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

This reverts commit 99b36442bb.

.gitignore

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

README.md

@@ -9,18 +9,30 @@
 A comprehensive toolset that streamlines organizing, downloading, and applying LoRA models in ComfyUI. With powerful features like recipe management, checkpoint organization, and one-click workflow integration, working with models becomes faster, smoother, and significantly easier. Access the interface at: `http://localhost:8188/loras`
 
 ![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)"
@@ -56,71 +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)
 
 ---
@@ -185,7 +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

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", {})
@@ -187,7 +284,7 @@ class MetadataProcessor:
                         sampler_params = metadata[SAMPLING][sampler_node_id].get("parameters", {})
                         params["sampler"] = sampler_params.get("sampler_name")
                     
-                    # 3. Trace guider input for CFGGuider, 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 and guider_node_id in prompt.original_prompt:
                         # Check if the guider node is a CFGGuider
@@ -207,21 +304,14 @@ class MetadataProcessor:
                             if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
                                 params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
                         else:
-                            # 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)
+                            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:
@@ -229,21 +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", "")
                 
@@ -273,13 +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:
@@ -289,7 +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,
@@ -391,11 +405,13 @@ 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

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/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/routes/api_routes.py

@@ -106,8 +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)

py/routes/checkpoints_routes.py

@@ -420,7 +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)

py/routes/misc_routes.py

@@ -4,13 +4,15 @@ import asyncio
 import json
 import time
 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
 
@@ -39,6 +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)
@@ -52,7 +57,59 @@ class MiscRoutes:
         
         # 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"""
@@ -354,6 +411,28 @@ class MiscRoutes:
             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
@@ -393,6 +472,13 @@ class MiscRoutes:
             
             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):
@@ -406,31 +492,10 @@ class MiscRoutes:
                 # 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)
+                        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)
@@ -506,35 +571,11 @@ class MiscRoutes:
                         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())
+                    # 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
@@ -777,7 +818,7 @@ class MiscRoutes:
         
         Expects a JSON body with:
         {
-            "node_ids": [123, 456], # List of node IDs to update
+            "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
         }
@@ -785,37 +826,59 @@ class MiscRoutes:
         try:
             # Parse the request body
             data = await request.json()
-            node_ids = data.get('node_ids', [])
+            node_ids = data.get('node_ids')
             lora_code = data.get('lora_code', '')
             mode = data.get('mode', 'append')
             
-            if not node_ids or not lora_code:
+            if not lora_code:
                 return web.json_response({
                     'success': False,
-                    'error': 'Missing node_ids or lora_code parameter'
+                    'error': 'Missing lora_code parameter'
                 }, status=400)
             
-            # Send the lora code update to each node
             results = []
-            for node_id in node_ids:
+            
+            # Desktop mode: no specific node_ids provided
+            if node_ids is None:
                 try:
-                    # Send the message to the frontend
+                    # Send broadcast message with id=-1 to all Lora Loader nodes
                     PromptServer.instance.send_sync("lora_code_update", {
-                        "id": node_id,
+                        "id": -1,
                         "lora_code": lora_code,
                         "mode": mode
                     })
                     results.append({
-                        'node_id': node_id,
+                        'node_id': 'broadcast',
                         'success': True
                     })
                 except Exception as e:
-                    logger.error(f"Error sending lora code to node {node_id}: {e}")
+                    logger.error(f"Error broadcasting lora code: {e}")
                     results.append({
-                        'node_id': node_id,
+                        '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,
@@ -828,3 +891,63 @@ class MiscRoutes:
                 '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/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
@@ -281,17 +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/model_scanner.py

@@ -5,8 +5,7 @@ import asyncio
 import time
 import shutil
 from typing import List, Dict, Optional, Type, Set
-
-from ..utils.model_utils import determine_base_model
+import msgpack  # Add MessagePack import for efficient serialization
 
 from ..utils.models import BaseModelMetadata
 from ..config import config
@@ -19,6 +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"""
     
@@ -41,6 +43,7 @@ class ModelScanner:
         self._tags_count = {}  # Dictionary to store tag counts
         self._is_initializing = False  # Flag to track initialization state
         self._excluded_models = []  # List to track excluded models
+        self._dirs_last_modified = {}  # Track directory modification times
         
         # Register this service
         asyncio.create_task(self._register_service())
@@ -50,6 +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:
@@ -68,7 +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,
@@ -113,6 +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({
@@ -282,8 +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:
@@ -295,10 +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()
@@ -486,6 +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)
@@ -539,113 +757,69 @@ class ModelScanner:
     # Common methods shared between scanners
     async def _process_model_file(self, file_path: str, root_path: str) -> Dict:
         """Process a single model file and return its metadata"""
-        needs_metadata_update = False
-        original_save_metadata = save_metadata
+        metadata = await load_metadata(file_path, self.model_class)
         
