feat: Update release notes and version to 0.8.7 with enhancements and bug fixes

Dev

README.md

@@ -20,6 +20,14 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 
 ## Release Notes
 
+### 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

__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/lora_manager.py

@@ -104,8 +104,6 @@ class LoraManager:
     async def _initialize_services(cls):
         """Initialize all services using the ServiceRegistry"""
         try:
-            logger.info("LoRA Manager: Initializing services via ServiceRegistry")
-            
             # Initialize CivitaiClient first to ensure it's ready for other services
             civitai_client = await ServiceRegistry.get_civitai_client()
             
@@ -115,12 +113,12 @@ 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()

py/metadata_collector/__init__.py

@@ -0,0 +1,18 @@
+import os
+import importlib
+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)

py/metadata_collector/constants.py

@@ -0,0 +1,12 @@
+"""Constants used by the metadata collector"""
+
+# Individual category constants
+MODELS = "models"
+PROMPTS = "prompts"
+SAMPLING = "sampling"
+LORAS = "loras"
+SIZE = "size"
+IMAGES = "images"  # Added new category for image results
+
+# Collection of categories for iteration
+METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES]  # Added IMAGES to categories

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 +1,245 @@
+import json
+
+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 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 KSamplerAdvanced 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:
+                # 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", "")
+                
+                # 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"""           
+        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 +1,280 @@
+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]
+
+# Registry of node-specific extractors
+NODE_EXTRACTORS = {
+    # Sampling
+    "KSampler": SamplerExtractor,
+    "KSamplerAdvanced": KSamplerAdvancedExtractor,  # Add KSamplerAdvanced
+    "SamplerCustomAdvanced": SamplerExtractor,  # Add SamplerCustomAdvanced
+    # 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/save_image.py

@@ -5,10 +5,10 @@ import re
 import numpy as np
 import folder_paths # type: ignore
 from ..services.lora_scanner import LoraScanner
-from ..workflow.parser import WorkflowParser
+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 +34,7 @@ class SaveImage:
                 "file_format": (["png", "jpeg", "webp"],),
             },
             "optional": {
-                "custom_prompt": ("STRING", {"default": "", "forceInput": True}),
+                "lossless_webp": ("BOOLEAN", {"default": False}),
-                "lossless_webp": ("BOOLEAN", {"default": True}),
                 "quality": ("INT", {"default": 100, "min": 1, "max": 100}),
                 "embed_workflow": ("BOOLEAN", {"default": False}),
                 "add_counter_to_filename": ("BOOLEAN", {"default": True}),
@@ -61,21 +60,17 @@ class SaveImage:
                 return item.get('sha256')
         return None
 
-    async def format_metadata(self, parsed_workflow, custom_prompt=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', '')
+        prompt = metadata_dict.get('prompt', '')
-        negative_prompt = parsed_workflow.get('negative_prompt', '')
+        negative_prompt = metadata_dict.get('negative_prompt', '')
-        
-        # Override prompt with custom_prompt if provided
-        if custom_prompt:
-            prompt = custom_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 +99,11 @@ class SaveImage:
         params = []
         
         # Add standard parameters in the correct order
-        if 'steps' in parsed_workflow:
+        if 'steps' in metadata_dict:
-            params.append(f"Steps: {parsed_workflow.get('steps')}")
+            params.append(f"Steps: {metadata_dict.get('steps')}")
         
-        if 'sampler' in parsed_workflow:
+        if 'sampler' in metadata_dict:
-            sampler = parsed_workflow.get('sampler')
+            sampler = metadata_dict.get('sampler')
             # Convert ComfyUI sampler names to user-friendly names
             sampler_mapping = {
                 'euler': 'Euler',
@@ -130,8 +125,8 @@ class SaveImage:
             sampler_name = sampler_mapping.get(sampler, sampler)
             params.append(f"Sampler: {sampler_name}")
         
