Update version to 0.8.16 and enhance release notes with new features, improvements, and bug fixes.

This reverts commit 99b36442bb.

.github/FUNDING.yml

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

.gitignore

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

README.md

@@ -10,16 +10,53 @@ A comprehensive toolset that streamlines organizing, downloading, and applying L
 
 ![Interface Preview](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/static/images/screenshot.png)
 
+One-click Integration:
+![One-Click Integration](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/static/images/one-click-send.jpg)
+
 ## 📺 Tutorial: One-Click LoRA Integration
 Watch this quick tutorial to learn how to use the new one-click LoRA integration feature:
 
-[![One-Click LoRA Integration Tutorial](https://img.youtube.com/vi/qS95OjX3e70/0.jpg)](https://youtu.be/qS95OjX3e70)
-[![LoRA Manager v0.8.10 - Checkpoint Management, Standalone Mode, and New Features!](https://img.youtube.com/vi/VKvTlCB78h4/0.jpg)](https://youtu.be/VKvTlCB78h4)
+[![One-Click LoRA Integration Tutorial](https://img.youtube.com/vi/hvKw31YpE-U/0.jpg)](https://youtu.be/hvKw31YpE-U)
 
 ---
 
 ## Release Notes
 
+### v0.8.16
+* **Dramatic Startup Speed Improvement** - Added cache serialization mechanism for significantly faster loading times, especially beneficial for large model collections
+* **Enhanced Refresh Options** - Extended functionality with "Full Rebuild (complete)" option alongside "Quick Refresh (incremental)" to fix potential memory cache issues without requiring application restart
+* **Customizable Display Density** - Replaced compact mode with adjustable display density settings for personalized layout customization
+* **Model Creator Information** - Added creator details to model information panels for better attribution
+* **Improved WebP Support** - Enhanced Save Image node with workflow embedding capability for WebP format images
+* **Direct Example Access** - Added "Open Example Images Folder" button to card interfaces for convenient browsing of downloaded model examples
+* **Enhanced Compatibility** - Full ComfyUI Desktop support for "Send lora or recipe to workflow" functionality
+* **Cache Management** - Added settings to clear existing cache files when needed
+* **Bug Fixes & Stability** - Various improvements for overall reliability and performance
+
+### v0.8.15
+* **Enhanced One-Click Integration** - Replaced copy button with direct send button allowing LoRAs/recipes to be sent directly to your current ComfyUI workflow without needing to paste
+* **Flexible Workflow Integration** - Click to append LoRAs/recipes to existing loader nodes or Shift+click to replace content, with additional right-click menu options for "Send to Workflow (Append)" or "Send to Workflow (Replace)"
+* **Improved LoRA Loader Controls** - Added header drag functionality for proportional strength adjustment of all LoRAs simultaneously (including CLIP strengths when expanded)
+* **Keyboard Navigation Support** - Implemented Page Up/Down for page scrolling, Home key to jump to top, and End key to jump to bottom for faster browsing through large collections
+
+### v0.8.14
+* **Virtualized Scrolling** - Completely rebuilt rendering mechanism for smooth browsing with no lag or freezing, now supporting virtually unlimited model collections with optimized layouts for large displays, improving space utilization and user experience
+* **Compact Display Mode** - Added space-efficient view option that displays more cards per row (7 on 1080p, 8 on 2K, 10 on 4K)
+* **Enhanced LoRA Node Functionality** - Comprehensive improvements to LoRA loader/stacker nodes including real-time trigger word updates (reflecting any change anywhere in the LoRA chain for precise updates) and expanded context menu with "Copy Notes" and "Copy Trigger Words" options for faster workflow
+
+### v0.8.13
+* **Enhanced Recipe Management** - Added "Find duplicates" feature to identify and batch delete duplicate recipes with duplicate detection notifications during imports
+* **Improved Source Tracking** - Source URLs are now saved with recipes imported via URL, allowing users to view original content with one click or manually edit links
+* **Advanced LoRA Control** - Double-click LoRAs in Loader/Stacker nodes to access expanded CLIP strength controls for more precise adjustments of model and CLIP strength separately
+* **Lycoris Model Support** - Added compatibility with Lycoris models for expanded creative options
+* **Bug Fixes & UX Improvements** - Resolved various issues and enhanced overall user experience with numerous optimizations
+
+### v0.8.12
+* **Enhanced Model Discovery** - Added alphabetical navigation bar to LoRAs page for faster browsing through large collections
+* **Optimized Example Images** - Improved download logic to automatically refresh stale metadata before fetching example images
+* **Model Exclusion System** - New right-click option to exclude specific LoRAs or checkpoints from management
+* **Improved Showcase Experience** - Enhanced interaction in LoRA and checkpoint showcase areas for better usability
+
 ### v0.8.11
 * **Offline Image Support** - Added functionality to download and save all model example images locally, ensuring access even when offline or if images are removed from CivitAI or the site is down
 * **Resilient Download System** - Implemented pause/resume capability with checkpoint recovery that persists through restarts or unexpected exits
@@ -31,71 +68,6 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 * **Enhanced Metadata Collection** - Added support for SamplerCustomAdvanced node in the metadata collector module
 * **Improved UI Organization** - Optimized Lora Loader node height to display up to 5 LoRAs at once with scrolling capability for larger collections
 
-### v0.8.9
-* **Favorites System** - New functionality to bookmark your favorite LoRAs and checkpoints for quick access and better organization
-* **Enhanced UI Controls** - Increased model card button sizes for improved usability and easier interaction
-* **Smoother Page Transitions** - Optimized interface switching between pages, eliminating flash issues particularly noticeable in dark theme
-* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
-
-### v0.8.8
-* **Real-time TriggerWord Updates** - Enhanced TriggerWord Toggle node to instantly update when connected Lora Loader or Lora Stacker nodes change, without requiring workflow execution
-* **Optimized Metadata Recovery** - Improved utilization of existing .civitai.info files for faster initialization and preservation of metadata from models deleted from CivitAI
-* **Migration Acceleration** - Further speed improvements for users transitioning from A1111/Forge environments
-* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
-
-### v0.8.7
-* **Enhanced Context Menu** - Added comprehensive context menu functionality to Recipes and Checkpoints pages for improved workflow
-* **Interactive LoRA Strength Control** - Implemented drag functionality in LoRA Loader for intuitive strength adjustment
-* **Metadata Collector Overhaul** - Rebuilt metadata collection system with optimized architecture for better performance
-* **Improved Save Image Node** - Enhanced metadata capture and image saving performance with the new metadata collector
-* **Streamlined Recipe Saving** - Optimized Save Recipe functionality to work independently without requiring Preview Image nodes
-* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
-
-### v0.8.6 Major Update
-* **Checkpoint Management** - Added comprehensive management for model checkpoints including scanning, searching, filtering, and deletion
-* **Enhanced Metadata Support** - New capabilities for retrieving and managing checkpoint metadata with improved operations
-* **Improved Initial Loading** - Optimized cache initialization with visual progress indicators for better user experience
-
-### v0.8.5
-* **Enhanced LoRA & Recipe Connectivity** - Added Recipes tab in LoRA details to see all recipes using a specific LoRA
-* **Improved Navigation** - New shortcuts to jump between related LoRAs and Recipes with one-click navigation
-* **Video Preview Controls** - Added "Autoplay Videos on Hover" setting to optimize performance and reduce resource usage
-* **UI Experience Refinements** - Smoother transitions between related content pages
-
-### v0.8.4
-* **Node Layout Improvements** - Fixed layout issues with LoRA Loader and Trigger Words Toggle nodes in newer ComfyUI frontend versions
-* **Recipe LoRA Reconnection** - Added ability to reconnect deleted LoRAs in recipes by clicking the "deleted" badge in recipe details
-* **Bug Fixes & Stability** - Resolved various issues for improved reliability
-
-### v0.8.3
-* **Enhanced Workflow Parser** - Rebuilt workflow analysis engine with improved support for ComfyUI core nodes and easier extensibility
-* **Improved Recipe System** - Refined the experimental Save Recipe functionality with better workflow integration
-* **New Save Image Node** - Added experimental node with metadata support for perfect CivitAI compatibility
-  * Supports dynamic filename prefixes with variables [1](https://github.com/nkchocoai/ComfyUI-SaveImageWithMetaData?tab=readme-ov-file#filename_prefix)
-* **Default LoRA Root Setting** - Added configuration option for setting your preferred LoRA directory
-
-### v0.8.2  
-* **Faster Initialization for Forge Users** - Improved first-run efficiency by utilizing existing `.json` and `.civitai.info` files from Forge’s CivitAI helper extension, making migration smoother.  
-* **LoRA Filename Editing** - Added support for renaming LoRA files directly within LoRA Manager.  
-* **Recipe Editing** - Users can now edit recipe names and tags.  
-* **Retain Deleted LoRAs in Recipes** - Deleted LoRAs will remain listed in recipes, allowing future functionality to reconnect them once re-obtained.  
-* **Download Missing LoRAs from Recipes** - Easily fetch missing LoRAs associated with a recipe.
-
-### v0.8.1
-* **Base Model Correction** - Added support for modifying base model associations to fix incorrect metadata for non-CivitAI LoRAs
-* **LoRA Loader Flexibility** - Made CLIP input optional for model-only workflows like Hunyuan video generation
-* **Expanded Recipe Support** - Added compatibility with 3 additional recipe metadata formats
-* **Enhanced Showcase Images** - Generation parameters now displayed alongside LoRA preview images
-* **UI Improvements & Bug Fixes** - Various interface refinements and stability enhancements
-
-### v0.8.0
-* **Introduced LoRA Recipes** - Create, import, save, and share your favorite LoRA combinations
-* **Recipe Management System** - Easily browse, search, and organize your LoRA recipes
-* **Workflow Integration** - Save recipes directly from your workflow with generation parameters preserved
-* **Simplified Workflow Application** - Quickly apply saved recipes to new projects
-* **Enhanced UI & UX** - Improved interface design and user experience
-* **Bug Fixes & Stability** - Resolved various issues and enhanced overall performance
-
 [View Update History](./update_logs.md)
 
 ---
@@ -160,7 +132,7 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 
 ### Option 2: **Portable Standalone Edition** (No ComfyUI required)
 
-1. Download the [Portable Package](https://github.com/willmiao/ComfyUI-Lora-Manager/releases/download/v0.8.10/lora_manager_portable.7z)
+1. Download the [Portable Package](https://github.com/willmiao/ComfyUI-Lora-Manager/releases/download/v0.8.15/lora_manager_portable.7z)
 2. Copy the provided `settings.json.example` file to create a new file named `settings.json` in `comfyui-lora-manager` folder
 3. Edit `settings.json` to include your correct model folder paths and CivitAI API key
 4. Run run.bat
@@ -283,6 +255,8 @@ If you find this project helpful, consider supporting its development:
 
 [![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/pixelpawsai)
 
+WeChat: [Click to view QR code](https://raw.githubusercontent.com/willmiao/ComfyUI-Lora-Manager/main/static/images/wechat-qr.webp)
+
 ## 💬 Community
 
 Join our Discord community for support, discussions, and updates:

py/metadata_collector/constants.py

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

py/metadata_collector/metadata_processor.py

@@ -4,33 +4,109 @@ import sys
 # Check if running in standalone mode
 standalone_mode = 'nodes' not in sys.modules
 
-from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE
+from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IS_SAMPLER
 
 class MetadataProcessor:
     """Process and format collected metadata"""
     
     @staticmethod
-    def find_primary_sampler(metadata):
-        """Find the primary KSampler node (with highest denoise value)"""
+    def find_primary_sampler(metadata, downstream_id=None):
+        """
+        Find the primary KSampler node that executed before the given downstream node
+        
+        Parameters:
+        - metadata: The workflow metadata
+        - downstream_id: Optional ID of a downstream node to help identify the specific primary sampler
+        """
+        # If we have a downstream_id and execution_order, use it to narrow down potential samplers
+        if downstream_id and "execution_order" in metadata:
+            execution_order = metadata["execution_order"]
+            
+            # Find the index of the downstream node in the execution order
+            if downstream_id in execution_order:
+                downstream_index = execution_order.index(downstream_id)
+                
+                # Extract all sampler nodes that executed before the downstream node
+                candidate_samplers = {}
+                for i in range(downstream_index):
+                    node_id = execution_order[i]
+                    # Use IS_SAMPLER flag to identify true sampler nodes
+                    if node_id in metadata.get(SAMPLING, {}) and metadata[SAMPLING][node_id].get(IS_SAMPLER, False):
+                        candidate_samplers[node_id] = metadata[SAMPLING][node_id]
+                
+                # If we found candidate samplers, apply primary sampler logic to these candidates only
+                if candidate_samplers:
+                    # Collect potential primary samplers based on different criteria
+                    custom_advanced_samplers = []
+                    advanced_add_noise_samplers = []
+                    high_denoise_samplers = []
+                    max_denoise = -1
+                    high_denoise_id = None
+                    
+                    # First, check for SamplerCustomAdvanced among candidates
+                    prompt = metadata.get("current_prompt")
+                    if prompt and prompt.original_prompt:
+                        for node_id in candidate_samplers:
+                            node_info = prompt.original_prompt.get(node_id, {})
+                            if node_info.get("class_type") == "SamplerCustomAdvanced":
+                                custom_advanced_samplers.append(node_id)
+                    
+                    # Next, check for KSamplerAdvanced with add_noise="enable" among candidates
+                    for node_id, sampler_info in candidate_samplers.items():
+                        parameters = sampler_info.get("parameters", {})
+                        add_noise = parameters.get("add_noise")
+                        if add_noise == "enable":
+                            advanced_add_noise_samplers.append(node_id)
+                    
+                    # Find the sampler with highest denoise value among candidates
+                    for node_id, sampler_info in candidate_samplers.items():
+                        parameters = sampler_info.get("parameters", {})
+                        denoise = parameters.get("denoise")
+                        if denoise is not None and denoise > max_denoise:
+                            max_denoise = denoise
+                            high_denoise_id = node_id
+                    
+                    if high_denoise_id:
+                        high_denoise_samplers.append(high_denoise_id)
+                    
+                    # Combine all potential primary samplers
+                    potential_samplers = custom_advanced_samplers + advanced_add_noise_samplers + high_denoise_samplers
+                    
+                    # Find the most recent potential primary sampler (closest to downstream node)
+                    for i in range(downstream_index - 1, -1, -1):
+                        node_id = execution_order[i]
+                        if node_id in potential_samplers:
+                            return node_id, candidate_samplers[node_id]
+                    
+                    # If no potential sampler found from our criteria, return the most recent sampler
+                    if candidate_samplers:
+                        for i in range(downstream_index - 1, -1, -1):
+                            node_id = execution_order[i]
+                            if node_id in candidate_samplers:
+                                return node_id, candidate_samplers[node_id]
+        
+        # If no downstream_id provided or no suitable sampler found, fall back to original logic
         primary_sampler = None
         primary_sampler_id = None
-        max_denoise = -1  # Track the highest denoise value
+        max_denoise = -1
         
         # First, check for SamplerCustomAdvanced
         prompt = metadata.get("current_prompt")
         if prompt and prompt.original_prompt:
             for node_id, node_info in prompt.original_prompt.items():
                 if node_info.get("class_type") == "SamplerCustomAdvanced":
-                    # Found a SamplerCustomAdvanced node
-                    if node_id in metadata.get(SAMPLING, {}):
+                    # Check if the node is in SAMPLING and has IS_SAMPLER flag
+                    if node_id in metadata.get(SAMPLING, {}) and metadata[SAMPLING][node_id].get(IS_SAMPLER, False):
                         return node_id, metadata[SAMPLING][node_id]
         
-        # Next, check for KSamplerAdvanced with add_noise="enable"
+        # Next, check for KSamplerAdvanced with add_noise="enable" using IS_SAMPLER flag
         for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
+            # Skip if not marked as a sampler
+            if not sampler_info.get(IS_SAMPLER, False):
+                continue
+                
             parameters = sampler_info.get("parameters", {})
             add_noise = parameters.get("add_noise")
-            
-            # If add_noise is "enable", this is likely the primary sampler for KSamplerAdvanced
             if add_noise == "enable":
                 primary_sampler = sampler_info
                 primary_sampler_id = node_id
@@ -39,10 +115,12 @@ class MetadataProcessor:
         # If no specialized sampler found, find the sampler with highest denoise value
         if primary_sampler is None:
             for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
+                # Skip if not marked as a sampler
+                if not sampler_info.get(IS_SAMPLER, False):
+                    continue
+                    
                 parameters = sampler_info.get("parameters", {})
                 denoise = parameters.get("denoise")
-                
-                # If denoise exists and is higher than current max, use this sampler
                 if denoise is not None and denoise > max_denoise:
                     max_denoise = denoise
                     primary_sampler = sampler_info
@@ -74,13 +152,18 @@ class MetadataProcessor:
         current_node_id = node_id
         current_input = input_name
         
+        # If we're just tracing to origin (no target_class), keep track of the last valid node
+        last_valid_node = None
+        
         while current_depth < max_depth:
             if current_node_id not in prompt.original_prompt:
-                return None
+                return last_valid_node if not target_class else None
                 
             node_inputs = prompt.original_prompt[current_node_id].get("inputs", {})
             if current_input not in node_inputs:
-                return None
+                # We've reached a node without the specified input - this is our origin node
+                # if we're not looking for a specific target_class
+                return current_node_id if not target_class else None
                 
             input_value = node_inputs[current_input]
             # Input connections are formatted as [node_id, output_index]
@@ -91,9 +174,9 @@ class MetadataProcessor:
                 if target_class and prompt.original_prompt[found_node_id].get("class_type") == target_class:
                     return found_node_id
                 
-                # If we're not looking for a specific class or haven't found it yet
+                # If we're not looking for a specific class, update the last valid node
                 if not target_class:
-                    return found_node_id
+                    last_valid_node = found_node_id
                 
                 # Continue tracing through intermediate nodes
                 current_node_id = found_node_id
@@ -101,16 +184,17 @@ class MetadataProcessor:
                 if "conditioning" in prompt.original_prompt[current_node_id].get("inputs", {}):
                     current_input = "conditioning"
                 else:
-                    # If there's no "conditioning" input, we can't trace further
+                    # If there's no "conditioning" input, return the current node
+                    # if we're not looking for a specific target_class
                     return found_node_id if not target_class else None
             else:
                 # We've reached a node with no further connections
-                return None
+                return last_valid_node if not target_class else None
             
             current_depth += 1
             
         # If we've reached max depth without finding target_class
-        return None
+        return last_valid_node if not target_class else None
     
     @staticmethod
     def find_primary_checkpoint(metadata):
@@ -126,8 +210,14 @@ class MetadataProcessor:
         return None
     
     @staticmethod
-    def extract_generation_params(metadata):
-        """Extract generation parameters from metadata using node relationships"""
+    def extract_generation_params(metadata, id=None):
+        """
+        Extract generation parameters from metadata using node relationships
+        
+        Parameters:
+        - metadata: The workflow metadata
+        - id: Optional ID of a downstream node to help identify the specific primary sampler
+        """
         params = {
             "prompt": "",
             "negative_prompt": "",
@@ -147,13 +237,20 @@ class MetadataProcessor:
         prompt = metadata.get("current_prompt")
         
         # Find the primary KSampler node
-        primary_sampler_id, primary_sampler = MetadataProcessor.find_primary_sampler(metadata)
+        primary_sampler_id, primary_sampler = MetadataProcessor.find_primary_sampler(metadata, id)
         
         # Directly get checkpoint from metadata instead of tracing
         checkpoint = MetadataProcessor.find_primary_checkpoint(metadata)
         if checkpoint:
             params["checkpoint"] = checkpoint
         
+        # Check if guidance parameter exists in any sampling node
+        for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
+            parameters = sampler_info.get("parameters", {})
+            if "guidance" in parameters and parameters["guidance"] is not None:
+                params["guidance"] = parameters["guidance"]
+                break
+        
         if primary_sampler:
             # Extract sampling parameters
             sampling_params = primary_sampler.get("parameters", {})
@@ -187,24 +284,34 @@ class MetadataProcessor:
                         sampler_params = metadata[SAMPLING][sampler_node_id].get("parameters", {})
                         params["sampler"] = sampler_params.get("sampler_name")
                     
-                    # 3. Trace guider input for FluxGuidance and CLIPTextEncode
+                    # 3. Trace guider input for CFGGuider and CLIPTextEncode
                     guider_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "guider", max_depth=5)
-                    if guider_node_id:
-                        # Look for FluxGuidance along the guider path
-                        flux_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", "FluxGuidance", max_depth=5)
-                        if flux_node_id and flux_node_id in metadata.get(SAMPLING, {}):
-                            flux_params = metadata[SAMPLING][flux_node_id].get("parameters", {})
-                            params["guidance"] = flux_params.get("guidance")
-                        
-                        # Find CLIPTextEncode for positive prompt (through conditioning)
-                        positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", "CLIPTextEncode", max_depth=10)
-                        if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
-                            params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
+                    if guider_node_id and guider_node_id in prompt.original_prompt:
+                        # Check if the guider node is a CFGGuider
+                        if prompt.original_prompt[guider_node_id].get("class_type") == "CFGGuider":
+                            # Extract cfg value from the CFGGuider
+                            if guider_node_id in metadata.get(SAMPLING, {}):
+                                cfg_params = metadata[SAMPLING][guider_node_id].get("parameters", {})
+                                params["cfg_scale"] = cfg_params.get("cfg")
+                            
+                            # Find CLIPTextEncode for positive prompt
+                            positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "positive", "CLIPTextEncode", max_depth=10)
+                            if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
+                                params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
+                            
+                            # Find CLIPTextEncode for negative prompt
+                            negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "negative", "CLIPTextEncode", max_depth=10)
+                            if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
+                                params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
+                        else:
+                            positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
+                            if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
+                                params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
                 
                 else:
                     # Original tracing for standard samplers
                     # Trace positive prompt - look specifically for CLIPTextEncode
-                    positive_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "CLIPTextEncode", max_depth=10)
+                    positive_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", max_depth=10)
                     if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
                         params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
                     else:
@@ -212,21 +319,9 @@ class MetadataProcessor:
                         positive_flux_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "CLIPTextEncodeFlux", max_depth=10)
                         if positive_flux_node_id and positive_flux_node_id in metadata.get(PROMPTS, {}):
                             params["prompt"] = metadata[PROMPTS][positive_flux_node_id].get("text", "")
-                            
-                            # Also extract guidance value if present in the sampling data
-                            if positive_flux_node_id in metadata.get(SAMPLING, {}):
-                                flux_params = metadata[SAMPLING][positive_flux_node_id].get("parameters", {})
-                                if "guidance" in flux_params:
-                                    params["guidance"] = flux_params.get("guidance")
-                    
-                    # Find any FluxGuidance nodes in the positive conditioning path
-                    flux_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "FluxGuidance", max_depth=5)
-                    if flux_node_id and flux_node_id in metadata.get(SAMPLING, {}):
-                        flux_params = metadata[SAMPLING][flux_node_id].get("parameters", {})
-                        params["guidance"] = flux_params.get("guidance")
                     
                     # Trace negative prompt - look specifically for CLIPTextEncode
-                    negative_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "negative", "CLIPTextEncode", max_depth=10)
+                    negative_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "negative", max_depth=10)
                     if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
                         params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
                 
@@ -256,13 +351,19 @@ class MetadataProcessor:
         return params
     
     @staticmethod
-    def to_dict(metadata):
-        """Convert extracted metadata to the ComfyUI output.json format"""           
+    def to_dict(metadata, id=None):
+        """
+        Convert extracted metadata to the ComfyUI output.json format
+        
+        Parameters:
+        - metadata: The workflow metadata
+        - id: Optional ID of a downstream node to help identify the specific primary sampler
+        """              
         if standalone_mode:
             # Return empty dictionary in standalone mode
             return {}
         
-        params = MetadataProcessor.extract_generation_params(metadata)
+        params = MetadataProcessor.extract_generation_params(metadata, id)
         
         # Convert all values to strings to match output.json format
         for key in params:
@@ -272,7 +373,7 @@ class MetadataProcessor:
         return params
     
     @staticmethod
-    def to_json(metadata):
+    def to_json(metadata, id=None):
         """Convert metadata to JSON string"""
-        params = MetadataProcessor.to_dict(metadata)
+        params = MetadataProcessor.to_dict(metadata, id)
         return json.dumps(params, indent=4)

py/metadata_collector/node_extractors.py

@@ -1,6 +1,6 @@
 import os
 
-from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES
+from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER
 
 
 class NodeMetadataExtractor:
@@ -61,7 +61,8 @@ class SamplerExtractor(NodeMetadataExtractor):
                 
         metadata[SAMPLING][node_id] = {
             "parameters": sampling_params,
-            "node_id": node_id
+            "node_id": node_id,
+            IS_SAMPLER: True  # Add sampler flag
         }
         
         # Extract latent image dimensions if available
@@ -98,7 +99,8 @@ class KSamplerAdvancedExtractor(NodeMetadataExtractor):
                 
         metadata[SAMPLING][node_id] = {
             "parameters": sampling_params,
-            "node_id": node_id
+            "node_id": node_id,
+            IS_SAMPLER: True  # Add sampler flag
         }
         
         # Extract latent image dimensions if available
@@ -269,7 +271,8 @@ class KSamplerSelectExtractor(NodeMetadataExtractor):
                 
         metadata[SAMPLING][node_id] = {
             "parameters": sampling_params,
-            "node_id": node_id
+            "node_id": node_id,
+            IS_SAMPLER: False  # Mark as non-primary sampler
         }
 
 class BasicSchedulerExtractor(NodeMetadataExtractor):
@@ -285,7 +288,8 @@ class BasicSchedulerExtractor(NodeMetadataExtractor):
                 
         metadata[SAMPLING][node_id] = {
             "parameters": sampling_params,
-            "node_id": node_id
+            "node_id": node_id,
+            IS_SAMPLER: False  # Mark as non-primary sampler
         }
 
 class SamplerCustomAdvancedExtractor(NodeMetadataExtractor):
@@ -303,7 +307,8 @@ class SamplerCustomAdvancedExtractor(NodeMetadataExtractor):
                 
         metadata[SAMPLING][node_id] = {
             "parameters": sampling_params,
-            "node_id": node_id
+            "node_id": node_id,
+            IS_SAMPLER: True  # Add sampler flag
         }
         
         # Extract latent image dimensions if available
@@ -338,11 +343,20 @@ class CLIPTextEncodeFluxExtractor(NodeMetadataExtractor):
         clip_l_text = inputs.get("clip_l", "")
         t5xxl_text = inputs.get("t5xxl", "")
         
-        # Create JSON string with T5 content first, then CLIP-L
-        combined_text = json.dumps({
-            "T5": t5xxl_text,
-            "CLIP-L": clip_l_text
-        })
+        # If both are empty, use empty string
+        if not clip_l_text and not t5xxl_text:
+            combined_text = ""
+        # If one is empty, use the non-empty one
+        elif not clip_l_text:
+            combined_text = t5xxl_text
+        elif not t5xxl_text:
+            combined_text = clip_l_text
+        # If both have content, use JSON format
+        else:
+            combined_text = json.dumps({
+                "T5": t5xxl_text,
+                "CLIP-L": clip_l_text
+            })
         
         metadata[PROMPTS][node_id] = {
             "text": combined_text,
@@ -362,6 +376,23 @@ class CLIPTextEncodeFluxExtractor(NodeMetadataExtractor):
                 
             metadata[SAMPLING][node_id]["parameters"]["guidance"] = guidance_value
 
+class CFGGuiderExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs or "cfg" not in inputs:
+            return
+            
+        cfg_value = inputs.get("cfg")
+        
+        # Store the cfg value in SAMPLING category
+        if SAMPLING not in metadata:
+            metadata[SAMPLING] = {}
+            
+        if node_id not in metadata[SAMPLING]:
+            metadata[SAMPLING][node_id] = {"parameters": {}, "node_id": node_id}
+            
+        metadata[SAMPLING][node_id]["parameters"]["cfg"] = cfg_value
+
 # Registry of node-specific extractors
 NODE_EXTRACTORS = {
     # Sampling
@@ -374,15 +405,18 @@ NODE_EXTRACTORS = {
     # Loaders
     "CheckpointLoaderSimple": CheckpointLoaderExtractor,
     "UNETLoader": UNETLoaderExtractor,          # Updated to use dedicated extractor
+    "UnetLoaderGGUF": UNETLoaderExtractor,  # Updated to use dedicated extractor
     "LoraLoader": LoraLoaderExtractor,
     "LoraManagerLoader": LoraLoaderManagerExtractor,
     # Conditioning
     "CLIPTextEncode": CLIPTextEncodeExtractor,
     "CLIPTextEncodeFlux": CLIPTextEncodeFluxExtractor,  # Add CLIPTextEncodeFlux
+    "WAS_Text_to_Conditioning": CLIPTextEncodeExtractor,
     # Latent
     "EmptyLatentImage": ImageSizeExtractor,
     # Flux
     "FluxGuidance": FluxGuidanceExtractor,      # Add FluxGuidance
+    "CFGGuider": CFGGuiderExtractor,            # Add CFGGuider
     # Image
     "VAEDecode": VAEDecodeExtractor,  # Added VAEDecode extractor
     # Add other nodes as needed

py/nodes/debug_metadata.py

@@ -14,20 +14,23 @@ class DebugMetadata:
             "required": {
                 "images": ("IMAGE",),
             },
+            "hidden": {
+                "id": "UNIQUE_ID",
+            },
         }
 
     RETURN_TYPES = ("STRING",)
     RETURN_NAMES = ("metadata_json",)
     FUNCTION = "process_metadata"
 
-    def process_metadata(self, images):
+    def process_metadata(self, images, id):
         try:
             # Get the current execution context's metadata
             from ..metadata_collector import get_metadata
             metadata = get_metadata()
             
             # Use the MetadataProcessor to convert it to JSON string
-            metadata_json = MetadataProcessor.to_json(metadata)
+            metadata_json = MetadataProcessor.to_json(metadata, id)
             
             return (metadata_json,)
         except Exception as e:

py/nodes/lora_loader.py

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

py/nodes/lora_stacker.py

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

py/nodes/save_image.py

@@ -41,6 +41,7 @@ class SaveImage:
                 "add_counter_to_filename": ("BOOLEAN", {"default": True}),
             },
             "hidden": {
+                "id": "UNIQUE_ID",
                 "prompt": "PROMPT",
                 "extra_pnginfo": "EXTRA_PNGINFO",
             },
@@ -223,7 +224,7 @@ class SaveImage:
         if lora_hashes:
             lora_hash_parts = []
             for lora_name, hash_value in lora_hashes.items():
-                lora_hash_parts.append(f"{lora_name}: {hash_value}")
+                lora_hash_parts.append(f"{lora_name}: {hash_value[:10]}")
             
             if lora_hash_parts:
                 params.append(f"Lora hashes: \"{', '.join(lora_hash_parts)}\"")
@@ -300,14 +301,14 @@ class SaveImage:
                     
         return filename
 
-    def save_images(self, images, filename_prefix, file_format, prompt=None, extra_pnginfo=None, 
+    def save_images(self, images, filename_prefix, file_format, id, prompt=None, extra_pnginfo=None, 
                    lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
         """Save images with metadata"""
         results = []
-        
+
         # Get metadata using the metadata collector
         raw_metadata = get_metadata()
-        metadata_dict = MetadataProcessor.to_dict(raw_metadata)
+        metadata_dict = MetadataProcessor.to_dict(raw_metadata, id)
             
         # Get or create metadata asynchronously
         metadata = asyncio.run(self.format_metadata(metadata_dict))
@@ -378,14 +379,23 @@ class SaveImage:
                             print(f"Error adding EXIF data: {e}")
                     img.save(file_path, format="JPEG", **save_kwargs)
                 elif file_format == "webp":
-                    # For WebP, also use piexif for metadata
-                    if metadata:
-                        try:
-                            exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
-                            exif_bytes = piexif.dump(exif_dict)
-                            save_kwargs["exif"] = exif_bytes
-                        except Exception as e:
-                            print(f"Error adding EXIF data: {e}")
+                    try:
+                        # For WebP, use piexif for metadata
+                        exif_dict = {}
+
+                        if metadata:
+                            exif_dict['Exif'] = {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}
+                        
+                        # Add workflow if needed
+                        if embed_workflow and extra_pnginfo is not None:
+                            workflow_json = json.dumps(extra_pnginfo["workflow"]) 
+                            exif_dict['0th'] = {piexif.ImageIFD.ImageDescription: "Workflow:" + workflow_json}
+                            
+                        exif_bytes = piexif.dump(exif_dict)
+                        save_kwargs["exif"] = exif_bytes
+                    except Exception as e:
+                        print(f"Error adding EXIF data: {e}")
+                    
                     img.save(file_path, format="WEBP", **save_kwargs)
                 
                 results.append({
@@ -399,7 +409,7 @@ class SaveImage:
         
         return results
 
-    def process_image(self, images, filename_prefix="ComfyUI", file_format="png", prompt=None, extra_pnginfo=None,
+    def process_image(self, images, id, filename_prefix="ComfyUI", file_format="png", prompt=None, extra_pnginfo=None,
                      lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
         """Process and save image with metadata"""
         # Make sure the output directory exists
@@ -416,6 +426,7 @@ class SaveImage:
             images, 
             filename_prefix, 
             file_format, 
+            id,
             prompt, 
             extra_pnginfo,
             lossless_webp,

py/recipes/__init__.py

@@ -0,0 +1,22 @@
+"""Recipe metadata parser package for ComfyUI-Lora-Manager."""
+
+from .base import RecipeMetadataParser
+from .factory import RecipeParserFactory
+from .constants import GEN_PARAM_KEYS, VALID_LORA_TYPES
+from .parsers import (
+    RecipeFormatParser,
+    ComfyMetadataParser,
+    MetaFormatParser,
+    AutomaticMetadataParser
+)
+
+__all__ = [
+    'RecipeMetadataParser',
+    'RecipeParserFactory',
+    'GEN_PARAM_KEYS',
+    'VALID_LORA_TYPES',
+    'RecipeFormatParser',
+    'ComfyMetadataParser',
+    'MetaFormatParser',
+    'AutomaticMetadataParser'
+]

py/recipes/base.py

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

py/recipes/constants.py

@@ -0,0 +1,16 @@
+"""Constants used across recipe parsers."""
+
+# Constants for generation parameters
+GEN_PARAM_KEYS = [
+    'prompt',
+    'negative_prompt', 
+    'steps',
+    'sampler',
+    'cfg_scale',
+    'seed',
+    'size',
+    'clip_skip',
+]
+
+# Valid Lora types
+VALID_LORA_TYPES = ['lora', 'locon']

py/recipes/factory.py

@@ -0,0 +1,43 @@
+"""Factory for creating recipe metadata parsers."""
+
+import logging
+from .parsers import (
+    RecipeFormatParser,
+    ComfyMetadataParser,
+    MetaFormatParser,
+    AutomaticMetadataParser
+)
+from .base import RecipeMetadataParser
+
+logger = logging.getLogger(__name__)
+
+class RecipeParserFactory:
+    """Factory for creating recipe metadata parsers"""
+    
+    @staticmethod
+    def create_parser(user_comment: str) -> RecipeMetadataParser:
+        """
+        Create appropriate parser based on the user comment content
+        
+        Args:
+            user_comment: The EXIF UserComment string from the image
+            
+        Returns:
+            Appropriate RecipeMetadataParser implementation
+        """
+        # Try ComfyMetadataParser first since it requires valid JSON
+        try:
+            if ComfyMetadataParser().is_metadata_matching(user_comment):
+                return ComfyMetadataParser()
+        except Exception:
+            # If JSON parsing fails, move on to other parsers
+            pass
+            
+        if RecipeFormatParser().is_metadata_matching(user_comment):
+            return RecipeFormatParser()
+        elif AutomaticMetadataParser().is_metadata_matching(user_comment):
+            return AutomaticMetadataParser()
+        elif MetaFormatParser().is_metadata_matching(user_comment):
+            return MetaFormatParser()
+        else:
+            return None

py/recipes/parsers/__init__.py

@@ -0,0 +1,13 @@
+"""Recipe parsers package."""
+
+from .recipe_format import RecipeFormatParser
+from .comfy import ComfyMetadataParser
+from .meta_format import MetaFormatParser
+from .automatic import AutomaticMetadataParser
+
+__all__ = [
+    'RecipeFormatParser',
+    'ComfyMetadataParser',
+    'MetaFormatParser',
+    'AutomaticMetadataParser',
+]

py/recipes/parsers/automatic.py

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

py/recipes/parsers/comfy.py

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

py/recipes/parsers/meta_format.py

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

py/recipes/parsers/recipe_format.py

@@ -0,0 +1,114 @@
+"""Parser for dedicated recipe metadata format."""
+
+import re
+import json
+import logging
+from typing import Dict, Any
+from ...config import config
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+class RecipeFormatParser(RecipeMetadataParser):
+    """Parser for images with dedicated recipe metadata format"""
+    
+    # Regular expression pattern for extracting recipe metadata
+    METADATA_MARKER = r'Recipe metadata: (\{.*\})'
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the metadata format"""
+        return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from images with dedicated recipe metadata format"""
+        try:
+            # Extract recipe metadata from user comment
+            try:
+                # Look for recipe metadata section
+                recipe_match = re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL)
+                if not recipe_match:
+                    recipe_metadata = None
+                else:
+                    recipe_json = recipe_match.group(1)
+                    recipe_metadata = json.loads(recipe_json)
+            except Exception as e:
+                logger.error(f"Error extracting recipe metadata: {e}")
+                recipe_metadata = None
+            if not recipe_metadata:
+                return {"error": "No recipe metadata found", "loras": []}
+                
+            # Process the recipe metadata
+            loras = []
+            for lora in recipe_metadata.get('loras', []):
+                # Convert recipe lora format to frontend format
+                lora_entry = {
+                    'id': lora.get('modelVersionId', ''),
+                    'name': lora.get('modelName', ''),
+                    'version': lora.get('modelVersionName', ''),
+                    'type': 'lora',
+                    'weight': lora.get('strength', 1.0),
+                    'file_name': lora.get('file_name', ''),
+                    'hash': lora.get('hash', '')
+                }
+                
+                # Check if this LoRA exists locally by SHA256 hash
+                if lora.get('hash') and recipe_scanner:
+                    lora_scanner = recipe_scanner._lora_scanner
+                    exists_locally = lora_scanner.has_lora_hash(lora['hash'])
+                    if exists_locally:
+                        lora_cache = await lora_scanner.get_cached_data()
+                        lora_item = next((item for item in lora_cache.raw_data if item['sha256'].lower() == lora['hash'].lower()), None)
+                        if lora_item:
+                            lora_entry['existsLocally'] = True
+                            lora_entry['localPath'] = lora_item['file_path']
+                            lora_entry['file_name'] = lora_item['file_name']
+                            lora_entry['size'] = lora_item['size']
+                            lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
+                            
+                    else:
+                        lora_entry['existsLocally'] = False
+                        lora_entry['localPath'] = None
+                        
+                        # Try to get additional info from Civitai if we have a model version ID
+                        if lora.get('modelVersionId') and civitai_client:
+                            try:
+                                civitai_info_tuple = await civitai_client.get_model_version_info(lora['modelVersionId'])
+                                # Populate lora entry with Civitai info
+                                populated_entry = await self.populate_lora_from_civitai(
+                                    lora_entry, 
+                                    civitai_info_tuple, 
+                                    recipe_scanner,
+                                    None,  # No need to track base model counts
+                                    lora['hash']
+                                )
+                                if populated_entry is None:
+                                    continue  # Skip invalid LoRA types
+                                lora_entry = populated_entry
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for LoRA: {e}")
+                                lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                
+                loras.append(lora_entry)
+
+            logger.info(f"Found {len(loras)} loras in recipe metadata")
+            
+            # Filter gen_params to only include recognized keys
+            filtered_gen_params = {}
+            if 'gen_params' in recipe_metadata:
+                for key, value in recipe_metadata['gen_params'].items():
+                    if key in GEN_PARAM_KEYS:
+                        filtered_gen_params[key] = value
+            
+            return {
+                'base_model': recipe_metadata.get('base_model', ''),
+                'loras': loras,
+                'gen_params': filtered_gen_params,
+                'tags': recipe_metadata.get('tags', []),
+                'title': recipe_metadata.get('title', ''),
+                'from_recipe_metadata': True
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing recipe format metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}

py/routes/api_routes.py

@@ -43,6 +43,7 @@ class ApiRoutes:
         app.on_startup.append(lambda _: routes.initialize_services())
         
         app.router.add_post('/api/delete_model', routes.delete_model)
+        app.router.add_post('/api/loras/exclude', routes.exclude_model)  # Add new exclude endpoint
         app.router.add_post('/api/fetch-civitai', routes.fetch_civitai)
         app.router.add_post('/api/replace_preview', routes.replace_preview)
         app.router.add_get('/api/loras', routes.get_loras)
@@ -69,6 +70,13 @@ class ApiRoutes:
         # Add the new trigger words route
         app.router.add_post('/loramanager/get_trigger_words', routes.get_trigger_words)
 
