feat: update release notes and version to 0.8.26, adding creator search and enhancing node usability

refactor: consolidate model API client creation into a factory function for better maintainability
feat: implement move operations for LoRAs and handle unsupported operations for Checkpoints and Embeddings

README.md

@@ -34,6 +34,41 @@ Enhance your Civitai browsing experience with our companion browser extension! S
 
 ## Release Notes
 
+### v0.8.26
+* **Creator Search Option**  
+  - Added ability to search models by creator name, making it easier to find models from specific authors.
+* **Enhanced Node Usability**  
+  - Improved user experience for Lora Loader, Lora Stacker, and WanVideo Lora Select nodes by fixing the maximum height of the text input area. Users can now freely and conveniently adjust the LoRA region within these nodes.
+* **Compatibility Fixes**  
+  - Resolved compatibility issues with ComfyUI and certain custom nodes, including ComfyUI-Custom-Scripts, ensuring smoother integration and operation.
+
+### v0.8.25
+* **LoRA List Reordering**  
+  - Drag & Drop: Easily rearrange LoRA entries using the drag handle.
+  - Keyboard Shortcuts:  
+    - Arrow keys: Navigate between LoRAs  
+    - Ctrl/Cmd + Arrow: Move selected LoRA up/down  
+    - Ctrl/Cmd + Home/End: Move selected LoRA to top/bottom  
+    - Delete/Backspace: Remove selected LoRA  
+  - Context Menu: Right-click for quick actions like Move Up, Move Down, Move to Top, Move to Bottom.
+* **Bulk Operations for Checkpoints & Embeddings**  
+  - Bulk Mode: Select multiple checkpoints or embeddings for batch actions.
+  - Bulk Refresh: Update Civitai metadata for selected models.
+  - Bulk Delete: Remove multiple models at once.
+  - Bulk Move (Embeddings): Move selected embeddings to a different folder.
+* **New Setting: Auto Download Example Images**  
+  - Automatically fetch example images for models missing previews (requires download location to be set). Enabled by default.
+* **General Improvements**  
+  - Various user experience enhancements and stability fixes.
+
+### v0.8.22
+* **Embeddings Management** - Added Embeddings page for comprehensive embedding model management.
+* **Advanced Sorting Options** - Introduced flexible sorting controls, allowing sorting by name, added date, or file size in both ascending and descending order.
+* **Custom Download Path Templates & Base Model Mapping** - Implemented UI settings for configuring download path templates and base model path mappings, allowing customized model organization and storage location when downloading models via LM Civitai Extension.
+* **LM Civitai Extension Enhancements** - Improved concurrent download performance and stability, with new support for canceling active downloads directly from the extension interface.
+* **Update Feature** - Added update functionality, allowing users to update LoRA Manager to the latest release version directly from the LoRA Manager UI.
+* **Bulk Operations: Refresh All** - Added bulk refresh functionality, allowing users to update Civitai metadata across multiple LoRAs.
+
 ### v0.8.20
 * **LM Civitai Extension** - Released [browser extension through Chrome Web Store](https://chromewebstore.google.com/detail/lm-civitai-extension/capigligggeijgmocnaflanlbghnamgm?utm_source=item-share-cb) that works seamlessly with LoRA Manager to enhance Civitai browsing experience, showing which models are already in your local library, enabling one-click downloads, and providing queue and parallel download support
 * **Enhanced Lora Loader** - Added support for nunchaku, improving convenience when working with ComfyUI-nunchaku workflows, plus new template workflows for quick onboarding
@@ -62,52 +97,6 @@ Enhance your Civitai browsing experience with our companion browser extension! S
 * **Intelligent Word Suggestions** - Implemented smart trigger word suggestions by reading class tokens and tag frequency from safetensors files, displaying recommendations when editing trigger words
 * **Model Version Management** - Added "Re-link to CivitAI" context menu option for connecting models to different CivitAI versions when needed
 
-### v0.8.16
-* **Dramatic Startup Speed Improvement** - Added cache serialization mechanism for significantly faster loading times, especially beneficial for large model collections
-* **Enhanced Refresh Options** - Extended functionality with "Full Rebuild (complete)" option alongside "Quick Refresh (incremental)" to fix potential memory cache issues without requiring application restart
-* **Customizable Display Density** - Replaced compact mode with adjustable display density settings for personalized layout customization
-* **Model Creator Information** - Added creator details to model information panels for better attribution
-* **Improved WebP Support** - Enhanced Save Image node with workflow embedding capability for WebP format images
-* **Direct Example Access** - Added "Open Example Images Folder" button to card interfaces for convenient browsing of downloaded model examples
-* **Enhanced Compatibility** - Full ComfyUI Desktop support for "Send lora or recipe to workflow" functionality
-* **Cache Management** - Added settings to clear existing cache files when needed
-* **Bug Fixes & Stability** - Various improvements for overall reliability and performance
-
-### v0.8.15
-* **Enhanced One-Click Integration** - Replaced copy button with direct send button allowing LoRAs/recipes to be sent directly to your current ComfyUI workflow without needing to paste
-* **Flexible Workflow Integration** - Click to append LoRAs/recipes to existing loader nodes or Shift+click to replace content, with additional right-click menu options for "Send to Workflow (Append)" or "Send to Workflow (Replace)"
-* **Improved LoRA Loader Controls** - Added header drag functionality for proportional strength adjustment of all LoRAs simultaneously (including CLIP strengths when expanded)
-* **Keyboard Navigation Support** - Implemented Page Up/Down for page scrolling, Home key to jump to top, and End key to jump to bottom for faster browsing through large collections
-
-### v0.8.14
-* **Virtualized Scrolling** - Completely rebuilt rendering mechanism for smooth browsing with no lag or freezing, now supporting virtually unlimited model collections with optimized layouts for large displays, improving space utilization and user experience
-* **Compact Display Mode** - Added space-efficient view option that displays more cards per row (7 on 1080p, 8 on 2K, 10 on 4K)
-* **Enhanced LoRA Node Functionality** - Comprehensive improvements to LoRA loader/stacker nodes including real-time trigger word updates (reflecting any change anywhere in the LoRA chain for precise updates) and expanded context menu with "Copy Notes" and "Copy Trigger Words" options for faster workflow
-
-### v0.8.13
-* **Enhanced Recipe Management** - Added "Find duplicates" feature to identify and batch delete duplicate recipes with duplicate detection notifications during imports
-* **Improved Source Tracking** - Source URLs are now saved with recipes imported via URL, allowing users to view original content with one click or manually edit links
-* **Advanced LoRA Control** - Double-click LoRAs in Loader/Stacker nodes to access expanded CLIP strength controls for more precise adjustments of model and CLIP strength separately
-* **Lycoris Model Support** - Added compatibility with Lycoris models for expanded creative options
-* **Bug Fixes & UX Improvements** - Resolved various issues and enhanced overall user experience with numerous optimizations
-
-### v0.8.12
-* **Enhanced Model Discovery** - Added alphabetical navigation bar to LoRAs page for faster browsing through large collections
-* **Optimized Example Images** - Improved download logic to automatically refresh stale metadata before fetching example images
-* **Model Exclusion System** - New right-click option to exclude specific LoRAs or checkpoints from management
-* **Improved Showcase Experience** - Enhanced interaction in LoRA and checkpoint showcase areas for better usability
-
-### v0.8.11
-* **Offline Image Support** - Added functionality to download and save all model example images locally, ensuring access even when offline or if images are removed from CivitAI or the site is down
-* **Resilient Download System** - Implemented pause/resume capability with checkpoint recovery that persists through restarts or unexpected exits
-* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
-
-### v0.8.10
-* **Standalone Mode** - Run LoRA Manager independently from ComfyUI for a lightweight experience that works even with other stable diffusion interfaces
-* **Portable Edition** - New one-click portable version for easy startup and updates in standalone mode
-* **Enhanced Metadata Collection** - Added support for SamplerCustomAdvanced node in the metadata collector module
-* **Improved UI Organization** - Optimized Lora Loader node height to display up to 5 LoRAs at once with scrolling capability for larger collections
-
 [View Update History](./update_logs.md)
 
 ---

py/config.py

@@ -59,6 +59,9 @@ class Config:
             
             if self.checkpoints_roots and len(self.checkpoints_roots) == 1 and "default_checkpoint_root" not in settings:
                 settings["default_checkpoint_root"] = self.checkpoints_roots[0]
+
+            if self.embeddings_roots and len(self.embeddings_roots) == 1 and "default_embedding_root" not in settings:
+                settings["default_embedding_root"] = self.embeddings_roots[0]
             
             # Save settings
             with open(settings_path, 'w', encoding='utf-8') as f:
@@ -201,16 +204,20 @@ class Config:
                     real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
                     unet_map[real_path] = unet_map.get(real_path, path.replace(os.sep, "/"))  # preserve first seen
             
+            # Merge both maps and deduplicate by real path
+            merged_map = {}
+            for real_path, orig_path in {**checkpoint_map, **unet_map}.items():
+                if real_path not in merged_map:
+                    merged_map[real_path] = orig_path
+
             # Now sort and use only the deduplicated real paths
-            unique_checkpoint_paths = sorted(checkpoint_map.values(), key=lambda p: p.lower())
-            unique_unet_paths = sorted(unet_map.values(), key=lambda p: p.lower())
+            unique_paths = sorted(merged_map.values(), key=lambda p: p.lower())
             
-            # Store individual paths in class properties
-            self.checkpoints_roots = unique_checkpoint_paths
-            self.unet_roots = unique_unet_paths
+            # Split back into checkpoints and unet roots for class properties
+            self.checkpoints_roots = [p for p in unique_paths if p in checkpoint_map.values()]
+            self.unet_roots = [p for p in unique_paths if p in unet_map.values()]
             
-            # Combine all checkpoint-related paths for return value
-            all_paths = unique_checkpoint_paths + unique_unet_paths
+            all_paths = unique_paths
             
             logger.info("Found checkpoint roots:" + ("\n - " + "\n - ".join(all_paths) if all_paths else "[]"))
             

py/metadata_collector/metadata_hook.py

@@ -146,52 +146,40 @@ class MetadataHook:
         # Store the original _async_map_node_over_list function
         original_map_node_over_list = getattr(execution, map_node_func_name)
         
-        # Define the wrapped async function - NOTE: Updated signature with prompt_id and unique_id!
-        async def async_map_node_over_list_with_metadata(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None):
+        # Wrapped async function, compatible with both stable and nightly
+        async def async_map_node_over_list_with_metadata(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, *args, **kwargs):
+            hidden_inputs = kwargs.get('hidden_inputs', None)
             # Only collect metadata when calling the main function of nodes
             if func == obj.FUNCTION and hasattr(obj, '__class__'):
                 try:
-                    # Get the current prompt_id from the registry
                     registry = MetadataRegistry()
-                    # We now have prompt_id directly from the function parameters
-                    
                     if prompt_id is not None:
-                        # Get node class type
                         class_type = obj.__class__.__name__
-                        
-                        # Use the passed unique_id parameter instead of trying to extract it
                         node_id = unique_id
-                        
-                        # Record inputs before execution
                         if node_id is not None:
                             registry.record_node_execution(node_id, class_type, input_data_all, None)
                 except Exception as e:
                     print(f"Error collecting metadata (pre-execution): {str(e)}")
             
-            # Execute the original async function with ALL parameters in the correct order
-            results = await original_map_node_over_list(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt, execution_block_cb, pre_execute_cb)
+            # Call original function with all args/kwargs
+            results = await original_map_node_over_list(
+                prompt_id, unique_id, obj, input_data_all, func,
+                allow_interrupt, execution_block_cb, pre_execute_cb, *args, **kwargs
+            )
             
-            # After execution, collect outputs for relevant nodes
             if func == obj.FUNCTION and hasattr(obj, '__class__'):
                 try:
-                    # Get the current prompt_id from the registry
                     registry = MetadataRegistry()
-                    
                     if prompt_id is not None:
-                        # Get node class type
                         class_type = obj.__class__.__name__
-                        
-                        # Use the passed unique_id parameter 
                         node_id = unique_id
-                        
-                        # Record outputs after execution
                         if node_id is not None:
                             registry.update_node_execution(node_id, class_type, results)
                 except Exception as e:
                     print(f"Error collecting metadata (post-execution): {str(e)}")
             
             return results
-            
+        
         # Also hook the execute function to track the current prompt_id
         original_execute = execution.execute
         

py/nodes/save_image.py

@@ -1,6 +1,5 @@
 import json
 import os
-import asyncio
 import re
 import numpy as np
 import folder_paths # type: ignore

py/recipes/base.py

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

py/recipes/parsers/automatic.py

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

py/recipes/parsers/civitai_image.py

@@ -153,10 +153,6 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             # Process civitaiResources array
             if "civitaiResources" in metadata and isinstance(metadata["civitaiResources"], list):
                 for resource in metadata["civitaiResources"]:
-                    # Skip resources that aren't LoRAs or LyCORIS
-                    if resource.get("type") not in ["lora", "lycoris"] and "type" not in resource:
-                        continue
-                        
                     # Get unique identifier for deduplication
                     version_id = str(resource.get("modelVersionId", ""))
                     
@@ -275,6 +271,66 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     
                         result["loras"].append(lora_entry)
             
+            # Check for LoRA info in the format "Lora_0 Model hash", "Lora_0 Model name", etc.
+            lora_index = 0
+            while f"Lora_{lora_index} Model hash" in metadata and f"Lora_{lora_index} Model name" in metadata:
+                lora_hash = metadata[f"Lora_{lora_index} Model hash"]
+                lora_name = metadata[f"Lora_{lora_index} Model name"]
+                lora_strength_model = float(metadata.get(f"Lora_{lora_index} Strength model", 1.0))
+                
+                # Skip if we've already added this LoRA by hash
+                if lora_hash and lora_hash in added_loras:
+                    lora_index += 1
+                    continue
+                
+                lora_entry = {
+                    'name': lora_name,
+                    'type': "lora",
+                    'weight': lora_strength_model,
+                    'hash': lora_hash,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': lora_name,
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+                
+                # Try to get info from Civitai if hash is available
+                if lora_entry['hash'] and civitai_client:
+                    try:
+                        civitai_info = await civitai_client.get_model_by_hash(lora_hash)
+                        
+                        populated_entry = await self.populate_lora_from_civitai(
+                            lora_entry,
+                            civitai_info,
+                            recipe_scanner,
+                            base_model_counts,
+                            lora_hash
+                        )
+                        
+                        if populated_entry is None:
+                            lora_index += 1
+                            continue  # Skip invalid LoRA types
+                            
+                        lora_entry = populated_entry
+                        
+                        # If we have a version ID from Civitai, track it for deduplication
+                        if 'id' in lora_entry and lora_entry['id']:
+                            added_loras[str(lora_entry['id'])] = len(result["loras"])
+                    except Exception as e:
+                        logger.error(f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}")
+                
+                # Track by hash if we have it
+                if lora_hash:
+                    added_loras[lora_hash] = len(result["loras"])
+                    
+                result["loras"].append(lora_entry)
+                
+                lora_index += 1
+            
             # If base model wasn't found earlier, use the most common one from LoRAs
             if not result["base_model"] and base_model_counts:
                 result["base_model"] = max(base_model_counts.items(), key=lambda x: x[1])[0]

py/recipes/parsers/recipe_format.py

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

py/routes/base_model_routes.py

@@ -38,7 +38,7 @@ class BaseModelRoutes(ABC):
             prefix: URL prefix (e.g., 'loras', 'checkpoints')
         """
         # Common model management routes
-        app.router.add_get(f'/api/{prefix}', self.get_models)
+        app.router.add_get(f'/api/{prefix}/list', self.get_models)
         app.router.add_post(f'/api/{prefix}/delete', self.delete_model)
         app.router.add_post(f'/api/{prefix}/exclude', self.exclude_model)
         app.router.add_post(f'/api/{prefix}/fetch-civitai', self.fetch_civitai)
@@ -48,6 +48,8 @@ class BaseModelRoutes(ABC):
         app.router.add_post(f'/api/{prefix}/rename', self.rename_model)
         app.router.add_post(f'/api/{prefix}/bulk-delete', self.bulk_delete_models)
         app.router.add_post(f'/api/{prefix}/verify-duplicates', self.verify_duplicates)
+        app.router.add_post(f'/api/{prefix}/move_model', self.move_model)
+        app.router.add_post(f'/api/{prefix}/move_models_bulk', self.move_models_bulk)
         
         # Common query routes
         app.router.add_get(f'/api/{prefix}/top-tags', self.get_top_tags)
@@ -175,6 +177,7 @@ class BaseModelRoutes(ABC):
             'filename': request.query.get('search_filename', 'true').lower() == 'true',
             'modelname': request.query.get('search_modelname', 'true').lower() == 'true',
             'tags': request.query.get('search_tags', 'false').lower() == 'true',
+            'creator': request.query.get('search_creator', 'false').lower() == 'true',
             'recursive': request.query.get('recursive', 'false').lower() == 'true',
         }
         
@@ -408,7 +411,7 @@ class BaseModelRoutes(ABC):
                         group["models"].append(await self.service.format_response(model))
                 
                 # Find the model from the main index too
-                hash_val = self.service.scanner._hash_index.get_hash_by_filename(filename)
+                hash_val = self.service.scanner.get_hash_by_filename(filename)
                 if hash_val:
                     main_path = self.service.get_path_by_hash(hash_val)
                     if main_path and main_path not in paths:
@@ -616,4 +619,81 @@ class BaseModelRoutes(ABC):
         # This will be implemented by subclasses as they need CivitAI client access
         return web.json_response({
             "error": "Not implemented in base class"
-        }, status=501)
+        }, status=501)
+    
+    # Common model move handlers
+    async def move_model(self, request: web.Request) -> web.Response:
+        """Handle model move request"""
+        try:
+            data = await request.json()
+            file_path = data.get('file_path')
+            target_path = data.get('target_path')
+            if not file_path or not target_path:
+                return web.Response(text='File path and target path are required', status=400)
+            import os
+            source_dir = os.path.dirname(file_path)
+            if os.path.normpath(source_dir) == os.path.normpath(target_path):
+                logger.info(f"Source and target directories are the same: {source_dir}")
+                return web.json_response({'success': True, 'message': 'Source and target directories are the same'})
+            file_name = os.path.basename(file_path)
+            target_file_path = os.path.join(target_path, file_name).replace(os.sep, '/')
+            if os.path.exists(target_file_path):
+                return web.json_response({
+                    'success': False, 
+                    'error': f"Target file already exists: {target_file_path}"
+                }, status=409)
+            success = await self.service.scanner.move_model(file_path, target_path)
+            if success:
+                return web.json_response({'success': True, 'new_file_path': target_file_path})
+            else:
+                return web.Response(text='Failed to move model', status=500)
+        except Exception as e:
+            logger.error(f"Error moving model: {e}", exc_info=True)
+            return web.Response(text=str(e), status=500)
+
+    async def move_models_bulk(self, request: web.Request) -> web.Response:
+        """Handle bulk model move request"""
+        try:
+            data = await request.json()
+            file_paths = data.get('file_paths', [])
+            target_path = data.get('target_path')
+            if not file_paths or not target_path:
+                return web.Response(text='File paths and target path are required', status=400)
+            results = []
+            import os
+            for file_path in file_paths:
+                source_dir = os.path.dirname(file_path)
+                if os.path.normpath(source_dir) == os.path.normpath(target_path):
+                    results.append({
+                        "path": file_path, 
+                        "success": True, 
+                        "message": "Source and target directories are the same"
+                    })
+                    continue
+                file_name = os.path.basename(file_path)
+                target_file_path = os.path.join(target_path, file_name).replace(os.sep, '/')
+                if os.path.exists(target_file_path):
+                    results.append({
+                        "path": file_path, 
+                        "success": False, 
+                        "message": f"Target file already exists: {target_file_path}"
+                    })
+                    continue
+                success = await self.service.scanner.move_model(file_path, target_path)
+                results.append({
+                    "path": file_path, 
+                    "success": success,
+                    "message": "Success" if success else "Failed to move model"
+                })
+            success_count = sum(1 for r in results if r["success"])
+            failure_count = len(results) - success_count
+            return web.json_response({
+                'success': True,
+                'message': f'Moved {success_count} of {len(file_paths)} models',
+                'results': results,
+                'success_count': success_count,
+                'failure_count': failure_count
+            })
+        except Exception as e:
+            logger.error(f"Error moving models in bulk: {e}", exc_info=True)
+            return web.Response(text=str(e), status=500)

py/routes/checkpoint_routes.py

@@ -4,6 +4,7 @@ from aiohttp import web
 from .base_model_routes import BaseModelRoutes
 from ..services.checkpoint_service import CheckpointService
 from ..services.service_registry import ServiceRegistry
+from ..config import config
 
 logger = logging.getLogger(__name__)
 
@@ -41,6 +42,10 @@ class CheckpointRoutes(BaseModelRoutes):
         
         # Checkpoint info by name
         app.router.add_get(f'/api/{prefix}/info/{{name}}', self.get_checkpoint_info)
+        
+        # Checkpoint roots and Unet roots
+        app.router.add_get(f'/api/{prefix}/checkpoints_roots', self.get_checkpoints_roots)
+        app.router.add_get(f'/api/{prefix}/unet_roots', self.get_unet_roots)
     
     async def get_checkpoint_info(self, request: web.Request) -> web.Response:
         """Get detailed information for a specific checkpoint by name"""
@@ -102,4 +107,34 @@ class CheckpointRoutes(BaseModelRoutes):
             return web.json_response(versions)
         except Exception as e:
             logger.error(f"Error fetching checkpoint model versions: {e}")
-            return web.Response(status=500, text=str(e))
+            return web.Response(status=500, text=str(e))
+    
+    async def get_checkpoints_roots(self, request: web.Request) -> web.Response:
+        """Return the list of checkpoint roots from config"""
+        try:
+            roots = config.checkpoints_roots
+            return web.json_response({
+                "success": True,
+                "roots": roots
+            })
+        except Exception as e:
+            logger.error(f"Error getting checkpoint roots: {e}", exc_info=True)
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)
+
+    async def get_unet_roots(self, request: web.Request) -> web.Response:
+        """Return the list of unet roots from config"""
+        try:
+            roots = config.unet_roots
+            return web.json_response({
+                "success": True,
+                "roots": roots
+            })
+        except Exception as e:
+            logger.error(f"Error getting unet roots: {e}", exc_info=True)
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)

py/routes/lora_routes.py

@@ -49,10 +49,6 @@ class LoraRoutes(BaseModelRoutes):
         app.router.add_get(f'/api/{prefix}/civitai-url', self.get_lora_civitai_url)
         app.router.add_get(f'/api/{prefix}/model-description', self.get_lora_model_description)
         
-        # LoRA-specific management routes
-        app.router.add_post(f'/api/{prefix}/move_model', self.move_model)
-        app.router.add_post(f'/api/{prefix}/move_models_bulk', self.move_models_bulk)
-        
         # CivitAI integration with LoRA-specific validation
         app.router.add_get(f'/api/{prefix}/civitai/versions/{{model_id}}', self.get_civitai_versions_lora)
         app.router.add_get(f'/api/{prefix}/civitai/model/version/{{modelVersionId}}', self.get_civitai_model_by_version)
@@ -284,105 +280,6 @@ class LoraRoutes(BaseModelRoutes):
                 "error": str(e)
             }, status=500)
     
-    # Model management methods
-    async def move_model(self, request: web.Request) -> web.Response:
-        """Handle model move request"""
-        try:
-            data = await request.json()
-            file_path = data.get('file_path')  # full path of the model file
-            target_path = data.get('target_path')  # folder path to move the model to
-            
-            if not file_path or not target_path:
-                return web.Response(text='File path and target path are required', status=400)
-
-            # Check if source and destination are the same
-            import os
-            source_dir = os.path.dirname(file_path)
-            if os.path.normpath(source_dir) == os.path.normpath(target_path):
-                logger.info(f"Source and target directories are the same: {source_dir}")
-                return web.json_response({'success': True, 'message': 'Source and target directories are the same'})
-
-            # Check if target file already exists
-            file_name = os.path.basename(file_path)
-            target_file_path = os.path.join(target_path, file_name).replace(os.sep, '/')
-            
-            if os.path.exists(target_file_path):
-                return web.json_response({
-                    'success': False, 
-                    'error': f"Target file already exists: {target_file_path}"
-                }, status=409)  # 409 Conflict
-
-            # Call scanner to handle the move operation
-            success = await self.service.scanner.move_model(file_path, target_path)
-            
-            if success:
-                return web.json_response({'success': True, 'new_file_path': target_file_path})
-            else:
-                return web.Response(text='Failed to move model', status=500)
-                
-        except Exception as e:
-            logger.error(f"Error moving model: {e}", exc_info=True)
-            return web.Response(text=str(e), status=500)
-    
-    async def move_models_bulk(self, request: web.Request) -> web.Response:
-        """Handle bulk model move request"""
-        try:
-            data = await request.json()
-            file_paths = data.get('file_paths', [])  # list of full paths of the model files
-            target_path = data.get('target_path')  # folder path to move the models to
-            
-            if not file_paths or not target_path:
-                return web.Response(text='File paths and target path are required', status=400)
-
-            results = []
-            import os
-            for file_path in file_paths:
-                # Check if source and destination are the same
-                source_dir = os.path.dirname(file_path)
-                if os.path.normpath(source_dir) == os.path.normpath(target_path):
-                    results.append({
-                        "path": file_path, 
-                        "success": True, 
-                        "message": "Source and target directories are the same"
-                    })
-                    continue
-                
-                # Check if target file already exists
-                file_name = os.path.basename(file_path)
-                target_file_path = os.path.join(target_path, file_name).replace(os.sep, '/')
-                
-                if os.path.exists(target_file_path):
-                    results.append({
-                        "path": file_path, 
-                        "success": False, 
-                        "message": f"Target file already exists: {target_file_path}"
-                    })
-                    continue
-                
-                # Try to move the model
-                success = await self.service.scanner.move_model(file_path, target_path)
-                results.append({
-                    "path": file_path, 
-                    "success": success,
-                    "message": "Success" if success else "Failed to move model"
-                })
-            
-            # Count successes and failures
-            success_count = sum(1 for r in results if r["success"])
-            failure_count = len(results) - success_count
-            
-            return web.json_response({
-                'success': True,
-                'message': f'Moved {success_count} of {len(file_paths)} models',
-                'results': results,
-                'success_count': success_count,
-                'failure_count': failure_count
-            })
-                
-        except Exception as e:
-            logger.error(f"Error moving models in bulk: {e}", exc_info=True)
-            return web.Response(text=str(e), status=500)
-    
     async def get_lora_model_description(self, request: web.Request) -> web.Response:
         """Get model description for a Lora model"""
         try:

py/routes/misc_routes.py

@@ -167,6 +167,9 @@ class MiscRoutes:
             
             # Validate and update settings
             for key, value in data.items():
+                if value == settings.get(key):
+                    # No change, skip
+                    continue
                 # Special handling for example_images_path - verify path exists
                 if key == 'example_images_path' and value:
                     if not os.path.exists(value):

py/routes/recipe_routes.py

@@ -22,7 +22,6 @@ from ..config import config
 # Check if running in standalone mode
 standalone_mode = 'nodes' not in sys.modules
 
-from ..utils.utils import download_civitai_image
 from ..services.service_registry import ServiceRegistry  # Add ServiceRegistry import
 
 # Only import MetadataRegistry in non-standalone mode
@@ -376,16 +375,6 @@ class RecipeRoutes:
                     # Use meta field from image_info as metadata
                     if 'meta' in image_info:
                         metadata = image_info['meta']
-                    
-                else:
-                    # Not a Civitai image URL, use the original download method
-                    temp_path = download_civitai_image(url)
-                    
-                    if not temp_path:
-                        return web.json_response({
-                            "error": "Failed to download image from URL",
-                            "loras": []
-                        }, status=400)
             