-        # Temporarily override save_metadata to prevent intermediate writes
-        async def no_op_save(*args, **kwargs):
-            nonlocal needs_metadata_update
-            needs_metadata_update = True
-            return None
-            
-        # Use a context manager to temporarily replace save_metadata
-        from contextlib import contextmanager
-        
-        @contextmanager
-        def prevent_metadata_writes():
-            nonlocal needs_metadata_update
-            # Replace the function temporarily
-            import sys
-            from .. import utils
-            original = utils.file_utils.save_metadata
-            utils.file_utils.save_metadata = no_op_save
-            try:
-                yield
-            finally:
-                # Restore the original function
-                utils.file_utils.save_metadata = original
-        
-        # Process with write prevention
-        with prevent_metadata_writes():
-            metadata = await load_metadata(file_path, self.model_class)
-            
-            if metadata is None:
+        if metadata is None:
+            civitai_info_path = f"{os.path.splitext(file_path)[0]}.civitai.info"
+            if os.path.exists(civitai_info_path):
+                try:
+                    with open(civitai_info_path, 'r', encoding='utf-8') as f:
+                        version_info = json.load(f)
+                    
+                    file_info = next((f for f in version_info.get('files', []) if f.get('primary')), None)
+                    if file_info:
+                        file_name = os.path.splitext(os.path.basename(file_path))[0]
+                        file_info['name'] = file_name
+                    
+                        metadata = self.model_class.from_civitai_info(version_info, file_info, file_path)
+                        metadata.preview_url = find_preview_file(file_name, os.path.dirname(file_path))
+                        await save_metadata(file_path, metadata)
+                        logger.debug(f"Created metadata from .civitai.info for {file_path}")
+                except Exception as e:
+                    logger.error(f"Error creating metadata from .civitai.info for {file_path}: {e}")
+        else:
+            # Check if metadata exists but civitai field is empty - try to restore from civitai.info
+            if metadata.civitai is None or metadata.civitai == {}:
                 civitai_info_path = f"{os.path.splitext(file_path)[0]}.civitai.info"
                 if os.path.exists(civitai_info_path):
                     try:
                         with open(civitai_info_path, 'r', encoding='utf-8') as f:
                             version_info = json.load(f)
                         
-                        file_info = next((f for f in version_info.get('files', []) if f.get('primary')), None)
-                        if file_info:
-                            file_name = os.path.splitext(os.path.basename(file_path))[0]
-                            file_info['name'] = file_name
+                        logger.debug(f"Restoring missing civitai data from .civitai.info for {file_path}")
+                        metadata.civitai = version_info
                         
-                            metadata = self.model_class.from_civitai_info(version_info, file_info, file_path)
-                            metadata.preview_url = find_preview_file(file_name, os.path.dirname(file_path))
-                            needs_metadata_update = True
-                            logger.debug(f"Created metadata from .civitai.info for {file_path}")
+                        # Ensure tags are also updated if they're missing
+                        if (not metadata.tags or len(metadata.tags) == 0) and 'model' in version_info:
+                            if 'tags' in version_info['model']:
+                                metadata.tags = version_info['model']['tags']
+                        
+                        # Also restore description if missing
+                        if (not metadata.modelDescription or metadata.modelDescription == "") and 'model' in version_info:
+                            if 'description' in version_info['model']:
+                                metadata.modelDescription = version_info['model']['description']
+                        
+                        # Save the updated metadata
+                        await save_metadata(file_path, metadata)
+                        logger.debug(f"Updated metadata with civitai info for {file_path}")
                     except Exception as e:
-                        logger.error(f"Error creating metadata from .civitai.info for {file_path}: {e}")
-            else:
-                # Check if metadata exists but civitai field is empty - try to restore from civitai.info
-                if metadata.civitai is None or metadata.civitai == {}:
-                    civitai_info_path = f"{os.path.splitext(file_path)[0]}.civitai.info"
-                    if os.path.exists(civitai_info_path):
-                        try:
-                            with open(civitai_info_path, 'r', encoding='utf-8') as f:
-                                version_info = json.load(f)
-                            
-                            logger.debug(f"Restoring missing civitai data from .civitai.info for {file_path}")
-                            metadata.civitai = version_info
-                            needs_metadata_update = True
-                            
-                            # Ensure tags are also updated if they're missing
-                            if (not metadata.tags or len(metadata.tags) == 0) and 'model' in version_info:
-                                if 'tags' in version_info['model']:
-                                    metadata.tags = version_info['model']['tags']
-                                    needs_metadata_update = True
-                            
-                            # Also restore description if missing
-                            if (not metadata.modelDescription or metadata.modelDescription == "") and 'model' in version_info:
-                                if 'description' in version_info['model']:
-                                    metadata.modelDescription = version_info['model']['description']
-                                    needs_metadata_update = True
-                        except Exception as e:
-                            logger.error(f"Error restoring civitai data from .civitai.info for {file_path}: {e}")
-                
-                # Check if base_model is consistent with civitai baseModel
-                if metadata.civitai and 'baseModel' in metadata.civitai:
-                    civitai_base_model = determine_base_model(metadata.civitai['baseModel'])
-                    if metadata.base_model != civitai_base_model:
-                        logger.debug(f"Updating base_model from {metadata.base_model} to {civitai_base_model} for {file_path}")
-                        metadata.base_model = civitai_base_model
-                        needs_metadata_update = True
+                        logger.error(f"Error restoring civitai data from .civitai.info for {file_path}: {e}")
             