+        # Add new endpoint for letter counts
+        app.router.add_get('/api/loras/letter-counts', routes.get_letter_counts)
+        
+        # Add new endpoints for copying lora data
+        app.router.add_get('/api/loras/get-notes', routes.get_lora_notes)
+        app.router.add_get('/api/loras/get-trigger-words', routes.get_lora_trigger_words)
+
         # Add update check routes
         UpdateRoutes.setup_routes(app)
 
@@ -78,6 +86,12 @@ class ApiRoutes:
             self.scanner = await ServiceRegistry.get_lora_scanner()
         return await ModelRouteUtils.handle_delete_model(request, self.scanner)
 
+    async def exclude_model(self, request: web.Request) -> web.Response:
+        """Handle model exclusion request"""
+        if self.scanner is None:
+            self.scanner = await ServiceRegistry.get_lora_scanner()
+        return await ModelRouteUtils.handle_exclude_model(request, self.scanner)
+
     async def fetch_civitai(self, request: web.Request) -> web.Response:
         """Handle CivitAI metadata fetch request"""
         if self.scanner is None:
@@ -92,8 +106,11 @@ class ApiRoutes:
     
     async def scan_loras(self, request: web.Request) -> web.Response:
         """Force a rescan of LoRA files"""
-        try:                
-            await self.scanner.get_cached_data(force_refresh=True)
+        try:
+            # Get full_rebuild parameter from query string, default to false
+            full_rebuild = request.query.get('full_rebuild', 'false').lower() == 'true'
+                
+            await self.scanner.get_cached_data(force_refresh=True, rebuild_cache=full_rebuild)
             return web.json_response({"status": "success", "message": "LoRA scan completed"})
         except Exception as e:
             logger.error(f"Error in scan_loras: {e}", exc_info=True)
@@ -126,6 +143,9 @@ class ApiRoutes:
             tags = request.query.get('tags', None)
             favorites_only = request.query.get('favorites_only', 'false').lower() == 'true'  # New parameter
             
+            # New parameter for alphabet filtering
+            first_letter = request.query.get('first_letter', None)
+            
             # New parameters for recipe filtering
             lora_hash = request.query.get('lora_hash', None)
             lora_hashes = request.query.get('lora_hashes', None)
@@ -156,7 +176,8 @@ class ApiRoutes:
                 tags=filters.get('tags', None),
                 search_options=search_options,
                 hash_filters=hash_filters,
-                favorites_only=favorites_only  # Pass favorites_only parameter
+                favorites_only=favorites_only,  # Pass favorites_only parameter
+                first_letter=first_letter  # Pass the new first_letter parameter
             )
 
             # Get all available folders from cache
@@ -372,10 +393,10 @@ class ApiRoutes:
             versions = response.get('modelVersions', [])
             model_type = response.get('type', '')
             
-            # Check model type - should be LORA
-            if model_type.lower() != 'lora':
+            # Check model type - should be LORA or LoCon
+            if model_type.lower() not in ['lora', 'locon']:
                 return web.json_response({
-                    'error': f"Model type mismatch. Expected LORA, got {model_type}"
+                    'error': f"Model type mismatch. Expected LORA or LoCon, got {model_type}"
                 }, status=400)
             
             # Check local availability for each version
@@ -494,7 +515,7 @@ class ApiRoutes:
                         logger.warning(f"Early access download failed: {error_message}")
                         return web.Response(
                             status=401,  # Use 401 status code to match Civitai's response
-                            text=f"Early Access Restriction: {error_message}"
+                            text=error_message
                         )
                     
                     return web.Response(status=500, text=error_message)
@@ -781,11 +802,13 @@ class ApiRoutes:
             # Check if we already have the description stored in metadata
             description = None
             tags = []
+            creator = {}
             if file_path:
                 metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
                 metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
                 description = metadata.get('modelDescription')
                 tags = metadata.get('tags', [])
+                creator = metadata.get('creator', {})
             
             # If description is not in metadata, fetch from CivitAI
             if not description:
@@ -795,6 +818,7 @@ class ApiRoutes:
                 if (model_metadata):
                     description = model_metadata.get('description')
                     tags = model_metadata.get('tags', [])
+                    creator = model_metadata.get('creator', {})
                 
                     # Save the metadata to file if we have a file path and got metadata
                     if file_path:
@@ -804,6 +828,7 @@ class ApiRoutes:
                             
                             metadata['modelDescription'] = description
                             metadata['tags'] = tags
+                            metadata['creator'] = creator
                             
                             with open(metadata_path, 'w', encoding='utf-8') as f:
                                 json.dump(metadata, f, indent=2, ensure_ascii=False)
@@ -814,7 +839,8 @@ class ApiRoutes:
             return web.json_response({
                 'success': True,
                 'description': description or "<p>No model description available.</p>",
-                'tags': tags
+                'tags': tags,
+                'creator': creator
             })
             
         except Exception as e:
@@ -1045,3 +1071,101 @@ class ApiRoutes:
                 "success": False,
                 "error": str(e)
             }, status=500)
+
+    async def get_letter_counts(self, request: web.Request) -> web.Response:
+        """Get count of loras for each letter of the alphabet"""
+        try:
+            if self.scanner is None:
+                self.scanner = await ServiceRegistry.get_lora_scanner()
+                
+            # Get letter counts
+            letter_counts = await self.scanner.get_letter_counts()
+            
+            return web.json_response({
+                'success': True,
+                'letter_counts': letter_counts
+            })
+        except Exception as e:
+            logger.error(f"Error getting letter counts: {e}")
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    async def get_lora_notes(self, request: web.Request) -> web.Response:
+        """Get notes for a specific LoRA file"""
+        try:
+            if self.scanner is None:
+                self.scanner = await ServiceRegistry.get_lora_scanner()
+                
+            # Get lora file name from query parameters
+            lora_name = request.query.get('name')
+            if not lora_name:
+                return web.Response(text='Lora file name is required', status=400)
+
+            # Get cache data
+            cache = await self.scanner.get_cached_data()
+            
+            # Search for the lora in cache data
+            for lora in cache.raw_data:
+                file_name = lora['file_name']
+                if file_name == lora_name:
+                    notes = lora.get('notes', '')
+                    
+                    return web.json_response({
+                        'success': True,
+                        'notes': notes
+                    })
+
+            # If lora not found
+            return web.json_response({
+                'success': False,
+                'error': 'LoRA not found in cache'
+            }, status=404)
+
+        except Exception as e:
+            logger.error(f"Error getting lora notes: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    async def get_lora_trigger_words(self, request: web.Request) -> web.Response:
+        """Get trigger words for a specific LoRA file"""
+        try:
+            if self.scanner is None:
+                self.scanner = await ServiceRegistry.get_lora_scanner()
+                
+            # Get lora file name from query parameters
+            lora_name = request.query.get('name')
+            if not lora_name:
+                return web.Response(text='Lora file name is required', status=400)
+
+            # Get cache data
+            cache = await self.scanner.get_cached_data()
+            
+            # Search for the lora in cache data
+            for lora in cache.raw_data:
+                file_name = lora['file_name']
+                if file_name == lora_name:
+                    # Get trigger words from civitai data
+                    civitai_data = lora.get('civitai', {})
+                    trigger_words = civitai_data.get('trainedWords', [])
+                    
+                    return web.json_response({
+                        'success': True,
+                        'trigger_words': trigger_words
+                    })
+
+            # If lora not found
+            return web.json_response({
+                'success': False,
+                'error': 'LoRA not found in cache'
+            }, status=404)
+
+        except Exception as e:
+            logger.error(f"Error getting lora trigger words: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/routes/checkpoints_routes.py

@@ -49,6 +49,7 @@ class CheckpointsRoutes:
         
         # Add new routes for model management similar to LoRA routes
         app.router.add_post('/api/checkpoints/delete', self.delete_model)
+        app.router.add_post('/api/checkpoints/exclude', self.exclude_model)  # Add new exclude endpoint
         app.router.add_post('/api/checkpoints/fetch-civitai', self.fetch_civitai)
         app.router.add_post('/api/checkpoints/replace-preview', self.replace_preview)
         app.router.add_post('/api/checkpoints/download', self.download_checkpoint)
@@ -419,7 +420,10 @@ class CheckpointsRoutes:
     async def scan_checkpoints(self, request):
         """Force a rescan of checkpoint files"""
         try:
-            await self.scanner.get_cached_data(force_refresh=True)
+            # Get the full_rebuild parameter and convert to bool, default to False
+            full_rebuild = request.query.get('full_rebuild', 'false').lower() == 'true'
+            
+            await self.scanner.get_cached_data(force_refresh=True, rebuild_cache=full_rebuild)
             return web.json_response({"status": "success", "message": "Checkpoint scan completed"})
         except Exception as e:
             logger.error(f"Error in scan_checkpoints: {e}", exc_info=True)
@@ -429,7 +433,7 @@ class CheckpointsRoutes:
         """Get detailed information for a specific checkpoint by name"""
         try:
             name = request.match_info.get('name', '')
-            checkpoint_info = await self.scanner.get_checkpoint_info_by_name(name)
+            checkpoint_info = await self.scanner.get_model_info_by_name(name)
             
             if checkpoint_info:
                 return web.json_response(checkpoint_info)
@@ -499,6 +503,10 @@ class CheckpointsRoutes:
     async def delete_model(self, request: web.Request) -> web.Response:
         """Handle checkpoint model deletion request"""
         return await ModelRouteUtils.handle_delete_model(request, self.scanner)
+
+    async def exclude_model(self, request: web.Request) -> web.Response:
+        """Handle checkpoint model exclusion request"""
+        return await ModelRouteUtils.handle_exclude_model(request, self.scanner)
     
     async def fetch_civitai(self, request: web.Request) -> web.Response:
         """Handle CivitAI metadata fetch request for checkpoints"""
@@ -653,7 +661,7 @@ class CheckpointsRoutes:
             model_type = response.get('type', '')
             
             # Check model type - should be Checkpoint
-            if model_type.lower() != 'checkpoint':
+            if (model_type.lower() != 'checkpoint'):
                 return web.json_response({
                     'error': f"Model type mismatch. Expected Checkpoint, got {model_type}"
                 }, status=400)

py/routes/misc_routes.py

@@ -3,15 +3,18 @@ import os
 import asyncio
 import json
 import time
-import tkinter as tk
-from tkinter import filedialog
 import aiohttp
+import re
+import subprocess
+import sys
+from server import PromptServer # type: ignore
 from aiohttp import web
 from ..services.settings_manager import settings
 from ..utils.usage_stats import UsageStats
 from ..services.service_registry import ServiceRegistry
-from ..utils.exif_utils import ExifUtils
-from ..utils.constants import EXAMPLE_IMAGE_WIDTH, SUPPORTED_MEDIA_EXTENSIONS
+from ..utils.constants import SUPPORTED_MEDIA_EXTENSIONS
+from ..services.civitai_client import CivitaiClient
+from ..utils.routes_common import ModelRouteUtils
 
 logger = logging.getLogger(__name__)
 
@@ -27,7 +30,8 @@ download_progress = {
     'last_error': None,
     'start_time': None,
     'end_time': None,
-    'processed_models': set()  # Track models that have been processed
+    'processed_models': set(),  # Track models that have been processed
+    'refreshed_models': set()  # Track models that had metadata refreshed
 }
 
 class MiscRoutes:
@@ -37,6 +41,9 @@ class MiscRoutes:
     def setup_routes(app):
         """Register miscellaneous routes"""
         app.router.add_post('/api/settings', MiscRoutes.update_settings)
+        
+        # Add new route for clearing cache
+        app.router.add_post('/api/clear-cache', MiscRoutes.clear_cache)
 
         # Usage stats routes
         app.router.add_post('/api/update-usage-stats', MiscRoutes.update_usage_stats)
@@ -48,6 +55,61 @@ class MiscRoutes:
         app.router.add_post('/api/pause-example-images', MiscRoutes.pause_example_images)
         app.router.add_post('/api/resume-example-images', MiscRoutes.resume_example_images)
         
+        # Lora code update endpoint
+        app.router.add_post('/api/update-lora-code', MiscRoutes.update_lora_code)
+        
+        # Add new route for opening example images folder
+        app.router.add_post('/api/open-example-images-folder', MiscRoutes.open_example_images_folder)
+
+    @staticmethod
+    async def clear_cache(request):
+        """Clear all cache files from the cache folder"""
+        try:
+            # Get the cache folder path (relative to project directory)
+            project_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+            cache_folder = os.path.join(project_dir, 'cache')
+            
+            # Check if cache folder exists
+            if not os.path.exists(cache_folder):
+                logger.info("Cache folder does not exist, nothing to clear")
+                return web.json_response({'success': True, 'message': 'No cache folder found'})
+            
+            # Get list of cache files before deleting for reporting
+            cache_files = [f for f in os.listdir(cache_folder) if os.path.isfile(os.path.join(cache_folder, f))]
+            deleted_files = []
+            
+            # Delete each .msgpack file in the cache folder
+            for filename in cache_files:
+                if filename.endswith('.msgpack'):
+                    file_path = os.path.join(cache_folder, filename)
+                    try:
+                        os.remove(file_path)
+                        deleted_files.append(filename)
+                        logger.info(f"Deleted cache file: {filename}")
+                    except Exception as e:
+                        logger.error(f"Failed to delete {filename}: {e}")
+                        return web.json_response({
+                            'success': False,
+                            'error': f"Failed to delete {filename}: {str(e)}"
+                        }, status=500)
+            
+            # If we want to completely remove the cache folder too (optional, 
+            # but we'll keep the folder structure in place here)
+            # shutil.rmtree(cache_folder)
+            
+            return web.json_response({
+                'success': True,
+                'message': f"Successfully cleared {len(deleted_files)} cache files",
+                'deleted_files': deleted_files
+            })
+            
+        except Exception as e:
+            logger.error(f"Error clearing cache files: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
     @staticmethod
     async def update_settings(request):
         """Update application settings"""
@@ -151,6 +213,7 @@ class MiscRoutes:
             # Create a copy for JSON serialization
             response_progress = download_progress.copy()
             response_progress['processed_models'] = list(download_progress['processed_models'])
+            response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
             
             return web.json_response({
                 'success': False,
@@ -164,7 +227,7 @@ class MiscRoutes:
             output_dir = data.get('output_dir')
             optimize = data.get('optimize', True)
             model_types = data.get('model_types', ['lora', 'checkpoint'])
-            delay = float(data.get('delay', 0.2))
+            delay = float(data.get('delay', 0.1)) # Default to 0.1 seconds
             
             if not output_dir:
                 return web.json_response({
@@ -213,6 +276,7 @@ class MiscRoutes:
             # Create a copy for JSON serialization
             response_progress = download_progress.copy()
             response_progress['processed_models'] = list(download_progress['processed_models'])
+            response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
             
             return web.json_response({
                 'success': True,
@@ -235,6 +299,7 @@ class MiscRoutes:
         # Create a copy of the progress dict with the set converted to a list for JSON serialization
         response_progress = download_progress.copy()
         response_progress['processed_models'] = list(download_progress['processed_models'])
+        response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
         
         return web.json_response({
             'success': True,
@@ -284,6 +349,243 @@ class MiscRoutes:
                 'error': f"Download is in '{download_progress['status']}' state, cannot resume"
             }, status=400)
 
+    @staticmethod
+    async def _refresh_model_metadata(model_hash, model_name, scanner_type, scanner):
+        """Refresh model metadata from CivitAI
+        
+        Args:
+            model_hash: SHA256 hash of the model
+            model_name: Name of the model (for logging)
+            scanner_type: Type of scanner ('lora' or 'checkpoint')
+            scanner: Scanner instance for this model type
+            
+        Returns:
+            bool: True if metadata was successfully refreshed, False otherwise
+        """
+        global download_progress
+        
+        try:
+            # Find the model in the scanner cache
+            cache = await scanner.get_cached_data()
+            model_data = None
+            
+            for item in cache.raw_data:
+                if item.get('sha256') == model_hash:
+                    model_data = item
+                    break
+            
+            if not model_data:
+                logger.warning(f"Model {model_name} with hash {model_hash} not found in cache")
+                return False
+            
+            file_path = model_data.get('file_path')
+            if not file_path:
+                logger.warning(f"Model {model_name} has no file path")
+                return False
+            
+            # Track that we're refreshing this model
+            download_progress['refreshed_models'].add(model_hash)
+            
+            # Use ModelRouteUtils to refresh the metadata
+            async def update_cache_func(old_path, new_path, metadata):
+                return await scanner.update_single_model_cache(old_path, new_path, metadata)
+            
+            success = await ModelRouteUtils.fetch_and_update_model(
+                model_hash, 
+                file_path, 
+                model_data,
+                update_cache_func
+            )
+            
+            if success:
+                logger.info(f"Successfully refreshed metadata for {model_name}")
+                return True
+            else:
+                logger.warning(f"Failed to refresh metadata for {model_name}")
+                return False
+                
+        except Exception as e:
+            error_msg = f"Error refreshing metadata for {model_name}: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            download_progress['errors'].append(error_msg)
+            download_progress['last_error'] = error_msg
+            return False
+    
+    @staticmethod
+    def _get_civitai_optimized_url(image_url):
+        """Convert a Civitai image URL to its optimized WebP version
+        
+        Args:
+            image_url: Original Civitai image URL
+            
+        Returns:
+            str: URL to optimized WebP version
+        """
+        # Match the base part of Civitai URLs
+        base_pattern = r'(https://image\.civitai\.com/[^/]+/[^/]+)'
+        match = re.match(base_pattern, image_url)
+        
+        if match:
+            base_url = match.group(1)
+            # Create the optimized WebP URL
+            return f"{base_url}/optimized=true/image.webp"
+        
+        # Return original URL if it doesn't match the expected format
+        return image_url
+    
+    @staticmethod
+    async def _process_model_images(model_hash, model_name, model_images, model_dir, optimize, independent_session, delay):
+        """Process and download images for a single model
+        
+        Args:
+            model_hash: SHA256 hash of the model
+            model_name: Name of the model
+            model_images: List of image objects from CivitAI
+            model_dir: Directory to save images to
+            optimize: Whether to optimize images
+            independent_session: aiohttp session for downloads
+            delay: Delay between downloads
+            
+        Returns:
+            bool: True if all images were processed successfully, False otherwise
+        """
+        global download_progress
+        
+        model_success = True
+        
+        for i, image in enumerate(model_images, 1):
+            image_url = image.get('url')
+            if not image_url:
+                continue
+            
+            # Get image filename from URL
+            image_filename = os.path.basename(image_url.split('?')[0])
+            image_ext = os.path.splitext(image_filename)[1].lower()
+            
+            # Handle both images and videos
+            is_image = image_ext in SUPPORTED_MEDIA_EXTENSIONS['images']
+            is_video = image_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']
+            
+            if not (is_image or is_video):
+                logger.debug(f"Skipping unsupported file type: {image_filename}")
+                continue
+            
+            save_filename = f"image_{i}{image_ext}"
+            
+            # If optimizing images and this is a Civitai image, use their pre-optimized WebP version
+            if is_image and optimize and 'civitai.com' in image_url:
+                # Transform URL to use Civitai's optimized WebP version
+                image_url = MiscRoutes._get_civitai_optimized_url(image_url)
+                # Update filename to use .webp extension
+                save_filename = f"image_{i}.webp"
+            
+            # Check if already downloaded
+            save_path = os.path.join(model_dir, save_filename)
+            if os.path.exists(save_path):
+                logger.debug(f"File already exists: {save_path}")
+                continue
+            
+            # Download the file
+            try:
+                logger.debug(f"Downloading {save_filename} for {model_name}")
+                
+                # Direct download using the independent session
+                async with independent_session.get(image_url, timeout=60) as response:
+                    if response.status == 200:
+                        with open(save_path, 'wb') as f:
+                            async for chunk in response.content.iter_chunked(8192):
+                                if chunk:
+                                    f.write(chunk)
+                    elif response.status == 404:
+                        error_msg = f"Failed to download file: {image_url}, status code: 404 - Model metadata might be stale"
+                        logger.warning(error_msg)
+                        download_progress['errors'].append(error_msg)
+                        download_progress['last_error'] = error_msg
+                        model_success = False  # Mark model as failed due to 404
+                        # Return early to trigger metadata refresh attempt
+                        return False, True  # (success, is_stale_metadata)
+                    else:
+                        error_msg = f"Failed to download file: {image_url}, status code: {response.status}"
+                        logger.warning(error_msg)
+                        download_progress['errors'].append(error_msg)
+                        download_progress['last_error'] = error_msg
+                        model_success = False  # Mark model as failed
+                
+                # Add a delay between downloads for remote files only
+                await asyncio.sleep(delay)
+            except Exception as e:
+                error_msg = f"Error downloading file {image_url}: {str(e)}"
+                logger.error(error_msg)
+                download_progress['errors'].append(error_msg)
+                download_progress['last_error'] = error_msg
+                model_success = False  # Mark model as failed
+        
+        return model_success, False  # (success, is_stale_metadata)
+    
+    @staticmethod
+    async def _process_local_example_images(model_file_path, model_file_name, model_name, model_dir, optimize):
+        """Process local example images for a model
+        
+        Args:
+            model_file_path: Path to the model file
+            model_file_name: Filename of the model
+            model_name: Name of the model
+            model_dir: Directory to save processed images to
+            optimize: Whether to optimize images
+            
+        Returns:
+            bool: True if local images were processed successfully, False otherwise
+        """
+        global download_progress
+        
+        try:
+            model_dir_path = os.path.dirname(model_file_path)
+            local_images = []
+            
+            # Look for files with pattern: filename.example.*.ext
+            if model_file_name:
+                example_prefix = f"{model_file_name}.example."
+                
+                if os.path.exists(model_dir_path):
+                    for file in os.listdir(model_dir_path):
+                        file_lower = file.lower()
+                        if file_lower.startswith(example_prefix.lower()):
+                            file_ext = os.path.splitext(file_lower)[1]
+                            is_supported = (file_ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or 
+                                           file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos'])
+                            
+                            if is_supported:
+                                local_images.append(os.path.join(model_dir_path, file))
+            
+            # Process local images if found
+            if local_images:
+                logger.info(f"Found {len(local_images)} local example images for {model_name}")
+                
+                for i, local_image_path in enumerate(local_images, 1):
+                    local_ext = os.path.splitext(local_image_path)[1].lower()
+                    save_filename = f"image_{i}{local_ext}"
+                    save_path = os.path.join(model_dir, save_filename)
+                    
+                    # Skip if already exists in output directory
+                    if os.path.exists(save_path):
+                        logger.debug(f"File already exists in output: {save_path}")
+                        continue
+                    
+                    # For local files, we just copy them without optimization
+                    # since we don't want to modify user files
+                    with open(local_image_path, 'rb') as src_file:
+                        with open(save_path, 'wb') as dst_file:
+                            dst_file.write(src_file.read())
+                
+                return True
+            return False
+        except Exception as e:
+            error_msg = f"Error processing local examples for {model_name}: {str(e)}"
+            logger.error(error_msg)
+            download_progress['errors'].append(error_msg)
+            download_progress['last_error'] = error_msg
+            return False
+
     @staticmethod
     async def _download_all_example_images(output_dir, optimize, model_types, delay):
         """Download example images for all models
@@ -332,14 +634,14 @@ class MiscRoutes:
                     for model in cache.raw_data:
                         # Only process models with images and a valid sha256
                         if model.get('civitai') and model.get('civitai', {}).get('images') and model.get('sha256'):
-                            all_models.append((scanner_type, model))
+                            all_models.append((scanner_type, model, scanner))
             
             # Update total count
             download_progress['total'] = len(all_models)
             logger.info(f"Found {download_progress['total']} models with example images")
             
             # Process each model
-            for scanner_type, model in all_models:
+            for scanner_type, model, scanner in all_models:
                 # Check if download is paused
                 while download_progress['status'] == 'paused':
                     await asyncio.sleep(1)
@@ -349,14 +651,13 @@ class MiscRoutes:
                     logger.info(f"Download stopped: {download_progress['status']}")
                     break
                 
-                model_success = True  # Track if all images for this model download successfully
+                model_hash = model.get('sha256', '').lower()
+                model_name = model.get('model_name', 'Unknown')
+                model_file_path = model.get('file_path', '')
+                model_file_name = model.get('file_name', '')
                 
                 try:
                     # Update current model info
-                    model_hash = model.get('sha256', '').lower()
-                    model_name = model.get('model_name', 'Unknown')
-                    model_file_path = model.get('file_path', '')
-                    model_file_name = model.get('file_name', '')
                     download_progress['current_model'] = f"{model_name} ({model_hash[:8]})"
                     
                     # Skip if already processed
@@ -381,156 +682,69 @@ class MiscRoutes:
                     # First check if we have local example images for this model
                     local_images_processed = False
                     if model_file_path:
-                        try:
-                            model_dir_path = os.path.dirname(model_file_path)
-                            local_images = []
-                            
-                            # Look for files with pattern: filename.example.*.ext
-                            if model_file_name:
-                                example_prefix = f"{model_file_name}.example."
-                                
-                                if os.path.exists(model_dir_path):
-                                    for file in os.listdir(model_dir_path):
-                                        file_lower = file.lower()
-                                        if file_lower.startswith(example_prefix.lower()):
-                                            file_ext = os.path.splitext(file_lower)[1]
-                                            is_supported = (file_ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or 
-                                                           file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos'])
-                                            
-                                            if is_supported:
-                                                local_images.append(os.path.join(model_dir_path, file))
-                            
-                            # Process local images if found
-                            if local_images:
-                                logger.info(f"Found {len(local_images)} local example images for {model_name}")
-                                
-                                for i, local_image_path in enumerate(local_images, 1):
-                                    local_ext = os.path.splitext(local_image_path)[1].lower()
-                                    save_filename = f"image_{i}{local_ext}"
-                                    save_path = os.path.join(model_dir, save_filename)
-                                    
-                                    # Skip if already exists in output directory
-                                    if os.path.exists(save_path):
-                                        logger.debug(f"File already exists in output: {save_path}")
-                                        continue
-                                    
-                                    # Handle image processing based on file type and optimize setting
-                                    is_image = local_ext in SUPPORTED_MEDIA_EXTENSIONS['images']
-                                    
-                                    if is_image and optimize:
-                                        # Optimize the image
-                                        with open(local_image_path, 'rb') as img_file:
-                                            image_data = img_file.read()
-                                        
-                                        optimized_data, ext = ExifUtils.optimize_image(
-                                            image_data, 
-                                            target_width=EXAMPLE_IMAGE_WIDTH, 
-                                            format='webp', 
-                                            quality=85, 
-                                            preserve_metadata=False
-                                        )
-                                        
-                                        # Update save filename if format changed
-                                        if ext == '.webp':
-                                            save_filename = os.path.splitext(save_filename)[0] + '.webp'
-                                            save_path = os.path.join(model_dir, save_filename)
-                                        
-                                        # Save the optimized image
-                                        with open(save_path, 'wb') as f:
-                                            f.write(optimized_data)
-                                    else:
-                                        # For videos or unoptimized images, copy directly
-                                        with open(local_image_path, 'rb') as src_file:
-                                            with open(save_path, 'wb') as dst_file:
-                                                dst_file.write(src_file.read())
-                                
-                                # Mark as successfully processed if all local images were processed
-                                download_progress['processed_models'].add(model_hash)
-                                local_images_processed = True
-                                logger.info(f"Successfully processed local examples for {model_name}")
+                        local_images_processed = await MiscRoutes._process_local_example_images(
+                            model_file_path, 
+                            model_file_name, 
+                            model_name, 
+                            model_dir, 
+                            optimize
+                        )
                         
-                        except Exception as e:
-                            error_msg = f"Error processing local examples for {model_name}: {str(e)}"
-                            logger.error(error_msg)
-                            download_progress['errors'].append(error_msg)
-                            download_progress['last_error'] = error_msg
-                            # Continue to remote download if local processing fails
+                        if local_images_processed:
+                            # Mark as successfully processed if all local images were processed
+                            download_progress['processed_models'].add(model_hash)
+                            logger.info(f"Successfully processed local examples for {model_name}")
                     
                     # If we didn't process local images, download from remote
                     if not local_images_processed:
-                        # Download example images
-                        for i, image in enumerate(images, 1):
-                            image_url = image.get('url')
-                            if not image_url:
-                                continue
+                        # Try to download images
+                        model_success, is_stale_metadata = await MiscRoutes._process_model_images(
+                            model_hash, 
+                            model_name, 
+                            images, 
+                            model_dir, 
+                            optimize, 
+                            independent_session, 
+                            delay
+                        )
+                        
+                        # If metadata is stale (404 error), try to refresh it and download again
+                        if is_stale_metadata and model_hash not in download_progress['refreshed_models']:
+                            logger.info(f"Metadata seems stale for {model_name}, attempting to refresh...")
                             