             # If metadata wasn't obtained from Civitai API, extract it from the image
             if metadata is None:
@@ -638,21 +627,6 @@ class RecipeRoutes:
                         image = base64.b64decode(image_base64)
                     except Exception as e:
                         return web.json_response({"error": f"Invalid base64 image data: {str(e)}"}, status=400)
-                elif image_url:
-                    # Download image from URL
-                    temp_path = download_civitai_image(image_url)
-                    if not temp_path:
-                        return web.json_response({"error": "Failed to download image from URL"}, status=400)
-                    
-                    # Read the downloaded image
-                    with open(temp_path, 'rb') as f:
-                        image = f.read()
-                    
-                    # Clean up temp file
-                    try:
-                        os.unlink(temp_path)
-                    except:
-                        pass
                 else:
                     return web.json_response({"error": "No image data provided"}, status=400)
             

py/routes/stats_routes.py

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

py/routes/update_routes.py

@@ -1,10 +1,11 @@
 import os
-import subprocess
 import aiohttp
 import logging
 import toml
 import git
-from datetime import datetime
+import zipfile
+import shutil
+import tempfile
 from aiohttp import web
 from typing import Dict, List
 
@@ -101,34 +102,36 @@ class UpdateRoutes:
     @staticmethod
     async def perform_update(request):
         """
-        Perform Git-based update to latest release tag or main branch
+        Perform Git-based update to latest release tag or main branch.
+        If .git is missing, fallback to ZIP download.
         """
         try:
-            # Parse request body
             body = await request.json() if request.has_body else {}
             nightly = body.get('nightly', False)
-            
-            # Get current plugin directory
+
             current_dir = os.path.dirname(os.path.abspath(__file__))
             plugin_root = os.path.dirname(os.path.dirname(current_dir))
-            
-            # Backup settings.json if it exists
+
             settings_path = os.path.join(plugin_root, 'settings.json')
             settings_backup = None
             if os.path.exists(settings_path):
                 with open(settings_path, 'r', encoding='utf-8') as f:
                     settings_backup = f.read()
                 logger.info("Backed up settings.json")
-            
-            # Perform Git update
-            success, new_version = await UpdateRoutes._perform_git_update(plugin_root, nightly)
-            
-            # Restore settings.json if we backed it up
+
+            git_folder = os.path.join(plugin_root, '.git')
+            if os.path.exists(git_folder):
+                # Git update
+                success, new_version = await UpdateRoutes._perform_git_update(plugin_root, nightly)
+            else:
+                # Fallback: Download ZIP and replace files
+                success, new_version = await UpdateRoutes._download_and_replace_zip(plugin_root)
+
             if settings_backup and success:
                 with open(settings_path, 'w', encoding='utf-8') as f:
                     f.write(settings_backup)
                 logger.info("Restored settings.json")
-            
+
             if success:
                 return web.json_response({
                     'success': True,
@@ -138,15 +141,96 @@ class UpdateRoutes:
             else:
                 return web.json_response({
                     'success': False,
-                    'error': 'Failed to complete Git update'
+                    'error': 'Failed to complete update'
                 })
-                
+
         except Exception as e:
             logger.error(f"Failed to perform update: {e}", exc_info=True)
             return web.json_response({
                 'success': False,
                 'error': str(e)
             })
+
+    @staticmethod
+    async def _download_and_replace_zip(plugin_root: str) -> tuple[bool, str]:
+        """
+        Download latest release ZIP from GitHub and replace plugin files.
+        Skips settings.json. Writes extracted file list to .tracking.
+        """
+        repo_owner = "willmiao"
+        repo_name = "ComfyUI-Lora-Manager"
+        github_api = f"https://api.github.com/repos/{repo_owner}/{repo_name}/releases/latest"
+
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(github_api) as resp:
+                    if resp.status != 200:
+                        logger.error(f"Failed to fetch release info: {resp.status}")
+                        return False, ""
+                    data = await resp.json()
+                    zip_url = data.get("zipball_url")
+                    version = data.get("tag_name", "unknown")
+
+                # Download ZIP
+                async with session.get(zip_url) as zip_resp:
+                    if zip_resp.status != 200:
+                        logger.error(f"Failed to download ZIP: {zip_resp.status}")
+                        return False, ""
+                    with tempfile.NamedTemporaryFile(delete=False, suffix=".zip") as tmp_zip:
+                        tmp_zip.write(await zip_resp.read())
+                        zip_path = tmp_zip.name
+
+                UpdateRoutes._clean_plugin_folder(plugin_root, skip_files=['settings.json'])
+
+                # Extract ZIP to temp dir
+                with tempfile.TemporaryDirectory() as tmp_dir:
+                    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+                        zip_ref.extractall(tmp_dir)
+                    # Find extracted folder (GitHub ZIP contains a root folder)
+                    extracted_root = next(os.scandir(tmp_dir)).path
+
+                    # Copy files, skipping settings.json
+                    for item in os.listdir(extracted_root):
+                        src = os.path.join(extracted_root, item)
+                        dst = os.path.join(plugin_root, item)
+                        if os.path.isdir(src):
+                            if os.path.exists(dst):
+                                shutil.rmtree(dst)
+                            shutil.copytree(src, dst, ignore=shutil.ignore_patterns('settings.json'))
+                        else:
+                            if item == 'settings.json':
+                                continue
+                            shutil.copy2(src, dst)
+
+                    # Write .tracking file: list all files under extracted_root, relative to extracted_root
+                    # for ComfyUI Manager to work properly
+                    tracking_info_file = os.path.join(plugin_root, '.tracking')
+                    tracking_files = []
+                    for root, dirs, files in os.walk(extracted_root):
+                        for file in files:
+                            rel_path = os.path.relpath(os.path.join(root, file), extracted_root)
+                            tracking_files.append(rel_path.replace("\\", "/"))
+                    with open(tracking_info_file, "w", encoding='utf-8') as file:
+                        file.write('\n'.join(tracking_files))
+
+                os.remove(zip_path)
+                logger.info(f"Updated plugin via ZIP to {version}")
+                return True, version
+
+        except Exception as e:
+            logger.error(f"ZIP update failed: {e}", exc_info=True)
+            return False, ""
+        
+    def _clean_plugin_folder(plugin_root, skip_files=None):
+        skip_files = skip_files or []
+        for item in os.listdir(plugin_root):
+            if item in skip_files:
+                continue
+            path = os.path.join(plugin_root, item)
+            if os.path.isdir(path):
+                shutil.rmtree(path)
+            else:
+                os.remove(path)
     
     @staticmethod
     async def _get_nightly_version() -> tuple[str, List[str]]:
@@ -288,65 +372,28 @@ class UpdateRoutes:
         """Get Git repository information"""
         current_dir = os.path.dirname(os.path.abspath(__file__))
         plugin_root = os.path.dirname(os.path.dirname(current_dir))
-        
+
         git_info = {
             'commit_hash': 'unknown',
-            'short_hash': 'unknown',
+            'short_hash': 'stable',
             'branch': 'unknown',
             'commit_date': 'unknown'
         }
-        
+
         try:
             # Check if we're in a git repository
             if not os.path.exists(os.path.join(plugin_root, '.git')):
                 return git_info
-                
-            # Get current commit hash
-            result = subprocess.run(
-                ['git', 'rev-parse', 'HEAD'],
-                cwd=plugin_root,
-                stdout=subprocess.PIPE,
-                stderr=subprocess.PIPE,
-                text=True,
-                check=False
-            )
-            if result.returncode == 0:
-                git_info['commit_hash'] = result.stdout.strip()
-                git_info['short_hash'] = git_info['commit_hash'][:7]
-            
-            # Get current branch name
-            result = subprocess.run(
-                ['git', 'rev-parse', '--abbrev-ref', 'HEAD'],
-                cwd=plugin_root,
-                stdout=subprocess.PIPE,
-                stderr=subprocess.PIPE,
-                text=True,
-                check=False
-            )
-            if result.returncode == 0:
-                git_info['branch'] = result.stdout.strip()
-            
-            # Get commit date
-            result = subprocess.run(
-                ['git', 'show', '-s', '--format=%ci', 'HEAD'],
-                cwd=plugin_root,
-                stdout=subprocess.PIPE,
-                stderr=subprocess.PIPE,
-                text=True,
-                check=False
-            )
-            if result.returncode == 0:
-                commit_date = result.stdout.strip()
-                # Format the date nicely if possible
-                try:
-                    date_obj = datetime.strptime(commit_date, '%Y-%m-%d %H:%M:%S %z')
-                    git_info['commit_date'] = date_obj.strftime('%Y-%m-%d')
-                except:
-                    git_info['commit_date'] = commit_date
-                    
+
+            repo = git.Repo(plugin_root)
+            commit = repo.head.commit
+            git_info['commit_hash'] = commit.hexsha
+            git_info['short_hash'] = commit.hexsha[:7]
+            git_info['branch'] = repo.active_branch.name if not repo.head.is_detached else 'detached'
+            git_info['commit_date'] = commit.committed_datetime.strftime('%Y-%m-%d')
         except Exception as e:
             logger.warning(f"Error getting git info: {e}")
-            
+
         return git_info
     
     @staticmethod

py/services/base_model_service.py

@@ -199,6 +199,22 @@ class BaseModelService(ABC):
                        for tag in item['tags']):
                     search_results.append(item)
                     continue
+            
+            # Search by creator
+            civitai = item.get('civitai')
+            creator_username = ''
+            if civitai and isinstance(civitai, dict):
+                creator = civitai.get('creator')
+                if creator and isinstance(creator, dict):
+                    creator_username = creator.get('username', '')
+            if search_options.get('creator', False) and creator_username:
+                if fuzzy_search:
+                    if fuzzy_match(creator_username, search):
+                        search_results.append(item)
+                        continue
+                elif search.lower() in creator_username.lower():
+                    search_results.append(item)
+                    continue
         
         return search_results
     

py/services/checkpoint_scanner.py

@@ -21,6 +21,14 @@ class CheckpointScanner(ModelScanner):
             hash_index=ModelHashIndex()
         )
 
+    def adjust_metadata(self, metadata, file_path, root_path):
+        if hasattr(metadata, "model_type"):
+            if root_path in config.checkpoints_roots:
+                metadata.model_type = "checkpoint"
+            elif root_path in config.unet_roots:
+                metadata.model_type = "diffusion_model"
+        return metadata
+
     def get_model_roots(self) -> List[str]:
         """Get checkpoint root directories"""
         return config.base_models_roots

py/services/model_hash_index.py

@@ -199,8 +199,6 @@ class ModelHashIndex:
     
     def get_hash_by_filename(self, filename: str) -> Optional[str]:
         """Get hash for a filename without extension"""
-        # Strip extension if present to make the function more flexible
-        filename = os.path.splitext(filename)[0]
         return self._filename_to_hash.get(filename)
     
     def clear(self) -> None:

py/services/model_scanner.py

@@ -569,12 +569,12 @@ class ModelScanner:
                     for entry in entries:
                         try:
                             if entry.is_file(follow_symlinks=True) and any(entry.name.endswith(ext) for ext in self.file_extensions):
-                                # Use original path instead of real path
                                 file_path = entry.path.replace(os.sep, "/")
-                                await self._process_single_file(file_path, original_root, models)
+                                result = await self._process_model_file(file_path, original_root)
+                                if result:
+                                    models.append(result)
                                 await asyncio.sleep(0)
                             elif entry.is_dir(follow_symlinks=True):
-                                # For directories, continue scanning with original path
                                 await scan_recursive(entry.path, visited_paths)
                         except Exception as e:
                             logger.error(f"Error processing entry {entry.path}: {e}")
@@ -583,15 +583,6 @@ class ModelScanner:
 
         await scan_recursive(root_path, set())
         return models
-
-    async def _process_single_file(self, file_path: str, root_path: str, models: list):
-        """Process a single file and add to results list"""
-        try:
-            result = await self._process_model_file(file_path, root_path)
-            if result:
-                models.append(result)
-        except Exception as e:
-            logger.error(f"Error processing {file_path}: {e}")
     
     def is_initializing(self) -> bool:
         """Check if the scanner is currently initializing"""
@@ -613,7 +604,10 @@ class ModelScanner:
                 return os.path.dirname(rel_path).replace(os.path.sep, '/')
         return ''
 
-    # Common methods shared between scanners
+    def adjust_metadata(self, metadata, file_path, root_path):
+        """Hook for subclasses: adjust metadata during scanning"""
+        return metadata
+
     async def _process_model_file(self, file_path: str, root_path: str) -> Dict:
         """Process a single model file and return its metadata"""
         metadata = await MetadataManager.load_metadata(file_path, self.model_class)
@@ -667,6 +661,9 @@ class ModelScanner:
         if metadata is None:
             metadata = await self._create_default_metadata(file_path)
         
+        # Hook: allow subclasses to adjust metadata
+        metadata = self.adjust_metadata(metadata, file_path, root_path)
+        
         model_data = metadata.to_dict()
         
         # Skip excluded models
@@ -732,48 +729,6 @@ class ModelScanner:
         except Exception as e:
             logger.error(f"Failed to update metadata from Civitai for {file_path}: {e}")
 
-    async def _scan_directory(self, root_path: str) -> List[Dict]:
-        """Base implementation for directory scanning"""
-        models = []
-        original_root = root_path
-
-        async def scan_recursive(path: str, visited_paths: set):
-            try:
-                real_path = os.path.realpath(path)
-                if real_path in visited_paths:
-                    logger.debug(f"Skipping already visited path: {path}")
-                    return
-                visited_paths.add(real_path)
-                
-                with os.scandir(path) as it:
-                    entries = list(it)
-                    for entry in entries:
-                        try:
-                            if entry.is_file(follow_symlinks=True):
-                                ext = os.path.splitext(entry.name)[1].lower()
-                                if ext in self.file_extensions:
-                                    file_path = entry.path.replace(os.sep, "/")
-                                    await self._process_single_file(file_path, original_root, models)
-                                    await asyncio.sleep(0)
-                            elif entry.is_dir(follow_symlinks=True):
-                                await scan_recursive(entry.path, visited_paths)
-                        except Exception as e:
-                            logger.error(f"Error processing entry {entry.path}: {e}")
-            except Exception as e:
-                logger.error(f"Error scanning {path}: {e}")
-        
-        await scan_recursive(root_path, set())
-        return models
-
-    async def _process_single_file(self, file_path: str, root_path: str, models_list: list):
-        """Process a single file and add to results list"""
-        try:
-            result = await self._process_model_file(file_path, root_path)
-            if result:
-                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
         

py/services/settings_manager.py

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

py/utils/constants.py

@@ -50,7 +50,8 @@ VALID_LORA_TYPES = ['lora', 'locon', 'dora']
 
 # Civitai model tags in priority order for subfolder organization
 CIVITAI_MODEL_TAGS = [
-    'character', 'style', 'concept', 'clothing', 'base model', 
+    'character', 'style', 'concept', 'clothing', 
+    # 'base model', # exclude 'base model'
     'poses', 'background', 'tool', 'vehicle', 'buildings', 
     'objects', 'assets', 'animal', 'action'
 ]

py/utils/example_images_download_manager.py

@@ -91,7 +91,7 @@ class DownloadManager:
                     with open(progress_file, 'r', encoding='utf-8') as f:
                         saved_progress = json.load(f)
                         download_progress['processed_models'] = set(saved_progress.get('processed_models', []))
-                        logger.info(f"Loaded previous progress, {len(download_progress['processed_models'])} models already processed")
+                        logger.debug(f"Loaded previous progress, {len(download_progress['processed_models'])} models already processed")
                 except Exception as e:
                     logger.error(f"Failed to load progress file: {e}")
                     download_progress['processed_models'] = set()
@@ -230,7 +230,7 @@ class DownloadManager:
             
             # Update total count
             download_progress['total'] = len(all_models)
-            logger.info(f"Found {download_progress['total']} models to process")
+            logger.debug(f"Found {download_progress['total']} models to process")
             
             # Process each model
             for i, (scanner_type, model, scanner) in enumerate(all_models):
@@ -250,7 +250,7 @@ class DownloadManager:
             # Mark as completed
             download_progress['status'] = 'completed'
             download_progress['end_time'] = time.time()
-            logger.info(f"Example images download completed: {download_progress['completed']}/{download_progress['total']} models processed")
+            logger.debug(f"Example images download completed: {download_progress['completed']}/{download_progress['total']} models processed")
             
         except Exception as e:
             error_msg = f"Error during example images download: {str(e)}"
@@ -307,7 +307,7 @@ class DownloadManager:
                     logger.debug(f"Skipping already processed model: {model_name}")
                     return False
                 else:
-                    logger.info(f"Model {model_name} marked as processed but folder empty or missing, reprocessing")
+                    logger.debug(f"Model {model_name} marked as processed but folder empty or missing, reprocessing")
             
             # Create model directory
             model_dir = os.path.join(output_dir, model_hash)

py/utils/example_images_file_manager.py

@@ -43,7 +43,15 @@ class ExampleImagesFileManager:
             
             # Construct folder path for this model
             model_folder = os.path.join(example_images_path, model_hash)
-            
+            model_folder = os.path.abspath(model_folder)  # Get absolute path
+
+            # Path validation: ensure model_folder is under example_images_path
+            if not model_folder.startswith(os.path.abspath(example_images_path)):
+                return web.json_response({
+                    'success': False,
+                    'error': 'Invalid model folder path'
+                }, status=400)
+
             # Check if folder exists
             if not os.path.exists(model_folder):
                 return web.json_response({

py/utils/utils.py

@@ -1,8 +1,5 @@
 from difflib import SequenceMatcher
-import requests
-import tempfile
 import os
-from bs4 import BeautifulSoup
 from ..services.service_registry import ServiceRegistry
 from ..config import config
 import asyncio
@@ -50,82 +47,7 @@ def get_lora_info(lora_name):
         # No event loop is running, we can use asyncio.run()
         return asyncio.run(_get_lora_info_async())
 
-def download_twitter_image(url):
-    """Download image from a URL containing twitter:image meta tag
-    
-    Args:
-        url (str): The URL to download image from
-        
-    Returns:
-        str: Path to downloaded temporary image file
-    """
-    try:
-        # Download page content
-        response = requests.get(url)
-        response.raise_for_status()
-        
-        # Parse HTML
-        soup = BeautifulSoup(response.text, 'html.parser')
-        
-        # Find twitter:image meta tag
-        meta_tag = soup.find('meta', attrs={'property': 'twitter:image'})
-        if not meta_tag:
-            return None
-            
-        image_url = meta_tag['content']
-        
-        # Download image
-        image_response = requests.get(image_url)
-        image_response.raise_for_status()
-        
-        # Save to temp file
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
-            temp_file.write(image_response.content)
-            return temp_file.name
-            
-    except Exception as e:
-        print(f"Error downloading twitter image: {e}")
-        return None
-
-def download_civitai_image(url):
-    """Download image from a URL containing avatar image with specific class and style attributes
-    
-    Args:
-        url (str): The URL to download image from
-        
-    Returns:
-        str: Path to downloaded temporary image file
-    """
-    try:
-        # Download page content
-        response = requests.get(url)
-        response.raise_for_status()
-        
-        # Parse HTML
-        soup = BeautifulSoup(response.text, 'html.parser')
-        
-        # Find image with specific class and style attributes
-        image = soup.select_one('img.EdgeImage_image__iH4_q.max-h-full.w-auto.max-w-full')
-        
-        if not image or 'src' not in image.attrs:
-            return None
-            
-        image_url = image['src']
-        
-        # Download image
-        image_response = requests.get(image_url)
-        image_response.raise_for_status()
-        
-        # Save to temp file
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
-            temp_file.write(image_response.content)
-            return temp_file.name
-            
-    except Exception as e:
-        print(f"Error downloading civitai avatar: {e}")
-        return None
-
-def fuzzy_match(text: str, pattern: str, threshold: float = 0.7) -> bool:
+def fuzzy_match(text: str, pattern: str, threshold: float = 0.85) -> bool:
         """
         Check if text matches pattern using fuzzy matching.
         Returns True if similarity ratio is above threshold.

pyproject.toml

@@ -1,17 +1,15 @@
 [project]
 name = "comfyui-lora-manager"
 description = "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!"
-version = "0.8.21"
+version = "0.8.26"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",
     "jinja2", 
     "safetensors",
-    "beautifulsoup4",
     "piexif",
     "Pillow",
     "olefile", # for getting rid of warning message
-    "requests",
     "toml",
     "natsort",
     "GitPython"

requirements.txt

@@ -1,13 +1,10 @@
 aiohttp
 jinja2
 safetensors
-beautifulsoup4
 piexif
 Pillow
 olefile
-requests
 toml
 numpy
 natsort
-pyyaml
 GitPython

static/css/components/banner.css

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

static/css/components/card.css

@@ -424,6 +424,33 @@
     font-size: 0.85em;
 }
 
+/* Style for version name */
+.version-name {
+    display: inline-block;
+    color: rgba(255,255,255,0.8); /* Muted white */
+    text-shadow: 1px 1px 2px rgba(0, 0, 0, 0.5);
+    font-size: 0.85em;
+    word-break: break-word;
+    overflow: hidden;
+    line-height: 1.4;
+    margin-top: 2px;
+    opacity: 0.8; /* Slightly transparent for better readability */
+    border: 1px solid rgba(255,255,255,0.25); /* Subtle border */
+    border-radius: var(--border-radius-xs);
+    padding: 1px 6px;
+    background: rgba(0,0,0,0.18); /* Optional: subtle background for contrast */
+}
+
+/* Medium density adjustments for version name */
+.medium-density .version-name {
+    font-size: 0.8em;
+}
+
+/* Compact density adjustments for version name */
+.compact-density .version-name {
+    font-size: 0.75em;
+}
+
 /* Prevent text selection on cards and interactive elements */
 .model-card,
 .model-card *,

static/css/components/header.css

@@ -19,6 +19,18 @@
   height: 100%;
 }
 
+/* Responsive header container for larger screens */
+@media (min-width: 2000px) {
+  .header-container {
+    max-width: 1800px;
+  }
+}
+@media (min-width: 3000px) {
+  .header-container {
+    max-width: 2400px;
+  }
+}
+
 /* Logo and title styling */
 .header-branding {
   display: flex;

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

@@ -183,7 +183,11 @@
     outline: none;
 }
 
-.edit-file-name-btn {
+/* 合并编辑按钮样式 */
+.edit-model-name-btn,
+.edit-file-name-btn,
+.edit-base-model-btn,
+.edit-model-description-btn {
     background: transparent;
     border: none;
     color: var(--text-color);
@@ -195,17 +199,28 @@
     margin-left: var(--space-1);
 }
 
+.edit-model-name-btn.visible,
 .edit-file-name-btn.visible,
-.file-name-wrapper:hover .edit-file-name-btn {
+.edit-base-model-btn.visible,
+.edit-model-description-btn.visible,
+.model-name-header:hover .edit-model-name-btn,
+.file-name-wrapper:hover .edit-file-name-btn,
+.base-model-display:hover .edit-base-model-btn,
+.model-name-header:hover .edit-model-description-btn {
     opacity: 0.5;
 }
 
-.edit-file-name-btn:hover {
+.edit-model-name-btn:hover,
+.edit-file-name-btn:hover,
+.edit-base-model-btn:hover,
+.edit-model-description-btn:hover {
     opacity: 0.8 !important;
     background: rgba(0, 0, 0, 0.05);
 }
 
-[data-theme="dark"] .edit-file-name-btn:hover {
+[data-theme="dark"] .edit-model-name-btn:hover,
+[data-theme="dark"] .edit-file-name-btn:hover,
+[data-theme="dark"] .edit-base-model-btn:hover {
     background: rgba(255, 255, 255, 0.05);
 }
 
@@ -234,32 +249,6 @@
     flex: 1;
 }
 
-.edit-base-model-btn {
-    background: transparent;
-    border: none;
-    color: var(--text-color);
-    opacity: 0;
-    cursor: pointer;
-    padding: 2px 5px;
-    border-radius: var(--border-radius-xs);
-    transition: all 0.2s ease;
-    margin-left: var(--space-1);
-}
-
-.edit-base-model-btn.visible,
-.base-model-display:hover .edit-base-model-btn {
-    opacity: 0.5;
-}
-
-.edit-base-model-btn:hover {
-    opacity: 0.8 !important;
-    background: rgba(0, 0, 0, 0.05);
-}
-
-[data-theme="dark"] .edit-base-model-btn:hover {
-    background: rgba(255, 255, 255, 0.05);
-}
-
 .base-model-selector {
     width: 100%;
     padding: 3px 5px;
@@ -316,32 +305,6 @@
     background: var(--bg-color);
 }
 
-.edit-model-name-btn {
-    background: transparent;
-    border: none;
-    color: var(--text-color);
-    opacity: 0;
-    cursor: pointer;
-    padding: 2px 5px;
-    border-radius: var(--border-radius-xs);
-    transition: all 0.2s ease;
-    margin-left: var(--space-1);
-}
-
-.edit-model-name-btn.visible,
-.model-name-header:hover .edit-model-name-btn {
-    opacity: 0.5;
-}
-
-.edit-model-name-btn:hover {
-    opacity: 0.8 !important;
-    background: rgba(0, 0, 0, 0.05);
-}
-
-[data-theme="dark"] .edit-model-name-btn:hover {
-    background: rgba(255, 255, 255, 0.05);
-}
-
 /* Tab System Styling */
 .showcase-tabs {
     display: flex;

static/css/components/statistics.css

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

static/css/style.css

@@ -6,6 +6,7 @@
 
 /* Import Components */
 @import 'components/header.css';
+@import 'components/banner.css';
 @import 'components/card.css';
 @import 'components/modal/_base.css';
 @import 'components/modal/delete-modal.css';

static/js/api/apiConfig.js

@@ -29,7 +29,7 @@ export const MODEL_CONFIG = {
         defaultPageSize: 100,
         supportsLetterFilter: false,
         supportsBulkOperations: true,
-        supportsMove: false,
+        supportsMove: true,
         templateName: 'checkpoints.html'
     },
     [MODEL_TYPES.EMBEDDING]: {
@@ -55,7 +55,7 @@ export function getApiEndpoints(modelType) {
     
     return {
         // Base CRUD operations
-        list: `/api/${modelType}`,
+        list: `/api/${modelType}/list`,
         delete: `/api/${modelType}/delete`,
         exclude: `/api/${modelType}/exclude`,
         rename: `/api/${modelType}/rename`,
@@ -63,6 +63,10 @@ export function getApiEndpoints(modelType) {
         
         // Bulk operations
         bulkDelete: `/api/${modelType}/bulk-delete`,
+
+        // Move operations (now common for all model types that support move)
+        moveModel: `/api/${modelType}/move_model`,
+        moveBulk: `/api/${modelType}/move_models_bulk`,
         
         // CivitAI integration
         fetchCivitai: `/api/${modelType}/fetch-civitai`,
@@ -99,14 +103,14 @@ export const MODEL_SPECIFIC_ENDPOINTS = {
         previewUrl: `/api/${MODEL_TYPES.LORA}/preview-url`,
         civitaiUrl: `/api/${MODEL_TYPES.LORA}/civitai-url`,
         modelDescription: `/api/${MODEL_TYPES.LORA}/model-description`,
-        moveModel: `/api/${MODEL_TYPES.LORA}/move_model`,
-        moveBulk: `/api/${MODEL_TYPES.LORA}/move_models_bulk`,
         getTriggerWordsPost: `/api/${MODEL_TYPES.LORA}/get_trigger_words`,
         civitaiModelByVersion: `/api/${MODEL_TYPES.LORA}/civitai/model/version`,
         civitaiModelByHash: `/api/${MODEL_TYPES.LORA}/civitai/model/hash`,
     },
     [MODEL_TYPES.CHECKPOINT]: {
         info: `/api/${MODEL_TYPES.CHECKPOINT}/info`,
+        checkpoints_roots: `/api/${MODEL_TYPES.CHECKPOINT}/checkpoints_roots`,
+        unet_roots: `/api/${MODEL_TYPES.CHECKPOINT}/unet_roots`,
     },
     [MODEL_TYPES.EMBEDDING]: {
     }

static/js/api/baseModelApi.js

@@ -8,12 +8,16 @@ import {
     DOWNLOAD_ENDPOINTS,
     WS_ENDPOINTS
 } from './apiConfig.js';
+import { resetAndReload } from './modelApiFactory.js';
 
 /**
- * Universal API client for all model types
+ * Abstract base class for all model API clients
  */
-class ModelApiClient {
+export class BaseModelApiClient {
     constructor(modelType = null) {
+        if (this.constructor === BaseModelApiClient) {
+            throw new Error("BaseModelApiClient is abstract and cannot be instantiated directly");
+        }
         this.modelType = modelType || getCurrentModelType();
         this.apiConfig = getCompleteApiConfig(this.modelType);
     }
@@ -42,9 +46,6 @@ class ModelApiClient {
         return pageState;
     }
 