-        if 'scheduler' in parsed_workflow:
+        if 'scheduler' in metadata_dict:
-            scheduler = parsed_workflow.get('scheduler')
+            scheduler = metadata_dict.get('scheduler')
             scheduler_mapping = {
                 'normal': 'Simple',
                 'karras': 'Karras',
@@ -142,27 +137,36 @@ class SaveImage:
             scheduler_name = scheduler_mapping.get(scheduler, scheduler)
             params.append(f"Schedule type: {scheduler_name}")
         
-        # CFG scale (cfg in parsed_workflow)
+        # CFG scale (cfg_scale in metadata_dict)
-        if 'cfg_scale' in parsed_workflow:
+        if 'cfg_scale' in metadata_dict:
-            params.append(f"CFG scale: {parsed_workflow.get('cfg_scale')}")
+            params.append(f"CFG scale: {metadata_dict.get('cfg_scale')}")
-        elif 'cfg' in parsed_workflow:
+        elif 'cfg' in metadata_dict:
-            params.append(f"CFG scale: {parsed_workflow.get('cfg')}")
+            params.append(f"CFG scale: {metadata_dict.get('cfg')}")
         
         # Seed
-        if 'seed' in parsed_workflow:
+        if 'seed' in metadata_dict:
-            params.append(f"Seed: {parsed_workflow.get('seed')}")
+            params.append(f"Seed: {metadata_dict.get('seed')}")
         
         # Size
-        if 'size' in parsed_workflow:
+        if 'size' in metadata_dict:
-            params.append(f"Size: {parsed_workflow.get('size')}")
+            params.append(f"Size: {metadata_dict.get('size')}")
         
         # Model info
-        if 'checkpoint' in parsed_workflow:
+        if 'checkpoint' in metadata_dict:
-            # Extract basename without path
+            # Ensure checkpoint is a string before processing
-            checkpoint = os.path.basename(parsed_workflow.get('checkpoint', ''))
+            checkpoint = metadata_dict.get('checkpoint')
-            # Remove extension if present
+            if checkpoint is not None:
-            checkpoint = os.path.splitext(checkpoint)[0]
+                # Handle both string and other types safely
-            params.append(f"Model: {checkpoint}")
+                if isinstance(checkpoint, str):
+                    # Extract basename without path
+                    checkpoint = os.path.basename(checkpoint)
+                    # Remove extension if present
+                    checkpoint = os.path.splitext(checkpoint)[0]
+                else:
+                    # Convert non-string to string
+                    checkpoint = str(checkpoint)
+                
+                params.append(f"Model: {checkpoint}")
         
         # Add LoRA hashes if available
         if lora_hashes:
@@ -181,9 +185,9 @@ 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 +195,30 @@ class SaveImage:
             parts = segment.replace("%", "").split(":")
             key = parts[0]
             
-            if key == "seed" and 'seed' in parsed_workflow:
+            if key == "seed" and 'seed' in metadata_dict:
-                filename = filename.replace(segment, str(parsed_workflow.get('seed', '')))
+                filename = filename.replace(segment, str(metadata_dict.get('seed', '')))
-            elif key == "width" and 'size' in parsed_workflow:
+            elif key == "width" and 'size' in metadata_dict:
-                size = parsed_workflow.get('size', 'x')
+                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:
+            elif key == "height" and 'size' in metadata_dict:
-                size = parsed_workflow.get('size', 'x')
+                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:
+            elif key == "pprompt" and 'prompt' in metadata_dict:
-                prompt = parsed_workflow.get('prompt', '').replace("\n", " ")
+                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:
+            elif key == "nprompt" and 'negative_prompt' in metadata_dict:
-                prompt = parsed_workflow.get('negative_prompt', '').replace("\n", " ")
+                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:
+            elif key == "model" and 'checkpoint' in metadata_dict:
-                model = parsed_workflow.get('checkpoint', '')
+                model = metadata_dict.get('checkpoint', '')
                 model = os.path.splitext(os.path.basename(model))[0]
                 if len(parts) >= 2:
                     length = int(parts[1])
@@ -224,12 +228,13 @@ class SaveImage:
                 from datetime import datetime
                 now = datetime.now()
                 date_table = {
-                    "yyyy": str(now.year),
+                    "yyyy": f"{now.year:04d}",
-                    "MM": str(now.month).zfill(2),
+                    "yy": f"{now.year % 100:02d}",
-                    "dd": str(now.day).zfill(2),
+                    "MM": f"{now.month:02d}",
-                    "hh": str(now.hour).zfill(2),
+                    "dd": f"{now.day:02d}",
-                    "mm": str(now.minute).zfill(2),
+                    "hh": f"{now.hour:02d}",
-                    "ss": str(now.second).zfill(2),
+                    "mm": f"{now.minute:02d}",
+                    "ss": f"{now.second:02d}",
                 }
                 if len(parts) >= 2:
                     date_format = parts[1]
@@ -245,23 +250,19 @@ 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,
+                   lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
-                   custom_prompt=None):
         """Save images with metadata"""
         results = []
         
