workflow)
+        filename_prefix = self.format_filename(filename_prefix, metadata_dict)
         
         # Get initial save path info once for the batch
         full_output_folder, filename, counter, subfolder, processed_prefix = folder_paths.get_save_image_path(
@@ -283,13 +335,14 @@ class SaveImage:
             if add_counter_to_filename:
                 # Use counter + i to ensure unique filenames for all images in batch
                 current_counter = counter + i
-                base_filename += f"_{current_counter:05}"
+                base_filename += f"_{current_counter:05}_"
                 
             # Set file extension and prepare saving parameters
             if file_format == "png":
                 file = base_filename + ".png"
                 file_extension = ".png"
-                save_kwargs = {"optimize": True, "compress_level": self.compress_level}
+                # Remove "optimize": True to match built-in node behavior
+                save_kwargs = {"compress_level": self.compress_level}
                 pnginfo = PngImagePlugin.PngInfo()
             elif file_format == "jpeg":
                 file = base_filename + ".jpg"
@@ -298,7 +351,8 @@ class SaveImage:
             elif file_format == "webp":
                 file = base_filename + ".webp" 
                 file_extension = ".webp"
-                save_kwargs = {"quality": quality, "lossless": lossless_webp}
+                # Add optimization param to control performance
+                save_kwargs = {"quality": quality, "lossless": lossless_webp, "method": 0}
             
             # Full save path
             file_path = os.path.join(full_output_folder, file)
@@ -346,8 +400,7 @@ class SaveImage:
         return results
 
     def process_image(self, images, filename_prefix="ComfyUI", file_format="png", prompt=None, extra_pnginfo=None,
-                     lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True,
-                     custom_prompt=""):
+                     lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
         """Process and save image with metadata"""
         # Make sure the output directory exists
         os.makedirs(self.output_dir, exist_ok=True)
@@ -368,8 +421,7 @@ class SaveImage:
             lossless_webp,
             quality,
             embed_workflow,
-            add_counter_to_filename,
-            custom_prompt if custom_prompt.strip() else None
+            add_counter_to_filename
         )
         
         return (images,)

py/nodes/trigger_word_toggle.py

@@ -47,10 +47,10 @@ class TriggerWordToggle:
         trigger_words = trigger_words_data if isinstance(trigger_words_data, str) else ""
         
         # Send trigger words to frontend
-        PromptServer.instance.send_sync("trigger_word_update", {
-            "id": id,
-            "message": trigger_words
-        })
+        # PromptServer.instance.send_sync("trigger_word_update", {
+        #     "id": id,
+        #     "message": trigger_words
+        # })
         
         filtered_triggers = trigger_words
         

py/nodes/utils.py

@@ -30,4 +30,55 @@ class FlexibleOptionalInputType(dict):
     return True
 
 
-any_type = AnyType("*")
+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
+
+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
+    basename = os.path.basename(lora_path)
+    return os.path.splitext(basename)[0]
+
+def get_loras_list(kwargs):
+    """Helper to extract loras list from either old or new kwargs format"""
+    if 'loras' not in kwargs:
+        return []
+        
+    loras_data = kwargs['loras']
+    # Handle new format: {'loras': {'__value__': [...]}}
+    if isinstance(loras_data, dict) and '__value__' in loras_data:
+        return loras_data['__value__']
+    # Handle old format: {'loras': [...]}
+    elif isinstance(loras_data, list):
+        return loras_data
+    # Unexpected format
+    else:
+        logger.warning(f"Unexpected loras format: {type(loras_data)}")
+        return []

py/routes/api_routes.py

@@ -3,8 +3,10 @@ import json
 import logging
 from aiohttp import web
 from typing import Dict
+from server import PromptServer # type: ignore
 
 from ..utils.routes_common import ModelRouteUtils
+from ..nodes.utils import get_lora_info
 
 from ..config import config
 from ..services.websocket_manager import ws_manager
@@ -50,8 +52,8 @@ class ApiRoutes:
         app.router.add_get('/api/lora-roots', routes.get_lora_roots)
         app.router.add_get('/api/folders', routes.get_folders)
         app.router.add_get('/api/civitai/versions/{model_id}', routes.get_civitai_versions)
-        app.router.add_get('/api/civitai/model/{modelVersionId}', routes.get_civitai_model)
-        app.router.add_get('/api/civitai/model/{hash}', routes.get_civitai_model)
+        app.router.add_get('/api/civitai/model/version/{modelVersionId}', routes.get_civitai_model_by_version)
+        app.router.add_get('/api/civitai/model/hash/{hash}', routes.get_civitai_model_by_hash)
         app.router.add_post('/api/download-lora', routes.download_lora)
         app.router.add_post('/api/settings', routes.update_settings)
         app.router.add_post('/api/move_model', routes.move_model)
@@ -64,6 +66,9 @@ class ApiRoutes:
         app.router.add_get('/api/lora-civitai-url', routes.get_lora_civitai_url)  # Add new route for Civitai URL
         app.router.add_post('/api/rename_lora', routes.rename_lora)  # Add new route for renaming LoRA files
         app.router.add_get('/api/loras/scan', routes.scan_loras)  # Add new route for scanning LoRA files
+        
+        # Add the new trigger words route
+        app.router.add_post('/loramanager/get_trigger_words', routes.get_trigger_words)
 
         # Add update check routes
         UpdateRoutes.setup_routes(app)
@@ -120,6 +125,7 @@ class ApiRoutes:
             # Get filter parameters
             base_models = request.query.get('base_models', None)
             tags = request.query.get('tags', None)
+            favorites_only = request.query.get('favorites_only', 'false').lower() == 'true'  # New parameter
             
             # New parameters for recipe filtering
             lora_hash = request.query.get('lora_hash', None)
@@ -150,7 +156,8 @@ class ApiRoutes:
                 base_models=filters.get('base_model', None),
                 tags=filters.get('tags', None),
                 search_options=search_options,
-                hash_filters=hash_filters
+                hash_filters=hash_filters,
+                favorites_only=favorites_only  # Pass favorites_only parameter
             )
 
             # Get all available folders from cache
@@ -190,6 +197,7 @@ class ApiRoutes:
             "from_civitai": lora.get("from_civitai", True),
             "usage_tips": lora.get("usage_tips", ""),
             "notes": lora.get("notes", ""),
+            "favorite": lora.get("favorite", False),  # Include favorite status in response
             "civitai": ModelRouteUtils.filter_civitai_data(lora.get("civitai", {}))
         }
 
@@ -226,7 +234,7 @@ class ApiRoutes:
                 target_width=CARD_PREVIEW_WIDTH,
                 format='webp',
                 quality=85,
-                preserve_metadata=True
+                preserve_metadata=False
             )
             extension = '.webp'  # Use .webp without .preview part
         
@@ -396,25 +404,52 @@ class ApiRoutes:
             logger.error(f"Error fetching model versions: {e}")
             return web.Response(status=500, text=str(e))
         
-    async def get_civitai_model(self, request: web.Request) -> web.Response:
-        """Get CivitAI model details by model version ID or hash"""
+    async def get_civitai_model_by_version(self, request: web.Request) -> web.Response:
+        """Get CivitAI model details by model version ID"""
         try:
             if self.civitai_client is None:
                 self.civitai_client = await ServiceRegistry.get_civitai_client()
                 
             model_version_id = request.match_info.get('modelVersionId')
-            if not model_version_id:
-                hash = request.match_info.get('hash')
-                model = await self.civitai_client.get_model_by_hash(hash)
-                return web.json_response(model)
             
             # Get model details from Civitai API    
-            model = await self.civitai_client.get_model_version_info(model_version_id)
+            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.Response(status=500, text=str(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:
+            if self.civitai_client is None:
+                self.civitai_client = await ServiceRegistry.get_civitai_client()
+                
+            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)
 
     async def download_lora(self, request: web.Request) -> web.Response:
         async with self._download_lock:
@@ -773,7 +808,7 @@ class ApiRoutes:
                 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:
+                if (model_metadata):
                     description = model_metadata.get('description')
                     tags = model_metadata.get('tags', [])
                 
@@ -994,4 +1029,35 @@ class ApiRoutes:
             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 = await get_lora_info(lora_name)
+                all_trigger_words.extend(trigger_words)
+            
+            # Format the trigger words
+            trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+            
+            # Send update to all connected trigger word toggle nodes
+            for node_id in node_ids:
+                PromptServer.instance.send_sync("trigger_word_update", {
+                    "id": node_id,
+                    "message": trigger_words_text
+                })
+            
+            return web.json_response({"success": True})
+
+        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/checkpoints_routes.py

@@ -69,6 +69,7 @@ class CheckpointsRoutes:
             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 = {
@@ -101,7 +102,8 @@ class CheckpointsRoutes:
                 base_models=base_models,
                 tags=tags,
                 search_options=search_options,
-                hash_filters=hash_filters
+                hash_filters=hash_filters,
+                favorites_only=favorites_only  # Pass favorites_only parameter
             )
             
             # Format response items
@@ -123,7 +125,8 @@ class CheckpointsRoutes:
     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):
+                               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()
 
@@ -181,6 +184,13 @@ class CheckpointsRoutes:
                 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):
@@ -276,6 +286,7 @@ class CheckpointsRoutes:
             "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", {}))
         }
     

py/routes/recipe_routes.py

@@ -1,20 +1,35 @@
 import os
 import time
+import numpy as np
+from PIL import Image
+import torch
+import io
 import logging
 from aiohttp import web
 from typing import Dict
 import tempfile
 import json
 import asyncio
+import sys
 from ..utils.exif_utils import ExifUtils
 from ..utils.recipe_parsers import RecipeParserFactory
 from ..utils.constants import CARD_PREVIEW_WIDTH
 
 from ..config import config
-from ..workflow.parser import WorkflowParser
+
+# Check if running in standalone mode
+standalone_mode = 'nodes' not in sys.modules
+
 from ..utils.utils import download_civitai_image
 from ..services.service_registry import ServiceRegistry  # Add ServiceRegistry import
 
+# Only import MetadataRegistry in non-standalone mode
+if not standalone_mode:
+    # Import metadata_collector functions and classes conditionally
+    from ..metadata_collector import get_metadata  # Add MetadataCollector import
+    from ..metadata_collector.metadata_processor import MetadataProcessor  # Add MetadataProcessor import
+    from ..metadata_collector.metadata_registry import MetadataRegistry
+
 logger = logging.getLogger(__name__)
 
 class RecipeRoutes:
@@ -24,7 +39,7 @@ class RecipeRoutes:
         # Initialize service references as None, will be set during async init
         self.recipe_scanner = None
         self.civitai_client = None
-        self.parser = WorkflowParser()
+        # Remove WorkflowParser instance
         
         # Pre-warm the cache
         self._init_cache_task = None
@@ -68,6 +83,9 @@ class RecipeRoutes:
         
         # Add route to get recipes for a specific Lora
         app.router.add_get('/api/recipes/for-lora', routes.get_recipes_for_lora)
+        
+        # Add new endpoint for scanning and rebuilding the recipe cache
+        app.router.add_get('/api/recipes/scan', routes.scan_recipes)
     
     async def _init_cache(self, app):
         """Initialize cache on startup"""
@@ -656,8 +674,8 @@ class RecipeRoutes:
             logger.error(f"Error retrieving base models: {e}", exc_info=True)
             return web.json_response({
                 'success': False,
-                'error': str(e)
-            }, status=500) 
+                'error': str(e)}
+            , status=500) 
 
     async def share_recipe(self, request: web.Request) -> web.Response:
         """Process a recipe image for sharing by adding metadata to EXIF"""
@@ -786,50 +804,75 @@ class RecipeRoutes:
             # Ensure services are initialized
             await self.init_services()
             
-            reader = await request.multipart()
+            # Get metadata using the metadata collector instead of workflow parsing
+            raw_metadata = get_metadata()
+            metadata_dict = MetadataProcessor.to_dict(raw_metadata)
             
-            # Process form data
-            workflow_json = None
+            # Check if we have valid metadata
+            if not metadata_dict:
+                return web.json_response({"error": "No generation metadata found"}, status=400)
             
-            while True:
-                field = await reader.next()
-                if field is None:
-                    break
+            # Get the most recent image from metadata registry instead of temp directory
+            if not standalone_mode:
+                metadata_registry = MetadataRegistry()
+                latest_image = metadata_registry.get_first_decoded_image()
+            else:
+                latest_image = None
+            
+            if not latest_image:
+                return web.json_response({"error": "No recent images found to use for recipe. Try generating an image first."}, status=400)
+            
+            # Convert the image data to bytes - handle tuple and tensor cases
+            logger.debug(f"Image type: {type(latest_image)}")
+            
+            try:
+                # Handle the tuple case first
+                if isinstance(latest_image, tuple):
+                    # Extract the tensor from the tuple
+                    if len(latest_image) > 0:
+                        tensor_image = latest_image[0]
+                    else:
+                        return web.json_response({"error": "Empty image tuple received"}, status=400)
+                else:
+                    tensor_image = latest_image
                 
-                if field.name == 'workflow_json':
-                    workflow_text = await field.text()
-                    try:
-                        workflow_json = json.loads(workflow_text)
-                    except:
-                        return web.json_response({"error": "Invalid workflow JSON"}, status=400)
+                # Get the shape info for debugging
+                if hasattr(tensor_image, 'shape'):
+                    shape_info = tensor_image.shape
+                    logger.debug(f"Tensor shape: {shape_info}, dtype: {tensor_image.dtype}")
+                
+                # Convert tensor to numpy array
+                if isinstance(tensor_image, torch.Tensor):
+                    image_np = tensor_image.cpu().numpy()
+                else:
+                    image_np = np.array(tensor_image)
+                
+                # Handle different tensor shapes
+                # Case: (1, 1, H, W, 3) or (1, H, W, 3) - batch or multi-batch
+                if len(image_np.shape) > 3:
+                    # Remove batch dimensions until we get to (H, W, 3)
+                    while len(image_np.shape) > 3:
+                        image_np = image_np[0]
+                
+                # If values are in [0, 1] range, convert to [0, 255]
+                if image_np.dtype == np.float32 or image_np.dtype == np.float64:
+                    if image_np.max() <= 1.0:
+                        image_np = (image_np * 255).astype(np.uint8)
+                
+                # Ensure image is in the right format (HWC with RGB channels)
+                if len(image_np.shape) == 3 and image_np.shape[2] == 3:
+                    pil_image = Image.fromarray(image_np)
+                    img_byte_arr = io.BytesIO()
+                    pil_image.save(img_byte_arr, format='PNG')
+                    image = img_byte_arr.getvalue()
+                else:
+                    return web.json_response({"error": f"Cannot handle this data shape: {image_np.shape}, {image_np.dtype}"}, status=400)
+            except Exception as e:
+                logger.error(f"Error processing image data: {str(e)}", exc_info=True)
+                return web.json_response({"error": f"Error processing image: {str(e)}"}, status=400)
             
-            if not workflow_json:
-                return web.json_response({"error": "Missing workflow JSON"}, status=400)
-            
-            # Find the latest image in the temp directory
-            temp_dir = config.temp_directory
-            image_files = []
-            
-            for file in os.listdir(temp_dir):
-                if file.lower().endswith(('.png', '.jpg', '.jpeg', '.webp')):
-                    file_path = os.path.join(temp_dir, file)
-                    image_files.append((file_path, os.path.getmtime(file_path)))
-            
-            if not image_files:
-                return web.json_response({"error": "No recent images found to use for recipe"}, status=400)
-            
-            # Sort by modification time (newest first)
-            image_files.sort(key=lambda x: x[1], reverse=True)
-            latest_image_path = image_files[0][0]
-            
-            # Parse the workflow to extract generation parameters and loras
-            parsed_workflow = self.parser.parse_workflow(workflow_json)
-
-            if not parsed_workflow:
-                return web.json_response({"error": "Could not extract parameters from workflow"}, status=400)
-            
-            # Get the lora stack from the parsed workflow
-            lora_stack = parsed_workflow.get("loras", "")
+            # Get the lora stack from the metadata
+            lora_stack = metadata_dict.get("loras", "")
             
             # Parse the lora stack format: "<lora:name:strength> <lora:name2:strength2> ..."
             import re
@@ -837,7 +880,7 @@ class RecipeRoutes:
             
             # Check if any loras were found
             if not lora_matches:
-                return web.json_response({"error": "No LoRAs found in the workflow"}, status=400)
+                return web.json_response({"error": "No LoRAs found in the generation metadata"}, status=400)
             
             # Generate recipe name from the first 3 loras (or less if fewer are available)
             loras_for_name = lora_matches[:3]  # Take at most 3 loras for the name
@@ -851,10 +894,6 @@ class RecipeRoutes:
             
             recipe_name = " ".join(recipe_name_parts)
             
-            # Read the image
-            with open(latest_image_path, 'rb') as f:
-                image = f.read()
-            
             # Create recipes directory if it doesn't exist
             recipes_dir = self.recipe_scanner.recipes_dir
             os.makedirs(recipes_dir, exist_ok=True)
@@ -922,8 +961,8 @@ class RecipeRoutes:
                 "created_date": time.time(),
                 "base_model": most_common_base_model,
                 "loras": loras_data,
-                "checkpoint": parsed_workflow.get("checkpoint", ""),
-                "gen_params": {key: value for key, value in parsed_workflow.items() 
+                "checkpoint": metadata_dict.get("checkpoint", ""),
+                "gen_params": {key: value for key, value in metadata_dict.items() 
                                if key not in ['checkpoint', 'loras']},
                 "loras_stack": lora_stack  # Include the original lora stack string
             }
@@ -1231,3 +1270,24 @@ class RecipeRoutes:
         except Exception as e:
             logger.error(f"Error getting recipes for Lora: {str(e)}")
             return web.json_response({'success': False, 'error': str(e)}, status=500)
+
+    async def scan_recipes(self, request: web.Request) -> web.Response:
+        """API endpoint for scanning and rebuilding the recipe cache"""
+        try:
+            # Ensure services are initialized
+            await self.init_services()
+            
+            # Force refresh the recipe cache
+            logger.info("Manually triggering recipe cache rebuild")
+            await self.recipe_scanner.get_cached_data(force_refresh=True)
+            
+            return web.json_response({
+                'success': True,
+                'message': 'Recipe cache refreshed successfully'
+            })
+        except Exception as e:
+            logger.error(f"Error refreshing recipe cache: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/routes/usage_stats_routes.py

@@ -0,0 +1,69 @@
+import logging
+from aiohttp import web
+from ..utils.usage_stats import UsageStats
+
+logger = logging.getLogger(__name__)
+
+class UsageStatsRoutes:
+    """Routes for handling usage statistics updates"""
+    
+    @staticmethod
+    def setup_routes(app):
+        """Register usage stats routes"""
+        app.router.add_post('/loras/api/update-usage-stats', UsageStatsRoutes.update_usage_stats)
+        app.router.add_get('/loras/api/get-usage-stats', UsageStatsRoutes.get_usage_stats)
+    
+    @staticmethod
+    async def update_usage_stats(request):
+        """
+        Update usage statistics based on a prompt_id
+        
+        Expects a JSON body with:
+        {
+            "prompt_id": "string"
+        }
+        """
+        try:
+            # Parse the request body
+            data = await request.json()
+            prompt_id = data.get('prompt_id')
+            
+            if not prompt_id:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing prompt_id'
+                }, status=400)
+            
+            # Call the UsageStats to process this prompt_id synchronously
+            usage_stats = UsageStats()
+            await usage_stats.process_execution(prompt_id)
+            
+            return web.json_response({
+                'success': True
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to update usage stats: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    @staticmethod
+    async def get_usage_stats(request):
+        """Get current usage statistics"""
+        try:
+            usage_stats = UsageStats()
+            stats = await usage_stats.get_stats()
+            
+            return web.json_response({
+                'success': True,
+                'data': stats
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to get usage stats: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/server_routes.py

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

py/services/civitai_client.py

@@ -34,6 +34,7 @@ class CivitaiClient:
             'User-Agent': 'ComfyUI-LoRA-Manager/1.0'
         }
         self._session = None
+        self._session_created_at = None
         # Set default buffer size to 1MB for higher throughput
         self.chunk_size = 1024 * 1024
     
@@ -44,8 +45,8 @@ class CivitaiClient:
             # Optimize TCP connection parameters
             connector = aiohttp.TCPConnector(
                 ssl=True,
-                limit=10,  # Increase parallel connections
-                ttl_dns_cache=300,  # DNS cache time
+                limit=3,  # Further reduced from 5 to 3
+                ttl_dns_cache=0,  # Disabled DNS caching completely
                 force_close=False,  # Keep connections for reuse
                 enable_cleanup_closed=True
             )
@@ -57,7 +58,18 @@ class CivitaiClient:
                 trust_env=trust_env,
                 timeout=timeout
             )
+            self._session_created_at = datetime.now()
         return self._session
+    
+    async def _ensure_fresh_session(self):
+        """Refresh session if it's been open too long"""
+        if self._session is not None:
+            if not hasattr(self, '_session_created_at') or \
+               (datetime.now() - self._session_created_at).total_seconds() > 300:  # 5 minutes
+                await self.close()
+                self._session = None
+        
+        return await self.session
 
     def _parse_content_disposition(self, header: str) -> str:
         """Parse filename from content-disposition header"""
@@ -103,13 +115,15 @@ class CivitaiClient:
         Returns:
             Tuple[bool, str]: (success, save_path or error message)
         """
-        session = await self.session
+        logger.debug(f"Resolving DNS for: {url}")
+        session = await self._ensure_fresh_session()
         try:
             headers = self._get_request_headers()
             
             # Add Range header to allow resumable downloads
             headers['Accept-Encoding'] = 'identity'  # Disable compression for better chunked downloads
             
+            logger.debug(f"Starting download from: {url}")
             async with session.get(url, headers=headers, allow_redirects=True) as response:
                 if response.status != 200:
                     # Handle 401 unauthorized responses
@@ -124,6 +138,7 @@ class CivitaiClient:
                         return False, "Access forbidden: You don't have permission to download this file."
                     