-    /**
-     * Fetch models with pagination
-     */
     async fetchModelsPage(page = 1, pageSize = null) {
         const pageState = this.getPageState();
         const actualPageSize = pageSize || pageState.pageSize || this.apiConfig.config.defaultPageSize;
@@ -79,9 +80,6 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Reset and reload models with virtual scrolling
-     */
     async loadMoreWithVirtualScroll(resetPage = false, updateFolders = false) {
         const pageState = this.getPageState();
         
@@ -93,26 +91,27 @@ class ModelApiClient {
                 pageState.currentPage = 1; // Reset to first page
             }
             
-            // Fetch the current page
-            const startTime = performance.now();
             const result = await this.fetchModelsPage(pageState.currentPage, pageState.pageSize);
-            const endTime = performance.now();
-            console.log(`fetchModelsPage耗时: ${(endTime - startTime).toFixed(2)} ms`);
             
-            // Update the virtual scroller
             state.virtualScroller.refreshWithData(
                 result.items,
                 result.totalItems,
                 result.hasMore
             );
             
-            // Update state
             pageState.hasMore = result.hasMore;
             pageState.currentPage = pageState.currentPage + 1;
             
-            // Update folders if needed
-            if (updateFolders && result.folders) {
-                updateFolderTags(result.folders);
+            if (updateFolders) {
+                const response = await fetch(this.apiConfig.endpoints.folders);
+                if (response.ok) {
+                    const data = await response.json();
+                    updateFolderTags(data.folders);
+                } else {
+                    const errorData = await response.json().catch(() => ({}));
+                    const errorMsg = errorData && errorData.error ? errorData.error : response.statusText;
+                    console.error(`Error getting folders: ${errorMsg}`);
+                }
             }
             
             return result;
@@ -126,9 +125,6 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Delete a model
-     */
     async deleteModel(filePath) {
         try {
             state.loadingManager.showSimpleLoading(`Deleting ${this.apiConfig.config.singularName}...`);
@@ -163,9 +159,6 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Exclude a model
-     */
     async excludeModel(filePath) {
         try {
             state.loadingManager.showSimpleLoading(`Excluding ${this.apiConfig.config.singularName}...`);
@@ -200,9 +193,6 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Rename a model file
-     */
     async renameModelFile(filePath, newFileName) {
         try {
             state.loadingManager.showSimpleLoading(`Renaming ${this.apiConfig.config.singularName} file...`);
@@ -239,9 +229,6 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Replace model preview
-     */
     replaceModelPreview(filePath) {
         const input = document.createElement('input');
         input.type = 'file';
@@ -257,9 +244,6 @@ class ModelApiClient {
         input.click();
     }
 
-    /**
-     * Upload preview image
-     */
     async uploadPreview(filePath, file, nsfwLevel = 0) {
         try {
             state.loadingManager.showSimpleLoading('Uploading preview...');
@@ -281,7 +265,6 @@ class ModelApiClient {
             const data = await response.json();
             const pageState = this.getPageState();
             
-            // Update the version timestamp
             const timestamp = Date.now();
             if (pageState.previewVersions) {
                 pageState.previewVersions.set(filePath, timestamp);
@@ -305,9 +288,6 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Save model metadata
-     */
     async saveModelMetadata(filePath, data) {
         try {
             state.loadingManager.showSimpleLoading('Saving metadata...');
@@ -332,9 +312,6 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Refresh models (scan)
-     */
     async refreshModels(fullRebuild = false) {
         try {
             state.loadingManager.showSimpleLoading(
@@ -349,6 +326,8 @@ class ModelApiClient {
             if (!response.ok) {
                 throw new Error(`Failed to refresh ${this.apiConfig.config.displayName}s: ${response.status} ${response.statusText}`);
             }
+
+            resetAndReload(true);
             
             showToast(`${fullRebuild ? 'Full rebuild' : 'Refresh'} complete`, 'success');
         } catch (error) {
@@ -360,9 +339,6 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Fetch CivitAI metadata for single model
-     */
     async refreshSingleModelMetadata(filePath) {
         try {
             state.loadingManager.showSimpleLoading('Refreshing metadata...');
@@ -399,9 +375,6 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Fetch CivitAI metadata for all models
-     */
     async fetchCivitaiMetadata() {
         let ws = null;
         
@@ -477,9 +450,6 @@ class ModelApiClient {
         });
     }
 
-    /**
-     * Fetch CivitAI metadata for multiple models with progress tracking
-     */
     async refreshBulkModelMetadata(filePaths) {
         if (!filePaths || filePaths.length === 0) {
             throw new Error('No file paths provided');
@@ -493,7 +463,6 @@ class ModelApiClient {
         const progressController = state.loadingManager.showEnhancedProgress('Starting metadata refresh...');
 
         try {
-            // Process files sequentially to avoid overwhelming the API
             for (let i = 0; i < filePaths.length; i++) {
                 const filePath = filePaths[i];
                 const fileName = filePath.split('/').pop();
@@ -535,7 +504,6 @@ class ModelApiClient {
                 processedCount++;
             }
 
-            // Show completion message
             let completionMessage;
             if (successCount === totalItems) {
                 completionMessage = `Successfully refreshed all ${successCount} ${this.apiConfig.config.displayName}s`;
@@ -575,113 +543,6 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Move a single model to target path
-     * @returns {string|null} - The new file path if moved, null if not moved
-     */
-    async moveSingleModel(filePath, targetPath) {
-        if (filePath.substring(0, filePath.lastIndexOf('/')) === targetPath) {
-            showToast('Model is already in the selected folder', 'info');
-            return null;
-        }
-
-        const response = await fetch(this.apiConfig.endpoints.specific.moveModel, {
-            method: 'POST',
-            headers: {
-                'Content-Type': 'application/json',
-            },
-            body: JSON.stringify({
-                file_path: filePath,
-                target_path: targetPath
-            })
-        });
-
-        const result = await response.json();
-
-        if (!response.ok) {
-            if (result && result.error) {
-                throw new Error(result.error);
-            }
-            throw new Error('Failed to move model');
-        }
-
-        if (result && result.message) {
-            showToast(result.message, 'info');
-        } else {
-            showToast('Model moved successfully', 'success');
-        }
-
-        // Return new file path if move succeeded
-        if (result.success) {
-            return result.new_file_path;
-        }
-        return null;
-    }
-
-    /**
-     * Move multiple models to target path
-     * @returns {Array<string>} - Array of new file paths that were moved successfully
-     */
-    async moveBulkModels(filePaths, targetPath) {
-        const movedPaths = filePaths.filter(path => {
-            return path.substring(0, path.lastIndexOf('/')) !== targetPath;
-        });
-
-        if (movedPaths.length === 0) {
-            showToast('All selected models are already in the target folder', 'info');
-            return [];
-        }
-
-        const response = await fetch(this.apiConfig.endpoints.specific.moveBulk, {
-            method: 'POST',
-            headers: {
-                'Content-Type': 'application/json',
-            },
-            body: JSON.stringify({
-                file_paths: movedPaths,
-                target_path: targetPath
-            })
-        });
-
-        const result = await response.json();
-
-        if (!response.ok) {
-            throw new Error('Failed to move models');
-        }
-
-        let successFilePaths = [];
-        if (result.success) {
-            if (result.failure_count > 0) {
-                showToast(`Moved ${result.success_count} models, ${result.failure_count} failed`, 'warning');
-                console.log('Move operation results:', result.results);
-                const failedFiles = result.results
-                    .filter(r => !r.success)
-                    .map(r => {
-                        const fileName = r.path.substring(r.path.lastIndexOf('/') + 1);
-                        return `${fileName}: ${r.message}`;
-                    });
-                if (failedFiles.length > 0) {
-                    const failureMessage = failedFiles.length <= 3 
-                        ? failedFiles.join('\n')
-                        : failedFiles.slice(0, 3).join('\n') + `\n(and ${failedFiles.length - 3} more)`;
-                    showToast(`Failed moves:\n${failureMessage}`, 'warning', 6000);
-                }
-            } else {
-                showToast(`Successfully moved ${result.success_count} models`, 'success');
-            }
-            // Collect new file paths for successful moves
-            successFilePaths = result.results
-                .filter(r => r.success)
-                .map(r => r.path);
-        } else {
-            throw new Error(result.message || 'Failed to move models');
-        }
-        return successFilePaths;
-    }
-
-    /**
-     * Fetch Civitai model versions
-     */
     async fetchCivitaiVersions(modelId) {
         try {
             const response = await fetch(`${this.apiConfig.endpoints.civitaiVersions}/${modelId}`);
@@ -699,9 +560,6 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Fetch model roots
-     */
     async fetchModelRoots() {
         try {
             const response = await fetch(this.apiConfig.endpoints.roots);
@@ -715,9 +573,6 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Fetch model folders
-     */
     async fetchModelFolders() {
         try {
             const response = await fetch(this.apiConfig.endpoints.folders);
@@ -731,9 +586,6 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Download a model
-     */
     async downloadModel(modelId, versionId, modelRoot, relativePath, downloadId) {
         try {
             const response = await fetch(DOWNLOAD_ENDPOINTS.download, {
@@ -759,13 +611,9 @@ class ModelApiClient {
         }
     }
 
-    /**
-     * Build query parameters for API requests
-     */
     _buildQueryParams(baseParams, pageState) {
         const params = new URLSearchParams(baseParams);
         
-        // Add common parameters
         if (pageState.activeFolder !== null) {
             params.append('folder', pageState.activeFolder);
         }
@@ -774,12 +622,10 @@ class ModelApiClient {
             params.append('favorites_only', 'true');
         }
         
-        // Add letter filter for supported model types
         if (this.apiConfig.config.supportsLetterFilter && pageState.activeLetterFilter) {
             params.append('first_letter', pageState.activeLetterFilter);
         }
 
-        // Add search parameters
         if (pageState.filters?.search) {
             params.append('search', pageState.filters.search);
             params.append('fuzzy', 'true');
@@ -790,11 +636,13 @@ class ModelApiClient {
                 if (pageState.searchOptions.tags !== undefined) {
                     params.append('search_tags', pageState.searchOptions.tags.toString());
                 }
+                if (pageState.searchOptions.creator !== undefined) {
+                    params.append('search_creator', pageState.searchOptions.creator.toString());
+                }
                 params.append('recursive', (pageState.searchOptions?.recursive ?? false).toString());
             }
         }
         
-        // Add filter parameters
         if (pageState.filters) {
             if (pageState.filters.tags && pageState.filters.tags.length > 0) {
                 pageState.filters.tags.forEach(tag => {
@@ -809,17 +657,12 @@ class ModelApiClient {
             }
         }
 
-        // Add model-specific parameters
         this._addModelSpecificParams(params, pageState);
 
         return params;
     }
 
-    /**
-     * Add model-specific parameters to query
-     */
     _addModelSpecificParams(params, pageState) {
-        // Override in specific implementations or handle via configuration
         if (this.modelType === 'loras') {
             const filterLoraHash = getSessionItem('recipe_to_lora_filterLoraHash');
             const filterLoraHashes = getSessionItem('recipe_to_lora_filterLoraHashes');
@@ -837,23 +680,149 @@ class ModelApiClient {
             }
         }
     }
-}
 
-// Export factory functions and utilities
-export function createModelApiClient(modelType = null) {
-    return new ModelApiClient(modelType);
-}
+    async moveSingleModel(filePath, targetPath) {
+        // Only allow move if supported
+        if (!this.apiConfig.config.supportsMove) {
+            showToast(`Moving ${this.apiConfig.config.displayName}s is not supported`, 'warning');
+            return null;
+        }
+        if (filePath.substring(0, filePath.lastIndexOf('/')) === targetPath) {
+            showToast(`${this.apiConfig.config.displayName} is already in the selected folder`, 'info');
+            return null;
+        }
 
-let _singletonClient = null;
+        const response = await fetch(this.apiConfig.endpoints.moveModel, {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_path: filePath,
+                target_path: targetPath
+            })
+        });
 
-export function getModelApiClient() {
-    if (!_singletonClient) {
-        _singletonClient = new ModelApiClient();
+        const result = await response.json();
+
+        if (!response.ok) {
+            if (result && result.error) {
+                throw new Error(result.error);
+            }
+            throw new Error(`Failed to move ${this.apiConfig.config.displayName}`);
+        }
+
+        if (result && result.message) {
+            showToast(result.message, 'info');
+        } else {
+            showToast(`${this.apiConfig.config.displayName} moved successfully`, 'success');
+        }
+
+        if (result.success) {
+            return result.new_file_path;
+        }
+        return null;
     }
-    _singletonClient.setModelType(state.currentPageType);
-    return _singletonClient;
-}
 
-export async function resetAndReload(updateFolders = false) {
-    return getModelApiClient().loadMoreWithVirtualScroll(true, updateFolders);
+    async moveBulkModels(filePaths, targetPath) {
+        if (!this.apiConfig.config.supportsMove) {
+            showToast(`Moving ${this.apiConfig.config.displayName}s is not supported`, 'warning');
+            return [];
+        }
+        const movedPaths = filePaths.filter(path => {
+            return path.substring(0, path.lastIndexOf('/')) !== targetPath;
+        });
+
+        if (movedPaths.length === 0) {
+            showToast(`All selected ${this.apiConfig.config.displayName}s are already in the target folder`, 'info');
+            return [];
+        }
+
+        const response = await fetch(this.apiConfig.endpoints.moveBulk, {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_paths: movedPaths,
+                target_path: targetPath
+            })
+        });
+
+        const result = await response.json();
+
+        if (!response.ok) {
+            throw new Error(`Failed to move ${this.apiConfig.config.displayName}s`);
+        }
+
+        let successFilePaths = [];
+        if (result.success) {
+            if (result.failure_count > 0) {
+                showToast(`Moved ${result.success_count} ${this.apiConfig.config.displayName}s, ${result.failure_count} failed`, 'warning');
+                console.log('Move operation results:', result.results);
+                const failedFiles = result.results
+                    .filter(r => !r.success)
+                    .map(r => {
+                        const fileName = r.path.substring(r.path.lastIndexOf('/') + 1);
+                        return `${fileName}: ${r.message}`;
+                    });
+                if (failedFiles.length > 0) {
+                    const failureMessage = failedFiles.length <= 3 
+                        ? failedFiles.join('\n')
+                        : failedFiles.slice(0, 3).join('\n') + `\n(and ${failedFiles.length - 3} more)`;
+                    showToast(`Failed moves:\n${failureMessage}`, 'warning', 6000);
+                }
+            } else {
+                showToast(`Successfully moved ${result.success_count} ${this.apiConfig.config.displayName}s`, 'success');
+            }
+            successFilePaths = result.results
+                .filter(r => r.success)
+                .map(r => r.path);
+        } else {
+            throw new Error(result.message || `Failed to move ${this.apiConfig.config.displayName}s`);
+        }
+        return successFilePaths;
+    }
+
+    async bulkDeleteModels(filePaths) {
+        if (!filePaths || filePaths.length === 0) {
+            throw new Error('No file paths provided');
+        }
+
+        try {
+            state.loadingManager.showSimpleLoading(`Deleting ${this.apiConfig.config.displayName.toLowerCase()}s...`);
+            
+            const response = await fetch(this.apiConfig.endpoints.bulkDelete, {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({
+                    file_paths: filePaths
+                })
+            });
+            
+            if (!response.ok) {
+                throw new Error(`Failed to delete ${this.apiConfig.config.displayName.toLowerCase()}s: ${response.statusText}`);
+            }
+            
+            const result = await response.json();
+            
+            if (result.success) {
+                return {
+                    success: true,
+                    deleted_count: result.deleted_count,
+                    failed_count: result.failed_count || 0,
+                    errors: result.errors || []
+                };
+            } else {
+                throw new Error(result.error || `Failed to delete ${this.apiConfig.config.displayName.toLowerCase()}s`);
+            }
+        } catch (error) {
+            console.error(`Error during bulk delete of ${this.apiConfig.config.displayName.toLowerCase()}s:`, error);
+            throw error;
+        } finally {
+            state.loadingManager.hide();
+        }
+    }
 }

static/js/api/checkpointApi.js

@@ -0,0 +1,93 @@
+import { BaseModelApiClient } from './baseModelApi.js';
+import { showToast } from '../utils/uiHelpers.js';
+
+/**
+ * Checkpoint-specific API client
+ */
+export class CheckpointApiClient extends BaseModelApiClient {
+    /**
+     * Get checkpoint information
+     */
+    async getCheckpointInfo(filePath) {
+        try {
+            const response = await fetch(this.apiConfig.endpoints.specific.info, {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ file_path: filePath })
+            });
+            
+            if (!response.ok) {
+                throw new Error('Failed to fetch checkpoint info');
+            }
+            
+            return await response.json();
+        } catch (error) {
+            console.error('Error fetching checkpoint info:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * Get checkpoint roots
+     */
+    async getCheckpointsRoots() {
+        try {
+            const response = await fetch(this.apiConfig.endpoints.specific.checkpoints_roots, {
+                method: 'GET'
+            });
+            if (!response.ok) {
+                throw new Error('Failed to fetch checkpoints roots');
+            }
+            return await response.json();
+        } catch (error) {
+            console.error('Error fetching checkpoints roots:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * Get unet roots
+     */
+    async getUnetRoots() {
+        try {
+            const response = await fetch(this.apiConfig.endpoints.specific.unet_roots, {
+                method: 'GET'
+            });
+            if (!response.ok) {
+                throw new Error('Failed to fetch unet roots');
+            }
+            return await response.json();
+        } catch (error) {
+            console.error('Error fetching unet roots:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * Get appropriate roots based on model type
+     */
+    async fetchModelRoots(modelType = 'checkpoint') {
+        try {
+            let response;
+            if (modelType === 'diffusion_model') {
+                response = await fetch(this.apiConfig.endpoints.specific.unet_roots, {
+                    method: 'GET'
+                });
+            } else {
+                response = await fetch(this.apiConfig.endpoints.specific.checkpoints_roots, {
+                    method: 'GET'
+                });
+            }
+            
+            if (!response.ok) {
+                throw new Error(`Failed to fetch ${modelType} roots`);
+            }
+            return await response.json();
+        } catch (error) {
+            console.error(`Error fetching ${modelType} roots:`, error);
+            throw error;
+        }
+    }
+}

static/js/api/embeddingApi.js

@@ -0,0 +1,8 @@
+import { BaseModelApiClient } from './baseModelApi.js';
+import { showToast } from '../utils/uiHelpers.js';
+
+/**
+ * Embedding-specific API client
+ */
+export class EmbeddingApiClient extends BaseModelApiClient {
+}

static/js/api/loraApi.js

@@ -0,0 +1,94 @@
+import { BaseModelApiClient } from './baseModelApi.js';
+import { showToast } from '../utils/uiHelpers.js';
+import { getSessionItem } from '../utils/storageHelpers.js';
+
+/**
+ * LoRA-specific API client
+ */
+export class LoraApiClient extends BaseModelApiClient {
+    /**
+     * Add LoRA-specific parameters to query
+     */
+    _addModelSpecificParams(params, pageState) {
+        const filterLoraHash = getSessionItem('recipe_to_lora_filterLoraHash');
+        const filterLoraHashes = getSessionItem('recipe_to_lora_filterLoraHashes');
+
+        if (filterLoraHash) {
+            params.append('lora_hash', filterLoraHash);
+        } else if (filterLoraHashes) {
+            try {
+                if (Array.isArray(filterLoraHashes) && filterLoraHashes.length > 0) {
+                    params.append('lora_hashes', filterLoraHashes.join(','));
+                }
+            } catch (error) {
+                console.error('Error parsing lora hashes from session storage:', error);
+            }
+        }
+    }
+
+    /**
+     * Get LoRA notes
+     */
+    async getLoraNote(filePath) {
+        try {
+            const response = await fetch(this.apiConfig.endpoints.specific.notes,
+                {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json'
+                    },
+                    body: JSON.stringify({ file_path: filePath })
+                }
+            );
+            
+            if (!response.ok) {
+                throw new Error('Failed to fetch LoRA notes');
+            }
+            
+            return await response.json();
+        } catch (error) {
+            console.error('Error fetching LoRA notes:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * Get LoRA trigger words
+     */
+    async getLoraTriggerWords(filePath) {
+        try {
+            const response = await fetch(this.apiConfig.endpoints.specific.triggerWords, {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ file_path: filePath })
+            });
+            
+            if (!response.ok) {
+                throw new Error('Failed to fetch trigger words');
+            }
+            
+            return await response.json();
+        } catch (error) {
+            console.error('Error fetching trigger words:', error);
+            throw error;
+        }
+    }
+
+    /**
+     * Get letter counts for LoRAs
+     */
+    async getLetterCounts() {
+        try {
+            const response = await fetch(this.apiConfig.endpoints.specific.letterCounts);
+            if (!response.ok) {
+                throw new Error('Failed to fetch letter counts');
+            }
+            return await response.json();
+        } catch (error) {
+            console.error('Error fetching letter counts:', error);
+            throw error;
+        }
+    }
+}

static/js/api/modelApiFactory.js

@@ -0,0 +1,35 @@
+import { LoraApiClient } from './loraApi.js';
+import { CheckpointApiClient } from './checkpointApi.js';
+import { EmbeddingApiClient } from './embeddingApi.js';
+import { MODEL_TYPES } from './apiConfig.js';
+import { state } from '../state/index.js';
+
+export function createModelApiClient(modelType) {
+    switch (modelType) {
+        case MODEL_TYPES.LORA:
+            return new LoraApiClient();
+        case MODEL_TYPES.CHECKPOINT:
+            return new CheckpointApiClient();
+        case MODEL_TYPES.EMBEDDING:
+            return new EmbeddingApiClient();
+        default:
+            throw new Error(`Unsupported model type: ${modelType}`);
+    }
+}
+
+let _singletonClient = null;
+
+export function getModelApiClient() {
+    const currentType = state.currentPageType;
+    
+    if (!_singletonClient || _singletonClient.modelType !== currentType) {
+        _singletonClient = createModelApiClient(currentType);
+    }
+    
+    return _singletonClient;
+}
+
+export function resetAndReload(updateFolders = false) {
+    const client = getModelApiClient();
+    return client.loadMoreWithVirtualScroll(true, updateFolders);
+}

static/js/components/ContextMenu/CheckpointContextMenu.js

@@ -1,8 +1,8 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
 import { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
-import { getModelApiClient, resetAndReload } from '../../api/baseModelApi.js';
-import { showToast } from '../../utils/uiHelpers.js';
+import { getModelApiClient, resetAndReload } from '../../api/modelApiFactory.js';
 import { showDeleteModal, showExcludeModal } from '../../utils/modalUtils.js';
+import { moveManager } from '../../managers/MoveManager.js';
 
 export class CheckpointContextMenu extends BaseContextMenu {
     constructor() {
@@ -54,8 +54,7 @@ export class CheckpointContextMenu extends BaseContextMenu {
                 apiClient.refreshSingleModelMetadata(this.currentCard.dataset.filepath);
                 break;
             case 'move':
-                // Move to folder (placeholder)
-                showToast('Move to folder feature coming soon', 'info');
+                moveManager.showMoveModal(this.currentCard.dataset.filepath, this.currentCard.dataset.model_type);
                 break;
             case 'exclude':
                 showExcludeModal(this.currentCard.dataset.filepath);

static/js/components/ContextMenu/EmbeddingContextMenu.js

@@ -1,7 +1,7 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
 import { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
-import { getModelApiClient, resetAndReload } from '../../api/baseModelApi.js';
-import { showToast } from '../../utils/uiHelpers.js';
+import { getModelApiClient, resetAndReload } from '../../api/modelApiFactory.js';
+import { moveManager } from '../../managers/MoveManager.js';
 import { showDeleteModal, showExcludeModal } from '../../utils/modalUtils.js';
 
 export class EmbeddingContextMenu extends BaseContextMenu {
@@ -54,8 +54,7 @@ export class EmbeddingContextMenu extends BaseContextMenu {
                 apiClient.refreshSingleModelMetadata(this.currentCard.dataset.filepath);
                 break;
             case 'move':
-                // Move to folder (placeholder)
-                showToast('Move to folder feature coming soon', 'info');
+                moveManager.showMoveModal(this.currentCard.dataset.filepath);
                 break;
             case 'exclude':
                 showExcludeModal(this.currentCard.dataset.filepath);

static/js/components/ContextMenu/LoraContextMenu.js

@@ -1,8 +1,9 @@
 import { BaseContextMenu } from './BaseContextMenu.js';
 import { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
-import { getModelApiClient, resetAndReload } from '../../api/baseModelApi.js';
-import { copyToClipboard, sendLoraToWorkflow } from '../../utils/uiHelpers.js';
+import { getModelApiClient, resetAndReload } from '../../api/modelApiFactory.js';
+import { copyLoraSyntax, sendLoraToWorkflow } from '../../utils/uiHelpers.js';
 import { showExcludeModal, showDeleteModal } from '../../utils/modalUtils.js';
+import { moveManager } from '../../managers/MoveManager.js';
 
 export class LoraContextMenu extends BaseContextMenu {
     constructor() {
@@ -36,7 +37,7 @@ export class LoraContextMenu extends BaseContextMenu {
                 break;
             case 'copyname':
                 // Generate and copy LoRA syntax
-                this.copyLoraSyntax();
+                copyLoraSyntax(this.currentCard);
                 break;
             case 'sendappend':
                 // Send LoRA to workflow (append mode)
@@ -66,16 +67,6 @@ export class LoraContextMenu extends BaseContextMenu {
         }
     }
 
-    // Specific LoRA methods
-    copyLoraSyntax() {
-        const card = this.currentCard;
-        const usageTips = JSON.parse(card.dataset.usage_tips || '{}');
-        const strength = usageTips.strength || 1;
-        const loraSyntax = `<lora:${card.dataset.file_name}:${strength}>`;
-        
-        copyToClipboard(loraSyntax, 'LoRA syntax copied to clipboard');
-    }
-
     sendLoraToWorkflow(replaceMode) {
         const card = this.currentCard;
         const usageTips = JSON.parse(card.dataset.usage_tips || '{}');

static/js/components/Header.js

@@ -16,7 +16,9 @@ export class HeaderManager {
       this.filterManager = null;
       
       // Initialize appropriate managers based on current page
-      this.initializeManagers();
+      if (this.currentPage !== 'statistics') {
+        this.initializeManagers();
+      }
       
       // Set up common header functionality
       this.initializeCommonElements();
@@ -37,11 +39,8 @@ export class HeaderManager {
       this.searchManager = new SearchManager({ page: this.currentPage });
       window.searchManager = this.searchManager;
       
-      // Initialize FilterManager for all page types that have filters
-      if (document.getElementById('filterButton')) {
-        this.filterManager = new FilterManager({ page: this.currentPage });
-        window.filterManager = this.filterManager;
-      }
+      this.filterManager = new FilterManager({ page: this.currentPage });
+      window.filterManager = this.filterManager;
     }
     
     initializeCommonElements() {

static/js/components/ModelDuplicatesManager.js

@@ -2,7 +2,7 @@
 import { showToast } from '../utils/uiHelpers.js';
 import { state, getCurrentPageState } from '../state/index.js';
 import { formatDate } from '../utils/formatters.js';
-import { resetAndReload} from '../api/baseModelApi.js';
+import { resetAndReload} from '../api/modelApiFactory.js';
 import { LoadingManager } from '../managers/LoadingManager.js';
 
 export class ModelDuplicatesManager {

static/js/components/alphabet/AlphabetBar.js

@@ -1,7 +1,7 @@
 // AlphabetBar.js - Component for alphabet filtering
 import { getCurrentPageState } from '../../state/index.js';
 import { getStorageItem, setStorageItem } from '../../utils/storageHelpers.js';
-import { resetAndReload } from '../../api/baseModelApi.js';
+import { resetAndReload } from '../../api/modelApiFactory.js';
 