-            if metadata is None:
-                metadata = await self._get_file_info(file_path)
-                needs_metadata_update = True
+        if metadata is None:
+            metadata = await self._get_file_info(file_path)
         
-        # Continue processing
-        model_data = metadata.to_dict() if metadata else None
+        model_data = metadata.to_dict()
         
         # Skip excluded models
-        if model_data and model_data.get('exclude', False):
+        if model_data.get('exclude', False):
             self._excluded_models.append(model_data['file_path'])
             return None
             
-        # Fetch missing metadata from Civitai if needed (with write prevention)
-        with prevent_metadata_writes():
-            await self._fetch_missing_metadata(file_path, model_data)
-            
+        await self._fetch_missing_metadata(file_path, model_data)
         rel_path = os.path.relpath(file_path, root_path)
         folder = os.path.dirname(rel_path)
         model_data['folder'] = folder.replace(os.path.sep, '/')
         
-        # Only save metadata if needed
-        if needs_metadata_update and metadata:
-            await original_save_metadata(file_path, metadata)
-        
         return model_data
 
     async def _fetch_missing_metadata(self, file_path: str, model_data: Dict) -> None:
@@ -697,9 +871,9 @@ class ModelScanner:
 
                     model_data['civitai']['creator'] = model_metadata['creator']
                     
-                    # Create a metadata object and save it using save_metadata
-                    metadata_obj = self.model_class.from_dict(model_data)
-                    await save_metadata(file_path, metadata_obj)
+                    metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+                    with open(metadata_path, 'w', encoding='utf-8') as f:
+                        json.dump(model_data, f, indent=2, ensure_ascii=False)
         except Exception as e:
             logger.error(f"Failed to update metadata from Civitai for {file_path}: {e}")
 
@@ -744,6 +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"""
@@ -789,25 +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)
             
@@ -885,6 +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:
@@ -977,4 +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/utils/file_utils.py

@@ -245,7 +245,8 @@ async def load_metadata(file_path: str, model_class: Type[BaseModelMetadata] = L
                     # needs_update = True
 
                 if needs_update:
-                    save_metadata(file_path, model_class.from_dict(data))
+                    with open(metadata_path, 'w', encoding='utf-8') as f:
+                        json.dump(data, f, indent=2, ensure_ascii=False)
                 
                 return model_class.from_dict(data)
                 

py/utils/routes_common.py

@@ -51,7 +51,7 @@ 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']
@@ -144,6 +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
@@ -167,21 +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()
@@ -195,7 +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}
 

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 = "0.8.15"
+version = "0.8.16"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",
@@ -14,7 +14,8 @@ dependencies = [
     "olefile", # for getting rid of warning message
     "requests",
     "toml",
-    "natsort"
+    "natsort",
+    "msgpack"
 ]
 
 [project.urls]

requirements.txt

@@ -10,4 +10,5 @@ requests
 toml
 numpy
 torch
-natsort
+natsort
+msgpack

static/css/components/card.css

@@ -7,9 +7,11 @@
     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 */
+    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 {
@@ -84,23 +86,39 @@
     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-mode .model-name {
+.compact-density .model-name {
     font-size: 0.9em;
     max-height: 2.4em;
 }
 
-.compact-mode .base-model-label {
+.compact-density .base-model-label {
     font-size: 0.8em;
     max-width: 110px;
 }
 