-        # Parse the workflow using the WorkflowParser
+        # Get metadata using the metadata collector
-        parser = WorkflowParser()
+        raw_metadata = get_metadata()
-        if prompt:
+        metadata_dict = MetadataProcessor.to_dict(raw_metadata)
-            parsed_workflow = parser.parse_workflow(prompt)
-        else:
-            parsed_workflow = {}
             
         # 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(
@@ -289,7 +290,8 @@ class SaveImage:
             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 +300,8 @@ 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 +349,7 @@ 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,
+                     lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
-                     custom_prompt=""):
         """Process and save image with metadata"""
         # Make sure the output directory exists
         os.makedirs(self.output_dir, exist_ok=True)
@@ -368,8 +370,7 @@ class SaveImage:
             lossless_webp,
             quality,
             embed_workflow,
-            add_counter_to_filename,
+            add_counter_to_filename
-            custom_prompt if custom_prompt.strip() else None
         )
         
         return (images,)

py/routes/api_routes.py

@@ -50,8 +50,8 @@ 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/version/{modelVersionId}', routes.get_civitai_model_by_version)
-        app.router.add_get('/api/civitai/model/{hash}', routes.get_civitai_model)
+        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)
@@ -226,7 +226,7 @@ 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 +396,52 @@ 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:
+    async def get_civitai_model_by_version(self, request: web.Request) -> web.Response:
-        """Get CivitAI model details by model version ID or hash"""
+        """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 +800,7 @@ 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', [])
                 

py/routes/recipe_routes.py

@@ -1,5 +1,9 @@
 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
@@ -11,9 +15,11 @@ from ..utils.recipe_parsers import RecipeParserFactory
 from ..utils.constants import CARD_PREVIEW_WIDTH
 
 from ..config import config
-from ..workflow.parser import WorkflowParser
+from ..metadata_collector import get_metadata  # Add MetadataCollector import
+from ..metadata_collector.metadata_processor import MetadataProcessor  # Add MetadataProcessor import
 from ..utils.utils import download_civitai_image
 from ..services.service_registry import ServiceRegistry  # Add ServiceRegistry import
+from ..metadata_collector.metadata_registry import MetadataRegistry
 
 logger = logging.getLogger(__name__)
 
@@ -24,7 +30,7 @@ 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
@@ -656,8 +662,8 @@ class RecipeRoutes:
             logger.error(f"Error retrieving base models: {e}", exc_info=True)
             return web.json_response({
                 'success': False,
-                'error': str(e)
+                'error': str(e)}
-            }, status=500) 
+            , 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 +792,72 @@ 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
+            # Check if we have valid metadata
-            workflow_json = None
+            if not metadata_dict:
+                return web.json_response({"error": "No generation metadata found"}, status=400)
             