 /**
  * AlphabetBar class - Handles the alphabet filtering UI and interactions

static/js/components/controls/CheckpointsControls.js

@@ -1,6 +1,6 @@
 // CheckpointsControls.js - Specific implementation for the Checkpoints page
 import { PageControls } from './PageControls.js';
-import { getModelApiClient, resetAndReload } from '../../api/baseModelApi.js';
+import { getModelApiClient, resetAndReload } from '../../api/modelApiFactory.js';
 import { showToast } from '../../utils/uiHelpers.js';
 import { downloadManager } from '../../managers/DownloadManager.js';
 

static/js/components/controls/EmbeddingsControls.js

@@ -1,6 +1,6 @@
 // EmbeddingsControls.js - Specific implementation for the Embeddings page
 import { PageControls } from './PageControls.js';
-import { getModelApiClient, resetAndReload } from '../../api/baseModelApi.js';
+import { getModelApiClient, resetAndReload } from '../../api/modelApiFactory.js';
 import { showToast } from '../../utils/uiHelpers.js';
 import { downloadManager } from '../../managers/DownloadManager.js';
 

static/js/components/controls/LorasControls.js

@@ -1,6 +1,6 @@
 // LorasControls.js - Specific implementation for the LoRAs page
 import { PageControls } from './PageControls.js';
-import { getModelApiClient, resetAndReload } from '../../api/baseModelApi.js';
+import { getModelApiClient, resetAndReload } from '../../api/modelApiFactory.js';
 import { getSessionItem, removeSessionItem } from '../../utils/storageHelpers.js';
 import { createAlphabetBar } from '../alphabet/index.js';
 import { downloadManager } from '../../managers/DownloadManager.js';

static/js/components/shared/ModelCard.js

@@ -1,11 +1,12 @@
-import { showToast, openCivitai, copyToClipboard, sendLoraToWorkflow, openExampleImagesFolder } from '../../utils/uiHelpers.js';
+import { showToast, openCivitai, copyToClipboard, copyLoraSyntax, sendLoraToWorkflow, openExampleImagesFolder } from '../../utils/uiHelpers.js';
 import { state, getCurrentPageState } from '../../state/index.js';
 import { showModelModal } from './ModelModal.js';
 import { toggleShowcase } from './showcase/ShowcaseView.js';
 import { bulkManager } from '../../managers/BulkManager.js';
 import { modalManager } from '../../managers/ModalManager.js';
 import { NSFW_LEVELS } from '../../utils/constants.js';
-import { getModelApiClient } from '../../api/baseModelApi.js';
+import { MODEL_TYPES } from '../../api/apiConfig.js';
+import { getModelApiClient } from '../../api/modelApiFactory.js';
 import { showDeleteModal } from '../../utils/modalUtils.js';
 
 // Add global event delegation handlers
@@ -152,7 +153,7 @@ async function toggleFavorite(card) {
 }
 
 function handleSendToWorkflow(card, replaceMode, modelType) {
-    if (modelType === 'loras') {
+    if (modelType === MODEL_TYPES.LORA) {
         const usageTips = JSON.parse(card.dataset.usage_tips || '{}');
         const strength = usageTips.strength || 1;
         const loraSyntax = `<lora:${card.dataset.file_name}:${strength}>`;
@@ -164,16 +165,13 @@ function handleSendToWorkflow(card, replaceMode, modelType) {
 }
 
 function handleCopyAction(card, modelType) {
-    if (modelType === 'loras') {
-        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');
-    } else if (modelType === 'checkpoints') {
+    if (modelType === MODEL_TYPES.LORA) {
+        copyLoraSyntax(card);
+    } else if (modelType === MODEL_TYPES.CHECKPOINT) {
         // Checkpoint copy functionality - copy checkpoint name
         const checkpointName = card.dataset.file_name;
         copyToClipboard(checkpointName, 'Checkpoint name copied');
-    } else if (modelType === 'embeddings') {
+    } else if (modelType === MODEL_TYPES.EMBEDDING) {
         const embeddingName = card.dataset.file_name;
         copyToClipboard(embeddingName, 'Embedding name copied');
     }
@@ -243,7 +241,7 @@ function showModelModalFromCard(card, modelType) {
         tags: JSON.parse(card.dataset.tags || '[]'),
         modelDescription: card.dataset.modelDescription || '',
         // LoRA specific fields
-        ...(modelType === 'lora' && {
+        ...(modelType === MODEL_TYPES.LORA && {
             usage_tips: card.dataset.usage_tips,
         })
     };
@@ -377,10 +375,15 @@ export function createModelCard(model, modelType) {
     card.dataset.favorite = model.favorite ? 'true' : 'false';
 
     // LoRA specific data
-    if (modelType === 'loras') {
+    if (modelType === MODEL_TYPES.LORA) {
         card.dataset.usage_tips = model.usage_tips;
     }
 
+    // checkpoint specific data
+    if (modelType === MODEL_TYPES.CHECKPOINT) {
+        card.dataset.model_type = model.model_type; // checkpoint or diffusion_model
+    }
+
     // Store metadata if available
     if (model.civitai) {
         card.dataset.meta = JSON.stringify(model.civitai || {});
@@ -406,7 +409,7 @@ export function createModelCard(model, modelType) {
     }
 
     // Apply selection state if in bulk mode and this card is in the selected set (LoRA only)
-    if (modelType === 'loras' && state.bulkMode && state.selectedLoras.has(model.file_path)) {
+    if (modelType === MODEL_TYPES.LORA && state.bulkMode && state.selectedLoras.has(model.file_path)) {
         card.classList.add('selected');
     }
 
@@ -454,7 +457,7 @@ export function createModelCard(model, modelType) {
     card.innerHTML = `
         <div class="card-preview ${shouldBlur ? 'blurred' : ''}">
             ${isVideo ? 
-                `<video ${videoAttrs}>
+                `<video ${videoAttrs} style="pointer-events: none;">
                     <source src="${versionedPreviewUrl}" type="video/mp4">
                 </video>` :
                 `<img src="${versionedPreviewUrl}" alt="${model.model_name}">`
@@ -482,6 +485,7 @@ export function createModelCard(model, modelType) {
             <div class="card-footer">
                 <div class="model-info">
                     <span class="model-name">${model.model_name}</span>
+                    ${model.civitai?.name ? `<span class="version-name">${model.civitai.name}</span>` : ''}
                 </div>
                 <div class="card-actions">
                     <i class="fas fa-folder-open" 

static/js/components/shared/ModelDescription.js

@@ -1,3 +1,5 @@
+import { showToast } from '../../utils/uiHelpers.js';
+
 /**
  * ModelDescription.js
  * Handles model description related functionality - General version
@@ -40,4 +42,99 @@ export function setupTabSwitching() {
             }
         });
     });
+}
+
+/**
+ * Set up model description editing functionality
+ * @param {string} filePath - File path
+ */
+export function setupModelDescriptionEditing(filePath) {
+    const descContent = document.querySelector('.model-description-content');
+    const descContainer = document.querySelector('.model-description-container');
+    if (!descContent || !descContainer) return;
+
+    // Add edit button if not present
+    let editBtn = descContainer.querySelector('.edit-model-description-btn');
+    if (!editBtn) {
+        editBtn = document.createElement('button');
+        editBtn.className = 'edit-model-description-btn';
+        editBtn.title = 'Edit model description';
+        editBtn.innerHTML = '<i class="fas fa-pencil-alt"></i>';
+        descContainer.insertBefore(editBtn, descContent);
+    }
+
+    // Show edit button on hover
+    descContainer.addEventListener('mouseenter', () => {
+        editBtn.classList.add('visible');
+    });
+    descContainer.addEventListener('mouseleave', () => {
+        if (!descContainer.classList.contains('editing')) {
+            editBtn.classList.remove('visible');
+        }
+    });
+
+    // Handle edit button click
+    editBtn.addEventListener('click', () => {
+        descContainer.classList.add('editing');
+        descContent.setAttribute('contenteditable', 'true');
+        descContent.dataset.originalValue = descContent.innerHTML.trim();
+        descContent.focus();
+
+        // Place cursor at the end
+        const range = document.createRange();
+        const sel = window.getSelection();
+        range.selectNodeContents(descContent);
+        range.collapse(false);
+        sel.removeAllRanges();
+        sel.addRange(range);
+
+        editBtn.classList.add('visible');
+    });
+
+    // Keyboard events
+    descContent.addEventListener('keydown', function(e) {
+        if (!this.getAttribute('contenteditable')) return;
+        if (e.key === 'Enter' && !e.shiftKey) {
+            e.preventDefault();
+            this.blur();
+        } else if (e.key === 'Escape') {
+            e.preventDefault();
+            this.innerHTML = this.dataset.originalValue;
+            exitEditMode();
+        }
+    });
+
+    // Save on blur
+    descContent.addEventListener('blur', async function() {
+        if (!this.getAttribute('contenteditable')) return;
+        const newValue = this.innerHTML.trim();
+        const originalValue = this.dataset.originalValue;
+        if (newValue === originalValue) {
+            exitEditMode();
+            return;
+        }
+        if (!newValue) {
+            this.innerHTML = originalValue;
+            showToast('Description cannot be empty', 'error');
+            exitEditMode();
+            return;
+        }
+        try {
+            // Save to backend
+            const { getModelApiClient } = await import('../../api/modelApiFactory.js');
+            await getModelApiClient().saveModelMetadata(filePath, { modelDescription: newValue });
+            showToast('Model description updated', 'success');
+        } catch (err) {
+            this.innerHTML = originalValue;
+            showToast('Failed to update model description', 'error');
+        } finally {
+            exitEditMode();
+        }
+    });
+
+    function exitEditMode() {
+        descContent.removeAttribute('contenteditable');
+        descContainer.classList.remove('editing');
+        editBtn.classList.remove('visible');
+    }
 }

static/js/components/shared/ModelMetadata.js

@@ -4,7 +4,7 @@
  */
 import { showToast } from '../../utils/uiHelpers.js';
 import { BASE_MODELS } from '../../utils/constants.js';
-import { getModelApiClient } from '../../api/baseModelApi.js';
+import { getModelApiClient } from '../../api/modelApiFactory.js';
 
 /**
  * Set up model name editing functionality

static/js/components/shared/ModelModal.js

@@ -6,14 +6,14 @@ import {
     scrollToTop,
     loadExampleImages
 } from './showcase/ShowcaseView.js';
-import { setupTabSwitching } from './ModelDescription.js';
+import { setupTabSwitching, setupModelDescriptionEditing } from './ModelDescription.js';
 import { 
     setupModelNameEditing, 
     setupBaseModelEditing, 
     setupFileNameEditing
 } from './ModelMetadata.js';
 import { setupTagEditMode } from './ModelTags.js';
-import { getModelApiClient } from '../../api/baseModelApi.js';
+import { getModelApiClient } from '../../api/modelApiFactory.js';
 import { renderCompactTags, setupTagTooltip, formatFileSize } from './utils.js';
 import { renderTriggerWords, setupTriggerWordsEditMode } from './TriggerWords.js';
 import { parsePresets, renderPresetTags } from './PresetTags.js';
@@ -33,7 +33,6 @@ export function showModelModal(model, modelType) {
         model.civitai.trainedWords.map(word => word.replace(/'/g, '\\\'')) : [];
     
     // Generate model type specific content
-    // const typeSpecificContent = modelType === 'loras' ? renderLoraSpecificContent(model, escapedWords) : '';
     let typeSpecificContent;
     if (modelType === 'loras') {
         typeSpecificContent = renderLoraSpecificContent(model, escapedWords);
@@ -211,6 +210,7 @@ export function showModelModal(model, modelType) {
     setupModelNameEditing(model.file_path);
     setupBaseModelEditing(model.file_path);
     setupFileNameEditing(model.file_path);
+    setupModelDescriptionEditing(model.file_path, model.modelDescription || '');
     setupEventHandlers(model.file_path);
     
     // LoRA specific setup

static/js/components/shared/ModelTags.js

@@ -3,7 +3,7 @@
  * Module for handling model tag editing functionality - 共享版本
  */
 import { showToast } from '../../utils/uiHelpers.js';
-import { getModelApiClient } from '../../api/baseModelApi.js';
+import { getModelApiClient } from '../../api/modelApiFactory.js';
 
 // Preset tag suggestions
 const PRESET_TAGS = [

static/js/components/shared/PresetTags.js

@@ -2,7 +2,7 @@
  * PresetTags.js
  * Handles LoRA model preset parameter tags - Shared version
  */
-import { getModelApiClient } from '../../api/baseModelApi.js';
+import { getModelApiClient } from '../../api/modelApiFactory.js';
 
 /**
  * Parse preset parameters

static/js/components/shared/TriggerWords.js

@@ -4,7 +4,7 @@
  * Moved to shared directory for consistency
  */
 import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
-import { getModelApiClient } from '../../api/baseModelApi.js';
+import { getModelApiClient } from '../../api/modelApiFactory.js';
 
 /**
  * Fetch trained words for a model

static/js/components/shared/showcase/MediaUtils.js

@@ -5,7 +5,7 @@
  */
 import { showToast, copyToClipboard } from '../../../utils/uiHelpers.js';
 import { state } from '../../../state/index.js';
-import { getModelApiClient } from '../../../api/baseModelApi.js';
+import { getModelApiClient } from '../../../api/modelApiFactory.js';
 
 /**
  * Try to load local image first, fall back to remote if local fails

static/js/core.js

@@ -5,8 +5,11 @@ import { modalManager } from './managers/ModalManager.js';
 import { updateService } from './managers/UpdateService.js';
 import { HeaderManager } from './components/Header.js';
 import { settingsManager } from './managers/SettingsManager.js';
+import { moveManager } from './managers/MoveManager.js';
+import { bulkManager } from './managers/BulkManager.js';
 import { exampleImagesManager } from './managers/ExampleImagesManager.js';
 import { helpManager } from './managers/HelpManager.js';
+import { bannerService } from './managers/BannerService.js';
 import { showToast, initTheme, initBackToTop } from './utils/uiHelpers.js';
 import { initializeInfiniteScroll } from './utils/infiniteScroll.js';
 import { migrateStorageItems } from './utils/storageHelpers.js';
@@ -27,15 +30,21 @@ export class AppCore {
         state.loadingManager = new LoadingManager();
         modalManager.initialize();
         updateService.initialize();
+        bannerService.initialize();
         window.modalManager = modalManager;
         window.settingsManager = settingsManager;
         window.exampleImagesManager = exampleImagesManager;
         window.helpManager = helpManager;
+        window.moveManager = moveManager;
+        window.bulkManager = bulkManager;
         
         // Initialize UI components
         window.headerManager = new HeaderManager();
         initTheme();
         initBackToTop();
+
+        // Initialize the bulk manager
+        bulkManager.initialize();
         
         // Initialize the example images manager
         exampleImagesManager.initialize();

static/js/loras.js

@@ -1,8 +1,6 @@
 import { appCore } from './core.js';
 import { state } from './state/index.js';
 import { updateCardsForBulkMode } from './components/shared/ModelCard.js';
-import { bulkManager } from './managers/BulkManager.js';
-import { moveManager } from './managers/MoveManager.js';
 import { LoraContextMenu } from './components/ContextMenu/index.js';
 import { createPageControls } from './components/controls/index.js';
 import { confirmDelete, closeDeleteModal, confirmExclude, closeExcludeModal } from './utils/modalUtils.js';
@@ -33,15 +31,6 @@ class LoraPageManager {
         window.closeDeleteModal = closeDeleteModal;
         window.confirmExclude = confirmExclude;
         window.closeExcludeModal = closeExcludeModal;
-        window.moveManager = moveManager;
-        
-        // Bulk operations
-        window.toggleBulkMode = () => bulkManager.toggleBulkMode();
-        window.clearSelection = () => bulkManager.clearSelection();
-        window.toggleCardSelection = (card) => bulkManager.toggleCardSelection(card);
-        window.copyAllLorasSyntax = () => bulkManager.copyAllLorasSyntax();
-        window.updateSelectedCount = () => bulkManager.updateSelectedCount();
-        window.bulkManager = bulkManager;
         
         // Expose duplicates manager
         window.modelDuplicatesManager = this.duplicatesManager;
@@ -56,9 +45,6 @@ class LoraPageManager {
         // Initialize cards for current bulk mode state (should be false initially)
         updateCardsForBulkMode(state.bulkMode);
         
-        // Initialize the bulk manager
-        bulkManager.initialize();
-        
         // Initialize common page features (virtual scroll)
         appCore.initializePageFeatures();
     }

static/js/managers/BannerService.js

@@ -0,0 +1,176 @@
+import { getStorageItem, setStorageItem } from '../utils/storageHelpers.js';
+
+/**
+ * Banner Service for managing notification banners
+ */
+class BannerService {
+    constructor() {
+        this.banners = new Map();
+        this.container = null;
+        this.initialized = false;
+    }
+
+    /**
+     * Initialize the banner service
+     */
+    initialize() {
+        if (this.initialized) return;
+
+        this.container = document.getElementById('banner-container');
+        if (!this.container) {
+            console.warn('Banner container not found');
+            return;
+        }
+
+        // Register default banners
+        this.registerBanner('civitai-extension', {
+            id: 'civitai-extension',
+            title: 'New Tool Available: LM Civitai Extension!',
+            content: 'LM Civitai Extension is a browser extension designed to work seamlessly with LoRA Manager to significantly enhance your Civitai browsing experience! See which models you already have, download new ones with a single click, and manage your downloads efficiently.',
+            actions: [
+                {
+                    text: 'Chrome Web Store',
+                    icon: 'fab fa-chrome',
+                    url: 'https://chromewebstore.google.com/detail/capigligggeijgmocnaflanlbghnamgm?utm_source=item-share-cb',
+                    type: 'secondary'
+                },
+                {
+                    text: 'Firefox Extension',
+                    icon: 'fab fa-firefox-browser',
+                    url: 'https://github.com/willmiao/lm-civitai-extension-firefox/releases/latest/download/extension.xpi',
+                    type: 'secondary'
+                },
+                {
+                    text: 'Read more...',
+                    icon: 'fas fa-book',
+                    url: 'https://github.com/willmiao/ComfyUI-Lora-Manager/wiki/LoRA-Manager-Civitai-Extension-(Chrome-Extension)',
+                    type: 'tertiary'
+                }
+            ],
+            dismissible: true,
+            priority: 1
+        });
+
+        this.showActiveBanners();
+        this.initialized = true;
+    }
+
+    /**
+     * Register a new banner
+     * @param {string} id - Unique banner ID
+     * @param {Object} bannerConfig - Banner configuration
+     */
+    registerBanner(id, bannerConfig) {
+        this.banners.set(id, bannerConfig);
+    }
+
+    /**
+     * Check if a banner has been dismissed
+     * @param {string} bannerId - Banner ID
+     * @returns {boolean}
+     */
+    isBannerDismissed(bannerId) {
+        const dismissedBanners = getStorageItem('dismissed_banners', []);
+        return dismissedBanners.includes(bannerId);
+    }
+
+    /**
+     * Dismiss a banner
+     * @param {string} bannerId - Banner ID
+     */
+    dismissBanner(bannerId) {
+        const dismissedBanners = getStorageItem('dismissed_banners', []);
+        if (!dismissedBanners.includes(bannerId)) {
+            dismissedBanners.push(bannerId);
+            setStorageItem('dismissed_banners', dismissedBanners);
+        }
+
+        // Remove banner from DOM
+        const bannerElement = document.querySelector(`[data-banner-id="${bannerId}"]`);
+        if (bannerElement) {
+            bannerElement.style.animation = 'banner-slide-up 0.3s ease-in-out forwards';
+            setTimeout(() => {
+                bannerElement.remove();
+                this.updateContainerVisibility();
+            }, 300);
+        }
+    }
+
+    /**
+     * Show all active (non-dismissed) banners
+     */
+    showActiveBanners() {
+        if (!this.container) return;
+
+        const activeBanners = Array.from(this.banners.values())
+            .filter(banner => !this.isBannerDismissed(banner.id))
+            .sort((a, b) => (b.priority || 0) - (a.priority || 0));
+
+        activeBanners.forEach(banner => {
+            this.renderBanner(banner);
+        });
+
+        this.updateContainerVisibility();
+    }
+
+    /**
+     * Render a banner to the DOM
+     * @param {Object} banner - Banner configuration
+     */
+    renderBanner(banner) {
+        const bannerElement = document.createElement('div');
+        bannerElement.className = 'banner-item';
+        bannerElement.setAttribute('data-banner-id', banner.id);
+
+        const actionsHtml = banner.actions ? banner.actions.map(action => 
+            `<a href="${action.url}" target="_blank" class="banner-action banner-action-${action.type}" rel="noopener noreferrer">
+                <i class="${action.icon}"></i>
+                <span>${action.text}</span>
+            </a>`
+        ).join('') : '';
+
+        const dismissButtonHtml = banner.dismissible ? 
+            `<button class="banner-dismiss" onclick="bannerService.dismissBanner('${banner.id}')" title="Dismiss">
+                <i class="fas fa-times"></i>
+            </button>` : '';
+
+        bannerElement.innerHTML = `
+            <div class="banner-content">
+                <div class="banner-text">
+                    <h4 class="banner-title">${banner.title}</h4>
+                    <p class="banner-description">${banner.content}</p>
+                </div>
+                <div class="banner-actions">
+                    ${actionsHtml}
+                </div>
+            </div>
+            ${dismissButtonHtml}
+        `;
+
+        this.container.appendChild(bannerElement);
+    }
+
+    /**
+     * Update container visibility based on active banners
+     */
+    updateContainerVisibility() {
+        if (!this.container) return;
+
+        const hasActiveBanners = this.container.children.length > 0;
+        this.container.style.display = hasActiveBanners ? 'block' : 'none';
+    }
+
+    /**
+     * Clear all dismissed banners (for testing/admin purposes)
+     */
+    clearDismissedBanners() {
+        setStorageItem('dismissed_banners', []);
+        location.reload();
+    }
+}
+
+// Create and export singleton instance
+export const bannerService = new BannerService();
+
+// Make it globally available
+window.bannerService = bannerService;

static/js/managers/BulkManager.js

@@ -1,147 +1,201 @@
-import { state } from '../state/index.js';
+import { state, getCurrentPageState } from '../state/index.js';
 import { showToast, copyToClipboard, sendLoraToWorkflow } from '../utils/uiHelpers.js';
 import { updateCardsForBulkMode } from '../components/shared/ModelCard.js';
 import { modalManager } from './ModalManager.js';
-import { getModelApiClient } from '../api/baseModelApi.js';
+import { moveManager } from './MoveManager.js';
+import { getModelApiClient } from '../api/modelApiFactory.js';
+import { MODEL_TYPES, MODEL_CONFIG } from '../api/apiConfig.js';
 
 export class BulkManager {
     constructor() {
         this.bulkBtn = document.getElementById('bulkOperationsBtn');
         this.bulkPanel = document.getElementById('bulkOperationsPanel');
-        this.isStripVisible = false; // Track strip visibility state
+        this.isStripVisible = false;
         
-        // Initialize selected loras set in state if not already there
-        if (!state.selectedLoras) {
-            state.selectedLoras = new Set();
-        }
+        this.stripMaxThumbnails = 50;
         
-        // Cache for lora metadata to handle non-visible selected loras
-        if (!state.loraMetadataCache) {
-            state.loraMetadataCache = new Map();
-        }
-
-        this.stripMaxThumbnails = 50; // Maximum thumbnails to show in strip
+        // Model type specific action configurations
+        this.actionConfig = {
+            [MODEL_TYPES.LORA]: {
+                sendToWorkflow: true,
+                copyAll: true,
+                refreshAll: true,
+                moveAll: true,
+                deleteAll: true
+            },
+            [MODEL_TYPES.EMBEDDING]: {
+                sendToWorkflow: false,
+                copyAll: false,
+                refreshAll: true,
+                moveAll: true,
+                deleteAll: true
+            },
+            [MODEL_TYPES.CHECKPOINT]: {
+                sendToWorkflow: false,
+                copyAll: false,
+                refreshAll: true,
+                moveAll: false,
+                deleteAll: true
+            }
+        };
     }
 
     initialize() {
-        // Add event listeners if needed
-        // (Already handled via onclick attributes in HTML, but could be moved here)
-        
-        // Add event listeners for the selected count to toggle thumbnail strip
+        this.setupEventListeners();
+        this.setupGlobalKeyboardListeners();
+    }
+
+    setupEventListeners() {
+        // Bulk operations button listeners
+        const sendToWorkflowBtn = this.bulkPanel?.querySelector('[data-action="send-to-workflow"]');
+        const copyAllBtn = this.bulkPanel?.querySelector('[data-action="copy-all"]');
+        const refreshAllBtn = this.bulkPanel?.querySelector('[data-action="refresh-all"]');
+        const moveAllBtn = this.bulkPanel?.querySelector('[data-action="move-all"]');
+        const deleteAllBtn = this.bulkPanel?.querySelector('[data-action="delete-all"]');
+        const clearBtn = this.bulkPanel?.querySelector('[data-action="clear"]');
+
+        if (sendToWorkflowBtn) {
+            sendToWorkflowBtn.addEventListener('click', () => this.sendAllModelsToWorkflow());
+        }
+        if (copyAllBtn) {
+            copyAllBtn.addEventListener('click', () => this.copyAllModelsSyntax());
+        }
+        if (refreshAllBtn) {
+            refreshAllBtn.addEventListener('click', () => this.refreshAllMetadata());
+        }
+        if (moveAllBtn) {
+            moveAllBtn.addEventListener('click', () => {
+                moveManager.showMoveModal('bulk');
+            });
+        }
+        if (deleteAllBtn) {
+            deleteAllBtn.addEventListener('click', () => this.showBulkDeleteModal());
+        }
+        if (clearBtn) {
+            clearBtn.addEventListener('click', () => this.clearSelection());
+        }
+
+        // Selected count click listener
         const selectedCount = document.getElementById('selectedCount');
         if (selectedCount) {
             selectedCount.addEventListener('click', () => this.toggleThumbnailStrip());
         }
+    }
 
-        // Add global keyboard event listener for Ctrl+A
+    setupGlobalKeyboardListeners() {
         document.addEventListener('keydown', (e) => {
-            // First check if any modal is currently open - if so, don't handle Ctrl+A
             if (modalManager.isAnyModalOpen()) {
-                return; // Exit early - let the browser handle Ctrl+A within the modal
+                return;
             }
 
-            // Check if search input is currently focused - if so, don't handle Ctrl+A
             const searchInput = document.getElementById('searchInput');
             if (searchInput && document.activeElement === searchInput) {
-                return; // Exit early - let the browser handle Ctrl+A within the search input
+                return;
             }
 
-            // Check if it's Ctrl+A (or Cmd+A on Mac)
             if ((e.ctrlKey || e.metaKey) && e.key.toLowerCase() === 'a') {
-
-                // Prevent default browser "Select All" behavior
                 e.preventDefault();
-                
-                // If not in bulk mode, enable it first
                 if (!state.bulkMode) {
                     this.toggleBulkMode();
-                    // Small delay to ensure DOM is updated
-                    setTimeout(() => this.selectAllVisibleLoras(), 50);
+                    setTimeout(() => this.selectAllVisibleModels(), 50);
                 } else {
-                    this.selectAllVisibleLoras();
+                    this.selectAllVisibleModels();
                 }
             } else if (e.key === 'Escape' && state.bulkMode) {
-                // If in bulk mode, exit it on Escape
                 this.toggleBulkMode();
             } else if (e.key.toLowerCase() === 'b') {
-                // If 'b' is pressed, toggle bulk mode
                 this.toggleBulkMode();
             }
         });
     }
 
     toggleBulkMode() {
-        // Toggle the state
         state.bulkMode = !state.bulkMode;
         
-        // Update UI
         this.bulkBtn.classList.toggle('active', state.bulkMode);
         
-        // Important: Remove the hidden class when entering bulk mode
         if (state.bulkMode) {
             this.bulkPanel.classList.remove('hidden');
-            // Use setTimeout to ensure the DOM updates before adding visible class
-            // This helps with the transition animation
+            this.updateActionButtonsVisibility();
             setTimeout(() => {
                 this.bulkPanel.classList.add('visible');
             }, 10);
         } else {
             this.bulkPanel.classList.remove('visible');
-            // Add hidden class back after transition completes
             setTimeout(() => {
                 this.bulkPanel.classList.add('hidden');
-            }, 400); // Match this with the transition duration in CSS
-            
-            // Hide thumbnail strip if it's visible
+            }, 400);
             this.hideThumbnailStrip();
         }
         
-        // First update all cards' visual state before clearing selection
         updateCardsForBulkMode(state.bulkMode);
         
-        // Clear selection if exiting bulk mode - do this after updating cards
         if (!state.bulkMode) {
             this.clearSelection();
             