                     # Generic error response for other status codes
+                    logger.error(f"Download failed for {url} with status {response.status}")
                     return False, f"Download failed with status {response.status}"
 
                 # Get filename from content-disposition header
@@ -170,7 +185,7 @@ class CivitaiClient:
 
     async def get_model_by_hash(self, model_hash: str) -> Optional[Dict]:
         try:
-            session = await self.session
+            session = await self._ensure_fresh_session()
             async with session.get(f"{self.base_url}/model-versions/by-hash/{model_hash}") as response:
                 if response.status == 200:
                     return await response.json()
@@ -181,7 +196,7 @@ class CivitaiClient:
 
     async def download_preview_image(self, image_url: str, save_path: str):
         try:
-            session = await self.session
+            session = await self._ensure_fresh_session()
             async with session.get(image_url) as response:
                 if response.status == 200:
                     content = await response.read()
@@ -196,7 +211,7 @@ class CivitaiClient:
     async def get_model_versions(self, model_id: str) -> List[Dict]:
         """Get all versions of a model with local availability info"""
         try:
-            session = await self.session  # 等待获取 session
+            session = await self._ensure_fresh_session()  # Use fresh session
             async with session.get(f"{self.base_url}/models/{model_id}") as response:
                 if response.status != 200:
                     return None
@@ -210,20 +225,46 @@ class CivitaiClient:
             logger.error(f"Error fetching model versions: {e}")
             return None
 
-    async def get_model_version_info(self, version_id: str) -> Optional[Dict]:
-        """Fetch model version metadata from Civitai"""
+    async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict], Optional[str]]:
+        """Fetch model version metadata from Civitai
+        
+        Args:
+            version_id: The Civitai model version ID
+            
+        Returns:
+            Tuple[Optional[Dict], Optional[str]]: A tuple containing:
+                - The model version data or None if not found
+                - An error message if there was an error, or None on success
+        """
         try:
-            session = await self.session
+            session = await self._ensure_fresh_session()
             url = f"{self.base_url}/model-versions/{version_id}"
             headers = self._get_request_headers()
             
+            logger.debug(f"Resolving DNS for model version info: {url}")
             async with session.get(url, headers=headers) as response:
                 if response.status == 200:
-                    return await response.json()
-                return None
+                    logger.debug(f"Successfully fetched model version info for: {version_id}")
+                    return await response.json(), None
+                
+                # Handle specific error cases
+                if response.status == 404:
+                    # Try to parse the error message
+                    try:
+                        error_data = await response.json()
+                        error_msg = error_data.get('error', f"Model not found (status 404)")
+                        logger.warning(f"Model version not found: {version_id} - {error_msg}")
+                        return None, error_msg
+                    except:
+                        return None, "Model not found (status 404)"
+                
+                # Other error cases
+                logger.error(f"Failed to fetch model info for {version_id} (status {response.status})")
+                return None, f"Failed to fetch model info (status {response.status})"
         except Exception as e:
-            logger.error(f"Error fetching model version info: {e}")
-            return None
+            error_msg = f"Error fetching model version info: {e}"
+            logger.error(error_msg)
+            return None, error_msg
 
     async def get_model_metadata(self, model_id: str) -> Tuple[Optional[Dict], int]:
         """Fetch model metadata (description and tags) from Civitai API
@@ -237,7 +278,7 @@ class CivitaiClient:
                 - The HTTP status code from the request
         """
         try:
-            session = await self.session
+            session = await self._ensure_fresh_session()
             headers = self._get_request_headers()
             url = f"{self.base_url}/models/{model_id}"
             
@@ -281,10 +322,11 @@ class CivitaiClient:
     async def _get_hash_from_civitai(self, model_version_id: str) -> Optional[str]:
         """Get hash from Civitai API"""
         try:
-            if not self._session:
+            session = await self._ensure_fresh_session()
+            if not session:
                 return None
             
-            version_info = await self._session.get(f"{self.base_url}/model-versions/{model_version_id}")
+            version_info = await session.get(f"{self.base_url}/model-versions/{model_version_id}")
             
             if not version_info or not version_info.json().get('files'):
                 return None

py/services/download_manager.py

@@ -86,21 +86,24 @@ class DownloadManager:
 
             # Get version info based on the provided identifier
             version_info = None
+            error_msg = None
             
-            if download_url:
-                # Extract version ID from download URL
-                version_id = download_url.split('/')[-1]
-                version_info = await civitai_client.get_model_version_info(version_id)
-            elif model_version_id:
-                # Use model version ID directly
-                version_info = await civitai_client.get_model_version_info(model_version_id)
-            elif model_hash:
+            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)
 
             
             if not version_info:
-                return {'success': False, 'error': 'Failed to fetch model metadata'}
+                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'}
 
             # Check if this is an early access model
             if version_info.get('earlyAccessEndsAt'):
@@ -202,7 +205,7 @@ class DownloadManager:
                 # Check if it's a video or an image
                 is_video = images[0].get('type') == 'video'
                 
-                if is_video:
+                if (is_video):
                     # For videos, use .mp4 extension
                     preview_ext = '.mp4'
                     preview_path = os.path.splitext(save_path)[0] + preview_ext
@@ -229,7 +232,7 @@ class DownloadManager:
                             target_width=CARD_PREVIEW_WIDTH,
                             format='webp',
                             quality=85,
-                            preserve_metadata=True
+                            preserve_metadata=False
                         )
                         
                         # Save the optimized image

py/services/file_monitor.py

@@ -408,7 +408,7 @@ class BaseFileMonitor:
     def start(self):
         """Start file monitoring"""
         if not ENABLE_FILE_MONITORING:
-            logger.info("File monitoring is disabled via ENABLE_FILE_MONITORING setting")
+            logger.debug("File monitoring is disabled via ENABLE_FILE_MONITORING setting")
             return
             
         for path in self.monitor_paths:
@@ -525,18 +525,18 @@ class CheckpointFileMonitor(BaseFileMonitor):
     def start(self):
         """Override start to check global enable flag"""
         if not ENABLE_FILE_MONITORING:
-            logger.info("Checkpoint file monitoring is disabled via ENABLE_FILE_MONITORING setting")
+            logger.debug("Checkpoint file monitoring is disabled via ENABLE_FILE_MONITORING setting")
             return
             
-        logger.info("Checkpoint file monitoring is temporarily disabled")
+        logger.debug("Checkpoint file monitoring is temporarily disabled")
         # Skip the actual monitoring setup
         pass
     
     async def initialize_paths(self):
         """Initialize monitor paths from scanner - currently disabled"""
         if not ENABLE_FILE_MONITORING:
-            logger.info("Checkpoint path initialization skipped (monitoring disabled)")
+            logger.debug("Checkpoint path initialization skipped (monitoring disabled)")
             return
             
-        logger.info("Checkpoint file path initialization skipped (monitoring disabled)")
+        logger.debug("Checkpoint file path initialization skipped (monitoring disabled)")
         pass

py/services/lora_scanner.py

@@ -9,7 +9,7 @@ from typing import List, Dict, Optional, Set
 from ..utils.models import LoraMetadata
 from ..config import config
 from .model_scanner import ModelScanner
-from .lora_hash_index import LoraHashIndex
+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
@@ -35,12 +35,12 @@ class LoraScanner(ModelScanner):
             # Define supported file extensions
             file_extensions = {'.safetensors'}
             
-            # Initialize parent class
+            # Initialize parent class with ModelHashIndex
             super().__init__(
                 model_type="lora",
                 model_class=LoraMetadata, 
                 file_extensions=file_extensions,
-                hash_index=LoraHashIndex()
+                hash_index=ModelHashIndex()  # Changed from LoraHashIndex to ModelHashIndex
             )
             self._initialized = True
     
@@ -122,7 +122,8 @@ class LoraScanner(ModelScanner):
     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) -> Dict:
+                               search_options: dict = None, hash_filters: dict = None,
+                               favorites_only: bool = False) -> Dict:
         """Get paginated and filtered lora data
         
         Args:
@@ -136,6 +137,7 @@ class LoraScanner(ModelScanner):
             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
         """
         cache = await self.get_cached_data()
 
@@ -194,6 +196,13 @@ class LoraScanner(ModelScanner):
                 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 folder filtering
         if folder is not None:
             if search_options.get('recursive', False):

py/services/model_hash_index.py

@@ -1,11 +1,12 @@
 from typing import Dict, Optional, Set
+import os
 
 class ModelHashIndex:
     """Index for looking up models by hash or path"""
     
     def __init__(self):
         self._hash_to_path: Dict[str, str] = {}
-        self._path_to_hash: Dict[str, str] = {}
+        self._filename_to_hash: Dict[str, str] = {}  # Changed from path_to_hash to filename_to_hash
     
     def add_entry(self, sha256: str, file_path: str) -> None:
         """Add or update hash index entry"""
@@ -15,37 +16,47 @@ class ModelHashIndex:
         # Ensure hash is lowercase for consistency
         sha256 = sha256.lower()
         
+        # Extract filename without extension
+        filename = self._get_filename_from_path(file_path)
+        
         # Remove old path mapping if hash exists
         if sha256 in self._hash_to_path:
             old_path = self._hash_to_path[sha256]
-            if old_path in self._path_to_hash:
-                del self._path_to_hash[old_path]
+            old_filename = self._get_filename_from_path(old_path)
+            if old_filename in self._filename_to_hash:
+                del self._filename_to_hash[old_filename]
         
-        # Remove old hash mapping if path exists
-        if file_path in self._path_to_hash:
-            old_hash = self._path_to_hash[file_path]
+        # Remove old hash mapping if filename exists
+        if filename in self._filename_to_hash:
+            old_hash = self._filename_to_hash[filename]
             if old_hash in self._hash_to_path:
                 del self._hash_to_path[old_hash]
         
         # Add new mappings
         self._hash_to_path[sha256] = file_path
-        self._path_to_hash[file_path] = sha256
+        self._filename_to_hash[filename] = sha256
+    
+    def _get_filename_from_path(self, file_path: str) -> str:
+        """Extract filename without extension from path"""
+        return os.path.splitext(os.path.basename(file_path))[0]
     
     def remove_by_path(self, file_path: str) -> None:
         """Remove entry by file path"""
-        if file_path in self._path_to_hash:
-            hash_val = self._path_to_hash[file_path]
+        filename = self._get_filename_from_path(file_path)
+        if filename in self._filename_to_hash:
+            hash_val = self._filename_to_hash[filename]
             if hash_val in self._hash_to_path:
                 del self._hash_to_path[hash_val]
-            del self._path_to_hash[file_path]
+            del self._filename_to_hash[filename]
     
     def remove_by_hash(self, sha256: str) -> None:
         """Remove entry by hash"""
         sha256 = sha256.lower()
         if sha256 in self._hash_to_path:
             path = self._hash_to_path[sha256]
-            if path in self._path_to_hash:
-                del self._path_to_hash[path]
+            filename = self._get_filename_from_path(path)
+            if filename in self._filename_to_hash:
+                del self._filename_to_hash[filename]
             del self._hash_to_path[sha256]
     
     def has_hash(self, sha256: str) -> bool:
@@ -58,20 +69,27 @@ class ModelHashIndex:
     
     def get_hash(self, file_path: str) -> Optional[str]:
         """Get hash for a file path"""
-        return self._path_to_hash.get(file_path)
+        filename = self._get_filename_from_path(file_path)
+        return self._filename_to_hash.get(filename)
+    
+    def get_hash_by_filename(self, filename: str) -> Optional[str]:
+        """Get hash for a filename without extension"""
+        # Strip extension if present to make the function more flexible
+        filename = os.path.splitext(filename)[0]
+        return self._filename_to_hash.get(filename)
     
     def clear(self) -> None:
         """Clear all entries"""
         self._hash_to_path.clear()
-        self._path_to_hash.clear()
+        self._filename_to_hash.clear()
     
     def get_all_hashes(self) -> Set[str]:
         """Get all hashes in the index"""
         return set(self._hash_to_path.keys())
     
-    def get_all_paths(self) -> Set[str]:
-        """Get all file paths in the index"""
-        return set(self._path_to_hash.keys())
+    def get_all_filenames(self) -> Set[str]:
+        """Get all filenames in the index"""
+        return set(self._filename_to_hash.keys())
     
     def __len__(self) -> int:
         """Get number of entries"""

py/services/model_scanner.py

@@ -292,7 +292,7 @@ class ModelScanner:
             )
 
         # If force refresh is requested, initialize the cache directly
-        if force_refresh:
+        if (force_refresh):
             if self._cache is None:
                 # For initial creation, do a full initialization
                 await self._initialize_cache()
@@ -553,9 +553,36 @@ class ModelScanner:
                         logger.debug(f"Created metadata from .civitai.info for {file_path}")
                 except Exception as e:
                     logger.error(f"Error creating metadata from .civitai.info for {file_path}: {e}")
+        else:
+            # Check if metadata exists but civitai field is empty - try to restore from civitai.info
+            if metadata.civitai is None or metadata.civitai == {}:
+                civitai_info_path = f"{os.path.splitext(file_path)[0]}.civitai.info"
+                if os.path.exists(civitai_info_path):
+                    try:
+                        with open(civitai_info_path, 'r', encoding='utf-8') as f:
+                            version_info = json.load(f)
+                        
+                        logger.debug(f"Restoring missing civitai data from .civitai.info for {file_path}")
+                        metadata.civitai = version_info
+                        
+                        # Ensure tags are also updated if they're missing
+                        if (not metadata.tags or len(metadata.tags) == 0) and 'model' in version_info:
+                            if 'tags' in version_info['model']:
+                                metadata.tags = version_info['model']['tags']
+                        
+                        # Also restore description if missing
+                        if (not metadata.modelDescription or metadata.modelDescription == "") and 'model' in version_info:
+                            if 'description' in version_info['model']:
+                                metadata.modelDescription = version_info['model']['description']
+                        
+                        # Save the updated metadata
+                        await save_metadata(file_path, metadata)
+                        logger.debug(f"Updated metadata with civitai info for {file_path}")
+                    except Exception as e:
+                        logger.error(f"Error restoring civitai data from .civitai.info for {file_path}: {e}")
             
-            if metadata is None:
-                metadata = await self._get_file_info(file_path)
+        if metadata is None:
+            metadata = await self._get_file_info(file_path)
         
         model_data = metadata.to_dict()
         
@@ -709,6 +736,12 @@ class ModelScanner:
                 shutil.move(source_metadata, target_metadata)
                 metadata = await self._update_metadata_paths(target_metadata, target_file)
             
+            # Move civitai.info file if exists
+            source_civitai = os.path.join(source_dir, f"{base_name}.civitai.info")
+            if os.path.exists(source_civitai):
+                target_civitai = os.path.join(target_path, f"{base_name}.civitai.info")
+                shutil.move(source_civitai, target_civitai)
+            
             for ext in PREVIEW_EXTENSIONS:
                 source_preview = os.path.join(source_dir, f"{base_name}{ext}")
                 if os.path.exists(source_preview):
@@ -805,6 +838,10 @@ class ModelScanner:
     def get_hash_by_path(self, file_path: str) -> Optional[str]:
         """Get hash for a model by its file path"""
         return self._hash_index.get_hash(file_path)
+        
+    def get_hash_by_filename(self, filename: str) -> Optional[str]:
+        """Get hash for a model by its filename without path"""
+        return self._hash_index.get_hash_by_filename(filename)
 
     # TODO: Adjust this method to use metadata instead of finding the file    
     def get_preview_url_by_hash(self, sha256: str) -> Optional[str]:

py/services/recipe_scanner.py

@@ -341,6 +341,10 @@ class RecipeScanner:
         metadata_updated = False
         
         for lora in recipe_data['loras']:
+            # Skip deleted loras that were already marked
+            if lora.get('isDeleted', False):
+                continue
+                
             # Skip if already has complete information
             if 'hash' in lora and 'file_name' in lora and lora['file_name']:
                 continue
@@ -356,10 +360,17 @@ class RecipeScanner:
                     metadata_updated = True
                 else:
                     # If not in cache, fetch from Civitai
-                    hash_from_civitai = await self._get_hash_from_civitai(model_version_id)
-                    if hash_from_civitai:
-                        lora['hash'] = hash_from_civitai
-                        metadata_updated = True
+                    result = await self._get_hash_from_civitai(model_version_id)
+                    if isinstance(result, tuple):
+                        hash_from_civitai, is_deleted = result
+                        if hash_from_civitai:
+                            lora['hash'] = hash_from_civitai
+                            metadata_updated = True
+                        elif is_deleted:
+                            # Mark the lora as deleted if it was not found on Civitai
+                            lora['isDeleted'] = True
+                            logger.warning(f"Marked lora with modelVersionId {model_version_id} as deleted")
+                            metadata_updated = True
                     else:
                         logger.debug(f"Could not get hash for modelVersionId {model_version_id}")
             
@@ -411,41 +422,26 @@ class RecipeScanner:
                 logger.error("Failed to get CivitaiClient from ServiceRegistry")
                 return None
                 
-            version_info = await civitai_client.get_model_version_info(model_version_id)
+            version_info, error_msg = await civitai_client.get_model_version_info(model_version_id)
             
-            if not version_info or not version_info.get('files'):
-                logger.debug(f"No files found in version info for ID: {model_version_id}")
-                return None
-                
+            if not version_info:
+                if error_msg and "model not found" in error_msg.lower():
+                    logger.warning(f"Model with version ID {model_version_id} was not found on Civitai - marking as deleted")
+                    return None, True  # Return None hash and True for isDeleted flag
+                else:
+                    logger.debug(f"Could not get hash for modelVersionId {model_version_id}: {error_msg}")
+                    return None, False  # Return None hash but not marked as deleted
+                    
             # Get hash from the first file
             for file_info in version_info.get('files', []):
                 if file_info.get('hashes', {}).get('SHA256'):
-                    return file_info['hashes']['SHA256']
+                    return file_info['hashes']['SHA256'], False  # Return hash with False for isDeleted flag
                     
             logger.debug(f"No SHA256 hash found in version info for ID: {model_version_id}")
-            return None
+            return None, False
         except Exception as e:
             logger.error(f"Error getting hash from Civitai: {e}")
-            return None
-
-    async def _get_model_version_name(self, model_version_id: str) -> Optional[str]:
-        """Get model version name from Civitai API"""
-        try:
-            # Get CivitaiClient from ServiceRegistry
-            civitai_client = await self._get_civitai_client()
-            if not civitai_client:
-                return None
-                
-            version_info = await civitai_client.get_model_version_info(model_version_id)
-            
-            if version_info and 'name' in version_info:
-                return version_info['name']
-                    
-            logger.debug(f"No version name found for modelVersionId {model_version_id}")
-            return None
-        except Exception as e:
-            logger.error(f"Error getting model version name from Civitai: {e}")
-            return None
+            return None, False
 
     async def _determine_base_model(self, loras: List[Dict]) -> Optional[str]:
         """Determine the most common base model among LoRAs"""

py/utils/exif_utils.py

@@ -203,7 +203,7 @@ class ExifUtils:
             return user_comment[:recipe_marker_index] + user_comment[next_line_index:]
             
     @staticmethod
-    def optimize_image(image_data, target_width=250, format='webp', quality=85, preserve_metadata=True):
+    def optimize_image(image_data, target_width=250, format='webp', quality=85, preserve_metadata=False):
         """
         Optimize an image by resizing and converting to WebP format
         
@@ -218,98 +218,144 @@ class ExifUtils:
             Tuple of (optimized_image_data, extension)
         """
         try:
-            # Extract metadata if needed
+            # First validate the image data is usable
+            img = None
+            if isinstance(image_data, str) and os.path.exists(image_data):
+                # It's a file path - validate file
+                try:
+                    with Image.open(image_data) as test_img:
+                        # Verify the image can be fully loaded by accessing its size
+                        width, height = test_img.size
+                    # If we got here, the image is valid
+                    img = Image.open(image_data)
+                except (IOError, OSError) as e:
+                    logger.error(f"Invalid or corrupt image file: {image_data}: {e}")
+                    raise ValueError(f"Cannot process corrupt image: {e}")
+            else:
+                # It's binary data - validate data
+                try:
+                    with BytesIO(image_data) as temp_buf:
+                        test_img = Image.open(temp_buf)
+                        # Verify the image can be fully loaded
+                        width, height = test_img.size
+                    # If successful, reopen for processing
+                    img = Image.open(BytesIO(image_data))
+                except Exception as e:
+                    logger.error(f"Invalid binary image data: {e}")
+                    raise ValueError(f"Cannot process corrupt image data: {e}")
+
+            # Extract metadata if needed and valid
             metadata = None
             if preserve_metadata:
-                if isinstance(image_data, str) and os.path.exists(image_data):
-                    # It's a file path
-                    metadata = ExifUtils.extract_image_metadata(image_data)
-                    img = Image.open(image_data)
-                else:
-                    # It's binary data
-                    temp_img = BytesIO(image_data)
-                    img = Image.open(temp_img)
-                    # Save to a temporary file to extract metadata
-                    import tempfile
-                    with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as temp_file:
-                        temp_path = temp_file.name
-                        temp_file.write(image_data)
-                    metadata = ExifUtils.extract_image_metadata(temp_path)
-                    os.unlink(temp_path)
-            else:
-                # Just open the image without extracting metadata
-                if isinstance(image_data, str) and os.path.exists(image_data):
-                    img = Image.open(image_data)
-                else:
-                    img = Image.open(BytesIO(image_data))
-            
+                try:
+                    if isinstance(image_data, str) and os.path.exists(image_data):
+                        # For file path, extract directly
+                        metadata = ExifUtils.extract_image_metadata(image_data)
+                    else:
+                        # For binary data, save to temp file first
+                        import tempfile
+                        with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as temp_file:
+                            temp_path = temp_file.name
+                            temp_file.write(image_data)
+                        try:
+                            metadata = ExifUtils.extract_image_metadata(temp_path)
+                        except Exception as e:
+                            logger.warning(f"Failed to extract metadata from temp file: {e}")
+                        finally:
+                            # Clean up temp file
+                            try:
+                                os.unlink(temp_path)
+                            except Exception:
+                                pass
+                except Exception as e:
+                    logger.warning(f"Failed to extract metadata, continuing without it: {e}")
+                    # Continue without metadata
+
             # Calculate new height to maintain aspect ratio
             width, height = img.size
             new_height = int(height * (target_width / width))
             
-            # Resize the image
-            resized_img = img.resize((target_width, new_height), Image.LANCZOS)
+            # Resize the image with error handling
+            try:
+                resized_img = img.resize((target_width, new_height), Image.LANCZOS)
+            except Exception as e:
+                logger.error(f"Failed to resize image: {e}")
+                # Return original image if resize fails
+                return image_data, '.jpg' if not isinstance(image_data, str) else os.path.splitext(image_data)[1]
             