-            while True:
+            # Get the most recent image from metadata registry instead of temp directory
-                field = await reader.next()
+            metadata_registry = MetadataRegistry()
-                if field is None:
+            latest_image = metadata_registry.get_first_decoded_image()
-                    break
+            
+            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':
+                # Get the shape info for debugging
-                    workflow_text = await field.text()
+                if hasattr(tensor_image, 'shape'):
-                    try:
+                    shape_info = tensor_image.shape
-                        workflow_json = json.loads(workflow_text)
+                    logger.debug(f"Tensor shape: {shape_info}, dtype: {tensor_image.dtype}")
-                    except:
+                
-                        return web.json_response({"error": "Invalid workflow JSON"}, status=400)
+                # 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:
+            # Get the lora stack from the metadata
-                return web.json_response({"error": "Missing workflow JSON"}, status=400)
+            lora_stack = metadata_dict.get("loras", "")
-            
-            # 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", "")
             
             # Parse the lora stack format: "<lora:name:strength> <lora:name2:strength2> ..."
             import re
@@ -837,7 +865,7 @@ 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 +879,6 @@ 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 +946,8 @@ class RecipeRoutes:
                 "created_date": time.time(),
                 "base_model": most_common_base_model,
                 "loras": loras_data,
-                "checkpoint": parsed_workflow.get("checkpoint", ""),
+                "checkpoint": metadata_dict.get("checkpoint", ""),
-                "gen_params": {key: value for key, value in parsed_workflow.items() 
+                "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
             }

py/services/civitai_client.py

@@ -210,8 +210,17 @@ class CivitaiClient:
             logger.error(f"Error fetching model versions: {e}")
             return None
 
-    async def get_model_version_info(self, version_id: str) -> Optional[Dict]:
+    async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict], Optional[str]]:
-        """Fetch model version metadata from Civitai"""
+        """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
             url = f"{self.base_url}/model-versions/{version_id}"
@@ -219,11 +228,25 @@ class CivitaiClient:
             
             async with session.get(url, headers=headers) as response:
                 if response.status == 200:
-                    return await response.json()
+                    return await response.json(), None
-                return 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
+                return None, f"Failed to fetch model info (status {response.status})"
         except Exception as e:
-            logger.error(f"Error fetching model version info: {e}")
+            error_msg = f"Error fetching model version info: {e}"
-            return None
+            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

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)
+                version_info, error_msg = 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)
+                version_info, error_msg = await civitai_client.get_model_version_info(model_version_id)
             elif model_hash:
                 # Get model by hash
                 version_info = await civitai_client.get_model_by_hash(model_hash)
 
             
             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/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)
+                    result = await self._get_hash_from_civitai(model_version_id)
-                    if hash_from_civitai:
+                    if isinstance(result, tuple):
-                        lora['hash'] = hash_from_civitai
+                        hash_from_civitai, is_deleted = result
-                        metadata_updated = True
+                        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'):
+            if not version_info:
-                logger.debug(f"No files found in version info for ID: {model_version_id}")
+                if error_msg and "model not found" in error_msg.lower():
-                return None
+                    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
+            return None, False
-
-    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
 