-            // TODO: fix this, no DOM manipulation should be done here
-            // Force a lightweight refresh of the cards to ensure proper display
-            // This is less disruptive than a full resetAndReload()
+            // TODO:
             document.querySelectorAll('.model-card').forEach(card => {
-                // Re-apply normal display mode to all card actions
                 const actions = card.querySelectorAll('.card-actions, .card-button');
                 actions.forEach(action => action.style.display = 'flex');
             });
         }
     }
 
+    updateActionButtonsVisibility() {
+        const currentModelType = state.currentPageType;
+        const config = this.actionConfig[currentModelType];
+        
+        if (!config) return;
+
+        // Update button visibility based on model type
+        const sendToWorkflowBtn = this.bulkPanel?.querySelector('[data-action="send-to-workflow"]');
+        const copyAllBtn = this.bulkPanel?.querySelector('[data-action="copy-all"]');
+        const refreshAllBtn = this.bulkPanel?.querySelector('[data-action="refresh-all"]');
+        const moveAllBtn = this.bulkPanel?.querySelector('[data-action="move-all"]');
+        const deleteAllBtn = this.bulkPanel?.querySelector('[data-action="delete-all"]');
+
+        if (sendToWorkflowBtn) {
+            sendToWorkflowBtn.style.display = config.sendToWorkflow ? 'block' : 'none';
+        }
+        if (copyAllBtn) {
+            copyAllBtn.style.display = config.copyAll ? 'block' : 'none';
+        }
+        if (refreshAllBtn) {
+            refreshAllBtn.style.display = config.refreshAll ? 'block' : 'none';
+        }
+        if (moveAllBtn) {
+            moveAllBtn.style.display = config.moveAll ? 'block' : 'none';
+        }
+        if (deleteAllBtn) {
+            deleteAllBtn.style.display = config.deleteAll ? 'block' : 'none';
+        }
+    }
+
     clearSelection() {
         document.querySelectorAll('.model-card.selected').forEach(card => {
             card.classList.remove('selected');
         });
-        state.selectedLoras.clear();
+        state.selectedModels.clear();
         this.updateSelectedCount();
-        
-        // Hide thumbnail strip if it's visible
         this.hideThumbnailStrip();
     }
 
     updateSelectedCount() {
         const countElement = document.getElementById('selectedCount');
+        const currentConfig = MODEL_CONFIG[state.currentPageType];
+        const displayName = currentConfig?.displayName || 'Models';
         
         if (countElement) {
-            // Set text content without the icon
-            countElement.textContent = `${state.selectedLoras.size} selected `;
+            countElement.textContent = `${state.selectedModels.size} ${displayName.toLowerCase()}(s) selected `;
             
-            // Update caret icon if it exists
             const existingCaret = countElement.querySelector('.dropdown-caret');
             if (existingCaret) {
                 existingCaret.className = `fas fa-caret-${this.isStripVisible ? 'down' : 'up'} dropdown-caret`;
-                existingCaret.style.visibility = state.selectedLoras.size > 0 ? 'visible' : 'hidden';
+                existingCaret.style.visibility = state.selectedModels.size > 0 ? 'visible' : 'hidden';
             } else {
-                // Create new caret icon if it doesn't exist
                 const caretIcon = document.createElement('i');
                 caretIcon.className = `fas fa-caret-${this.isStripVisible ? 'down' : 'up'} dropdown-caret`;
-                caretIcon.style.visibility = state.selectedLoras.size > 0 ? 'visible' : 'hidden';
+                caretIcon.style.visibility = state.selectedModels.size > 0 ? 'visible' : 'hidden';
                 countElement.appendChild(caretIcon);
             }
         }
@@ -149,16 +203,18 @@ export class BulkManager {
 
     toggleCardSelection(card) {
         const filepath = card.dataset.filepath;
+        const pageState = getCurrentPageState();
         
         if (card.classList.contains('selected')) {
             card.classList.remove('selected');
-            state.selectedLoras.delete(filepath);
+            state.selectedModels.delete(filepath);
         } else {
             card.classList.add('selected');
-            state.selectedLoras.add(filepath);
+            state.selectedModels.add(filepath);
             
-            // Cache the metadata for this lora
-            state.loraMetadataCache.set(filepath, {
+            // Cache the metadata for this model
+            const metadataCache = this.getMetadataCache();
+            metadataCache.set(filepath, {
                 fileName: card.dataset.file_name,
                 usageTips: card.dataset.usage_tips,
                 previewUrl: this.getCardPreviewUrl(card),
@@ -169,35 +225,49 @@ export class BulkManager {
         
         this.updateSelectedCount();
         
-        // Update thumbnail strip if it's visible
         if (this.isStripVisible) {
             this.updateThumbnailStrip();
         }
     }
+
+    getMetadataCache() {
+        const currentType = state.currentPageType;
+        const pageState = getCurrentPageState();
+        
+        // Initialize metadata cache if it doesn't exist
+        if (currentType === MODEL_TYPES.LORA) {
+            if (!state.loraMetadataCache) {
+                state.loraMetadataCache = new Map();
+            }
+            return state.loraMetadataCache;
+        } else {
+            if (!pageState.metadataCache) {
+                pageState.metadataCache = new Map();
+            }
+            return pageState.metadataCache;
+        }
+    }
     
-    // Helper method to get preview URL from a card
     getCardPreviewUrl(card) {
         const img = card.querySelector('img');
         const video = card.querySelector('video source');
         return img ? img.src : (video ? video.src : '/loras_static/images/no-preview.png');
     }
     
-    // Helper method to check if preview is a video
     isCardPreviewVideo(card) {
         return card.querySelector('video') !== null;
     }
 
-    // Apply selection state to cards after they are refreshed
     applySelectionState() {
         if (!state.bulkMode) return;
         
         document.querySelectorAll('.model-card').forEach(card => {
             const filepath = card.dataset.filepath;
-            if (state.selectedLoras.has(filepath)) {
+            if (state.selectedModels.has(filepath)) {
                 card.classList.add('selected');
                 
-                // Update the cache with latest data
-                state.loraMetadataCache.set(filepath, {
+                const metadataCache = this.getMetadataCache();
+                metadataCache.set(filepath, {
                     fileName: card.dataset.file_name,
                     usageTips: card.dataset.usage_tips,
                     previewUrl: this.getCardPreviewUrl(card),
@@ -212,30 +282,33 @@ export class BulkManager {
         this.updateSelectedCount();
     }
 
-    async copyAllLorasSyntax() {
-        if (state.selectedLoras.size === 0) {
+    async copyAllModelsSyntax() {
+        if (state.currentPageType !== MODEL_TYPES.LORA) {
+            showToast('Copy syntax is only available for LoRAs', 'warning');
+            return;
+        }
+        
+        if (state.selectedModels.size === 0) {
             showToast('No LoRAs selected', 'warning');
             return;
         }
         
         const loraSyntaxes = [];
         const missingLoras = [];
+        const metadataCache = this.getMetadataCache();
         
-        // Process all selected loras using our metadata cache
-        for (const filepath of state.selectedLoras) {
-            const metadata = state.loraMetadataCache.get(filepath);
+        for (const filepath of state.selectedModels) {
+            const metadata = metadataCache.get(filepath);
             
             if (metadata) {
                 const usageTips = JSON.parse(metadata.usageTips || '{}');
                 const strength = usageTips.strength || 1;
                 loraSyntaxes.push(`<lora:${metadata.fileName}:${strength}>`);
             } else {
-                // If we don't have metadata, this is an error case
                 missingLoras.push(filepath);
             }
         }
         
-        // Handle any loras with missing metadata
         if (missingLoras.length > 0) {
             console.warn('Missing metadata for some selected loras:', missingLoras);
             showToast(`Missing data for ${missingLoras.length} LoRAs`, 'warning');
@@ -249,31 +322,33 @@ export class BulkManager {
         await copyToClipboard(loraSyntaxes.join(', '), `Copied ${loraSyntaxes.length} LoRA syntaxes to clipboard`);
     }
     
-    // Add method to send all selected loras to workflow
-    async sendAllLorasToWorkflow() {
-        if (state.selectedLoras.size === 0) {
+    async sendAllModelsToWorkflow() {
+        if (state.currentPageType !== MODEL_TYPES.LORA) {
+            showToast('Send to workflow is only available for LoRAs', 'warning');
+            return;
+        }
+        
+        if (state.selectedModels.size === 0) {
             showToast('No LoRAs selected', 'warning');
             return;
         }
         
         const loraSyntaxes = [];
         const missingLoras = [];
+        const metadataCache = this.getMetadataCache();
         
-        // Process all selected loras using our metadata cache
-        for (const filepath of state.selectedLoras) {
-            const metadata = state.loraMetadataCache.get(filepath);
+        for (const filepath of state.selectedModels) {
+            const metadata = metadataCache.get(filepath);
             
             if (metadata) {
                 const usageTips = JSON.parse(metadata.usageTips || '{}');
                 const strength = usageTips.strength || 1;
                 loraSyntaxes.push(`<lora:${metadata.fileName}:${strength}>`);
             } else {
-                // If we don't have metadata, this is an error case
                 missingLoras.push(filepath);
             }
         }
         
-        // Handle any loras with missing metadata
         if (missingLoras.length > 0) {
             console.warn('Missing metadata for some selected loras:', missingLoras);
             showToast(`Missing data for ${missingLoras.length} LoRAs`, 'warning');
@@ -284,82 +359,48 @@ export class BulkManager {
             return;
         }
         
-        // Send the loras to the workflow
         await sendLoraToWorkflow(loraSyntaxes.join(', '), false, 'lora');
     }
     
-    // Show the bulk delete confirmation modal
     showBulkDeleteModal() {
-        if (state.selectedLoras.size === 0) {
-            showToast('No LoRAs selected', 'warning');
+        if (state.selectedModels.size === 0) {
+            showToast('No models selected', 'warning');
             return;
         }
         
-        // Update the count in the modal
         const countElement = document.getElementById('bulkDeleteCount');
         if (countElement) {
-            countElement.textContent = state.selectedLoras.size;
+            countElement.textContent = state.selectedModels.size;
         }
         
-        // Show the modal
         modalManager.showModal('bulkDeleteModal');
     }
     
-    // Confirm bulk delete action
     async confirmBulkDelete() {
-        if (state.selectedLoras.size === 0) {
-            showToast('No LoRAs selected', 'warning');
+        if (state.selectedModels.size === 0) {
+            showToast('No models selected', 'warning');
             modalManager.closeModal('bulkDeleteModal');
             return;
         }
         
-        // Close the modal first before showing loading indicator
         modalManager.closeModal('bulkDeleteModal');
         
         try {
-            // Show loading indicator
-            state.loadingManager.showSimpleLoading('Deleting models...');
+            const apiClient = getModelApiClient();
+            const filePaths = Array.from(state.selectedModels);
             
-            // Gather all file paths for deletion
-            const filePaths = Array.from(state.selectedLoras);
-            
-            // Call the backend API
-            const response = await fetch('/api/loras/bulk-delete', {
-                method: 'POST',
-                headers: {
-                    'Content-Type': 'application/json'
-                },
-                body: JSON.stringify({
-                    file_paths: filePaths
-                })
-            });
-            
-            const result = await response.json();
+            const result = await apiClient.bulkDeleteModels(filePaths);
             
             if (result.success) {
-                showToast(`Successfully deleted ${result.deleted_count} models`, 'success');
+                const currentConfig = MODEL_CONFIG[state.currentPageType];
+                showToast(`Successfully deleted ${result.deleted_count} ${currentConfig.displayName.toLowerCase()}(s)`, 'success');
                 
-                // If virtual scroller exists, update the UI without page reload
-                if (state.virtualScroller) {
-                    // Remove each deleted item from the virtual scroller
-                    filePaths.forEach(path => {
-                        state.virtualScroller.removeItemByFilePath(path);
-                    });
-                    
-                    // Clear the selection
-                    this.clearSelection();
-                } else {
-                    // Clear the selection
-                    this.clearSelection();
-                    
-                    // Fall back to page reload for non-virtual scroll mode
-                    setTimeout(() => {
-                        window.location.reload();
-                    }, 1500);
-                }
+                filePaths.forEach(path => {
+                    state.virtualScroller.removeItemByFilePath(path);
+                });
+                this.clearSelection();
 
                 if (window.modelDuplicatesManager) {
-                    // Update duplicates badge after refresh
                     window.modelDuplicatesManager.updateDuplicatesBadgeAfterRefresh();
                 }
             } else {
@@ -368,16 +409,11 @@ export class BulkManager {
         } catch (error) {
             console.error('Error during bulk delete:', error);
             showToast('Failed to delete models', 'error');
-        } finally {
-            // Hide loading indicator
-            state.loadingManager.hide();
         }
     }
 
-    // Create and show the thumbnail strip of selected LoRAs
     toggleThumbnailStrip() {
-        // If no items are selected, do nothing
-        if (state.selectedLoras.size === 0) return;
+        if (state.selectedModels.size === 0) return;
         
         const existing = document.querySelector('.selected-thumbnails-strip');
         if (existing) {
@@ -388,38 +424,30 @@ export class BulkManager {
     }
     
     showThumbnailStrip() {
-        // Create the thumbnail strip container
         const strip = document.createElement('div');
         strip.className = 'selected-thumbnails-strip';
         
-        // Create a container for the thumbnails (for scrolling)
         const thumbnailContainer = document.createElement('div');
         thumbnailContainer.className = 'thumbnails-container';
         strip.appendChild(thumbnailContainer);
         
-        // Position the strip above the bulk operations panel
         this.bulkPanel.parentNode.insertBefore(strip, this.bulkPanel);
         
-        // Populate the thumbnails
         this.updateThumbnailStrip();
         
-        // Update strip visibility state and caret direction
         this.isStripVisible = true;
-        this.updateSelectedCount(); // Update caret
+        this.updateSelectedCount();
         
-        // Add animation class after a short delay to trigger transition
         setTimeout(() => strip.classList.add('visible'), 10);
     }
     
     hideThumbnailStrip() {
         const strip = document.querySelector('.selected-thumbnails-strip');
-        if (strip && this.isStripVisible) {  // Only hide if actually visible
+        if (strip && this.isStripVisible) {
             strip.classList.remove('visible');
             
-            // Update strip visibility state
             this.isStripVisible = false;
             
-            // Update caret without triggering another hide
             const countElement = document.getElementById('selectedCount');
             if (countElement) {
                 const caret = countElement.querySelector('.dropdown-caret');
@@ -428,7 +456,6 @@ export class BulkManager {
                 }
             }
             
-            // Wait for animation to complete before removing
             setTimeout(() => {
                 if (strip.parentNode) {
                     strip.parentNode.removeChild(strip);
@@ -441,33 +468,28 @@ export class BulkManager {
         const container = document.querySelector('.thumbnails-container');
         if (!container) return;
         
-        // Clear existing thumbnails
         container.innerHTML = '';
         
-        // Get all selected loras
-        const selectedLoras = Array.from(state.selectedLoras);
+        const selectedModels = Array.from(state.selectedModels);
         
-        // Create counter if we have more thumbnails than we'll show
-        if (selectedLoras.length > this.stripMaxThumbnails) {
+        if (selectedModels.length > this.stripMaxThumbnails) {
             const counter = document.createElement('div');
             counter.className = 'strip-counter';
-            counter.textContent = `Showing ${this.stripMaxThumbnails} of ${selectedLoras.length} selected`;
+            counter.textContent = `Showing ${this.stripMaxThumbnails} of ${selectedModels.length} selected`;
             container.appendChild(counter);
         }
         
-        // Limit the number of thumbnails to display
-        const thumbnailsToShow = selectedLoras.slice(0, this.stripMaxThumbnails);
+        const thumbnailsToShow = selectedModels.slice(0, this.stripMaxThumbnails);
+        const metadataCache = this.getMetadataCache();
         
-        // Add a thumbnail for each selected LoRA (limited to max)
         thumbnailsToShow.forEach(filepath => {
-            const metadata = state.loraMetadataCache.get(filepath);
+            const metadata = metadataCache.get(filepath);
             if (!metadata) return;
             
             const thumbnail = document.createElement('div');
             thumbnail.className = 'selected-thumbnail';
             thumbnail.dataset.filepath = filepath;
             
-            // Create the visual element (image or video)
             if (metadata.isVideo) {
                 thumbnail.innerHTML = `
                     <video autoplay loop muted playsinline>
@@ -484,14 +506,12 @@ export class BulkManager {
                 `;
             }
             
-            // Add click handler for deselection
             thumbnail.addEventListener('click', (e) => {
                 if (!e.target.closest('.thumbnail-remove')) {
                     this.deselectItem(filepath);
                 }
             });
             
-            // Add click handler for the remove button
             thumbnail.querySelector('.thumbnail-remove').addEventListener('click', (e) => {
                 e.stopPropagation();
                 this.deselectItem(filepath);
@@ -502,43 +522,36 @@ export class BulkManager {
     }
     
     deselectItem(filepath) {
-        // Find and deselect the corresponding card if it's in the DOM
         const card = document.querySelector(`.model-card[data-filepath="${filepath}"]`);
         if (card) {
             card.classList.remove('selected');
         }
         
-        // Remove from the selection set
-        state.selectedLoras.delete(filepath);
+        state.selectedModels.delete(filepath);
         
-        // Update UI
         this.updateSelectedCount();
         this.updateThumbnailStrip();
         
-        // Hide the strip if no more selections
-        if (state.selectedLoras.size === 0) {
+        if (state.selectedModels.size === 0) {
             this.hideThumbnailStrip();
         }
     }
 
-    // Add method to select all visible loras
-    selectAllVisibleLoras() {
-        // Only select loras already in the VirtualScroller's data model
+    selectAllVisibleModels() {
         if (!state.virtualScroller || !state.virtualScroller.items) {
             showToast('Unable to select all items', 'error');
             return;
         }
         
-        const oldCount = state.selectedLoras.size;
+        const oldCount = state.selectedModels.size;
+        const metadataCache = this.getMetadataCache();
         
-        // Add all loaded loras to the selection set
         state.virtualScroller.items.forEach(item => {
             if (item && item.file_path) {
-                state.selectedLoras.add(item.file_path);
+                state.selectedModels.add(item.file_path);
                 
-                // Add to metadata cache if not already there
-                if (!state.loraMetadataCache.has(item.file_path)) {
-                    state.loraMetadataCache.set(item.file_path, {
+                if (!metadataCache.has(item.file_path)) {
+                    metadataCache.set(item.file_path, {
                         fileName: item.file_name,
                         usageTips: item.usage_tips || '{}',
                         previewUrl: item.preview_url || '/loras_static/images/no-preview.png',
@@ -549,45 +562,37 @@ export class BulkManager {
             }
         });
         
-        // Update visual state
         this.applySelectionState();
         
-        // Show success message
-        const newlySelected = state.selectedLoras.size - oldCount;
-        showToast(`Selected ${newlySelected} additional LoRAs`, 'success');
+        const newlySelected = state.selectedModels.size - oldCount;
+        const currentConfig = MODEL_CONFIG[state.currentPageType];
+        showToast(`Selected ${newlySelected} additional ${currentConfig.displayName.toLowerCase()}(s)`, 'success');
         
-        // Update thumbnail strip if visible
         if (this.isStripVisible) {
             this.updateThumbnailStrip();
         }
     }
 
-    // Add method to refresh metadata for all selected models
     async refreshAllMetadata() {
-        if (state.selectedLoras.size === 0) {
+        if (state.selectedModels.size === 0) {
             showToast('No models selected', 'warning');
             return;
         }
         
         try {
-            // Get the API client for the current model type
             const apiClient = getModelApiClient();
+            const filePaths = Array.from(state.selectedModels);
             
-            // Convert Set to Array for processing
-            const filePaths = Array.from(state.selectedLoras);
-            
-            // Call the bulk refresh method
             const result = await apiClient.refreshBulkModelMetadata(filePaths);
             
             if (result.success) {
-                // Update the metadata cache for successfully refreshed items
-                for (const filepath of state.selectedLoras) {
-                    const metadata = state.loraMetadataCache.get(filepath);
+                const metadataCache = this.getMetadataCache();
+                for (const filepath of state.selectedModels) {
+                    const metadata = metadataCache.get(filepath);
                     if (metadata) {
-                        // Find the corresponding card to get updated data
                         const card = document.querySelector(`.model-card[data-filepath="${filepath}"]`);
                         if (card) {
-                            state.loraMetadataCache.set(filepath, {
+                            metadataCache.set(filepath, {
                                 ...metadata,
                                 fileName: card.dataset.file_name,
                                 usageTips: card.dataset.usage_tips,
@@ -599,7 +604,6 @@ export class BulkManager {
                     }
                 }
                 
-                // Update thumbnail strip if visible
                 if (this.isStripVisible) {
                     this.updateThumbnailStrip();
                 }
@@ -612,5 +616,4 @@ export class BulkManager {
     }
 }
 
-// Create a singleton instance
 export const bulkManager = new BulkManager();

static/js/managers/DownloadManager.js

@@ -1,7 +1,7 @@
 import { modalManager } from './ModalManager.js';
 import { showToast } from '../utils/uiHelpers.js';
 import { LoadingManager } from './LoadingManager.js';
-import { getModelApiClient, resetAndReload } from '../api/baseModelApi.js';
+import { getModelApiClient, resetAndReload } from '../api/modelApiFactory.js';
 import { getStorageItem, setStorageItem } from '../utils/storageHelpers.js';
 
 export class DownloadManager {
@@ -297,7 +297,10 @@ export class DownloadManager {
             // Set default root if available
             const defaultRootKey = `default_${this.apiClient.modelType}_root`;
             const defaultRoot = getStorageItem('settings', {})[defaultRootKey];
+            console.log(`Default root for ${this.apiClient.modelType}:`, defaultRoot);
+            console.log('Available roots:', rootsData.roots);
             if (defaultRoot && rootsData.roots.includes(defaultRoot)) {
+                console.log(`Setting default root: ${defaultRoot}`);
                 modelRoot.value = defaultRoot;
             }
 

static/js/managers/ExampleImagesManager.js

@@ -1,4 +1,5 @@
 import { showToast } from '../utils/uiHelpers.js';
+import { state } from '../state/index.js';
 import { getStorageItem, setStorageItem } from '../utils/storageHelpers.js';
 
 // ExampleImagesManager.js
@@ -14,6 +15,12 @@ class ExampleImagesManager {
         this.isMigrating = false; // Track migration state separately from downloading
         this.hasShownCompletionToast = false; // Flag to track if completion toast has been shown
         
+        // Auto download properties
+        this.autoDownloadInterval = null;
+        this.lastAutoDownloadCheck = 0;
+        this.autoDownloadCheckInterval = 10 * 60 * 1000; // 10 minutes in milliseconds
+        this.pageInitTime = Date.now(); // Track when page was initialized
+        
         // Initialize download path field and check download status
         this.initializePathOptions();
         this.checkDownloadStatus();
@@ -48,6 +55,14 @@ class ExampleImagesManager {
         if (collapseBtn) {
             collapseBtn.onclick = () => this.toggleProgressPanel();
         }
+
+        // Setup auto download if enabled
+        if (state.global.settings.autoDownloadExampleImages) {
+            this.setupAutoDownload();
+        }
+
+        // Make this instance globally accessible
+        window.exampleImagesManager = this;
     }
     
     // Initialize event listeners for buttons
@@ -133,6 +148,15 @@ class ExampleImagesManager {
                         console.error('Failed to update example images path:', error);
                     }
                 }
+
+                // Setup or clear auto download based on path availability
+                if (state.global.settings.autoDownloadExampleImages) {
+                    if (hasPath) {
+                        this.setupAutoDownload();
+                    } else {
+                        this.clearAutoDownload();
+                    }
+                }
             });
         } catch (error) {
             console.error('Failed to initialize path options:', error);
@@ -646,6 +670,106 @@ class ExampleImagesManager {
             }
         }
     }
+
+    setupAutoDownload() {
+        // Only setup if conditions are met
+        if (!this.canAutoDownload()) {
+            return;
+        }
+
+        // Clear any existing interval
+        this.clearAutoDownload();
+
+        // Wait at least 30 seconds after page initialization before first check
+        const timeSinceInit = Date.now() - this.pageInitTime;
+        const initialDelay = Math.max(60000 - timeSinceInit, 5000); // At least 5 seconds, up to 60 seconds
+
+        console.log(`Setting up auto download with initial delay of ${initialDelay}ms`);
+
+        setTimeout(() => {
+            // Do initial check
+            this.performAutoDownloadCheck();
+
+            // Set up recurring interval
+            this.autoDownloadInterval = setInterval(() => {
+                this.performAutoDownloadCheck();
+            }, this.autoDownloadCheckInterval);
+
+        }, initialDelay);
+    }
+
+    clearAutoDownload() {
+        if (this.autoDownloadInterval) {
+            clearInterval(this.autoDownloadInterval);
+            this.autoDownloadInterval = null;
+            console.log('Auto download interval cleared');
+        }
+    }
+
+    canAutoDownload() {
+        // Check if auto download is enabled
+        if (!state.global.settings.autoDownloadExampleImages) {
+            return false;
+        }
+
+        // Check if download path is set
+        const pathInput = document.getElementById('exampleImagesPath');
+        if (!pathInput || !pathInput.value.trim()) {
+            return false;
+        }
+
+        // Check if already downloading
+        if (this.isDownloading) {
+            return false;
+        }
+
+        return true;
+    }
+
+    async performAutoDownloadCheck() {
+        const now = Date.now();
+        
+        // Prevent too frequent checks (minimum 2 minutes between checks)
+        if (now - this.lastAutoDownloadCheck < 2 * 60 * 1000) {
+            console.log('Skipping auto download check - too soon since last check');
+            return;
+        }
+
+        this.lastAutoDownloadCheck = now;
+
+        if (!this.canAutoDownload()) {
+            console.log('Auto download conditions not met, skipping check');
+            return;
+        }
+
+        try {
+            console.log('Performing auto download check...');
+            
+            const outputDir = document.getElementById('exampleImagesPath').value;
+            const optimize = document.getElementById('optimizeExampleImages').checked;
+            
+            const response = await fetch('/api/download-example-images', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({
+                    output_dir: outputDir,
+                    optimize: optimize,
+                    model_types: ['lora', 'checkpoint', 'embedding'],
+                    auto_mode: true // Flag to indicate this is an automatic download
+                })
+            });
+            
+            const data = await response.json();
+            
+            if (!data.success) {
+                console.warn('Auto download check failed:', data.error);
+            }
+        } catch (error) {
+            console.error('Auto download check error:', error);
+        }
+    }
 }
 
 // Create singleton instance

static/js/managers/FilterManager.js

@@ -1,7 +1,6 @@
-import { BASE_MODEL_CLASSES } from '../utils/constants.js';
 import { getCurrentPageState } from '../state/index.js';
 import { showToast, updatePanelPositions } from '../utils/uiHelpers.js';
-import { getModelApiClient } from '../api/baseModelApi.js';
+import { getModelApiClient } from '../api/modelApiFactory.js';
 import { removeStorageItem, setStorageItem, getStorageItem } from '../utils/storageHelpers.js';
 
 export class FilterManager {
@@ -67,14 +66,7 @@ export class FilterManager {
             tagsContainer.innerHTML = '<div class="tags-loading">Loading tags...</div>';
             
             // Determine the API endpoint based on the page type
-            let tagsEndpoint = '/api/loras/top-tags?limit=20';
-            if (this.currentPage === 'recipes') {
-                tagsEndpoint = '/api/recipes/top-tags?limit=20';
-            } else if (this.currentPage === 'checkpoints') {
-                tagsEndpoint = '/api/checkpoints/top-tags?limit=20';
-            } else if (this.currentPage === 'embeddings') {
-                tagsEndpoint = '/api/embeddings/top-tags?limit=20';
-            }
+            const tagsEndpoint = `/api/${this.currentPage}/top-tags?limit=20`;
 
             const response = await fetch(tagsEndpoint);
             if (!response.ok) throw new Error('Failed to fetch tags');
@@ -141,19 +133,8 @@ export class FilterManager {
         const baseModelTagsContainer = document.getElementById('baseModelTags');
         if (!baseModelTagsContainer) return;
         
-        // Set the appropriate API endpoint based on current page
-        let apiEndpoint = '';
-        if (this.currentPage === 'loras') {
-            apiEndpoint = '/api/loras/base-models';
-        } else if (this.currentPage === 'recipes') {
-            apiEndpoint = '/api/recipes/base-models';
-        } else if (this.currentPage === 'checkpoints') {
-            apiEndpoint = '/api/checkpoints/base-models';
-        } else if (this.currentPage === 'embeddings') {
-            apiEndpoint = '/api/embeddings/base-models';
-        } else {
-            return;
-        }
+        // Set the API endpoint based on current page
+        const apiEndpoint = `/api/${this.currentPage}/base-models`;
         