             # Save to BytesIO in the specified format
             output = BytesIO()
             
-            # WebP format
+            # Set format and extension
             if format.lower() == 'webp':
-                resized_img.save(output, format='WEBP', quality=quality)
-                extension = '.webp'
-            # JPEG format
+                save_format, extension = 'WEBP', '.webp'
             elif format.lower() in ('jpg', 'jpeg'):
-                resized_img.save(output, format='JPEG', quality=quality)
-                extension = '.jpg'
-            # PNG format
+                save_format, extension = 'JPEG', '.jpg'
             elif format.lower() == 'png':
-                resized_img.save(output, format='PNG', optimize=True)
-                extension = '.png'
+                save_format, extension = 'PNG', '.png'
             else:
-                # Default to WebP
-                resized_img.save(output, format='WEBP', quality=quality)
-                extension = '.webp'
+                save_format, extension = 'WEBP', '.webp'
+            
+            # Save with error handling
+            try:
+                if save_format == 'PNG':
+                    resized_img.save(output, format=save_format, optimize=True)
+                else:
+                    resized_img.save(output, format=save_format, quality=quality)
+            except Exception as e:
+                logger.error(f"Failed to save optimized image: {e}")
+                # Return original image if save fails
+                return image_data, '.jpg' if not isinstance(image_data, str) else os.path.splitext(image_data)[1]
             
             # Get the optimized image data
             optimized_data = output.getvalue()
             
-            # If we need to preserve metadata, write it to a temporary file
+            # Handle metadata preservation if requested and available
             if preserve_metadata and metadata:
-                # For WebP format, we'll directly save with metadata
-                if format.lower() == 'webp':
-                    # Create a new BytesIO with metadata
-                    output_with_metadata = BytesIO()
-                    
-                    # Create EXIF data with user comment
-                    exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
-                    exif_bytes = piexif.dump(exif_dict)
-                    
-                    # Save with metadata
-                    resized_img.save(output_with_metadata, format='WEBP', exif=exif_bytes, quality=quality)
-                    optimized_data = output_with_metadata.getvalue()
-                else:
-                    # For other formats, use the temporary file approach
-                    import tempfile
-                    with tempfile.NamedTemporaryFile(suffix=extension, delete=False) as temp_file:
-                        temp_path = temp_file.name
-                        temp_file.write(optimized_data)
-                    
-                    # Add the metadata back
-                    ExifUtils.update_image_metadata(temp_path, metadata)
-                    
-                    # Read the file with metadata
-                    with open(temp_path, 'rb') as f:
-                        optimized_data = f.read()
-                    
-                    # Clean up
-                    os.unlink(temp_path)
+                try:
+                    if save_format == 'WEBP':
+                        # For WebP format, directly save with metadata
+                        try:
+                            output_with_metadata = BytesIO()
+                            exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
+                            exif_bytes = piexif.dump(exif_dict)
+                            resized_img.save(output_with_metadata, format='WEBP', exif=exif_bytes, quality=quality)
+                            optimized_data = output_with_metadata.getvalue()
+                        except Exception as e:
+                            logger.warning(f"Failed to add metadata to WebP, continuing without it: {e}")
+                    else:
+                        # For other formats, use temporary file
+                        import tempfile
+                        with tempfile.NamedTemporaryFile(suffix=extension, delete=False) as temp_file:
+                            temp_path = temp_file.name
+                            temp_file.write(optimized_data)
+                        
+                        try:
+                            # Add metadata
+                            ExifUtils.update_image_metadata(temp_path, metadata)
+                            # Read back the file
+                            with open(temp_path, 'rb') as f:
+                                optimized_data = f.read()
+                        except Exception as e:
+                            logger.warning(f"Failed to add metadata to image, continuing without it: {e}")
+                        finally:
+                            # Clean up temp file
+                            try:
+                                os.unlink(temp_path)
+                            except Exception:
+                                pass
+                except Exception as e:
+                    logger.warning(f"Failed to preserve metadata: {e}, continuing with unmodified output")
             
             return optimized_data, extension
             
         except Exception as e:
             logger.error(f"Error optimizing image: {e}", exc_info=True)
-            # Return original data if optimization fails
+            # Return original data if optimization completely fails
             if isinstance(image_data, str) and os.path.exists(image_data):
-                with open(image_data, 'rb') as f:
-                    return f.read(), os.path.splitext(image_data)[1]
+                try:
+                    with open(image_data, 'rb') as f:
+                        return f.read(), os.path.splitext(image_data)[1]
+                except Exception:
+                    return image_data, '.jpg'  # Last resort fallback
             return image_data, '.jpg'

py/utils/file_utils.py

@@ -42,7 +42,7 @@ def find_preview_file(base_name: str, dir_path: str) -> str:
                         target_width=CARD_PREVIEW_WIDTH,
                         format='webp',
                         quality=85,
-                        preserve_metadata=True
+                        preserve_metadata=False  # Changed from True to False
                     )
                     
                     # Save the optimized webp file

py/utils/models.py

@@ -21,6 +21,8 @@ class BaseModelMetadata:
     civitai: Optional[Dict] = None  # Civitai API data if available
     tags: List[str] = None      # Model tags
     modelDescription: str = ""  # Full model description
+    civitai_deleted: bool = False  # Whether deleted from Civitai
+    favorite: bool = False      # Whether the model is a favorite
 
     def __post_init__(self):
         # Initialize empty lists to avoid mutable default parameter issue
@@ -64,6 +66,15 @@ class LoraMetadata(BaseModelMetadata):
         file_name = file_info['name']
         base_model = determine_base_model(version_info.get('baseModel', ''))
         
+        # 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]),
@@ -75,7 +86,9 @@ class LoraMetadata(BaseModelMetadata):
             preview_url=None,  # Will be updated after preview download
             preview_nsfw_level=0, # Will be updated after preview download
             from_civitai=True,
-            civitai=version_info
+            civitai=version_info,
+            tags=tags,
+            modelDescription=description
         )
 
 @dataclass
@@ -90,6 +103,15 @@ class CheckpointMetadata(BaseModelMetadata):
         base_model = determine_base_model(version_info.get('baseModel', ''))
         model_type = version_info.get('type', 'checkpoint')
         
+        # 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]),
@@ -102,6 +124,8 @@ class CheckpointMetadata(BaseModelMetadata):
             preview_nsfw_level=0,
             from_civitai=True,
             civitai=version_info,
-            model_type=model_type
+            model_type=model_type,
+            tags=tags,
+            modelDescription=description
         )
 

py/utils/recipe_parsers.py

@@ -45,14 +45,14 @@ class RecipeMetadataParser(ABC):
         """
         pass
     
-    async def populate_lora_from_civitai(self, lora_entry: Dict[str, Any], civitai_info: Dict[str, Any], 
+    async def populate_lora_from_civitai(self, lora_entry: Dict[str, Any], civitai_info_tuple: Tuple[Dict[str, Any], Optional[str]], 
                                          recipe_scanner=None, base_model_counts=None, hash_value=None) -> Dict[str, Any]:
         """
         Populate a lora entry with information from Civitai API response
         
         Args:
             lora_entry: The lora entry to populate
-            civitai_info: The response from Civitai API
+            civitai_info_tuple: The response tuple from Civitai API (data, error_msg)
             recipe_scanner: Optional recipe scanner for local file lookup
             base_model_counts: Optional dict to track base model counts
             hash_value: Optional hash value to use if not available in civitai_info
@@ -61,6 +61,9 @@ class RecipeMetadataParser(ABC):
             The populated lora_entry dict
         """
         try:
+            # Unpack the tuple to get the actual data
+            civitai_info, error_msg = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
+            
             if civitai_info and civitai_info.get("error") != "Model not found":
                 # Check if this is an early access lora
                 if civitai_info.get('earlyAccessEndsAt'):
@@ -94,8 +97,9 @@ class RecipeMetadataParser(ABC):
                 
                 # Process file information if available
                 if 'files' in civitai_info:
+                    # Find the primary model file (type="Model" and primary=true) in the files list
                     model_file = next((file for file in civitai_info.get('files', []) 
-                                    if file.get('type') == 'Model'), None)
+                                      if file.get('type') == 'Model' and file.get('primary') == True), None)
                     
                     if model_file:
                         # Get size
@@ -241,11 +245,11 @@ class RecipeFormatParser(RecipeMetadataParser):
                         # Try to get additional info from Civitai if we have a model version ID
                         if lora.get('modelVersionId') and civitai_client:
                             try:
-                                civitai_info = await civitai_client.get_model_version_info(lora['modelVersionId'])
+                                civitai_info_tuple = await civitai_client.get_model_version_info(lora['modelVersionId'])
                                 # Populate lora entry with Civitai info
                                 lora_entry = await self.populate_lora_from_civitai(
                                     lora_entry, 
-                                    civitai_info, 
+                                    civitai_info_tuple, 
                                     recipe_scanner,
                                     None,  # No need to track base model counts
                                     lora['hash']
@@ -336,12 +340,13 @@ class StandardMetadataParser(RecipeMetadataParser):
                 # Get additional info from Civitai if client is available
                 if civitai_client:
                     try:
-                        civitai_info = await civitai_client.get_model_version_info(model_version_id)
+                        civitai_info_tuple = await civitai_client.get_model_version_info(model_version_id)
                         # Populate lora entry with Civitai info
                         lora_entry = await self.populate_lora_from_civitai(
                             lora_entry, 
-                            civitai_info, 
-                            recipe_scanner
+                            civitai_info_tuple, 
+                            recipe_scanner,
+                            base_model_counts
                         )
                     except Exception as e:
                         logger.error(f"Error fetching Civitai info for LoRA: {e}")
@@ -398,27 +403,43 @@ class StandardMetadataParser(RecipeMetadataParser):
             
             # Extract Civitai resources
             if 'Civitai resources:' in user_comment:
-                resources_part = user_comment.split('Civitai resources:', 1)[1]
-                if '],' in resources_part:
-                    resources_json = resources_part.split('],', 1)[0] + ']'
-                    try:
-                        resources = json.loads(resources_json)
-                        # Filter loras and checkpoints
-                        for resource in resources:
-                            if resource.get('type') == 'lora':
-                                # 确保 weight 字段被正确保留
-                                lora_entry = resource.copy()
-                                # 如果找不到 weight，默认为 1.0
-                                if 'weight' not in lora_entry:
-                                    lora_entry['weight'] = 1.0
-                                # Ensure modelVersionName is included
-                                if 'modelVersionName' not in lora_entry:
-                                    lora_entry['modelVersionName'] = ''
-                                metadata['loras'].append(lora_entry)
-                            elif resource.get('type') == 'checkpoint':
-                                metadata['checkpoint'] = resource
-                    except json.JSONDecodeError:
-                        pass
+                resources_part = user_comment.split('Civitai resources:', 1)[1].strip()
+                
+                # Look for the opening and closing brackets to extract the JSON array
+                if resources_part.startswith('['):
+                    # Find the position of the closing bracket
+                    bracket_count = 0
+                    end_pos = -1
+                    
+                    for i, char in enumerate(resources_part):
+                        if char == '[':
+                            bracket_count += 1
+                        elif char == ']':
+                            bracket_count -= 1
+                            if bracket_count == 0:
+                                end_pos = i
+                                break
+                    
+                    if end_pos != -1:
+                        resources_json = resources_part[:end_pos+1]
+                        try:
+                            resources = json.loads(resources_json)
+                            # Filter loras and checkpoints
+                            for resource in resources:
+                                if resource.get('type') == 'lora':
+                                    # 确保 weight 字段被正确保留
+                                    lora_entry = resource.copy()
+                                    # 如果找不到 weight，默认为 1.0
+                                    if 'weight' not in lora_entry:
+                                        lora_entry['weight'] = 1.0
+                                    # Ensure modelVersionName is included
+                                    if 'modelVersionName' not in lora_entry:
+                                        lora_entry['modelVersionName'] = ''
+                                    metadata['loras'].append(lora_entry)
+                                elif resource.get('type') == 'checkpoint':
+                                    metadata['checkpoint'] = resource
+                        except json.JSONDecodeError:
+                            pass
             
             return metadata
         except Exception as e:
@@ -621,11 +642,11 @@ class ComfyMetadataParser(RecipeMetadataParser):
                 # Get additional info from Civitai if client is available
                 if civitai_client:
                     try:
-                        civitai_info = await civitai_client.get_model_version_info(model_version_id)
+                        civitai_info_tuple = await civitai_client.get_model_version_info(model_version_id)
                         # Populate lora entry with Civitai info
                         lora_entry = await self.populate_lora_from_civitai(
                             lora_entry, 
-                            civitai_info, 
+                            civitai_info_tuple, 
                             recipe_scanner
                         )
                     except Exception as e:
@@ -660,7 +681,8 @@ class ComfyMetadataParser(RecipeMetadataParser):
                         # Get additional checkpoint info from Civitai
                         if civitai_client:
                             try:
-                                civitai_info = await civitai_client.get_model_version_info(checkpoint_version_id)
+                                civitai_info_tuple = await civitai_client.get_model_version_info(checkpoint_version_id)
+                                civitai_info, _ = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
                                 # Populate checkpoint with Civitai info
                                 checkpoint = await self.populate_checkpoint_from_civitai(checkpoint, civitai_info)
                             except Exception as e:

py/utils/routes_common.py

@@ -95,7 +95,7 @@ class ModelRouteUtils:
                                 target_width=CARD_PREVIEW_WIDTH,
                                 format='webp',
                                 quality=85,
-                                preserve_metadata=True
+                                preserve_metadata=False
                             )
                             
                             # Save the optimized WebP image
@@ -387,7 +387,7 @@ class ModelRouteUtils:
                     target_width=CARD_PREVIEW_WIDTH,
                     format='webp',
                     quality=85,
-                    preserve_metadata=True
+                    preserve_metadata=False
                 )
                 extension = '.webp'  # Use .webp without .preview part
             

py/utils/usage_stats.py

@@ -0,0 +1,273 @@
+import os
+import json
+import sys
+import time
+import asyncio
+import logging
+from typing import Dict, Set
+
+from ..config import config
+from ..services.service_registry import ServiceRegistry
+
+# Check if running in standalone mode
+standalone_mode = 'nodes' not in sys.modules
+
+if not standalone_mode:
+    from ..metadata_collector.metadata_registry import MetadataRegistry
+    from ..metadata_collector.constants import MODELS, LORAS
+
+logger = logging.getLogger(__name__)
+
+class UsageStats:
+    """Track usage statistics for models and save to JSON"""
+    
+    _instance = None
+    _lock = asyncio.Lock()  # For thread safety
+    
+    # Default stats file name
+    STATS_FILENAME = "lora_manager_stats.json"
+    
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._initialized = False
+        return cls._instance
+    
+    def __init__(self):
+        if self._initialized:
+            return
+            
+        # Initialize stats storage
+        self.stats = {
+            "checkpoints": {},  # sha256 -> count
+            "loras": {},        # sha256 -> count
+            "total_executions": 0,
+            "last_save_time": 0
+        }
+        
+        # Queue for prompt_ids to process
+        self.pending_prompt_ids = set()
+        
+        # Load existing stats if available
+        self._stats_file_path = self._get_stats_file_path()
+        self._load_stats()
+        
+        # Save interval in seconds
+        self.save_interval = 90  # 1.5 minutes
+        
+        # Start background task to process queued prompt_ids
+        self._bg_task = asyncio.create_task(self._background_processor())
+        
+        self._initialized = True
+        logger.info("Usage statistics tracker initialized")
+    
+    def _get_stats_file_path(self) -> str:
+        """Get the path to the stats JSON file"""
+        if not config.loras_roots or len(config.loras_roots) == 0:
+            # Fallback to temporary directory if no lora roots
+            return os.path.join(config.temp_directory, self.STATS_FILENAME)
+        
+        # Use the first lora root
+        return os.path.join(config.loras_roots[0], self.STATS_FILENAME)
+    
+    def _load_stats(self):
+        """Load existing statistics from file"""
+        try:
+            if os.path.exists(self._stats_file_path):
+                with open(self._stats_file_path, 'r', encoding='utf-8') as f:
+                    loaded_stats = json.load(f)
+                
+                # Update our stats with loaded data
+                if isinstance(loaded_stats, dict):
+                    # Update individual sections to maintain structure
+                    if "checkpoints" in loaded_stats and isinstance(loaded_stats["checkpoints"], dict):
+                        self.stats["checkpoints"] = loaded_stats["checkpoints"]
+                    
+                    if "loras" in loaded_stats and isinstance(loaded_stats["loras"], dict):
+                        self.stats["loras"] = loaded_stats["loras"]
+                    
+                    if "total_executions" in loaded_stats:
+                        self.stats["total_executions"] = loaded_stats["total_executions"]
+                    
+                logger.info(f"Loaded usage statistics from {self._stats_file_path}")
+        except Exception as e:
+            logger.error(f"Error loading usage statistics: {e}")
+    
+    async def save_stats(self, force=False):
+        """Save statistics to file"""
+        try:
+            # Only save if it's been at least save_interval since last save or force is True
+            current_time = time.time()
+            if not force and (current_time - self.stats.get("last_save_time", 0)) < self.save_interval:
+                return False
+                
+            # Use a lock to prevent concurrent writes
+            async with self._lock:
+                # Update last save time
+                self.stats["last_save_time"] = current_time
+                
+                # Create directory if it doesn't exist
+                os.makedirs(os.path.dirname(self._stats_file_path), exist_ok=True)
+                
+                # Write to a temporary file first, then move it to avoid corruption
+                temp_path = f"{self._stats_file_path}.tmp"
+                with open(temp_path, 'w', encoding='utf-8') as f:
+                    json.dump(self.stats, f, indent=2, ensure_ascii=False)
+                
+                # Replace the old file with the new one
+                os.replace(temp_path, self._stats_file_path)
+                
+                logger.debug(f"Saved usage statistics to {self._stats_file_path}")
+                return True
+        except Exception as e:
+            logger.error(f"Error saving usage statistics: {e}", exc_info=True)
+            return False
+    
+    def register_execution(self, prompt_id):
+        """Register a completed execution by prompt_id for later processing"""
+        if prompt_id:
+            self.pending_prompt_ids.add(prompt_id)
+    
+    async def _background_processor(self):
+        """Background task to process queued prompt_ids"""
+        try:
+            while True:
+                # Wait a short interval before checking for new prompt_ids
+                await asyncio.sleep(5)  # Check every 5 seconds
+                
+                # Process any pending prompt_ids
+                if self.pending_prompt_ids:
+                    async with self._lock:
+                        # Get a copy of the set and clear original
+                        prompt_ids = self.pending_prompt_ids.copy()
+                        self.pending_prompt_ids.clear()
+                    
+                    # Process each prompt_id
+                    registry = MetadataRegistry()
+                    for prompt_id in prompt_ids:
+                        try:
+                            metadata = registry.get_metadata(prompt_id)
+                            await self._process_metadata(metadata)
+                        except Exception as e:
+                            logger.error(f"Error processing prompt_id {prompt_id}: {e}")
+                
+                # Periodically save stats
+                await self.save_stats()
+        except asyncio.CancelledError:
+            # Task was cancelled, clean up
+            await self.save_stats(force=True)
+        except Exception as e:
+            logger.error(f"Error in background processing task: {e}", exc_info=True)
+            # Restart the task after a delay if it fails
+            asyncio.create_task(self._restart_background_task())
+    
+    async def _restart_background_task(self):
+        """Restart the background task after a delay"""
+        await asyncio.sleep(30)  # Wait 30 seconds before restarting
+        self._bg_task = asyncio.create_task(self._background_processor())
+    
+    async def _process_metadata(self, metadata):
+        """Process metadata from an execution"""
+        if not metadata or not isinstance(metadata, dict):
+            return
+            
+        # Increment total executions count
+        self.stats["total_executions"] += 1
+        
+        # Process checkpoints
+        if MODELS in metadata and isinstance(metadata[MODELS], dict):
+            await self._process_checkpoints(metadata[MODELS])
+        
+        # Process loras
+        if LORAS in metadata and isinstance(metadata[LORAS], dict):
+            await self._process_loras(metadata[LORAS])
+    
+    async def _process_checkpoints(self, models_data):
+        """Process checkpoint models from metadata"""
+        try:
+            # Get checkpoint scanner service
+            checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+            if not checkpoint_scanner:
+                logger.warning("Checkpoint scanner not available for usage tracking")
+                return
+            
+            for node_id, model_info in models_data.items():
+                if not isinstance(model_info, dict):
+                    continue
+                    
+                # Check if this is a checkpoint model
+                model_type = model_info.get("type")
+                if model_type == "checkpoint":
+                    model_name = model_info.get("name")
+                    if not model_name:
+                        continue
+                    
+                    # Clean up filename (remove extension if present)
+                    model_filename = os.path.splitext(os.path.basename(model_name))[0]
+                    
+                    # Get hash for this checkpoint
+                    model_hash = checkpoint_scanner.get_hash_by_filename(model_filename)
+                    if model_hash:
+                        # Update stats for this checkpoint
+                        self.stats["checkpoints"][model_hash] = self.stats["checkpoints"].get(model_hash, 0) + 1
+        except Exception as e:
+            logger.error(f"Error processing checkpoint usage: {e}", exc_info=True)
+    
+    async def _process_loras(self, loras_data):
+        """Process LoRA models from metadata"""
+        try:
+            # Get LoRA scanner service
+            lora_scanner = await ServiceRegistry.get_lora_scanner()
+            if not lora_scanner:
+                logger.warning("LoRA scanner not available for usage tracking")
+                return
+            
+            for node_id, lora_info in loras_data.items():
+                if not isinstance(lora_info, dict):
+                    continue
+                
+                # Get the list of LoRAs from standardized format
+                lora_list = lora_info.get("lora_list", [])
+                for lora in lora_list:
+                    if not isinstance(lora, dict):
+                        continue
+                        
+                    lora_name = lora.get("name")
+                    if not lora_name:
+                        continue
+                    
+                    # Get hash for this LoRA
+                    lora_hash = lora_scanner.get_hash_by_filename(lora_name)
+                    if lora_hash:
+                        # Update stats for this LoRA
+                        self.stats["loras"][lora_hash] = self.stats["loras"].get(lora_hash, 0) + 1
+        except Exception as e:
+            logger.error(f"Error processing LoRA usage: {e}", exc_info=True)
+    
+    async def get_stats(self):
+        """Get current usage statistics"""
+        return self.stats
+    
+    async def get_model_usage_count(self, model_type, sha256):
+        """Get usage count for a specific model by hash"""
+        if model_type == "checkpoint":
+            return self.stats["checkpoints"].get(sha256, 0)
+        elif model_type == "lora":
+            return self.stats["loras"].get(sha256, 0)
+        return 0
+    
+    async def process_execution(self, prompt_id):
+        """Process a prompt execution immediately (synchronous approach)"""
+        if not prompt_id:
+            return
+            
+        try:
+            # Process metadata for this prompt_id
+            registry = MetadataRegistry()
+            metadata = registry.get_metadata(prompt_id)
+            if metadata:
+                await self._process_metadata(metadata)
+                # Save stats if needed
+                await self.save_stats()
+        except Exception as e:
+            logger.error(f"Error processing prompt_id {prompt_id}: {e}", exc_info=True)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-lora-manager"
 description = "LoRA Manager for ComfyUI - Access it at http://localhost:8188/loras for managing LoRA models with previews and metadata integration."
-version = "0.8.6"
+version = "0.8.10"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",
@@ -12,7 +12,8 @@ dependencies = [
     "piexif",
     "Pillow",
     "olefile", # for getting rid of warning message
-    "requests"
+    "requests",
+    "toml"
 ]
 