     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):
+                try:
-                    # It's a file path
+                    if isinstance(image_data, str) and os.path.exists(image_data):
-                    metadata = ExifUtils.extract_image_metadata(image_data)
+                        # For file path, extract directly
-                    img = Image.open(image_data)
+                        metadata = ExifUtils.extract_image_metadata(image_data)
-                else:
+                    else:
-                    # It's binary data
+                        # For binary data, save to temp file first
-                    temp_img = BytesIO(image_data)
+                        import tempfile
-                    img = Image.open(temp_img)
+                        with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as temp_file:
-                    # Save to a temporary file to extract metadata
+                            temp_path = temp_file.name
-                    import tempfile
+                            temp_file.write(image_data)
-                    with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as temp_file:
+                        try:
-                        temp_path = temp_file.name
+                            metadata = ExifUtils.extract_image_metadata(temp_path)
-                        temp_file.write(image_data)
+                        except Exception as e:
-                    metadata = ExifUtils.extract_image_metadata(temp_path)
+                            logger.warning(f"Failed to extract metadata from temp file: {e}")
-                    os.unlink(temp_path)
+                        finally:
-            else:
+                            # Clean up temp file
-                # Just open the image without extracting metadata
+                            try:
-                if isinstance(image_data, str) and os.path.exists(image_data):
+                                os.unlink(temp_path)
-                    img = Image.open(image_data)
+                            except Exception:
-                else:
+                                pass
-                    img = Image.open(BytesIO(image_data))
+                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
+            # Resize the image with error handling
-            resized_img = img.resize((target_width, new_height), Image.LANCZOS)
+            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)
+                save_format, extension = 'WEBP', '.webp'
-                extension = '.webp'
-            # JPEG format
             elif format.lower() in ('jpg', 'jpeg'):
-                resized_img.save(output, format='JPEG', quality=quality)
+                save_format, extension = 'JPEG', '.jpg'
-                extension = '.jpg'
-            # PNG format
             elif format.lower() == 'png':
-                resized_img.save(output, format='PNG', optimize=True)
+                save_format, extension = 'PNG', '.png'
-                extension = '.png'
             else:
-                # Default to WebP
+                save_format, extension = 'WEBP', '.webp'
-                resized_img.save(output, format='WEBP', quality=quality)
+            
-                extension = '.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
+                try:
-                if format.lower() == 'webp':
+                    if save_format == 'WEBP':
-                    # Create a new BytesIO with metadata
+                        # For WebP format, directly save with metadata
-                    output_with_metadata = BytesIO()
+                        try:
-                    
+                            output_with_metadata = BytesIO()
-                    # Create EXIF data with user comment
+                            exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
-                    exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
+                            exif_bytes = piexif.dump(exif_dict)
-                    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()
-                    # Save with metadata
+                        except Exception as e:
-                    resized_img.save(output_with_metadata, format='WEBP', exif=exif_bytes, quality=quality)
+                            logger.warning(f"Failed to add metadata to WebP, continuing without it: {e}")
-                    optimized_data = output_with_metadata.getvalue()
+                    else:
-                else:
+                        # For other formats, use temporary file
-                    # For other formats, use the temporary file approach
+                        import tempfile
-                    import tempfile
+                        with tempfile.NamedTemporaryFile(suffix=extension, delete=False) as temp_file:
-                    with tempfile.NamedTemporaryFile(suffix=extension, delete=False) as temp_file:
+                            temp_path = temp_file.name
-                        temp_path = temp_file.name
+                            temp_file.write(optimized_data)
-                        temp_file.write(optimized_data)
+                        
-                    
+                        try:
-                    # Add the metadata back
+                            # Add metadata
-                    ExifUtils.update_image_metadata(temp_path, metadata)
+                            ExifUtils.update_image_metadata(temp_path, metadata)
-                    
+                            # Read back the file
-                    # Read the file with metadata
+                            with open(temp_path, 'rb') as f:
-                    with open(temp_path, 'rb') as f:
+                                optimized_data = f.read()
-                        optimized_data = f.read()
+                        except Exception as e:
-                    
+                            logger.warning(f"Failed to add metadata to image, continuing without it: {e}")
-                    # Clean up
+                        finally:
-                    os.unlink(temp_path)
+                            # 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:
+                try:
-                    return f.read(), os.path.splitext(image_data)[1]
+                    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/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
             

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 = "0.8.7"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",

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)
@@ -424,12 +424,20 @@ 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
+            // Get the current page's previewVersions Map based on model type
-            if (modelType === 'lora') {
+            const pageType = modelType === 'checkpoint' ? 'checkpoints' : 'loras';
-                state.previewVersions?.set(filePath, Date.now());
+            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 +55,29 @@ 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 checkpoint metadata (similar to the Lora version)
+export async function saveCheckpointMetadata(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 await response.json();
 }