         // Fetch base models
         fetch(apiEndpoint)

static/js/managers/ImportManager.js

@@ -1,7 +1,5 @@
 import { modalManager } from './ModalManager.js';
-import { showToast } from '../utils/uiHelpers.js';
 import { LoadingManager } from './LoadingManager.js';
-import { getStorageItem } from '../utils/storageHelpers.js';
 import { ImportStepManager } from './import/ImportStepManager.js';
 import { ImageProcessor } from './import/ImageProcessor.js';
 import { RecipeDataManager } from './import/RecipeDataManager.js';
@@ -86,8 +84,8 @@ export class ImportManager {
         const uploadError = document.getElementById('uploadError');
         if (uploadError) uploadError.textContent = '';
         
-        const urlError = document.getElementById('urlError');
-        if (urlError) urlError.textContent = '';
+        const importUrlError = document.getElementById('importUrlError');
+        if (importUrlError) importUrlError.textContent = '';
         
         const recipeName = document.getElementById('recipeName');
         if (recipeName) recipeName.value = '';
@@ -167,10 +165,10 @@ export class ImportManager {
         
         // Clear error messages
         const uploadError = document.getElementById('uploadError');
-        const urlError = document.getElementById('urlError');
+        const importUrlError = document.getElementById('importUrlError');
         
         if (uploadError) uploadError.textContent = '';
-        if (urlError) urlError.textContent = '';
+        if (importUrlError) importUrlError.textContent = '';
     }
 
     handleImageUpload(event) {
@@ -224,8 +222,8 @@ export class ImportManager {
         const uploadError = document.getElementById('uploadError');
         if (uploadError) uploadError.textContent = '';
         
-        const urlError = document.getElementById('urlError');
-        if (urlError) urlError.textContent = '';
+        const importUrlError = document.getElementById('importUrlError');
+        if (importUrlError) importUrlError.textContent = '';
     }
 
     backToDetails() {

static/js/managers/MoveManager.js

@@ -1,107 +1,116 @@
 import { showToast, updateFolderTags } from '../utils/uiHelpers.js';
 import { state, getCurrentPageState } from '../state/index.js';
 import { modalManager } from './ModalManager.js';
+import { bulkManager } from './BulkManager.js';
 import { getStorageItem } from '../utils/storageHelpers.js';
-import { getModelApiClient } from '../api/baseModelApi.js';
+import { getModelApiClient } from '../api/modelApiFactory.js';
 
 class MoveManager {
     constructor() {
         this.currentFilePath = null;
         this.bulkFilePaths = null;
         this.modal = document.getElementById('moveModal');
-        this.loraRootSelect = document.getElementById('moveLoraRoot');
+        this.modelRootSelect = document.getElementById('moveModelRoot');
         this.folderBrowser = document.getElementById('moveFolderBrowser');
         this.newFolderInput = document.getElementById('moveNewFolder');
         this.pathDisplay = document.getElementById('moveTargetPathDisplay');
         this.modalTitle = document.getElementById('moveModalTitle');
+        this.rootLabel = document.getElementById('moveRootLabel');
 
         this.initializeEventListeners();
     }
 
     initializeEventListeners() {
-        // 初始化LoRA根目录选择器
-        this.loraRootSelect.addEventListener('change', () => this.updatePathPreview());
+        // Initialize model root directory selector
+        this.modelRootSelect.addEventListener('change', () => this.updatePathPreview());
 
-        // 文件夹选择事件
+        // Folder selection event
         this.folderBrowser.addEventListener('click', (e) => {
             const folderItem = e.target.closest('.folder-item');
             if (!folderItem) return;
 
-            // 如果点击已选中的文件夹，则取消选择
+            // If clicking already selected folder, deselect it
             if (folderItem.classList.contains('selected')) {
                 folderItem.classList.remove('selected');
             } else {
-                // 取消其他选中状态
+                // Deselect other folders
                 this.folderBrowser.querySelectorAll('.folder-item').forEach(item => {
                     item.classList.remove('selected');
                 });
-                // 设置当前选中状态
+                // Select current folder
                 folderItem.classList.add('selected');
             }
             
             this.updatePathPreview();
         });
 
-        // 新文件夹输入事件
+        // New folder input event
         this.newFolderInput.addEventListener('input', () => this.updatePathPreview());
     }
 
-    async showMoveModal(filePath) {
+    async showMoveModal(filePath, modelType = null) {
         // Reset state
         this.currentFilePath = null;
         this.bulkFilePaths = null;
         
+        const apiClient = getModelApiClient();
+        const currentPageType = state.currentPageType;
+        const modelConfig = apiClient.apiConfig.config;
+        
         // Handle bulk mode
         if (filePath === 'bulk') {
-            const selectedPaths = Array.from(state.selectedLoras);
+            const selectedPaths = Array.from(state.selectedModels);
             if (selectedPaths.length === 0) {
-                showToast('No LoRAs selected', 'warning');
+                showToast('No models selected', 'warning');
                 return;
             }
             this.bulkFilePaths = selectedPaths;
-            this.modalTitle.textContent = `Move ${selectedPaths.length} LoRAs`;
+            this.modalTitle.textContent = `Move ${selectedPaths.length} ${modelConfig.displayName}s`;
         } else {
             // Single file mode
             this.currentFilePath = filePath;
-            this.modalTitle.textContent = "Move Model";
+            this.modalTitle.textContent = `Move ${modelConfig.displayName}`;
         }
         
-        // 清除之前的选择
+        // Update UI labels based on model type
+        this.rootLabel.textContent = `Select ${modelConfig.displayName} Root:`;
+        this.pathDisplay.querySelector('.path-text').textContent = `Select a ${modelConfig.displayName.toLowerCase()} root directory`;
+        
+        // Clear previous selections
         this.folderBrowser.querySelectorAll('.folder-item').forEach(item => {
             item.classList.remove('selected');
         });
         this.newFolderInput.value = '';
 
         try {
-            // Fetch LoRA roots
-            const rootsResponse = await fetch('/api/loras/roots');
-            if (!rootsResponse.ok) {
-                throw new Error('Failed to fetch LoRA roots');
+            // Fetch model roots
+            let rootsData;
+            if (modelType) {
+                // For checkpoints, use the specific API method that considers modelType
+                rootsData = await apiClient.fetchModelRoots(modelType);
+            } else {
+                // For other model types, use the generic method
+                rootsData = await apiClient.fetchModelRoots();
             }
             
-            const rootsData = await rootsResponse.json();
             if (!rootsData.roots || rootsData.roots.length === 0) {
-                throw new Error('No LoRA roots found');
+                throw new Error(`No ${modelConfig.displayName.toLowerCase()} roots found`);
             }
 
-            // 填充LoRA根目录选择器
-            this.loraRootSelect.innerHTML = rootsData.roots.map(root => 
+            // Populate model root selector
+            this.modelRootSelect.innerHTML = rootsData.roots.map(root => 
                 `<option value="${root}">${root}</option>`
             ).join('');
 
-            // Set default lora root if available
-            const defaultRoot = getStorageItem('settings', {}).default_loras_root;
+            // Set default root if available
+            const settingsKey = `default_${currentPageType.slice(0, -1)}_root`; // Remove 's' from plural
+            const defaultRoot = getStorageItem('settings', {})[settingsKey];
             if (defaultRoot && rootsData.roots.includes(defaultRoot)) {
-                this.loraRootSelect.value = defaultRoot;
+                this.modelRootSelect.value = defaultRoot;
             }
 
             // Fetch folders dynamically
-            const foldersResponse = await fetch('/api/loras/folders');
-            if (!foldersResponse.ok) {
-                throw new Error('Failed to fetch folders');
-            }
-            
-            const foldersData = await foldersResponse.json();
+            const foldersData = await apiClient.fetchModelFolders();
             
             // Update folder browser with dynamic content
             this.folderBrowser.innerHTML = foldersData.folders.map(folder => 
@@ -112,13 +121,13 @@ class MoveManager {
             modalManager.showModal('moveModal');
             
         } catch (error) {
-            console.error('Error fetching LoRA roots or folders:', error);
+            console.error(`Error fetching ${modelConfig.displayName.toLowerCase()} roots or folders:`, error);
             showToast(error.message, 'error');
         }
     }
 
     updatePathPreview() {
-        const selectedRoot = this.loraRootSelect.value;
+        const selectedRoot = this.modelRootSelect.value;
         const selectedFolder = this.folderBrowser.querySelector('.folder-item.selected')?.dataset.folder || '';
         const newFolder = this.newFolderInput.value.trim();
 
@@ -134,7 +143,7 @@ class MoveManager {
     }
 
     async moveModel() {
-        const selectedRoot = this.loraRootSelect.value;
+        const selectedRoot = this.modelRootSelect.value;
         const selectedFolder = this.folderBrowser.querySelector('.folder-item.selected')?.dataset.folder || '';
         const newFolder = this.newFolderInput.value.trim();
 
@@ -191,11 +200,8 @@ class MoveManager {
 
             // Refresh folder tags after successful move
             try {
-                const foldersResponse = await fetch('/api/loras/folders');
-                if (foldersResponse.ok) {
-                    const foldersData = await foldersResponse.json();
-                    updateFolderTags(foldersData.folders);
-                }
+                const foldersData = await apiClient.fetchModelFolders();
+                updateFolderTags(foldersData.folders);
             } catch (error) {
                 console.error('Error refreshing folder tags:', error);
             }
@@ -204,7 +210,7 @@ class MoveManager {
             
             // If we were in bulk mode, exit it after successful move
             if (this.bulkFilePaths && state.bulkMode) {
-                toggleBulkMode();
+                bulkManager.toggleBulkMode();
             }
 
         } catch (error) {

static/js/managers/SearchManager.js

@@ -1,6 +1,6 @@
 import { updatePanelPositions } from "../utils/uiHelpers.js";
 import { getCurrentPageState } from "../state/index.js";
-import { getModelApiClient } from "../api/baseModelApi.js";
+import { getModelApiClient } from "../api/modelApiFactory.js";
 import { setStorageItem, getStorageItem } from "../utils/storageHelpers.js";
 /**
  * SearchManager - Handles search functionality across different pages
@@ -318,6 +318,7 @@ export class SearchManager {
             filename: options.filename || false,
             modelname: options.modelname || false,
             tags: options.tags || false,
+            creator: options.creator || false,
             recursive: recursive
           };
         } else if (this.currentPage === 'checkpoints') {
@@ -325,6 +326,7 @@ export class SearchManager {
             filename: options.filename || false,
             modelname: options.modelname || false,
             tags: options.tags || false,
+            creator: options.creator || false,
             recursive: recursive
           };
         }

static/js/managers/SettingsManager.js

@@ -1,7 +1,7 @@
 import { modalManager } from './ModalManager.js';
 import { showToast } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
-import { resetAndReload } from '../api/baseModelApi.js';
+import { resetAndReload } from '../api/modelApiFactory.js';
 import { setStorageItem, getStorageItem } from '../utils/storageHelpers.js';
 import { DOWNLOAD_PATH_TEMPLATES, MAPPABLE_BASE_MODELS } from '../utils/constants.js';
 
@@ -15,29 +15,44 @@ export class SettingsManager {
         
         // Ensure settings are loaded from localStorage
         this.loadSettingsFromStorage();
-        
+
+        // Sync settings to backend if needed
+        this.syncSettingsToBackendIfNeeded();
+
         this.initialize();
     }
 
     loadSettingsFromStorage() {
         // Get saved settings from localStorage
         const savedSettings = getStorageItem('settings');
-        
+
+        // Migrate legacy default_loras_root to default_lora_root if present
+        if (savedSettings && savedSettings.default_loras_root && !savedSettings.default_lora_root) {
+            savedSettings.default_lora_root = savedSettings.default_loras_root;
+            delete savedSettings.default_loras_root;
+            setStorageItem('settings', savedSettings);
+        }
+
         // Apply saved settings to state if available
         if (savedSettings) {
             state.global.settings = { ...state.global.settings, ...savedSettings };
         }
-        
+
         // Initialize default values for new settings if they don't exist
         if (state.global.settings.compactMode === undefined) {
             state.global.settings.compactMode = false;
         }
-        
+
         // Set default for optimizeExampleImages if undefined
         if (state.global.settings.optimizeExampleImages === undefined) {
             state.global.settings.optimizeExampleImages = true;
         }
-        
+
+        // Set default for autoDownloadExampleImages if undefined
+        if (state.global.settings.autoDownloadExampleImages === undefined) {
+            state.global.settings.autoDownloadExampleImages = true;
+        }
+
         // Set default for cardInfoDisplay if undefined
         if (state.global.settings.cardInfoDisplay === undefined) {
             state.global.settings.cardInfoDisplay = 'always';
@@ -67,6 +82,60 @@ export class SettingsManager {
         if (state.global.settings.base_model_path_mappings === undefined) {
             state.global.settings.base_model_path_mappings = {};
         }
+
+        // Set default for defaultEmbeddingRoot if undefined
+        if (state.global.settings.default_embedding_root === undefined) {
+            state.global.settings.default_embedding_root = '';
+        }
+
+        // Set default for includeTriggerWords if undefined
+        if (state.global.settings.includeTriggerWords === undefined) {
+            state.global.settings.includeTriggerWords = false;
+        }
+    }
+
+    async syncSettingsToBackendIfNeeded() {
+        // Get local settings from storage
+        const localSettings = getStorageItem('settings') || {};
+
+        // Fields that need to be synced to backend
+        const fieldsToSync = [
+            'civitai_api_key',
+            'default_lora_root',
+            'default_checkpoint_root',
+            'default_embedding_root',
+            'base_model_path_mappings',
+            'download_path_template'
+        ];
+
+        // Build payload for syncing
+        const payload = {};
+
+        fieldsToSync.forEach(key => {
+            if (localSettings[key] !== undefined) {
+                if (key === 'base_model_path_mappings') {
+                    payload[key] = JSON.stringify(localSettings[key]);
+                } else {
+                    payload[key] = localSettings[key];
+                }
+            }
+        });
+
+        // Only send request if there is something to sync
+        if (Object.keys(payload).length > 0) {
+            try {
+                await fetch('/api/settings', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify(payload)
+                });
+                // Log success to console
+                console.log('Settings synced to backend');
+            } catch (e) {
+                // Log error to console
+                console.error('Failed to sync settings to backend:', e);
+            }
+        }
     }
 
     initialize() {
@@ -136,6 +205,12 @@ export class SettingsManager {
             optimizeExampleImagesCheckbox.checked = state.global.settings.optimizeExampleImages || false;
         }
 
+        // Set auto download example images setting
+        const autoDownloadExampleImagesCheckbox = document.getElementById('autoDownloadExampleImages');
+        if (autoDownloadExampleImagesCheckbox) {
+            autoDownloadExampleImagesCheckbox.checked = state.global.settings.autoDownloadExampleImages || false;
+        }
+
         // Set download path template setting
         const downloadPathTemplateSelect = document.getElementById('downloadPathTemplate');
         if (downloadPathTemplateSelect) {
@@ -143,6 +218,12 @@ export class SettingsManager {
             this.updatePathTemplatePreview();
         }
 
+        // Set include trigger words setting
+        const includeTriggerWordsCheckbox = document.getElementById('includeTriggerWords');
+        if (includeTriggerWordsCheckbox) {
+            includeTriggerWordsCheckbox.checked = state.global.settings.includeTriggerWords || false;
+        }
+
         // Load base model path mappings
         this.loadBaseModelMappings();
 
@@ -151,8 +232,9 @@ export class SettingsManager {
         
         // Load default checkpoint root
         await this.loadCheckpointRoots();
-        
-        // Backend settings are loaded from the template directly
+
+        // Load default embedding root
+        await this.loadEmbeddingRoots();
     }
 
     async loadLoraRoots() {
@@ -185,7 +267,7 @@ export class SettingsManager {
             });
             
             // Set selected value from settings
-            const defaultRoot = state.global.settings.default_loras_root || '';
+            const defaultRoot = state.global.settings.default_lora_root || '';
             defaultLoraRootSelect.value = defaultRoot;
             
         } catch (error) {
@@ -233,6 +315,45 @@ export class SettingsManager {
         }
     }
 
+    async loadEmbeddingRoots() {
+        try {
+            const defaultEmbeddingRootSelect = document.getElementById('defaultEmbeddingRoot');
+            if (!defaultEmbeddingRootSelect) return;
+
+            // Fetch embedding roots
+            const response = await fetch('/api/embeddings/roots');
+            if (!response.ok) {
+                throw new Error('Failed to fetch embedding roots');
+            }
+
+            const data = await response.json();
+            if (!data.roots || data.roots.length === 0) {
+                throw new Error('No embedding roots found');
+            }
+
+            // Clear existing options except the first one (No Default)
+            const noDefaultOption = defaultEmbeddingRootSelect.querySelector('option[value=""]');
+            defaultEmbeddingRootSelect.innerHTML = '';
+            defaultEmbeddingRootSelect.appendChild(noDefaultOption);
+
+            // Add options for each root
+            data.roots.forEach(root => {
+                const option = document.createElement('option');
+                option.value = root;
+                option.textContent = root;
+                defaultEmbeddingRootSelect.appendChild(option);
+            });
+
+            // Set selected value from settings
+            const defaultRoot = state.global.settings.default_embedding_root || '';
+            defaultEmbeddingRootSelect.value = defaultRoot;
+
+        } catch (error) {
+            console.error('Error loading embedding roots:', error);
+            showToast('Failed to load embedding roots: ' + error.message, 'error');
+        }
+    }
+
     loadBaseModelMappings() {
         const mappingsContainer = document.getElementById('baseModelMappingsContainer');
         if (!mappingsContainer) return;
@@ -448,8 +569,12 @@ export class SettingsManager {
             state.global.settings.autoplayOnHover = value;
         } else if (settingKey === 'optimize_example_images') {
             state.global.settings.optimizeExampleImages = value;
+        } else if (settingKey === 'auto_download_example_images') {
+            state.global.settings.autoDownloadExampleImages = value;
         } else if (settingKey === 'compact_mode') {
             state.global.settings.compactMode = value;
+        } else if (settingKey === 'include_trigger_words') {
+            state.global.settings.includeTriggerWords = value;
         } else {
             // For any other settings that might be added in the future
             state.global.settings[settingKey] = value;
@@ -460,7 +585,7 @@ export class SettingsManager {
         
         try {
             // For backend settings, make API call
-            if (['show_only_sfw', 'blur_mature_content', 'autoplay_on_hover', 'optimize_example_images', 'use_centralized_examples'].includes(settingKey)) {
+            if (['show_only_sfw'].includes(settingKey)) {
                 const payload = {};
                 payload[settingKey] = value;
                 
@@ -475,14 +600,23 @@ export class SettingsManager {
                 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();
             
-            if (settingKey === 'show_only_sfw') {
+            // Trigger auto download setup/teardown when setting changes
+            if (settingKey === 'auto_download_example_images' && window.exampleImagesManager) {
+                if (value) {
+                    window.exampleImagesManager.setupAutoDownload();
+                } else {
+                    window.exampleImagesManager.clearAutoDownload();
+                }
+            }
+            
+            if (settingKey === 'show_only_sfw' || settingKey === 'blur_mature_content') {
                 this.reloadContent();
             }
             
@@ -505,9 +639,11 @@ export class SettingsManager {
         
         // Update frontend state
         if (settingKey === 'default_lora_root') {
-            state.global.settings.default_loras_root = value;
+            state.global.settings.default_lora_root = value;
         } else if (settingKey === 'default_checkpoint_root') {
             state.global.settings.default_checkpoint_root = value;
+        } else if (settingKey === 'default_embedding_root') {
+            state.global.settings.default_embedding_root = value;
         } else if (settingKey === 'display_density') {
             state.global.settings.displayDensity = value;
             
@@ -528,7 +664,7 @@ export class SettingsManager {
         
         try {
             // For backend settings, make API call
-            if (settingKey === 'default_lora_root' || settingKey === 'default_checkpoint_root' || settingKey === 'download_path_template') {
+            if (settingKey === 'default_lora_root' || settingKey === 'default_checkpoint_root' || settingKey === 'default_embedding_root' || settingKey === 'download_path_template') {
                 const payload = {};
                 payload[settingKey] = value;
                 
@@ -583,10 +719,7 @@ export class SettingsManager {
             // Update state
             state.global.settings[settingKey] = value;
             
-            // Save to localStorage if appropriate
-            if (!settingKey.includes('api_key')) { // Don't store API keys in localStorage for security
-                setStorageItem('settings', state.global.settings);
-            }
+            setStorageItem('settings', state.global.settings);
             
             // For backend settings, make API call
             const payload = {};
@@ -665,83 +798,13 @@ export class SettingsManager {
         } else if (this.currentPage === 'checkpoints') {
             // Reload the checkpoints without updating folders
             await resetAndReload(false);
-        }
-    }
-
-    async saveSettings() {
-        // Get frontend settings from UI
-        const blurMatureContent = document.getElementById('blurMatureContent').checked;
-        const showOnlySFW = document.getElementById('showOnlySFW').checked;
-        const defaultLoraRoot = document.getElementById('defaultLoraRoot').value;
-        const defaultCheckpointRoot = document.getElementById('defaultCheckpointRoot').value;
-        const autoplayOnHover = document.getElementById('autoplayOnHover').checked;
-        const optimizeExampleImages = document.getElementById('optimizeExampleImages').checked;
-        
-        // Get backend settings
-        const apiKey = document.getElementById('civitaiApiKey').value;
-        
-        // Update frontend state and save to localStorage
-        state.global.settings.blurMatureContent = blurMatureContent;
-        state.global.settings.show_only_sfw = showOnlySFW;
-        state.global.settings.default_loras_root = defaultLoraRoot;
-        state.global.settings.default_checkpoint_root = defaultCheckpointRoot;
-        state.global.settings.autoplayOnHover = autoplayOnHover;
-        state.global.settings.optimizeExampleImages = optimizeExampleImages;
-        
-        // Save settings to localStorage
-        setStorageItem('settings', state.global.settings);
-        
-        try {
-            // Save backend settings via API
-            const response = await fetch('/api/settings', {
-                method: 'POST',
-                headers: {
-                    'Content-Type': 'application/json',
-                },
-                body: JSON.stringify({
-                    civitai_api_key: apiKey,
-                    show_only_sfw: showOnlySFW,
-                    optimize_example_images: optimizeExampleImages,
-                    default_checkpoint_root: defaultCheckpointRoot
-                })
-            });
-
-            if (!response.ok) {
-                throw new Error('Failed to save settings');
-            }
-
-            showToast('Settings saved successfully', 'success');
-            modalManager.closeModal('settingsModal');
-            
-            // Apply frontend settings immediately
-            this.applyFrontendSettings();
-            
-            if (this.currentPage === 'loras') {
-                // Reload the loras without updating folders
-                await resetAndReload(false);
-            } else if (this.currentPage === 'recipes') {
-                // Reload the recipes without updating folders
-                await window.recipeManager.loadRecipes();
-            } else if (this.currentPage === 'checkpoints') {
-                // Reload the checkpoints without updating folders
-                await window.checkpointsManager.loadCheckpoints();
-            }
-        } catch (error) {
-            showToast('Failed to save settings: ' + error.message, 'error');
+        } else if (this.currentPage === 'embeddings') {
+            // Reload the embeddings without updating folders
+            await resetAndReload(false);
         }
     }
 
     applyFrontendSettings() {
-        // Apply blur setting to existing content
-        const blurSetting = state.global.settings.blurMatureContent;
-        document.querySelectorAll('.model-card[data-nsfw="true"] .card-image').forEach(img => {
-            if (blurSetting) {
-                img.classList.add('nsfw-blur');
-            } else {
-                img.classList.remove('nsfw-blur');
-            }
-        });
-        
         // Apply autoplay setting to existing videos in card previews
         const autoplayOnHover = state.global.settings.autoplayOnHover;
         document.querySelectorAll('.card-preview video').forEach(video => {

static/js/managers/UpdateService.js

@@ -358,9 +358,10 @@ export class UpdateService {
                     <i class="fas fa-check-circle" style="margin-right: 8px;"></i>
                     Successfully updated to ${newVersion}!
                     <br><br>
-                    <small style="opacity: 0.8;">
-                        Please restart ComfyUI to complete the update process.
-                    </small>
+                    <div style="opacity: 0.95; color: var(--lora-error); font-size: 1em;">
+                        Please restart ComfyUI or LoRA Manager to apply update.<br>
+                        Make sure to reload your browser for both LoRA Manager and ComfyUI.
+                    </div>
                 </div>
             `;
         }
@@ -370,10 +371,10 @@ export class UpdateService {
         this.updateAvailable = false;
         
         // Refresh the modal content
-        setTimeout(() => {
-            this.updateModalContent();
-            this.showUpdateProgress(false);
-        }, 2000);
+        // setTimeout(() => {
+        //     this.updateModalContent();
+        //     this.showUpdateProgress(false);
+        // }, 2000);
     }
     
     // Simple markdown parser for changelog items

static/js/managers/import/FolderBrowser.js

@@ -112,7 +112,7 @@ export class FolderBrowser {
                 ).join('');
                 
                 // Set default lora root if available
-                const defaultRoot = getStorageItem('settings', {}).default_loras_root;
+                const defaultRoot = getStorageItem('settings', {}).default_lora_root;
                 if (defaultRoot && rootsData.roots.includes(defaultRoot)) {
                     loraRoot.value = defaultRoot;
                 }

static/js/managers/import/ImageProcessor.js

@@ -27,7 +27,7 @@ export class ImageProcessor {
 
     async handleUrlInput() {
         const urlInput = document.getElementById('imageUrlInput');
-        const errorElement = document.getElementById('urlError');
+        const errorElement = document.getElementById('importUrlError');
         const input = urlInput.value.trim();
         
         // Validate input

static/js/state/index.js

@@ -37,6 +37,7 @@ export const state = {
                 filename: true,
                 modelname: true,
                 tags: false,
+                creator: false,
                 recursive: false
             },
             filters: {
@@ -83,12 +84,17 @@ export const state = {
             searchOptions: {
                 filename: true,
                 modelname: true,
+                creator: false,
                 recursive: false
             },
             filters: {
                 baseModel: [],
                 tags: []
             },
+            modelType: 'checkpoint', // 'checkpoint' or 'diffusion_model'
+            bulkMode: false,
+            selectedModels: new Set(),
+            metadataCache: new Map(),
             showFavoritesOnly: false,
             duplicatesMode: false,
         },
@@ -106,12 +112,16 @@ export const state = {
                 filename: true,
                 modelname: true,
                 tags: false,
+                creator: false,
                 recursive: false
             },
             filters: {
                 baseModel: [],
                 tags: []
             },
+            bulkMode: false,
+            selectedModels: new Set(),
+            metadataCache: new Map(),
             showFavoritesOnly: false,
             duplicatesMode: false,
         }
@@ -154,12 +164,43 @@ export const state = {
     get filters() { return this.pages[this.currentPageType].filters; },
     set filters(value) { this.pages[this.currentPageType].filters = value; },
     
-    get bulkMode() { return this.pages.loras.bulkMode; },
-    set bulkMode(value) { this.pages.loras.bulkMode = value; },
+    get bulkMode() { 
+        const currentType = this.currentPageType;
+        if (currentType === MODEL_TYPES.LORA) {
+            return this.pages.loras.bulkMode;
+        } else {
+            return this.pages[currentType].bulkMode;
+        }
+    },
+    set bulkMode(value) { 
+        const currentType = this.currentPageType;
+        if (currentType === MODEL_TYPES.LORA) {
+            this.pages.loras.bulkMode = value;
+        } else {
+            this.pages[currentType].bulkMode = value;
+        }
+    },
     
     get selectedLoras() { return this.pages.loras.selectedLoras; },
     set selectedLoras(value) { this.pages.loras.selectedLoras = value; },
     