 [project.urls]

requirements.txt

@@ -6,4 +6,7 @@ beautifulsoup4
 piexif
 Pillow
 olefile
-requests
+requests
+toml
+numpy
+torch

settings.json.example

@@ -0,0 +1,14 @@
+{
+  "civitai_api_key": "your_civitai_api_key_here",
+  "show_only_sfw": false,
+  "folder_paths": {
+    "loras": [
+      "C:/path/to/your/loras_folder",
+      "C:/path/to/another/loras_folder"
+    ],
+    "checkpoints": [
+      "C:/path/to/your/checkpoints_folder",
+      "C:/path/to/another/checkpoints_folder"
+    ]
+  }
+}

standalone.py

@@ -0,0 +1,347 @@
+import os
+import sys
+import json
+
+# Create mock folder_paths module BEFORE any other imports
+class MockFolderPaths:
+    @staticmethod
+    def get_folder_paths(folder_name):
+        # Load paths from settings.json
+        settings_path = os.path.join(os.path.dirname(__file__), 'settings.json')
+        try:
+            if os.path.exists(settings_path):
+                with open(settings_path, 'r', encoding='utf-8') as f:
+                    settings = json.load(f)
+                
+                # For diffusion_models, combine unet and diffusers paths
+                if folder_name == "diffusion_models":
+                    paths = []
+                    if 'folder_paths' in settings:
+                        if 'unet' in settings['folder_paths']:
+                            paths.extend(settings['folder_paths']['unet'])
+                        if 'diffusers' in settings['folder_paths']:
+                            paths.extend(settings['folder_paths']['diffusers'])
+                    # Filter out paths that don't exist
+                    valid_paths = [p for p in paths if os.path.exists(p)]
+                    if valid_paths:
+                        return valid_paths
+                    else:
+                        print(f"Warning: No valid paths found for {folder_name}")
+                # For other folder names, return their paths directly
+                elif 'folder_paths' in settings and folder_name in settings['folder_paths']:
+                    paths = settings['folder_paths'][folder_name]
+                    valid_paths = [p for p in paths if os.path.exists(p)]
+                    if valid_paths:
+                        return valid_paths
+                    else:
+                        print(f"Warning: No valid paths found for {folder_name}")
+        except Exception as e:
+            print(f"Error loading folder paths from settings: {e}")
+        
+        # Fallback to empty list if no paths found
+        return []
+    
+    @staticmethod
+    def get_temp_directory():
+        return os.path.join(os.path.dirname(__file__), 'temp')
+    
+    @staticmethod
+    def set_temp_directory(path):
+        os.makedirs(path, exist_ok=True)
+        return path
+
+# Create mock server module with PromptServer
+class MockPromptServer:
+    def __init__(self):
+        self.app = None
+        
+    def send_sync(self, *args, **kwargs):
+        pass
+
+# Create mock metadata_collector module
+class MockMetadataCollector:
+    def init(self):
+        pass
+    
+    def get_metadata(self, prompt_id=None):
+        return {}
+
+# Initialize basic mocks before any imports
+sys.modules['folder_paths'] = MockFolderPaths()
+sys.modules['server'] = type('server', (), {'PromptServer': MockPromptServer()})
+sys.modules['py.metadata_collector'] = MockMetadataCollector()
+
+# Now we can safely import modules that depend on folder_paths and server
+import argparse
+import asyncio
+import logging
+from aiohttp import web
+
+# Setup logging
+logging.basicConfig(level=logging.INFO, 
+                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger("lora-manager-standalone")
+
+# Configure aiohttp access logger to be less verbose
+logging.getLogger('aiohttp.access').setLevel(logging.WARNING)
+
+# Now we can import the global config from our local modules
+from py.config import config
+
+class StandaloneServer:
+    """Server implementation for standalone mode"""
+    
+    def __init__(self):
+        self.app = web.Application(logger=logger)
+        self.instance = self  # Make it compatible with PromptServer.instance pattern
+        
+        # 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"""
+        # Create placeholders for compatibility with ComfyUI's implementation
+        self.last_prompt_id = None
+        self.last_node_id = None
+        self.client_id = None
+        
+        # Set up routes
+        self.setup_routes()
+        
+        # Add startup and shutdown handlers
+        self.app.on_startup.append(self.on_startup)
+        self.app.on_shutdown.append(self.on_shutdown)
+    
+    def setup_routes(self):
+        """Set up basic routes"""
+        # Add a simple status endpoint
+        self.app.router.add_get('/', self.handle_status)
+    
+    async def handle_status(self, request):
+        """Handle status request by redirecting to loras page"""
+        # Redirect to loras page instead of showing status
+        raise web.HTTPFound('/loras')
+        
+        # Original JSON response (commented out)
+        # return web.json_response({
+        #     "status": "running",
+        #     "mode": "standalone",
+        #     "loras_roots": config.loras_roots,
+        #     "checkpoints_roots": config.checkpoints_roots
+        # })
+    
+    async def on_startup(self, app):
+        """Startup handler"""
+        logger.info("LoRA Manager standalone server starting...")
+    
+    async def on_shutdown(self, app):
+        """Shutdown handler"""
+        logger.info("LoRA Manager standalone server shutting down...")
+    
+    def send_sync(self, event_type, data, sid=None):
+        """Stub for compatibility with PromptServer"""
+        # In standalone mode, we don't have the same websocket system
+        pass
+    
+    async def start(self, host='127.0.0.1', port=8188):
+        """Start the server"""
+        runner = web.AppRunner(self.app)
+        await runner.setup()
+        site = web.TCPSite(runner, host, port)
+        await site.start()
+
+        # Log the server address with a clickable localhost URL regardless of the actual binding
+        logger.info(f"Server started at http://127.0.0.1:{port}")
+        
+        # Keep the server running
+        while True:
+            await asyncio.sleep(3600)  # Sleep for a long time
+    
+    async def publish_loop(self):
+        """Stub for compatibility with PromptServer"""
+        # This method exists in ComfyUI's server but we don't need it
+        pass
+
+# After all mocks are in place, import LoraManager
+from py.lora_manager import LoraManager
+
+class StandaloneLoraManager(LoraManager):
+    """Extended LoraManager for standalone mode"""
+    
+    @classmethod
+    def add_routes(cls, server_instance):
+        """Initialize and register all routes for standalone mode"""
+        app = server_instance.app
+        
+        # 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
+        
+        # Add static routes for each lora root
+        for idx, root in enumerate(config.loras_roots, start=1):
+            if not os.path.exists(root):
+                logger.warning(f"Lora root path does not exist: {root}")
+                continue
+                
+            preview_path = f'/loras_static/root{idx}/preview'
+            
+            # Check if this root is a link path in the mappings
+            real_root = root
+            for target, link in config._path_mappings.items():
+                if os.path.normpath(link) == os.path.normpath(root):
+                    # If so, route should point to the target (real path)
+                    real_root = target
+                    break
+            
+            # Normalize and standardize path display for consistency
+            display_root = real_root.replace('\\', '/')
+            
+            # Add static route for original path - use the normalized path
+            app.router.add_static(preview_path, real_root)
+            logger.info(f"Added static route {preview_path} -> {display_root}")
+            
+            # Record route mapping with normalized path
+            config.add_route_mapping(real_root, preview_path)
+            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):
+            if not os.path.exists(root):
+                logger.warning(f"Checkpoint root path does not exist: {root}")
+                continue
+                
+            preview_path = f'/checkpoints_static/root{idx}/preview'
+            
+            # Check if this root is a link path in the mappings
+            real_root = root
+            for target, link in config._path_mappings.items():
+                if os.path.normpath(link) == os.path.normpath(root):
+                    # If so, route should point to the target (real path)
+                    real_root = target
+                    break
+            
+            # Normalize and standardize path display for consistency
+            display_root = real_root.replace('\\', '/')
+            
+            # Add static route for original path
+            app.router.add_static(preview_path, real_root)
+            logger.info(f"Added static route {preview_path} -> {display_root}")
+            
+            # Record route mapping
+            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
+        }
+        
+        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)
+                
+                if is_checkpoint:
+                    route_path = f'/checkpoints_static/link_{link_idx["checkpoint"]}/preview'
+                    link_idx["checkpoint"] += 1
+                else:
+                    route_path = f'/loras_static/link_{link_idx["lora"]}/preview'
+                    link_idx["lora"] += 1
+                
+                # Display path with forward slashes for consistency
+                display_target = target_path.replace('\\', '/')
+                
+                app.router.add_static(route_path, target_path)
+                logger.info(f"Added static route for link target {route_path} -> {display_target}")
+                config.add_route_mapping(target_path, route_path)
+                added_targets.add(norm_target)
+        
+        # Add static route for plugin assets
+        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.routes.recipe_routes import RecipeRoutes
+        from py.routes.checkpoints_routes import CheckpointsRoutes
+        from py.routes.update_routes import UpdateRoutes
+        from py.routes.usage_stats_routes import UsageStatsRoutes
+        
+        lora_routes = LoraRoutes()
+        checkpoints_routes = CheckpointsRoutes()
+        
+        # Initialize routes
+        lora_routes.setup_routes(app)
+        checkpoints_routes.setup_routes(app)
+        ApiRoutes.setup_routes(app)
+        RecipeRoutes.setup_routes(app)
+        UpdateRoutes.setup_routes(app)
+        UsageStatsRoutes.setup_routes(app)
+        
+        # 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"""
+    parser = argparse.ArgumentParser(description="LoRA Manager Standalone Server")
+    parser.add_argument("--host", type=str, default="0.0.0.0", 
+                        help="Host address to bind the server to (default: 0.0.0.0)")
+    parser.add_argument("--port", type=int, default=8188,
+                        help="Port to bind the server to (default: 8188, access via http://localhost:8188/loras)")
+    # parser.add_argument("--loras", type=str, nargs="+", 
+    #                     help="Additional paths to LoRA model directories (optional if settings.json has paths)")
+    # parser.add_argument("--checkpoints", type=str, nargs="+",
+    #                     help="Additional paths to checkpoint model directories (optional if settings.json has paths)")
+    parser.add_argument("--log-level", type=str, default="INFO",
+                        choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
+                        help="Logging level")
+    return parser.parse_args()
+
+async def main():
+    """Main entry point for standalone mode"""
+    args = parse_args()
+    
+    # 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()
+    
+    # Initialize routes via the standalone lora manager
+    StandaloneLoraManager.add_routes(server)
+    
+    # Set up and start the server
+    await server.setup()
+    await server.start(host=args.host, port=args.port)
+
+if __name__ == "__main__":
+    try:
+        # Run the main function
+        asyncio.run(main())
+    except KeyboardInterrupt:
+        logger.info("Server stopped by user")

static/css/base.css

@@ -59,6 +59,16 @@ html, body {
     --scrollbar-width: 8px; /* 添加滚动条宽度变量 */
 }
 
+html[data-theme="dark"] {
+    background-color: #1a1a1a !important;
+    color-scheme: dark;
+}
+
+html[data-theme="light"] {
+    background-color: #ffffff !important;
+    color-scheme: light;
+}
+
 [data-theme="dark"] {
     --bg-color: #1a1a1a;
     --text-color: #e0e0e0;

static/css/components/card.css

@@ -192,12 +192,43 @@
     margin-left: var(--space-1);
     cursor: pointer;
     color: white;
-    transition: opacity 0.2s;
-    font-size: 0.9em;
+    transition: opacity 0.2s, transform 0.15s ease;
+    font-size: 1.0em; /* Increased from 0.9em for better visibility */
+    width: 16px; /* Fixed width for consistent spacing */
+    height: 16px; /* Fixed height for larger touch target */
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    border-radius: 50%;
+    padding: 4px; /* Add padding to increase clickable area */
+    box-sizing: content-box; /* Ensure padding adds to dimensions */
+    position: relative; /* For proper positioning */
+    margin: 0; /* Reset margin */
+}
+
+.card-actions i::before {
+    position: absolute; /* Position the icon glyph */
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%); /* Center the icon */
+}
+
+.card-actions {
+    display: flex;
+    gap: var(--space-1); /* Use gap instead of margin for spacing between icons */
+    align-items: center;
 }
 
 .card-actions i:hover {
-    opacity: 0.8;
+    opacity: 0.9;
+    transform: scale(1.1);
+    background-color: rgba(255, 255, 255, 0.1);
+}
+
+/* Style for active favorites */
+.favorite-active {
+    color: #ffc107 !important; /* Gold color for favorites */
+    text-shadow: 0 0 5px rgba(255, 193, 7, 0.5);
 }
 
 /* 响应式设计 */

static/css/layout.css

@@ -81,6 +81,22 @@
     opacity: 1;
 }
 
+/* Controls */
+.control-group button.favorite-filter {
+    position: relative;
+    overflow: hidden;
+}
+
+.control-group button.favorite-filter.active {
+    background: var(--lora-accent);
+    color: white;
+}
+
+.control-group button.favorite-filter i {
+    margin-right: 4px;
+    color: #ffc107;
+}
+
 /* Active state for buttons that can be toggled */
 .control-group button.active {
     background: var(--lora-accent);

static/js/api/baseModelApi.js

@@ -2,7 +2,7 @@
 import { state, getCurrentPageState } from '../state/index.js';
 import { showToast } from '../utils/uiHelpers.js';
 import { showDeleteModal, confirmDelete } from '../utils/modalUtils.js';
-import { getSessionItem } from '../utils/storageHelpers.js';
+import { getSessionItem, saveMapToStorage } from '../utils/storageHelpers.js';
 
 /**
  * Shared functionality for handling models (loras and checkpoints)
@@ -45,6 +45,11 @@ export async function loadMoreModels(options = {}) {
             params.append('folder', pageState.activeFolder);
         }
 
+        // Add favorites filter parameter if enabled
+        if (pageState.showFavoritesOnly) {
+            params.append('favorites_only', 'true');
+        }
+
         // Add search parameters if there's a search term
         if (pageState.filters?.search) {
             params.append('search', pageState.filters.search);
@@ -424,12 +429,20 @@ async function uploadPreview(filePath, file, modelType = 'lora') {
             const previewContainer = card.querySelector('.card-preview');
             const oldPreview = previewContainer.querySelector('img, video');
             
-            // For LoRA models, use timestamp to prevent caching
-            if (modelType === 'lora') {
-                state.previewVersions?.set(filePath, Date.now());
+            // Get the current page's previewVersions Map based on model type
+            const pageType = modelType === 'checkpoint' ? 'checkpoints' : 'loras';
+            const previewVersions = state.pages[pageType].previewVersions;
+            
+            // Update the version timestamp
+            const timestamp = Date.now();
+            if (previewVersions) {
+                previewVersions.set(filePath, timestamp);
+                
+                // Save the updated Map to localStorage
+                const storageKey = modelType === 'checkpoint' ? 'checkpoint_preview_versions' : 'lora_preview_versions';
+                saveMapToStorage(storageKey, previewVersions);
             }
             
-            const timestamp = Date.now();
             const previewUrl = data.preview_url ? 
                 `${data.preview_url}?t=${timestamp}` : 
                 `/api/model/preview_image?path=${encodeURIComponent(filePath)}&t=${timestamp}`;

static/js/api/checkpointApi.js

@@ -5,7 +5,8 @@ import {
     refreshModels as baseRefreshModels,
     deleteModel as baseDeleteModel,
     replaceModelPreview,
-    fetchCivitaiMetadata
+    fetchCivitaiMetadata,
+    refreshSingleModelMetadata
 } from './baseModelApi.js';
 
 // Load more checkpoints with pagination
@@ -54,4 +55,34 @@ export async function fetchCivitai() {
         fetchEndpoint: '/api/checkpoints/fetch-all-civitai',
         resetAndReloadFunction: resetAndReload
     });
+}
+
+// Refresh single checkpoint metadata
+export async function refreshSingleCheckpointMetadata(filePath) {
+    return 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) {
+    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');
+    }
+    
+    return response.json();
 }

static/js/api/loraApi.js

@@ -9,6 +9,31 @@ import {
     refreshSingleModelMetadata
 } from './baseModelApi.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) {
+    const response = await fetch('/api/loras/save-metadata', {
+        method: 'POST',
+        headers: {
+            'Content-Type': 'application/json',
+        },
+        body: JSON.stringify({
+            file_path: filePath,
+            ...data
+        })
+    });
+
+    if (!response.ok) {
+        throw new Error('Failed to save metadata');
+    }
+    
+    return response.json();
+}
+
 export async function loadMoreLoras(resetPage = false, updateFolders = false) {
     return loadMoreModels({
         resetPage,

static/js/checkpoints.js

@@ -4,6 +4,7 @@ import { confirmDelete, closeDeleteModal } 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';
 
 // Initialize the Checkpoints page
 class CheckpointsPageManager {
@@ -34,6 +35,9 @@ class CheckpointsPageManager {
         this.pageControls.restoreFolderFilter();
         this.pageControls.initFolderTagsVisibility();
         
+        // Initialize context menu
+        new CheckpointContextMenu();
+        
         // Initialize infinite scroll
         initializeInfiniteScroll('checkpoints');
         

static/js/components/CheckpointCard.js

@@ -1,8 +1,8 @@
-import { showToast } from '../utils/uiHelpers.js';
+import { showToast, copyToClipboard } 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 } from '../api/checkpointApi.js';
+import { replaceCheckpointPreview as apiReplaceCheckpointPreview, saveModelMetadata } from '../api/checkpointApi.js';
 
 export function createCheckpointCard(checkpoint) {
     const card = document.createElement('div');
@@ -17,6 +17,7 @@ export function createCheckpointCard(checkpoint) {
     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) {
@@ -44,7 +45,10 @@ export function createCheckpointCard(checkpoint) {
 
     // Determine preview URL
     const previewUrl = checkpoint.preview_url || '/loras_static/images/no-preview.png';
-    const version = state.previewVersions ? state.previewVersions.get(checkpoint.file_path) : null;
+    
+    // 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
@@ -62,6 +66,9 @@ export function createCheckpointCard(checkpoint) {
     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 ? 
@@ -79,6 +86,9 @@ export function createCheckpointCard(checkpoint) {
                     ${checkpoint.base_model}
                 </span>
                 <div class="card-actions">
+                    <i class="${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"' : ''}>
@@ -195,27 +205,46 @@ export function createCheckpointCard(checkpoint) {
         });
     }
 
+    // Favorite button click event
+    card.querySelector('.fa-star')?.addEventListener('click', async e => {
+        e.stopPropagation();
+        const starIcon = e.currentTarget;
+        const isFavorite = starIcon.classList.contains('fas');
+        const newFavoriteState = !isFavorite;
+        
+        try {
+            // Save the new favorite state to the server
+            await saveModelMetadata(card.dataset.filepath, { 
+                favorite: newFavoriteState 
+            });
+
+            // Update the UI
+            if (newFavoriteState) {
+                starIcon.classList.remove('far');
+                starIcon.classList.add('fas', 'favorite-active');
+                starIcon.title = 'Remove from favorites';
+                card.dataset.favorite = 'true';
+                showToast('Added to favorites', 'success');
+            } else {
+                starIcon.classList.remove('fas', 'favorite-active');
+                starIcon.classList.add('far');
+                starIcon.title = 'Add to favorites';
+                card.dataset.favorite = 'false';
+                showToast('Removed from favorites', 'success');
+            }
+        } catch (error) {
+            console.error('Failed to update favorite status:', error);
+            showToast('Failed to update favorite status', 'error');
+        }
+    });
+
     // Copy button click event
     card.querySelector('.fa-copy')?.addEventListener('click', async e => {
         e.stopPropagation();
         const checkpointName = card.dataset.file_name;
         
         try {
-            // Modern clipboard API
-            if (navigator.clipboard && window.isSecureContext) {
-                await navigator.clipboard.writeText(checkpointName);
-            } else {
-                // Fallback for older browsers
-                const textarea = document.createElement('textarea');
-                textarea.value = checkpointName;
-                textarea.style.position = 'absolute';
-                textarea.style.left = '-99999px';
-                document.body.appendChild(textarea);
-                textarea.select();
-                document.execCommand('copy');
-                document.body.removeChild(textarea);
-            }
-            showToast('Checkpoint name copied', 'success');
+            await copyToClipboard(checkpointName, 'Checkpoint name copied');
         } catch (err) {
             console.error('Copy failed:', err);
             showToast('Copy failed', 'error');

static/js/components/ContextMenu.js

@@ -366,4 +366,7 @@ export class LoraContextMenu {
         this.menu.style.display = 'none';
         this.currentCard = null;
     }
-}
+}
+
+// For backward compatibility, re-export the LoraContextMenu class
+// export { LoraContextMenu } from './ContextMenu/LoraContextMenu.js';