-.compact-mode .card-actions i {
+.compact-density .card-actions i {
     font-size: 0.95em;
     padding: 3px;
 }
 
-.compact-mode .model-info {
+.compact-density .model-info {
     padding-bottom: 2px;
 }
 
@@ -448,10 +466,12 @@
     display: block;
     position: relative;
     margin: 0 auto;
-    padding: 6px 0; /* Add top/bottom padding equivalent to card padding */
+    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 */

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;
@@ -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

@@ -151,7 +151,7 @@ body.modal-open {
 
 .modal-content h2 {
     color: var(--text-color);
-    margin-bottom: var(--space-2);
+    margin-bottom: var(--space-1);
     font-size: 1.5em;
 }
 
@@ -682,4 +682,15 @@ input:checked + .toggle-slider:before {
 
 [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/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 {
@@ -333,6 +333,66 @@
     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;
@@ -378,4 +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/js/api/baseModelApi.js

@@ -499,13 +499,18 @@ 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}`);
@@ -515,10 +520,10 @@ export async function refreshModels(options = {}) {
             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();

static/js/api/checkpointApi.js

@@ -76,11 +76,12 @@ export async function resetAndReload(updateFolders = false) {
 }
 
 // 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
     });
 }
 
@@ -119,22 +120,30 @@ 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();
     }
-    
-    return response.json();
 }
 
 /**
@@ -144,4 +153,40 @@ export async function saveModelMetadata(filePath, data) {
  */
 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();
+    }
 }

static/js/api/loraApi.js

@@ -21,22 +21,30 @@ import { state, getCurrentPageState } from '../state/index.js';
  * @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();
 }
 
 /**
@@ -143,11 +151,12 @@ export async function resetAndReload(updateFolders = false) {
     }
 }
 
-export async function refreshLoras() {
+export async function refreshLoras(fullRebuild = false) {
     return baseRefreshModels({
         modelType: 'lora',
         scanEndpoint: '/api/loras/scan',
-        resetAndReloadFunction: resetAndReload
+        resetAndReloadFunction: resetAndReload,
+        fullRebuild: fullRebuild
     });
 }
 
@@ -172,4 +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/components/CheckpointCard.js

@@ -1,4 +1,4 @@
-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';
@@ -115,8 +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>
@@ -272,6 +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) {

static/js/components/ContextMenu/CheckpointContextMenu.js

@@ -1,6 +1,6 @@
 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';
@@ -23,10 +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

static/js/components/ContextMenu/LoraContextMenu.js

@@ -1,9 +1,9 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
-import { refreshSingleLoraMetadata, saveModelMetadata } from '../../api/loraApi.js';
-import { showToast, getNSFWLevelName, copyToClipboard, sendLoraToWorkflow } 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 } from '../../utils/modalUtils.js';
+import { showExcludeModal, showDeleteModal } from '../../utils/modalUtils.js';
 
 export class LoraContextMenu extends BaseContextMenu {
     constructor() {
@@ -47,10 +47,16 @@ export class LoraContextMenu extends BaseContextMenu {
                 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);

static/js/components/LoraCard.js

@@ -1,4 +1,4 @@
-import { showToast, openCivitai, copyToClipboard, sendLoraToWorkflow } 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';
@@ -57,9 +57,9 @@ function handleLoraCardEvent(event) {
         return;
     }
     
-    if (event.target.closest('.fa-trash')) {
+    if (event.target.closest('.fa-copy')) {
         event.stopPropagation();
-        showDeleteModal(card.dataset.filepath);
+        copyLoraSyntax(card);
         return;
     }
     
@@ -69,6 +69,12 @@ function handleLoraCardEvent(event) {
         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
@@ -182,6 +188,15 @@ async function sendLoraToComfyUI(card, replaceMode) {
     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');
     card.className = 'lora-card';
@@ -273,8 +288,8 @@ export function createLoraCard(lora) {
                     <i class="fas fa-paper-plane" 
                        title="Send to ComfyUI (Click: Append, Shift+Click: Replace)">
                     </i>
-                    <i class="fas fa-trash" 
-                       title="Delete Model">
+                    <i class="fas fa-copy" 
+                       title="Copy LoRA Syntax">
                     </i>
                 </div>
             </div>
@@ -291,14 +306,14 @@ 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>
     `;
-
+    
     // Add a special class for virtual scroll positioning if needed
     if (state.virtualScroller) {
         card.classList.add('virtual-scroll-item');

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', () => {
@@ -303,6 +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', () => {
@@ -400,19 +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/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