-                            # Get image filename from URL
-                            image_filename = os.path.basename(image_url.split('?')[0])
-                            image_ext = os.path.splitext(image_filename)[1].lower()
+                            # Refresh metadata from CivitAI
+                            refresh_success = await MiscRoutes._refresh_model_metadata(
+                                model_hash, 
+                                model_name, 
+                                scanner_type, 
+                                scanner
+                            )
                             
-                            # Handle both images and videos
-                            is_image = image_ext in SUPPORTED_MEDIA_EXTENSIONS['images']
-                            is_video = image_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']
-                            
-                            if not (is_image or is_video):
-                                logger.debug(f"Skipping unsupported file type: {image_filename}")
-                                continue
-                            
-                            save_filename = f"image_{i}{image_ext}"
-                            
-                            # Check if already downloaded
-                            save_path = os.path.join(model_dir, save_filename)
-                            if os.path.exists(save_path):
-                                logger.debug(f"File already exists: {save_path}")
-                                continue
-                            
-                            # Download the file
-                            try:
-                                logger.debug(f"Downloading {save_filename} for {model_name}")
+                            if refresh_success:
+                                # Get updated model data
+                                updated_cache = await scanner.get_cached_data()
+                                updated_model = None
                                 
-                                # Direct download using the independent session
-                                async with independent_session.get(image_url, timeout=60) as response:
-                                    if response.status == 200:
-                                        if is_image and optimize:
-                                            # For images, optimize if requested
-                                            image_data = await response.read()
-                                            optimized_data, ext = ExifUtils.optimize_image(
-                                                image_data, 
-                                                target_width=EXAMPLE_IMAGE_WIDTH, 
-                                                format='webp', 
-                                                quality=85, 
-                                                preserve_metadata=False
-                                            )
-                                            
-                                            # Update save filename if format changed
-                                            if ext == '.webp':
-                                                save_filename = os.path.splitext(save_filename)[0] + '.webp'
-                                                save_path = os.path.join(model_dir, save_filename)
-                                            
-                                            # Save the optimized image
-                                            with open(save_path, 'wb') as f:
-                                                f.write(optimized_data)
-                                        else:
-                                            # For videos or unoptimized images, save directly
-                                            with open(save_path, 'wb') as f:
-                                                async for chunk in response.content.iter_chunked(8192):
-                                                    if chunk:
-                                                        f.write(chunk)
-                                    else:
-                                        error_msg = f"Failed to download file: {image_url}, status code: {response.status}"
-                                        logger.warning(error_msg)
-                                        download_progress['errors'].append(error_msg)
-                                        download_progress['last_error'] = error_msg
-                                        model_success = False  # Mark model as failed
+                                for item in updated_cache.raw_data:
+                                    if item.get('sha256') == model_hash:
+                                        updated_model = item
+                                        break
                                 
-                                # Add a delay between downloads for remote files only
-                                await asyncio.sleep(delay)
-                            except Exception as e:
-                                error_msg = f"Error downloading file {image_url}: {str(e)}"
-                                logger.error(error_msg)
-                                download_progress['errors'].append(error_msg)
-                                download_progress['last_error'] = error_msg
-                                model_success = False  # Mark model as failed
+                                if updated_model and updated_model.get('civitai', {}).get('images'):
+                                    # Try downloading with updated metadata
+                                    logger.info(f"Retrying download with refreshed metadata for {model_name}")
+                                    updated_images = updated_model.get('civitai', {}).get('images', [])
+                                    
+                                    # Retry download with new images
+                                    model_success, _ = await MiscRoutes._process_model_images(
+                                        model_hash, 
+                                        model_name, 
+                                        updated_images, 
+                                        model_dir, 
+                                        optimize, 
+                                        independent_session, 
+                                        delay
+                                    )
                         
                         # Only mark model as processed if all images downloaded successfully
                         if model_success:
@@ -544,6 +758,7 @@ class MiscRoutes:
                         with open(progress_file, 'w', encoding='utf-8') as f:
                             json.dump({
                                 'processed_models': list(download_progress['processed_models']),
+                                'refreshed_models': list(download_progress['refreshed_models']),
                                 'completed': download_progress['completed'],
                                 'total': download_progress['total'],
                                 'last_update': time.time()
@@ -584,6 +799,7 @@ class MiscRoutes:
                 with open(progress_file, 'w', encoding='utf-8') as f:
                     json.dump({
                         'processed_models': list(download_progress['processed_models']),
+                        'refreshed_models': list(download_progress['refreshed_models']),
                         'completed': download_progress['completed'],
                         'total': download_progress['total'],
                         'last_update': time.time(),
@@ -594,3 +810,144 @@ class MiscRoutes:
             
             # Set download status to not downloading
             is_downloading = False
+    
+    @staticmethod
+    async def update_lora_code(request):
+        """
+        Update Lora code in ComfyUI nodes
+        
+        Expects a JSON body with:
+        {
+            "node_ids": [123, 456], # Optional - List of node IDs to update (for browser mode)
+            "lora_code": "<lora:modelname:1.0>", # The Lora code to send
+            "mode": "append" # or "replace" - whether to append or replace existing code
+        }
+        """
+        try:
+            # Parse the request body
+            data = await request.json()
+            node_ids = data.get('node_ids')
+            lora_code = data.get('lora_code', '')
+            mode = data.get('mode', 'append')
+            
+            if not lora_code:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing lora_code parameter'
+                }, status=400)
+            
+            results = []
+            
+            # Desktop mode: no specific node_ids provided
+            if node_ids is None:
+                try:
+                    # Send broadcast message with id=-1 to all Lora Loader nodes
+                    PromptServer.instance.send_sync("lora_code_update", {
+                        "id": -1,
+                        "lora_code": lora_code,
+                        "mode": mode
+                    })
+                    results.append({
+                        'node_id': 'broadcast',
+                        'success': True
+                    })
+                except Exception as e:
+                    logger.error(f"Error broadcasting lora code: {e}")
+                    results.append({
+                        'node_id': 'broadcast',
+                        'success': False,
+                        'error': str(e)
+                    })
+            else:
+                # Browser mode: send to specific nodes
+                for node_id in node_ids:
+                    try:
+                        # Send the message to the frontend
+                        PromptServer.instance.send_sync("lora_code_update", {
+                            "id": node_id,
+                            "lora_code": lora_code,
+                            "mode": mode
+                        })
+                        results.append({
+                            'node_id': node_id,
+                            'success': True
+                        })
+                    except Exception as e:
+                        logger.error(f"Error sending lora code to node {node_id}: {e}")
+                        results.append({
+                            'node_id': node_id,
+                            'success': False,
+                            'error': str(e)
+                        })
+            
+            return web.json_response({
+                'success': True,
+                'results': results
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to update lora code: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    @staticmethod
+    async def open_example_images_folder(request):
+        """
+        Open the example images folder for a specific model
+        
+        Expects a JSON body with:
+        {
+            "model_hash": "sha256_hash"  # SHA256 hash of the model
+        }
+        """
+        try:
+            # Parse the request body
+            data = await request.json()
+            model_hash = data.get('model_hash')
+            
+            if not model_hash:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing model_hash parameter'
+                }, status=400)
+            
+            # Get the example images path from settings
+            example_images_path = settings.get('example_images_path')
+            if not example_images_path:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No example images path configured. Please set it in the settings panel first.'
+                }, status=400)
+            
+            # Construct the folder path for this model
+            model_folder = os.path.join(example_images_path, model_hash)
+            
+            # Check if the folder exists
+            if not os.path.exists(model_folder):
+                return web.json_response({
+                    'success': False,
+                    'error': 'No example images found for this model. Download example images first.'
+                }, status=404)
+            
+            # Open the folder in the file explorer
+            if os.name == 'nt':  # Windows
+                os.startfile(model_folder)
+            elif os.name == 'posix':  # macOS and Linux
+                if sys.platform == 'darwin':  # macOS
+                    subprocess.Popen(['open', model_folder])
+                else:  # Linux
+                    subprocess.Popen(['xdg-open', model_folder])
+            
+            return web.json_response({
+                'success': True,
+                'message': f'Opened example images folder for model {model_hash}'
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to open example images folder: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/routes/recipe_routes.py

@@ -1,5 +1,6 @@
 import os
 import time
+import base64
 import numpy as np
 from PIL import Image
 import torch
@@ -12,7 +13,7 @@ import json
 import asyncio
 import sys
 from ..utils.exif_utils import ExifUtils
-from ..utils.recipe_parsers import RecipeParserFactory
+from ..recipes import RecipeParserFactory
 from ..utils.constants import CARD_PREVIEW_WIDTH
 
 from ..config import config
@@ -56,6 +57,7 @@ class RecipeRoutes:
         app.router.add_get('/api/recipes', routes.get_recipes)
         app.router.add_get('/api/recipe/{recipe_id}', routes.get_recipe_detail)
         app.router.add_post('/api/recipes/analyze-image', routes.analyze_recipe_image)
+        app.router.add_post('/api/recipes/analyze-local-image', routes.analyze_local_image)
         app.router.add_post('/api/recipes/save', routes.save_recipe)
         app.router.add_delete('/api/recipe/{recipe_id}', routes.delete_recipe)
         
@@ -70,12 +72,18 @@ class RecipeRoutes:
         # Add new endpoint for getting recipe syntax
         app.router.add_get('/api/recipe/{recipe_id}/syntax', routes.get_recipe_syntax)
         
-        # Add new endpoint for updating recipe metadata (name and tags)
+        # Add new endpoint for updating recipe metadata (name, tags and source_path)
         app.router.add_put('/api/recipe/{recipe_id}/update', routes.update_recipe)
         
         # Add new endpoint for reconnecting deleted LoRAs
         app.router.add_post('/api/recipe/lora/reconnect', routes.reconnect_lora)
         
+        # Add new endpoint for finding duplicate recipes
+        app.router.add_get('/api/recipes/find-duplicates', routes.find_duplicates)
+        
+        # Add new endpoint for bulk deletion of recipes
+        app.router.add_post('/api/recipes/bulk-delete', routes.bulk_delete)
+        
         # Start cache initialization
         app.on_startup.append(routes._init_cache)
         
@@ -300,7 +308,6 @@ class RecipeRoutes:
                 
                 # For URL mode, include the image data as base64
                 if is_url_mode and temp_path:
-                    import base64
                     with open(temp_path, "rb") as image_file:
                         result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
                     
@@ -317,7 +324,6 @@ class RecipeRoutes:
                 
                 # For URL mode, include the image data as base64
                 if is_url_mode and temp_path:
-                    import base64
                     with open(temp_path, "rb") as image_file:
                         result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
                     
@@ -332,7 +338,6 @@ class RecipeRoutes:
             
             # For URL mode, include the image data as base64
             if is_url_mode and temp_path:
-                import base64
                 with open(temp_path, "rb") as image_file:
                     result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
             
@@ -340,6 +345,21 @@ class RecipeRoutes:
             if "error" in result and not result.get("loras"):
                 return web.json_response(result, status=200)
             
+            # Calculate fingerprint from parsed loras
+            from ..utils.utils import calculate_recipe_fingerprint
+            fingerprint = calculate_recipe_fingerprint(result.get("loras", []))
+            
+            # Add fingerprint to result
+            result["fingerprint"] = fingerprint
+            
+            # Find matching recipes with the same fingerprint
+            matching_recipes = []
+            if fingerprint:
+                matching_recipes = await self.recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+                
+            # Add matching recipes to result
+            result["matching_recipes"] = matching_recipes
+            
             return web.json_response(result)
             
         except Exception as e:
@@ -355,7 +375,100 @@ class RecipeRoutes:
                     os.unlink(temp_path)
                 except Exception as e:
                     logger.error(f"Error deleting temporary file: {e}")
+    
+    async def analyze_local_image(self, request: web.Request) -> web.Response:
+        """Analyze a local image file for recipe metadata"""
+        try:
+            # Ensure services are initialized 
+            await self.init_services()
+            
+            # Get JSON data from request
+            data = await request.json()
+            file_path = data.get('path')
+            
+            if not file_path:
+                return web.json_response({
+                    'error': 'No file path provided',
+                    'loras': []
+                }, status=400)
+                
+            # Normalize file path for cross-platform compatibility
+            file_path = os.path.normpath(file_path.strip('"').strip("'"))
+            
+            # Validate that the file exists
+            if not os.path.isfile(file_path):
+                return web.json_response({
+                    'error': 'File not found',
+                    'loras': []
+                }, status=404)
+                
+            # Extract metadata from the image using ExifUtils
+            metadata = ExifUtils.extract_image_metadata(file_path)
+            
+            # If no metadata found, return error
+            if not metadata:
+                # Get base64 image data
+                with open(file_path, "rb") as image_file:
+                    image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
+                    
+                return web.json_response({
+                    "error": "No metadata found in this image",
+                    "loras": [],  # Return empty loras array to prevent client-side errors
+                    "image_base64": image_base64
+                }, status=200)
+            
+            # Use the parser factory to get the appropriate parser
+            parser = RecipeParserFactory.create_parser(metadata)
 
+            if parser is None:
+                # Get base64 image data
+                with open(file_path, "rb") as image_file:
+                    image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
+                    
+                return web.json_response({
+                    "error": "No parser found for this image",
+                    "loras": [],  # Return empty loras array to prevent client-side errors
+                    "image_base64": image_base64
+                }, status=200)
+            
+            # Parse the metadata
+            result = await parser.parse_metadata(
+                metadata, 
+                recipe_scanner=self.recipe_scanner, 
+                civitai_client=self.civitai_client
+            )
+            
+            # Add base64 image data to result
+            with open(file_path, "rb") as image_file:
+                result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
+            
+            # Check for errors
+            if "error" in result and not result.get("loras"):
+                return web.json_response(result, status=200)
+            
+            # Calculate fingerprint from parsed loras
+            from ..utils.utils import calculate_recipe_fingerprint
+            fingerprint = calculate_recipe_fingerprint(result.get("loras", []))
+            
+            # Add fingerprint to result
+            result["fingerprint"] = fingerprint
+            
+            # Find matching recipes with the same fingerprint
+            matching_recipes = []
+            if fingerprint:
+                matching_recipes = await self.recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+                
+            # Add matching recipes to result
+            result["matching_recipes"] = matching_recipes
+            
+            return web.json_response(result)
+            
+        except Exception as e:
+            logger.error(f"Error analyzing local image: {e}", exc_info=True)
+            return web.json_response({
+                'error': str(e),
+                'loras': []  # Return empty loras array to prevent client-side errors
+            }, status=500)
 
     async def save_recipe(self, request: web.Request) -> web.Response:
         """Save a recipe to the recipes folder"""
@@ -425,7 +538,6 @@ class RecipeRoutes:
             if not image:
                 if image_base64:
                     # Convert base64 to binary
-                    import base64
                     try:
                         # Remove potential data URL prefix
                         if ',' in image_base64:
@@ -474,7 +586,7 @@ class RecipeRoutes:
             with open(image_path, 'wb') as f:
                 f.write(optimized_image)
             
-            # Create the recipe JSON
+            # Create the recipe data structure
             current_time = time.time()
             
             # Format loras data according to the recipe.json format
@@ -514,6 +626,10 @@ class RecipeRoutes:
                     "clip_skip": raw_metadata.get("clip_skip", "")
                 }
             
+            # Calculate recipe fingerprint 
+            from ..utils.utils import calculate_recipe_fingerprint
+            fingerprint = calculate_recipe_fingerprint(loras_data)
+            
             # Create the recipe data structure
             recipe_data = {
                 "id": recipe_id,
@@ -523,13 +639,18 @@ class RecipeRoutes:
                 "created_date": current_time,
                 "base_model": metadata.get("base_model", ""),
                 "loras": loras_data,
-                "gen_params": gen_params
+                "gen_params": gen_params,
+                "fingerprint": fingerprint
             }
             
             # Add tags if provided
             if tags:
                 recipe_data["tags"] = tags
             
+            # Add source_path if provided in metadata
+            if metadata.get("source_path"):
+                recipe_data["source_path"] = metadata.get("source_path")
+            
             # Save the recipe JSON
             json_filename = f"{recipe_id}.recipe.json"
             json_path = os.path.join(recipes_dir, json_filename)
@@ -539,6 +660,14 @@ class RecipeRoutes:
             # Add recipe metadata to the image
             ExifUtils.append_recipe_metadata(image_path, recipe_data)
             
+            # Check for duplicates
+            matching_recipes = []
+            if fingerprint:
+                matching_recipes = await self.recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+                # Remove current recipe from matches
+                if recipe_id in matching_recipes:
+                    matching_recipes.remove(recipe_id)
+            
             # Simplified cache update approach
             # Instead of trying to update the cache directly, just set it to None
             # to force a refresh on the next get_cached_data call
@@ -554,7 +683,8 @@ class RecipeRoutes:
                 'success': True,
                 'recipe_id': recipe_id,
                 'image_path': image_path,
-                'json_path': json_path
+                'json_path': json_path,
+                'matching_recipes': matching_recipes
             })
             
         except Exception as e:
@@ -1089,9 +1219,9 @@ class RecipeRoutes:
             data = await request.json()
             
             # Validate required fields
-            if 'title' not in data and 'tags' not in data:
+            if 'title' not in data and 'tags' not in data and 'source_path' not in data:
                 return web.json_response({
-                    "error": "At least one field to update must be provided (title or tags)"
+                    "error": "At least one field to update must be provided (title or tags or source_path)"
                 }, status=400)
             
             # Use the recipe scanner's update method
@@ -1186,6 +1316,10 @@ class RecipeRoutes:
                     
             if not found:
                 return web.json_response({"error": "Could not find matching deleted LoRA in recipe"}, status=404)
+            
+            # Recalculate recipe fingerprint after updating LoRA
+            from ..utils.utils import calculate_recipe_fingerprint
+            recipe_data['fingerprint'] = calculate_recipe_fingerprint(recipe_data.get('loras', []))
                 
             # Save updated recipe
             with open(recipe_path, 'w', encoding='utf-8') as f:
@@ -1201,6 +1335,8 @@ class RecipeRoutes:
                     if cache_item.get('id') == recipe_id:
                         # Replace loras array with updated version
                         cache_item['loras'] = recipe_data['loras']
+                        # Update fingerprint in cache
+                        cache_item['fingerprint'] = recipe_data['fingerprint']
                         
                         # Resort the cache
                         asyncio.create_task(scanner._cache.resort())
@@ -1211,11 +1347,20 @@ class RecipeRoutes:
             if image_path and os.path.exists(image_path):
                 from ..utils.exif_utils import ExifUtils
                 ExifUtils.append_recipe_metadata(image_path, recipe_data)
+            
+            # Find other recipes with the same fingerprint
+            matching_recipes = []
+            if 'fingerprint' in recipe_data:
+                matching_recipes = await scanner.find_recipes_by_fingerprint(recipe_data['fingerprint'])
+                # Remove current recipe from matches
+                if recipe_id in matching_recipes:
+                    matching_recipes.remove(recipe_id)
                 
             return web.json_response({
                 "success": True,
                 "recipe_id": recipe_id,
-                "updated_lora": updated_lora
+                "updated_lora": updated_lora,
+                "matching_recipes": matching_recipes
             })
             
         except Exception as e:
@@ -1291,3 +1436,150 @@ class RecipeRoutes:
                 'success': False,
                 'error': str(e)
             }, status=500)
+
+    async def find_duplicates(self, request: web.Request) -> web.Response:
+        """Find all duplicate recipes based on fingerprints"""
+        try:
+            # Ensure services are initialized
+            await self.init_services()
+            
+            # Get all duplicate recipes
+            duplicate_groups = await self.recipe_scanner.find_all_duplicate_recipes()
+            
+            # Create response data with additional recipe information
+            response_data = []
+            
+            for fingerprint, recipe_ids in duplicate_groups.items():
+                # Skip groups with only one recipe (not duplicates)
+                if len(recipe_ids) <= 1:
+                    continue
+                    
+                # Get recipe details for each recipe in the group
+                recipes = []
+                for recipe_id in recipe_ids:
+                    recipe = await self.recipe_scanner.get_recipe_by_id(recipe_id)
+                    if recipe:
+                        # Add only needed fields to keep response size manageable
+                        recipes.append({
+                            'id': recipe.get('id'),
+                            'title': recipe.get('title'),
+                            'file_url': recipe.get('file_url') or self._format_recipe_file_url(recipe.get('file_path', '')),
+                            'modified': recipe.get('modified'),
+                            'created_date': recipe.get('created_date'),
+                            'lora_count': len(recipe.get('loras', [])),
+                        })
+                        
+                # Only include groups with at least 2 valid recipes
+                if len(recipes) >= 2:
+                    # Sort recipes by modified date (newest first)
+                    recipes.sort(key=lambda x: x.get('modified', 0), reverse=True)
+                    
+                    response_data.append({
+                        'fingerprint': fingerprint,
+                        'count': len(recipes),
+                        'recipes': recipes
+                    })
+            
+            # Sort groups by count (highest first)
+            response_data.sort(key=lambda x: x['count'], reverse=True)
+            
+            return web.json_response({
+                'success': True,
+                'duplicate_groups': response_data
+            })
+            
+        except Exception as e:
+            logger.error(f"Error finding duplicate recipes: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def bulk_delete(self, request: web.Request) -> web.Response:
+        """Delete multiple recipes by ID"""
+        try:
+            # Ensure services are initialized
+            await self.init_services()
+            
+            # Parse request data
+            data = await request.json()
+            recipe_ids = data.get('recipe_ids', [])
+            
+            if not recipe_ids:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No recipe IDs provided'
+                }, status=400)
+                
+            # Get recipes directory
+            recipes_dir = self.recipe_scanner.recipes_dir
+            if not recipes_dir or not os.path.exists(recipes_dir):
+                return web.json_response({
+                    'success': False,
+                    'error': 'Recipes directory not found'
+                }, status=404)
+            
+            # Track deleted and failed recipes
+            deleted_recipes = []
+            failed_recipes = []
+            
+            # Process each recipe ID
+            for recipe_id in recipe_ids:
+                # Find recipe JSON file
+                recipe_json_path = os.path.join(recipes_dir, f"{recipe_id}.recipe.json")
+                
+                if not os.path.exists(recipe_json_path):
+                    failed_recipes.append({
+                        'id': recipe_id,
+                        'reason': 'Recipe not found'
+                    })
+                    continue
+                
+                try:
+                    # Load recipe data to get image path
+                    with open(recipe_json_path, 'r', encoding='utf-8') as f:
+                        recipe_data = json.load(f)
+                    
+                    # Get image path
+                    image_path = recipe_data.get('file_path')
+                    
+                    # Delete recipe JSON file
+                    os.remove(recipe_json_path)
+                    
+                    # Delete recipe image if it exists
+                    if image_path and os.path.exists(image_path):
+                        os.remove(image_path)
+                        
+                    deleted_recipes.append(recipe_id)
+                    
+                except Exception as e:
+                    failed_recipes.append({
+                        'id': recipe_id,
+                        'reason': str(e)
+                    })
+            
+            # Update cache if any recipes were deleted
+            if deleted_recipes and self.recipe_scanner._cache is not None:
+                # Remove deleted recipes from raw_data
+                self.recipe_scanner._cache.raw_data = [
+                    r for r in self.recipe_scanner._cache.raw_data 
+                    if r.get('id') not in deleted_recipes
+                ]
+                # Resort the cache
+                asyncio.create_task(self.recipe_scanner._cache.resort())
+                logger.info(f"Removed {len(deleted_recipes)} recipes from cache")
+            
+            return web.json_response({
+                'success': True,
+                'deleted': deleted_recipes,
+                'failed': failed_recipes,
+                'total_deleted': len(deleted_recipes),
+                'total_failed': len(failed_recipes)
+            })
+            
+        except Exception as e:
+            logger.error(f"Error performing bulk delete: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/routes/update_routes.py

@@ -150,11 +150,16 @@ class UpdateRoutes:
         """
         Compare two semantic version strings
         Returns True if version2 is newer than version1
+        Ignores any suffixes after '-' (e.g., -bugfix, -alpha)
         """
         try:
+            # Clean version strings - remove any suffix after '-'
+            v1_clean = version1.split('-')[0]
+            v2_clean = version2.split('-')[0]
+            
             # Split versions into components
-            v1_parts = [int(x) for x in version1.split('.')]
-            v2_parts = [int(x) for x in version2.split('.')]
+            v1_parts = [int(x) for x in v1_clean.split('.')]
+            v2_parts = [int(x) for x in v2_clean.split('.')]
             
             # Ensure both have 3 components (major.minor.patch)
             while len(v1_parts) < 3:

py/server_routes.py

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

py/services/civitai_client.py

@@ -267,7 +267,7 @@ class CivitaiClient:
             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
+        """Fetch model metadata (description, tags, and creator info) from Civitai API
         
         Args:
             model_id: The Civitai model ID
@@ -294,10 +294,14 @@ class CivitaiClient:
                 # Extract relevant metadata
                 metadata = {
                     "description": data.get("description") or "No model description available",
-                    "tags": data.get("tags", [])
+                    "tags": data.get("tags", []),
+                    "creator": {
+                        "username": data.get("creator", {}).get("username"),
+                        "image": data.get("creator", {}).get("image")
+                    }
                 }
                 
-                if metadata["description"] or metadata["tags"]:
+                if metadata["description"] or metadata["tags"] or metadata["creator"]["username"]:
                     return metadata, status_code
                 else:
                     logger.warning(f"No metadata found for model {model_id}")

py/services/download_manager.py

@@ -2,8 +2,7 @@ import logging
 import os
 import json
 import asyncio
-from typing import Optional, Dict, Any
-from .civitai_client import CivitaiClient
+from typing import Dict
 from ..utils.models import LoraMetadata, CheckpointMetadata
 from ..utils.constants import CARD_PREVIEW_WIDTH
 from ..utils.exif_utils import ExifUtils
@@ -136,15 +135,9 @@ class DownloadManager:
             # 3. Prepare download
             file_name = file_info['name']
             save_path = os.path.join(save_dir, file_name)
-            file_size = file_info.get('sizeKB', 0) * 1024
 
             # 4. Notify file monitor - use normalized path and file size
-            file_monitor = await self._get_lora_monitor() if model_type == "lora" else await self._get_checkpoint_monitor()
-            if file_monitor and file_monitor.handler:
-                file_monitor.handler.add_ignore_path(
-                    save_path.replace(os.sep, '/'),
-                    file_size
-                )
+            # file monitor is despreted, so we don't need to use it
 
             # 5. Prepare metadata based on model type
             if model_type == "checkpoint":
@@ -154,7 +147,7 @@ class DownloadManager:
                 metadata = LoraMetadata.from_civitai_info(version_info, file_info, save_path)
                 logger.info(f"Creating LoraMetadata for {file_name}")
             
-            # 5.1 Get and update model tags and description
+            # 5.1 Get and update model tags, description and creator info
             model_id = version_info.get('modelId')
             if model_id:
                 model_metadata, _ = await civitai_client.get_model_metadata(str(model_id))
@@ -163,6 +156,8 @@ class DownloadManager:
                         metadata.tags = model_metadata.get("tags", [])
                     if model_metadata.get("description"):
                         metadata.modelDescription = model_metadata.get("description", "")
+                    if model_metadata.get("creator"):
+                        metadata.civitai["creator"] = model_metadata.get("creator")
             
             # 6. Start download process
             result = await self._execute_download(
@@ -285,17 +280,11 @@ class DownloadManager:
                 scanner = await self._get_lora_scanner()
                 logger.info(f"Updating lora cache for {save_path}")
                 
-            cache = await scanner.get_cached_data()
+            # Convert metadata to dictionary
             metadata_dict = metadata.to_dict()
-            metadata_dict['folder'] = relative_path
-            cache.raw_data.append(metadata_dict)
-            await cache.resort()
-            all_folders = set(cache.folders)
-            all_folders.add(relative_path)
-            cache.folders = sorted(list(all_folders), key=lambda x: x.lower())
-            
-            # Update the hash index with the new model entry
-            scanner._hash_index.add_entry(metadata_dict['sha256'], metadata_dict['file_path'])
+
+            # Add model to cache and save to disk in a single operation
+            await scanner.add_model_to_cache(metadata_dict, relative_path)
 
             # Report 100% completion
             if progress_callback:

py/services/lora_cache.py

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

py/services/lora_scanner.py

@@ -4,6 +4,7 @@ import logging
 import asyncio
 import shutil
 import time
+import re
 from typing import List, Dict, Optional, Set
 
 from ..utils.models import LoraMetadata
@@ -123,7 +124,7 @@ class LoraScanner(ModelScanner):
                                folder: str = None, search: str = None, fuzzy_search: bool = False,
                                base_models: list = None, tags: list = None,
                                search_options: dict = None, hash_filters: dict = None,
-                               favorites_only: bool = False) -> Dict:
+                               favorites_only: bool = False, first_letter: str = None) -> Dict:
         """Get paginated and filtered lora data
         
         Args:
@@ -138,6 +139,7 @@ class LoraScanner(ModelScanner):
             search_options: Dictionary with search options (filename, modelname, tags, recursive)
             hash_filters: Dictionary with hash filtering options (single_hash or multiple_hashes)
             favorites_only: Filter for favorite models only
+            first_letter: Filter by first letter of model name
         """
         cache = await self.get_cached_data()
 
@@ -202,6 +204,10 @@ class LoraScanner(ModelScanner):
                 lora for lora in filtered_data
                 if lora.get('favorite', False) is True
             ]
+            
+        # Apply first letter filtering
+        if first_letter:
+            filtered_data = self._filter_by_first_letter(filtered_data, first_letter)
         
         # Apply folder filtering
         if folder is not None:
@@ -273,6 +279,101 @@ class LoraScanner(ModelScanner):
         
         return result
 
+    def _filter_by_first_letter(self, data, letter):
+        """Filter data by first letter of model name
+        
+        Special handling:
+        - '#': Numbers (0-9)
+        - '@': Special characters (not alphanumeric)
+        - '漢': CJK characters
+        """
+        filtered_data = []
+        
+        for lora in data:
+            model_name = lora.get('model_name', '')
+            if not model_name:
+                continue
+                
+            first_char = model_name[0].upper()
+            
+            if letter == '#' and first_char.isdigit():
+                filtered_data.append(lora)
+            elif letter == '@' and not first_char.isalnum():
+                # Special characters (not alphanumeric)
+                filtered_data.append(lora)
+            elif letter == '漢' and self._is_cjk_character(first_char):
+                # CJK characters
+                filtered_data.append(lora)
+            elif letter.upper() == first_char:
+                # Regular alphabet matching
+                filtered_data.append(lora)
+                
+        return filtered_data
+    
+    def _is_cjk_character(self, char):
+        """Check if character is a CJK character"""
+        # Define Unicode ranges for CJK characters
+        cjk_ranges = [
+            (0x4E00, 0x9FFF),   # CJK Unified Ideographs
+            (0x3400, 0x4DBF),   # CJK Unified Ideographs Extension A
+            (0x20000, 0x2A6DF), # CJK Unified Ideographs Extension B
+            (0x2A700, 0x2B73F), # CJK Unified Ideographs Extension C
+            (0x2B740, 0x2B81F), # CJK Unified Ideographs Extension D
+            (0x2B820, 0x2CEAF), # CJK Unified Ideographs Extension E
+            (0x2CEB0, 0x2EBEF), # CJK Unified Ideographs Extension F
+            (0x30000, 0x3134F), # CJK Unified Ideographs Extension G
+            (0xF900, 0xFAFF),   # CJK Compatibility Ideographs
+            (0x3300, 0x33FF),   # CJK Compatibility
+            (0x3200, 0x32FF),   # Enclosed CJK Letters and Months
+            (0x3100, 0x312F),   # Bopomofo
+            (0x31A0, 0x31BF),   # Bopomofo Extended
+            (0x3040, 0x309F),   # Hiragana
+            (0x30A0, 0x30FF),   # Katakana
+            (0x31F0, 0x31FF),   # Katakana Phonetic Extensions
+            (0xAC00, 0xD7AF),   # Hangul Syllables
+            (0x1100, 0x11FF),   # Hangul Jamo
+            (0xA960, 0xA97F),   # Hangul Jamo Extended-A
+            (0xD7B0, 0xD7FF),   # Hangul Jamo Extended-B
+        ]
+        
+        code_point = ord(char)
+        return any(start <= code_point <= end for start, end in cjk_ranges)
+    
+    async def get_letter_counts(self):
+        """Get count of models for each letter of the alphabet"""
+        cache = await self.get_cached_data()
+        data = cache.sorted_by_name
+        
+        # Define letter categories
+        letters = {
+            '#': 0,  # Numbers
+            'A': 0, 'B': 0, 'C': 0, 'D': 0, 'E': 0, 'F': 0, 'G': 0, 'H': 0,
+            'I': 0, 'J': 0, 'K': 0, 'L': 0, 'M': 0, 'N': 0, 'O': 0, 'P': 0,
+            'Q': 0, 'R': 0, 'S': 0, 'T': 0, 'U': 0, 'V': 0, 'W': 0, 'X': 0,
+            'Y': 0, 'Z': 0,
+            '@': 0,  # Special characters
+            '漢': 0   # CJK characters
+        }
+        
+        # Count models for each letter
+        for lora in data:
+            model_name = lora.get('model_name', '')
+            if not model_name:
+                continue
+                
+            first_char = model_name[0].upper()
+            
+            if first_char.isdigit():
+                letters['#'] += 1
+            elif first_char in letters:
+                letters[first_char] += 1
+            elif self._is_cjk_character(first_char):
+                letters['漢'] += 1
+            elif not first_char.isalnum():
+                letters['@'] += 1
+                
+        return letters
+
     async def _update_metadata_paths(self, metadata_path: str, lora_path: str) -> Dict:
         """Update file paths in metadata file"""
         try:

py/services/model_cache.py

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

py/services/model_scanner.py

@@ -5,6 +5,7 @@ import asyncio
 import time
 import shutil
 from typing import List, Dict, Optional, Type, Set
+import msgpack  # Add MessagePack import for efficient serialization
 
 from ..utils.models import BaseModelMetadata
 from ..config import config
@@ -17,6 +18,9 @@ from .websocket_manager import ws_manager
 
 logger = logging.getLogger(__name__)
 
+# Define cache version to handle future format changes
+CACHE_VERSION = 1
+
 class ModelScanner:
     """Base service for scanning and managing model files"""
     
@@ -38,6 +42,8 @@ class ModelScanner:
         self._hash_index = hash_index or ModelHashIndex()
         self._tags_count = {}  # Dictionary to store tag counts
         self._is_initializing = False  # Flag to track initialization state
+        self._excluded_models = []  # List to track excluded models
+        self._dirs_last_modified = {}  # Track directory modification times
         
         # Register this service
         asyncio.create_task(self._register_service())
@@ -47,6 +53,171 @@ class ModelScanner:
         service_name = f"{self.model_type}_scanner"
         await ServiceRegistry.register_service(service_name, self)
     
+    def _get_cache_file_path(self) -> Optional[str]:
+        """Get the path to the cache file"""
+        # Get the directory where this module is located
+        current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
+        
+        # Create a cache directory within the project if it doesn't exist
+        cache_dir = os.path.join(current_dir, "cache")
+        os.makedirs(cache_dir, exist_ok=True)
+        
+        # Create filename based on model type
+        cache_filename = f"lm_{self.model_type}_cache.msgpack"
+        return os.path.join(cache_dir, cache_filename)
+
+    def _prepare_for_msgpack(self, data):
+        """Preprocess data to accommodate MessagePack serialization limitations
+        
+        Converts integers exceeding safe range to strings
+        
+        Args:
+            data: Any type of data structure
+            
+        Returns:
+            Preprocessed data structure with large integers converted to strings
+        """
+        if isinstance(data, dict):
+            return {k: self._prepare_for_msgpack(v) for k, v in data.items()}
+        elif isinstance(data, list):
+            return [self._prepare_for_msgpack(item) for item in data]
+        elif isinstance(data, int) and (data > 9007199254740991 or data < -9007199254740991):
+            # Convert integers exceeding JavaScript's safe integer range (2^53-1) to strings
+            return str(data)
+        else:
+            return data
+
+    async def _save_cache_to_disk(self) -> bool:
+        """Save cache data to disk using MessagePack"""
+        if self._cache is None or not self._cache.raw_data:
+            logger.debug(f"No {self.model_type} cache data to save")
+            return False
+            
+        cache_path = self._get_cache_file_path()
+        if not cache_path:
+            logger.warning(f"Cannot determine {self.model_type} cache file location")
+            return False
+            
+        try:
+            # Create cache data structure
+            cache_data = {
+                "version": CACHE_VERSION,
+                "timestamp": time.time(),
+                "model_type": self.model_type,
+                "raw_data": self._cache.raw_data,
+                "hash_index": {
+                    "hash_to_path": self._hash_index._hash_to_path,
+                    "filename_to_hash": self._hash_index._filename_to_hash  # Fix: changed from path_to_hash to filename_to_hash
+                },
+                "tags_count": self._tags_count,
+                "dirs_last_modified": self._get_dirs_last_modified()
+            }
+            
+            # Preprocess data to handle large integers
+            processed_cache_data = self._prepare_for_msgpack(cache_data)
+            
+            # Write to temporary file first (atomic operation)
+            temp_path = f"{cache_path}.tmp"
+            with open(temp_path, 'wb') as f:
+                msgpack.pack(processed_cache_data, f)
+                
+            # Replace the old file with the new one
+            if os.path.exists(cache_path):
+                os.replace(temp_path, cache_path)
+            else:
+                os.rename(temp_path, cache_path)
+                
+            logger.info(f"Saved {self.model_type} cache with {len(self._cache.raw_data)} models to {cache_path}")
+            return True
+        except Exception as e:
+            logger.error(f"Error saving {self.model_type} cache to disk: {e}")
+            # Try to clean up temp file if it exists
+            if 'temp_path' in locals() and os.path.exists(temp_path):
+                try:
+                    os.remove(temp_path)
+                except:
+                    pass
+            return False
+    
+    def _get_dirs_last_modified(self) -> Dict[str, float]:
+        """Get last modified time for all model directories"""
+        dirs_info = {}
+        for root in self.get_model_roots():
+            if os.path.exists(root):
+                dirs_info[root] = os.path.getmtime(root)
+                # Also check immediate subdirectories for changes
+                try:
+                    with os.scandir(root) as it:
+                        for entry in it:
+                            if entry.is_dir(follow_symlinks=True):
+                                dirs_info[entry.path] = entry.stat().st_mtime
+                except Exception as e:
+                    logger.error(f"Error getting directory info for {root}: {e}")
+        return dirs_info
+                
+    def _is_cache_valid(self, cache_data: Dict) -> bool:
+        """Validate if the loaded cache is still valid"""
+        if not cache_data or cache_data.get("version") != CACHE_VERSION:
+            return False
+            
+        if cache_data.get("model_type") != self.model_type:
+            return False
+            
+        # Check if directories have changed
+        stored_dirs = cache_data.get("dirs_last_modified", {})
+        current_dirs = self._get_dirs_last_modified()
+        
+        # If directory structure has changed, cache is invalid
+        if set(stored_dirs.keys()) != set(current_dirs.keys()):
+            return False
+            
+        # Remove the modification time check to make cache validation less strict
+        # This allows the cache to be valid even when files have changed
+        # Users can explicitly refresh the cache when needed
+                
+        return True
+        
+    async def _load_cache_from_disk(self) -> bool:
+        """Load cache data from disk using MessagePack"""
+        start_time = time.time()
+        cache_path = self._get_cache_file_path()
+        if not cache_path or not os.path.exists(cache_path):
+            return False
+            
+        try:
+            with open(cache_path, 'rb') as f:
+                cache_data = msgpack.unpack(f)
+                
+            # Validate cache data
+            if not self._is_cache_valid(cache_data):
+                logger.info(f"{self.model_type.capitalize()} cache file found but invalid or outdated")
+                return False
+                
+            # Load data into memory
+            self._cache = ModelCache(
+                raw_data=cache_data["raw_data"],
+                sorted_by_name=[],
+                sorted_by_date=[],
+                folders=[]
+            )
+            
+            # Load hash index
+            hash_index_data = cache_data.get("hash_index", {})
+            self._hash_index._hash_to_path = hash_index_data.get("hash_to_path", {})
+            self._hash_index._filename_to_hash = hash_index_data.get("filename_to_hash", {})  # Fix: changed from path_to_hash to filename_to_hash
+            
+            # Load tags count
+            self._tags_count = cache_data.get("tags_count", {})
+            
+            # Resort the cache
+            await self._cache.resort()
+            
+            logger.info(f"Loaded {self.model_type} cache from disk with {len(self._cache.raw_data)} models in {time.time() - start_time:.2f} seconds")
+            return True
+        except Exception as e:
+            logger.error(f"Error loading {self.model_type} cache from disk: {e}")
+            return False
+
     async def initialize_in_background(self) -> None:
         """Initialize cache in background using thread pool"""
         try:
@@ -65,7 +236,31 @@ class ModelScanner:
             # Determine the page type based on model type
             page_type = 'loras' if self.model_type == 'lora' else 'checkpoints'
             