static/js/components/ContextMenu/BaseContextMenu.js

@@ -0,0 +1,84 @@
+export class BaseContextMenu {
+    constructor(menuId, cardSelector) {
+        this.menu = document.getElementById(menuId);
+        this.cardSelector = cardSelector;
+        this.currentCard = null;
+        
+        if (!this.menu) {
+            console.error(`Context menu element with ID ${menuId} not found`);
+            return;
+        }
+        
+        this.init();
+    }
+
+    init() {
+        // Hide menu on regular clicks
+        document.addEventListener('click', () => this.hideMenu());
+        
+        // Show menu on right-click on cards
+        document.addEventListener('contextmenu', (e) => {
+            const card = e.target.closest(this.cardSelector);
+            if (!card) {
+                this.hideMenu();
+                return;
+            }
+            e.preventDefault();
+            this.showMenu(e.clientX, e.clientY, card);
+        });
+
+        // Handle menu item clicks
+        this.menu.addEventListener('click', (e) => {
+            const menuItem = e.target.closest('.context-menu-item');
+            if (!menuItem || !this.currentCard) return;
+
+            const action = menuItem.dataset.action;
+            if (!action) return;
+            
+            this.handleMenuAction(action, menuItem);
+            this.hideMenu();
+        });
+    }
+    
+    handleMenuAction(action, menuItem) {
+        // Override in subclass
+        console.warn('handleMenuAction not implemented');
+    }
+
+    showMenu(x, y, card) {
+        this.currentCard = card;
+        this.menu.style.display = 'block';
+
+        // Get menu dimensions
+        const menuRect = this.menu.getBoundingClientRect();
+        
+        // Get viewport dimensions
+        const viewportWidth = document.documentElement.clientWidth;
+        const viewportHeight = document.documentElement.clientHeight;
+        
+        // Calculate position
+        let finalX = x;
+        let finalY = y;
+        
+        // Ensure menu doesn't go offscreen right
+        if (x + menuRect.width > viewportWidth) {
+            finalX = x - menuRect.width;
+        }
+        
+        // Ensure menu doesn't go offscreen bottom
+        if (y + menuRect.height > viewportHeight) {
+            finalY = y - menuRect.height;
+        }
+        
+        // Position menu
+        this.menu.style.left = `${finalX}px`;
+        this.menu.style.top = `${finalY}px`;
+    }
+
+    hideMenu() {
+        if (this.menu) {
+            this.menu.style.display = 'none';
+        }
+        this.currentCard = null;
+    }
+}

static/js/components/ContextMenu/CheckpointContextMenu.js

@@ -0,0 +1,315 @@
+import { BaseContextMenu } from './BaseContextMenu.js';
+import { refreshSingleCheckpointMetadata, saveModelMetadata } from '../../api/checkpointApi.js';
+import { showToast, getNSFWLevelName } from '../../utils/uiHelpers.js';
+import { NSFW_LEVELS } from '../../utils/constants.js';
+import { getStorageItem } from '../../utils/storageHelpers.js';
+
+export class CheckpointContextMenu extends BaseContextMenu {
+    constructor() {
+        super('checkpointContextMenu', '.lora-card');
+        this.nsfwSelector = document.getElementById('nsfwLevelSelector');
+        
+        // Initialize NSFW Level Selector events
+        if (this.nsfwSelector) {
+            this.initNSFWSelector();
+        }
+    }
+    
+    handleMenuAction(action) {
+        switch(action) {
+            case 'details':
+                // Show checkpoint details
+                this.currentCard.click();
+                break;
+            case 'preview':
+                // Replace checkpoint preview
+                if (this.currentCard.querySelector('.fa-image')) {
+                    this.currentCard.querySelector('.fa-image').click();
+                }
+                break;
+            case 'civitai':
+                // Open civitai page
+                if (this.currentCard.dataset.from_civitai === 'true') {
+                    if (this.currentCard.querySelector('.fa-globe')) {
+                        this.currentCard.querySelector('.fa-globe').click();
+                    }
+                } else {
+                    showToast('No CivitAI information available', 'info');
+                }
+                break;
+            case 'delete':
+                // Delete checkpoint
+                if (this.currentCard.querySelector('.fa-trash')) {
+                    this.currentCard.querySelector('.fa-trash').click();
+                }
+                break;
+            case 'copyname':
+                // Copy checkpoint name
+                if (this.currentCard.querySelector('.fa-copy')) {
+                    this.currentCard.querySelector('.fa-copy').click();
+                }
+                break;
+            case 'refresh-metadata':
+                // Refresh metadata from CivitAI
+                refreshSingleCheckpointMetadata(this.currentCard.dataset.filepath);
+                break;
+            case 'set-nsfw':
+                // Set NSFW level
+                this.showNSFWLevelSelector(null, null, this.currentCard);
+                break;
+            case 'move':
+                // Move to folder (placeholder)
+                showToast('Move to folder feature coming soon', 'info');
+                break;
+        }
+    }
+
+    // NSFW Selector methods
+    initNSFWSelector() {
+        // Close button
+        const closeBtn = this.nsfwSelector.querySelector('.close-nsfw-selector');
+        closeBtn.addEventListener('click', () => {
+            this.nsfwSelector.style.display = 'none';
+        });
+
+        // Level buttons
+        const levelButtons = this.nsfwSelector.querySelectorAll('.nsfw-level-btn');
+        levelButtons.forEach(btn => {
+            btn.addEventListener('click', async () => {
+                const level = parseInt(btn.dataset.level);
+                const filePath = this.nsfwSelector.dataset.cardPath;
+                
+                if (!filePath) return;
+                
+                try {
+                    await saveModelMetadata(filePath, { preview_nsfw_level: level });
+                    
+                    // Update card data
+                    const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
+                    if (card) {
+                        let metaData = {};
+                        try {
+                            metaData = JSON.parse(card.dataset.meta || '{}');
+                        } catch (err) {
+                            console.error('Error parsing metadata:', err);
+                        }
+                        
+                        metaData.preview_nsfw_level = level;
+                        card.dataset.meta = JSON.stringify(metaData);
+                        card.dataset.nsfwLevel = level.toString();
+                        
+                        // Apply blur effect immediately
+                        this.updateCardBlurEffect(card, level);
+                    }
+                    
+                    showToast(`Content rating set to ${getNSFWLevelName(level)}`, 'success');
+                    this.nsfwSelector.style.display = 'none';
+                } catch (error) {
+                    showToast(`Failed to set content rating: ${error.message}`, 'error');
+                }
+            });
+        });
+        
+        // Close when clicking outside
+        document.addEventListener('click', (e) => {
+            if (this.nsfwSelector.style.display === 'block' && 
+                !this.nsfwSelector.contains(e.target) && 
+                !e.target.closest('.context-menu-item[data-action="set-nsfw"]')) {
+                this.nsfwSelector.style.display = 'none';
+            }
+        });
+    }
+
+    updateCardBlurEffect(card, level) {
+        // Get user settings for blur threshold
+        const blurThreshold = parseInt(getStorageItem('nsfwBlurLevel') || '4');
+        
+        // Get card preview container
+        const previewContainer = card.querySelector('.card-preview');
+        if (!previewContainer) return;
+        
+        // Get preview media element
+        const previewMedia = previewContainer.querySelector('img') || previewContainer.querySelector('video');
+        if (!previewMedia) return;
+        
+        // Check if blur should be applied
+        if (level >= blurThreshold) {
+            // Add blur class to the preview container
+            previewContainer.classList.add('blurred');
+            
+            // Get or create the NSFW overlay
+            let nsfwOverlay = previewContainer.querySelector('.nsfw-overlay');
+            if (!nsfwOverlay) {
+                // Create new overlay
+                nsfwOverlay = document.createElement('div');
+                nsfwOverlay.className = 'nsfw-overlay';
+                
+                // Create and configure the warning content
+                const warningContent = document.createElement('div');
+                warningContent.className = 'nsfw-warning';
+                
+                // Determine NSFW warning text based on level
+                let nsfwText = "Mature Content";
+                if (level >= NSFW_LEVELS.XXX) {
+                    nsfwText = "XXX-rated Content";
+                } else if (level >= NSFW_LEVELS.X) {
+                    nsfwText = "X-rated Content";
+                } else if (level >= NSFW_LEVELS.R) {
+                    nsfwText = "R-rated Content";
+                }
+                
+                // Add warning text and show button
+                warningContent.innerHTML = `
+                    <p>${nsfwText}</p>
+                    <button class="show-content-btn">Show</button>
+                `;
+                
+                // Add click event to the show button
+                const showBtn = warningContent.querySelector('.show-content-btn');
+                showBtn.addEventListener('click', (e) => {
+                    e.stopPropagation();
+                    previewContainer.classList.remove('blurred');
+                    nsfwOverlay.style.display = 'none';
+                    
+                    // Update toggle button icon if it exists
+                    const toggleBtn = card.querySelector('.toggle-blur-btn');
+                    if (toggleBtn) {
+                        toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
+                    }
+                });
+                
+                nsfwOverlay.appendChild(warningContent);
+                previewContainer.appendChild(nsfwOverlay);
+            } else {
+                // Update existing overlay
+                const warningText = nsfwOverlay.querySelector('p');
+                if (warningText) {
+                    let nsfwText = "Mature Content";
+                    if (level >= NSFW_LEVELS.XXX) {
+                        nsfwText = "XXX-rated Content";
+                    } else if (level >= NSFW_LEVELS.X) {
+                        nsfwText = "X-rated Content";
+                    } else if (level >= NSFW_LEVELS.R) {
+                        nsfwText = "R-rated Content";
+                    }
+                    warningText.textContent = nsfwText;
+                }
+                nsfwOverlay.style.display = 'flex';
+            }
+            
+            // Get or create the toggle button in the header
+            const cardHeader = previewContainer.querySelector('.card-header');
+            if (cardHeader) {
+                let toggleBtn = cardHeader.querySelector('.toggle-blur-btn');
+                
+                if (!toggleBtn) {
+                    toggleBtn = document.createElement('button');
+                    toggleBtn.className = 'toggle-blur-btn';
+                    toggleBtn.title = 'Toggle blur';
+                    toggleBtn.innerHTML = '<i class="fas fa-eye"></i>';
+                    
+                    // Add click event to toggle button
+                    toggleBtn.addEventListener('click', (e) => {
+                        e.stopPropagation();
+                        const isBlurred = previewContainer.classList.toggle('blurred');
+                        const icon = toggleBtn.querySelector('i');
+                        
+                        // Update icon and overlay visibility
+                        if (isBlurred) {
+                            icon.className = 'fas fa-eye';
+                            nsfwOverlay.style.display = 'flex';
+                        } else {
+                            icon.className = 'fas fa-eye-slash';
+                            nsfwOverlay.style.display = 'none';
+                        }
+                    });
+                    
+                    // Add to the beginning of header
+                    cardHeader.insertBefore(toggleBtn, cardHeader.firstChild);
+                    
+                    // Update base model label class
+                    const baseModelLabel = cardHeader.querySelector('.base-model-label');
+                    if (baseModelLabel && !baseModelLabel.classList.contains('with-toggle')) {
+                        baseModelLabel.classList.add('with-toggle');
+                    }
+                } else {
+                    // Update existing toggle button
+                    toggleBtn.querySelector('i').className = 'fas fa-eye';
+                }
+            }
+        } else {
+            // Remove blur
+            previewContainer.classList.remove('blurred');
+            
+            // Hide overlay if it exists
+            const overlay = previewContainer.querySelector('.nsfw-overlay');
+            if (overlay) overlay.style.display = 'none';
+            
+            // Remove toggle button when content is set to PG or PG13
+            const cardHeader = previewContainer.querySelector('.card-header');
+            if (cardHeader) {
+                const toggleBtn = cardHeader.querySelector('.toggle-blur-btn');
+                if (toggleBtn) {
+                    // Remove the toggle button completely
+                    toggleBtn.remove();
+                    
+                    // Update base model label class if it exists
+                    const baseModelLabel = cardHeader.querySelector('.base-model-label');
+                    if (baseModelLabel && baseModelLabel.classList.contains('with-toggle')) {
+                        baseModelLabel.classList.remove('with-toggle');
+                    }
+                }
+            }
+        }
+    }
+
+    showNSFWLevelSelector(x, y, card) {
+        const selector = document.getElementById('nsfwLevelSelector');
+        const currentLevelEl = document.getElementById('currentNSFWLevel');
+        
+        // Get current NSFW level
+        let currentLevel = 0;
+        try {
+            const metaData = JSON.parse(card.dataset.meta || '{}');
+            currentLevel = metaData.preview_nsfw_level || 0;
+            
+            // Update if we have no recorded level but have a dataset attribute
+            if (!currentLevel && card.dataset.nsfwLevel) {
+                currentLevel = parseInt(card.dataset.nsfwLevel) || 0;
+            }
+        } catch (err) {
+            console.error('Error parsing metadata:', err);
+        }
+        
+        currentLevelEl.textContent = getNSFWLevelName(currentLevel);
+        
+        // Position the selector
+        if (x && y) {
+            const viewportWidth = document.documentElement.clientWidth;
+            const viewportHeight = document.documentElement.clientHeight;
+            const selectorRect = selector.getBoundingClientRect();
+            
+            // Center the selector if no coordinates provided
+            let finalX = (viewportWidth - selectorRect.width) / 2;
+            let finalY = (viewportHeight - selectorRect.height) / 2;
+            
+            selector.style.left = `${finalX}px`;
+            selector.style.top = `${finalY}px`;
+        }
+        
+        // Highlight current level button
+        document.querySelectorAll('.nsfw-level-btn').forEach(btn => {
+            if (parseInt(btn.dataset.level) === currentLevel) {
+                btn.classList.add('active');
+            } else {
+                btn.classList.remove('active');
+            }
+        });
+        
+        // Store reference to current card
+        selector.dataset.cardPath = card.dataset.filepath;
+        
+        // Show selector
+        selector.style.display = 'block';
+    }
+}

static/js/components/ContextMenu/LoraContextMenu.js

@@ -0,0 +1,309 @@
+import { BaseContextMenu } from './BaseContextMenu.js';
+import { refreshSingleLoraMetadata, saveModelMetadata } from '../../api/loraApi.js';
+import { showToast, getNSFWLevelName } from '../../utils/uiHelpers.js';
+import { NSFW_LEVELS } from '../../utils/constants.js';
+import { getStorageItem } from '../../utils/storageHelpers.js';
+
+export class LoraContextMenu extends BaseContextMenu {
+    constructor() {
+        super('loraContextMenu', '.lora-card');
+        this.nsfwSelector = document.getElementById('nsfwLevelSelector');
+        
+        // Initialize NSFW Level Selector events
+        if (this.nsfwSelector) {
+            this.initNSFWSelector();
+        }
+    }
+
+    handleMenuAction(action, menuItem) {
+        switch(action) {
+            case 'detail':
+                // Trigger the main card click which shows the modal
+                this.currentCard.click();
+                break;
+            case 'civitai':
+                // Only trigger if the card is from civitai
+                if (this.currentCard.dataset.from_civitai === 'true') {
+                    if (this.currentCard.dataset.meta === '{}') {
+                        showToast('Please fetch metadata from CivitAI first', 'info');
+                    } else {
+                        this.currentCard.querySelector('.fa-globe')?.click();
+                    }
+                } else {
+                    showToast('No CivitAI information available', 'info');
+                }
+                break;
+            case 'copyname':
+                this.currentCard.querySelector('.fa-copy')?.click();
+                break;
+            case 'preview':
+                this.currentCard.querySelector('.fa-image')?.click();
+                break;
+            case 'delete':
+                this.currentCard.querySelector('.fa-trash')?.click();
+                break;
+            case 'move':
+                moveManager.showMoveModal(this.currentCard.dataset.filepath);
+                break;
+            case 'refresh-metadata':
+                refreshSingleLoraMetadata(this.currentCard.dataset.filepath);
+                break;
+            case 'set-nsfw':
+                this.showNSFWLevelSelector(null, null, this.currentCard);
+                break;
+        }
+    }
+
+    // NSFW Selector methods from the original context menu
+    initNSFWSelector() {
+        // Close button
+        const closeBtn = this.nsfwSelector.querySelector('.close-nsfw-selector');
+        closeBtn.addEventListener('click', () => {
+            this.nsfwSelector.style.display = 'none';
+        });
+
+        // Level buttons
+        const levelButtons = this.nsfwSelector.querySelectorAll('.nsfw-level-btn');
+        levelButtons.forEach(btn => {
+            btn.addEventListener('click', async () => {
+                const level = parseInt(btn.dataset.level);
+                const filePath = this.nsfwSelector.dataset.cardPath;
+                
+                if (!filePath) return;
+                
+                try {
+                    await this.saveModelMetadata(filePath, { preview_nsfw_level: level });
+                    
+                    // Update card data
+                    const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
+                    if (card) {
+                        let metaData = {};
+                        try {
+                            metaData = JSON.parse(card.dataset.meta || '{}');
+                        } catch (err) {
+                            console.error('Error parsing metadata:', err);
+                        }
+                        
+                        metaData.preview_nsfw_level = level;
+                        card.dataset.meta = JSON.stringify(metaData);
+                        card.dataset.nsfwLevel = level.toString();
+                        
+                        // Apply blur effect immediately
+                        this.updateCardBlurEffect(card, level);
+                    }
+                    
+                    showToast(`Content rating set to ${getNSFWLevelName(level)}`, 'success');
+                    this.nsfwSelector.style.display = 'none';
+                } catch (error) {
+                    showToast(`Failed to set content rating: ${error.message}`, 'error');
+                }
+            });
+        });
+        
+        // Close when clicking outside
+        document.addEventListener('click', (e) => {
+            if (this.nsfwSelector.style.display === 'block' && 
+                !this.nsfwSelector.contains(e.target) && 
+                !e.target.closest('.context-menu-item[data-action="set-nsfw"]')) {
+                this.nsfwSelector.style.display = 'none';
+            }
+        });
+    }
+
+    async saveModelMetadata(filePath, data) {
+        return saveModelMetadata(filePath, data);
+    }
+
+    updateCardBlurEffect(card, level) {
+        // Get user settings for blur threshold
+        const blurThreshold = parseInt(getStorageItem('nsfwBlurLevel') || '4');
+        
+        // Get card preview container
+        const previewContainer = card.querySelector('.card-preview');
+        if (!previewContainer) return;
+        
+        // Get preview media element
+        const previewMedia = previewContainer.querySelector('img') || previewContainer.querySelector('video');
+        if (!previewMedia) return;
+        
+        // Check if blur should be applied
+        if (level >= blurThreshold) {
+            // Add blur class to the preview container
+            previewContainer.classList.add('blurred');
+            
+            // Get or create the NSFW overlay
+            let nsfwOverlay = previewContainer.querySelector('.nsfw-overlay');
+            if (!nsfwOverlay) {
+                // Create new overlay
+                nsfwOverlay = document.createElement('div');
+                nsfwOverlay.className = 'nsfw-overlay';
+                
+                // Create and configure the warning content
+                const warningContent = document.createElement('div');
+                warningContent.className = 'nsfw-warning';
+                
+                // Determine NSFW warning text based on level
+                let nsfwText = "Mature Content";
+                if (level >= NSFW_LEVELS.XXX) {
+                    nsfwText = "XXX-rated Content";
+                } else if (level >= NSFW_LEVELS.X) {
+                    nsfwText = "X-rated Content";
+                } else if (level >= NSFW_LEVELS.R) {
+                    nsfwText = "R-rated Content";
+                }
+                
+                // Add warning text and show button
+                warningContent.innerHTML = `
+                    <p>${nsfwText}</p>
+                    <button class="show-content-btn">Show</button>
+                `;
+                
+                // Add click event to the show button
+                const showBtn = warningContent.querySelector('.show-content-btn');
+                showBtn.addEventListener('click', (e) => {
+                    e.stopPropagation();
+                    previewContainer.classList.remove('blurred');
+                    nsfwOverlay.style.display = 'none';
+                    
+                    // Update toggle button icon if it exists
+                    const toggleBtn = card.querySelector('.toggle-blur-btn');
+                    if (toggleBtn) {
+                        toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
+                    }
+                });
+                
+                nsfwOverlay.appendChild(warningContent);
+                previewContainer.appendChild(nsfwOverlay);
+            } else {
+                // Update existing overlay
+                const warningText = nsfwOverlay.querySelector('p');
+                if (warningText) {
+                    let nsfwText = "Mature Content";
+                    if (level >= NSFW_LEVELS.XXX) {
+                        nsfwText = "XXX-rated Content";
+                    } else if (level >= NSFW_LEVELS.X) {
+                        nsfwText = "X-rated Content";
+                    } else if (level >= NSFW_LEVELS.R) {
+                        nsfwText = "R-rated Content";
+                    }
+                    warningText.textContent = nsfwText;
+                }
+                nsfwOverlay.style.display = 'flex';
+            }
+            
+            // Get or create the toggle button in the header
+            const cardHeader = previewContainer.querySelector('.card-header');
+            if (cardHeader) {
+                let toggleBtn = cardHeader.querySelector('.toggle-blur-btn');
+                
+                if (!toggleBtn) {
+                    toggleBtn = document.createElement('button');
+                    toggleBtn.className = 'toggle-blur-btn';
+                    toggleBtn.title = 'Toggle blur';
+                    toggleBtn.innerHTML = '<i class="fas fa-eye"></i>';
+                    
+                    // Add click event to toggle button
+                    toggleBtn.addEventListener('click', (e) => {
+                        e.stopPropagation();
+                        const isBlurred = previewContainer.classList.toggle('blurred');
+                        const icon = toggleBtn.querySelector('i');
+                        
+                        // Update icon and overlay visibility
+                        if (isBlurred) {
+                            icon.className = 'fas fa-eye';
+                            nsfwOverlay.style.display = 'flex';
+                        } else {
+                            icon.className = 'fas fa-eye-slash';
+                            nsfwOverlay.style.display = 'none';
+                        }
+                    });
+                    
+                    // Add to the beginning of header
+                    cardHeader.insertBefore(toggleBtn, cardHeader.firstChild);
+                    
+                    // Update base model label class
+                    const baseModelLabel = cardHeader.querySelector('.base-model-label');
+                    if (baseModelLabel && !baseModelLabel.classList.contains('with-toggle')) {
+                        baseModelLabel.classList.add('with-toggle');
+                    }
+                } else {
+                    // Update existing toggle button
+                    toggleBtn.querySelector('i').className = 'fas fa-eye';
+                }
+            }
+        } else {
+            // Remove blur
+            previewContainer.classList.remove('blurred');
+            
+            // Hide overlay if it exists
+            const overlay = previewContainer.querySelector('.nsfw-overlay');
+            if (overlay) overlay.style.display = 'none';
+            
+            // Remove toggle button when content is set to PG or PG13
+            const cardHeader = previewContainer.querySelector('.card-header');
+            if (cardHeader) {
+                const toggleBtn = cardHeader.querySelector('.toggle-blur-btn');
+                if (toggleBtn) {
+                    // Remove the toggle button completely
+                    toggleBtn.remove();
+                    
+                    // Update base model label class if it exists
+                    const baseModelLabel = cardHeader.querySelector('.base-model-label');
+                    if (baseModelLabel && baseModelLabel.classList.contains('with-toggle')) {
+                        baseModelLabel.classList.remove('with-toggle');
+                    }
+                }
+            }
+        }
+    }
+
+    showNSFWLevelSelector(x, y, card) {
+        const selector = document.getElementById('nsfwLevelSelector');
+        const currentLevelEl = document.getElementById('currentNSFWLevel');
+        
+        // Get current NSFW level
+        let currentLevel = 0;
+        try {
+            const metaData = JSON.parse(card.dataset.meta || '{}');
+            currentLevel = metaData.preview_nsfw_level || 0;
+            
+            // Update if we have no recorded level but have a dataset attribute
+            if (!currentLevel && card.dataset.nsfwLevel) {
+                currentLevel = parseInt(card.dataset.nsfwLevel) || 0;
+            }
+        } catch (err) {
+            console.error('Error parsing metadata:', err);
+        }
+        
+        currentLevelEl.textContent = getNSFWLevelName(currentLevel);
+        
+        // Position the selector
+        if (x && y) {
+            const viewportWidth = document.documentElement.clientWidth;
+            const viewportHeight = document.documentElement.clientHeight;
+            const selectorRect = selector.getBoundingClientRect();
+            
+            // Center the selector if no coordinates provided
+            let finalX = (viewportWidth - selectorRect.width) / 2;
+            let finalY = (viewportHeight - selectorRect.height) / 2;
+            
+            selector.style.left = `${finalX}px`;
+            selector.style.top = `${finalY}px`;
+        }
+        
+        // Highlight current level button
+        document.querySelectorAll('.nsfw-level-btn').forEach(btn => {
+            if (parseInt(btn.dataset.level) === currentLevel) {
+                btn.classList.add('active');
+            } else {
+                btn.classList.remove('active');
+            }
+        });
+        
+        // Store reference to current card
+        selector.dataset.cardPath = card.dataset.filepath;
+        
+        // Show selector
+        selector.style.display = 'block';
+    }
+}