@@ -36,8 +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

static/js/components/controls/PageControls.js

@@ -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/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');
+    }
 }
 
 /**
@@ -410,19 +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/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/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

@@ -170,6 +170,18 @@ export class ModalManager {
             });
         }
         
+        // 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) {
@@ -233,7 +245,7 @@ export class ModalManager {
         // Store current scroll position before showing modal
         this.scrollPosition = window.scrollY;
 
-        if (id === 'deleteModal' || id === 'excludeModal' || id === 'duplicateDeleteModal') {
+        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

@@ -31,6 +31,16 @@ export class SettingsManager {
         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() {
@@ -82,10 +92,10 @@ export class SettingsManager {
             autoplayOnHoverCheckbox.checked = state.global.settings.autoplayOnHover || false;
         }
         
-        // Set compact mode setting
-        const compactModeCheckbox = document.getElementById('compactMode');
-        if (compactModeCheckbox) {
-            compactModeCheckbox.checked = state.global.settings.compactMode || false;
+        // Set display density setting
+        const displayDensitySelect = document.getElementById('displayDensity');
+        if (displayDensitySelect) {
+            displayDensitySelect.value = state.global.settings.displayDensity || 'default';
         }
 
         // Load default lora root
@@ -219,6 +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;
@@ -229,22 +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');
@@ -310,6 +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
@@ -419,8 +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`);
+        }
     }
 }
 

static/js/utils/VirtualScroller.js

@@ -15,6 +15,10 @@ export class VirtualScroller {
         this.itemAspectRatio = 896/1152; // Aspect ratio of cards
         this.rowGap = options.rowGap || 20; // Add vertical gap between rows (default 20px)
         
+        // Add container padding properties
+        this.containerPaddingTop = options.containerPaddingTop || 4; // Default top padding from CSS
+        this.containerPaddingBottom = options.containerPaddingBottom || 4; // Default bottom padding from CSS
+        
         // Add data windowing enable/disable flag
         this.enableDataWindowing = options.enableDataWindowing !== undefined ? options.enableDataWindowing : false;
 
@@ -74,6 +78,10 @@ export class VirtualScroller {
         this.gridElement.style.position = 'relative';
         this.gridElement.style.minHeight = '0';
         
+        // Apply padding directly to ensure consistency
+        this.gridElement.style.paddingTop = `${this.containerPaddingTop}px`;
+        this.gridElement.style.paddingBottom = `${this.containerPaddingBottom}px`;
+        
         // Place the spacer inside the grid container
         this.gridElement.appendChild(this.spacerElement);
     }
@@ -88,22 +96,40 @@ export class VirtualScroller {
         // Calculate available content width (excluding padding)
         const availableContentWidth = containerWidth - paddingLeft - paddingRight;
         
-        // Get compact mode setting
-        const compactMode = state.global.settings?.compactMode || false;
+        // Get display density setting
+        const displayDensity = state.global.settings?.displayDensity || 'default';
         
         // Set exact column counts and grid widths to match CSS container widths
         let maxColumns, maxGridWidth;
         
-        // Match exact column counts and CSS container width values
+        // Match exact column counts and CSS container width values based on density
         if (window.innerWidth >= 3000) { // 4K
-            maxColumns = compactMode ? 10 : 8;
+            if (displayDensity === 'default') {
+                maxColumns = 8;
+            } else if (displayDensity === 'medium') {
+                maxColumns = 9;
+            } else { // compact
+                maxColumns = 10;
+            }
             maxGridWidth = 2400; // Match exact CSS container width for 4K
         } else if (window.innerWidth >= 2000) { // 2K/1440p
-            maxColumns = compactMode ? 8 : 6;
+            if (displayDensity === 'default') {
+                maxColumns = 6;
+            } else if (displayDensity === 'medium') {
+                maxColumns = 7;
+            } else { // compact
+                maxColumns = 8;
+            }
             maxGridWidth = 1800; // Match exact CSS container width for 2K
         } else {
             // 1080p
-            maxColumns = compactMode ? 7 : 5;
+            if (displayDensity === 'default') {
+                maxColumns = 5;
+            } else if (displayDensity === 'medium') {
+                maxColumns = 6;
+            } else { // compact
+                maxColumns = 7;
+            }
             maxGridWidth = 1400; // Match exact CSS container width for 1080p
         }
         
@@ -145,7 +171,7 @@ export class VirtualScroller {
             leftOffset: this.leftOffset,
             paddingLeft,
             paddingRight,
-            compactMode,
+            displayDensity,
             maxColumns,
             baseCardWidth,
             rowGap: this.rowGap
@@ -154,12 +180,9 @@ export class VirtualScroller {
         // Update grid element max-width to match available width
         this.gridElement.style.maxWidth = `${actualGridWidth}px`;
         