-            # First, count all model files to track progress
+            # First, try to load from cache
+            await ws_manager.broadcast_init_progress({
+                'stage': 'loading_cache',
+                'progress': 0,
+                'details': f"Loading {self.model_type} cache...",
+                'scanner_type': self.model_type,
+                'pageType': page_type
+            })
+            
+            cache_loaded = await self._load_cache_from_disk()
+            
+            if cache_loaded:
+                # Cache loaded successfully, broadcast complete message
+                await ws_manager.broadcast_init_progress({
+                    'stage': 'finalizing',
+                    'progress': 100,
+                    'status': 'complete',
+                    'details': f"Loaded {len(self._cache.raw_data)} {self.model_type} files from cache.",
+                    'scanner_type': self.model_type,
+                    'pageType': page_type
+                })
+                self._is_initializing = False
+                return
+                
+            # If cache loading failed, proceed with full scan
             await ws_manager.broadcast_init_progress({
                 'stage': 'scan_folders',
                 'progress': 0,
@@ -110,6 +305,9 @@ class ModelScanner:
             
             logger.info(f"{self.model_type.capitalize()} cache initialized in {time.time() - start_time:.2f} seconds. Found {len(self._cache.raw_data)} models")
             
+            # Save the cache to disk after initialization
+            await self._save_cache_to_disk()
+            
             # Send completion message
             await asyncio.sleep(0.5)  # Small delay to ensure final progress message is sent
             await ws_manager.broadcast_init_progress({
@@ -279,8 +477,13 @@ class ModelScanner:
             # Clean up the event loop
             loop.close()
 
-    async def get_cached_data(self, force_refresh: bool = False) -> ModelCache:
-        """Get cached model data, refresh if needed"""
+    async def get_cached_data(self, force_refresh: bool = False, rebuild_cache: bool = False) -> ModelCache:
+        """Get cached model data, refresh if needed
+        
+        Args:
+            force_refresh: Whether to refresh the cache
+            rebuild_cache: Whether to completely rebuild the cache by reloading from disk first
+        """
         # If cache is not initialized, return an empty cache
         # Actual initialization should be done via initialize_in_background
         if self._cache is None and not force_refresh:
@@ -292,10 +495,25 @@ class ModelScanner:
             )
 
         # If force refresh is requested, initialize the cache directly
-        if (force_refresh):
+        if force_refresh:
+            # If rebuild_cache is True, try to reload from disk before reconciliation
+            if rebuild_cache:
+                logger.info(f"{self.model_type.capitalize()} Scanner: Attempting to rebuild cache from disk...")
+                cache_loaded = await self._load_cache_from_disk()
+                if cache_loaded:
+                    logger.info(f"{self.model_type.capitalize()} Scanner: Successfully reloaded cache from disk")
+                else:
+                    logger.info(f"{self.model_type.capitalize()} Scanner: Could not reload cache from disk, proceeding with complete rebuild")
+                    # If loading from disk failed, do a complete rebuild and save to disk
+                    await self._initialize_cache()
+                    await self._save_cache_to_disk()
+                    return self._cache
+            
             if self._cache is None:
                 # For initial creation, do a full initialization
                 await self._initialize_cache()
+                # Save the newly built cache
+                await self._save_cache_to_disk()
             else:
                 # For subsequent refreshes, use fast reconciliation
                 await self._reconcile_cache()
@@ -394,6 +612,9 @@ class ModelScanner:
                             if file_path in cached_paths:
                                 found_paths.add(file_path)
                                 continue
+
+                            if file_path in self._excluded_models:
+                                continue
                                 
                             # Try case-insensitive match on Windows
                             if os.name == 'nt':
@@ -406,7 +627,7 @@ class ModelScanner:
                                         break
                                 if matched:
                                     continue
-                            
+                                
                             # This is a new file to process
                             new_files.append(file_path)
                     
@@ -480,6 +701,9 @@ class ModelScanner:
                 # Resort cache
                 await self._cache.resort()
                 
+                # Save updated cache to disk
+                await self._save_cache_to_disk()
+                
             logger.info(f"{self.model_type.capitalize()} Scanner: Cache reconciliation completed in {time.time() - start_time:.2f} seconds. Added {total_added}, removed {total_removed} models.")
         except Exception as e:
             logger.error(f"{self.model_type.capitalize()} Scanner: Error reconciling cache: {e}", exc_info=True)
@@ -586,6 +810,11 @@ class ModelScanner:
         
         model_data = metadata.to_dict()
         
+        # Skip excluded models
+        if model_data.get('exclude', False):
+            self._excluded_models.append(model_data['file_path'])
+            return None
+            
         await self._fetch_missing_metadata(file_path, model_data)
         rel_path = os.path.relpath(file_path, root_path)
         folder = os.path.dirname(rel_path)
@@ -610,7 +839,10 @@ class ModelScanner:
                     model_id = str(model_id)
                     tags_missing = not model_data.get('tags') or len(model_data.get('tags', [])) == 0
                     desc_missing = not model_data.get('modelDescription') or model_data.get('modelDescription') in (None, "")
-                    needs_metadata_update = tags_missing or desc_missing
+                    # TODO: not for now, but later we should check if the creator is missing
+                    # creator_missing = not model_data.get('civitai', {}).get('creator')
+                    creator_missing = False
+                    needs_metadata_update = tags_missing or desc_missing or creator_missing
             
             if needs_metadata_update and model_id:
                 logger.debug(f"Fetching missing metadata for {file_path} with model ID {model_id}")
@@ -636,6 +868,8 @@ class ModelScanner:
                     
                     if model_metadata.get('description') and (not model_data.get('modelDescription') or model_data.get('modelDescription') in (None, "")):
                         model_data['modelDescription'] = model_metadata['description']
+
+                    model_data['civitai']['creator'] = model_metadata['creator']
                     
                     metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
                     with open(metadata_path, 'w', encoding='utf-8') as f:
@@ -684,6 +918,44 @@ class ModelScanner:
                 models_list.append(result)
         except Exception as e:
             logger.error(f"Error processing {file_path}: {e}")
+
+    async def add_model_to_cache(self, metadata_dict: Dict, folder: str = '') -> bool:
+        """Add a model to the cache and save to disk
+        
+        Args:
+            metadata_dict: The model metadata dictionary
+            folder: The relative folder path for the model
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            if self._cache is None:
+                await self.get_cached_data()
+                
+            # Update folder in metadata
+            metadata_dict['folder'] = folder
+            
+            # Add to cache
+            self._cache.raw_data.append(metadata_dict)
+            
+            # Resort cache data
+            await self._cache.resort()
+            
+            # Update folders list
+            all_folders = set(self._cache.folders)
+            all_folders.add(folder)
+            self._cache.folders = sorted(list(all_folders), key=lambda x: x.lower())
+            
+            # Update the hash index
+            self._hash_index.add_entry(metadata_dict['sha256'], metadata_dict['file_path'])
+                
+            # Save to disk
+            await self._save_cache_to_disk()
+            return True
+        except Exception as e:
+            logger.error(f"Error adding model to cache: {e}")
+            return False
     
     async def move_model(self, source_path: str, target_path: str) -> bool:
         """Move a model and its associated files to a new location"""
@@ -729,25 +1001,40 @@ class ModelScanner:
             
             shutil.move(real_source, real_target)
             
-            source_metadata = os.path.join(source_dir, f"{base_name}.metadata.json")
+            # Move all associated files with the same base name
+            source_metadata = None
+            moved_metadata_path = None
+            
+            # Find all files with the same base name in the source directory
+            files_to_move = []
+            try:
+                for file in os.listdir(source_dir):
+                    if file.startswith(base_name + ".") and file != os.path.basename(source_path):
+                        source_file_path = os.path.join(source_dir, file)
+                        # Store metadata file path for special handling
+                        if file == f"{base_name}.metadata.json":
+                            source_metadata = source_file_path
+                            moved_metadata_path = os.path.join(target_path, file)
+                        else:
+                            files_to_move.append((source_file_path, os.path.join(target_path, file)))
+            except Exception as e:
+                logger.error(f"Error listing files in {source_dir}: {e}")
+            
+            # Move all associated files
             metadata = None
-            if os.path.exists(source_metadata):
-                target_metadata = os.path.join(target_path, f"{base_name}.metadata.json")
-                shutil.move(source_metadata, target_metadata)
-                metadata = await self._update_metadata_paths(target_metadata, target_file)
+            for source_file, target_file_path in files_to_move:
+                try:
+                    shutil.move(source_file, target_file_path)
+                except Exception as e:
+                    logger.error(f"Error moving associated file {source_file}: {e}")
             
-            # Move civitai.info file if exists
-            source_civitai = os.path.join(source_dir, f"{base_name}.civitai.info")
-            if os.path.exists(source_civitai):
-                target_civitai = os.path.join(target_path, f"{base_name}.civitai.info")
-                shutil.move(source_civitai, target_civitai)
-            
-            for ext in PREVIEW_EXTENSIONS:
-                source_preview = os.path.join(source_dir, f"{base_name}{ext}")
-                if os.path.exists(source_preview):
-                    target_preview = os.path.join(target_path, f"{base_name}{ext}")
-                    shutil.move(source_preview, target_preview)
-                    break
+            # Handle metadata file specially to update paths
+            if source_metadata and os.path.exists(source_metadata):
+                try:
+                    shutil.move(source_metadata, moved_metadata_path)
+                    metadata = await self._update_metadata_paths(moved_metadata_path, target_file)
+                except Exception as e:
+                    logger.error(f"Error moving metadata file: {e}")
             
             await self.update_single_model_cache(source_path, target_file, metadata)
             
@@ -825,6 +1112,9 @@ class ModelScanner:
         
         await cache.resort()
         
+        # Save the updated cache
+        await self._save_cache_to_disk()
+        
         return True
         
     def has_hash(self, sha256: str) -> bool:
@@ -900,6 +1190,10 @@ class ModelScanner:
             logger.error(f"Error getting model info by name: {e}", exc_info=True)
             return None
         
+    def get_excluded_models(self) -> List[str]:
+        """Get list of excluded model file paths"""
+        return self._excluded_models.copy()
+
     async def update_preview_in_cache(self, file_path: str, preview_url: str) -> bool:
         """Update preview URL in cache for a specific lora
         
@@ -913,4 +1207,8 @@ class ModelScanner:
         if self._cache is None:
             return False
 
-        return await self._cache.update_preview_url(file_path, preview_url)
+        updated = await self._cache.update_preview_url(file_path, preview_url)
+        if updated:
+            # Save updated cache to disk
+            await self._save_cache_to_disk()
+        return updated

py/services/recipe_cache.py

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

py/services/recipe_scanner.py

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

py/utils/file_utils.py

@@ -233,6 +233,17 @@ async def load_metadata(file_path: str, model_class: Type[BaseModelMetadata] = L
                     data['usage_tips'] = "{}"
                     needs_update = True
                 
+                # Update preview_nsfw_level if needed
+                civitai_data = data.get('civitai', {})
+                civitai_images = civitai_data.get('images', []) if civitai_data else []
+                if (data.get('preview_url') and 
+                    data.get('preview_nsfw_level', 0) == 0 and 
+                    civitai_images and 
+                    civitai_images[0].get('nsfwLevel', 0) != 0):
+                    data['preview_nsfw_level'] = civitai_images[0]['nsfwLevel']
+                    # TODO: write to metadata file
+                    # needs_update = True
+
                 if needs_update:
                     with open(metadata_path, 'w', encoding='utf-8') as f:
                         json.dump(data, f, indent=2, ensure_ascii=False)

py/utils/lora_metadata.py

@@ -2,6 +2,9 @@ from safetensors import safe_open
 from typing import Dict
 from .model_utils import determine_base_model
 import os
+import logging
+
+logger = logging.getLogger(__name__)
 
 async def extract_lora_metadata(file_path: str) -> Dict:
     """Extract essential metadata from safetensors file"""

py/utils/models.py

@@ -23,6 +23,7 @@ class BaseModelMetadata:
     modelDescription: str = ""  # Full model description
     civitai_deleted: bool = False  # Whether deleted from Civitai
     favorite: bool = False      # Whether the model is a favorite
+    exclude: bool = False       # Whether to exclude this model from the cache
 
     def __post_init__(self):
         # Initialize empty lists to avoid mutable default parameter issue

py/utils/routes_common.py

@@ -40,6 +40,7 @@ class ModelRouteUtils:
                                   civitai_metadata: Dict, client: CivitaiClient) -> None:
         """Update local metadata with CivitAI data"""
         local_metadata['civitai'] = civitai_metadata
+        local_metadata['from_civitai'] = True
         
         # Update model name if available
         if 'model' in civitai_metadata:
@@ -50,9 +51,10 @@ class ModelRouteUtils:
             model_id = civitai_metadata['modelId']
             if model_id:
                 model_metadata, _ = await client.get_model_metadata(str(model_id))
-                if model_metadata:
+                if (model_metadata):
                     local_metadata['modelDescription'] = model_metadata.get('description', '')
                     local_metadata['tags'] = model_metadata.get('tags', [])
+                    local_metadata['civitai']['creator'] = model_metadata['creator']
         
         # Update base model
         local_metadata['base_model'] = determine_base_model(civitai_metadata.get('baseModel'))
@@ -142,6 +144,11 @@ class ModelRouteUtils:
         """
         client = CivitaiClient()
         try:
+            # Validate input parameters
+            if not isinstance(model_data, dict):
+                logger.error(f"Invalid model_data type: {type(model_data)}")
+                return False
+
             metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
             
             # Check if model metadata exists
@@ -165,21 +172,25 @@ class ModelRouteUtils:
                 client
             )
             
-            # Update cache object directly
-            model_data.update({
+            # Update cache object directly using safe .get() method
+            update_dict = {
                 'model_name': local_metadata.get('model_name'),
                 'preview_url': local_metadata.get('preview_url'),
                 'from_civitai': True,
                 'civitai': civitai_metadata
-            })
+            }
+            model_data.update(update_dict)
             
             # Update cache using the provided function
             await update_cache_func(file_path, file_path, local_metadata)
                 
             return True
 
+        except KeyError as e:
+            logger.error(f"Error fetching CivitAI data - Missing key: {e} in model_data={model_data}")
+            return False
         except Exception as e:
-            logger.error(f"Error fetching CivitAI data: {e}")
+            logger.error(f"Error fetching CivitAI data: {str(e)}", exc_info=True)  # Include stack trace
             return False
         finally:
             await client.close()
@@ -193,7 +204,7 @@ class ModelRouteUtils:
         fields = [
             "id", "modelId", "name", "createdAt", "updatedAt", 
             "publishedAt", "trainedWords", "baseModel", "description",
-            "model", "images"
+            "model", "images", "creator"
         ]
         return {k: data[k] for k in fields if k in data}
 
@@ -424,6 +435,65 @@ class ModelRouteUtils:
             logger.error(f"Error replacing preview: {e}", exc_info=True)
             return web.Response(text=str(e), status=500)
 
+    @staticmethod
+    async def handle_exclude_model(request: web.Request, scanner) -> web.Response:
+        """Handle model exclusion request
+        
+        Args:
+            request: The aiohttp request
+            scanner: The model scanner instance with cache management methods
+            
+        Returns:
+            web.Response: The HTTP response
+        """
+        try:
+            data = await request.json()
+            file_path = data.get('file_path')
+            if not file_path:
+                return web.Response(text='Model path is required', status=400)
+
+            # Update metadata to mark as excluded
+            metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+            metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
+            metadata['exclude'] = True
+            
+            # Save updated metadata
+            with open(metadata_path, 'w', encoding='utf-8') as f:
+                json.dump(metadata, f, indent=2, ensure_ascii=False)
+
+            # Update cache
+            cache = await scanner.get_cached_data()
+
+            # Find and remove model from cache
+            model_to_remove = next((item for item in cache.raw_data if item['file_path'] == file_path), None)
+            if model_to_remove:
+                # Update tags count
+                for tag in model_to_remove.get('tags', []):
+                    if tag in scanner._tags_count:
+                        scanner._tags_count[tag] = max(0, scanner._tags_count[tag] - 1)
+                        if scanner._tags_count[tag] == 0:
+                            del scanner._tags_count[tag]
+
+                # Remove from hash index if available
+                if hasattr(scanner, '_hash_index') and scanner._hash_index:
+                    scanner._hash_index.remove_by_path(file_path)
+
+                # Remove from cache data
+                cache.raw_data = [item for item in cache.raw_data if item['file_path'] != file_path]
+                await cache.resort()
+            
+            # Add to excluded models list
+            scanner._excluded_models.append(file_path)
+            
+            return web.json_response({
+                'success': True,
+                'message': f"Model {os.path.basename(file_path)} excluded"
+            })
+            
+        except Exception as e:
+            logger.error(f"Error excluding model: {e}", exc_info=True)
+            return web.Response(text=str(e), status=500)
+
     @staticmethod
     async def handle_download_model(request: web.Request, download_manager: DownloadManager, model_type="lora") -> web.Response:
         """Handle model download request
@@ -500,4 +570,4 @@ class ModelRouteUtils:
                 )
             
             logger.error(f"Error downloading {model_type}: {error_message}")
-            return web.Response(status=500, text=error_message)
+            return web.Response(status=500, text=error_message)

py/utils/utils.py

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

py/workflow/__init__.py

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

py/workflow/cli.py

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

py/workflow/ext/__init__.py

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

py/workflow/ext/comfyui_core.py

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

py/workflow/ext/kjnodes.py

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

py/workflow/main.py

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

py/workflow/mappers.py

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

py/workflow/parser.py

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

py/workflow/test.py

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

py/workflow/utils.py

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

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-lora-manager"
 description = "LoRA Manager for ComfyUI - Access it at http://localhost:8188/loras for managing LoRA models with previews and metadata integration."
-version = "0.8.11"
+version = "0.8.16"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",
@@ -13,7 +13,9 @@ dependencies = [
     "Pillow",
     "olefile", # for getting rid of warning message
     "requests",
-    "toml"
+    "toml",
+    "natsort",
+    "msgpack"
 ]
 
 [project.urls]

requirements.txt

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

static/css/base.css

@@ -38,7 +38,7 @@ html, body {
     --lora-border: oklch(90% 0.02 256 / 0.15);
     --lora-text: oklch(95% 0.02 256);
     --lora-error: oklch(75% 0.32 29);
-    --lora-warning: oklch(75% 0.25 80); /* Add warning color for deleted LoRAs */
+    --lora-warning: oklch(75% 0.25 80); /* Modified to be used with oklch() */
     
     /* Spacing Scale */
     --space-1: calc(8px * 1);
@@ -79,7 +79,7 @@ html[data-theme="light"] {
     --lora-surface: oklch(25% 0.02 256 / 0.98);
     --lora-border: oklch(90% 0.02 256 / 0.15);
     --lora-text: oklch(98% 0.02 256);
-    --lora-warning: oklch(75% 0.25 80); /* Add warning color for dark theme too */
+    --lora-warning: oklch(75% 0.25 80); /* Modified to be used with oklch() */
 }
 
 body {

static/css/components/alphabet-bar.css

@@ -0,0 +1,165 @@
+/* Alphabet Bar Component */
+.alphabet-bar-container {
+    position: fixed;
+    left: 0;
+    top: 50%;
+    transform: translateY(-50%);
+    z-index: 100;
+    display: flex;
+    transition: transform 0.3s ease;
+}
+
+.alphabet-bar-container.collapsed {
+    transform: translateY(-50%) translateX(-90%);
+}
+
+/* New visual indicator for when a letter is active and bar is collapsed */
+.alphabet-bar-container.collapsed .toggle-alphabet-bar.has-active-letter {
+    border-color: var(--lora-accent);
+    background: oklch(var(--lora-accent) / 0.15);
+}
+
+.alphabet-bar-container.collapsed .toggle-alphabet-bar.has-active-letter::after {
+    content: '';
+    position: absolute;
+    top: 7px;
+    right: 7px;
+    width: 8px;
+    height: 8px;
+    background-color: var(--lora-accent);
+    border-radius: 50%;
+    animation: pulse-active 2s infinite;
+}
+
+@keyframes pulse-active {
+    0% { transform: scale(0.8); opacity: 0.7; }
+    50% { transform: scale(1.1); opacity: 1; }
+    100% { transform: scale(0.8); opacity: 0.7; }
+}
+
+.alphabet-bar {
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: 0 var(--border-radius-xs) var(--border-radius-xs) 0;
+    padding: 8px 4px;
+    display: flex;
+    flex-direction: column;
+    gap: 6px;
+    align-items: center;
+    box-shadow: 2px 0 8px rgba(0, 0, 0, 0.1);
+    max-height: 80vh;
+    overflow-y: auto;
+    scrollbar-width: thin;
+}
+
+.alphabet-bar::-webkit-scrollbar {
+    width: 4px;
+}
+
+.alphabet-bar::-webkit-scrollbar-thumb {
+    background: var(--border-color);
+    border-radius: 4px;
+}
+
+.toggle-alphabet-bar {
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-left: none;
+    border-radius: 0 var(--border-radius-xs) var(--border-radius-xs) 0;
+    padding: 8px 4px;
+    cursor: pointer;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    color: var(--text-color);
+    width: 20px;
+    height: 40px;
+    align-self: center;
+    box-shadow: 2px 0 8px rgba(0, 0, 0, 0.1);
+}
+
+.toggle-alphabet-bar:hover {
+    background: var(--bg-hover);
+}
+
+.toggle-alphabet-bar i {
+    transition: transform 0.3s ease;
+}
+
+.alphabet-bar-container.collapsed .toggle-alphabet-bar i {
+    transform: rotate(180deg);
+}
+
+.letter-chip {
+    padding: 4px 2px;
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    color: var(--text-color);
+    cursor: pointer;
+    min-width: 24px;
+    text-align: center;
+    font-size: 0.85em;
+    transition: all 0.2s ease;
+    border: 1px solid var(--border-color);
+}
+
+.letter-chip:hover {
+    background: var(--lora-accent);
+    color: white;
+    transform: scale(1.1);
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+}
+
+.letter-chip.active {
+    background: var(--lora-accent);
+    color: white;
+    border-color: var(--lora-accent);
+}
+
+.letter-chip.disabled {
+    opacity: 0.5;
+    pointer-events: none;
+    cursor: default;
+}
+
+/* Hide the count by default, only show in tooltip */
+.letter-chip .count {
+    display: none;
+}
+
+.alphabet-bar-title {
+    font-size: 0.75em;
+    color: var(--text-color);
+    opacity: 0.7;
+    margin-bottom: 6px;
+    writing-mode: vertical-lr;
+    transform: rotate(180deg);
+    white-space: nowrap;
+}
+
+@media (max-width: 768px) {
+    .alphabet-bar-container {
+        transform: translateY(-50%) translateX(-90%);
+    }
+    
+    .alphabet-bar-container.active {
+        transform: translateY(-50%) translateX(0);
+    }
+    
+    .letter-chip {
+        padding: 3px 1px;
+        min-width: 20px;
+        font-size: 0.75em;
+    }
+}
+
+/* Keyframe animations for the active letter */
+@keyframes pulse {
+    0% { transform: scale(1); }
+    50% { transform: scale(1.1); }
+    100% { transform: scale(1); }
+}
+
+.letter-chip.active {
+    animation: pulse 1s ease-in-out 1;
+}

static/css/components/card.css

@@ -1,14 +1,17 @@
 /* 卡片网格布局 */
 .card-grid {
     display: grid;
-    grid-template-columns: repeat(auto-fill, minmax(260px, 1fr)); /* Adjusted from 320px */
-    gap: 12px; /* Reduced from var(--space-2) for tighter horizontal spacing */
+    grid-template-columns: repeat(auto-fill, minmax(260px, 1fr)); /* Base size */
+    gap: 12px; /* Consistent gap for both row and column spacing */
+    row-gap: 20px; /* Increase vertical spacing between rows */
     margin-top: var(--space-2);
     padding-top: 4px; /* 添加顶部内边距，为悬停动画提供空间 */
     padding-bottom: 4px; /* 添加底部内边距，为悬停动画提供空间 */
-    max-width: 1400px; /* Container width control */
+    width: 100%; /* Ensure it takes full width of container */
+    max-width: 1400px; /* Base container width */
     margin-left: auto;
     margin-right: auto;
+    box-sizing: border-box; /* Include padding in width calculation */
 }
 
 .lora-card {
@@ -17,13 +20,14 @@
     border-radius: var(--border-radius-base);
     backdrop-filter: blur(16px);
     transition: transform 160ms ease-out;
-    aspect-ratio: 896/1152;
-    max-width: 260px; /* Adjusted from 320px to fit 5 cards */
+    aspect-ratio: 896/1152; /* Preserve aspect ratio */
+    max-width: 260px; /* Base size */
+    width: 100%;
     margin: 0 auto;
-    cursor: pointer; /* Added from recipe-card */
-    display: flex; /* Added from recipe-card */
-    flex-direction: column; /* Added from recipe-card */
-    overflow: hidden; /* Add overflow hidden to contain children */
+    cursor: pointer;
+    display: flex;
+    flex-direction: column;
+    overflow: hidden;
 }
 
 .lora-card:hover {
@@ -36,6 +40,30 @@
     outline-offset: 2px;
 }
 
+/* Responsive adjustments for 1440p screens (2K) */
+@media (min-width: 2000px) {
+    .card-grid {
+        max-width: 1800px; /* Increased for 2K screens */
+        grid-template-columns: repeat(auto-fill, minmax(270px, 1fr));
+    }
+    
+    .lora-card {
+        max-width: 270px;
+    }
+}
+
+/* Responsive adjustments for 4K screens */
+@media (min-width: 3000px) {
+    .card-grid {
+        max-width: 2400px; /* Increased for 4K screens */
+        grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
+    }
+    
+    .lora-card {
+        max-width: 280px;
+    }
+}
+
 /* Responsive adjustments */
 @media (max-width: 1400px) {
     .card-grid {
@@ -58,6 +86,42 @@
     min-height: 0; /* Fix for potential flexbox sizing issue in Firefox */
 }
 
+/* Smaller text for medium density */
+.medium-density .model-name {
+    font-size: 0.95em;
+    max-height: 2.6em;
+}
+
+.medium-density .base-model-label {
+    font-size: 0.85em;
+    max-width: 120px;
+}
+
+.medium-density .card-actions i {
+    font-size: 0.98em;
+    padding: 4px;
+}
+
+/* Smaller text for compact mode */
+.compact-density .model-name {
+    font-size: 0.9em;
+    max-height: 2.4em;
+}
+
+.compact-density .base-model-label {
+    font-size: 0.8em;
+    max-width: 110px;
+}
+
+.compact-density .card-actions i {
+    font-size: 0.95em;
+    padding: 3px;
+}
+
+.compact-density .model-info {
+    padding-bottom: 2px;
+}
+
 .card-preview img,
 .card-preview video {
     width: 100%;
@@ -313,6 +377,25 @@
     font-size: 0.85em;
 }
 
+/* Prevent text selection on cards and interactive elements */
+.lora-card,
+.lora-card *,
+.card-actions,
+.card-actions i,
+.toggle-blur-btn,
+.show-content-btn,
+.card-preview img,
+.card-preview video,
+.card-footer,
+.card-header,
+.model-name,
+.base-model-label {
+    -webkit-user-select: none;
+    -moz-user-select: none;
+    -ms-user-select: none;
+    user-select: none;
+}
+
 /* Recipe specific elements - migrated from recipe-card.css */
 .recipe-indicator {
     position: absolute;
@@ -362,4 +445,44 @@
     padding: 2rem;
     background: var(--lora-surface-alt);
     border-radius: var(--border-radius-base);
+}
+
+/* Virtual scrolling specific styles - updated */
+.virtual-scroll-item {
+    position: absolute;
+    box-sizing: border-box;
+    transition: transform 160ms ease-out;
+    margin: 0; /* Remove margins, positioning is handled by VirtualScroller */
+    width: 100%; /* Allow width to be set by the VirtualScroller */
+}
+
+.virtual-scroll-item:hover {
+    transform: translateY(-2px); /* Keep hover effect */
+    z-index: 1; /* Ensure hovered items appear above others */
+}
+
+/* When using virtual scroll, adjust container */
+.card-grid.virtual-scroll {
+    display: block;
+    position: relative;
+    margin: 0 auto;
+    padding: 4px 0; /* Add top/bottom padding equivalent to card padding */
+    height: auto;
+    width: 100%;
+    max-width: 1400px; /* Keep the max-width from original grid */
+    box-sizing: border-box; /* Include padding in width calculation */
+    overflow-x: hidden; /* Prevent horizontal overflow */
+}
+
+/* For larger screens, allow more space for the cards */
+@media (min-width: 2000px) {
+    .card-grid.virtual-scroll {
+        max-width: 1800px;
+    }
+}
+
+@media (min-width: 3000px) {
+    .card-grid.virtual-scroll {
+        max-width: 2400px;
+    }
 }

static/css/components/duplicates.css

@@ -0,0 +1,272 @@
+/* Duplicates Management Styles */
+
+/* Duplicates banner */
+.duplicates-banner {
+    position: relative; /* Changed from sticky to relative */
+    width: 100%;
+    background-color: var(--card-bg);
+    color: var(--text-color);
+    border-bottom: 1px solid var(--border-color);
+    z-index: var(--z-overlay);
+    padding: 12px 0; /* Removed horizontal padding */
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.15);
+    transition: all 0.3s ease;
+    margin-bottom: 20px; /* Add margin to create space below the banner */
+}
+
+.duplicates-banner .banner-content {
+    max-width: 1400px; /* Match the container max-width */
+    margin: 0 auto;
+    display: flex;
+    align-items: center;
+    gap: 12px;
+    padding: 0 16px; /* Move horizontal padding to the content */
+}
+
+/* Responsive container for larger screens - match container in layout.css */
+@media (min-width: 2000px) {
+    .duplicates-banner .banner-content {
+        max-width: 1800px;
+    }
+}
+
+@media (min-width: 3000px) {
+    .duplicates-banner .banner-content {
+        max-width: 2400px;
+    }
+}
+
+.duplicates-banner i.fa-exclamation-triangle {
+    font-size: 18px;
+    color: oklch(var(--lora-warning));
+}
+
+.duplicates-banner .banner-actions {
+    margin-left: auto;
+    display: flex;
+    gap: 8px;
+    align-items: center;
+}
+
+.duplicates-banner button {
+    min-width: 100px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 4px;
+    border-radius: var(--border-radius-xs);
+    padding: 4px 10px;
+    border: 1px solid var(--border-color);
+    background: var(--card-bg);
+    color: var(--text-color);
+    font-size: 0.85em;
+    transition: all 0.2s ease;
+    cursor: pointer;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+}
+
+.duplicates-banner button:hover {
+    border-color: var(--lora-accent);
+    background: var(--bg-color);
+    transform: translateY(-1px);
+    box-shadow: 0 3px 5px rgba(0, 0, 0, 0.08);
+}
+
+.duplicates-banner button.btn-exit {
+    min-width: unset;
+    width: 28px;
+    height: 28px;
+    padding: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    border-radius: 50%;
+}
+
+.duplicates-banner button.disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+/* Duplicate groups */
+.duplicate-group {
+    position: relative;
+    border: 2px solid oklch(var(--lora-warning));
+    border-radius: var(--border-radius-base);
+    padding: 16px;
+    margin-bottom: 24px;
+    background: var(--card-bg);
+}
+
+.duplicate-group-header {
+    background-color: var(--bg-color);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+    padding: 8px 16px;
+    border-radius: var(--border-radius-xs);
+    margin-bottom: 16px;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+}
+
+.duplicate-group-header span:last-child {
+    display: flex;
+    gap: 8px;
+    align-items: center;
+}
+
+.duplicate-group-header button {
+    min-width: 80px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 4px;
+    border-radius: var(--border-radius-xs);
+    padding: 4px 8px;
+    border: 1px solid var(--border-color);
+    background: var(--card-bg);
+    color: var(--text-color);
+    font-size: 0.85em;
+    transition: all 0.2s ease;
+    cursor: pointer;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+    margin-left: 8px;
+}
+
+.duplicate-group-header button:hover {
+    border-color: var(--lora-accent);
+    background: var(--bg-color);
+    transform: translateY(-1px);
+    box-shadow: 0 3px 5px rgba(0, 0, 0, 0.08);
+}
+
+.card-group-container {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 16px;
+    justify-content: flex-start;
+    align-items: flex-start;
+}
+
+/* Make cards in duplicate groups have consistent width */
+.card-group-container .lora-card {
+    flex: 0 0 auto;
+    width: 240px;
+    margin: 0;
+    cursor: pointer; /* Indicate the card is clickable */
+}
+
+/* Ensure the grid layout is only applied to the main recipe grid, not duplicate groups */
+.duplicate-mode .card-grid {
+    display: block;
+}
+
+/* Scrollable container for large duplicate groups */
+.card-group-container.scrollable {
+    max-height: 450px;
+    overflow-y: auto;
+    padding-right: 8px;
+}
+
+/* Add a toggle button to expand/collapse large duplicate groups */
+.group-toggle-btn {
+    position: absolute;
+    right: 16px;
+    bottom: -12px;
+    background: var(--card-bg);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+    border-radius: 50%;
+    width: 24px;
+    height: 24px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    cursor: pointer;
+    z-index: 1;
+    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
+    transition: all 0.2s ease;
+}
+
+.group-toggle-btn:hover {
+    border-color: var(--lora-accent);
+    transform: translateY(-1px);
+    box-shadow: 0 3px 5px rgba(0, 0, 0, 0.08);
+}
+
+/* Duplicate card styling */
+.lora-card.duplicate {
+    position: relative;
+    transition: all 0.2s ease;
+}
+
+.lora-card.duplicate:hover {
+    border-color: var(--lora-accent);
+}
+
+.lora-card.duplicate.latest {
+    border-style: solid;
+    border-color: oklch(var(--lora-warning));
+}
+
+.lora-card.duplicate-selected {
+    border: 2px solid oklch(var(--lora-accent));
+    box-shadow: 0 0 8px rgba(0, 0, 0, 0.2);
+}
+
+.lora-card .selector-checkbox {
+    position: absolute;
+    top: 10px;
+    right: 10px;
+    z-index: 10;
+    width: 20px;
+    height: 20px;
+    cursor: pointer;
+}
+
+/* Latest indicator */
+.lora-card.duplicate.latest::after {
+    content: "Latest";
+    position: absolute;
+    top: 10px;
+    left: 10px;
+    background: oklch(var(--lora-accent));
+    color: white;
+    font-size: 12px;
+    padding: 2px 6px;
+    border-radius: var(--border-radius-xs);
+    z-index: 5;
+}
+
+/* Responsive adjustments */
+@media (max-width: 768px) {
+    .duplicates-banner .banner-content {
+        flex-direction: column;
+        align-items: flex-start;
+        gap: 8px;
+    }
+    
+    .duplicates-banner .banner-actions {
+        width: 100%;
+        margin-left: 0;
+        justify-content: space-between;
+    }
+    
+    .duplicate-group-header {
+        flex-direction: column;
+        gap: 8px;
+        align-items: flex-start;
+    }
+    
+    .duplicate-group-header span:last-child {
+        display: flex;
+        gap: 8px;
+        width: 100%;
+    }
+    
+    .duplicate-group-header button {
+        margin-left: 0;
+        flex: 1;
+    }
+}

static/css/components/import-modal.css

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

static/css/components/keyboard-nav.css

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

static/css/components/lora-modal.css

@@ -673,11 +673,6 @@
     opacity: 0.9;
 }
 
-/* Model name field styles - complete replacement */
-.model-name-field {
-    display: none;
-}
-
 /* New Model Name Header Styles */
 .model-name-header {
     display: flex;
@@ -1133,8 +1128,8 @@
     pointer-events: none;
 }
 
-/* Show metadata panel only on hover */
-.media-wrapper:hover .image-metadata-panel {
+/* Show metadata panel only when the 'visible' class is added */
+.media-wrapper .image-metadata-panel.visible {
     transform: translateY(0);
     opacity: 0.98;
     pointer-events: auto;
@@ -1323,4 +1318,61 @@
 
 .recipes-error i {
     color: var(--lora-error);
+}
+
+/* Creator Information Styles */
+.creator-info {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    margin-bottom: var(--space-1);
+    padding: 6px 10px;
+    background: rgba(0, 0, 0, 0.03);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: var(--border-radius-sm);
+    max-width: fit-content;
+}
+
+[data-theme="dark"] .creator-info {
+    background: rgba(255, 255, 255, 0.03);
+    border: 1px solid var(--lora-border);
+}
+
+.creator-avatar {
+    width: 28px;
+    height: 28px;
+    border-radius: 50%;
+    overflow: hidden;
+    flex-shrink: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    background: var(--lora-surface);
+    border: 1px solid var(--lora-border);
+}
+
+.creator-avatar img {
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+.creator-placeholder {
+    background: var(--lora-accent);
+    color: white;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.creator-username {
+    font-size: 0.9em;
+    font-weight: 500;
+    color: var(--text-color);
+}
+
+/* Optional: add hover effect for creator info */
+.creator-info:hover {
+    background: oklch(var(--lora-accent) / 0.1);
+    border-color: var(--lora-accent);
 }

static/css/components/modal.css

@@ -44,26 +44,12 @@ body.modal-open {
 }
 
 /* Delete Modal specific styles */
-.delete-modal-content {
-    max-width: 500px;
-    text-align: center;
-}
 