+    get selectedModels() { 
+        const currentType = this.currentPageType;
+        if (currentType === MODEL_TYPES.LORA) {
+            return this.pages.loras.selectedLoras;
+        } else {
+            return this.pages[currentType].selectedModels;
+        }
+    },
+    set selectedModels(value) { 
+        const currentType = this.currentPageType;
+        if (currentType === MODEL_TYPES.LORA) {
+            this.pages.loras.selectedLoras = value;
+        } else {
+            this.pages[currentType].selectedModels = value;
+        }
+    },
+    
     get loraMetadataCache() { return this.pages.loras.loraMetadataCache; },
     set loraMetadataCache(value) { this.pages.loras.loraMetadataCache = value; },
     

static/js/statistics.js

@@ -150,6 +150,12 @@ class StatisticsManager {
                 value: this.data.collection.checkpoint_count,
                 label: 'Checkpoints',
                 format: 'number'
+            },
+            {
+                icon: 'fas fa-code',
+                value: this.data.collection.embedding_count,
+                label: 'Embeddings',
+                format: 'number'
             }
         ];
 
@@ -195,7 +201,9 @@ class StatisticsManager {
         if (!this.data.collection) return 0;
         
         const totalModels = this.data.collection.total_models;
-        const unusedModels = this.data.collection.unused_loras + this.data.collection.unused_checkpoints;
+        const unusedModels = this.data.collection.unused_loras + 
+                           this.data.collection.unused_checkpoints + 
+                           this.data.collection.unused_embeddings;
         const usedModels = totalModels - unusedModels;
         
         return totalModels > 0 ? (usedModels / totalModels) * 100 : 0;
@@ -233,12 +241,17 @@ class StatisticsManager {
         if (!ctx || !this.data.collection) return;
 
         const data = {
-            labels: ['LoRAs', 'Checkpoints'],
+            labels: ['LoRAs', 'Checkpoints', 'Embeddings'],
             datasets: [{
-                data: [this.data.collection.lora_count, this.data.collection.checkpoint_count],
+                data: [
+                    this.data.collection.lora_count, 
+                    this.data.collection.checkpoint_count,
+                    this.data.collection.embedding_count
+                ],
                 backgroundColor: [
                     'oklch(68% 0.28 256)',
-                    'oklch(68% 0.28 200)'
+                    'oklch(68% 0.28 200)',
+                    'oklch(68% 0.28 120)'
                 ],
                 borderWidth: 2,
                 borderColor: getComputedStyle(document.documentElement).getPropertyValue('--border-color')
@@ -266,8 +279,13 @@ class StatisticsManager {
 
         const loraData = this.data.baseModels.loras;
         const checkpointData = this.data.baseModels.checkpoints;
+        const embeddingData = this.data.baseModels.embeddings;
         
-        const allModels = new Set([...Object.keys(loraData), ...Object.keys(checkpointData)]);
+        const allModels = new Set([
+            ...Object.keys(loraData), 
+            ...Object.keys(checkpointData),
+            ...Object.keys(embeddingData)
+        ]);
         
         const data = {
             labels: Array.from(allModels),
@@ -281,6 +299,11 @@ class StatisticsManager {
                     label: 'Checkpoints',
                     data: Array.from(allModels).map(model => checkpointData[model] || 0),
                     backgroundColor: 'oklch(68% 0.28 200 / 0.7)'
+                },
+                {
+                    label: 'Embeddings',
+                    data: Array.from(allModels).map(model => embeddingData[model] || 0),
+                    backgroundColor: 'oklch(68% 0.28 120 / 0.7)'
                 }
             ]
         };
@@ -325,6 +348,13 @@ class StatisticsManager {
                     borderColor: 'oklch(68% 0.28 200)',
                     backgroundColor: 'oklch(68% 0.28 200 / 0.1)',
                     fill: true
+                },
+                {
+                    label: 'Embedding Usage',
+                    data: timeline.map(item => item.embedding_usage),
+                    borderColor: 'oklch(68% 0.28 120)',
+                    backgroundColor: 'oklch(68% 0.28 120 / 0.1)',
+                    fill: true
                 }
             ]
         };
@@ -365,11 +395,13 @@ class StatisticsManager {
 
         const topLoras = this.data.usage.top_loras || [];
         const topCheckpoints = this.data.usage.top_checkpoints || [];
+        const topEmbeddings = this.data.usage.top_embeddings || [];
         
         // Combine and sort all models by usage
         const allModels = [
             ...topLoras.map(m => ({ ...m, type: 'LoRA' })),
-            ...topCheckpoints.map(m => ({ ...m, type: 'Checkpoint' }))
+            ...topCheckpoints.map(m => ({ ...m, type: 'Checkpoint' })),
+            ...topEmbeddings.map(m => ({ ...m, type: 'Embedding' }))
         ].sort((a, b) => b.usage_count - a.usage_count).slice(0, 10);
 
         const data = {
@@ -377,9 +409,14 @@ class StatisticsManager {
             datasets: [{
                 label: 'Usage Count',
                 data: allModels.map(model => model.usage_count),
-                backgroundColor: allModels.map(model => 
-                    model.type === 'LoRA' ? 'oklch(68% 0.28 256)' : 'oklch(68% 0.28 200)'
-                )
+                backgroundColor: allModels.map(model => {
+                    switch(model.type) {
+                        case 'LoRA': return 'oklch(68% 0.28 256)';
+                        case 'Checkpoint': return 'oklch(68% 0.28 200)';
+                        case 'Embedding': return 'oklch(68% 0.28 120)';
+                        default: return 'oklch(68% 0.28 256)';
+                    }
+                })
             }]
         };
 
@@ -404,12 +441,17 @@ class StatisticsManager {
         if (!ctx || !this.data.collection) return;
 
         const data = {
-            labels: ['LoRAs', 'Checkpoints'],
+            labels: ['LoRAs', 'Checkpoints', 'Embeddings'],
             datasets: [{
-                data: [this.data.collection.lora_size, this.data.collection.checkpoint_size],
+                data: [
+                    this.data.collection.lora_size, 
+                    this.data.collection.checkpoint_size,
+                    this.data.collection.embedding_size
+                ],
                 backgroundColor: [
                     'oklch(68% 0.28 256)',
-                    'oklch(68% 0.28 200)'
+                    'oklch(68% 0.28 200)',
+                    'oklch(68% 0.28 120)'
                 ]
             }]
         };
@@ -443,10 +485,12 @@ class StatisticsManager {
 
         const loraData = this.data.storage.loras || [];
         const checkpointData = this.data.storage.checkpoints || [];
+        const embeddingData = this.data.storage.embeddings || [];
         
         const allData = [
             ...loraData.map(item => ({ ...item, type: 'LoRA' })),
-            ...checkpointData.map(item => ({ ...item, type: 'Checkpoint' }))
+            ...checkpointData.map(item => ({ ...item, type: 'Checkpoint' })),
+            ...embeddingData.map(item => ({ ...item, type: 'Embedding' }))
         ];
 
         const data = {
@@ -458,9 +502,14 @@ class StatisticsManager {
                     name: item.name,
                     type: item.type
                 })),
-                backgroundColor: allData.map(item => 
-                    item.type === 'LoRA' ? 'oklch(68% 0.28 256 / 0.6)' : 'oklch(68% 0.28 200 / 0.6)'
-                )
+                backgroundColor: allData.map(item => {
+                    switch(item.type) {
+                        case 'LoRA': return 'oklch(68% 0.28 256 / 0.6)';
+                        case 'Checkpoint': return 'oklch(68% 0.28 200 / 0.6)';
+                        case 'Embedding': return 'oklch(68% 0.28 120 / 0.6)';
+                        default: return 'oklch(68% 0.28 256 / 0.6)';
+                    }
+                })
             }]
         };
 
@@ -502,6 +551,7 @@ class StatisticsManager {
     renderTopModelsLists() {
         this.renderTopLorasList();
         this.renderTopCheckpointsList();
+        this.renderTopEmbeddingsList();
         this.renderLargestModelsList();
     }
 
@@ -555,17 +605,44 @@ class StatisticsManager {
         `).join('');
     }
 
+    renderTopEmbeddingsList() {
+        const container = document.getElementById('topEmbeddingsList');
+        if (!container || !this.data.usage?.top_embeddings) return;
+
+        const topEmbeddings = this.data.usage.top_embeddings;
+        
+        if (topEmbeddings.length === 0) {
+            container.innerHTML = '<div class="loading-placeholder">No usage data available</div>';
+            return;
+        }
+
+        container.innerHTML = topEmbeddings.map(embedding => `
+            <div class="model-item">
+                <img src="${embedding.preview_url || '/loras_static/images/no-preview.png'}" 
+                     alt="${embedding.name}" class="model-preview"
+                     onerror="this.src='/loras_static/images/no-preview.png'">
+                <div class="model-info">
+                    <div class="model-name" title="${embedding.name}">${embedding.name}</div>
+                    <div class="model-meta">${embedding.base_model} • ${embedding.folder}</div>
+                </div>
+                <div class="model-usage">${embedding.usage_count}</div>
+            </div>
+        `).join('');
+    }
+
     renderLargestModelsList() {
         const container = document.getElementById('largestModelsList');
         if (!container || !this.data.storage) return;
 
         const loraModels = this.data.storage.loras || [];
         const checkpointModels = this.data.storage.checkpoints || [];
+        const embeddingModels = this.data.storage.embeddings || [];
         
         // Combine and sort by size
         const allModels = [
             ...loraModels.map(m => ({ ...m, type: 'LoRA' })),
-            ...checkpointModels.map(m => ({ ...m, type: 'Checkpoint' }))
+            ...checkpointModels.map(m => ({ ...m, type: 'Checkpoint' })),
+            ...embeddingModels.map(m => ({ ...m, type: 'Embedding' }))
         ].sort((a, b) => b.size - a.size).slice(0, 10);
 
         if (allModels.length === 0) {

static/js/utils/infiniteScroll.js

@@ -1,7 +1,7 @@
 import { state, getCurrentPageState } from '../state/index.js';
 import { VirtualScroller } from './VirtualScroller.js';
 import { createModelCard, setupModelCardEventDelegation } from '../components/shared/ModelCard.js';
-import { getModelApiClient } from '../api/baseModelApi.js';
+import { getModelApiClient } from '../api/modelApiFactory.js';
 import { showToast } from './uiHelpers.js';
 
 // Function to dynamically import the appropriate card creator based on page type

static/js/utils/modalUtils.js

@@ -1,5 +1,5 @@
 import { modalManager } from '../managers/ModalManager.js';
-import { getModelApiClient } from '../api/baseModelApi.js';
+import { getModelApiClient } from '../api/modelApiFactory.js';
 
 const apiClient = getModelApiClient();
 

static/js/utils/storageHelpers.js

@@ -141,7 +141,8 @@ export function migrateStorageItems() {
         'recipes_search_prefs',
         'checkpoints_search_prefs',
         'show_update_notifications',
-        'last_update_check'
+        'last_update_check',
+        'dismissed_banners'
     ];
     
     // Migrate each known key

static/js/utils/uiHelpers.js

@@ -1,4 +1,4 @@
-import { getCurrentPageState } from '../state/index.js';
+import { state, getCurrentPageState } from '../state/index.js';
 import { getStorageItem, setStorageItem } from './storageHelpers.js';
 import { NODE_TYPE_ICONS, DEFAULT_NODE_COLOR } from './constants.js';
 
@@ -285,6 +285,76 @@ export function getNSFWLevelName(level) {
     return 'Unknown';
 }
 
+export function copyLoraSyntax(card) {
+  const usageTips = JSON.parse(card.dataset.usage_tips || "{}");
+  const strength = usageTips.strength || 1;
+  const baseSyntax = `<lora:${card.dataset.file_name}:${strength}>`;
+
+  // Check if trigger words should be included
+  const includeTriggerWords = state.global.settings.includeTriggerWords;
+
+  if (!includeTriggerWords) {
+    copyToClipboard(baseSyntax, "LoRA syntax copied to clipboard");
+    return;
+  }
+
+  // Get trigger words from metadata
+  const meta = card.dataset.meta ? JSON.parse(card.dataset.meta) : null;
+  const trainedWords = meta?.trainedWords;
+
+  if (
+    !trainedWords ||
+    !Array.isArray(trainedWords) ||
+    trainedWords.length === 0
+  ) {
+    copyToClipboard(
+      baseSyntax,
+      "LoRA syntax copied to clipboard (no trigger words found)"
+    );
+    return;
+  }
+
+  let finalSyntax = baseSyntax;
+
+  if (trainedWords.length === 1) {
+    // Single group: append trigger words to the same line
+    const triggers = trainedWords[0]
+      .split(",")
+      .map((word) => word.trim())
+      .filter((word) => word);
+    if (triggers.length > 0) {
+      finalSyntax = `${baseSyntax}, ${triggers.join(", ")}`;
+    }
+    copyToClipboard(
+      finalSyntax,
+      "LoRA syntax with trigger words copied to clipboard"
+    );
+  } else {
+    // Multiple groups: format with separators
+    const groups = trainedWords
+      .map((group) => {
+        const triggers = group
+          .split(",")
+          .map((word) => word.trim())
+          .filter((word) => word);
+        return triggers.join(", ");
+      })
+      .filter((group) => group);
+
+    if (groups.length > 0) {
+      // Use separator between all groups except the first
+      finalSyntax = baseSyntax + ", " + groups[0];
+      for (let i = 1; i < groups.length; i++) {
+        finalSyntax += `\n${"-".repeat(17)}\n${groups[i]}`;
+      }
+    }
+    copyToClipboard(
+      finalSyntax,
+      "LoRA syntax with trigger word groups copied to clipboard"
+    );
+  }
+}
+
 /**
  * Sends LoRA syntax to the active ComfyUI workflow
  * @param {string} loraSyntax - The LoRA syntax to send

templates/base.html

@@ -82,6 +82,11 @@
         </button>
 
         <div class="container">
+            <!-- Banner component -->
+            <div id="banner-container" class="banner-container" style="display: none;">
+                <!-- Banners will be dynamically inserted here -->
+            </div>
+
             {% if is_initializing %}
                 <!-- Show initialization component when initializing -->
                 {% include 'components/initialization.html' %}

templates/checkpoints.html

@@ -9,7 +9,7 @@
 
 {% block init_title %}Initializing Checkpoints Manager{% endblock %}
 {% block init_message %}Scanning and building checkpoints cache. This may take a few moments...{% endblock %}
-{% block init_check_url %}/api/checkpoints?page=1&page_size=1{% endblock %}
+{% block init_check_url %}/api/checkpoints/list?page=1&page_size=1{% endblock %}
 
 {% block additional_components %}
 

templates/components/controls.html

@@ -50,14 +50,11 @@
                     <i class="fas fa-cloud-download-alt"></i> Download
                 </button>
             </div>
-            <!-- Conditional buttons based on page -->
-            {% if request.path == '/loras' %}
             <div class="control-group">
                 <button id="bulkOperationsBtn" data-action="bulk" title="Bulk Operations (Press B)">
                     <i class="fas fa-th-large"></i> <span>Bulk <div class="shortcut-key">B</div></span>
                 </button>
             </div>
-            {% endif %}
             <div class="control-group">
                 <button id="findDuplicatesBtn" data-action="find-duplicates" title="Find duplicate models">
                     <i class="fas fa-clone"></i> Duplicates
@@ -119,22 +116,22 @@
             0 selected <i class="fas fa-caret-down dropdown-caret"></i>
         </span>
         <div class="bulk-operations-actions">
-            <button onclick="bulkManager.sendAllLorasToWorkflow()" title="Send all selected LoRAs to workflow">
+            <button data-action="send-to-workflow" title="Send all selected LoRAs to workflow">
                 <i class="fas fa-arrow-right"></i> Send to Workflow
             </button>
-            <button onclick="bulkManager.copyAllLorasSyntax()" title="Copy all selected LoRAs syntax">
+            <button data-action="copy-all" title="Copy all selected LoRAs syntax">
                 <i class="fas fa-copy"></i> Copy All
             </button>
-            <button onclick="bulkManager.refreshAllMetadata()" title="Refresh CivitAI metadata for selected models">
+            <button data-action="refresh-all" title="Refresh CivitAI metadata for selected models">
                 <i class="fas fa-sync-alt"></i> Refresh All
             </button>
-            <button onclick="moveManager.showMoveModal('bulk')" title="Move selected LoRAs to folder">
+            <button data-action="move-all" title="Move selected models to folder">
                 <i class="fas fa-folder-open"></i> Move All
             </button>
-            <button onclick="bulkManager.showBulkDeleteModal()" title="Delete selected LoRAs" class="danger-btn">
+            <button data-action="delete-all" title="Delete selected models" class="danger-btn">
                 <i class="fas fa-trash"></i> Delete All
             </button>
-            <button onclick="bulkManager.clearSelection()" title="Clear selection">
+            <button data-action="clear" title="Clear selection">
                 <i class="fas fa-times"></i> Clear
             </button>
         </div>

templates/components/header.html

@@ -86,15 +86,18 @@
       <div class="search-option-tag active" data-option="filename">Filename</div>
       <div class="search-option-tag active" data-option="modelname">Checkpoint Name</div>
       <div class="search-option-tag active" data-option="tags">Tags</div>
+      <div class="search-option-tag" data-option="creator">Creator</div>
       {% elif request.path == '/embeddings' %}
       <div class="search-option-tag active" data-option="filename">Filename</div>
       <div class="search-option-tag active" data-option="modelname">Embedding Name</div>
       <div class="search-option-tag active" data-option="tags">Tags</div>
+      <div class="search-option-tag" data-option="creator">Creator</div>
       {% else %}
       <!-- Default options for LoRAs page -->
       <div class="search-option-tag active" data-option="filename">Filename</div>
       <div class="search-option-tag active" data-option="modelname">Model Name</div>
       <div class="search-option-tag active" data-option="tags">Tags</div>
+      <div class="search-option-tag" data-option="creator">Creator</div>
       {% endif %}
     </div>
   </div>

templates/components/import_modal.html

@@ -25,7 +25,7 @@
                             <i class="fas fa-download"></i> Fetch Image
                         </button>
                     </div>
-                    <div class="error-message" id="urlError"></div>
+                    <div class="error-message" id="importUrlError"></div>
                 </div>
             </div>
             

templates/components/modals/move_modal.html

@@ -9,13 +9,13 @@
             <div class="path-preview">
                 <label>Target Location Preview:</label>
                 <div class="path-display" id="moveTargetPathDisplay">
-                    <span class="path-text">Select a LoRA root directory</span>
+                    <span class="path-text">Select a model root directory</span>
                 </div>
             </div>
 
             <div class="input-group">
-                <label>Select LoRA Root:</label>
-                <select id="moveLoraRoot"></select>
+                <label id="moveRootLabel">Select Model Root:</label>
+                <select id="moveModelRoot"></select>
             </div>
             <div class="input-group">
                 <label>Target Folder:</label>

templates/components/modals/settings_modal.html

@@ -128,6 +128,23 @@
                         Set the default checkpoint root directory for downloads, imports and moves
                     </div>
                 </div>
+
+                <div class="setting-item">
+                    <div class="setting-row">
+                        <div class="setting-info">
+                            <label for="defaultEmbeddingRoot">Default Embedding Root</label>
+                        </div>
+                        <div class="setting-control select-control">
+                            <select id="defaultEmbeddingRoot" onchange="settingsManager.saveSelectSetting('defaultEmbeddingRoot', 'default_embedding_root')">
+                                <option value="">No Default</option>
+                                <!-- Options will be loaded dynamically -->
+                            </select>
+                        </div>
+                    </div>
+                    <div class="input-help">
+                        Set the default embedding root directory for downloads, imports and moves
+                    </div>
+                </div>
             </div>
 
             <!-- Default Path Customization Section -->
@@ -255,6 +272,24 @@
                     </div>
                 </div>
                 
+                <div class="setting-item">
+                    <div class="setting-row">
+                        <div class="setting-info">
+                            <label for="autoDownloadExampleImages">Auto Download Example Images</label>
+                        </div>
+                        <div class="setting-control">
+                            <label class="toggle-switch">
+                                <input type="checkbox" id="autoDownloadExampleImages" checked 
+                                       onchange="settingsManager.saveToggleSetting('autoDownloadExampleImages', 'auto_download_example_images')">
+                                <span class="toggle-slider"></span>
+                            </label>
+                        </div>
+                    </div>
+                    <div class="input-help">
+                        Automatically download example images for models that don't have them (requires download location to be set)
+                    </div>
+                </div>
+                
                 <div class="setting-item">
                     <div class="setting-row">
                         <div class="setting-info">
@@ -273,6 +308,28 @@
                     </div>
                 </div>
             </div>
+            
+            <!-- Misc. Section -->
+            <div class="settings-section">
+                <h3>Misc.</h3>
+                <div class="setting-item">
+                    <div class="setting-row">
+                        <div class="setting-info">
+                            <label for="includeTriggerWords">Include Trigger Words in LoRA Syntax</label>
+                        </div>
+                        <div class="setting-control">
+                            <label class="toggle-switch">
+                                <input type="checkbox" id="includeTriggerWords" 
+                                       onchange="settingsManager.saveToggleSetting('includeTriggerWords', 'include_trigger_words')">
+                                <span class="toggle-slider"></span>
+                            </label>
+                        </div>
+                    </div>
+                    <div class="input-help">
+                        Include trained trigger words when copying LoRA syntax to clipboard
+                    </div>
+                </div>
+            </div>
         </div>
     </div>
 </div>

templates/embeddings.html

@@ -9,7 +9,7 @@
 
 {% block init_title %}Initializing Embeddings Manager{% endblock %}
 {% block init_message %}Scanning and building embeddings cache. This may take a few moments...{% endblock %}
-{% block init_check_url %}/api/embeddings?page=1&page_size=1{% endblock %}
+{% block init_check_url %}/api/embeddings/list?page=1&page_size=1{% endblock %}
 
 {% block additional_components %}
 

templates/loras.html

@@ -11,7 +11,7 @@
 
 {% block init_title %}Initializing LoRA Manager{% endblock %}
 {% block init_message %}Scanning and building LoRA cache. This may take a few minutes...{% endblock %}
-{% block init_check_url %}/api/loras?page=1&page_size=1{% endblock %}
+{% block init_check_url %}/api/loras/list?page=1&page_size=1{% endblock %}
 
 {% block content %}
     {% include 'components/controls.html' %}

templates/statistics.html

@@ -98,6 +98,14 @@
                         </div>
                     </div>
 
+                    <!-- Top Used Embeddings -->
+                    <div class="list-container">
+                        <h3><i class="fas fa-code"></i> Most Used Embeddings</h3>
+                        <div class="model-list" id="topEmbeddingsList">
+                            <!-- List will be populated by JavaScript -->
+                        </div>
+                    </div>
+
                     <!-- Usage Distribution Chart -->
                     <div class="chart-container full-width">
                         <h3><i class="fas fa-chart-bar"></i> Usage Distribution</h3>

update_logs.md

@@ -2,6 +2,52 @@
 
 ---
 
+### v0.8.16
+* **Dramatic Startup Speed Improvement** - Added cache serialization mechanism for significantly faster loading times, especially beneficial for large model collections
+* **Enhanced Refresh Options** - Extended functionality with "Full Rebuild (complete)" option alongside "Quick Refresh (incremental)" to fix potential memory cache issues without requiring application restart
+* **Customizable Display Density** - Replaced compact mode with adjustable display density settings for personalized layout customization
+* **Model Creator Information** - Added creator details to model information panels for better attribution
+* **Improved WebP Support** - Enhanced Save Image node with workflow embedding capability for WebP format images
+* **Direct Example Access** - Added "Open Example Images Folder" button to card interfaces for convenient browsing of downloaded model examples
+* **Enhanced Compatibility** - Full ComfyUI Desktop support for "Send lora or recipe to workflow" functionality
+* **Cache Management** - Added settings to clear existing cache files when needed
+* **Bug Fixes & Stability** - Various improvements for overall reliability and performance
+
+### v0.8.15
+* **Enhanced One-Click Integration** - Replaced copy button with direct send button allowing LoRAs/recipes to be sent directly to your current ComfyUI workflow without needing to paste
+* **Flexible Workflow Integration** - Click to append LoRAs/recipes to existing loader nodes or Shift+click to replace content, with additional right-click menu options for "Send to Workflow (Append)" or "Send to Workflow (Replace)"
+* **Improved LoRA Loader Controls** - Added header drag functionality for proportional strength adjustment of all LoRAs simultaneously (including CLIP strengths when expanded)
+* **Keyboard Navigation Support** - Implemented Page Up/Down for page scrolling, Home key to jump to top, and End key to jump to bottom for faster browsing through large collections
+
+### v0.8.14
+* **Virtualized Scrolling** - Completely rebuilt rendering mechanism for smooth browsing with no lag or freezing, now supporting virtually unlimited model collections with optimized layouts for large displays, improving space utilization and user experience
+* **Compact Display Mode** - Added space-efficient view option that displays more cards per row (7 on 1080p, 8 on 2K, 10 on 4K)
+* **Enhanced LoRA Node Functionality** - Comprehensive improvements to LoRA loader/stacker nodes including real-time trigger word updates (reflecting any change anywhere in the LoRA chain for precise updates) and expanded context menu with "Copy Notes" and "Copy Trigger Words" options for faster workflow
+
+### v0.8.13
+* **Enhanced Recipe Management** - Added "Find duplicates" feature to identify and batch delete duplicate recipes with duplicate detection notifications during imports
+* **Improved Source Tracking** - Source URLs are now saved with recipes imported via URL, allowing users to view original content with one click or manually edit links
+* **Advanced LoRA Control** - Double-click LoRAs in Loader/Stacker nodes to access expanded CLIP strength controls for more precise adjustments of model and CLIP strength separately
+* **Lycoris Model Support** - Added compatibility with Lycoris models for expanded creative options
+* **Bug Fixes & UX Improvements** - Resolved various issues and enhanced overall user experience with numerous optimizations
+
+### v0.8.12
+* **Enhanced Model Discovery** - Added alphabetical navigation bar to LoRAs page for faster browsing through large collections
+* **Optimized Example Images** - Improved download logic to automatically refresh stale metadata before fetching example images
+* **Model Exclusion System** - New right-click option to exclude specific LoRAs or checkpoints from management
+* **Improved Showcase Experience** - Enhanced interaction in LoRA and checkpoint showcase areas for better usability
+
+### v0.8.11
+* **Offline Image Support** - Added functionality to download and save all model example images locally, ensuring access even when offline or if images are removed from CivitAI or the site is down
+* **Resilient Download System** - Implemented pause/resume capability with checkpoint recovery that persists through restarts or unexpected exits
+* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
+
+### v0.8.10
+* **Standalone Mode** - Run LoRA Manager independently from ComfyUI for a lightweight experience that works even with other stable diffusion interfaces
+* **Portable Edition** - New one-click portable version for easy startup and updates in standalone mode
+* **Enhanced Metadata Collection** - Added support for SamplerCustomAdvanced node in the metadata collector module
+* **Improved UI Organization** - Optimized Lora Loader node height to display up to 5 LoRAs at once with scrolling capability for larger collections
+
 ### v0.8.9
 * **Favorites System** - New functionality to bookmark your favorite LoRAs and checkpoints for quick access and better organization
 * **Enhanced UI Controls** - Increased model card button sizes for improved usability and easier interaction

web/comfyui/json_display_widget.js

@@ -5,9 +5,6 @@ export function addJsonDisplayWidget(node, name, opts) {
   
   // Set initial height
   const defaultHeight = 200;
-  container.style.setProperty('--comfy-widget-min-height', `${defaultHeight}px`);
-  container.style.setProperty('--comfy-widget-max-height', `${defaultHeight * 2}px`);
-  container.style.setProperty('--comfy-widget-height', `${defaultHeight}px`);
   
   Object.assign(container.style, {
     display: "block",
@@ -113,16 +110,6 @@ export function addJsonDisplayWidget(node, name, opts) {
       widgetValue = v;
       displayJson(widgetValue, widget);
     },
-    getMinHeight: function() {
-      return parseInt(container.style.getPropertyValue('--comfy-widget-min-height')) || defaultHeight;
-    },
-    getMaxHeight: function() {
-      return parseInt(container.style.getPropertyValue('--comfy-widget-max-height')) || defaultHeight * 2;
-    },
-    getHeight: function() {
-      // Return actual container height to reduce the gap
-      return parseInt(container.style.getPropertyValue('--comfy-widget-height')) || defaultHeight;
-    },
     hideOnZoom: true
   });
 

web/comfyui/lora_loader.js

@@ -4,39 +4,11 @@ import {
     LORA_PATTERN,
     collectActiveLorasFromChain,
     updateConnectedTriggerWords,
-    chainCallback
+    chainCallback,
+    mergeLoras
 } from "./utils.js";
 import { addLorasWidget } from "./loras_widget.js";
 