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

@@ -44,7 +44,10 @@ 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

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, saveCheckpointMetadata } 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 saveCheckpointMetadata(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 +1,324 @@
+import { BaseContextMenu } from './BaseContextMenu.js';
+import { refreshSingleLoraMetadata } from '../../api/loraApi.js';
+import { showToast, getNSFWLevelName } from '../../utils/uiHelpers.js';
+import { NSFW_LEVELS } from '../../utils/constants.js';
+import { getStorageItem } from '../../utils/storageHelpers.js';
+
+export class LoraContextMenu 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) {
+        const response = await fetch('/api/loras/save-metadata', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_path: filePath,
+                ...data
+            })
+        });
+    
+        if (!response.ok) {
+            throw new Error('Failed to save metadata');
+        }
+        
+        return await response.json();
+    }
+
+    updateCardBlurEffect(card, level) {
+        // Get user settings for blur threshold
+        const blurThreshold = parseInt(getStorageItem('nsfwBlurLevel') || '4');
+        
+        // Get card preview container
+        const previewContainer = card.querySelector('.card-preview');
+        if (!previewContainer) return;
+        
+        // Get preview media element
+        const previewMedia = previewContainer.querySelector('img') || previewContainer.querySelector('video');
+        if (!previewMedia) return;
+        
+        // Check if blur should be applied
+        if (level >= blurThreshold) {
+            // Add blur class to the preview container
+            previewContainer.classList.add('blurred');
+            
+            // Get or create the NSFW overlay
+            let nsfwOverlay = previewContainer.querySelector('.nsfw-overlay');
+            if (!nsfwOverlay) {
+                // Create new overlay
+                nsfwOverlay = document.createElement('div');
+                nsfwOverlay.className = 'nsfw-overlay';
+                
+                // Create and configure the warning content
+                const warningContent = document.createElement('div');
+                warningContent.className = 'nsfw-warning';
+                
+                // Determine NSFW warning text based on level
+                let nsfwText = "Mature Content";
+                if (level >= NSFW_LEVELS.XXX) {
+                    nsfwText = "XXX-rated Content";
+                } else if (level >= NSFW_LEVELS.X) {
+                    nsfwText = "X-rated Content";
+                } else if (level >= NSFW_LEVELS.R) {
+                    nsfwText = "R-rated Content";
+                }
+                
+                // Add warning text and show button
+                warningContent.innerHTML = `
+                    <p>${nsfwText}</p>
+                    <button class="show-content-btn">Show</button>
+                `;
+                
+                // Add click event to the show button
+                const showBtn = warningContent.querySelector('.show-content-btn');
+                showBtn.addEventListener('click', (e) => {
+                    e.stopPropagation();
+                    previewContainer.classList.remove('blurred');
+                    nsfwOverlay.style.display = 'none';
+                    
+                    // Update toggle button icon if it exists
+                    const toggleBtn = card.querySelector('.toggle-blur-btn');
+                    if (toggleBtn) {
+                        toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
+                    }
+                });
+                
+                nsfwOverlay.appendChild(warningContent);
+                previewContainer.appendChild(nsfwOverlay);
+            } else {
+                // Update existing overlay
+                const warningText = nsfwOverlay.querySelector('p');
+                if (warningText) {
+                    let nsfwText = "Mature Content";
+                    if (level >= NSFW_LEVELS.XXX) {
+                        nsfwText = "XXX-rated Content";
+                    } else if (level >= NSFW_LEVELS.X) {
+                        nsfwText = "X-rated Content";
+                    } else if (level >= NSFW_LEVELS.R) {
+                        nsfwText = "R-rated Content";
+                    }
+                    warningText.textContent = nsfwText;
+                }
+                nsfwOverlay.style.display = 'flex';
+            }
+            
+            // Get or create the toggle button in the header
+            const cardHeader = previewContainer.querySelector('.card-header');
+            if (cardHeader) {
+                let toggleBtn = cardHeader.querySelector('.toggle-blur-btn');
+                
+                if (!toggleBtn) {
+                    toggleBtn = document.createElement('button');
+                    toggleBtn.className = 'toggle-blur-btn';
+                    toggleBtn.title = 'Toggle blur';
+                    toggleBtn.innerHTML = '<i class="fas fa-eye"></i>';
+                    
+                    // Add click event to toggle button
+                    toggleBtn.addEventListener('click', (e) => {
+                        e.stopPropagation();
+                        const isBlurred = previewContainer.classList.toggle('blurred');
+                        const icon = toggleBtn.querySelector('i');
+                        
+                        // Update icon and overlay visibility
+                        if (isBlurred) {
+                            icon.className = 'fas fa-eye';
+                            nsfwOverlay.style.display = 'flex';
+                        } else {
+                            icon.className = 'fas fa-eye-slash';
+                            nsfwOverlay.style.display = 'none';
+                        }
+                    });
+                    
+                    // Add to the beginning of header
+                    cardHeader.insertBefore(toggleBtn, cardHeader.firstChild);
+                    
+                    // Update base model label class
+                    const baseModelLabel = cardHeader.querySelector('.base-model-label');
+                    if (baseModelLabel && !baseModelLabel.classList.contains('with-toggle')) {
+                        baseModelLabel.classList.add('with-toggle');
+                    }
+                } else {
+                    // Update existing toggle button
+                    toggleBtn.querySelector('i').className = 'fas fa-eye';
+                }
+            }
+        } else {
+            // Remove blur
+            previewContainer.classList.remove('blurred');
+            
+            // Hide overlay if it exists
+            const overlay = previewContainer.querySelector('.nsfw-overlay');
+            if (overlay) overlay.style.display = 'none';
+            
+            // 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