 .delete-message {
     color: var(--text-color);
     margin: var(--space-2) 0;
 }
 
-.delete-model-info {
-    background: var(--lora-surface);
-    border: 1px solid var(--lora-border);
-    border-radius: var(--border-radius-sm);
-    padding: var(--space-2);
-    margin: var(--space-2) 0;
-    color: var(--text-color);
-    word-break: break-all;
-}
-
 /* Update delete modal styles */
 .delete-modal {
     display: none;  /* Set initial display to none */
@@ -92,7 +78,8 @@ body.modal-open {
     animation: modalFadeIn 0.2s ease-out;
 }
 
-.delete-model-info {
+.delete-model-info,
+.exclude-model-info {
     /* Update info display styling */
     background: var(--lora-surface);
     border: 1px solid var(--lora-border);
@@ -123,7 +110,7 @@ body.modal-open {
     margin-top: var(--space-3);
 }
 
-.cancel-btn, .delete-btn {
+.cancel-btn, .delete-btn, .exclude-btn {
     padding: 8px var(--space-2);
     border-radius: 6px;
     border: none;
@@ -143,6 +130,12 @@ body.modal-open {
     color: white;
 }
 
+/* Style for exclude button - different from delete button */
+.exclude-btn {
+    background: var(--lora-accent, #4f46e5);
+    color: white;
+}
+
 .cancel-btn:hover {
     background: var(--lora-border);
 }
@@ -151,9 +144,14 @@ body.modal-open {
     opacity: 0.9;
 }
 
+.exclude-btn:hover {
+    opacity: 0.9;
+    background: oklch(from var(--lora-accent, #4f46e5) l c h / 85%);
+}
+
 .modal-content h2 {
     color: var(--text-color);
-    margin-bottom: var(--space-2);
+    margin-bottom: var(--space-1);
     font-size: 1.5em;
 }
 
@@ -587,7 +585,7 @@ input:checked + .toggle-slider:before {
     border-radius: var(--border-radius-xs);
     border: 1px solid var(--border-color);
     background-color: var(--lora-surface);
-    color: var(--text-color);
+    color: var (--text-color);
     font-size: 0.95em;
     height: 32px;
 }
@@ -674,4 +672,25 @@ input:checked + .toggle-slider:before {
 
 .changelog-item a:hover {
     text-decoration: underline;
+}
+
+/* Add warning text style for settings */
+.warning-text {
+    color: var(--lora-warning, #e67e22);
+    font-weight: 500;
+}
+
+[data-theme="dark"] .warning-text {
+    color: var(--lora-warning, #f39c12);
+}
+
+/* Add styles for density description list */
+.density-description {
+    margin: 8px 0;
+    padding-left: 20px;
+    font-size: 0.9em;
+}
+
+.density-description li {
+    margin-bottom: 4px;
 }

static/css/components/progress-panel.css

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

static/css/components/recipe-modal.css

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

static/css/components/support-modal.css

@@ -117,9 +117,50 @@
     box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
 }
 
+/* QR Code section styles */
+.qrcode-toggle {
+    width: 100%;
+    margin-top: var(--space-2);
+    justify-content: center;
+    position: relative;
+}
+
+.qrcode-toggle .toggle-icon {
+    margin-left: 8px;
+    transition: transform 0.3s ease;
+}
+
+.qrcode-toggle.active .toggle-icon {
+    transform: rotate(180deg);
+}
+
+.qrcode-container {
+    max-height: 0;
+    overflow: hidden;
+    transition: max-height 0.4s ease, opacity 0.3s ease;
+    opacity: 0;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+}
+
+.qrcode-container.show {
+    max-height: 500px;
+    opacity: 1;
+    margin-top: var(--space-3);
+}
+
+.qrcode-image {
+    max-width: 80%;
+    height: auto;
+    border-radius: var(--border-radius-sm);
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+    border: 1px solid var(--lora-border);
+    aspect-ratio: 1/1; /* Ensure proper aspect ratio for the square QR code */
+}
+
 .support-footer {
     text-align: center;
-    margin-top: var(--space-1);
     font-style: italic;
     color: var(--text-color);
 }

static/css/layout.css

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

static/css/style.css

@@ -21,6 +21,9 @@
 @import 'components/filter-indicator.css';
 @import 'components/initialization.css';
 @import 'components/progress-panel.css';
+@import 'components/alphabet-bar.css'; /* Add alphabet bar component */
+@import 'components/duplicates.css'; /* Add duplicates component */
+@import 'components/keyboard-nav.css'; /* Add keyboard navigation component */
 
 .initialization-notice {
     display: flex;

static/js/api/baseModelApi.js

@@ -1,7 +1,6 @@
 // filepath: d:\Workspace\ComfyUI\custom_nodes\ComfyUI-Lora-Manager\static\js\api\baseModelApi.js
 import { state, getCurrentPageState } from '../state/index.js';
 import { showToast } from '../utils/uiHelpers.js';
-import { showDeleteModal, confirmDelete } from '../utils/modalUtils.js';
 import { getSessionItem, saveMapToStorage } from '../utils/storageHelpers.js';
 
 /**
@@ -49,6 +48,11 @@ export async function loadMoreModels(options = {}) {
         if (pageState.showFavoritesOnly) {
             params.append('favorites_only', 'true');
         }
+        
+        // Add active letter filter if set
+        if (pageState.activeLetterFilter) {
+            params.append('first_letter', pageState.activeLetterFilter);
+        }
 
         // Add search parameters if there's a search term
         if (pageState.filters?.search) {
@@ -155,6 +159,231 @@ export async function loadMoreModels(options = {}) {
     }
 }
 
+// New method for virtual scrolling fetch
+export async function fetchModelsPage(options = {}) {
+    const {
+        modelType = 'lora',
+        page = 1,
+        pageSize = 100,
+        endpoint = '/api/loras'
+    } = options;
+
+    const pageState = getCurrentPageState();
+    
+    try {
+        const params = new URLSearchParams({
+            page: page,
+            page_size: pageSize || pageState.pageSize || 20,
+            sort_by: pageState.sortBy
+        });
+        
+        if (pageState.activeFolder !== null) {
+            params.append('folder', pageState.activeFolder);
+        }
+
+        // Add favorites filter parameter if enabled
+        if (pageState.showFavoritesOnly) {
+            params.append('favorites_only', 'true');
+        }
+        
+        // Add active letter filter if set
+        if (pageState.activeLetterFilter) {
+            params.append('first_letter', pageState.activeLetterFilter);
+        }
+
+        // Add search parameters if there's a search term
+        if (pageState.filters?.search) {
+            params.append('search', pageState.filters.search);
+            params.append('fuzzy', 'true');
+            
+            // Add search option parameters if available
+            if (pageState.searchOptions) {
+                params.append('search_filename', pageState.searchOptions.filename.toString());
+                params.append('search_modelname', pageState.searchOptions.modelname.toString());
+                if (pageState.searchOptions.tags !== undefined) {
+                    params.append('search_tags', pageState.searchOptions.tags.toString());
+                }
+                params.append('recursive', (pageState.searchOptions?.recursive ?? false).toString());
+            }
+        }
+        
+        // Add filter parameters if active
+        if (pageState.filters) {
+            // Handle tags filters
+            if (pageState.filters.tags && pageState.filters.tags.length > 0) {
+                // Checkpoints API expects individual 'tag' parameters, Loras API expects comma-separated 'tags'
+                if (modelType === 'checkpoint') {
+                    pageState.filters.tags.forEach(tag => {
+                        params.append('tag', tag);
+                    });
+                } else {
+                    params.append('tags', pageState.filters.tags.join(','));
+                }
+            }
+            
+            // Handle base model filters
+            if (pageState.filters.baseModel && pageState.filters.baseModel.length > 0) {
+                if (modelType === 'checkpoint') {
+                    pageState.filters.baseModel.forEach(model => {
+                        params.append('base_model', model);
+                    });
+                } else {
+                    params.append('base_models', pageState.filters.baseModel.join(','));
+                }
+            }
+        }
+
+        // Add model-specific parameters
+        if (modelType === 'lora') {
+            // Check for recipe-based filtering parameters from session storage
+            const filterLoraHash = getSessionItem('recipe_to_lora_filterLoraHash');
+            const filterLoraHashes = getSessionItem('recipe_to_lora_filterLoraHashes');
+
+            // Add hash filter parameter if present
+            if (filterLoraHash) {
+                params.append('lora_hash', filterLoraHash);
+            } 
+            // Add multiple hashes filter if present
+            else if (filterLoraHashes) {
+                try {
+                    if (Array.isArray(filterLoraHashes) && filterLoraHashes.length > 0) {
+                        params.append('lora_hashes', filterLoraHashes.join(','));
+                    }
+                } catch (error) {
+                    console.error('Error parsing lora hashes from session storage:', error);
+                }
+            }
+        }
+
+        const response = await fetch(`${endpoint}?${params}`);
+        if (!response.ok) {
+            throw new Error(`Failed to fetch models: ${response.statusText}`);
+        }
+        
+        const data = await response.json();
+        
+        return {
+            items: data.items,
+            totalItems: data.total,
+            totalPages: data.total_pages,
+            currentPage: page,
+            hasMore: page < data.total_pages,
+            folders: data.folders
+        };
+        
+    } catch (error) {
+        console.error(`Error fetching ${modelType}s:`, error);
+        showToast(`Failed to fetch ${modelType}s: ${error.message}`, 'error');
+        throw error;
+    }
+}
+
+/**
+ * Reset and reload models using virtual scrolling
+ * @param {Object} options - Operation options
+ * @returns {Promise<Object>} The fetch result
+ */
+export async function resetAndReloadWithVirtualScroll(options = {}) {
+    const {
+        modelType = 'lora',
+        updateFolders = false,
+        fetchPageFunction
+    } = options;
+    
+    const pageState = getCurrentPageState();
+    
+    try {
+        pageState.isLoading = true;
+        document.body.classList.add('loading');
+        
+        // Reset page counter
+        pageState.currentPage = 1;
+        
+        // Fetch the first page
+        const result = await fetchPageFunction(1, pageState.pageSize || 50);
+        
+        // Update the virtual scroller
+        state.virtualScroller.refreshWithData(
+            result.items,
+            result.totalItems,
+            result.hasMore
+        );
+        
+        // Update state
+        pageState.hasMore = result.hasMore;
+        pageState.currentPage = 2; // Next page will be 2
+        
+        // Update folders if needed
+        if (updateFolders && result.folders) {
+            updateFolderTags(result.folders);
+        }
+        
+        return result;
+    } catch (error) {
+        console.error(`Error reloading ${modelType}s:`, error);
+        showToast(`Failed to reload ${modelType}s: ${error.message}`, 'error');
+        throw error;
+    } finally {
+        pageState.isLoading = false;
+        document.body.classList.remove('loading');
+    }
+}
+
+/**
+ * Load more models using virtual scrolling
+ * @param {Object} options - Operation options
+ * @returns {Promise<Object>} The fetch result
+ */
+export async function loadMoreWithVirtualScroll(options = {}) {
+    const {
+        modelType = 'lora',
+        resetPage = false,
+        updateFolders = false,
+        fetchPageFunction
+    } = options;
+    
+    const pageState = getCurrentPageState();
+    
+    try {
+        // Start loading state
+        pageState.isLoading = true;
+        document.body.classList.add('loading');
+        
+        // Reset to first page if requested
+        if (resetPage) {
+            pageState.currentPage = 1;
+        }
+        
+        // Fetch the first page of data
+        const result = await fetchPageFunction(pageState.currentPage, pageState.pageSize || 50);
+        
+        // Update virtual scroller with the new data
+        state.virtualScroller.refreshWithData(
+            result.items,
+            result.totalItems,
+            result.hasMore
+        );
+        
+        // Update state
+        pageState.hasMore = result.hasMore;
+        pageState.currentPage = 2; // Next page to load would be 2
+        
+        // Update folders if needed
+        if (updateFolders && result.folders) {
+            updateFolderTags(result.folders);
+        }
+        
+        return result;
+    } catch (error) {
+        console.error(`Error loading ${modelType}s:`, error);
+        showToast(`Failed to load ${modelType}s: ${error.message}`, 'error');
+        throw error;
+    } finally {
+        pageState.isLoading = false;
+        document.body.classList.remove('loading');
+    }
+}
+
 // Update folder tags in the UI
 export function updateFolderTags(folders) {
     const folderTagsContainer = document.querySelector('.folder-tags');
@@ -203,13 +432,49 @@ export function replaceModelPreview(filePath, modelType = 'lora') {
 }
 
 // Delete a model (generic)
-export function deleteModel(filePath, modelType = 'lora') {
-    if (modelType === 'checkpoint') {
-        confirmDelete('Are you sure you want to delete this checkpoint?', () => {
-            performDelete(filePath, modelType);
+export async function deleteModel(filePath, modelType = 'lora') {
+    try {
+        const endpoint = modelType === 'checkpoint' 
+            ? '/api/checkpoints/delete' 
+            : '/api/delete_model';
+            
+        const response = await fetch(endpoint, {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_path: filePath
+            })
         });
-    } else {
-        showDeleteModal(filePath);
+        
+        if (!response.ok) {
+            throw new Error(`Failed to delete ${modelType}: ${response.statusText}`);
+        }
+        
+        const data = await response.json();
+        
+        if (data.success) {
+            // If virtual scroller exists, update its data
+            if (state.virtualScroller) {
+                state.virtualScroller.removeItemByFilePath(filePath);
+            } else {
+                // Legacy approach: remove the card from UI directly
+                const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
+                if (card) {
+                    card.remove();
+                }
+            }
+            
+            showToast(`${modelType} deleted successfully`, 'success');
+            return true;
+        } else {
+            throw new Error(data.error || `Failed to delete ${modelType}`);
+        }
+    } catch (error) {
+        console.error(`Error deleting ${modelType}:`, error);
+        showToast(`Failed to delete ${modelType}: ${error.message}`, 'error');
+        return false;
     }
 }
 
@@ -234,26 +499,31 @@ export async function refreshModels(options = {}) {
     const { 
         modelType = 'lora',
         scanEndpoint = '/api/loras/scan',
-        resetAndReloadFunction
+        resetAndReloadFunction,
+        fullRebuild = false // New parameter with default value false
     } = options;
     
     try {
-        state.loadingManager.showSimpleLoading(`Refreshing ${modelType}s...`);
+        state.loadingManager.showSimpleLoading(`${fullRebuild ? 'Full rebuild' : 'Refreshing'} ${modelType}s...`);
         
-        const response = await fetch(scanEndpoint);
+        // Add fullRebuild parameter to the request
+        const url = new URL(scanEndpoint, window.location.origin);
+        url.searchParams.append('full_rebuild', fullRebuild);
+        
+        const response = await fetch(url);
         
         if (!response.ok) {
             throw new Error(`Failed to refresh ${modelType}s: ${response.status} ${response.statusText}`);
         }
         
         if (typeof resetAndReloadFunction === 'function') {
-            await resetAndReloadFunction();
+            await resetAndReloadFunction(true); // update folders
         }
         
-        showToast(`Refresh complete`, 'success');
+        showToast(`${fullRebuild ? 'Full rebuild' : 'Refresh'} complete`, 'success');
     } catch (error) {
         console.error(`Refresh failed:`, error);
-        showToast(`Failed to refresh ${modelType}s`, 'error');
+        showToast(`Failed to ${fullRebuild ? 'rebuild' : 'refresh'} ${modelType}s`, 'error');
     } finally {
         state.loadingManager.hide();
         state.loadingManager.restoreProgressBar();
@@ -389,6 +659,53 @@ export async function refreshSingleModelMetadata(filePath, modelType = 'lora') {
     }
 }
 
+// Generic function to exclude a model
+export async function excludeModel(filePath, modelType = 'lora') {
+    try {
+        const endpoint = modelType === 'checkpoint' 
+            ? '/api/checkpoints/exclude' 
+            : '/api/loras/exclude';
+            
+        const response = await fetch(endpoint, {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_path: filePath
+            })
+        });
+        
+        if (!response.ok) {
+            throw new Error(`Failed to exclude ${modelType}: ${response.statusText}`);
+        }
+        
+        const data = await response.json();
+        
+        if (data.success) {
+            // If virtual scroller exists, update its data
+            if (state.virtualScroller) {
+                state.virtualScroller.removeItemByFilePath(filePath);
+            } else {
+                // Legacy approach: remove the card from UI directly
+                const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
+                if (card) {
+                    card.remove();
+                }
+            }
+            
+            showToast(`${modelType} excluded successfully`, 'success');
+            return true;
+        } else {
+            throw new Error(data.error || `Failed to exclude ${modelType}`);
+        }
+    } catch (error) {
+        console.error(`Error excluding ${modelType}:`, error);
+        showToast(`Failed to exclude ${modelType}: ${error.message}`, 'error');
+        return false;
+    }
+}
+
 // Private methods
 
 // Upload a preview image

static/js/api/checkpointApi.js

@@ -1,40 +1,87 @@
 import { createCheckpointCard } from '../components/CheckpointCard.js';
 import {
     loadMoreModels,
+    fetchModelsPage,
     resetAndReload as baseResetAndReload,
+    resetAndReloadWithVirtualScroll,
+    loadMoreWithVirtualScroll,
     refreshModels as baseRefreshModels,
     deleteModel as baseDeleteModel,
     replaceModelPreview,
     fetchCivitaiMetadata,
-    refreshSingleModelMetadata
+    refreshSingleModelMetadata,
+    excludeModel as baseExcludeModel
 } from './baseModelApi.js';
+import { state } from '../state/index.js';
 
-// Load more checkpoints with pagination
-export async function loadMoreCheckpoints(resetPagination = true) {
-    return loadMoreModels({
-        resetPage: resetPagination,
-        updateFolders: true,
+/**
+ * Fetch checkpoints with pagination for virtual scrolling
+ * @param {number} page - Page number to fetch
+ * @param {number} pageSize - Number of items per page
+ * @returns {Promise<Object>} Object containing items, total count, and pagination info
+ */
+export async function fetchCheckpointsPage(page = 1, pageSize = 100) {
+    return fetchModelsPage({
         modelType: 'checkpoint',
-        createCardFunction: createCheckpointCard,
+        page,
+        pageSize,
         endpoint: '/api/checkpoints'
     });
 }
 
+/**
+ * Load more checkpoints with pagination - updated to work with VirtualScroller
+ * @param {boolean} resetPage - Whether to reset to the first page
+ * @param {boolean} updateFolders - Whether to update folder tags
+ * @returns {Promise<void>}
+ */
+export async function loadMoreCheckpoints(resetPage = false, updateFolders = false) {
+    // Check if virtual scroller is available
+    if (state.virtualScroller) {
+        return loadMoreWithVirtualScroll({
+            modelType: 'checkpoint',
+            resetPage,
+            updateFolders,
+            fetchPageFunction: fetchCheckpointsPage
+        });
+    } else {
+        // Fall back to the original implementation if virtual scroller isn't available
+        return loadMoreModels({
+            resetPage,
+            updateFolders,
+            modelType: 'checkpoint',
+            createCardFunction: createCheckpointCard,
+            endpoint: '/api/checkpoints'
+        });
+    }
+}
+
 // Reset and reload checkpoints
-export async function resetAndReload() {
-    return baseResetAndReload({
-        updateFolders: true,
-        modelType: 'checkpoint',
-        loadMoreFunction: loadMoreCheckpoints
-    });
+export async function resetAndReload(updateFolders = false) {
+    // Check if virtual scroller is available
+    if (state.virtualScroller) {
+        return resetAndReloadWithVirtualScroll({
+            modelType: 'checkpoint',
+            updateFolders,
+            fetchPageFunction: fetchCheckpointsPage
+        });
+    } else {
+        // Fall back to original implementation
+        return baseResetAndReload({
+            updateFolders,
+            modelType: 'checkpoint',
+            loadMoreFunction: loadMoreCheckpoints
+        });
+    }
 }
 
 // Refresh checkpoints
-export async function refreshCheckpoints() {
+export async function refreshCheckpoints(fullRebuild = false) {
     return baseRefreshModels({
         modelType: 'checkpoint',
         scanEndpoint: '/api/checkpoints/scan',
-        resetAndReloadFunction: resetAndReload
+        resetAndReloadFunction: resetAndReload,
+        fullRebuild: fullRebuild
     });
 }
 
@@ -59,7 +106,11 @@ export async function fetchCivitai() {
 
 // Refresh single checkpoint metadata
 export async function refreshSingleCheckpointMetadata(filePath) {
-    return refreshSingleModelMetadata(filePath, 'checkpoint');
+    const success = await refreshSingleModelMetadata(filePath, 'checkpoint');
+    if (success) {
+        // Reload the current view to show updated data
+        await resetAndReload();
+    }
 }
 
 /**
@@ -69,20 +120,73 @@ export async function refreshSingleCheckpointMetadata(filePath) {
  * @returns {Promise} - Promise that resolves with the server response
  */
 export async function saveModelMetadata(filePath, data) {
-    const response = await fetch('/api/checkpoints/save-metadata', {
-        method: 'POST',
-        headers: {
-            'Content-Type': 'application/json',
-        },
-        body: JSON.stringify({
-            file_path: filePath,
-            ...data
-        })
-    });
+    try {
+        // Show loading indicator
+        state.loadingManager.showSimpleLoading('Saving metadata...');
+        
+        const response = await fetch('/api/checkpoints/save-metadata', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_path: filePath,
+                ...data
+            })
+        });
 
-    if (!response.ok) {
-        throw new Error('Failed to save metadata');
+        if (!response.ok) {
+            throw new Error('Failed to save metadata');
+        }
+        
+        return response.json();
+    } finally {
+        // Always hide the loading indicator when done
+        state.loadingManager.hide();
+    }
+}
+
+/**
+ * Exclude a checkpoint model from being shown in the UI
+ * @param {string} filePath - File path of the checkpoint to exclude
+ * @returns {Promise<boolean>} Promise resolving to success status
+ */
+export function excludeCheckpoint(filePath) {
+    return baseExcludeModel(filePath, 'checkpoint');
+}
+
+/**
+ * Rename a checkpoint file
+ * @param {string} filePath - Current file path
+ * @param {string} newFileName - New file name (without path)
+ * @returns {Promise<Object>} - Promise that resolves with the server response
+ */
+export async function renameCheckpointFile(filePath, newFileName) {
+    try {
+        // Show loading indicator
+        state.loadingManager.showSimpleLoading('Renaming checkpoint file...');
+        
+        const response = await fetch('/api/rename_checkpoint', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_path: filePath,
+                new_file_name: newFileName
+            })
+        });
+        
+        if (!response.ok) {
+            throw new Error(`Server returned ${response.status}: ${response.statusText}`);
+        }
+        
+        return await response.json();
+    } catch (error) {
+        console.error('Error renaming checkpoint file:', error);
+        throw error;
+    } finally {
+        // Hide loading indicator
+        state.loadingManager.hide();
     }
-    
-    return response.json();
 }

static/js/api/loraApi.js

@@ -1,13 +1,18 @@
 import { createLoraCard } from '../components/LoraCard.js';
 import {
     loadMoreModels,
+    fetchModelsPage,
     resetAndReload as baseResetAndReload,
+    resetAndReloadWithVirtualScroll,
+    loadMoreWithVirtualScroll,
     refreshModels as baseRefreshModels,
     deleteModel as baseDeleteModel,
     replaceModelPreview,
     fetchCivitaiMetadata,
-    refreshSingleModelMetadata
+    refreshSingleModelMetadata,
+    excludeModel as baseExcludeModel
 } from './baseModelApi.js';
+import { state, getCurrentPageState } from '../state/index.js';
 
 /**
  * Save model metadata to the server
@@ -16,30 +21,81 @@ import {
  * @returns {Promise} Promise of the save operation
  */
 export async function saveModelMetadata(filePath, data) {
-    const response = await fetch('/api/loras/save-metadata', {
-        method: 'POST',
-        headers: {
-            'Content-Type': 'application/json',
-        },
-        body: JSON.stringify({
-            file_path: filePath,
-            ...data
-        })
-    });
+    try {
+        // Show loading indicator
+        state.loadingManager.showSimpleLoading('Saving metadata...');
+        
+        const response = await fetch('/api/loras/save-metadata', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_path: filePath,
+                ...data
+            })
+        });
 
-    if (!response.ok) {
-        throw new Error('Failed to save metadata');
+        if (!response.ok) {
+            throw new Error('Failed to save metadata');
+        }
+        
+        return response.json();
+    } finally {
+        // Always hide the loading indicator when done
+        state.loadingManager.hide();
     }
-    
-    return response.json();
 }
 
+/**
+ * Exclude a lora model from being shown in the UI
+ * @param {string} filePath - File path of the model to exclude
+ * @returns {Promise<boolean>} Promise resolving to success status
+ */
+export async function excludeLora(filePath) {
+    return baseExcludeModel(filePath, 'lora');
+}
+
+/**
+ * Load more loras with pagination - updated to work with VirtualScroller
+ * @param {boolean} resetPage - Whether to reset to the first page
+ * @param {boolean} updateFolders - Whether to update folder tags
+ * @returns {Promise<void>}
+ */
 export async function loadMoreLoras(resetPage = false, updateFolders = false) {
-    return loadMoreModels({
-        resetPage,
-        updateFolders,
+    const pageState = getCurrentPageState();
+    
+    // Check if virtual scroller is available
+    if (state.virtualScroller) {
+        return loadMoreWithVirtualScroll({
+            modelType: 'lora',
+            resetPage,
+            updateFolders,
+            fetchPageFunction: fetchLorasPage
+        });
+    } else {
+        // Fall back to the original implementation if virtual scroller isn't available
+        return loadMoreModels({
+            resetPage,
+            updateFolders,
+            modelType: 'lora',
+            createCardFunction: createLoraCard,
+            endpoint: '/api/loras'
+        });
+    }
+}
+
+/**
+ * Fetch loras with pagination for virtual scrolling
+ * @param {number} page - Page number to fetch
+ * @param {number} pageSize - Number of items per page
+ * @returns {Promise<Object>} Object containing items, total count, and pagination info
+ */
+export async function fetchLorasPage(page = 1, pageSize = 100) {
+    return fetchModelsPage({
         modelType: 'lora',
-        createCardFunction: createLoraCard,
+        page,
+        pageSize,
         endpoint: '/api/loras'
     });
 }
@@ -61,28 +117,46 @@ export async function replacePreview(filePath) {
 }
 
 export function appendLoraCards(loras) {
-    const grid = document.getElementById('loraGrid');
-    const sentinel = document.getElementById('scroll-sentinel');
-    
-    loras.forEach(lora => {
-        const card = createLoraCard(lora);
-        grid.appendChild(card);
-    });
+    // This function is no longer needed with virtual scrolling
+    // but kept for compatibility
+    if (state.virtualScroller) {
+        console.warn('appendLoraCards is deprecated when using virtual scrolling');
+    } else {
+        const grid = document.getElementById('loraGrid');
+        
+        loras.forEach(lora => {
+            const card = createLoraCard(lora);
+            grid.appendChild(card);
+        });
+    }
 }
 
 export async function resetAndReload(updateFolders = false) {
-    return baseResetAndReload({
-        updateFolders,
-        modelType: 'lora',
-        loadMoreFunction: loadMoreLoras
-    });
+    const pageState = getCurrentPageState();
+    
+    // Check if virtual scroller is available
+    if (state.virtualScroller) {
+        return resetAndReloadWithVirtualScroll({
+            modelType: 'lora',
+            updateFolders,
+            fetchPageFunction: fetchLorasPage
+        });
+    } else {
+        // Fall back to original implementation
+        return baseResetAndReload({
+            updateFolders,
+            modelType: 'lora',
+            loadMoreFunction: loadMoreLoras
+        });
+    }
 }
 
-export async function refreshLoras() {
+export async function refreshLoras(fullRebuild = false) {
     return baseRefreshModels({
         modelType: 'lora',
         scanEndpoint: '/api/loras/scan',
-        resetAndReloadFunction: resetAndReload
+        resetAndReloadFunction: resetAndReload,
+        fullRebuild: fullRebuild
     });
 }
 
@@ -107,4 +181,40 @@ export async function fetchModelDescription(modelId, filePath) {
         console.error('Error fetching model description:', error);
         throw error;
     }
+}
+
+/**
+ * Rename a LoRA file
+ * @param {string} filePath - Current file path
+ * @param {string} newFileName - New file name (without path)
+ * @returns {Promise<Object>} - Promise that resolves with the server response
+ */
+export async function renameLoraFile(filePath, newFileName) {
+    try {
+        // Show loading indicator
+        state.loadingManager.showSimpleLoading('Renaming LoRA file...');
+        
+        const response = await fetch('/api/rename_lora', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_path: filePath,
+                new_file_name: newFileName
+            })
+        });
+        
+        if (!response.ok) {
+            throw new Error(`Server returned ${response.status}: ${response.statusText}`);
+        }
+        
+        return await response.json();
+    } catch (error) {
+        console.error('Error renaming LoRA file:', error);
+        throw error;
+    } finally {
+        // Hide loading indicator
+        state.loadingManager.hide();
+    }
 }

static/js/api/recipeApi.js

@@ -0,0 +1,174 @@
+import { RecipeCard } from '../components/RecipeCard.js';
+import {
+    fetchModelsPage,
+    resetAndReloadWithVirtualScroll,
+    loadMoreWithVirtualScroll
+} from './baseModelApi.js';
+import { state, getCurrentPageState } from '../state/index.js';
+import { showToast } from '../utils/uiHelpers.js';
+
+/**
+ * Fetch recipes with pagination for virtual scrolling
+ * @param {number} page - Page number to fetch
+ * @param {number} pageSize - Number of items per page
+ * @returns {Promise<Object>} Object containing items, total count, and pagination info
+ */
+export async function fetchRecipesPage(page = 1, pageSize = 100) {
+    const pageState = getCurrentPageState();
+    
+    try {
+        const params = new URLSearchParams({
+            page: page,
+            page_size: pageSize || pageState.pageSize || 20,
+            sort_by: pageState.sortBy
+        });
+        
+        // If we have a specific recipe ID to load
+        if (pageState.customFilter?.active && pageState.customFilter?.recipeId) {
+            // Special case: load specific recipe
+            const response = await fetch(`/api/recipe/${pageState.customFilter.recipeId}`);
+            
+            if (!response.ok) {
+                throw new Error(`Failed to load recipe: ${response.statusText}`);
+            }
+            
+            const recipe = await response.json();
+            
+            // Return in expected format
+            return {
+                items: [recipe],
+                totalItems: 1,
+                totalPages: 1,
+                currentPage: 1,
+                hasMore: false
+            };
+        }
+        
+        // Add custom filter for Lora if present
+        if (pageState.customFilter?.active && pageState.customFilter?.loraHash) {
+            params.append('lora_hash', pageState.customFilter.loraHash);
+            params.append('bypass_filters', 'true');
+        } else {
+            // Normal filtering logic
+            
+            // Add search filter if present
+            if (pageState.filters?.search) {
+                params.append('search', pageState.filters.search);
+                
+                // Add search option parameters
+                if (pageState.searchOptions) {
+                    params.append('search_title', pageState.searchOptions.title.toString());
+                    params.append('search_tags', pageState.searchOptions.tags.toString());
+                    params.append('search_lora_name', pageState.searchOptions.loraName.toString());
+                    params.append('search_lora_model', pageState.searchOptions.loraModel.toString());
+                    params.append('fuzzy', 'true');
+                }
+            }
+            
+            // Add base model filters
+            if (pageState.filters?.baseModel && pageState.filters.baseModel.length) {
+                params.append('base_models', pageState.filters.baseModel.join(','));
+            }
+            
+            // Add tag filters
+            if (pageState.filters?.tags && pageState.filters.tags.length) {
+                params.append('tags', pageState.filters.tags.join(','));
+            }
+        }
+
+        // Fetch recipes
+        const response = await fetch(`/api/recipes?${params.toString()}`);
+        
+        if (!response.ok) {
+            throw new Error(`Failed to load recipes: ${response.statusText}`);
+        }
+        
+        const data = await response.json();
+        
+        return {
+            items: data.items,
+            totalItems: data.total,
+            totalPages: data.total_pages,
+            currentPage: page,
+            hasMore: page < data.total_pages
+        };
+    } catch (error) {
+        console.error('Error fetching recipes:', error);
+        showToast(`Failed to fetch recipes: ${error.message}`, 'error');
+        throw error;
+    }
+}
+
+/**
+ * Reset and reload recipes using virtual scrolling
+ * @param {boolean} updateFolders - Whether to update folder tags
+ * @returns {Promise<Object>} The fetch result
+ */
+export async function resetAndReload(updateFolders = false) {
+    return resetAndReloadWithVirtualScroll({
+        modelType: 'recipe',
+        updateFolders,
+        fetchPageFunction: fetchRecipesPage
+    });
+}
+
+/**
+ * Refreshes the recipe list by first rebuilding the cache and then loading recipes
+ */
+export async function refreshRecipes() {
+    try {
+        state.loadingManager.showSimpleLoading('Refreshing recipes...');
+        
+        // Call the API endpoint to rebuild the recipe cache
+        const response = await fetch('/api/recipes/scan');
+        
+        if (!response.ok) {
+            const data = await response.json();
+            throw new Error(data.error || 'Failed to refresh recipe cache');
+        }
+        
+        // After successful cache rebuild, reload the recipes
+        await resetAndReload();
+        
+        showToast('Refresh complete', 'success');
+    } catch (error) {
+        console.error('Error refreshing recipes:', error);
+        showToast(error.message || 'Failed to refresh recipes', 'error');
+    } finally {
+        state.loadingManager.hide();
+        state.loadingManager.restoreProgressBar();
+    }
+}
+
+/**
+ * Load more recipes with pagination - updated to work with VirtualScroller
+ * @param {boolean} resetPage - Whether to reset to the first page
+ * @returns {Promise<void>}
+ */
+export async function loadMoreRecipes(resetPage = false) {
+    const pageState = getCurrentPageState();
+    
+    // Use virtual scroller if available
+    if (state.virtualScroller) {
+        return loadMoreWithVirtualScroll({
+            modelType: 'recipe',
+            resetPage,
+            updateFolders: false,
+            fetchPageFunction: fetchRecipesPage
+        });
+    }
+}
+
+/**
+ * Create a recipe card instance from recipe data
+ * @param {Object} recipe - Recipe data
+ * @returns {HTMLElement} Recipe card DOM element
+ */
+export function createRecipeCard(recipe) {
+    const recipeCard = new RecipeCard(recipe, (recipe) => {
+        if (window.recipeManager) {
+            window.recipeManager.showRecipeDetails(recipe);
+        }
+    });
+    return recipeCard.element;
+}

static/js/checkpoints.js

@@ -1,6 +1,5 @@
 import { appCore } from './core.js';
-import { initializeInfiniteScroll } from './utils/infiniteScroll.js';
-import { confirmDelete, closeDeleteModal } from './utils/modalUtils.js';
+import { confirmDelete, closeDeleteModal, confirmExclude, closeExcludeModal } from './utils/modalUtils.js';
 import { createPageControls } from './components/controls/index.js';
 import { loadMoreCheckpoints } from './api/checkpointApi.js';
 import { CheckpointDownloadManager } from './managers/CheckpointDownloadManager.js';
@@ -23,6 +22,8 @@ class CheckpointsPageManager {
         // Minimal set of functions that need to remain global
         window.confirmDelete = confirmDelete;
         window.closeDeleteModal = closeDeleteModal;
+        window.confirmExclude = confirmExclude;
+        window.closeExcludeModal = closeExcludeModal;
         
         // Add loadCheckpoints function to window for FilterManager compatibility
         window.checkpointManager = {
@@ -38,9 +39,6 @@ class CheckpointsPageManager {
         // Initialize context menu
         new CheckpointContextMenu();
         
-        // Initialize infinite scroll
-        initializeInfiniteScroll('checkpoints');
-        
         // Initialize common page features
         appCore.initializePageFeatures();
         

static/js/components/CheckpointCard.js

@@ -1,8 +1,9 @@
-import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
+import { showToast, copyToClipboard, openExampleImagesFolder } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { showCheckpointModal } from './checkpointModal/index.js';
 import { NSFW_LEVELS } from '../utils/constants.js';
 import { replaceCheckpointPreview as apiReplaceCheckpointPreview, saveModelMetadata } from '../api/checkpointApi.js';
+import { showDeleteModal } from '../utils/modalUtils.js';
 
 export function createCheckpointCard(checkpoint) {
     const card = document.createElement('div');
@@ -114,8 +115,8 @@ export function createCheckpointCard(checkpoint) {
                     <span class="model-name">${checkpoint.model_name}</span>
                 </div>
                 <div class="card-actions">
-                    <i class="fas fa-image" 
-                       title="Replace Preview Image">
+                    <i class="fas fa-folder-open" 
+                       title="Open Example Images Folder">
                     </i>
                 </div>
             </div>
@@ -262,7 +263,7 @@ export function createCheckpointCard(checkpoint) {
     // Delete button click event
     card.querySelector('.fa-trash')?.addEventListener('click', e => {
         e.stopPropagation();
-        deleteCheckpoint(checkpoint.file_path);
+        showDeleteModal(checkpoint.file_path);
     });
 