-        // Add or remove compact-mode class for style adjustments
-        if (compactMode) {
-            this.gridElement.classList.add('compact-mode');
-        } else {
-            this.gridElement.classList.remove('compact-mode');
-        }
+        // Add or remove density classes for style adjustments
+        this.gridElement.classList.remove('default-density', 'medium-density', 'compact-density');
+        this.gridElement.classList.add(`${displayDensity}-density`);
         
         // Update spacer height
         this.updateSpacerHeight();
@@ -321,8 +344,11 @@ export class VirtualScroller {
         // Add row gaps to the total height calculation
         const totalHeight = totalRows * this.itemHeight + (totalRows - 1) * this.rowGap;
         
+        // Include container padding in the total height
+        const spacerHeight = totalHeight + this.containerPaddingTop + this.containerPaddingBottom;
+        
         // Update spacer height to represent all items
-        this.spacerElement.style.height = `${totalHeight}px`;
+        this.spacerElement.style.height = `${spacerHeight}px`;
     }
 
     getVisibleRange() {
@@ -450,7 +476,8 @@ export class VirtualScroller {
         const col = index % this.columnsCount;
         
         // Calculate precise positions with row gap included
-        const topPos = row * (this.itemHeight + this.rowGap);
+        // Add the top padding to account for container padding
+        const topPos = this.containerPaddingTop + (row * (this.itemHeight + this.rowGap));
         
         // Position correctly with leftOffset (no need to add padding as absolute
         // positioning is already relative to the padding edge of the container)

static/js/utils/infiniteScroll.js

@@ -122,6 +122,8 @@ async function initializeVirtualScroll(pageType) {
             fetchItemsFn: fetchDataFn,
             pageSize: 100,
             rowGap: 20,
+            containerPaddingTop: 4,
+            containerPaddingBottom: 4,
             enableDataWindowing: false // Explicitly set to false to disable data windowing
         });
         

static/js/utils/uiHelpers.js

@@ -362,29 +362,31 @@ export function getNSFWLevelName(level) {
  */
 export async function sendLoraToWorkflow(loraSyntax, replaceMode = false, syntaxType = 'lora') {
   try {
+    let loraNodes = [];
+    let isDesktopMode = false;
+    
     // Get the current workflow from localStorage
     const workflowData = localStorage.getItem('workflow');
-    if (!workflowData) {
-      showToast('No active workflow found', 'error');
-      return false;
-    }
-    
-    // Parse the workflow JSON
-    const workflow = JSON.parse(workflowData);
-    
-    // Find all Lora Loader (LoraManager) nodes
-    const loraNodes = [];
-    if (workflow.nodes && Array.isArray(workflow.nodes)) {
-      for (const node of workflow.nodes) {
-        if (node.type === "Lora Loader (LoraManager)") {
-          loraNodes.push(node.id);
+    if (workflowData) {
+      // Web browser mode - extract node IDs from workflow
+      const workflow = JSON.parse(workflowData);
+      
+      // Find all Lora Loader (LoraManager) nodes
+      if (workflow.nodes && Array.isArray(workflow.nodes)) {
+        for (const node of workflow.nodes) {
+          if (node.type === "Lora Loader (LoraManager)") {
+            loraNodes.push(node.id);
+          }
         }
       }
-    }
-    
-    if (loraNodes.length === 0) {
-      showToast('No Lora Loader nodes found in the workflow', 'warning');
-      return false;
+      
+      if (loraNodes.length === 0) {
+        showToast('No Lora Loader nodes found in the workflow', 'warning');
+        return false;
+      }
+    } else {
+      // ComfyUI Desktop mode - don't specify node IDs and let backend handle it
+      isDesktopMode = true;
     }
     