static/js/components/ContextMenu/RecipeContextMenu.js

@@ -0,0 +1,205 @@
+import { BaseContextMenu } from './BaseContextMenu.js';
+import { showToast } from '../../utils/uiHelpers.js';
+import { setSessionItem, removeSessionItem } from '../../utils/storageHelpers.js';
+import { state } from '../../state/index.js';
+
+export class RecipeContextMenu extends BaseContextMenu {
+    constructor() {
+        super('recipeContextMenu', '.lora-card');
+    }
+    
+    showMenu(x, y, card) {
+        // Call the parent method first to handle basic positioning
+        super.showMenu(x, y, card);
+        
+        // Get recipe data to check for missing LoRAs
+        const recipeId = card.dataset.id;
+        const missingLorasItem = this.menu.querySelector('.download-missing-item');
+        
+        if (recipeId && missingLorasItem) {
+            // Check if this card has missing LoRAs
+            const loraCountElement = card.querySelector('.lora-count');
+            const hasMissingLoras = loraCountElement && loraCountElement.classList.contains('missing');
+            
+            // Show/hide the download missing LoRAs option based on missing status
+            if (hasMissingLoras) {
+                missingLorasItem.style.display = 'flex';
+            } else {
+                missingLorasItem.style.display = 'none';
+            }
+        }
+    }
+    
+    handleMenuAction(action) {
+        const recipeId = this.currentCard.dataset.id;
+        
+        switch(action) {
+            case 'details':
+                // Show recipe details
+                this.currentCard.click();
+                break;
+            case 'copy':
+                // Copy recipe to clipboard
+                this.currentCard.querySelector('.fa-copy')?.click();
+                break;
+            case 'share':
+                // Share recipe
+                this.currentCard.querySelector('.fa-share-alt')?.click();
+                break;
+            case 'delete':
+                // Delete recipe
+                this.currentCard.querySelector('.fa-trash')?.click();
+                break;
+            case 'viewloras':
+                // View all LoRAs in the recipe
+                this.viewRecipeLoRAs(recipeId);
+                break;
+            case 'download-missing':
+                // Download missing LoRAs
+                this.downloadMissingLoRAs(recipeId);
+                break;
+        }
+    }
+    
+    // View all LoRAs in the recipe
+    viewRecipeLoRAs(recipeId) {
+        if (!recipeId) {
+            showToast('Cannot view LoRAs: Missing recipe ID', 'error');
+            return;
+        }
+        
+        // First get the recipe details to access its LoRAs
+        fetch(`/api/recipe/${recipeId}`)
+            .then(response => response.json())
+            .then(recipe => {
+                // Clear any previous filters first
+                removeSessionItem('recipe_to_lora_filterLoraHash');
+                removeSessionItem('recipe_to_lora_filterLoraHashes');
+                removeSessionItem('filterRecipeName');
+                removeSessionItem('viewLoraDetail');
+                
+                // Collect all hashes from the recipe's LoRAs
+                const loraHashes = recipe.loras
+                    .filter(lora => lora.hash)
+                    .map(lora => lora.hash.toLowerCase());
+                    
+                if (loraHashes.length > 0) {
+                    // Store the LoRA hashes and recipe name in session storage
+                    setSessionItem('recipe_to_lora_filterLoraHashes', JSON.stringify(loraHashes));
+                    setSessionItem('filterRecipeName', recipe.title);
+                    
+                    // Navigate to the LoRAs page
+                    window.location.href = '/loras';
+                } else {
+                    showToast('No LoRAs found in this recipe', 'info');
+                }
+            })
+            .catch(error => {
+                console.error('Error loading recipe LoRAs:', error);
+                showToast('Error loading recipe LoRAs: ' + error.message, 'error');
+            });
+    }
+    
+    // Download missing LoRAs
+    async downloadMissingLoRAs(recipeId) {
+        if (!recipeId) {
+            showToast('Cannot download LoRAs: Missing recipe ID', 'error');
+            return;
+        }
+        
+        try {
+            // First get the recipe details
+            const response = await fetch(`/api/recipe/${recipeId}`);
+            const recipe = await response.json();
+            
+            // Get missing LoRAs
+            const missingLoras = recipe.loras.filter(lora => !lora.inLibrary && !lora.isDeleted);
+            
+            if (missingLoras.length === 0) {
+                showToast('No missing LoRAs to download', 'info');
+                return;
+            }
+            
+            // Show loading toast
+            state.loadingManager.showSimpleLoading('Getting version info for missing LoRAs...');
+            
+            // Get version info for each missing LoRA
+            const missingLorasWithVersionInfoPromises = missingLoras.map(async lora => {
+                let endpoint;
+                
+                // Determine which endpoint to use based on available data
+                if (lora.modelVersionId) {
+                    endpoint = `/api/civitai/model/version/${lora.modelVersionId}`;
+                } else if (lora.hash) {
+                    endpoint = `/api/civitai/model/hash/${lora.hash}`;
+                } else {
+                    console.error("Missing both hash and modelVersionId for lora:", lora);
+                    return null;
+                }
+                
+                const versionResponse = await fetch(endpoint);
+                const versionInfo = await versionResponse.json();
+                
+                // Return original lora data combined with version info
+                return {
+                    ...lora,
+                    civitaiInfo: versionInfo
+                };
+            });
+            
+            // Wait for all API calls to complete
+            const lorasWithVersionInfo = await Promise.all(missingLorasWithVersionInfoPromises);
+            
+            // Filter out null values (failed requests)
+            const validLoras = lorasWithVersionInfo.filter(lora => lora !== null);
+            
+            if (validLoras.length === 0) {
+                showToast('Failed to get information for missing LoRAs', 'error');
+                return;
+            }
+            
+            // Prepare data for import manager using the retrieved information
+            const recipeData = {
+                loras: validLoras.map(lora => {
+                    const civitaiInfo = lora.civitaiInfo;
+                    const modelFile = civitaiInfo.files ? 
+                        civitaiInfo.files.find(file => file.type === 'Model') : null;
+                    
+                    return {
+                        // Basic lora info
+                        name: civitaiInfo.model?.name || lora.name,
+                        version: civitaiInfo.name || '',
+                        strength: lora.strength || 1.0,
+                        
+                        // Model identifiers
+                        hash: modelFile?.hashes?.SHA256?.toLowerCase() || lora.hash,
+                        modelVersionId: civitaiInfo.id || lora.modelVersionId,
+                        
+                        // Metadata
+                        thumbnailUrl: civitaiInfo.images?.[0]?.url || '',
+                        baseModel: civitaiInfo.baseModel || '',
+                        downloadUrl: civitaiInfo.downloadUrl || '',
+                        size: modelFile ? (modelFile.sizeKB * 1024) : 0,
+                        file_name: modelFile ? modelFile.name.split('.')[0] : '',
+                        
+                        // Status flags
+                        existsLocally: false,
+                        isDeleted: civitaiInfo.error === "Model not found",
+                        isEarlyAccess: !!civitaiInfo.earlyAccessEndsAt,
+                        earlyAccessEndsAt: civitaiInfo.earlyAccessEndsAt || ''
+                    };
+                })
+            };
+            
+            // Call ImportManager's download missing LoRAs method
+            window.importManager.downloadMissingLoras(recipeData, recipeId);
+        } catch (error) {
+            console.error('Error downloading missing LoRAs:', error);
+            showToast('Error preparing LoRAs for download: ' + error.message, 'error');
+        } finally {
+            if (state.loadingManager) {
+                state.loadingManager.hide();
+            }
+        }
+    }
+}

static/js/components/ContextMenu/index.js

@@ -0,0 +1,3 @@
+export { LoraContextMenu } from './LoraContextMenu.js';
+export { RecipeContextMenu } from './RecipeContextMenu.js';
+export { CheckpointContextMenu } from './CheckpointContextMenu.js';

static/js/components/LoraCard.js

@@ -1,9 +1,9 @@
-import { showToast, openCivitai } from '../utils/uiHelpers.js';
+import { showToast, openCivitai, copyToClipboard } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { showLoraModal } from './loraModal/index.js';
 import { bulkManager } from '../managers/BulkManager.js';
 import { NSFW_LEVELS } from '../utils/constants.js';
-import { replacePreview, deleteModel } from '../api/loraApi.js'
+import { replacePreview, deleteModel, saveModelMetadata } from '../api/loraApi.js'
 
 export function createLoraCard(lora) {
     const card = document.createElement('div');
@@ -20,6 +20,7 @@ export function createLoraCard(lora) {
     card.dataset.usage_tips = lora.usage_tips;
     card.dataset.notes = lora.notes;
     card.dataset.meta = JSON.stringify(lora.civitai || {});
+    card.dataset.favorite = lora.favorite ? 'true' : 'false';
     
     // Store tags and model description
     if (lora.tags && Array.isArray(lora.tags)) {
@@ -44,7 +45,9 @@ export function createLoraCard(lora) {
         card.classList.add('selected');
     }
 
-    const version = state.previewVersions.get(lora.file_path);
+    // Get the page-specific previewVersions map
+    const previewVersions = state.pages.loras.previewVersions || new Map();
+    const version = previewVersions.get(lora.file_path);
     const previewUrl = lora.preview_url || '/loras_static/images/no-preview.png';
     const versionedPreviewUrl = version ? `${previewUrl}?t=${version}` : previewUrl;
 
@@ -63,6 +66,9 @@ export function createLoraCard(lora) {
     const isVideo = previewUrl.endsWith('.mp4');
     const videoAttrs = autoplayOnHover ? 'controls muted loop' : 'controls autoplay muted loop';
 
+    // Get favorite status from the lora data
+    const isFavorite = lora.favorite === true;
+
     card.innerHTML = `
         <div class="card-preview ${shouldBlur ? 'blurred' : ''}">
             ${isVideo ? 
@@ -80,6 +86,9 @@ export function createLoraCard(lora) {
                     ${lora.base_model}
                 </span>
                 <div class="card-actions">
+                    <i class="${isFavorite ? 'fas fa-star favorite-active' : 'far fa-star'}" 
+                       title="${isFavorite ? 'Remove from favorites' : 'Add to favorites'}">
+                    </i>
                     <i class="fas fa-globe" 
                        title="${lora.from_civitai ? 'View on Civitai' : 'Not available from Civitai'}"
                        ${!lora.from_civitai ? 'style="opacity: 0.5; cursor: not-allowed"' : ''}>
@@ -133,6 +142,7 @@ export function createLoraCard(lora) {
                 base_model: card.dataset.base_model,
                 usage_tips: card.dataset.usage_tips,
                 notes: card.dataset.notes,
+                favorite: card.dataset.favorite === 'true',
                 // Parse civitai metadata from the card's dataset
                 civitai: (() => {
                     try {
@@ -196,6 +206,39 @@ export function createLoraCard(lora) {
         });
     }
 
+    // Favorite button click event
+    card.querySelector('.fa-star')?.addEventListener('click', async e => {
+        e.stopPropagation();
+        const starIcon = e.currentTarget;
+        const isFavorite = starIcon.classList.contains('fas');
+        const newFavoriteState = !isFavorite;
+        
+        try {
+            // Save the new favorite state to the server
+            await saveModelMetadata(card.dataset.filepath, { 
+                favorite: newFavoriteState 
+            });
+
+            // Update the UI
+            if (newFavoriteState) {
+                starIcon.classList.remove('far');
+                starIcon.classList.add('fas', 'favorite-active');
+                starIcon.title = 'Remove from favorites';
+                card.dataset.favorite = 'true';
+                showToast('Added to favorites', 'success');
+            } else {
+                starIcon.classList.remove('fas', 'favorite-active');
+                starIcon.classList.add('far');
+                starIcon.title = 'Add to favorites';
+                card.dataset.favorite = 'false';
+                showToast('Removed from favorites', 'success');
+            }
+        } catch (error) {
+            console.error('Failed to update favorite status:', error);
+            showToast('Failed to update favorite status', 'error');
+        }
+    });
+
     // Copy button click event
     card.querySelector('.fa-copy')?.addEventListener('click', async e => {
         e.stopPropagation();
@@ -203,26 +246,7 @@ export function createLoraCard(lora) {
         const strength = usageTips.strength || 1;
         const loraSyntax = `<lora:${card.dataset.file_name}:${strength}>`;
         
-        try {
-            // Modern clipboard API
-            if (navigator.clipboard && window.isSecureContext) {
-                await navigator.clipboard.writeText(loraSyntax);
-            } else {
-                // Fallback for older browsers
-                const textarea = document.createElement('textarea');
-                textarea.value = loraSyntax;
-                textarea.style.position = 'absolute';
-                textarea.style.left = '-99999px';
-                document.body.appendChild(textarea);
-                textarea.select();
-                document.execCommand('copy');
-                document.body.removeChild(textarea);
-            }
-            showToast('LoRA syntax copied', 'success');
-        } catch (err) {
-            console.error('Copy failed:', err);
-            showToast('Copy failed', 'error');
-        }
+        await copyToClipboard(loraSyntax, 'LoRA syntax copied');
     });
 
     // Civitai button click event

static/js/components/RecipeCard.js

@@ -1,5 +1,5 @@
 // Recipe Card Component
-import { showToast } from '../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
 import { modalManager } from '../managers/ModalManager.js';
 
 class RecipeCard {
@@ -109,14 +109,11 @@ class RecipeCard {
                 .then(response => response.json())
                 .then(data => {
                     if (data.success && data.syntax) {
-                        return navigator.clipboard.writeText(data.syntax);
+                        return copyToClipboard(data.syntax, 'Recipe syntax copied to clipboard');
                     } else {
                         throw new Error(data.error || 'No syntax returned');
                     }
                 })
-                .then(() => {
-                    showToast('Recipe syntax copied to clipboard', 'success');
-                })
                 .catch(err => {
                     console.error('Failed to copy: ', err);
                     showToast('Failed to copy recipe syntax', 'error');
@@ -279,4 +276,4 @@ class RecipeCard {
     }
 }
 
-export { RecipeCard }; 
+export { RecipeCard };

static/js/components/RecipeModal.js

@@ -1,5 +1,5 @@
 // Recipe Modal Component
-import { showToast } from '../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { setSessionItem, removeSessionItem } from '../utils/storageHelpers.js';
 
@@ -747,9 +747,8 @@ class RecipeModal {
             const data = await response.json();
             
             if (data.success && data.syntax) {
-                // Copy to clipboard
-                await navigator.clipboard.writeText(data.syntax);
-                showToast('Recipe syntax copied to clipboard', 'success');
+                // Use the centralized copyToClipboard utility function
+                await copyToClipboard(data.syntax, 'Recipe syntax copied to clipboard');
             } else {
                 throw new Error(data.error || 'No syntax returned from server');
             }
@@ -761,12 +760,7 @@ class RecipeModal {
     
     // Helper method to copy text to clipboard
     copyToClipboard(text, successMessage) {
-        navigator.clipboard.writeText(text).then(() => {
-            showToast(successMessage, 'success');
-        }).catch(err => {
-            console.error('Failed to copy text: ', err);
-            showToast('Failed to copy text', 'error');
-        });
+        copyToClipboard(text, successMessage);
     }
 
     // Add new method to handle downloading missing LoRAs
@@ -790,9 +784,9 @@ class RecipeModal {
                 
                 // Determine which endpoint to use based on available data
                 if (lora.modelVersionId) {
-                    endpoint = `/api/civitai/model/${lora.modelVersionId}`;
+                    endpoint = `/api/civitai/model/version/${lora.modelVersionId}`;
                 } else if (lora.hash) {
-                    endpoint = `/api/civitai/model/${lora.hash}`;
+                    endpoint = `/api/civitai/model/hash/${lora.hash}`;
                 } else {
                     console.error("Missing both hash and modelVersionId for lora:", lora);
                     return null;

static/js/components/checkpointModal/ModelMetadata.js

@@ -5,31 +5,7 @@
 import { showToast } from '../../utils/uiHelpers.js';
 import { BASE_MODELS } from '../../utils/constants.js';
 import { updateCheckpointCard } from '../../utils/cardUpdater.js';
-
-/**
- * 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) {
-    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');
-    }
-    
-    return response.json();
-}
+import { saveModelMetadata } from '../../api/checkpointApi.js';
 
 /**
  * Set up model name editing functionality

static/js/components/checkpointModal/ShowcaseView.js

@@ -2,7 +2,7 @@
  * ShowcaseView.js
  * Handles showcase content (images, videos) display for checkpoint modal
  */
-import { showToast } from '../../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
 import { state } from '../../state/index.js';
 import { NSFW_LEVELS } from '../../utils/constants.js';
 
@@ -307,8 +307,7 @@ function initMetadataPanelHandlers(container) {
                 if (!promptElement) return;
                 
                 try {
-                    await navigator.clipboard.writeText(promptElement.textContent);
-                    showToast('Prompt copied to clipboard', 'success');
+                    await copyToClipboard(promptElement.textContent, 'Prompt copied to clipboard');
                 } catch (err) {
                     console.error('Copy failed:', err);
                     showToast('Copy failed', 'error');

static/js/components/checkpointModal/index.js

@@ -11,9 +11,9 @@ import { setupTabSwitching, loadModelDescription } from './ModelDescription.js';
 import { 
     setupModelNameEditing, 
     setupBaseModelEditing, 
-    setupFileNameEditing,
-    saveModelMetadata
+    setupFileNameEditing
 } from './ModelMetadata.js';
+import { saveModelMetadata } from '../../api/checkpointApi.js';
 import { renderCompactTags, setupTagTooltip, formatFileSize } from './utils.js';
 import { updateCheckpointCard } from '../../utils/cardUpdater.js';
 

static/js/components/controls/LorasControls.js

@@ -2,7 +2,6 @@
 import { PageControls } from './PageControls.js';
 import { loadMoreLoras, fetchCivitai, resetAndReload, refreshLoras } from '../../api/loraApi.js';
 import { getSessionItem, removeSessionItem } from '../../utils/storageHelpers.js';
-import { showToast } from '../../utils/uiHelpers.js';
 
 /**
  * LorasControls class - Extends PageControls for LoRA-specific functionality

static/js/components/controls/PageControls.js

@@ -1,6 +1,6 @@
 // PageControls.js - Manages controls for both LoRAs and Checkpoints pages
 import { state, getCurrentPageState, setCurrentPageType } from '../../state/index.js';
-import { getStorageItem, setStorageItem } from '../../utils/storageHelpers.js';
+import { getStorageItem, setStorageItem, getSessionItem, setSessionItem } from '../../utils/storageHelpers.js';
 import { showToast } from '../../utils/uiHelpers.js';
 
 /**
@@ -26,6 +26,9 @@ export class PageControls {
         // Initialize event listeners
         this.initEventListeners();
         
+        // Initialize favorites filter button state
+        this.initFavoritesFilter();
+        
         console.log(`PageControls initialized for ${pageType} page`);
     }
     
@@ -121,6 +124,12 @@ export class PageControls {
                 bulkButton.addEventListener('click', () => this.toggleBulkMode());
             }
         }
+        
+        // Favorites filter button handler
+        const favoriteFilterBtn = document.getElementById('favoriteFilterBtn');
+        if (favoriteFilterBtn) {
+            favoriteFilterBtn.addEventListener('click', () => this.toggleFavoritesOnly());
+        }
     }
     
     /**
@@ -385,4 +394,50 @@ export class PageControls {
             showToast('Failed to clear custom filter: ' + error.message, 'error');
         }
     }
+    
+    /**
+     * Initialize the favorites filter button state
+     */
+    initFavoritesFilter() {
+        const favoriteFilterBtn = document.getElementById('favoriteFilterBtn');
+        if (favoriteFilterBtn) {
+            // Get current state from session storage with page-specific key
+            const storageKey = `show_favorites_only_${this.pageType}`;
+            const showFavoritesOnly = getSessionItem(storageKey, false);
+            
+            // Update button state
+            if (showFavoritesOnly) {
+                favoriteFilterBtn.classList.add('active');
+            }
+            
+            // Update app state
+            this.pageState.showFavoritesOnly = showFavoritesOnly;
+        }
+    }
+    
+    /**
+     * Toggle favorites-only filter and reload models
+     */
+    async toggleFavoritesOnly() {
+        const favoriteFilterBtn = document.getElementById('favoriteFilterBtn');
+        
+        // Toggle the filter state in storage
+        const storageKey = `show_favorites_only_${this.pageType}`;
+        const currentState = this.pageState.showFavoritesOnly;
+        const newState = !currentState;
+        
+        // Update session storage
+        setSessionItem(storageKey, newState);
+        
+        // Update state
+        this.pageState.showFavoritesOnly = newState;
+        
+        // Update button appearance
+        if (favoriteFilterBtn) {
+            favoriteFilterBtn.classList.toggle('active', newState);
+        }
+        
+        // Reload models with new filter
+        await this.resetAndReload(true);
+    }
 }

static/js/components/loraModal/ModelMetadata.js

@@ -5,31 +5,7 @@
 import { showToast } from '../../utils/uiHelpers.js';
 import { BASE_MODELS } from '../../utils/constants.js';
 import { updateLoraCard } from '../../utils/cardUpdater.js';
-
-/**
- * 保存模型元数据到服务器
- * @param {string} filePath - 文件路径
- * @param {Object} data - 要保存的数据
- * @returns {Promise} 保存操作的Promise
- */
-export async function saveModelMetadata(filePath, data) {
-    const response = await fetch('/api/loras/save-metadata', {
-        method: 'POST',
-        headers: {
-            'Content-Type': 'application/json',
-        },
-        body: JSON.stringify({
-            file_path: filePath,
-            ...data
-        })
-    });
-
-    if (!response.ok) {
-        throw new Error('Failed to save metadata');
-    }
-    
-    return response.json();
-}
+import { saveModelMetadata } from '../../api/loraApi.js';
 