     // Replace preview button click event
@@ -271,6 +272,12 @@ export function createCheckpointCard(checkpoint) {
         replaceCheckpointPreview(checkpoint.file_path);
     });
 
+    // Open example images folder button click event
+    card.querySelector('.fa-folder-open')?.addEventListener('click', e => {
+        e.stopPropagation();
+        openExampleImagesFolder(checkpoint.sha256);
+    });
+
     // Add autoplayOnHover handlers for video elements if needed
     const videoElement = card.querySelector('video');
     if (videoElement && autoplayOnHover) {
@@ -322,17 +329,6 @@ function openCivitai(modelName) {
     }
 }
 
-function deleteCheckpoint(filePath) {
-    if (window.deleteCheckpoint) {
-        window.deleteCheckpoint(filePath);
-    } else {
-        // Use the modal delete functionality
-        import('../utils/modalUtils.js').then(({ showDeleteModal }) => {
-            showDeleteModal(filePath, 'checkpoint');
-        });
-    }
-}
-
 function replaceCheckpointPreview(filePath) {
     if (window.replaceCheckpointPreview) {
         window.replaceCheckpointPreview(filePath);

static/js/components/ContextMenu/CheckpointContextMenu.js

@@ -1,8 +1,9 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
-import { refreshSingleCheckpointMetadata, saveModelMetadata } from '../../api/checkpointApi.js';
-import { showToast, getNSFWLevelName } from '../../utils/uiHelpers.js';
+import { refreshSingleCheckpointMetadata, saveModelMetadata, replaceCheckpointPreview } from '../../api/checkpointApi.js';
+import { showToast, getNSFWLevelName, openExampleImagesFolder } from '../../utils/uiHelpers.js';
 import { NSFW_LEVELS } from '../../utils/constants.js';
 import { getStorageItem } from '../../utils/storageHelpers.js';
+import { showExcludeModal } from '../../utils/modalUtils.js';
 
 export class CheckpointContextMenu extends BaseContextMenu {
     constructor() {
@@ -22,10 +23,12 @@ export class CheckpointContextMenu extends BaseContextMenu {
                 this.currentCard.click();
                 break;
             case 'preview':
-                // Replace checkpoint preview
-                if (this.currentCard.querySelector('.fa-image')) {
-                    this.currentCard.querySelector('.fa-image').click();
-                }
+                // Open example images folder instead of replacing preview
+                openExampleImagesFolder(this.currentCard.dataset.sha256);
+                break;
+            case 'replace-preview':
+                // Add new action for replacing preview images
+                replaceCheckpointPreview(this.currentCard.dataset.filepath);
                 break;
             case 'civitai':
                 // Open civitai page
@@ -61,6 +64,10 @@ export class CheckpointContextMenu extends BaseContextMenu {
                 // Move to folder (placeholder)
                 showToast('Move to folder feature coming soon', 'info');
                 break;
+            case 'exclude':
+                showExcludeModal(this.currentCard.dataset.filepath, 'checkpoint');
+                break;
+
         }
     }
 

static/js/components/ContextMenu/LoraContextMenu.js

@@ -1,8 +1,9 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
-import { refreshSingleLoraMetadata, saveModelMetadata } from '../../api/loraApi.js';
-import { showToast, getNSFWLevelName } from '../../utils/uiHelpers.js';
+import { refreshSingleLoraMetadata, saveModelMetadata, replacePreview } from '../../api/loraApi.js';
+import { showToast, getNSFWLevelName, copyToClipboard, sendLoraToWorkflow, openExampleImagesFolder } from '../../utils/uiHelpers.js';
 import { NSFW_LEVELS } from '../../utils/constants.js';
 import { getStorageItem } from '../../utils/storageHelpers.js';
+import { showExcludeModal, showDeleteModal } from '../../utils/modalUtils.js';
 
 export class LoraContextMenu extends BaseContextMenu {
     constructor() {
@@ -34,13 +35,28 @@ export class LoraContextMenu extends BaseContextMenu {
                 }
                 break;
             case 'copyname':
-                this.currentCard.querySelector('.fa-copy')?.click();
+                // Generate and copy LoRA syntax
+                this.copyLoraSyntax();
+                break;
+            case 'sendappend':
+                // Send LoRA to workflow (append mode)
+                this.sendLoraToWorkflow(false);
+                break;
+            case 'sendreplace':
+                // Send LoRA to workflow (replace mode)
+                this.sendLoraToWorkflow(true);
                 break;
             case 'preview':
-                this.currentCard.querySelector('.fa-image')?.click();
+                // Open example images folder instead of showing preview image dialog
+                openExampleImagesFolder(this.currentCard.dataset.sha256);
+                break;
+            case 'replace-preview':
+                // Add a new action for replacing preview images
+                replacePreview(this.currentCard.dataset.filepath);
                 break;
             case 'delete':
-                this.currentCard.querySelector('.fa-trash')?.click();
+                // Call showDeleteModal directly instead of clicking the trash button
+                showDeleteModal(this.currentCard.dataset.filepath);
                 break;
             case 'move':
                 moveManager.showMoveModal(this.currentCard.dataset.filepath);
@@ -51,9 +67,32 @@ export class LoraContextMenu extends BaseContextMenu {
             case 'set-nsfw':
                 this.showNSFWLevelSelector(null, null, this.currentCard);
                 break;
+            case 'exclude':
+                showExcludeModal(this.currentCard.dataset.filepath);
+                break;
         }
     }
 
+    // New method to handle copy syntax functionality
+    copyLoraSyntax() {
+        const card = this.currentCard;
+        const usageTips = JSON.parse(card.dataset.usage_tips || '{}');
+        const strength = usageTips.strength || 1;
+        const loraSyntax = `<lora:${card.dataset.file_name}:${strength}>`;
+        
+        copyToClipboard(loraSyntax, 'LoRA syntax copied to clipboard');
+    }
+
+    // New method to handle send to workflow functionality
+    sendLoraToWorkflow(replaceMode) {
+        const card = this.currentCard;
+        const usageTips = JSON.parse(card.dataset.usage_tips || '{}');
+        const strength = usageTips.strength || 1;
+        const loraSyntax = `<lora:${card.dataset.file_name}:${strength}>`;
+        
+        sendLoraToWorkflow(loraSyntax, replaceMode, 'lora');
+    }
+
     // NSFW Selector methods from the original context menu
     initNSFWSelector() {
         // Close button

static/js/components/ContextMenu/RecipeContextMenu.js

@@ -1,5 +1,5 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
-import { showToast } from '../../utils/uiHelpers.js';
+import { showToast, copyToClipboard, sendLoraToWorkflow } from '../../utils/uiHelpers.js';
 import { setSessionItem, removeSessionItem } from '../../utils/storageHelpers.js';
 import { state } from '../../state/index.js';
 
@@ -39,8 +39,16 @@ export class RecipeContextMenu extends BaseContextMenu {
                 this.currentCard.click();
                 break;
             case 'copy':
-                // Copy recipe to clipboard
-                this.currentCard.querySelector('.fa-copy')?.click();
+                // Copy recipe syntax to clipboard
+                this.copyRecipeSyntax();
+                break;
+            case 'sendappend':
+                // Send recipe to workflow (append mode)
+                this.sendRecipeToWorkflow(false);
+                break;
+            case 'sendreplace':
+                // Send recipe to workflow (replace mode)
+                this.sendRecipeToWorkflow(true);
                 break;
             case 'share':
                 // Share recipe
@@ -61,6 +69,52 @@ export class RecipeContextMenu extends BaseContextMenu {
         }
     }
     
+    // New method to copy recipe syntax to clipboard
+    copyRecipeSyntax() {
+        const recipeId = this.currentCard.dataset.id;
+        if (!recipeId) {
+            showToast('Cannot copy recipe: Missing recipe ID', 'error');
+            return;
+        }
+
+        fetch(`/api/recipe/${recipeId}/syntax`)
+            .then(response => response.json())
+            .then(data => {
+                if (data.success && data.syntax) {
+                    copyToClipboard(data.syntax, 'Recipe syntax copied to clipboard');
+                } else {
+                    throw new Error(data.error || 'No syntax returned');
+                }
+            })
+            .catch(err => {
+                console.error('Failed to copy recipe syntax: ', err);
+                showToast('Failed to copy recipe syntax', 'error');
+            });
+    }
+    
+    // New method to send recipe to workflow
+    sendRecipeToWorkflow(replaceMode) {
+        const recipeId = this.currentCard.dataset.id;
+        if (!recipeId) {
+            showToast('Cannot send recipe: Missing recipe ID', 'error');
+            return;
+        }
+
+        fetch(`/api/recipe/${recipeId}/syntax`)
+            .then(response => response.json())
+            .then(data => {
+                if (data.success && data.syntax) {
+                    return sendLoraToWorkflow(data.syntax, replaceMode, 'recipe');
+                } else {
+                    throw new Error(data.error || 'No syntax returned');
+                }
+            })
+            .catch(err => {
+                console.error('Failed to send recipe to workflow: ', err);
+                showToast('Failed to send recipe to workflow', 'error');
+            });
+    }
+    
     // View all LoRAs in the recipe
     viewRecipeLoRAs(recipeId) {
         if (!recipeId) {

static/js/components/DuplicatesManager.js

@@ -0,0 +1,402 @@
+// Duplicates Manager Component
+import { showToast } from '../utils/uiHelpers.js';
+import { RecipeCard } from './RecipeCard.js';
+import { state, getCurrentPageState } from '../state/index.js';
+import { initializeInfiniteScroll } from '../utils/infiniteScroll.js';
+
+export class DuplicatesManager {
+    constructor(recipeManager) {
+        this.recipeManager = recipeManager;
+        this.duplicateGroups = [];
+        this.inDuplicateMode = false;
+        this.selectedForDeletion = new Set();
+    }
+    
+    async findDuplicates() {
+        try {
+            document.body.classList.add('loading');
+            
+            const response = await fetch('/api/recipes/find-duplicates');
+            if (!response.ok) {
+                throw new Error('Failed to find duplicates');
+            }
+            
+            const data = await response.json();
+            if (!data.success) {
+                throw new Error(data.error || 'Unknown error finding duplicates');
+            }
+            
+            this.duplicateGroups = data.duplicate_groups || [];
+            
+            if (this.duplicateGroups.length === 0) {
+                showToast('No duplicate recipes found', 'info');
+                return false;
+            }
+            
+            this.enterDuplicateMode();
+            return true;
+        } catch (error) {
+            console.error('Error finding duplicates:', error);
+            showToast('Failed to find duplicates: ' + error.message, 'error');
+            return false;
+        } finally {
+            document.body.classList.remove('loading');
+        }
+    }
+    
+    enterDuplicateMode() {
+        this.inDuplicateMode = true;
+        this.selectedForDeletion.clear();
+        
+        // Update state
+        const pageState = getCurrentPageState();
+        pageState.duplicatesMode = true;
+        
+        // Show duplicates banner
+        const banner = document.getElementById('duplicatesBanner');
+        const countSpan = document.getElementById('duplicatesCount');
+        
+        if (banner && countSpan) {
+            countSpan.textContent = `Found ${this.duplicateGroups.length} duplicate group${this.duplicateGroups.length !== 1 ? 's' : ''}`;
+            banner.style.display = 'block';
+        }
+        
+        // Disable virtual scrolling if active
+        if (state.virtualScroller) {
+            state.virtualScroller.disable();
+        }
+        
+        // Add duplicate-mode class to the body
+        document.body.classList.add('duplicate-mode');
+        
+        // Render duplicate groups
+        this.renderDuplicateGroups();
+        
+        // Update selected count
+        this.updateSelectedCount();
+    }
+    
+    exitDuplicateMode() {
+        this.inDuplicateMode = false;
+        this.selectedForDeletion.clear();
+        
+        // Update state
+        const pageState = getCurrentPageState();
+        pageState.duplicatesMode = false;
+        
+        // Hide duplicates banner
+        const banner = document.getElementById('duplicatesBanner');
+        if (banner) {
+            banner.style.display = 'none';
+        }
+        
+        // Remove duplicate-mode class from the body
+        document.body.classList.remove('duplicate-mode');
+        
+        // Clear the recipe grid first
+        const recipeGrid = document.getElementById('recipeGrid');
+        if (recipeGrid) {
+            recipeGrid.innerHTML = '';
+        }
+        
+        // Re-enable virtual scrolling
+        if (state.virtualScroller) {
+            state.virtualScroller.enable();
+        } else {
+            // If virtual scroller doesn't exist, reinitialize it
+            setTimeout(() => {
+                initializeInfiniteScroll('recipes');
+            }, 100);
+        }
+    }
+    
+    renderDuplicateGroups() {
+        const recipeGrid = document.getElementById('recipeGrid');
+        if (!recipeGrid) return;
+        
+        // Clear existing content
+        recipeGrid.innerHTML = '';
+        
+        // Render each duplicate group
+        this.duplicateGroups.forEach((group, groupIndex) => {
+            const groupDiv = document.createElement('div');
+            groupDiv.className = 'duplicate-group';
+            groupDiv.dataset.fingerprint = group.fingerprint;
+            
+            // Create group header
+            const header = document.createElement('div');
+            header.className = 'duplicate-group-header';
+            header.innerHTML = `
+                <span>Duplicate Group #${groupIndex + 1} (${group.recipes.length} recipes)</span>
+                <span>
+                    <button class="btn-select-all" onclick="recipeManager.duplicatesManager.toggleSelectAllInGroup('${group.fingerprint}')">
+                        Select All
+                    </button>
+                    <button class="btn-select-latest" onclick="recipeManager.duplicatesManager.selectLatestInGroup('${group.fingerprint}')">
+                        Keep Latest
+                    </button>
+                </span>
+            `;
+            groupDiv.appendChild(header);
+            
+            // Create cards container
+            const cardsDiv = document.createElement('div');
+            cardsDiv.className = 'card-group-container';
+            
+            // Add scrollable class if there are many recipes in the group
+            if (group.recipes.length > 6) {
+                cardsDiv.classList.add('scrollable');
+                
+                // Add expand/collapse toggle button
+                const toggleBtn = document.createElement('button');
+                toggleBtn.className = 'group-toggle-btn';
+                toggleBtn.innerHTML = '<i class="fas fa-chevron-down"></i>';
+                toggleBtn.title = "Expand/Collapse";
+                toggleBtn.onclick = function() {
+                    cardsDiv.classList.toggle('scrollable');
+                    this.innerHTML = cardsDiv.classList.contains('scrollable') ? 
+                        '<i class="fas fa-chevron-down"></i>' : 
+                        '<i class="fas fa-chevron-up"></i>';
+                };
+                groupDiv.appendChild(toggleBtn);
+            }
+            
+            // Sort recipes by date (newest first)
+            const sortedRecipes = [...group.recipes].sort((a, b) => b.modified - a.modified);
+            
+            // Add all recipe cards in this group
+            sortedRecipes.forEach((recipe, index) => {
+                // Create recipe card
+                const recipeCard = new RecipeCard(recipe, (recipe) => {
+                    this.recipeManager.showRecipeDetails(recipe);
+                });
+                const card = recipeCard.element;
+                
+                // Add duplicate class
+                card.classList.add('duplicate');
+                
+                // Mark the latest one
+                if (index === 0) {
+                    card.classList.add('latest');
+                }
+                
+                // Add selection checkbox
+                const checkbox = document.createElement('input');
+                checkbox.type = 'checkbox';
+                checkbox.className = 'selector-checkbox';
+                checkbox.dataset.recipeId = recipe.id;
+                checkbox.dataset.groupFingerprint = group.fingerprint;
+                
+                // Check if already selected
+                if (this.selectedForDeletion.has(recipe.id)) {
+                    checkbox.checked = true;
+                    card.classList.add('duplicate-selected');
+                }
+                
+                // Add change event to checkbox
+                checkbox.addEventListener('change', (e) => {
+                    e.stopPropagation();
+                    this.toggleCardSelection(recipe.id, card, checkbox);
+                });
+                
+                // Make the entire card clickable for selection
+                card.addEventListener('click', (e) => {
+                    // Don't toggle if clicking on the checkbox directly or card actions
+                    if (e.target === checkbox || e.target.closest('.card-actions')) {
+                        return;
+                    }
+                    
+                    // Toggle checkbox state
+                    checkbox.checked = !checkbox.checked;
+                    this.toggleCardSelection(recipe.id, card, checkbox);
+                });
+                
+                card.appendChild(checkbox);
+                cardsDiv.appendChild(card);
+            });
+            
+            groupDiv.appendChild(cardsDiv);
+            recipeGrid.appendChild(groupDiv);
+        });
+    }
+    
+    // Helper method to toggle card selection state
+    toggleCardSelection(recipeId, card, checkbox) {
+        if (checkbox.checked) {
+            this.selectedForDeletion.add(recipeId);
+            card.classList.add('duplicate-selected');
+        } else {
+            this.selectedForDeletion.delete(recipeId);
+            card.classList.remove('duplicate-selected');
+        }
+        
+        this.updateSelectedCount();
+    }
+    
+    updateSelectedCount() {
+        const selectedCountEl = document.getElementById('selectedCount');
+        if (selectedCountEl) {
+            selectedCountEl.textContent = this.selectedForDeletion.size;
+        }
+        
+        // Update delete button state
+        const deleteBtn = document.querySelector('.btn-delete-selected');
+        if (deleteBtn) {
+            deleteBtn.disabled = this.selectedForDeletion.size === 0;
+            deleteBtn.classList.toggle('disabled', this.selectedForDeletion.size === 0);
+        }
+    }
+    
+    toggleSelectAllInGroup(fingerprint) {
+        const checkboxes = document.querySelectorAll(`.selector-checkbox[data-group-fingerprint="${fingerprint}"]`);
+        const allSelected = Array.from(checkboxes).every(checkbox => checkbox.checked);
+        
+        // If all are selected, deselect all; otherwise select all
+        checkboxes.forEach(checkbox => {
+            checkbox.checked = !allSelected;
+            const recipeId = checkbox.dataset.recipeId;
+            const card = checkbox.closest('.lora-card');
+            
+            if (!allSelected) {
+                this.selectedForDeletion.add(recipeId);
+                card.classList.add('duplicate-selected');
+            } else {
+                this.selectedForDeletion.delete(recipeId);
+                card.classList.remove('duplicate-selected');
+            }
+        });
+        
+        // Update the button text
+        const button = document.querySelector(`.duplicate-group[data-fingerprint="${fingerprint}"] .btn-select-all`);
+        if (button) {
+            button.textContent = !allSelected ? "Deselect All" : "Select All";
+        }
+        
+        this.updateSelectedCount();
+    }
+    
+    selectAllInGroup(fingerprint) {
+        const checkboxes = document.querySelectorAll(`.selector-checkbox[data-group-fingerprint="${fingerprint}"]`);
+        checkboxes.forEach(checkbox => {
+            checkbox.checked = true;
+            this.selectedForDeletion.add(checkbox.dataset.recipeId);
+            checkbox.closest('.lora-card').classList.add('duplicate-selected');
+        });
+        
+        // Update the button text
+        const button = document.querySelector(`.duplicate-group[data-fingerprint="${fingerprint}"] .btn-select-all`);
+        if (button) {
+            button.textContent = "Deselect All";
+        }
+        
+        this.updateSelectedCount();
+    }
+    
+    selectLatestInGroup(fingerprint) {
+        // Find all checkboxes in this group
+        const checkboxes = document.querySelectorAll(`.selector-checkbox[data-group-fingerprint="${fingerprint}"]`);
+        
+        // Get all the recipes in this group
+        const group = this.duplicateGroups.find(g => g.fingerprint === fingerprint);
+        if (!group) return;
+        
+        // Sort recipes by date (newest first)
+        const sortedRecipes = [...group.recipes].sort((a, b) => b.modified - a.modified);
+        
+        // Skip the first (latest) one and select the rest for deletion
+        for (let i = 1; i < sortedRecipes.length; i++) {
+            const recipeId = sortedRecipes[i].id;
+            const checkbox = document.querySelector(`.selector-checkbox[data-recipe-id="${recipeId}"]`);
+            
+            if (checkbox) {
+                checkbox.checked = true;
+                this.selectedForDeletion.add(recipeId);
+                checkbox.closest('.lora-card').classList.add('duplicate-selected');
+            }
+        }
+        
+        // Make sure the latest one is not selected
+        const latestId = sortedRecipes[0].id;
+        const latestCheckbox = document.querySelector(`.selector-checkbox[data-recipe-id="${latestId}"]`);
+        
+        if (latestCheckbox) {
+            latestCheckbox.checked = false;
+            this.selectedForDeletion.delete(latestId);
+            latestCheckbox.closest('.lora-card').classList.remove('duplicate-selected');
+        }
+        
+        this.updateSelectedCount();
+    }
+    
+    selectLatestDuplicates() {
+        // For each duplicate group, select all but the latest recipe
+        this.duplicateGroups.forEach(group => {
+            this.selectLatestInGroup(group.fingerprint);
+        });
+    }
+    
+    async deleteSelectedDuplicates() {
+        if (this.selectedForDeletion.size === 0) {
+            showToast('No recipes selected for deletion', 'info');
+            return;
+        }
+        
+        try {
+            // Show the delete confirmation modal instead of a simple confirm
+            const duplicateDeleteCount = document.getElementById('duplicateDeleteCount');
+            if (duplicateDeleteCount) {
+                duplicateDeleteCount.textContent = this.selectedForDeletion.size;
+            }
+            
+            // Use the modal manager to show the confirmation modal
+            modalManager.showModal('duplicateDeleteModal');
+        } catch (error) {
+            console.error('Error preparing delete:', error);
+            showToast('Error: ' + error.message, 'error');
+        }
+    }
+    
+    // Add new method to execute deletion after confirmation
+    async confirmDeleteDuplicates() {
+        try {
+            document.body.classList.add('loading');
+            
+            // Close the modal
+            modalManager.closeModal('duplicateDeleteModal');
+            
+            // Prepare recipe IDs for deletion
+            const recipeIds = Array.from(this.selectedForDeletion);
+            
+            // Call API to bulk delete
+            const response = await fetch('/api/recipes/bulk-delete', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ recipe_ids: recipeIds })
+            });
+            
+            if (!response.ok) {
+                throw new Error('Failed to delete selected recipes');
+            }
+            
+            const data = await response.json();
+            if (!data.success) {
+                throw new Error(data.error || 'Unknown error deleting recipes');
+            }
+            
+            showToast(`Successfully deleted ${data.total_deleted} recipes`, 'success');
+            
+            // Exit duplicate mode if deletions were successful
+            if (data.total_deleted > 0) {
+                this.exitDuplicateMode();
+            }
+            
+        } catch (error) {
+            console.error('Error deleting recipes:', error);
+            showToast('Failed to delete recipes: ' + error.message, 'error');
+        } finally {
+            document.body.classList.remove('loading');
+        }
+    }
+}

static/js/components/Header.js

@@ -78,5 +78,33 @@ export class HeaderManager {
           // Handle support panel logic
         });
       }
+
+      // Handle QR code toggle
+      const qrToggle = document.getElementById('toggleQRCode');
+      const qrContainer = document.getElementById('qrCodeContainer');
+      
+      if (qrToggle && qrContainer) {
+          qrToggle.addEventListener('click', function() {
+              qrContainer.classList.toggle('show');
+              qrToggle.classList.toggle('active');
+              
+              const toggleText = qrToggle.querySelector('.toggle-text');
+              if (qrContainer.classList.contains('show')) {
+                  toggleText.textContent = 'Hide WeChat QR Code';
+                  // Add small delay to ensure DOM is updated before scrolling
+                  setTimeout(() => {
+                      const supportModal = document.querySelector('.support-modal');
+                      if (supportModal) {
+                          supportModal.scrollTo({
+                              top: supportModal.scrollHeight,
+                              behavior: 'smooth'
+                          });
+                      }
+                  }, 250);
+              } else {
+                  toggleText.textContent = 'Show WeChat QR Code';
+              }
+          });
+      }
     }
 }

static/js/components/LoraCard.js

@@ -1,9 +1,201 @@
-import { showToast, openCivitai, copyToClipboard } from '../utils/uiHelpers.js';
+import { showToast, openCivitai, copyToClipboard, sendLoraToWorkflow, openExampleImagesFolder } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { showLoraModal } from './loraModal/index.js';
 import { bulkManager } from '../managers/BulkManager.js';
 import { NSFW_LEVELS } from '../utils/constants.js';
-import { replacePreview, deleteModel, saveModelMetadata } from '../api/loraApi.js'
+import { replacePreview, saveModelMetadata } from '../api/loraApi.js'
+import { showDeleteModal } from '../utils/modalUtils.js';
+
+// Add a global event delegation handler
+export function setupLoraCardEventDelegation() {
+    const gridElement = document.getElementById('loraGrid');
+    if (!gridElement) return;
+    
+    // Remove any existing event listener to prevent duplication
+    gridElement.removeEventListener('click', handleLoraCardEvent);
+    
+    // Add the event delegation handler
+    gridElement.addEventListener('click', handleLoraCardEvent);
+}
+
+// Event delegation handler for all lora card events
+function handleLoraCardEvent(event) {
+    // Find the closest card element
+    const card = event.target.closest('.lora-card');
+    if (!card) return;
+    
+    // Handle specific elements within the card
+    if (event.target.closest('.toggle-blur-btn')) {
+        event.stopPropagation();
+        toggleBlurContent(card);
+        return;
+    }
+    
+    if (event.target.closest('.show-content-btn')) {
+        event.stopPropagation();
+        showBlurredContent(card);
+        return;
+    }
+    
+    if (event.target.closest('.fa-star')) {
+        event.stopPropagation();
+        toggleFavorite(card);
+        return;
+    }
+    
+    if (event.target.closest('.fa-globe')) {
+        event.stopPropagation();
+        if (card.dataset.from_civitai === 'true') {
+            openCivitai(card.dataset.name);
+        }
+        return;
+    }
+    
+    if (event.target.closest('.fa-paper-plane')) {
+        event.stopPropagation();
+        sendLoraToComfyUI(card, event.shiftKey);
+        return;
+    }
+    
+    if (event.target.closest('.fa-copy')) {
+        event.stopPropagation();
+        copyLoraSyntax(card);
+        return;
+    }
+    
+    if (event.target.closest('.fa-image')) {
+        event.stopPropagation();
+        replacePreview(card.dataset.filepath);
+        return;
+    }
+    
+    if (event.target.closest('.fa-folder-open')) {
+        event.stopPropagation();
+        openExampleImagesFolder(card.dataset.sha256);
+        return;
+    }
+    
+    // If no specific element was clicked, handle the card click (show modal or toggle selection)
+    if (state.bulkMode) {
+        // Toggle selection using the bulk manager
+        bulkManager.toggleCardSelection(card);
+    } else {
+        // Normal behavior - show modal
+        const loraMeta = {
+            sha256: card.dataset.sha256,
+            file_path: card.dataset.filepath,
+            model_name: card.dataset.name,
+            file_name: card.dataset.file_name,
+            folder: card.dataset.folder,
+            modified: card.dataset.modified,
+            file_size: card.dataset.file_size,
+            from_civitai: card.dataset.from_civitai === 'true',
+            base_model: card.dataset.base_model,
+            usage_tips: card.dataset.usage_tips,
+            notes: card.dataset.notes,
+            favorite: card.dataset.favorite === 'true',
+            // Parse civitai metadata from the card's dataset
+            civitai: (() => {
+                try {
+                    // Attempt to parse the JSON string
+                    return JSON.parse(card.dataset.meta || '{}');
+                } catch (e) {
+                    console.error('Failed to parse civitai metadata:', e);
+                    return {}; // Return empty object on error
+                }
+            })(),
+            tags: JSON.parse(card.dataset.tags || '[]'),
+            modelDescription: card.dataset.modelDescription || ''
+        };
+        showLoraModal(loraMeta);
+    }
+}
+
+// Helper functions for event handling
+function toggleBlurContent(card) {
+    const preview = card.querySelector('.card-preview');
+    const isBlurred = preview.classList.toggle('blurred');
+    const icon = card.querySelector('.toggle-blur-btn i');
+    
+    // Update the icon based on blur state
+    if (isBlurred) {
+        icon.className = 'fas fa-eye';
+    } else {
+        icon.className = 'fas fa-eye-slash';
+    }
+    
+    // Toggle the overlay visibility
+    const overlay = card.querySelector('.nsfw-overlay');
+    if (overlay) {
+        overlay.style.display = isBlurred ? 'flex' : 'none';
+    }
+}
+
+function showBlurredContent(card) {
+    const preview = card.querySelector('.card-preview');
+    preview.classList.remove('blurred');
+    
+    // Update the toggle button icon
+    const toggleBtn = card.querySelector('.toggle-blur-btn');
+    if (toggleBtn) {
+        toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
+    }
+    
+    // Hide the overlay
+    const overlay = card.querySelector('.nsfw-overlay');
+    if (overlay) {
+        overlay.style.display = 'none';
+    }
+}
+
+async function toggleFavorite(card) {
+    const starIcon = card.querySelector('.fa-star');
+    const isFavorite = starIcon.classList.contains('fas');
+    const newFavoriteState = !isFavorite;
+    
+    try {
+        // Save the new favorite state to the server
+        await saveModelMetadata(card.dataset.filepath, { 
+            favorite: newFavoriteState 
+        });
+
+        // Update the UI
+        if (newFavoriteState) {
+            starIcon.classList.remove('far');
+            starIcon.classList.add('fas', 'favorite-active');
+            starIcon.title = 'Remove from favorites';
+            card.dataset.favorite = 'true';
+            showToast('Added to favorites', 'success');
+        } else {
+            starIcon.classList.remove('fas', 'favorite-active');
+            starIcon.classList.add('far');
+            starIcon.title = 'Add to favorites';
+            card.dataset.favorite = 'false';
+            showToast('Removed from favorites', 'success');
+        }
+    } catch (error) {
+        console.error('Failed to update favorite status:', error);
+        showToast('Failed to update favorite status', 'error');
+    }
+}
+
+// Function to send LoRA to ComfyUI workflow
+async function sendLoraToComfyUI(card, replaceMode) {
+    const usageTips = JSON.parse(card.dataset.usage_tips || '{}');
+    const strength = usageTips.strength || 1;
+    const loraSyntax = `<lora:${card.dataset.file_name}:${strength}>`;
+    
+    sendLoraToWorkflow(loraSyntax, replaceMode, 'lora');
+}
+
+// Add function to copy lora syntax
+function copyLoraSyntax(card) {
+    const usageTips = JSON.parse(card.dataset.usage_tips || '{}');
+    const strength = usageTips.strength || 1;
+    const loraSyntax = `<lora:${card.dataset.file_name}:${strength}>`;
+    
+    copyToClipboard(loraSyntax, 'LoRA syntax copied to clipboard');
+}
 
 export function createLoraCard(lora) {
     const card = document.createElement('div');
@@ -93,12 +285,12 @@ export function createLoraCard(lora) {
                        title="${lora.from_civitai ? 'View on Civitai' : 'Not available from Civitai'}"
                        ${!lora.from_civitai ? 'style="opacity: 0.5; cursor: not-allowed"' : ''}>
                     </i>
+                    <i class="fas fa-paper-plane" 
+                       title="Send to ComfyUI (Click: Append, Shift+Click: Replace)">
+                    </i>
                     <i class="fas fa-copy" 
                        title="Copy LoRA Syntax">
                     </i>
-                    <i class="fas fa-trash" 
-                       title="Delete Model">
-                    </i>
                 </div>
             </div>
             ${shouldBlur ? `
@@ -114,170 +306,20 @@ export function createLoraCard(lora) {
                     <span class="model-name">${lora.model_name}</span>
                 </div>
                 <div class="card-actions">
-                    <i class="fas fa-image" 
-                       title="Replace Preview Image">
+                    <i class="fas fa-folder-open" 
+                       title="Open Example Images Folder">
                     </i>
                 </div>
             </div>
         </div>
     `;
-
-    // Main card click event - modified to handle bulk mode
-    card.addEventListener('click', () => {
-        // Check if we're in bulk mode
-        if (state.bulkMode) {
-            // Toggle selection using the bulk manager
-            bulkManager.toggleCardSelection(card);
-        } else {
-            // Normal behavior - show modal
-            const loraMeta = {
-                sha256: card.dataset.sha256,
-                file_path: card.dataset.filepath,
-                model_name: card.dataset.name,
-                file_name: card.dataset.file_name,
-                folder: card.dataset.folder,
-                modified: card.dataset.modified,
-                file_size: card.dataset.file_size,
-                from_civitai: card.dataset.from_civitai === 'true',
-                base_model: card.dataset.base_model,
-                usage_tips: card.dataset.usage_tips,
-                notes: card.dataset.notes,
-                favorite: card.dataset.favorite === 'true',
-                // Parse civitai metadata from the card's dataset
-                civitai: (() => {
-                    try {
-                        // Attempt to parse the JSON string
-                        return JSON.parse(card.dataset.meta || '{}');
-                    } catch (e) {
-                        console.error('Failed to parse civitai metadata:', e);
-                        return {}; // Return empty object on error
-                    }
-                })(),
-                tags: JSON.parse(card.dataset.tags || '[]'),
-                modelDescription: card.dataset.modelDescription || ''
-            };
-            showLoraModal(loraMeta);
-        }
-    });
-
-    // Toggle blur button functionality
-    const toggleBlurBtn = card.querySelector('.toggle-blur-btn');
-    if (toggleBlurBtn) {
-        toggleBlurBtn.addEventListener('click', (e) => {
-            e.stopPropagation();
-            const preview = card.querySelector('.card-preview');
-            const isBlurred = preview.classList.toggle('blurred');
-            const icon = toggleBlurBtn.querySelector('i');
-            
-            // Update the icon based on blur state
-            if (isBlurred) {
-                icon.className = 'fas fa-eye';
-            } else {
-                icon.className = 'fas fa-eye-slash';
-            }
-            
-            // Toggle the overlay visibility
-            const overlay = card.querySelector('.nsfw-overlay');
-            if (overlay) {
-                overlay.style.display = isBlurred ? 'flex' : 'none';
-            }
-        });
-    }
-
-    // Show content button functionality
-    const showContentBtn = card.querySelector('.show-content-btn');
-    if (showContentBtn) {
-        showContentBtn.addEventListener('click', (e) => {
-            e.stopPropagation();
-            const preview = card.querySelector('.card-preview');
-            preview.classList.remove('blurred');
-            
-            // Update the toggle button icon
-            const toggleBtn = card.querySelector('.toggle-blur-btn');
-            if (toggleBtn) {
-                toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
-            }
-            
-            // Hide the overlay
-            const overlay = card.querySelector('.nsfw-overlay');
-            if (overlay) {
-                overlay.style.display = 'none';
-            }
-        });
-    }
-
-    // Favorite button click event
-    card.querySelector('.fa-star')?.addEventListener('click', async e => {
-        e.stopPropagation();
-        const starIcon = e.currentTarget;
-        const isFavorite = starIcon.classList.contains('fas');
-        const newFavoriteState = !isFavorite;
-        
-        try {
-            // Save the new favorite state to the server
-            await saveModelMetadata(card.dataset.filepath, { 
-                favorite: newFavoriteState 
-            });
-
-            // Update the UI
-            if (newFavoriteState) {
-                starIcon.classList.remove('far');
-                starIcon.classList.add('fas', 'favorite-active');
-                starIcon.title = 'Remove from favorites';
-                card.dataset.favorite = 'true';
-                showToast('Added to favorites', 'success');
-            } else {
-                starIcon.classList.remove('fas', 'favorite-active');
-                starIcon.classList.add('far');
-                starIcon.title = 'Add to favorites';
-                card.dataset.favorite = 'false';
-                showToast('Removed from favorites', 'success');
-            }
-        } catch (error) {
-            console.error('Failed to update favorite status:', error);
-            showToast('Failed to update favorite status', 'error');
-        }
-    });
-
-    // Copy button click event
-    card.querySelector('.fa-copy')?.addEventListener('click', async e => {
-        e.stopPropagation();
-        const usageTips = JSON.parse(card.dataset.usage_tips || '{}');
-        const strength = usageTips.strength || 1;
-        const loraSyntax = `<lora:${card.dataset.file_name}:${strength}>`;
-        
-        await copyToClipboard(loraSyntax, 'LoRA syntax copied');
-    });
-
-    // Civitai button click event
-    if (lora.from_civitai) {
-        card.querySelector('.fa-globe')?.addEventListener('click', e => {
-            e.stopPropagation();
-            openCivitai(lora.model_name);
-        });
-    }
-
-    // Delete button click event
-    card.querySelector('.fa-trash')?.addEventListener('click', e => {
-        e.stopPropagation();
-        deleteModel(lora.file_path);
-    });
-
-    // Replace preview button click event
-    card.querySelector('.fa-image')?.addEventListener('click', e => {
-        e.stopPropagation();
-        replacePreview(lora.file_path);
-    });
     