     // Call the backend API to update the lora code
@@ -394,7 +396,7 @@ export async function sendLoraToWorkflow(loraSyntax, replaceMode = false, syntax
         'Content-Type': 'application/json'
       },
       body: JSON.stringify({
-        node_ids: loraNodes,
+        node_ids: isDesktopMode ? undefined : loraNodes,
         lora_code: loraSyntax,
         mode: replaceMode ? 'replace' : 'append'
       })
@@ -419,4 +421,36 @@ export async function sendLoraToWorkflow(loraSyntax, replaceMode = false, syntax
     showToast(`Failed to send ${syntaxType === 'recipe' ? 'recipe' : 'LoRA'} to workflow`, 'error');
     return false;
   }
+}
+
+/**
+ * Opens the example images folder for a specific model
+ * @param {string} modelHash - The SHA256 hash of the model
+ */
+export async function openExampleImagesFolder(modelHash) {
+  try {
+    const response = await fetch('/api/open-example-images-folder', {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json'
+      },
+      body: JSON.stringify({
+        model_hash: modelHash
+      })
+    });
+    
+    const result = await response.json();
+    
+    if (result.success) {
+      showToast('Opening example images folder', 'success');
+      return true;
+    } else {
+      showToast(result.error || 'Failed to open example images folder', 'error');
+      return false;
+    }
+  } catch (error) {
+    console.error('Failed to open example images folder:', error);
+    showToast('Failed to open example images folder', 'error');
+    return false;
+  }
 }

templates/checkpoints.html

@@ -19,7 +19,8 @@
     <div class="context-menu-item" data-action="civitai"><i class="fas fa-external-link-alt"></i> View on CivitAI</div>
     <div class="context-menu-item" data-action="refresh-metadata"><i class="fas fa-sync"></i> Refresh Civitai Data</div>
     <div class="context-menu-item" data-action="copyname"><i class="fas fa-copy"></i> Copy Model Filename</div>
-    <div class="context-menu-item" data-action="preview"><i class="fas fa-image"></i> Replace Preview</div>
+    <div class="context-menu-item" data-action="preview"><i class="fas fa-folder-open"></i> Open Examples Folder</div>
+    <div class="context-menu-item" data-action="replace-preview"><i class="fas fa-image"></i> Replace Preview</div>
     <div class="context-menu-item" data-action="set-nsfw"><i class="fas fa-exclamation-triangle"></i> Set Content Rating</div>
     <div class="context-menu-separator"></div>
     <div class="context-menu-item" data-action="move"><i class="fas fa-folder-open"></i> Move to Folder</div>

templates/components/context_menu.html

@@ -18,6 +18,9 @@
         <i class="fas fa-exchange-alt"></i> Send to Workflow (Replace)
     </div>
     <div class="context-menu-item" data-action="preview">
+        <i class="fas fa-folder-open"></i> Open Examples Folder
+    </div>
+    <div class="context-menu-item" data-action="replace-preview">
         <i class="fas fa-image"></i> Replace Preview
     </div>
     <div class="context-menu-item" data-action="set-nsfw">

templates/components/controls.html

@@ -15,8 +15,19 @@
                 <option value="date">Date</option>
                 </select>
             </div>
-            <div title="Refresh model list" class="control-group">
-                <button data-action="refresh"><i class="fas fa-sync"></i> Refresh</button>
+            <div title="Refresh model list" class="control-group dropdown-group">
+                <button data-action="refresh" class="dropdown-main"><i class="fas fa-sync"></i> Refresh</button>
+                <button class="dropdown-toggle" aria-label="Show refresh options">
+                    <i class="fas fa-caret-down"></i>
+                </button>
+                <div class="dropdown-menu">
+                    <div class="dropdown-item" data-action="quick-refresh">
+                        <i class="fas fa-bolt"></i> Quick Refresh (incremental)
+                    </div>
+                    <div class="dropdown-item" data-action="full-rebuild">
+                        <i class="fas fa-tools"></i> Full Rebuild (complete)
+                    </div>
+                </div>
             </div>
             