@@ -44,7 +44,9 @@ 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;
 

static/js/components/RecipeModal.js

@@ -790,9 +790,9 @@ 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/loraModal/TriggerWords.js

@@ -235,8 +235,8 @@ function addNewTriggerWord(word) {
     
     // Validation: Check total number
     const currentTags = tagsContainer.querySelectorAll('.trigger-word-tag');
-    if (currentTags.length >= 10) {
+    if (currentTags.length >= 30) {
-        showToast('Maximum 10 trigger words allowed', 'error');
+        showToast('Maximum 30 trigger words allowed', 'error');
         return;
     }
     
@@ -336,7 +336,22 @@ async function saveTriggerWords() {
  */
 window.copyTriggerWord = async function(word) {
     try {
-        await navigator.clipboard.writeText(word);
+        // Modern clipboard API - with fallback for non-secure contexts
+        if (navigator.clipboard && window.isSecureContext) {
+            await navigator.clipboard.writeText(word);
+        } else {
+            // Fallback for older browsers or non-secure contexts
+            const textarea = document.createElement('textarea');
+            textarea.value = word;
+            textarea.style.position = 'absolute';
+            textarea.style.left = '-99999px';
+            document.body.appendChild(textarea);
+            textarea.select();
+            const success = document.execCommand('copy');
+            document.body.removeChild(textarea);
+            
+            if (!success) throw new Error('Copy command failed');
+        }
         showToast('Trigger word copied', 'success');
     } catch (err) {
         console.error('Copy failed:', err);

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/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();
         

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,
@@ -66,6 +70,7 @@ export const state = {
             hasMore: true,
             sortBy: 'name',
             activeFolder: null,
+            previewVersions: checkpointPreviewVersions,
             searchManager: null,
             searchOptions: {
                 filename: true,

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);
+        existingSentinel.remove();
-    } 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);
     }
     
-    // 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 + 200) {
-        if (rect.top < windowHeight + 500) {
             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
+    // Clear any existing interval
-    setTimeout(() => {
+    if (state.scrollCheckInterval) {
-        const sentinel = document.getElementById('scroll-sentinel');
+        clearInterval(state.scrollCheckInterval);
-        if (sentinel && sentinel.getBoundingClientRect().top < window.innerHeight) {
+        state.scrollCheckInterval = null;
-            debouncedLoadMore();
+    }
-        }
-    }, 100);
 }

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();
+    }
 }

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/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 %}

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);
     });
   };
 

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 = () => {
@@ -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
+    // Send the request to the backend API without workflow data
-    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',
+      method: 'POST'
-      body: formData
     });
     
     const result = await response.json();
@@ -917,4 +1014,4 @@ async function saveRecipeDirectly(widget) {
       });
     }
   }
-} 
+}