-    // Apply bulk mode styling if currently in bulk mode
-    if (state.bulkMode) {
-        const actions = card.querySelectorAll('.card-actions');
-        actions.forEach(actionGroup => {
-            actionGroup.style.display = 'none';
-        });
+    // Add a special class for virtual scroll positioning if needed
+    if (state.virtualScroller) {
+        card.classList.add('virtual-scroll-item');
     }
     
-    // Add autoplayOnHover handlers for video elements if needed
+    // Add video auto-play on hover functionality if needed
     const videoElement = card.querySelector('video');
     if (videoElement && autoplayOnHover) {
         const cardPreview = card.querySelector('.card-preview');
@@ -286,15 +328,10 @@ export function createLoraCard(lora) {
         videoElement.removeAttribute('autoplay');
         videoElement.pause();
         
-        // Add mouse events to trigger play/pause
-        cardPreview.addEventListener('mouseenter', () => {
-            videoElement.play();
-        });
-        
-        cardPreview.addEventListener('mouseleave', () => {
-            videoElement.pause();
-            videoElement.currentTime = 0;
-        });
+        // Add mouse events to trigger play/pause using event attributes
+        // This approach reduces the number of event listeners created
+        cardPreview.setAttribute('onmouseenter', 'this.querySelector("video")?.play()');
+        cardPreview.setAttribute('onmouseleave', 'const v=this.querySelector("video"); if(v){v.pause();v.currentTime=0;}');
     }
 
     return card;
@@ -307,7 +344,7 @@ export function updateCardsForBulkMode(isBulkMode) {
     
     document.body.classList.toggle('bulk-mode', isBulkMode);
     
-    // Get all lora cards
+    // Get all lora cards - this can now be from the DOM or through the virtual scroller
     const loraCards = document.querySelectorAll('.lora-card');
     
     loraCards.forEach(card => {
@@ -329,6 +366,11 @@ export function updateCardsForBulkMode(isBulkMode) {
         }
     });
     
+    // If using virtual scroller, we need to rerender after toggling bulk mode
+    if (state.virtualScroller && typeof state.virtualScroller.scheduleRender === 'function') {
+        state.virtualScroller.scheduleRender();
+    }
+    
     // Apply selection state to cards if entering bulk mode
     if (isBulkMode) {
         bulkManager.applySelectionState();

static/js/components/RecipeCard.js

@@ -1,12 +1,16 @@
 // Recipe Card Component
-import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
+import { showToast, copyToClipboard, sendLoraToWorkflow } from '../utils/uiHelpers.js';
 import { modalManager } from '../managers/ModalManager.js';
+import { getCurrentPageState } from '../state/index.js';
 
 class RecipeCard {
     constructor(recipe, clickHandler) {
         this.recipe = recipe;
         this.clickHandler = clickHandler;
         this.element = this.createCardElement();
+        
+        // Store reference to this instance on the DOM element for updates
+        this.element._recipeCardInstance = this;
     }
     
     createCardElement() {
@@ -33,33 +37,41 @@ class RecipeCard {
                          (this.recipe.file_path ? `/loras_static/root1/preview/${this.recipe.file_path.split('/').pop()}` : 
                          '/loras_static/images/no-preview.png');
 
+        // Check if in duplicates mode
+        const pageState = getCurrentPageState();
+        const isDuplicatesMode = pageState.duplicatesMode;
+
         card.innerHTML = `
-            <div class="recipe-indicator" title="Recipe">R</div>
+            ${!isDuplicatesMode ? `<div class="recipe-indicator" title="Recipe">R</div>` : ''}
             <div class="card-preview">
                 <img src="${imageUrl}" alt="${this.recipe.title}">
+                ${!isDuplicatesMode ? `
                 <div class="card-header">
                     <div class="base-model-wrapper">
                         ${baseModel ? `<span class="base-model-label" title="${baseModel}">${baseModel}</span>` : ''}
                     </div>
                     <div class="card-actions">
                         <i class="fas fa-share-alt" title="Share Recipe"></i>
-                        <i class="fas fa-copy" title="Copy Recipe Syntax"></i>
+                        <i class="fas fa-paper-plane" title="Send Recipe to Workflow (Click: Append, Shift+Click: Replace)"></i>
                         <i class="fas fa-trash" title="Delete Recipe"></i>
                     </div>
                 </div>
+                ` : ''}
                 <div class="card-footer">
                     <div class="model-info">
                         <span class="model-name">${this.recipe.title}</span>
                     </div>
+                    ${!isDuplicatesMode ? `
                     <div class="lora-count ${allLorasAvailable ? 'ready' : (lorasCount > 0 ? 'missing' : '')}" 
                          title="${this.getLoraStatusTitle(lorasCount, missingLorasCount)}">
                         <i class="fas fa-layer-group"></i> ${lorasCount}
                     </div>
+                    ` : ''}
                 </div>
             </div>
         `;
         
-        this.attachEventListeners(card);
+        this.attachEventListeners(card, isDuplicatesMode);
         return card;
     }
     
@@ -69,58 +81,59 @@ class RecipeCard {
         return `${missingCount} of ${totalCount} LoRAs missing`;
     }
     
-    attachEventListeners(card) {
-        // Recipe card click event
-        card.addEventListener('click', () => {
-            this.clickHandler(this.recipe);
-        });
-        
-        // Share button click event - prevent propagation to card
-        card.querySelector('.fa-share-alt')?.addEventListener('click', (e) => {
-            e.stopPropagation();
-            this.shareRecipe();
-        });
-        
-        // Copy button click event - prevent propagation to card
-        card.querySelector('.fa-copy')?.addEventListener('click', (e) => {
-            e.stopPropagation();
-            this.copyRecipeSyntax();
-        });
-        
-        // Delete button click event - prevent propagation to card
-        card.querySelector('.fa-trash')?.addEventListener('click', (e) => {
-            e.stopPropagation();
-            this.showDeleteConfirmation();
-        });
+    attachEventListeners(card, isDuplicatesMode) {
+        // Recipe card click event - only attach if not in duplicates mode
+        if (!isDuplicatesMode) {
+            card.addEventListener('click', () => {
+                this.clickHandler(this.recipe);
+            });
+            
+            // Share button click event - prevent propagation to card
+            card.querySelector('.fa-share-alt')?.addEventListener('click', (e) => {
+                e.stopPropagation();
+                this.shareRecipe();
+            });
+            
+            // Send button click event - prevent propagation to card
+            card.querySelector('.fa-paper-plane')?.addEventListener('click', (e) => {
+                e.stopPropagation();
+                this.sendRecipeToWorkflow(e.shiftKey);
+            });
+            
+            // Delete button click event - prevent propagation to card
+            card.querySelector('.fa-trash')?.addEventListener('click', (e) => {
+                e.stopPropagation();
+                this.showDeleteConfirmation();
+            });
+        }
     }
     
-    copyRecipeSyntax() {
+    // Replace copyRecipeSyntax with sendRecipeToWorkflow
+    sendRecipeToWorkflow(replaceMode = false) {
         try {
             // Get recipe ID
             const recipeId = this.recipe.id;
             if (!recipeId) {
-                showToast('Cannot copy recipe syntax: Missing recipe ID', 'error');
+                showToast('Cannot send recipe: Missing recipe ID', 'error');
                 return;
             }
 
-            
-            // Fallback if button not found
             fetch(`/api/recipe/${recipeId}/syntax`)
                 .then(response => response.json())
                 .then(data => {
                     if (data.success && data.syntax) {
-                        return copyToClipboard(data.syntax, 'Recipe syntax copied to clipboard');
+                        return sendLoraToWorkflow(data.syntax, replaceMode, 'recipe');
                     } else {
                         throw new Error(data.error || 'No syntax returned');
                     }
                 })
                 .catch(err => {
-                    console.error('Failed to copy: ', err);
-                    showToast('Failed to copy recipe syntax', 'error');
+                    console.error('Failed to send recipe to workflow: ', err);
+                    showToast('Failed to send recipe to workflow', 'error');
                 });
         } catch (error) {
-            console.error('Error copying recipe syntax:', error);
-            showToast('Error copying recipe syntax', 'error');
+            console.error('Error sending recipe to workflow:', error);
+            showToast('Error sending recipe to workflow', 'error');
         }
     }
     

static/js/components/RecipeModal.js

@@ -2,6 +2,7 @@
 import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { setSessionItem, removeSessionItem } from '../utils/storageHelpers.js';
+import { updateRecipeCard } from '../utils/cardUpdater.js';
 
 class RecipeModal {
     constructor() {
@@ -82,7 +83,7 @@ class RecipeModal {
     
     showRecipeDetails(recipe) {
         // Store the full recipe for editing
-        this.currentRecipe = JSON.parse(JSON.stringify(recipe)); // 深拷贝以避免对原始对象的修改
+        this.currentRecipe = recipe;
         
         // Set modal title with edit icon
         const modalTitle = document.getElementById('recipeModalTitle');
@@ -245,6 +246,45 @@ class RecipeModal {
                 imgElement.alt = recipe.title || 'Recipe Preview';
                 mediaContainer.appendChild(imgElement);
             }
+
+            // Add source URL container if the recipe has a source_path
+            const sourceUrlContainer = document.createElement('div');
+            sourceUrlContainer.className = 'source-url-container';
+            const hasSourceUrl = recipe.source_path && recipe.source_path.trim().length > 0;
+            const sourceUrl = hasSourceUrl ? recipe.source_path : '';
+            const isValidUrl = hasSourceUrl && (sourceUrl.startsWith('http://') || sourceUrl.startsWith('https://'));
+            
+            sourceUrlContainer.innerHTML = `
+                <div class="source-url-content">
+                    <span class="source-url-icon"><i class="fas fa-link"></i></span>
+                    <span class="source-url-text" title="${isValidUrl ? 'Click to open source URL' : 'No valid URL'}">${
+                        hasSourceUrl ? sourceUrl : 'No source URL'
+                    }</span>
+                </div>
+                <button class="source-url-edit-btn" title="Edit source URL">
+                    <i class="fas fa-pencil-alt"></i>
+                </button>
+            `;
+            
+            // Add source URL editor
+            const sourceUrlEditor = document.createElement('div');
+            sourceUrlEditor.className = 'source-url-editor';
+            sourceUrlEditor.innerHTML = `
+                <input type="text" class="source-url-input" placeholder="Enter source URL (e.g., https://civitai.com/...)" value="${sourceUrl}">
+                <div class="source-url-actions">
+                    <button class="source-url-cancel-btn">Cancel</button>
+                    <button class="source-url-save-btn">Save</button>
+                </div>
+            `;
+            
+            // Append both containers to the media container
+            mediaContainer.appendChild(sourceUrlContainer);
+            mediaContainer.appendChild(sourceUrlEditor);
+            
+            // Set up event listeners for source URL functionality
+            setTimeout(() => {
+                this.setupSourceUrlHandlers();
+            }, 50);
         }
         
         // Set generation parameters
@@ -451,8 +491,6 @@ class RecipeModal {
             lorasListElement.innerHTML = '<div class="no-loras">No LoRAs associated with this recipe</div>';
             this.recipeLorasSyntax = '';
         }
-
-        console.log(this.currentRecipe.loras);
         
         // Show the modal
         modalManager.showModal('recipeModal');
@@ -648,50 +686,8 @@ class RecipeModal {
                 // 更新当前recipe对象的属性
                 Object.assign(this.currentRecipe, updates);
                 
-                // 确保这个更新也传播到卡片视图
-                // 尝试找到可能显示这个recipe的卡片并更新它
-                try {
-                    const recipeCards = document.querySelectorAll('.recipe-card');
-                    recipeCards.forEach(card => {
-                        if (card.dataset.recipeId === this.recipeId) {
-                            // 更新卡片标题
-                            if (updates.title) {
-                                const titleElement = card.querySelector('.recipe-title');
-                                if (titleElement) {
-                                    titleElement.textContent = updates.title;
-                                }
-                            }
-                            
-                            // 更新卡片标签
-                            if (updates.tags) {
-                                const tagsElement = card.querySelector('.recipe-tags');
-                                if (tagsElement) {
-                                    if (updates.tags.length > 0) {
-                                        tagsElement.innerHTML = updates.tags.map(
-                                            tag => `<div class="recipe-tag">${tag}</div>`
-                                        ).join('');
-                                    } else {
-                                        tagsElement.innerHTML = '';
-                                    }
-                                }
-                            }
-                        }
-                    });
-                } catch (err) {
-                    console.log("Non-critical error updating recipe cards:", err);
-                }
-                
-                // 重要：强制刷新recipes列表，确保从服务器获取最新数据
-                try {
-                    if (window.recipeManager && typeof window.recipeManager.loadRecipes === 'function') {
-                        // 异步刷新recipes列表，不阻塞用户界面
-                        setTimeout(() => {
-                            window.recipeManager.loadRecipes(true);
-                        }, 500);
-                    }
-                } catch (err) {
-                    console.log("Error refreshing recipes list:", err);
-                }
+                // Update the recipe card in the UI
+                updateRecipeCard(this.recipeId, updates);
             } else {
                 showToast(`Failed to update recipe: ${data.error}`, 'error');
             }
@@ -951,8 +947,8 @@ class RecipeModal {
             let loraSyntaxMatch = inputValue.match(/<lora:([^:>]+)(?::[^>]+)?>/);
             let fileName = loraSyntaxMatch ? loraSyntaxMatch[1] : inputValue.trim();
             
-            // Remove any file extension if present
-            fileName = fileName.replace(/\.\w+$/, '');
+            // Remove .safetensors extension if present
+            fileName = fileName.replace(/\.safetensors$/, '');
             
             // Get the deleted lora data
             const deletedLora = this.currentRecipe.loras[loraIndex];
@@ -1069,6 +1065,56 @@ class RecipeModal {
             });
         });
     }
+
+    // New method to set up source URL handlers
+    setupSourceUrlHandlers() {
+        const sourceUrlContainer = document.querySelector('.source-url-container');
+        const sourceUrlEditor = document.querySelector('.source-url-editor');
+        const sourceUrlText = sourceUrlContainer.querySelector('.source-url-text');
+        const sourceUrlEditBtn = sourceUrlContainer.querySelector('.source-url-edit-btn');
+        const sourceUrlCancelBtn = sourceUrlEditor.querySelector('.source-url-cancel-btn');
+        const sourceUrlSaveBtn = sourceUrlEditor.querySelector('.source-url-save-btn');
+        const sourceUrlInput = sourceUrlEditor.querySelector('.source-url-input');
+        
+        // Show editor on edit button click
+        sourceUrlEditBtn.addEventListener('click', () => {
+            sourceUrlContainer.classList.add('hide');
+            sourceUrlEditor.classList.add('active');
+            sourceUrlInput.focus();
+        });
+        
+        // Cancel editing
+        sourceUrlCancelBtn.addEventListener('click', () => {
+            sourceUrlEditor.classList.remove('active');
+            sourceUrlContainer.classList.remove('hide');
+            sourceUrlInput.value = this.currentRecipe.source_path || '';
+        });
+        
+        // Save new source URL
+        sourceUrlSaveBtn.addEventListener('click', () => {
+            const newSourceUrl = sourceUrlInput.value.trim();
+            if (newSourceUrl && newSourceUrl !== this.currentRecipe.source_path) {
+                // Update source URL in the UI
+                sourceUrlText.textContent = newSourceUrl;
+                sourceUrlText.title = newSourceUrl.startsWith('http://') || newSourceUrl.startsWith('https://') ? 'Click to open source URL' : 'No valid URL';
+                
+                // Update the recipe on the server
+                this.updateRecipeMetadata({ source_path: newSourceUrl });
+            }
+            
+            // Hide editor
+            sourceUrlEditor.classList.remove('active');
+            sourceUrlContainer.classList.remove('hide');
+        });
+        
+        // Open source URL in a new tab if it's valid
+        sourceUrlText.addEventListener('click', () => {
+            const url = sourceUrlText.textContent.trim();
+            if (url.startsWith('http://') || url.startsWith('https://')) {
+                window.open(url, '_blank');
+            }
+        });
+    }
 }
 
 export { RecipeModal };

static/js/components/alphabet/AlphabetBar.js

@@ -0,0 +1,319 @@
+// AlphabetBar.js - Component for alphabet filtering
+import { getCurrentPageState, setCurrentPageType } from '../../state/index.js';
+import { getStorageItem, setStorageItem } from '../../utils/storageHelpers.js';
+import { resetAndReload } from '../../api/loraApi.js';
+
+/**
+ * AlphabetBar class - Handles the alphabet filtering UI and interactions
+ */
+export class AlphabetBar {
+    constructor(pageType = 'loras') {
+        // Store the page type
+        this.pageType = pageType;
+        
+        // Get the current page state
+        this.pageState = getCurrentPageState();
+        
+        // Initialize letter counts
+        this.letterCounts = {};
+        
+        // Initialize the component
+        this.initializeComponent();
+    }
+    
+    /**
+     * Initialize the alphabet bar component
+     */
+    async initializeComponent() {
+        // Get letter counts from API
+        await this.fetchLetterCounts();
+        
+        // Initialize event listeners
+        this.initEventListeners();
+        
+        // Restore the active letter filter from storage if available
+        this.restoreActiveLetterFilter();
+        
+        // Restore collapse state from storage
+        this.restoreCollapseState();
+
+        // Update the toggle button indicator if there's an active letter filter
+        this.updateToggleIndicator();
+    }
+    
+    /**
+     * Fetch letter counts from the API
+     */
+    async fetchLetterCounts() {
+        try {
+            const response = await fetch('/api/loras/letter-counts');
+            
+            if (!response.ok) {
+                throw new Error(`Failed to fetch letter counts: ${response.statusText}`);
+            }
+            
+            const data = await response.json();
+            
+            if (data.success && data.letter_counts) {
+                this.letterCounts = data.letter_counts;
+                
+                // Update the count display in the UI
+                this.updateLetterCountsDisplay();
+            }
+        } catch (error) {
+            console.error('Error fetching letter counts:', error);
+        }
+    }
+    
+    /**
+     * Update the letter counts display in the UI
+     */
+    updateLetterCountsDisplay() {
+        const letterChips = document.querySelectorAll('.letter-chip');
+        
+        letterChips.forEach(chip => {
+            const letter = chip.dataset.letter;
+            const count = this.letterCounts[letter] || 0;
+            
+            // Update the title attribute for tooltip display
+            if (count > 0) {
+                chip.title = `${letter}: ${count} LoRAs`;
+                chip.classList.remove('disabled');
+            } else {
+                chip.title = `${letter}: No LoRAs`;
+                chip.classList.add('disabled');
+            }
+            
+            // Keep the count span for backward compatibility
+            const countSpan = chip.querySelector('.count');
+            if (countSpan) {
+                countSpan.textContent = ` (${count})`;
+            }
+        });
+    }
+    
+    /**
+     * Initialize event listeners for the alphabet bar
+     */
+    initEventListeners() {
+        const alphabetBar = document.querySelector('.alphabet-bar');
+        const toggleButton = document.querySelector('.toggle-alphabet-bar');
+        const alphabetBarContainer = document.querySelector('.alphabet-bar-container');
+        
+        if (alphabetBar) {
+            // Use event delegation for letter chips
+            alphabetBar.addEventListener('click', (e) => {
+                const letterChip = e.target.closest('.letter-chip');
+                
+                if (letterChip && !letterChip.classList.contains('disabled')) {
+                    this.handleLetterClick(letterChip);
+                }
+            });
+            
+            // Add toggle button listener
+            if (toggleButton && alphabetBarContainer) {
+                toggleButton.addEventListener('click', () => {
+                    alphabetBarContainer.classList.toggle('collapsed');
+                    
+                    // If expanding and there's an active letter, scroll it into view
+                    if (!alphabetBarContainer.classList.contains('collapsed')) {
+                        this.scrollActiveLetterIntoView();
+                    }
+                    
+                    // Save collapse state to storage
+                    setStorageItem(`${this.pageType}_alphabetBarCollapsed`, 
+                        alphabetBarContainer.classList.contains('collapsed'));
+
+                    // Update toggle indicator
+                    this.updateToggleIndicator();
+                });
+            }
+            
+            // Add keyboard shortcut listeners
+            document.addEventListener('keydown', (e) => {
+                // Alt + letter shortcuts
+                if (e.altKey && !e.ctrlKey && !e.metaKey) {
+                    const key = e.key.toUpperCase();
+                    
+                    // Check if it's a letter A-Z
+                    if (/^[A-Z]$/.test(key)) {
+                        const letterChip = document.querySelector(`.letter-chip[data-letter="${key}"]`);
+                        
+                        if (letterChip && !letterChip.classList.contains('disabled')) {
+                            this.handleLetterClick(letterChip);
+                            e.preventDefault();
+                        }
+                    } 
+                    // Special cases for non-letter filters
+                    else if (e.key === '0' || e.key === ')') {
+                        // Alt+0 for numbers (#)
+                        const letterChip = document.querySelector('.letter-chip[data-letter="#"]');
+                        
+                        if (letterChip && !letterChip.classList.contains('disabled')) {
+                            this.handleLetterClick(letterChip);
+                            e.preventDefault();
+                        }
+                    } else if (e.key === '2' || e.key === '@') {
+                        // Alt+@ for special characters
+                        const letterChip = document.querySelector('.letter-chip[data-letter="@"]');
+                        
+                        if (letterChip && !letterChip.classList.contains('disabled')) {
+                            this.handleLetterClick(letterChip);
+                            e.preventDefault();
+                        }
+                    } else if (e.key === 'c' || e.key === 'C') {
+                        // Alt+C for CJK characters
+                        const letterChip = document.querySelector('.letter-chip[data-letter="漢"]');
+                        
+                        if (letterChip && !letterChip.classList.contains('disabled')) {
+                            this.handleLetterClick(letterChip);
+                            e.preventDefault();
+                        }
+                    }
+                }
+            });
+        }
+    }
+    
+    /**
+     * Restore the collapse state from storage
+     */
+    restoreCollapseState() {
+        const alphabetBarContainer = document.querySelector('.alphabet-bar-container');
+        
+        if (alphabetBarContainer) {
+            const isCollapsed = getStorageItem(`${this.pageType}_alphabetBarCollapsed`);
+            
+            // If there's a stored preference, apply it
+            if (isCollapsed !== null) {
+                if (isCollapsed) {
+                    alphabetBarContainer.classList.add('collapsed');
+                } else {
+                    alphabetBarContainer.classList.remove('collapsed');
+                }
+            }
+        }
+    }
+    
+    /**
+     * Handle letter chip click
+     * @param {HTMLElement} letterChip - The letter chip that was clicked
+     */
+    handleLetterClick(letterChip) {
+        const letter = letterChip.dataset.letter;
+        const wasActive = letterChip.classList.contains('active');
+        
+        // Remove active class from all letter chips
+        document.querySelectorAll('.letter-chip').forEach(chip => {
+            chip.classList.remove('active');
+        });
+        
+        if (!wasActive) {
+            // Set the new active letter
+            letterChip.classList.add('active');
+            this.pageState.activeLetterFilter = letter;
+            
+            // Save to storage
+            setStorageItem(`${this.pageType}_activeLetterFilter`, letter);
+        } else {
+            // Clear the active letter filter
+            this.pageState.activeLetterFilter = null;
+            
+            // Remove from storage
+            setStorageItem(`${this.pageType}_activeLetterFilter`, null);
+        }
+        
+        // Update visual indicator on toggle button
+        this.updateToggleIndicator();
+        
+        // Trigger a reload with the new filter
+        resetAndReload(true);
+    }
+    
+    /**
+     * Restore the active letter filter from storage
+     */
+    restoreActiveLetterFilter() {
+        const activeLetterFilter = getStorageItem(`${this.pageType}_activeLetterFilter`);
+        
+        if (activeLetterFilter) {
+            const letterChip = document.querySelector(`.letter-chip[data-letter="${activeLetterFilter}"]`);
+            
+            if (letterChip && !letterChip.classList.contains('disabled')) {
+                letterChip.classList.add('active');
+                this.pageState.activeLetterFilter = activeLetterFilter;
+                
+                // Scroll the active letter into view if the alphabet bar is expanded
+                this.scrollActiveLetterIntoView();
+            }
+        }
+    }
+    
+    /**
+     * Clear the active letter filter
+     */
+    clearActiveLetterFilter() {
+        // Remove active class from all letter chips
+        document.querySelectorAll('.letter-chip').forEach(chip => {
+            chip.classList.remove('active');
+        });
+        
+        // Clear the active letter filter
+        this.pageState.activeLetterFilter = null;
+        
+        // Remove from storage
+        setStorageItem(`${this.pageType}_activeLetterFilter`, null);
+        
+        // Update the toggle button indicator
+        this.updateToggleIndicator();
+    }
+    
+    /**
+     * Update letter counts with new data
+     * @param {Object} newCounts - New letter count data
+     */
+    updateCounts(newCounts) {
+        this.letterCounts = { ...newCounts };
+        this.updateLetterCountsDisplay();
+    }
+    
+    /**
+     * Update the toggle button visual indicator based on active filter
+     */
+    updateToggleIndicator() {
+        const toggleButton = document.querySelector('.toggle-alphabet-bar');
+        const hasActiveFilter = this.pageState.activeLetterFilter !== null;
+        
+        if (toggleButton) {
+            if (hasActiveFilter) {
+                toggleButton.classList.add('has-active-letter');
+            } else {
+                toggleButton.classList.remove('has-active-letter');
+            }
+        }
+    }
+    
+    /**
+     * Scroll the active letter into view if the alphabet bar is expanded
+     */
+    scrollActiveLetterIntoView() {
+        if (!this.pageState.activeLetterFilter) return;
+
+        
+        const alphabetBarContainer = document.querySelector('.alphabet-bar-container');
+        if (alphabetBarContainer) {
+            const activeLetterChip = document.querySelector(`.letter-chip.active`);
+            
+            if (activeLetterChip) {
+                // Use a small timeout to ensure the alphabet bar is fully expanded
+                setTimeout(() => {
+                    activeLetterChip.scrollIntoView({
+                        behavior: 'smooth',
+                        block: 'center',
+                        inline: 'center'
+                    });
+                }, 300);
+            }
+        }
+    }
+}

static/js/components/alphabet/index.js

@@ -0,0 +1,14 @@
+// Alphabet component index file
+import { AlphabetBar } from './AlphabetBar.js';
+
+// Export the class
+export { AlphabetBar };
+
+/**
+ * Factory function to create the appropriate alphabet bar
+ * @param {string} pageType - The type of page ('loras' or 'checkpoints')
+ * @returns {AlphabetBar} - The alphabet bar instance
+ */
+export function createAlphabetBar(pageType) {
+    return new AlphabetBar(pageType);
+}

static/js/components/checkpointModal/ModelMetadata.js

@@ -5,7 +5,7 @@
 import { showToast } from '../../utils/uiHelpers.js';
 import { BASE_MODELS } from '../../utils/constants.js';
 import { updateCheckpointCard } from '../../utils/cardUpdater.js';
-import { saveModelMetadata } from '../../api/checkpointApi.js';
+import { saveModelMetadata, renameCheckpointFile } from '../../api/checkpointApi.js';
 
 /**
  * Set up model name editing functionality
@@ -17,6 +17,9 @@ export function setupModelNameEditing(filePath) {
     
     if (!modelNameContent || !editBtn) return;
     
+    // Store the file path in a data attribute for later use
+    modelNameContent.dataset.filePath = filePath;
+    
     // Show edit button on hover
     const modelNameHeader = document.querySelector('.model-name-header');
     modelNameHeader.addEventListener('mouseenter', () => {
@@ -24,14 +27,17 @@ export function setupModelNameEditing(filePath) {
     });
     
     modelNameHeader.addEventListener('mouseleave', () => {
-        if (!modelNameContent.getAttribute('data-editing')) {
+        if (!modelNameHeader.classList.contains('editing')) {
             editBtn.classList.remove('visible');
         }
     });
     
     // Handle edit button click
     editBtn.addEventListener('click', () => {
-        modelNameContent.setAttribute('data-editing', 'true');
+        modelNameHeader.classList.add('editing');
+        modelNameContent.setAttribute('contenteditable', 'true');
+        // Store original value for comparison later
+        modelNameContent.dataset.originalValue = modelNameContent.textContent.trim();
         modelNameContent.focus();
         
         // Place cursor at the end
@@ -47,33 +53,25 @@ export function setupModelNameEditing(filePath) {
         editBtn.classList.add('visible');
     });
     
-    // Handle focus out
-    modelNameContent.addEventListener('blur', function() {
-        this.removeAttribute('data-editing');
-        editBtn.classList.remove('visible');
-        
-        if (this.textContent.trim() === '') {
-            // Restore original model name if empty
-            // Use the passed filePath to find the card
-            const checkpointCard = document.querySelector(`.checkpoint-card[data-filepath="${filePath}"]`);
-            if (checkpointCard) {
-                this.textContent = checkpointCard.dataset.model_name;
-            }
-        }
-    });
-    
-    // Handle enter key
+    // Handle keyboard events in edit mode
     modelNameContent.addEventListener('keydown', function(e) {
+        if (!this.getAttribute('contenteditable')) return;
+        
         if (e.key === 'Enter') {
             e.preventDefault();
-            // Use the passed filePath
-            saveModelName(filePath);
-            this.blur();
+            this.blur(); // Trigger save on Enter
+        } else if (e.key === 'Escape') {
+            e.preventDefault();
+            // Restore original value
+            this.textContent = this.dataset.originalValue;
+            exitEditMode();
         }
     });
     
     // Limit model name length
     modelNameContent.addEventListener('input', function() {
+        if (!this.getAttribute('contenteditable')) return;
+        
         if (this.textContent.length > 100) {
             this.textContent = this.textContent.substring(0, 100);
             // Place cursor at the end
@@ -87,44 +85,59 @@ export function setupModelNameEditing(filePath) {
             showToast('Model name is limited to 100 characters', 'warning');
         }
     });
-}
-
-/**
- * Save model name
- * @param {string} filePath - File path
- */
-async function saveModelName(filePath) {
-    const modelNameElement = document.querySelector('.model-name-content');
-    const newModelName = modelNameElement.textContent.trim();
     
-    // Validate model name
-    if (!newModelName) {
-        showToast('Model name cannot be empty', 'error');
-        return;
-    }
+    // Handle focus out - save changes
+    modelNameContent.addEventListener('blur', async function() {
+        if (!this.getAttribute('contenteditable')) return;
+        
+        const newModelName = this.textContent.trim();
+        const originalValue = this.dataset.originalValue;
+        
+        // Basic validation
+        if (!newModelName) {
+            // Restore original value if empty
+            this.textContent = originalValue;
+            showToast('Model name cannot be empty', 'error');
+            exitEditMode();
+            return;
+        }
+        
+        if (newModelName === originalValue) {
+            // No changes, just exit edit mode
+            exitEditMode();
+            return;
+        }
+        
+        try {
+            // Get the file path from the dataset
+            const filePath = this.dataset.filePath;
+            
+            await saveModelMetadata(filePath, { model_name: newModelName });
+            
+            // Update the corresponding checkpoint card's dataset and display
+            updateCheckpointCard(filePath, { model_name: newModelName });
+            
+            // BUGFIX: Directly update the card's dataset.name attribute to ensure
+            // it's correctly read when reopening the modal
+            const checkpointCard = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
+            if (checkpointCard) {
+                checkpointCard.dataset.name = newModelName;
+            }
+            
+            showToast('Model name updated successfully', 'success');
+        } catch (error) {
+            console.error('Error updating model name:', error);
+            this.textContent = originalValue; // Restore original model name
+            showToast('Failed to update model name', 'error');
+        } finally {
+            exitEditMode();
+        }
+    });
     
-    // Check if model name is too long
-    if (newModelName.length > 100) {
-        showToast('Model name is too long (maximum 100 characters)', 'error');
-        // Truncate the displayed text
-        modelNameElement.textContent = newModelName.substring(0, 100);
-        return;
-    }
-    
-    try {
-        await saveModelMetadata(filePath, { model_name: newModelName });
-        
-        // Update the card with the new model name
-        updateCheckpointCard(filePath, { name: newModelName });
-        
-        showToast('Model name updated successfully', 'success');
-        
-        // No need to reload the entire page
-        // setTimeout(() => {
-        //     window.location.reload();
-        // }, 1500);
-    } catch (error) {
-        showToast('Failed to update model name', 'error');
+    function exitEditMode() {
+        modelNameContent.removeAttribute('contenteditable');
+        modelNameHeader.classList.remove('editing');
+        editBtn.classList.remove('visible');
     }
 }
 
@@ -138,6 +151,9 @@ export function setupBaseModelEditing(filePath) {
     
     if (!baseModelContent || !editBtn) return;
     
+    // Store the file path in a data attribute for later use
+    baseModelContent.dataset.filePath = filePath;
+    
     // Show edit button on hover
     const baseModelDisplay = document.querySelector('.base-model-display');
     baseModelDisplay.addEventListener('mouseenter', () => {
@@ -171,12 +187,13 @@ export function setupBaseModelEditing(filePath) {
             'Stable Diffusion 2.x': [BASE_MODELS.SD_2_0, BASE_MODELS.SD_2_1],
             'Stable Diffusion 3.x': [BASE_MODELS.SD_3, BASE_MODELS.SD_3_5, BASE_MODELS.SD_3_5_MEDIUM, BASE_MODELS.SD_3_5_LARGE, BASE_MODELS.SD_3_5_LARGE_TURBO],
             'SDXL': [BASE_MODELS.SDXL, BASE_MODELS.SDXL_LIGHTNING, BASE_MODELS.SDXL_HYPER],
-            'Video Models': [BASE_MODELS.SVD, BASE_MODELS.WAN_VIDEO, BASE_MODELS.HUNYUAN_VIDEO],
+            'Video Models': [BASE_MODELS.SVD, BASE_MODELS.LTXV, BASE_MODELS.WAN_VIDEO, BASE_MODELS.HUNYUAN_VIDEO],
             'Other Models': [
                 BASE_MODELS.FLUX_1_D, BASE_MODELS.FLUX_1_S, BASE_MODELS.AURAFLOW,
                 BASE_MODELS.PIXART_A, BASE_MODELS.PIXART_E, BASE_MODELS.HUNYUAN_1,
                 BASE_MODELS.LUMINA, BASE_MODELS.KOLORS, BASE_MODELS.NOOBAI,
-                BASE_MODELS.ILLUSTRIOUS, BASE_MODELS.PONY, BASE_MODELS.UNKNOWN
+                BASE_MODELS.ILLUSTRIOUS, BASE_MODELS.PONY, BASE_MODELS.HIDREAM,
+                BASE_MODELS.UNKNOWN
             ]
         };
         
@@ -302,6 +319,9 @@ export function setupFileNameEditing(filePath) {
     
     if (!fileNameContent || !editBtn) return;
     
+    // Store the original file path
+    fileNameContent.dataset.filePath = filePath;
+    
     // Show edit button on hover
     const fileNameWrapper = document.querySelector('.file-name-wrapper');
     fileNameWrapper.addEventListener('mouseenter', () => {
@@ -399,19 +419,8 @@ export function setupFileNameEditing(filePath) {
         
         try {
             // Use the passed filePath (which includes the original filename)
-            // Call API to rename the file
-            const response = await fetch('/api/rename_checkpoint', {
-                method: 'POST',
-                headers: {
-                    'Content-Type': 'application/json',
-                },
-                body: JSON.stringify({
-                    file_path: filePath, // Use the full original path
-                    new_file_name: newFileName
-                })
-            });
-            
-            const result = await response.json();
+            // Call API to rename the file using the new function from checkpointApi.js
+            const result = await renameCheckpointFile(filePath, newFileName);
             
             if (result.success) {
                 showToast('File name updated successfully', 'success');

static/js/components/checkpointModal/ShowcaseView.js

@@ -322,8 +322,72 @@ function initMetadataPanelHandlers(container) {
     const mediaWrappers = container.querySelectorAll('.media-wrapper');
     
     mediaWrappers.forEach(wrapper => {
+        // Get the metadata panel and media element (img or video)
         const metadataPanel = wrapper.querySelector('.image-metadata-panel');
-        if (!metadataPanel) return;
+        const mediaElement = wrapper.querySelector('img, video');
+        
+        if (!metadataPanel || !mediaElement) return;
+        
+        let isOverMetadataPanel = false;
+        
+        // Add event listeners to the wrapper for mouse tracking
+        wrapper.addEventListener('mousemove', (e) => {
+            // Get mouse position relative to wrapper
+            const rect = wrapper.getBoundingClientRect();
+            const mouseX = e.clientX - rect.left;
+            const mouseY = e.clientY - rect.top;
+            
+            // Get the actual displayed dimensions of the media element
+            const mediaRect = getRenderedMediaRect(mediaElement, rect.width, rect.height);
+            
+            // Check if mouse is over the actual media content
+            const isOverMedia = (
+                mouseX >= mediaRect.left && 
+                mouseX <= mediaRect.right && 
+                mouseY >= mediaRect.top && 
+                mouseY <= mediaRect.bottom
+            );
+            
+            // Show metadata panel when over media content or metadata panel itself
+            if (isOverMedia || isOverMetadataPanel) {
+                metadataPanel.classList.add('visible');
+            } else {
+                metadataPanel.classList.remove('visible');
+            }
+        });
+        
+        wrapper.addEventListener('mouseleave', () => {
+            // Only hide panel when mouse leaves the wrapper and not over the metadata panel
+            if (!isOverMetadataPanel) {
+                metadataPanel.classList.remove('visible');
+            }
+        });
+        
+        // Add mouse enter/leave events for the metadata panel itself
+        metadataPanel.addEventListener('mouseenter', () => {
+            isOverMetadataPanel = true;
+            metadataPanel.classList.add('visible');
+        });
+        
+        metadataPanel.addEventListener('mouseleave', () => {
+            isOverMetadataPanel = false;
+            // Only hide if mouse is not over the media
+            const rect = wrapper.getBoundingClientRect();
+            const mediaRect = getRenderedMediaRect(mediaElement, rect.width, rect.height);
+            const mouseX = event.clientX - rect.left;
+            const mouseY = event.clientY - rect.top;
+            
+            const isOverMedia = (
+                mouseX >= mediaRect.left && 
+                mouseX <= mediaRect.right && 
+                mouseY >= mediaRect.top && 
+                mouseY <= mediaRect.bottom
+            );
+            
+            if (!isOverMedia) {
+                metadataPanel.classList.remove('visible');
+            }
+        });
         