             <div class="control-group">

templates/components/modals.html

@@ -39,6 +39,21 @@
     </div>
 </div>
 
+<!-- Cache Clear Confirmation Modal -->
+<div id="clearCacheModal" class="modal delete-modal">
+    <div class="modal-content delete-modal-content">
+        <h2>Clear Cache Files</h2>
+        <p class="delete-message">Are you sure you want to clear all cache files?</p>
+        <div class="delete-model-info">
+            <p>This will remove all cached model data. The system will need to rebuild the cache on next startup, which may take some time depending on your model collection size.</p>
+        </div>
+        <div class="modal-actions">
+            <button class="cancel-btn" onclick="modalManager.closeModal('clearCacheModal')">Cancel</button>
+            <button class="delete-btn" onclick="settingsManager.executeClearCache()">Clear Cache</button>
+        </div>
+    </div>
+</div>
+
 <!-- Settings Modal -->
 <div id="settingsModal" class="modal">
     <div class="modal-content settings-modal">
@@ -161,18 +176,47 @@
                 <div class="setting-item">
                     <div class="setting-row">
                         <div class="setting-info">
-                            <label for="compactMode">Compact Mode</label>
+                            <label for="displayDensity">Display Density</label>
                         </div>
-                        <div class="setting-control">
-                            <label class="toggle-switch">
-                                <input type="checkbox" id="compactMode" 
-                                       onchange="settingsManager.saveToggleSetting('compactMode', 'compact_mode')">
-                                <span class="toggle-slider"></span>
-                            </label>
+                        <div class="setting-control select-control">
+                            <select id="displayDensity" onchange="settingsManager.saveSelectSetting('displayDensity', 'display_density')">
+                                <option value="default">Default</option>
+                                <option value="medium">Medium</option>
+                                <option value="compact">Compact</option>
+                            </select>
                         </div>
                     </div>
                     <div class="input-help">
-                        Display more cards per row (7 on 1080p, 8 on 2K, 10 on 4K). <span class="warning-text">Warning: May cause performance issues (lag and lower FPS) on systems with limited resources.</span>
+                        Choose how many cards to display per row:
+                        <ul class="density-description">
+                            <li><strong>Default:</strong> 5 (1080p), 6 (2K), 8 (4K)</li>
+                            <li><strong>Medium:</strong> 6 (1080p), 7 (2K), 9 (4K)</li>
+                            <li><strong>Compact:</strong> 7 (1080p), 8 (2K), 10 (4K)</li>
+                        </ul>
+                        <span class="warning-text">Warning: Higher densities may cause performance issues on systems with limited resources.</span>
+                    </div>
+                </div>
+            </div>
+            
+            <!-- Add Cache Management Section -->
+            <div class="settings-section">
+                <h3>Cache Management</h3>
+                
+                <div class="setting-item">
+                    <div class="setting-row">
+                        <div class="setting-info">
+                            <label>Clear Cache Files</label>
+                        </div>
+                        <div class="setting-control">
+                            <button id="clearCacheBtn" class="secondary-btn" onclick="settingsManager.confirmClearCache()">
+                                <i class="fas fa-trash"></i>
+                                <span>Clear Cache</span>
+                            </button>
+                        </div>
+                    </div>
+                    <div class="input-help">
+                        Removes cached model data. System will rebuild cache on next startup or when triggered manually.
+                        <span class="warning-text">May cause temporary performance impact during rebuild.</span>
                     </div>
                 </div>
             </div>

update_logs.md

@@ -2,6 +2,71 @@
 
 ---
 
+### 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
+
 ### v0.7.37
 * Added NSFW content control settings (blur mature content and SFW-only filter)
 * Implemented intelligent blur effects for previews and showcase media

web/comfyui/lora_loader.js

@@ -49,12 +49,42 @@ app.registerExtension({
     
     // Handle lora code updates from Python
     handleLoraCodeUpdate(id, loraCode, mode) {
+        // Handle broadcast mode (for Desktop/non-browser support)
+        if (id === -1) {
+            // Find all Lora Loader nodes in the current graph
+            const loraLoaderNodes = [];
+            for (const nodeId in app.graph._nodes_by_id) {
+                const node = app.graph._nodes_by_id[nodeId];
+                if (node.comfyClass === "Lora Loader (LoraManager)") {
+                    loraLoaderNodes.push(node);
+                }
+            }
+            
+            // Update each Lora Loader node found
+            if (loraLoaderNodes.length > 0) {
+                loraLoaderNodes.forEach(node => {
+                    this.updateNodeLoraCode(node, loraCode, mode);
+                });
+                console.log(`Updated ${loraLoaderNodes.length} Lora Loader nodes in broadcast mode`);
+            } else {
+                console.warn("No Lora Loader nodes found in the workflow for broadcast update");
+            }
+            
+            return;
+        }
+        
+        // Standard mode - update a specific node
         const node = app.graph.getNodeById(+id);
         if (!node || node.comfyClass !== "Lora Loader (LoraManager)") {
             console.warn("Node not found or not a LoraLoader:", id);
             return;
         }
         
+        this.updateNodeLoraCode(node, loraCode, mode);
+    },
+    
+    // Helper method to update a single node's lora code
+    updateNodeLoraCode(node, loraCode, mode) {
         // Update the input widget with new lora code
         const inputWidget = node.widgets[0];
         if (!inputWidget) return;

web/comfyui/usage_stats.js

@@ -4,7 +4,7 @@ import { api } from "../../scripts/api.js";
 
 // Register the extension
 app.registerExtension({
-    name: "ComfyUI-Lora-Manager.UsageStats",
+    name: "Lora-Manager.UsageStats",
     
     init() {
         // Listen for successful executions