 /**
  * 设置模型名称编辑功能

static/js/components/loraModal/PresetTags.js

@@ -2,8 +2,7 @@
  * PresetTags.js
  * 处理LoRA模型预设参数标签相关的功能模块
  */
-import { saveModelMetadata } from './ModelMetadata.js';
-import { showToast } from '../../utils/uiHelpers.js';
+import { saveModelMetadata } from '../../api/loraApi.js';
 
 /**
  * 解析预设参数

static/js/components/loraModal/RecipeTab.js

@@ -1,7 +1,7 @@
 /**
  * RecipeTab - Handles the recipes tab in the Lora Modal
  */
-import { showToast } from '../../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
 import { setSessionItem, removeSessionItem } from '../../utils/storageHelpers.js';
 
 /**
@@ -172,14 +172,11 @@ function copyRecipeSyntax(recipeId) {
         .then(response => response.json())
         .then(data => {
             if (data.success && data.syntax) {
-                return navigator.clipboard.writeText(data.syntax);
+                return copyToClipboard(data.syntax, 'Recipe syntax copied to clipboard');
             } else {
                 throw new Error(data.error || 'No syntax returned');
             }
         })
-        .then(() => {
-            showToast('Recipe syntax copied to clipboard', 'success');
-        })
         .catch(err => {
             console.error('Failed to copy: ', err);
             showToast('Failed to copy recipe syntax', 'error');

static/js/components/loraModal/ShowcaseView.js

@@ -2,7 +2,7 @@
  * ShowcaseView.js
  * 处理LoRA模型展示内容（图片、视频）的功能模块
  */
-import { showToast } from '../../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
 import { state } from '../../state/index.js';
 import { NSFW_LEVELS } from '../../utils/constants.js';
 
@@ -311,8 +311,7 @@ function initMetadataPanelHandlers(container) {
                 if (!promptElement) return;
                 
                 try {
-                    await navigator.clipboard.writeText(promptElement.textContent);
-                    showToast('Prompt copied to clipboard', 'success');
+                    await copyToClipboard(promptElement.textContent, 'Prompt copied to clipboard');
                 } catch (err) {
                     console.error('Copy failed:', err);
                     showToast('Copy failed', 'error');

static/js/components/loraModal/TriggerWords.js

@@ -2,8 +2,8 @@
  * TriggerWords.js
  * 处理LoRA模型触发词相关的功能模块
  */
-import { showToast } from '../../utils/uiHelpers.js';
-import { saveModelMetadata } from './ModelMetadata.js';
+import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
+import { saveModelMetadata } from '../../api/loraApi.js';
 
 /**
  * 渲染触发词
@@ -235,8 +235,8 @@ function addNewTriggerWord(word) {
     
     // Validation: Check total number
     const currentTags = tagsContainer.querySelectorAll('.trigger-word-tag');
-    if (currentTags.length >= 10) {
-        showToast('Maximum 10 trigger words allowed', 'error');
+    if (currentTags.length >= 30) {
+        showToast('Maximum 30 trigger words allowed', 'error');
         return;
     }
     
@@ -336,8 +336,7 @@ async function saveTriggerWords() {
  */
 window.copyTriggerWord = async function(word) {
     try {
-        await navigator.clipboard.writeText(word);
-        showToast('Trigger word copied', 'success');
+        await copyToClipboard(word, 'Trigger word copied');
     } catch (err) {
         console.error('Copy failed:', err);
         showToast('Copy failed', 'error');

static/js/components/loraModal/index.js

@@ -3,8 +3,7 @@
  * 
  * 将原始的LoraModal.js拆分成多个功能模块后的主入口文件
  */
-import { showToast } from '../../utils/uiHelpers.js';
-import { state } from '../../state/index.js';
+import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
 import { modalManager } from '../../managers/ModalManager.js';
 import { renderShowcaseContent, toggleShowcase, setupShowcaseScroll, scrollToTop } from './ShowcaseView.js';
 import { setupTabSwitching, loadModelDescription } from './ModelDescription.js';
@@ -14,9 +13,9 @@ import { loadRecipesForLora } from './RecipeTab.js'; // Add import for recipe ta
 import { 
     setupModelNameEditing, 
     setupBaseModelEditing, 
-    setupFileNameEditing,
-    saveModelMetadata
+    setupFileNameEditing
 } from './ModelMetadata.js';
+import { saveModelMetadata } from '../../api/loraApi.js';
 import { renderCompactTags, setupTagTooltip, formatFileSize } from './utils.js';
 import { updateLoraCard } from '../../utils/cardUpdater.js';
 
@@ -174,8 +173,7 @@ export function showLoraModal(lora) {
 // Copy file name function
 window.copyFileName = async function(fileName) {
     try {
-        await navigator.clipboard.writeText(fileName);
-        showToast('File name copied', 'success');
+        await copyToClipboard(fileName, 'File name copied');
     } catch (err) {
         console.error('Copy failed:', err);
         showToast('Copy failed', 'error');

static/js/loras.js

@@ -6,7 +6,7 @@ import { updateCardsForBulkMode } from './components/LoraCard.js';
 import { bulkManager } from './managers/BulkManager.js';
 import { DownloadManager } from './managers/DownloadManager.js';
 import { moveManager } from './managers/MoveManager.js';
-import { LoraContextMenu } from './components/ContextMenu.js';
+import { LoraContextMenu } from './components/ContextMenu/index.js';
 import { createPageControls } from './components/controls/index.js';
 import { confirmDelete, closeDeleteModal } from './utils/modalUtils.js';
 

static/js/managers/BulkManager.js

@@ -1,5 +1,5 @@
 import { state } from '../state/index.js';
-import { showToast } from '../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
 import { updateCardsForBulkMode } from '../components/LoraCard.js';
 
 export class BulkManager {
@@ -205,13 +205,7 @@ export class BulkManager {
             return;
         }
         
-        try {
-            await navigator.clipboard.writeText(loraSyntaxes.join(', '));
-            showToast(`Copied ${loraSyntaxes.length} LoRA syntaxes to clipboard`, 'success');
-        } catch (err) {
-            console.error('Copy failed:', err);
-            showToast('Copy failed', 'error');
-        }
+        await copyToClipboard(loraSyntaxes.join(', '), `Copied ${loraSyntaxes.length} LoRA syntaxes to clipboard`);
     }
     
     // Create and show the thumbnail strip of selected LoRAs

static/js/managers/ImportManager.js

@@ -146,6 +146,18 @@ export class ImportManager {
         if (totalSizeDisplay) {
             totalSizeDisplay.textContent = 'Calculating...';
         }
+        
+        // Remove any existing deleted LoRAs warning
+        const deletedLorasWarning = document.getElementById('deletedLorasWarning');
+        if (deletedLorasWarning) {
+            deletedLorasWarning.remove();
+        }
+        
+        // Remove any existing early access warning
+        const earlyAccessWarning = document.getElementById('earlyAccessWarning');
+        if (earlyAccessWarning) {
+            earlyAccessWarning.remove();
+        }
     }
 
     toggleImportMode(mode) {
@@ -532,17 +544,17 @@ export class ImportManager {
         const nextButton = document.querySelector('#detailsStep .primary-btn');
         if (!nextButton) return;
         
+        // Always clean up previous warnings first
+        const existingWarning = document.getElementById('deletedLorasWarning');
+        if (existingWarning) {
+            existingWarning.remove();
+        }
+        
         // Count deleted LoRAs
         const deletedLoras = this.recipeData.loras.filter(lora => lora.isDeleted).length;
         
         // If we have deleted LoRAs, show a warning and update button text
         if (deletedLoras > 0) {
-            // Remove any existing warning
-            const existingWarning = document.getElementById('deletedLorasWarning');
-            if (existingWarning) {
-                existingWarning.remove();
-            }
-            
             // Create a new warning container above the buttons
             const buttonsContainer = document.querySelector('#detailsStep .modal-actions') || nextButton.parentNode;
             const warningContainer = document.createElement('div');

static/js/recipes.js

@@ -5,6 +5,7 @@ import { RecipeCard } from './components/RecipeCard.js';
 import { RecipeModal } from './components/RecipeModal.js';
 import { getCurrentPageState } from './state/index.js';
 import { getSessionItem, removeSessionItem } from './utils/storageHelpers.js';
+import { RecipeContextMenu } from './components/ContextMenu/index.js';
 
 class RecipeManager {
     constructor() {
@@ -37,6 +38,9 @@ class RecipeManager {
         // Set default search options if not already defined
         this._initSearchOptions();
         
+        // Initialize context menu
+        new RecipeContextMenu();
+        
         // Check for custom filter parameters in session storage
         this._checkCustomFilter();
         
@@ -264,6 +268,32 @@ class RecipeManager {
         }
     }
     
+    /**
+     * Refreshes the recipe list by first rebuilding the cache and then loading recipes
+     */
+    async refreshRecipes() {
+        try {           
+            // Call the new endpoint to rebuild the recipe cache
+            const response = await fetch('/api/recipes/scan');
+            
+            if (!response.ok) {
+                const data = await response.json();
+                throw new Error(data.error || 'Failed to refresh recipe cache');
+            }
+            
+            // After successful cache rebuild, load the recipes
+            await this.loadRecipes(true);
+            
+            appCore.showToast('Refresh complete', 'success');
+        } catch (error) {
+            console.error('Error refreshing recipes:', error);
+            appCore.showToast(error.message || 'Failed to refresh recipes', 'error');
+            
+            // Still try to load recipes even if scan failed
+            await this.loadRecipes(true);
+        }
+    }
+    
     async _loadSpecificRecipe(recipeId) {
         try {
             // Fetch specific recipe by ID

static/js/state/index.js

@@ -1,5 +1,5 @@
 // Create the new hierarchical state structure
-import { getStorageItem } from '../utils/storageHelpers.js';
+import { getStorageItem, getMapFromStorage } from '../utils/storageHelpers.js';
 
 // Load settings from localStorage or use defaults
 const savedSettings = getStorageItem('settings', {
@@ -7,6 +7,10 @@ const savedSettings = getStorageItem('settings', {
     show_only_sfw: false
 });
 
+// Load preview versions from localStorage
+const loraPreviewVersions = getMapFromStorage('lora_preview_versions');
+const checkpointPreviewVersions = getMapFromStorage('checkpoint_preview_versions');
+
 export const state = {
     // Global state
     global: {
@@ -23,7 +27,7 @@ export const state = {
             hasMore: true,
             sortBy: 'name',
             activeFolder: null,
-            previewVersions: new Map(),
+            previewVersions: loraPreviewVersions,
             searchManager: null,
             searchOptions: {
                 filename: true,
@@ -38,6 +42,7 @@ export const state = {
             bulkMode: false,
             selectedLoras: new Set(),
             loraMetadataCache: new Map(),
+            showFavoritesOnly: false,
         },
         
         recipes: {
@@ -57,7 +62,8 @@ export const state = {
                 tags: [],
                 search: ''
             },
-            pageSize: 20
+            pageSize: 20,
+            showFavoritesOnly: false,
         },
         
         checkpoints: {
@@ -66,6 +72,7 @@ export const state = {
             hasMore: true,
             sortBy: 'name',
             activeFolder: null,
+            previewVersions: checkpointPreviewVersions,
             searchManager: null,
             searchOptions: {
                 filename: true,
@@ -75,7 +82,8 @@ export const state = {
             filters: {
                 baseModel: [],
                 tags: []
-            }
+            },
+            showFavoritesOnly: false,
         }
     },
     

static/js/utils/infiniteScroll.js

@@ -4,6 +4,7 @@ import { loadMoreCheckpoints } from '../api/checkpointApi.js';
 import { debounce } from './debounce.js';
 
 export function initializeInfiniteScroll(pageType = 'loras') {
+    // Clean up any existing observer
     if (state.observer) {
         state.observer.disconnect();
     }
@@ -47,53 +48,53 @@ export function initializeInfiniteScroll(pageType = 'loras') {
     }
 
     const debouncedLoadMore = debounce(loadMoreFunction, 100);
-
-    // Create a more robust observer with lower threshold and root margin
-    state.observer = new IntersectionObserver(
-        (entries) => {
-            const target = entries[0];
-            if (target.isIntersecting && !pageState.isLoading && pageState.hasMore) {
-                debouncedLoadMore();
-            }
-        },
-        { 
-            threshold: 0.01,  // Lower threshold to detect even minimal visibility
-            rootMargin: '0px 0px 300px 0px' // Increase bottom margin to trigger earlier
-        }
-    );
-
+    
     const grid = document.getElementById(gridId);
     if (!grid) {
         console.warn(`Grid with ID "${gridId}" not found for infinite scroll`);
         return;
     }
-
+    
+    // Remove any existing sentinel
     const existingSentinel = document.getElementById('scroll-sentinel');
     if (existingSentinel) {
-        state.observer.observe(existingSentinel);
-    } else {
-        // Create a wrapper div that will be placed after the grid
-        const sentinelWrapper = document.createElement('div');
-        sentinelWrapper.style.width = '100%';
-        sentinelWrapper.style.height = '30px'; // Increased height for better visibility
-        sentinelWrapper.style.margin = '0';
-        sentinelWrapper.style.padding = '0';
-        
-        // Create the actual sentinel element
-        const sentinel = document.createElement('div');
-        sentinel.id = 'scroll-sentinel';
-        sentinel.style.height = '30px'; // Match wrapper height
-        
-        // Add the sentinel to the wrapper
-        sentinelWrapper.appendChild(sentinel);
-        
-        // Insert the wrapper after the grid instead of inside it
-        grid.parentNode.insertBefore(sentinelWrapper, grid.nextSibling);
-        
-        state.observer.observe(sentinel);
+        existingSentinel.remove();
     }
     
-    // Add a scroll event backup to handle edge cases
+    // Create a sentinel element after the grid (not inside it)
+    const sentinel = document.createElement('div');
+    sentinel.id = 'scroll-sentinel';
+    sentinel.style.width = '100%';
+    sentinel.style.height = '20px';
+    sentinel.style.visibility = 'hidden'; // Make it invisible but still affect layout
+    
+    // Insert after grid instead of inside
+    grid.parentNode.insertBefore(sentinel, grid.nextSibling);
+    
+    // Create observer with appropriate settings, slightly different for checkpoints page
+    const observerOptions = {
+        threshold: 0.1,
+        rootMargin: pageType === 'checkpoints' ? '0px 0px 200px 0px' : '0px 0px 100px 0px'
+    };
+    
+    // Initialize the observer
+    state.observer = new IntersectionObserver((entries) => {
+        const target = entries[0];
+        if (target.isIntersecting && !pageState.isLoading && pageState.hasMore) {
+            debouncedLoadMore();
+        }
+    }, observerOptions);
+    
+    // Start observing
+    state.observer.observe(sentinel);
+    
+    // Clean up any existing scroll event listener
+    if (state.scrollHandler) {
+        window.removeEventListener('scroll', state.scrollHandler);
+        state.scrollHandler = null;
+    }
+    
+    // Add a simple backup scroll handler
     const handleScroll = debounce(() => {
         if (pageState.isLoading || !pageState.hasMore) return;
         
@@ -103,26 +104,17 @@ export function initializeInfiniteScroll(pageType = 'loras') {
         const rect = sentinel.getBoundingClientRect();
         const windowHeight = window.innerHeight;
         
-        // If sentinel is within 500px of viewport bottom, load more
-        if (rect.top < windowHeight + 500) {
+        if (rect.top < windowHeight + 200) {
             debouncedLoadMore();
         }
     }, 200);
     
-    // Clean up existing scroll listener if any
-    if (state.scrollHandler) {
-        window.removeEventListener('scroll', state.scrollHandler);
-    }
-    
-    // Save reference to the handler for cleanup
     state.scrollHandler = handleScroll;
     window.addEventListener('scroll', state.scrollHandler);
     
-    // Check position immediately in case content is already visible
-    setTimeout(() => {
-        const sentinel = document.getElementById('scroll-sentinel');
-        if (sentinel && sentinel.getBoundingClientRect().top < window.innerHeight) {
-            debouncedLoadMore();
-        }
-    }, 100);
+    // Clear any existing interval
+    if (state.scrollCheckInterval) {
+        clearInterval(state.scrollCheckInterval);
+        state.scrollCheckInterval = null;
+    }
 }

static/js/utils/storageHelpers.js

@@ -171,4 +171,45 @@ export function migrateStorageItems() {
     localStorage.setItem(STORAGE_PREFIX + 'migration_completed', 'true');
     
     console.log('Lora Manager: Storage migration completed');
+}
+
+/**
+ * Save a Map to localStorage
+ * @param {string} key - The localStorage key
+ * @param {Map} map - The Map to save
+ */
+export function saveMapToStorage(key, map) {
+    if (!(map instanceof Map)) {
+        console.error('Cannot save non-Map object:', map);
+        return;
+    }
+
+    try {
+        const prefixedKey = STORAGE_PREFIX + key;
+        // Convert Map to array of entries and save as JSON
+        const entries = Array.from(map.entries());
+        localStorage.setItem(prefixedKey, JSON.stringify(entries));
+    } catch (error) {
+        console.error(`Error saving Map to localStorage (${key}):`, error);
+    }
+}
+
+/**
+ * Load a Map from localStorage
+ * @param {string} key - The localStorage key
+ * @returns {Map} - The loaded Map or a new empty Map
+ */
+export function getMapFromStorage(key) {
+    try {
+        const prefixedKey = STORAGE_PREFIX + key;
+        const data = localStorage.getItem(prefixedKey);
+        if (!data) return new Map();
+        
+        // Parse JSON and convert back to Map
+        const entries = JSON.parse(data);
+        return new Map(entries);
+    } catch (error) {
+        console.error(`Error loading Map from localStorage (${key}):`, error);
+        return new Map();
+    }
 }

static/js/utils/uiHelpers.js

@@ -2,6 +2,40 @@ import { state } from '../state/index.js';
 import { resetAndReload } from '../api/loraApi.js';
 import { getStorageItem, setStorageItem } from './storageHelpers.js';
 
+/**
+ * Utility function to copy text to clipboard with fallback for older browsers
+ * @param {string} text - The text to copy to clipboard
+ * @param {string} successMessage - Optional success message to show in toast
+ * @returns {Promise<boolean>} - Promise that resolves to true if copy was successful
+ */
+export async function copyToClipboard(text, successMessage = 'Copied to clipboard') {
+    try {
+        // Modern clipboard API
+        if (navigator.clipboard && window.isSecureContext) {
+            await navigator.clipboard.writeText(text);
+        } else {
+            // Fallback for older browsers
+            const textarea = document.createElement('textarea');
+            textarea.value = text;
+            textarea.style.position = 'absolute';
+            textarea.style.left = '-99999px';
+            document.body.appendChild(textarea);
+            textarea.select();
+            document.execCommand('copy');
+            document.body.removeChild(textarea);
+        }
+        
+        if (successMessage) {
+            showToast(successMessage, 'success');
+        }
+        return true;
+    } catch (err) {
+        console.error('Copy failed:', err);
+        showToast('Copy failed', 'error');
+        return false;
+    }
+}
+
 export function showToast(message, type = 'info') {
     const toast = document.createElement('div');
     toast.className = `toast toast-${type}`;
@@ -80,13 +114,55 @@ export function restoreFolderFilter() {
 }
 
 export function initTheme() {
-    document.body.dataset.theme = getStorageItem('theme') || 'dark';
+    const savedTheme = getStorageItem('theme') || 'auto';
+    applyTheme(savedTheme);
+    
+    // Update theme when system preference changes (for 'auto' mode)
+    window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', () => {
+        const currentTheme = getStorageItem('theme') || 'auto';
+        if (currentTheme === 'auto') {
+            applyTheme('auto');
+        }
+    });
 }
 
 export function toggleTheme() {
-    const theme = document.body.dataset.theme === 'light' ? 'dark' : 'light';
-    document.body.dataset.theme = theme;
-    setStorageItem('theme', theme);
+    const currentTheme = getStorageItem('theme') || 'auto';
+    let newTheme;
+    
+    if (currentTheme === 'dark') {
+        newTheme = 'light';
+    } else {
+        newTheme = 'dark';
+    }
+    
+    setStorageItem('theme', newTheme);
+    applyTheme(newTheme);
+    
+    // Force a repaint to ensure theme changes are applied immediately
+    document.body.style.display = 'none';
+    document.body.offsetHeight; // Trigger a reflow
+    document.body.style.display = '';
+    
+    return newTheme;
+}
+
+// Add a new helper function to apply the theme
+function applyTheme(theme) {
+    const prefersDark = window.matchMedia('(prefers-color-scheme: dark)').matches;
+    const htmlElement = document.documentElement;
+    
+    // Remove any existing theme attributes
+    htmlElement.removeAttribute('data-theme');
+    
+    // Apply the appropriate theme
+    if (theme === 'dark' || (theme === 'auto' && prefersDark)) {
+        htmlElement.setAttribute('data-theme', 'dark');
+        document.body.dataset.theme = 'dark';
+    } else {
+        htmlElement.setAttribute('data-theme', 'light');
+        document.body.dataset.theme = 'light';
+    }
 }
 
 export function toggleFolder(tag) {
@@ -108,12 +184,6 @@ export function toggleFolder(tag) {
     resetAndReload();
 }
 
-export function copyTriggerWord(word) {
-    navigator.clipboard.writeText(word).then(() => {
-        showToast('Trigger word copied', 'success');
-    });
-}
-
 function filterByFolder(folderPath) {
     document.querySelectorAll('.lora-card').forEach(card => {
         card.style.display = card.dataset.folder === folderPath ? '' : 'none';

templates/base.html

@@ -6,7 +6,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1">
     <link rel="stylesheet" href="/loras_static/css/style.css">
     {% block page_css %}{% endblock %}
-    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css"
+    <link rel="stylesheet" href="/loras_static/vendor/font-awesome/css/all.min.css"
         crossorigin="anonymous" referrerpolicy="no-referrer">
     <link rel="icon" type="image/png" sizes="32x32" href="/loras_static/images/favicon-32x32.png">
     <link rel="icon" type="image/png" sizes="16x16" href="/loras_static/images/favicon-16x16.png">
@@ -17,7 +17,7 @@
     {% block preload %}{% endblock %}
 