         // Prevent events from bubbling
         metadataPanel.addEventListener('click', (e) => {
@@ -352,11 +416,61 @@ function initMetadataPanelHandlers(container) {
         
         // Prevent panel scroll from causing modal scroll
         metadataPanel.addEventListener('wheel', (e) => {
-            e.stopPropagation();
-        });
+            const isAtTop = metadataPanel.scrollTop === 0;
+            const isAtBottom = metadataPanel.scrollHeight - metadataPanel.scrollTop === metadataPanel.clientHeight;
+            
+            // Only prevent default if scrolling would cause the panel to scroll
+            if ((e.deltaY < 0 && !isAtTop) || (e.deltaY > 0 && !isAtBottom)) {
+                e.stopPropagation();
+            }
+        }, { passive: true });
     });
 }
 
+/**
+ * Get the actual rendered rectangle of a media element with object-fit: contain
+ * @param {HTMLElement} mediaElement - The img or video element
+ * @param {number} containerWidth - Width of the container
+ * @param {number} containerHeight - Height of the container
+ * @returns {Object} - Rect with left, top, right, bottom coordinates
+ */
+function getRenderedMediaRect(mediaElement, containerWidth, containerHeight) {
+    // Get natural dimensions of the media
+    const naturalWidth = mediaElement.naturalWidth || mediaElement.videoWidth || mediaElement.clientWidth;
+    const naturalHeight = mediaElement.naturalHeight || mediaElement.videoHeight || mediaElement.clientHeight;
+    
+    if (!naturalWidth || !naturalHeight) {
+        // Fallback if dimensions cannot be determined
+        return { left: 0, top: 0, right: containerWidth, bottom: containerHeight };
+    }
+    
+    // Calculate aspect ratios
+    const containerRatio = containerWidth / containerHeight;
+    const mediaRatio = naturalWidth / naturalHeight;
+    
+    let renderedWidth, renderedHeight, left = 0, top = 0;
+    
+    // Apply object-fit: contain logic
+    if (containerRatio > mediaRatio) {
+        // Container is wider than media - will have empty space on sides
+        renderedHeight = containerHeight;
+        renderedWidth = renderedHeight * mediaRatio;
+        left = (containerWidth - renderedWidth) / 2;
+    } else {
+        // Container is taller than media - will have empty space top/bottom
+        renderedWidth = containerWidth;
+        renderedHeight = renderedWidth / mediaRatio;
+        top = (containerHeight - renderedHeight) / 2;
+    }
+    
+    return {
+        left,
+        top,
+        right: left + renderedWidth,
+        bottom: top + renderedHeight
+    };
+}
+
 /**
  * Initialize blur toggle handlers
  */

static/js/components/checkpointModal/index.js

@@ -27,11 +27,25 @@ export function showCheckpointModal(checkpoint) {
             <button class="close" onclick="modalManager.closeModal('checkpointModal')">&times;</button>
             <header class="modal-header">
                 <div class="model-name-header">
-                    <h2 class="model-name-content" contenteditable="true" spellcheck="false">${checkpoint.model_name || 'Checkpoint Details'}</h2>
+                    <h2 class="model-name-content">${checkpoint.model_name || 'Checkpoint Details'}</h2>
                     <button class="edit-model-name-btn" title="Edit model name">
                         <i class="fas fa-pencil-alt"></i>
                     </button>
                 </div>
+
+                ${checkpoint.civitai?.creator ? `
+                <div class="creator-info">
+                    ${checkpoint.civitai.creator.image ? 
+                        `<div class="creator-avatar">
+                            <img src="${checkpoint.civitai.creator.image}" alt="${checkpoint.civitai.creator.username}" onerror="this.onerror=null; this.src='static/icons/user-placeholder.png';">
+                        </div>` : 
+                        `<div class="creator-avatar creator-placeholder">
+                            <i class="fas fa-user"></i>
+                        </div>`
+                    }
+                    <span class="creator-username">${checkpoint.civitai.creator.username}</span>
+                </div>` : ''}
+
                 ${renderCompactTags(checkpoint.tags || [])}
             </header>
 

static/js/components/controls/CheckpointsControls.js

@@ -33,8 +33,8 @@ export class CheckpointsControls extends PageControls {
                 return await resetAndReload(updateFolders);
             },
             
-            refreshModels: async () => {
-                return await refreshCheckpoints();
+            refreshModels: async (fullRebuild = false) => {
+                return await refreshCheckpoints(fullRebuild);
             },
             
             // Add fetch from Civitai functionality for checkpoints

static/js/components/controls/LorasControls.js

@@ -2,6 +2,7 @@
 import { PageControls } from './PageControls.js';
 import { loadMoreLoras, fetchCivitai, resetAndReload, refreshLoras } from '../../api/loraApi.js';
 import { getSessionItem, removeSessionItem } from '../../utils/storageHelpers.js';
+import { createAlphabetBar } from '../alphabet/index.js';
 
 /**
  * LorasControls class - Extends PageControls for LoRA-specific functionality
@@ -16,6 +17,9 @@ export class LorasControls extends PageControls {
         
         // Check for custom filters (e.g., from recipe navigation)
         this.checkCustomFilters();
+        
+        // Initialize alphabet bar component
+        this.initAlphabetBar();
     }
     
     /**
@@ -32,8 +36,8 @@ export class LorasControls extends PageControls {
                 return await resetAndReload(updateFolders);
             },
             
-            refreshModels: async () => {
-                return await refreshLoras();
+            refreshModels: async (fullRebuild = false) => {
+                return await refreshLoras(fullRebuild);
             },
             
             // LoRA-specific API functions
@@ -142,4 +146,15 @@ export class LorasControls extends PageControls {
     _truncateText(text, maxLength) {
         return text.length > maxLength ? text.substring(0, maxLength - 3) + '...' : text;
     }
+    
+    /**
+     * Initialize the alphabet bar component
+     */
+    initAlphabetBar() {
+        // Create the alphabet bar component
+        this.alphabetBar = createAlphabetBar('loras');
+        
+        // Expose the alphabet bar to the global scope for debugging
+        window.alphabetBar = this.alphabetBar;
+    }
 }

static/js/components/controls/PageControls.js

@@ -37,7 +37,7 @@ export class PageControls {
      */
     initializeState() {
         // Set default values
-        this.pageState.pageSize = 20; 
+        this.pageState.pageSize = 100;
         this.pageState.isLoading = false;
         this.pageState.hasMore = true;
         
@@ -83,9 +83,12 @@ export class PageControls {
         // Refresh button handler
         const refreshBtn = document.querySelector('[data-action="refresh"]');
         if (refreshBtn) {
-            refreshBtn.addEventListener('click', () => this.refreshModels());
+            refreshBtn.addEventListener('click', () => this.refreshModels(false)); // Regular refresh (incremental)
         }
         
+        // Initialize dropdown functionality
+        this.initDropdowns();
+        
         // Toggle folders button
         const toggleFoldersBtn = document.querySelector('.toggle-folders-btn');
         if (toggleFoldersBtn) {
@@ -102,6 +105,61 @@ export class PageControls {
         this.initPageSpecificListeners();
     }
     
+    /**
+     * Initialize dropdown functionality
+     */
+    initDropdowns() {
+        // Handle dropdown toggles
+        const dropdownToggles = document.querySelectorAll('.dropdown-toggle');
+        dropdownToggles.forEach(toggle => {
+            toggle.addEventListener('click', (e) => {
+                e.stopPropagation(); // Prevent triggering parent button
+                const dropdownGroup = toggle.closest('.dropdown-group');
+                
+                // Close all other open dropdowns first
+                document.querySelectorAll('.dropdown-group.active').forEach(group => {
+                    if (group !== dropdownGroup) {
+                        group.classList.remove('active');
+                    }
+                });
+                
+                // Toggle current dropdown
+                dropdownGroup.classList.toggle('active');
+            });
+        });
+        
+        // Handle quick refresh option
+        const quickRefreshOption = document.querySelector('[data-action="quick-refresh"]');
+        if (quickRefreshOption) {
+            quickRefreshOption.addEventListener('click', (e) => {
+                e.stopPropagation();
+                this.refreshModels(false);
+                // Close the dropdown
+                document.querySelector('.dropdown-group.active')?.classList.remove('active');
+            });
+        }
+        
+        // Handle full rebuild option
+        const fullRebuildOption = document.querySelector('[data-action="full-rebuild"]');
+        if (fullRebuildOption) {
+            fullRebuildOption.addEventListener('click', (e) => {
+                e.stopPropagation();
+                this.refreshModels(true);
+                // Close the dropdown
+                document.querySelector('.dropdown-group.active')?.classList.remove('active');
+            });
+        }
+        
+        // Close dropdowns when clicking outside
+        document.addEventListener('click', (e) => {
+            if (!e.target.closest('.dropdown-group')) {
+                document.querySelectorAll('.dropdown-group.active').forEach(group => {
+                    group.classList.remove('active');
+                });
+            }
+        });
+    }
+    
     /**
      * Initialize page-specific event listeners
      */
@@ -327,18 +385,19 @@ export class PageControls {
     
     /**
      * Refresh models list
+     * @param {boolean} fullRebuild - Whether to perform a full rebuild
      */
-    async refreshModels() {
+    async refreshModels(fullRebuild = false) {
         if (!this.api) {
             console.error('API methods not registered');
             return;
         }
 
         try {
-            await this.api.refreshModels();
+            await this.api.refreshModels(fullRebuild);
         } catch (error) {
-            console.error(`Error refreshing ${this.pageType}:`, error);
-            showToast(`Failed to refresh ${this.pageType}: ${error.message}`, 'error');
+            console.error(`Error ${fullRebuild ? 'rebuilding' : 'refreshing'} ${this.pageType}:`, error);
+            showToast(`Failed to ${fullRebuild ? 'rebuild' : 'refresh'} ${this.pageType}: ${error.message}`, 'error');
         }
     }
     

static/js/components/controls/index.js

@@ -2,6 +2,7 @@
 import { PageControls } from './PageControls.js';
 import { LorasControls } from './LorasControls.js';
 import { CheckpointsControls } from './CheckpointsControls.js';
+import { refreshVirtualScroll } from '../../utils/infiniteScroll.js';
 
 // Export the classes
 export { PageControls, LorasControls, CheckpointsControls };
@@ -20,4 +21,17 @@ export function createPageControls(pageType) {
         console.error(`Unknown page type: ${pageType}`);
         return null;
     }
+}
+
+// Example for a filter method:
+function applyFilter(filterType, value) {
+    // ...existing filter logic...
+    
+    // After filters are applied, refresh the virtual scroll if it exists
+    if (state.virtualScroller) {
+        refreshVirtualScroll();
+    } else {
+        // Fall back to existing reset and reload logic
+        resetAndReload(true);
+    }
 }

static/js/components/loraModal/ModelMetadata.js

@@ -5,7 +5,7 @@
 import { showToast } from '../../utils/uiHelpers.js';
 import { BASE_MODELS } from '../../utils/constants.js';
 import { updateLoraCard } from '../../utils/cardUpdater.js';
-import { saveModelMetadata } from '../../api/loraApi.js';
+import { saveModelMetadata, renameLoraFile } from '../../api/loraApi.js';
 
 /**
  * 设置模型名称编辑功能
@@ -27,14 +27,17 @@ export function setupModelNameEditing(filePath) {
     });
     
     modelNameHeader.addEventListener('mouseleave', () => {
-        if (!modelNameContent.getAttribute('data-editing')) {
+        if (!modelNameHeader.classList.contains('editing')) {
             editBtn.classList.remove('visible');
         }
     });
     
     // Handle edit button click
     editBtn.addEventListener('click', () => {
-        modelNameContent.setAttribute('data-editing', 'true');
+        modelNameHeader.classList.add('editing');
+        modelNameContent.setAttribute('contenteditable', 'true');
+        // Store original value for comparison later
+        modelNameContent.dataset.originalValue = modelNameContent.textContent.trim();
         modelNameContent.focus();
         
         // Place cursor at the end
@@ -50,33 +53,25 @@ export function setupModelNameEditing(filePath) {
         editBtn.classList.add('visible');
     });
     
-    // Handle focus out
-    modelNameContent.addEventListener('blur', function() {
-        this.removeAttribute('data-editing');
-        editBtn.classList.remove('visible');
-        
-        if (this.textContent.trim() === '') {
-            // Restore original model name if empty
-            const filePath = this.dataset.filePath;
-            const loraCard = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
-            if (loraCard) {
-                this.textContent = loraCard.dataset.model_name;
-            }
-        }
-    });
-    
-    // Handle enter key
+    // Handle keyboard events in edit mode
     modelNameContent.addEventListener('keydown', function(e) {
+        if (!this.getAttribute('contenteditable')) return;
+        
         if (e.key === 'Enter') {
             e.preventDefault();
-            const filePath = this.dataset.filePath;
-            saveModelName(filePath);
-            this.blur();
+            this.blur(); // Trigger save on Enter
+        } else if (e.key === 'Escape') {
+            e.preventDefault();
+            // Restore original value
+            this.textContent = this.dataset.originalValue;
+            exitEditMode();
         }
     });
     
     // Limit model name length
     modelNameContent.addEventListener('input', function() {
+        if (!this.getAttribute('contenteditable')) return;
+        
         // Limit model name length
         if (this.textContent.length > 100) {
             this.textContent = this.textContent.substring(0, 100);
@@ -91,6 +86,60 @@ export function setupModelNameEditing(filePath) {
             showToast('Model name is limited to 100 characters', 'warning');
         }
     });
+    
+    // Handle focus out - save changes
+    modelNameContent.addEventListener('blur', async function() {
+        if (!this.getAttribute('contenteditable')) return;
+        
+        const newModelName = this.textContent.trim();
+        const originalValue = this.dataset.originalValue;
+        
+        // Basic validation
+        if (!newModelName) {
+            // Restore original value if empty
+            this.textContent = originalValue;
+            showToast('Model name cannot be empty', 'error');
+            exitEditMode();
+            return;
+        }
+        
+        if (newModelName === originalValue) {
+            // No changes, just exit edit mode
+            exitEditMode();
+            return;
+        }
+        
+        try {
+            // Get the file path from the dataset
+            const filePath = this.dataset.filePath;
+            
+            await saveModelMetadata(filePath, { model_name: newModelName });
+            
+            // Update the corresponding lora card's dataset and display
+            updateLoraCard(filePath, { model_name: newModelName });
+            
+            // BUGFIX: Directly update the card's dataset.name attribute to ensure
+            // it's correctly read when reopening the modal
+            const loraCard = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
+            if (loraCard) {
+                loraCard.dataset.name = newModelName;
+            }
+            
+            showToast('Model name updated successfully', 'success');
+        } catch (error) {
+            console.error('Error updating model name:', error);
+            this.textContent = originalValue; // Restore original model name
+            showToast('Failed to update model name', 'error');
+        } finally {
+            exitEditMode();
+        }
+    });
+    
+    function exitEditMode() {
+        modelNameContent.removeAttribute('contenteditable');
+        modelNameHeader.classList.remove('editing');
+        editBtn.classList.remove('visible');
+    }
 }
 
 /**
@@ -173,12 +222,13 @@ export function setupBaseModelEditing(filePath) {
             'Stable Diffusion 2.x': [BASE_MODELS.SD_2_0, BASE_MODELS.SD_2_1],
             'Stable Diffusion 3.x': [BASE_MODELS.SD_3, BASE_MODELS.SD_3_5, BASE_MODELS.SD_3_5_MEDIUM, BASE_MODELS.SD_3_5_LARGE, BASE_MODELS.SD_3_5_LARGE_TURBO],
             'SDXL': [BASE_MODELS.SDXL, BASE_MODELS.SDXL_LIGHTNING, BASE_MODELS.SDXL_HYPER],
-            'Video Models': [BASE_MODELS.SVD, BASE_MODELS.WAN_VIDEO, BASE_MODELS.HUNYUAN_VIDEO],
+            'Video Models': [BASE_MODELS.SVD, BASE_MODELS.LTXV, BASE_MODELS.WAN_VIDEO, BASE_MODELS.HUNYUAN_VIDEO],
             'Other Models': [
                 BASE_MODELS.FLUX_1_D, BASE_MODELS.FLUX_1_S, BASE_MODELS.AURAFLOW,
                 BASE_MODELS.PIXART_A, BASE_MODELS.PIXART_E, BASE_MODELS.HUNYUAN_1,
                 BASE_MODELS.LUMINA, BASE_MODELS.KOLORS, BASE_MODELS.NOOBAI,
-                BASE_MODELS.ILLUSTRIOUS, BASE_MODELS.PONY, BASE_MODELS.UNKNOWN
+                BASE_MODELS.ILLUSTRIOUS, BASE_MODELS.PONY, BASE_MODELS.HIDREAM,
+                BASE_MODELS.UNKNOWN
             ]
         };
         
@@ -409,19 +459,8 @@ export function setupFileNameEditing(filePath) {
             // Get the file path from the dataset
             const filePath = this.dataset.filePath;
                 
-            // Call API to rename the file
-            const response = await fetch('/api/rename_lora', {
-                method: 'POST',
-                headers: {
-                    'Content-Type': 'application/json',
-                },
-                body: JSON.stringify({
-                    file_path: filePath,
-                    new_file_name: newFileName
-                })
-            });
-            
-            const result = await response.json();
+            // Call API to rename the file using the new function from loraApi.js
+            const result = await renameLoraFile(filePath, newFileName);
             
             if (result.success) {
                 showToast('File name updated successfully', 'success');

static/js/components/loraModal/ShowcaseView.js

@@ -329,9 +329,72 @@ function initMetadataPanelHandlers(container) {
     const mediaWrappers = container.querySelectorAll('.media-wrapper');
     
     mediaWrappers.forEach(wrapper => {
-        // Get the metadata panel
+        // Get the metadata panel and media element (img or video)
         const metadataPanel = wrapper.querySelector('.image-metadata-panel');
-        if (!metadataPanel) return;
+        const mediaElement = wrapper.querySelector('img, video');
+        
+        if (!metadataPanel || !mediaElement) return;
+        
+        let isOverMetadataPanel = false;
+        
+        // Add event listeners to the wrapper for mouse tracking
+        wrapper.addEventListener('mousemove', (e) => {
+            // Get mouse position relative to wrapper
+            const rect = wrapper.getBoundingClientRect();
+            const mouseX = e.clientX - rect.left;
+            const mouseY = e.clientY - rect.top;
+            
+            // Get the actual displayed dimensions of the media element
+            const mediaRect = getRenderedMediaRect(mediaElement, rect.width, rect.height);
+            
+            // Check if mouse is over the actual media content
+            const isOverMedia = (
+                mouseX >= mediaRect.left && 
+                mouseX <= mediaRect.right && 
+                mouseY >= mediaRect.top && 
+                mouseY <= mediaRect.bottom
+            );
+            
+            // Show metadata panel when over media content
+            if (isOverMedia || isOverMetadataPanel) {
+                metadataPanel.classList.add('visible');
+            } else {
+                metadataPanel.classList.remove('visible');
+            }
+        });
+        
+        wrapper.addEventListener('mouseleave', () => {
+            // Only hide panel when mouse leaves the wrapper and not over the metadata panel
+            if (!isOverMetadataPanel) {
+                metadataPanel.classList.remove('visible');
+            }
+        });
+        
+        // Add mouse enter/leave events for the metadata panel itself
+        metadataPanel.addEventListener('mouseenter', () => {
+            isOverMetadataPanel = true;
+            metadataPanel.classList.add('visible');
+        });
+        
+        metadataPanel.addEventListener('mouseleave', () => {
+            isOverMetadataPanel = false;
+            // Only hide if mouse is not over the media
+            const rect = wrapper.getBoundingClientRect();
+            const mediaRect = getRenderedMediaRect(mediaElement, rect.width, rect.height);
+            const mouseX = event.clientX - rect.left;
+            const mouseY = event.clientY - rect.top;
+            
+            const isOverMedia = (
+                mouseX >= mediaRect.left && 
+                mouseX <= mediaRect.right && 
+                mouseY >= mediaRect.top && 
+                mouseY <= mediaRect.bottom
+            );
+            
+            if (!isOverMedia) {
+                metadataPanel.classList.remove('visible');
+            }
+        });
         
         // Prevent events from the metadata panel from bubbling
         metadataPanel.addEventListener('click', (e) => {
@@ -371,6 +434,50 @@ function initMetadataPanelHandlers(container) {
     });
 }
 
+/**
+ * Get the actual rendered rectangle of a media element with object-fit: contain
+ * @param {HTMLElement} mediaElement - The img or video element
+ * @param {number} containerWidth - Width of the container
+ * @param {number} containerHeight - Height of the container
+ * @returns {Object} - Rect with left, top, right, bottom coordinates
+ */
+function getRenderedMediaRect(mediaElement, containerWidth, containerHeight) {
+    // Get natural dimensions of the media
+    const naturalWidth = mediaElement.naturalWidth || mediaElement.videoWidth || mediaElement.clientWidth;
+    const naturalHeight = mediaElement.naturalHeight || mediaElement.videoHeight || mediaElement.clientHeight;
+    
+    if (!naturalWidth || !naturalHeight) {
+        // Fallback if dimensions cannot be determined
+        return { left: 0, top: 0, right: containerWidth, bottom: containerHeight };
+    }
+    
+    // Calculate aspect ratios
+    const containerRatio = containerWidth / containerHeight;
+    const mediaRatio = naturalWidth / naturalHeight;
+    
+    let renderedWidth, renderedHeight, left = 0, top = 0;
+    
+    // Apply object-fit: contain logic
+    if (containerRatio > mediaRatio) {
+        // Container is wider than media - will have empty space on sides
+        renderedHeight = containerHeight;
+        renderedWidth = renderedHeight * mediaRatio;
+        left = (containerWidth - renderedWidth) / 2;
+    } else {
+        // Container is taller than media - will have empty space top/bottom
+        renderedWidth = containerWidth;
+        renderedHeight = renderedWidth / mediaRatio;
+        top = (containerHeight - renderedHeight) / 2;
+    }
+    
+    return {
+        left,
+        top,
+        right: left + renderedWidth,
+        bottom: top + renderedHeight
+    };
+}
+
 /**
  * 初始化模糊切换处理
  */

static/js/components/loraModal/index.js

@@ -24,6 +24,7 @@ import { updateLoraCard } from '../../utils/cardUpdater.js';
  * @param {Object} lora - LoRA模型数据
  */
 export function showLoraModal(lora) {
+    console.log('Lora data:', lora);
     const escapedWords = lora.civitai?.trainedWords?.length ? 
         lora.civitai.trainedWords.map(word => word.replace(/'/g, '\\\'')) : [];
 
@@ -32,11 +33,25 @@ export function showLoraModal(lora) {
             <button class="close" onclick="modalManager.closeModal('loraModal')">&times;</button>
             <header class="modal-header">
                 <div class="model-name-header">
-                    <h2 class="model-name-content" contenteditable="true" spellcheck="false">${lora.model_name}</h2>
+                    <h2 class="model-name-content">${lora.model_name}</h2>
                     <button class="edit-model-name-btn" title="Edit model name">
                         <i class="fas fa-pencil-alt"></i>
                     </button>
                 </div>
+                
+                ${lora.civitai?.creator ? `
+                <div class="creator-info">
+                    ${lora.civitai.creator.image ? 
+                      `<div class="creator-avatar">
+                         <img src="${lora.civitai.creator.image}" alt="${lora.civitai.creator.username}" onerror="this.onerror=null; this.src='static/icons/user-placeholder.png';">
+                       </div>` : 
+                      `<div class="creator-avatar creator-placeholder">
+                         <i class="fas fa-user"></i>
+                       </div>`
+                    }
+                    <span class="creator-username">${lora.civitai.creator.username}</span>
+                </div>` : ''}
+                
                 ${renderCompactTags(lora.tags || [])}
             </header>
 

static/js/core.js

@@ -9,6 +9,7 @@ import { exampleImagesManager } from './managers/ExampleImagesManager.js';
 import { showToast, initTheme, initBackToTop, lazyLoadImages } from './utils/uiHelpers.js';
 import { initializeInfiniteScroll } from './utils/infiniteScroll.js';
 import { migrateStorageItems } from './utils/storageHelpers.js';
+import { setupLoraCardEventDelegation } from './components/LoraCard.js';
 
 // Core application class
 export class AppCore {
@@ -63,7 +64,12 @@ export class AppCore {
         // Initialize lazy loading for images on all pages
         lazyLoadImages();
         
-        // Initialize infinite scroll for pages that need it
+        // Setup event delegation for lora cards if on the loras page
+        if (pageType === 'loras') {
+            setupLoraCardEventDelegation();
+        }
+        
+        // Initialize virtual scroll for pages that need it
         if (['loras', 'recipes', 'checkpoints'].includes(pageType)) {
             initializeInfiniteScroll(pageType);
         }

static/js/loras.js

@@ -8,7 +8,7 @@ import { DownloadManager } from './managers/DownloadManager.js';
 import { moveManager } from './managers/MoveManager.js';
 import { LoraContextMenu } from './components/ContextMenu/index.js';
 import { createPageControls } from './components/controls/index.js';
-import { confirmDelete, closeDeleteModal } from './utils/modalUtils.js';
+import { confirmDelete, closeDeleteModal, confirmExclude, closeExcludeModal } from './utils/modalUtils.js';
 
 // Initialize the LoRA page
 class LoraPageManager {
@@ -35,6 +35,8 @@ class LoraPageManager {
         window.showLoraModal = showLoraModal;
         window.confirmDelete = confirmDelete;
         window.closeDeleteModal = closeDeleteModal;
+        window.confirmExclude = confirmExclude;
+        window.closeExcludeModal = closeExcludeModal;
         window.downloadManager = this.downloadManager;
         window.moveManager = moveManager;
         window.toggleShowcase = toggleShowcase;
@@ -61,8 +63,14 @@ class LoraPageManager {
         // Initialize the bulk manager
         bulkManager.initialize();
         
-        // Initialize common page features (lazy loading, infinite scroll)
+        // Initialize common page features (virtual scroll)
         appCore.initializePageFeatures();
+        
+        // Add virtual scroll class to grid for CSS adjustments
+        const loraGrid = document.getElementById('loraGrid');
+        if (loraGrid && state.virtualScroller) {
+            loraGrid.classList.add('virtual-scroll');
+        }
     }
 }
 

static/js/managers/CheckpointDownloadManager.js

@@ -36,6 +36,14 @@ export class CheckpointDownloadManager {
             this.cleanupFolderBrowser();
         });
         this.resetSteps();
+        
+        // Auto-focus on the URL input
+        setTimeout(() => {
+            const urlInput = document.getElementById('checkpointUrl');
+            if (urlInput) {
+                urlInput.focus();
+            }
+        }, 100); // Small delay to ensure the modal is fully displayed
     }
 
     resetSteps() {
@@ -178,6 +186,11 @@ export class CheckpointDownloadManager {
             `;
         }).join('');
         
+        // Auto-select the version if there's only one
+        if (this.versions.length === 1 && !this.currentVersion) {
+            this.selectVersion(this.versions[0].id.toString());
+        }
+        
         // Update Next button state based on initial selection
         this.updateNextButtonState();
     }

static/js/managers/DownloadManager.js

@@ -38,6 +38,14 @@ export class DownloadManager {
             this.cleanupFolderBrowser();
         });
         this.resetSteps();
+        
+        // Auto-focus on the URL input
+        setTimeout(() => {
+            const urlInput = document.getElementById('loraUrl');
+            if (urlInput) {
+                urlInput.focus();
+            }
+        }, 100); // Small delay to ensure the modal is fully displayed
     }
 
     resetSteps() {
@@ -182,6 +190,11 @@ export class DownloadManager {
             `;
         }).join('');
         
+        // Auto-select the version if there's only one
+        if (this.versions.length === 1 && !this.currentVersion) {
+            this.selectVersion(this.versions[0].id.toString());
+        }
+        
         // Update Next button state based on initial selection
         this.updateNextButtonState();
     }

static/js/managers/ModalManager.js

@@ -59,6 +59,19 @@ export class ModalManager {
                 }
             });
         }
+        
+        // Add excludeModal registration
+        const excludeModal = document.getElementById('excludeModal');
+        if (excludeModal) {
+            this.registerModal('excludeModal', {
+                element: excludeModal,
+                onClose: () => {
+                    this.getModal('excludeModal').element.classList.remove('show');
+                    document.body.classList.remove('modal-open');
+                },
+                closeOnOutsideClick: true
+            });
+        }
 
         // Add downloadModal registration
         const downloadModal = document.getElementById('downloadModal');
@@ -145,6 +158,30 @@ export class ModalManager {
             });
         }
 
+        // Add duplicateDeleteModal registration
+        const duplicateDeleteModal = document.getElementById('duplicateDeleteModal');
+        if (duplicateDeleteModal) {
+            this.registerModal('duplicateDeleteModal', {
+                element: duplicateDeleteModal,
+                onClose: () => {
+                    this.getModal('duplicateDeleteModal').element.classList.remove('show');
+                    document.body.classList.remove('modal-open');
+                }
+            });
+        }
+        
+        // Add clearCacheModal registration
+        const clearCacheModal = document.getElementById('clearCacheModal');
+        if (clearCacheModal) {
+            this.registerModal('clearCacheModal', {
+                element: clearCacheModal,
+                onClose: () => {
+                    this.getModal('clearCacheModal').element.classList.remove('show');
+                    document.body.classList.remove('modal-open');
+                }
+            });
+        }
+        
         // Set up event listeners for modal toggles
         const supportToggle = document.getElementById('supportToggleBtn');
         if (supportToggle) {
@@ -208,7 +245,7 @@ export class ModalManager {
         // Store current scroll position before showing modal
         this.scrollPosition = window.scrollY;
 
-        if (id === 'deleteModal') {
+        if (id === 'deleteModal' || id === 'excludeModal' || id === 'duplicateDeleteModal' || id === 'clearCacheModal') {
             modal.element.classList.add('show');
         } else {
             modal.element.style.display = 'block';

static/js/managers/SettingsManager.js

@@ -26,6 +26,21 @@ export class SettingsManager {
         if (savedSettings) {
             state.global.settings = { ...state.global.settings, ...savedSettings };
         }
+        
+        // Initialize default values for new settings if they don't exist
+        if (state.global.settings.compactMode === undefined) {
+            state.global.settings.compactMode = false;
+        }
+        
+        // Convert old boolean compactMode to new displayDensity string
+        if (typeof state.global.settings.displayDensity === 'undefined') {
+            if (state.global.settings.compactMode === true) {
+                state.global.settings.displayDensity = 'compact';
+            } else {
+                state.global.settings.displayDensity = 'default';
+            }
+            // We can delete the old setting, but keeping it for backwards compatibility
+        }
     }
 
     initialize() {
@@ -76,6 +91,12 @@ export class SettingsManager {
         if (autoplayOnHoverCheckbox) {
             autoplayOnHoverCheckbox.checked = state.global.settings.autoplayOnHover || false;
         }
+        
+        // Set display density setting
+        const displayDensitySelect = document.getElementById('displayDensity');
+        if (displayDensitySelect) {
+            displayDensitySelect.value = state.global.settings.displayDensity || 'default';
+        }
 
         // Load default lora root
         await this.loadLoraRoots();
@@ -149,6 +170,8 @@ export class SettingsManager {
             state.global.settings.autoplayOnHover = value;
         } else if (settingKey === 'optimize_example_images') {
             state.global.settings.optimizeExampleImages = value;
+        } else if (settingKey === 'compact_mode') {
+            state.global.settings.compactMode = value;
         } else {
             // For any other settings that might be added in the future
             state.global.settings[settingKey] = value;
@@ -185,6 +208,12 @@ export class SettingsManager {
                 this.reloadContent();
             }
             
+            // Recalculate layout when compact mode changes
+            if (settingKey === 'compact_mode' && state.virtualScroller) {
+                state.virtualScroller.calculateLayout();
+                showToast(`Compact Mode ${value ? 'enabled' : 'disabled'}`, 'success');
+            }
+            
         } catch (error) {
             showToast('Failed to save setting: ' + error.message, 'error');
         }
@@ -200,6 +229,11 @@ export class SettingsManager {
         // Update frontend state
         if (settingKey === 'default_lora_root') {
             state.global.settings.default_loras_root = value;
+        } else if (settingKey === 'display_density') {
+            state.global.settings.displayDensity = value;
+            
+            // Also update compactMode for backwards compatibility
+            state.global.settings.compactMode = (value !== 'default');
         } else {
             // For any other settings that might be added in the future
             state.global.settings[settingKey] = value;
@@ -210,22 +244,38 @@ export class SettingsManager {
         
         try {
             // For backend settings, make API call
-            const payload = {};
-            payload[settingKey] = value;
-            
-            const response = await fetch('/api/settings', {
-                method: 'POST',
-                headers: {
-                    'Content-Type': 'application/json',
-                },
-                body: JSON.stringify(payload)
-            });
+            if (settingKey === 'default_lora_root') {
+                const payload = {};
+                payload[settingKey] = value;
+                
+                const response = await fetch('/api/settings', {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json',
+                    },
+                    body: JSON.stringify(payload)
+                });
 
-            if (!response.ok) {
-                throw new Error('Failed to save setting');
+                if (!response.ok) {
+                    throw new Error('Failed to save setting');
+                }
+                
+                showToast(`Settings updated: ${settingKey.replace(/_/g, ' ')}`, 'success');
             }
             
-            showToast(`Settings updated: ${settingKey.replace(/_/g, ' ')}`, 'success');
+            // Apply frontend settings immediately
+            this.applyFrontendSettings();
+            
+            // Recalculate layout when display density changes
+            if (settingKey === 'display_density' && state.virtualScroller) {
+                state.virtualScroller.calculateLayout();
+                
+                let densityName = "Default";
+                if (value === 'medium') densityName = "Medium";
+                if (value === 'compact') densityName = "Compact";
+                
+                showToast(`Display Density set to ${densityName}`, 'success');
+            }
             
         } catch (error) {
             showToast('Failed to save setting: ' + error.message, 'error');
@@ -291,6 +341,37 @@ export class SettingsManager {
         }
     }
 
+    confirmClearCache() {
+        // Show confirmation modal
+        modalManager.showModal('clearCacheModal');
+    }
+
+    async executeClearCache() {
+        try {
+            // Call the API endpoint to clear cache files
+            const response = await fetch('/api/clear-cache', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                }
+            });
+
+            const result = await response.json();
+            
+            if (result.success) {
+                showToast('Cache files have been cleared successfully. Cache will rebuild on next action.', 'success');
+            } else {
+                showToast(`Failed to clear cache: ${result.error}`, 'error');
+            }
+            
+            // Close the confirmation modal
+            modalManager.closeModal('clearCacheModal');
+        } catch (error) {
+            showToast(`Error clearing cache: ${error.message}`, 'error');
+            modalManager.closeModal('clearCacheModal');
+        }
+    }
+
     async reloadContent() {
         if (this.currentPage === 'loras') {
             // Reload the loras without updating folders
@@ -400,8 +481,17 @@ export class SettingsManager {
             videoParent.replaceChild(videoClone, video);
         });
         
-        // For show_only_sfw, there's no immediate action needed as it affects content loading
-        // The setting will take effect on next reload
+        // Apply display density class to grid
+        const grid = document.querySelector('.card-grid');
+        if (grid) {
+            const density = state.global.settings.displayDensity || 'default';
+            
+            // Remove all density classes first
+            grid.classList.remove('default-density', 'medium-density', 'compact-density');
+            
+            // Add the appropriate density class
+            grid.classList.add(`${density}-density`);
+        }
     }
 }