     <!-- 优化字体加载 -->
-    <link rel="preload" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/webfonts/fa-solid-900.woff2"
+    <link rel="preload" href="/loras_static/vendor/font-awesome/webfonts/fa-solid-900.woff2"
         as="font" type="font/woff2" crossorigin>
 
     <!-- 添加性能监控 -->
@@ -35,7 +35,7 @@
 
     <!-- 添加资源加载策略 -->
     <link rel="preconnect" href="https://civitai.com">
-    <link rel="preconnect" href="https://cdnjs.cloudflare.com">
+    <!-- <link rel="preconnect" href="https://cdnjs.cloudflare.com"> -->
 
     <script>
         // 计算滚动条宽度并设置CSS变量
@@ -48,6 +48,20 @@
             document.documentElement.style.setProperty('--scrollbar-width', scrollbarWidth + 'px');
         });
     </script>
+    <script>
+      (function() {
+        // Apply theme immediately based on stored preference
+        const STORAGE_PREFIX = 'lora_manager_';
+        const savedTheme = localStorage.getItem(STORAGE_PREFIX + 'theme') || 'auto';
+        const prefersDark = window.matchMedia('(prefers-color-scheme: dark)').matches;
+        
+        if (savedTheme === 'dark' || (savedTheme === 'auto' && prefersDark)) {
+          document.documentElement.setAttribute('data-theme', 'dark');
+        } else {
+          document.documentElement.setAttribute('data-theme', 'light');
+        }
+      })();
+    </script>
     {% block head_scripts %}{% endblock %}
 </head>
 

templates/checkpoints.html

@@ -13,6 +13,18 @@
 
 {% block additional_components %}
 {% include 'components/checkpoint_modals.html' %}
+
+<div id="checkpointContextMenu" class="context-menu" style="display: none;">
+    <div class="context-menu-item" data-action="details"><i class="fas fa-info-circle"></i> View Details</div>
+    <div class="context-menu-item" data-action="civitai"><i class="fas fa-external-link-alt"></i> View on CivitAI</div>
+    <div class="context-menu-item" data-action="refresh-metadata"><i class="fas fa-sync"></i> Refresh Civitai Data</div>
+    <div class="context-menu-item" data-action="copyname"><i class="fas fa-copy"></i> Copy Model Filename</div>
+    <div class="context-menu-item" data-action="preview"><i class="fas fa-image"></i> Replace Preview</div>
+    <div class="context-menu-item" data-action="set-nsfw"><i class="fas fa-exclamation-triangle"></i> Set Content Rating</div>
+    <div class="context-menu-separator"></div>
+    <div class="context-menu-item" data-action="move"><i class="fas fa-folder-open"></i> Move to Folder</div>
+    <div class="context-menu-item delete-item" data-action="delete"><i class="fas fa-trash"></i> Delete Model</div>
+</div>
 {% endblock %}
 
 {% block content %}

templates/components/controls.html

@@ -35,6 +35,11 @@
                 </button>
             </div>
             {% endif %}
+            <div class="control-group">
+                <button id="favoriteFilterBtn" data-action="toggle-favorites" class="favorite-filter" title="Show favorites only">
+                    <i class="fas fa-star"></i> Favorites
+                </button>
+            </div>
             <div id="customFilterIndicator" class="control-group hidden">
                 <div class="filter-active">
                     <i class="fas fa-filter"></i> <span class="customFilterText" title=""></span>

templates/recipes.html

@@ -16,6 +16,16 @@
 {% block additional_components %}
 {% include 'components/import_modal.html' %}
 {% include 'components/recipe_modal.html' %}
+
+<div id="recipeContextMenu" class="context-menu" style="display: none;">
+    <div class="context-menu-item" data-action="details"><i class="fas fa-info-circle"></i> View Details</div>
+    <div class="context-menu-item" data-action="share"><i class="fas fa-share-alt"></i> Share Recipe</div>
+    <div class="context-menu-item" data-action="copy"><i class="fas fa-copy"></i> Copy Recipe Syntax</div>
+    <div class="context-menu-item" data-action="viewloras"><i class="fas fa-layer-group"></i> View All LoRAs</div>
+    <div class="context-menu-item download-missing-item" data-action="download-missing"><i class="fas fa-download"></i> Download Missing LoRAs</div>
+    <div class="context-menu-separator"></div>
+    <div class="context-menu-item delete-item" data-action="delete"><i class="fas fa-trash"></i> Delete Recipe</div>
+</div>
 {% endblock %}
 
 {% block init_title %}Initializing Recipe Manager{% endblock %}
@@ -27,7 +37,7 @@
     <div class="controls">
         <div class="action-buttons">
             <div title="Refresh recipe list" class="control-group">
-                <button onclick="recipeManager.loadRecipes(true)"><i class="fas fa-sync"></i> Refresh</button>
+                <button onclick="recipeManager.refreshRecipes()"><i class="fas fa-sync"></i> Refresh</button>
             </div>
             <div title="Import recipes" class="control-group">
                 <button onclick="importManager.showImportModal()"><i class="fas fa-file-import"></i> Import</button>

web/comfyui/legacy_loras_widget.js

@@ -287,6 +287,108 @@ export function addLorasWidget(node, name, opts, callback) {
   // 创建预览tooltip实例
   const previewTooltip = new PreviewTooltip();
 
+  // Function to handle strength adjustment via dragging
+  const handleStrengthDrag = (name, initialStrength, initialX, event, widget) => {
+    // Calculate drag sensitivity (how much the strength changes per pixel)
+    // Using 0.01 per 10 pixels of movement
+    const sensitivity = 0.001;
+    
+    // Get the current mouse position
+    const currentX = event.clientX;
+    
+    // Calculate the distance moved
+    const deltaX = currentX - initialX;
+    
+    // Calculate the new strength value based on movement
+    // Moving right increases, moving left decreases
+    let newStrength = Number(initialStrength) + (deltaX * sensitivity);
+    
+    // Limit the strength to reasonable bounds (now between -10 and 10)
+    newStrength = Math.max(-10, Math.min(10, newStrength));
+    newStrength = Number(newStrength.toFixed(2));
+    
+    // Update the lora data
+    const lorasData = parseLoraValue(widget.value);
+    const loraIndex = lorasData.findIndex(l => l.name === name);
+    
+    if (loraIndex >= 0) {
+      lorasData[loraIndex].strength = newStrength;
+      
+      // Update the widget value
+      widget.value = formatLoraValue(lorasData);
+      
+      // Force re-render to show updated strength value
+      renderLoras(widget.value, widget);
+    }
+  };
+  
+  // Function to initialize drag operation
+  const initDrag = (loraEl, nameEl, name, widget) => {
+    let isDragging = false;
+    let initialX = 0;
+    let initialStrength = 0;
+    
+    // Create a style element for drag cursor override if it doesn't exist
+    if (!document.getElementById('comfy-lora-drag-style')) {
+      const styleEl = document.createElement('style');
+      styleEl.id = 'comfy-lora-drag-style';
+      styleEl.textContent = `
+        body.comfy-lora-dragging,
+        body.comfy-lora-dragging * {
+          cursor: ew-resize !important;
+        }
+      `;
+      document.head.appendChild(styleEl);
+    }
+    
+    // Create a drag handler that's applied to the entire lora entry
+    // except toggle and strength controls
+    loraEl.addEventListener('mousedown', (e) => {
+      // Skip if clicking on toggle or strength control areas
+      if (e.target.closest('.comfy-lora-toggle') || 
+          e.target.closest('input') || 
+          e.target.closest('.comfy-lora-arrow')) {
+        return;
+      }
+      
+      // Store initial values
+      const lorasData = parseLoraValue(widget.value);
+      const loraData = lorasData.find(l => l.name === name);
+      
+      if (!loraData) return;
+      
+      initialX = e.clientX;
+      initialStrength = loraData.strength;
+      isDragging = true;
+      
+      // Add class to body to enforce cursor style globally
+      document.body.classList.add('comfy-lora-dragging');
+      
+      // Prevent text selection during drag
+      e.preventDefault();
+    });
+    
+    // Use the document for move and up events to ensure drag continues
+    // even if mouse leaves the element
+    document.addEventListener('mousemove', (e) => {
+      if (!isDragging) return;
+      
+      // Call the strength adjustment function
+      handleStrengthDrag(name, initialStrength, initialX, e, widget);
+      
+      // Prevent showing the preview tooltip during drag
+      previewTooltip.hide();
+    });
+    
+    document.addEventListener('mouseup', () => {
+      if (isDragging) {
+        isDragging = false;
+        // Remove the class to restore normal cursor behavior
+        document.body.classList.remove('comfy-lora-dragging');
+      }
+    });
+  };
+
   // Function to create menu item
   const createMenuItem = (text, icon, onClick) => {
     const menuItem = document.createElement('div');
@@ -756,6 +858,9 @@ export function addLorasWidget(node, name, opts, callback) {
       loraEl.appendChild(strengthControl);
 
       container.appendChild(loraEl);
+
+      // Initialize drag functionality
+      initDrag(loraEl, nameEl, name, widget);
     });
   };
 
@@ -822,10 +927,6 @@ export function addLorasWidget(node, name, opts, callback) {
 // Function to directly save the recipe without dialog
 async function saveRecipeDirectly(widget) {
   try {
-    // Get the workflow data from the ComfyUI app
-    const prompt = await app.graphToPrompt();
-    console.log('Prompt:', prompt);
-    
     // Show loading toast
     if (app && app.extensionManager && app.extensionManager.toast) {
       app.extensionManager.toast.add({
@@ -836,14 +937,9 @@ async function saveRecipeDirectly(widget) {
       });
     }
     
-    // Prepare the data - only send workflow JSON
-    const formData = new FormData();
-    formData.append('workflow_json', JSON.stringify(prompt.output));
-    
     // Send the request
     const response = await fetch('/api/recipes/save-from-widget', {
-      method: 'POST',
-      body: formData
+      method: 'POST'
     });
     
     const result = await response.json();

web/comfyui/lora_loader.js

@@ -9,6 +9,54 @@ async function getLorasWidgetModule() {
     return await dynamicImportByVersion("./loras_widget.js", "./legacy_loras_widget.js");
 }
 
+// Function to get connected trigger toggle nodes
+function getConnectedTriggerToggleNodes(node) {
+    const connectedNodes = [];
+    
+    // Check if node has outputs
+    if (node.outputs && node.outputs.length > 0) {
+        // For each output slot
+        for (const output of node.outputs) {
+            // Check if this output has any links
+            if (output.links && output.links.length > 0) {
+                // For each link, get the target node
+                for (const linkId of output.links) {
+                    const link = app.graph.links[linkId];
+                    if (link) {
+                        const targetNode = app.graph.getNodeById(link.target_id);
+                        if (targetNode && targetNode.comfyClass === "TriggerWord Toggle (LoraManager)") {
+                            connectedNodes.push(targetNode.id);
+                        }
+                    }
+                }
+            }
+        }
+    }
+    return connectedNodes;
+}
+
+// Function to update trigger words for connected toggle nodes
+function updateConnectedTriggerWords(node, text) {
+    const connectedNodeIds = getConnectedTriggerToggleNodes(node);
+    if (connectedNodeIds.length > 0) {
+        const loraNames = new Set();
+        let match;
+        LORA_PATTERN.lastIndex = 0;
+        while ((match = LORA_PATTERN.exec(text)) !== null) {
+            loraNames.add(match[1]);
+        }
+        
+        fetch("/loramanager/get_trigger_words", {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({
+                lora_names: Array.from(loraNames),
+                node_ids: connectedNodeIds
+            })
+        }).catch(err => console.error("Error fetching trigger words:", err));
+    }
+}
+
 function mergeLoras(lorasText, lorasArr) {
     const result = [];
     let match;
@@ -99,6 +147,9 @@ app.registerExtension({
                         newText = newText.replace(/\s+/g, ' ').trim();
                         
                         inputWidget.value = newText;
+                        
+                        // Add this line to update trigger words when lorasWidget changes cause inputWidget value to change
+                        updateConnectedTriggerWords(node, newText);
                     } finally {
                         isUpdating = false;
                     }
@@ -117,6 +168,9 @@ app.registerExtension({
                         const mergedLoras = mergeLoras(value, currentLoras);
                         
                         node.lorasWidget.value = mergedLoras;
+                        
+                        // Replace the existing trigger word update code with the new function
+                        updateConnectedTriggerWords(node, value);
                     } finally {
                         isUpdating = false;
                     }

web/comfyui/lora_stacker.js

@@ -1,9 +1,58 @@
 import { app } from "../../scripts/app.js";
-import { addLorasWidget } from "./loras_widget.js";
+import { dynamicImportByVersion } from "./utils.js";
 
 // Extract pattern into a constant for consistent use
 const LORA_PATTERN = /<lora:([^:]+):([-\d\.]+)>/g;
 
+// Function to get the appropriate loras widget based on ComfyUI version
+async function getLorasWidgetModule() {
+    return await dynamicImportByVersion("./loras_widget.js", "./legacy_loras_widget.js");
+}
+
+// Function to get connected trigger toggle nodes
+function getConnectedTriggerToggleNodes(node) {
+    const connectedNodes = [];
+    
+    if (node.outputs && node.outputs.length > 0) {
+        for (const output of node.outputs) {
+            if (output.links && output.links.length > 0) {
+                for (const linkId of output.links) {
+                    const link = app.graph.links[linkId];
+                    if (link) {
+                        const targetNode = app.graph.getNodeById(link.target_id);
+                        if (targetNode && targetNode.comfyClass === "TriggerWord Toggle (LoraManager)") {
+                            connectedNodes.push(targetNode.id);
+                        }
+                    }
+                }
+            }
+        }
+    }
+    return connectedNodes;
+}
+
+// Function to update trigger words for connected toggle nodes
+function updateConnectedTriggerWords(node, text) {
+    const connectedNodeIds = getConnectedTriggerToggleNodes(node);
+    if (connectedNodeIds.length > 0) {
+        const loraNames = new Set();
+        let match;
+        LORA_PATTERN.lastIndex = 0;
+        while ((match = LORA_PATTERN.exec(text)) !== null) {
+            loraNames.add(match[1]);
+        }
+        
+        fetch("/loramanager/get_trigger_words", {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({
+                lora_names: Array.from(loraNames),
+                node_ids: connectedNodeIds
+            })
+        }).catch(err => console.error("Error fetching trigger words:", err));
+    }
+}
+
 function mergeLoras(lorasText, lorasArr) {
     const result = [];
     let match;
@@ -40,7 +89,7 @@ app.registerExtension({
             });
 
             // Wait for node to be properly initialized
-            requestAnimationFrame(() => {               
+            requestAnimationFrame(async () => {               
                 // Restore saved value if exists
                 let existingLoras = [];
                 if (node.widgets_values && node.widgets_values.length > 0) {
@@ -64,7 +113,10 @@ app.registerExtension({
                 // Add flag to prevent callback loops
                 let isUpdating = false;
                  
-                // Get the widget object directly from the returned object
+                // Dynamically load the appropriate widget module
+                const lorasModule = await getLorasWidgetModule();
+                const { addLorasWidget } = lorasModule;
+                
                 const result = addLorasWidget(node, "loras", {
                     defaultVal: mergedLoras  // Pass object directly
                 }, (value) => {
@@ -86,6 +138,9 @@ app.registerExtension({
                         newText = newText.replace(/\s+/g, ' ').trim();
                         
                         inputWidget.value = newText;
+                        
+                        // Update trigger words when lorasWidget changes
+                        updateConnectedTriggerWords(node, newText);
                     } finally {
                         isUpdating = false;
                     }
@@ -104,6 +159,9 @@ app.registerExtension({
                         const mergedLoras = mergeLoras(value, currentLoras);
                         
                         node.lorasWidget.value = mergedLoras;
+                        
+                        // Update trigger words when input changes
+                        updateConnectedTriggerWords(node, value);
                     } finally {
                         isUpdating = false;
                     }

web/comfyui/loras_widget.js

@@ -366,6 +366,108 @@ export function addLorasWidget(node, name, opts, callback) {
     return menuItem;
   };
 
+  // Function to handle strength adjustment via dragging
+  const handleStrengthDrag = (name, initialStrength, initialX, event, widget) => {
+    // Calculate drag sensitivity (how much the strength changes per pixel)
+    // Using 0.01 per 10 pixels of movement
+    const sensitivity = 0.001;
+    
+    // Get the current mouse position
+    const currentX = event.clientX;
+    
+    // Calculate the distance moved
+    const deltaX = currentX - initialX;
+    
+    // Calculate the new strength value based on movement
+    // Moving right increases, moving left decreases
+    let newStrength = Number(initialStrength) + (deltaX * sensitivity);
+    
+    // Limit the strength to reasonable bounds (now between -10 and 10)
+    newStrength = Math.max(-10, Math.min(10, newStrength));
+    newStrength = Number(newStrength.toFixed(2));
+    
+    // Update the lora data
+    const lorasData = parseLoraValue(widget.value);
+    const loraIndex = lorasData.findIndex(l => l.name === name);
+    
+    if (loraIndex >= 0) {
+      lorasData[loraIndex].strength = newStrength;
+      
+      // Update the widget value
+      widget.value = formatLoraValue(lorasData);
+      
+      // Force re-render to show updated strength value
+      renderLoras(widget.value, widget);
+    }
+  };
+  
+  // Function to initialize drag operation
+  const initDrag = (loraEl, nameEl, name, widget) => {
+    let isDragging = false;
+    let initialX = 0;
+    let initialStrength = 0;
+    
+    // Create a style element for drag cursor override if it doesn't exist
+    if (!document.getElementById('comfy-lora-drag-style')) {
+      const styleEl = document.createElement('style');
+      styleEl.id = 'comfy-lora-drag-style';
+      styleEl.textContent = `
+        body.comfy-lora-dragging,
+        body.comfy-lora-dragging * {
+          cursor: ew-resize !important;
+        }
+      `;
+      document.head.appendChild(styleEl);
+    }
+    
+    // Create a drag handler that's applied to the entire lora entry
+    // except toggle and strength controls
+    loraEl.addEventListener('mousedown', (e) => {
+      // Skip if clicking on toggle or strength control areas
+      if (e.target.closest('.comfy-lora-toggle') || 
+          e.target.closest('input') || 
+          e.target.closest('.comfy-lora-arrow')) {
+        return;
+      }
+      
+      // Store initial values
+      const lorasData = parseLoraValue(widget.value);
+      const loraData = lorasData.find(l => l.name === name);
+      
+      if (!loraData) return;
+      
+      initialX = e.clientX;
+      initialStrength = loraData.strength;
+      isDragging = true;
+      
+      // Add class to body to enforce cursor style globally
+      document.body.classList.add('comfy-lora-dragging');
+      
+      // Prevent text selection during drag
+      e.preventDefault();
+    });
+    
+    // Use the document for move and up events to ensure drag continues
+    // even if mouse leaves the element
+    document.addEventListener('mousemove', (e) => {
+      if (!isDragging) return;
+      
+      // Call the strength adjustment function
+      handleStrengthDrag(name, initialStrength, initialX, e, widget);
+      
+      // Prevent showing the preview tooltip during drag
+      previewTooltip.hide();
+    });
+    
+    document.addEventListener('mouseup', () => {
+      if (isDragging) {
+        isDragging = false;
+        // Remove the class to restore normal cursor behavior
+        document.body.classList.remove('comfy-lora-dragging');
+      }
+    });
+  };
+
   // Function to create context menu
   const createContextMenu = (x, y, loraName, widget) => {
     // Hide preview tooltip first
@@ -649,6 +751,9 @@ export function addLorasWidget(node, name, opts, callback) {
         e.stopPropagation();
         previewTooltip.hide();
       });
+      
+      // Initialize drag functionality for strength adjustment
+      initDrag(loraEl, nameEl, name, widget);
 
       // Remove the preview tooltip events from loraEl
       loraEl.onmouseenter = () => {
@@ -795,7 +900,7 @@ export function addLorasWidget(node, name, opts, callback) {
     });
     
     // Calculate height based on number of loras and fixed sizes
-    const calculatedHeight = CONTAINER_PADDING + HEADER_HEIGHT + (lorasData.length * LORA_ENTRY_HEIGHT);
+    const calculatedHeight = CONTAINER_PADDING + HEADER_HEIGHT + (Math.min(lorasData.length, 5) * LORA_ENTRY_HEIGHT);
     updateWidgetHeight(calculatedHeight);
   };
 
@@ -861,9 +966,6 @@ export function addLorasWidget(node, name, opts, callback) {
 // Function to directly save the recipe without dialog
 async function saveRecipeDirectly(widget) {
   try {
-    // Get the workflow data from the ComfyUI app
-    const prompt = await app.graphToPrompt();
-    
     // Show loading toast
     if (app && app.extensionManager && app.extensionManager.toast) {
       app.extensionManager.toast.add({
@@ -874,14 +976,9 @@ async function saveRecipeDirectly(widget) {
       });
     }
     
-    // Prepare the data - only send workflow JSON
-    const formData = new FormData();
-    formData.append('workflow_json', JSON.stringify(prompt.output));
-    
-    // Send the request
+    // Send the request to the backend API without workflow data
     const response = await fetch('/api/recipes/save-from-widget', {
-      method: 'POST',
-      body: formData
+      method: 'POST'
     });
     
     const result = await response.json();
@@ -917,4 +1014,4 @@ async function saveRecipeDirectly(widget) {
       });
     }
   }
-} 
+}

web/comfyui/usage_stats.js

@@ -0,0 +1,36 @@
+// ComfyUI extension to track model usage statistics
+import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js";
+
+// Register the extension
+app.registerExtension({
+    name: "ComfyUI-Lora-Manager.UsageStats",
+    
+    init() {
+        // Listen for successful executions
+        api.addEventListener("execution_success", ({ detail }) => {
+            if (detail && detail.prompt_id) {
+                this.updateUsageStats(detail.prompt_id);
+            }
+        });
+    },
+    
+    async updateUsageStats(promptId) {
+        try {
+            // Call backend endpoint with the prompt_id
+            const response = await fetch(`/loras/api/update-usage-stats`, {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({ prompt_id: promptId }),
+            });
+            
+            if (!response.ok) {
+                console.warn("Failed to update usage statistics:", response.statusText);
+            }
+        } catch (error) {
+            console.error("Error updating usage statistics:", error);
+        }
+    }
+});