-function mergeLoras(lorasText, lorasArr) {
-    const result = [];
-    let match;
-
-    // Reset pattern index before using
-    LORA_PATTERN.lastIndex = 0;
-    
-    // Parse text input and create initial entries
-    while ((match = LORA_PATTERN.exec(lorasText)) !== null) {
-        const name = match[1];
-        const modelStrength = Number(match[2]);
-        // Extract clip strength if provided, otherwise use model strength
-        const clipStrength = match[3] ? Number(match[3]) : modelStrength;
-        
-        // Find if this lora exists in the array data
-        const existingLora = lorasArr.find(l => l.name === name);
-        
-        result.push({
-            name: name,
-            // Use existing strength if available, otherwise use input strength
-            strength: existingLora ? existingLora.strength : modelStrength,
-            active: existingLora ? existingLora.active : true,
-            clipStrength: existingLora ? existingLora.clipStrength : clipStrength,
-        });
-    }
-
-    return result;
-}
-
 app.registerExtension({
     name: "LoraManager.LoraLoader",
     
@@ -184,6 +156,7 @@ app.registerExtension({
 
             // Update input widget callback
             const inputWidget = this.widgets[0];
+            inputWidget.options.getMaxHeight = () => 100;
             this.inputWidget = inputWidget;
             inputWidget.callback = (value) => {
               if (isUpdating) return;

web/comfyui/lora_stacker.js

@@ -4,39 +4,11 @@ import {
     getActiveLorasFromNode,
     collectActiveLorasFromChain,
     updateConnectedTriggerWords,
-    chainCallback
+    chainCallback,
+    mergeLoras
 } from "./utils.js";
 import { addLorasWidget } from "./loras_widget.js";
 
-function mergeLoras(lorasText, lorasArr) {
-    const result = [];
-    let match;
-
-    // Reset pattern index before using
-    LORA_PATTERN.lastIndex = 0;
-    
-    // Parse text input and create initial entries
-    while ((match = LORA_PATTERN.exec(lorasText)) !== null) {
-        const name = match[1];
-        const modelStrength = Number(match[2]);
-        // Extract clip strength if provided, otherwise use model strength
-        const clipStrength = match[3] ? Number(match[3]) : modelStrength;
-        
-        // Find if this lora exists in the array data
-        const existingLora = lorasArr.find(l => l.name === name);
-        
-        result.push({
-            name: name,
-            // Use existing strength if available, otherwise use input strength
-            strength: existingLora ? existingLora.strength : modelStrength,
-            active: existingLora ? existingLora.active : true,
-            clipStrength: existingLora ? existingLora.clipStrength : clipStrength,
-        });
-    }
-
-    return result;
-}
-
 app.registerExtension({
     name: "LoraManager.LoraStacker",
     
@@ -105,6 +77,7 @@ app.registerExtension({
 
                 // Update input widget callback
                 const inputWidget = this.widgets[0];
+                inputWidget.options.getMaxHeight = () => 100;
                 this.inputWidget = inputWidget;
                 inputWidget.callback = (value) => {
                     if (isUpdating) return;

web/comfyui/loras_widget.js

@@ -1,5 +1,4 @@
-import { app } from "../../scripts/app.js";
-import { createToggle, createArrowButton, PreviewTooltip } from "./loras_widget_components.js";
+import { createToggle, createArrowButton, PreviewTooltip, createDragHandle, updateEntrySelection } from "./loras_widget_components.js";
 import { 
   parseLoraValue, 
   formatLoraValue, 
@@ -11,7 +10,7 @@ import {
   CONTAINER_PADDING, 
   EMPTY_CONTAINER_HEIGHT 
 } from "./loras_widget_utils.js";
-import { initDrag, createContextMenu, initHeaderDrag } from "./loras_widget_events.js";
+import { initDrag, createContextMenu, initHeaderDrag, initReorderDrag, handleKeyboardNavigation } from "./loras_widget_events.js";
 
 export function addLorasWidget(node, name, opts, callback) {
   // Create container for loras
@@ -20,9 +19,6 @@ export function addLorasWidget(node, name, opts, callback) {
   
   // Set initial height using CSS variables approach
   const defaultHeight = 200;
-  container.style.setProperty('--comfy-widget-min-height', `${defaultHeight}px`);
-  container.style.setProperty('--comfy-widget-max-height', `${defaultHeight * 2}px`);
-  container.style.setProperty('--comfy-widget-height', `${defaultHeight}px`);
   
   Object.assign(container.style, {
     display: "flex",
@@ -42,6 +38,30 @@ export function addLorasWidget(node, name, opts, callback) {
   // Create preview tooltip instance
   const previewTooltip = new PreviewTooltip();
   
+  // Selection state - only one LoRA can be selected at a time
+  let selectedLora = null;
+  
+  // Function to select a LoRA
+  const selectLora = (loraName) => {
+    selectedLora = loraName;
+    // Update visual feedback for all entries
+    container.querySelectorAll('.comfy-lora-entry').forEach(entry => {
+      const entryLoraName = entry.dataset.loraName;
+      updateEntrySelection(entry, entryLoraName === selectedLora);
+    });
+  };
+  
+  // Add keyboard event listener to container
+  container.addEventListener('keydown', (e) => {
+    if (handleKeyboardNavigation(e, selectedLora, widget, renderLoras, selectLora)) {
+      e.stopPropagation();
+    }
+  });
+  
+  // Make container focusable for keyboard events
+  container.tabIndex = 0;
+  container.style.outline = 'none';
+  
   // Function to render loras from data
   const renderLoras = (value, widget) => {
     // Clear existing content
@@ -185,6 +205,26 @@ export function addLorasWidget(node, name, opts, callback) {
         marginBottom: "4px",
       });
 
+      // Store lora name and active state in dataset for selection
+      loraEl.dataset.loraName = name;
+      loraEl.dataset.active = active;
+
+      // Add click handler for selection
+      loraEl.addEventListener('click', (e) => {
+        // Skip if clicking on interactive elements
+        if (e.target.closest('.comfy-lora-toggle') || 
+            e.target.closest('input') || 
+            e.target.closest('.comfy-lora-arrow') ||
+            e.target.closest('.comfy-lora-drag-handle')) {
+          return;
+        }
+        
+        e.preventDefault();
+        e.stopPropagation();
+        selectLora(name);
+        container.focus(); // Focus container for keyboard events
+      });
+
       // Add double-click handler to toggle clip entry
       loraEl.addEventListener('dblclick', (e) => {
         // Skip if clicking on toggle or strength control areas
@@ -220,6 +260,12 @@ export function addLorasWidget(node, name, opts, callback) {
         }
       });
 
+      // Create drag handle for reordering
+      const dragHandle = createDragHandle();
+      
+      // Initialize reorder drag functionality
+      initReorderDrag(dragHandle, name, widget, renderLoras);
+
       // Create toggle for this lora
       const toggle = createToggle(active, (newActive) => {
         // Update this lora's active state
@@ -416,6 +462,7 @@ export function addLorasWidget(node, name, opts, callback) {
         minWidth: "0", // Allow shrinking
       });
       
+      leftSection.appendChild(dragHandle); // Add drag handle first
       leftSection.appendChild(toggle);
       leftSection.appendChild(nameEl);
       
@@ -424,6 +471,9 @@ export function addLorasWidget(node, name, opts, callback) {
 
       container.appendChild(loraEl);
 
+      // Update selection state
+      updateEntrySelection(loraEl, name === selectedLora);
+
       // If expanded, show the clip entry
       if (isExpanded) {
         totalVisibleEntries++;
@@ -444,6 +494,10 @@ export function addLorasWidget(node, name, opts, callback) {
           marginTop: "-2px"
         });
 
+        // Store the same lora name in clip entry dataset
+        clipEl.dataset.loraName = name;
+        clipEl.dataset.active = active;
+
         // Create clip name display
         const clipNameEl = document.createElement("div");
         clipNameEl.textContent = "[clip] " + name;
@@ -601,7 +655,7 @@ export function addLorasWidget(node, name, opts, callback) {
     });
     
     // Calculate height based on number of loras and fixed sizes
-    const calculatedHeight = CONTAINER_PADDING + HEADER_HEIGHT + (Math.min(totalVisibleEntries, 10) * LORA_ENTRY_HEIGHT);
+    const calculatedHeight = CONTAINER_PADDING + HEADER_HEIGHT + (Math.min(totalVisibleEntries, 12) * LORA_ENTRY_HEIGHT);
     updateWidgetHeight(container, calculatedHeight, defaultHeight, node);
   };
 
@@ -655,23 +709,8 @@ export function addLorasWidget(node, name, opts, callback) {
       widgetValue = updatedValue;
       renderLoras(widgetValue, widget);
     },
-    getMinHeight: function() {
-      return parseInt(container.style.getPropertyValue('--comfy-widget-min-height')) || defaultHeight;
-    },
-    getMaxHeight: function() {
-      return parseInt(container.style.getPropertyValue('--comfy-widget-max-height')) || defaultHeight * 2;
-    },
-    getHeight: function() {
-      return parseInt(container.style.getPropertyValue('--comfy-widget-height')) || defaultHeight;
-    },
     hideOnZoom: true,
-    selectOn: ['click', 'focus'],
-    afterResize: function(node) {
-      // Re-render after node resize
-      if (this.value && this.value.length > 0) {
-        renderLoras(this.value, this);
-      }
-    }
+    selectOn: ['click', 'focus']
   });
 
   widget.value = defaultValue;
@@ -685,6 +724,8 @@ export function addLorasWidget(node, name, opts, callback) {
   widget.onRemove = () => {
     container.remove(); 
     previewTooltip.cleanup();
+    // Remove keyboard event listener
+    container.removeEventListener('keydown', handleKeyboardNavigation);
   };
 
   return { minWidth: 400, minHeight: defaultHeight, widget };

web/comfyui/loras_widget_components.js

@@ -78,6 +78,87 @@ export function createArrowButton(direction, onClick) {
   return button;
 }
 
+// Function to create drag handle
+export function createDragHandle() {
+  const handle = document.createElement("div");
+  handle.className = "comfy-lora-drag-handle";
+  handle.innerHTML = "≡";
+  handle.title = "Drag to reorder LoRA";
+  
+  Object.assign(handle.style, {
+    width: "16px",
+    height: "16px",
+    display: "flex",
+    alignItems: "center",
+    justifyContent: "center",
+    cursor: "grab",
+    userSelect: "none",
+    fontSize: "14px",
+    color: "rgba(226, 232, 240, 0.6)",
+    transition: "all 0.2s ease",
+    marginRight: "8px",
+    flexShrink: "0"
+  });
+  
+  // Add hover effect
+  handle.onmouseenter = () => {
+    handle.style.color = "rgba(226, 232, 240, 0.9)";
+    handle.style.transform = "scale(1.1)";
+  };
+  
+  handle.onmouseleave = () => {
+    handle.style.color = "rgba(226, 232, 240, 0.6)";
+    handle.style.transform = "scale(1)";
+  };
+  
+  // Change cursor when dragging
+  handle.onmousedown = () => {
+    handle.style.cursor = "grabbing";
+  };
+  
+  return handle;
+}
+
+// Function to create drop indicator
+export function createDropIndicator() {
+  const indicator = document.createElement("div");
+  indicator.className = "comfy-lora-drop-indicator";
+  
+  Object.assign(indicator.style, {
+    position: "absolute",
+    left: "0",
+    right: "0",
+    height: "3px",
+    backgroundColor: "rgba(66, 153, 225, 0.9)",
+    borderRadius: "2px",
+    opacity: "0",
+    transition: "opacity 0.2s ease",
+    boxShadow: "0 0 6px rgba(66, 153, 225, 0.8)",
+    zIndex: "10",
+    pointerEvents: "none"
+  });
+  
+  return indicator;
+}
+
+// Function to update entry selection state
+export function updateEntrySelection(entryEl, isSelected) {
+  const baseColor = entryEl.dataset.active === 'true' ? 
+    "rgba(45, 55, 72, 0.7)" : "rgba(35, 40, 50, 0.5)";
+  const selectedColor = entryEl.dataset.active === 'true' ? 
+    "rgba(66, 153, 225, 0.3)" : "rgba(66, 153, 225, 0.2)";
+  
+  if (isSelected) {
+    entryEl.style.backgroundColor = selectedColor;
+    entryEl.style.border = "1px solid rgba(66, 153, 225, 0.6)";
+    entryEl.style.boxShadow = "0 0 0 1px rgba(66, 153, 225, 0.3)";
+  } else {
+    entryEl.style.backgroundColor = baseColor;
+    entryEl.style.border = "1px solid transparent";
+    entryEl.style.boxShadow = "none";
+  }
+}
+
 // Function to create menu item
 export function createMenuItem(text, icon, onClick) {
   const menuItem = document.createElement('div');

web/comfyui/loras_widget_events.js

@@ -1,6 +1,7 @@
 import { api } from "../../scripts/api.js";
-import { createMenuItem } from "./loras_widget_components.js";
-import { parseLoraValue, formatLoraValue, syncClipStrengthIfCollapsed, saveRecipeDirectly, copyToClipboard, showToast } from "./loras_widget_utils.js";
+import { app } from "../../scripts/app.js";
+import { createMenuItem, createDropIndicator } from "./loras_widget_components.js";
+import { parseLoraValue, formatLoraValue, syncClipStrengthIfCollapsed, saveRecipeDirectly, copyToClipboard, showToast, moveLoraByDirection, getDropTargetIndex } from "./loras_widget_utils.js";
 
 // Function to handle strength adjustment via dragging
 export function handleStrengthDrag(name, initialStrength, initialX, event, widget, isClipStrength = false) {
@@ -227,6 +228,223 @@ export function initHeaderDrag(headerEl, widget, renderFunction) {
   });
 }
 
+// Function to initialize drag-and-drop for reordering
+export function initReorderDrag(dragHandle, loraName, widget, renderFunction) {
+  let isDragging = false;
+  let draggedElement = null;
+  let dropIndicator = null;
+  let container = null;
+  let scale = 1;
+  
+  dragHandle.addEventListener('mousedown', (e) => {
+    e.preventDefault();
+    e.stopPropagation();
+    
+    isDragging = true;
+    draggedElement = dragHandle.closest('.comfy-lora-entry');
+    container = draggedElement.parentElement;
+    
+    // Add dragging class and visual feedback
+    draggedElement.classList.add('comfy-lora-dragging');
+    draggedElement.style.opacity = '0.5';
+    draggedElement.style.transform = 'scale(0.98)';
+    
+    // Create single drop indicator with absolute positioning
+    dropIndicator = createDropIndicator();
+    
+    // Make container relatively positioned for absolute indicator
+    const originalPosition = container.style.position;
+    container.style.position = 'relative';
+    container.appendChild(dropIndicator);
+    
+    // Store original position for cleanup
+    container._originalPosition = originalPosition;
+    
+    // Add global cursor style
+    document.body.style.cursor = 'grabbing';
+
+    // Store workflow scale for accurate positioning
+    scale = app.canvas.ds.scale;
+  });
+  
+  document.addEventListener('mousemove', (e) => {
+    if (!isDragging || !draggedElement || !dropIndicator) return;
+    
+    const targetIndex = getDropTargetIndex(container, e.clientY);
+    const entries = container.querySelectorAll('.comfy-lora-entry, .comfy-lora-clip-entry');
+    
+    if (targetIndex === 0) {
+      // Show at top
+      const firstEntry = entries[0];
+      if (firstEntry) {
+        const rect = firstEntry.getBoundingClientRect();
+        const containerRect = container.getBoundingClientRect();
+        dropIndicator.style.top = `${(rect.top - containerRect.top - 2) / scale}px`;
+        dropIndicator.style.opacity = '1';
+      }
+    } else if (targetIndex < entries.length) {
+      // Show between entries
+      const targetEntry = entries[targetIndex];
+      if (targetEntry) {
+        const rect = targetEntry.getBoundingClientRect();
+        const containerRect = container.getBoundingClientRect();
+        dropIndicator.style.top = `${(rect.top - containerRect.top - 2) / scale}px`;
+        dropIndicator.style.opacity = '1';
+      }
+    } else {
+      // Show at bottom
+      const lastEntry = entries[entries.length - 1];
+      if (lastEntry) {
+        const rect = lastEntry.getBoundingClientRect();
+        const containerRect = container.getBoundingClientRect();
+        dropIndicator.style.top = `${(rect.bottom - containerRect.top + 2) / scale}px`;
+        dropIndicator.style.opacity = '1';
+      }
+    }
+  });
+  
+  document.addEventListener('mouseup', (e) => {
+    if (!isDragging || !draggedElement) return;
+    
+    const targetIndex = getDropTargetIndex(container, e.clientY);
+    
+    // Get current LoRA data
+    const lorasData = parseLoraValue(widget.value);
+    const currentIndex = lorasData.findIndex(l => l.name === loraName);
+    
+    if (currentIndex !== -1 && currentIndex !== targetIndex) {
+      // Calculate actual target index (excluding clip entries from count)
+      const loraEntries = container.querySelectorAll('.comfy-lora-entry');
+      let actualTargetIndex = targetIndex;
+      
+      // Adjust target index if it's beyond the number of actual LoRA entries
+      if (actualTargetIndex > loraEntries.length) {
+        actualTargetIndex = loraEntries.length;
+      }
+      
+      // Move the LoRA
+      const newLoras = [...lorasData];
+      const [moved] = newLoras.splice(currentIndex, 1);
+      newLoras.splice(actualTargetIndex > currentIndex ? actualTargetIndex - 1 : actualTargetIndex, 0, moved);
+      
+      widget.value = formatLoraValue(newLoras);
+      
+      if (widget.callback) {
+        widget.callback(widget.value);
+      }
+      
+      // Re-render
+      if (renderFunction) {
+        renderFunction(widget.value, widget);
+      }
+    }
+    
+    // Cleanup
+    isDragging = false;
+    if (draggedElement) {
+      draggedElement.classList.remove('comfy-lora-dragging');
+      draggedElement.style.opacity = '';
+      draggedElement.style.transform = '';
+      draggedElement = null;
+    }
+    
+    if (dropIndicator && container) {
+      container.removeChild(dropIndicator);
+      // Restore original position
+      container.style.position = container._originalPosition || '';
+      delete container._originalPosition;
+      dropIndicator = null;
+    }
+    
+    // Reset cursor
+    document.body.style.cursor = '';
+    container = null;
+  });
+}
+
+// Function to handle keyboard navigation
+export function handleKeyboardNavigation(event, selectedLora, widget, renderFunction, selectLora) {
+  if (!selectedLora) return false;
+  
+  const lorasData = parseLoraValue(widget.value);
+  let handled = false;
+  
+  // Check for Ctrl/Cmd modifier for reordering
+  if (event.ctrlKey || event.metaKey) {
+    switch (event.key) {
+      case 'ArrowUp':
+        event.preventDefault();
+        const newLorasUp = moveLoraByDirection(lorasData, selectedLora, 'up');
+        widget.value = formatLoraValue(newLorasUp);
+        if (widget.callback) widget.callback(widget.value);
+        if (renderFunction) renderFunction(widget.value, widget);
+        handled = true;
+        break;
+        
+      case 'ArrowDown':
+        event.preventDefault();
+        const newLorasDown = moveLoraByDirection(lorasData, selectedLora, 'down');
+        widget.value = formatLoraValue(newLorasDown);
+        if (widget.callback) widget.callback(widget.value);
+        if (renderFunction) renderFunction(widget.value, widget);
+        handled = true;
+        break;
+        
+      case 'Home':
+        event.preventDefault();
+        const newLorasTop = moveLoraByDirection(lorasData, selectedLora, 'top');
+        widget.value = formatLoraValue(newLorasTop);
+        if (widget.callback) widget.callback(widget.value);
+        if (renderFunction) renderFunction(widget.value, widget);
+        handled = true;
+        break;
+        
+      case 'End':
+        event.preventDefault();
+        const newLorasBottom = moveLoraByDirection(lorasData, selectedLora, 'bottom');
+        widget.value = formatLoraValue(newLorasBottom);
+        if (widget.callback) widget.callback(widget.value);
+        if (renderFunction) renderFunction(widget.value, widget);
+        handled = true;
+        break;
+    }
+  } else {
+    // Normal navigation without Ctrl/Cmd
+    switch (event.key) {
+      case 'ArrowUp':
+        event.preventDefault();
+        const currentIndex = lorasData.findIndex(l => l.name === selectedLora);
+        if (currentIndex > 0) {
+          selectLora(lorasData[currentIndex - 1].name);
+        }
+        handled = true;
+        break;
+        
+      case 'ArrowDown':
+        event.preventDefault();
+        const currentIndexDown = lorasData.findIndex(l => l.name === selectedLora);
+        if (currentIndexDown < lorasData.length - 1) {
+          selectLora(lorasData[currentIndexDown + 1].name);
+        }
+        handled = true;
+        break;
+        
+      case 'Delete':
+      case 'Backspace':
+        event.preventDefault();
+        const filtered = lorasData.filter(l => l.name !== selectedLora);
+        widget.value = formatLoraValue(filtered);
+        if (widget.callback) widget.callback(widget.value);
+        if (renderFunction) renderFunction(widget.value, widget);
+        selectLora(null); // Clear selection
+        handled = true;
+        break;
+    }
+  }
+  
+  return handled;
+}
+
 // Function to create context menu
 export function createContextMenu(x, y, loraName, widget, previewTooltip, renderFunction) {
   // Hide preview tooltip first
@@ -398,6 +616,94 @@ export function createContextMenu(x, y, loraName, widget, previewTooltip, render
     }
   );
 
+  // Move Up option with arrow up icon
+  const moveUpOption = createMenuItem(
+    'Move Up',
+    '<svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M18 15l-6-6-6 6"></path></svg>',
+    () => {
+      menu.remove();
+      document.removeEventListener('click', closeMenu);
+      
+      const lorasData = parseLoraValue(widget.value);
+      const newLoras = moveLoraByDirection(lorasData, loraName, 'up');
+      widget.value = formatLoraValue(newLoras);
+      
+      if (widget.callback) {
+        widget.callback(widget.value);
+      }
+      
+      if (renderFunction) {
+        renderFunction(widget.value, widget);
+      }
+    }
+  );
+
+  // Move Down option with arrow down icon
+  const moveDownOption = createMenuItem(
+    'Move Down',
+    '<svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M6 9l6 6 6-6"></path></svg>',
+    () => {
+      menu.remove();
+      document.removeEventListener('click', closeMenu);
+      
+      const lorasData = parseLoraValue(widget.value);
+      const newLoras = moveLoraByDirection(lorasData, loraName, 'down');
+      widget.value = formatLoraValue(newLoras);
+      
+      if (widget.callback) {
+        widget.callback(widget.value);
+      }
+      
+      if (renderFunction) {
+        renderFunction(widget.value, widget);
+      }
+    }
+  );
+
+  // Move to Top option with chevrons up icon
+  const moveTopOption = createMenuItem(
+    'Move to Top',
+    '<svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M17 11l-5-5-5 5M17 18l-5-5-5 5"></path></svg>',
+    () => {
+      menu.remove();
+      document.removeEventListener('click', closeMenu);
+      
+      const lorasData = parseLoraValue(widget.value);
+      const newLoras = moveLoraByDirection(lorasData, loraName, 'top');
+      widget.value = formatLoraValue(newLoras);
+      
+      if (widget.callback) {
+        widget.callback(widget.value);
+      }
+      
+      if (renderFunction) {
+        renderFunction(widget.value, widget);
+      }
+    }
+  );
+
+  // Move to Bottom option with chevrons down icon
+  const moveBottomOption = createMenuItem(
+    'Move to Bottom',
+    '<svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M7 13l5 5 5-5M7 6l5 5 5-5"></path></svg>',
+    () => {
+      menu.remove();
+      document.removeEventListener('click', closeMenu);
+      
+      const lorasData = parseLoraValue(widget.value);
+      const newLoras = moveLoraByDirection(lorasData, loraName, 'bottom');
+      widget.value = formatLoraValue(newLoras);
+      
+      if (widget.callback) {
+        widget.callback(widget.value);
+      }
+      
+      if (renderFunction) {
+        renderFunction(widget.value, widget);
+      }
+    }
+  );
+
   // Add separator
   const separator1 = document.createElement('div');
   Object.assign(separator1.style, {
@@ -412,9 +718,21 @@ export function createContextMenu(x, y, loraName, widget, previewTooltip, render
     borderTop: '1px solid rgba(255, 255, 255, 0.1)',
   });
 
+  // Add separator for order options
+  const orderSeparator = document.createElement('div');
+  Object.assign(orderSeparator.style, {
+    margin: '4px 0',
+    borderTop: '1px solid rgba(255, 255, 255, 0.1)',
+  });
+
   menu.appendChild(viewOnCivitaiOption);
   menu.appendChild(deleteOption);
   menu.appendChild(separator1);
+  menu.appendChild(moveUpOption);
+  menu.appendChild(moveDownOption);
+  menu.appendChild(moveTopOption);
+  menu.appendChild(moveBottomOption);
+  menu.appendChild(orderSeparator);
   menu.appendChild(copyNotesOption);
   menu.appendChild(copyTriggerWordsOption);
   menu.appendChild(separator2);

web/comfyui/loras_widget_utils.js

@@ -3,7 +3,7 @@ import { app } from "../../scripts/app.js";
 // Fixed sizes for component calculations
 export const LORA_ENTRY_HEIGHT = 40; // Height of a single lora entry
 export const CLIP_ENTRY_HEIGHT = 40; // Height of a clip entry
-export const HEADER_HEIGHT = 40; // Height of the header section
+export const HEADER_HEIGHT = 32; // Height of the header section
 export const CONTAINER_PADDING = 12; // Top and bottom padding
 export const EMPTY_CONTAINER_HEIGHT = 100; // Height when no loras are present
 
@@ -164,3 +164,71 @@ export function showToast(message, type = 'info') {
         }
     }
 }
+
+/**
+ * Move a LoRA to a new position in the array
+ * @param {Array} loras - Array of LoRA objects
+ * @param {number} fromIndex - Current index of the LoRA
+ * @param {number} toIndex - Target index for the LoRA
+ * @returns {Array} - New array with LoRA moved
+ */
+export function moveLoraInArray(loras, fromIndex, toIndex) {
+  const newLoras = [...loras];
+  const [removed] = newLoras.splice(fromIndex, 1);
+  newLoras.splice(toIndex, 0, removed);
+  return newLoras;
+}
+
+/**
+ * Move a LoRA by name to a specific position
+ * @param {Array} loras - Array of LoRA objects
+ * @param {string} loraName - Name of the LoRA to move
+ * @param {string} direction - 'up', 'down', 'top', 'bottom'
+ * @returns {Array} - New array with LoRA moved
+ */
+export function moveLoraByDirection(loras, loraName, direction) {
+  const currentIndex = loras.findIndex(l => l.name === loraName);
+  if (currentIndex === -1) return loras;
+  
+  let newIndex;
+  switch (direction) {
+    case 'up':
+      newIndex = Math.max(0, currentIndex - 1);
+      break;
+    case 'down':
+      newIndex = Math.min(loras.length - 1, currentIndex + 1);
+      break;
+    case 'top':
+      newIndex = 0;
+      break;
+    case 'bottom':
+      newIndex = loras.length - 1;
+      break;
+    default:
+      return loras;
+  }
+  
+  if (newIndex === currentIndex) return loras;
+  return moveLoraInArray(loras, currentIndex, newIndex);
+}
+
+/**
+ * Get the drop target index based on mouse position
+ * @param {HTMLElement} container - The container element
+ * @param {number} clientY - Mouse Y position
+ * @returns {number} - Target index for dropping
+ */
+export function getDropTargetIndex(container, clientY) {
+  const entries = container.querySelectorAll('.comfy-lora-entry');
+  let targetIndex = entries.length;
+  
+  for (let i = 0; i < entries.length; i++) {
+    const rect = entries[i].getBoundingClientRect();
+    if (clientY < rect.top + rect.height / 2) {
+      targetIndex = i;
+      break;
+    }
+  }
+  
+  return targetIndex;
+}

web/comfyui/tags_widget.js

@@ -5,9 +5,6 @@ export function addTagsWidget(node, name, opts, callback) {
   
   // Set initial height
   const defaultHeight = 150;
-  container.style.setProperty('--comfy-widget-min-height', `${defaultHeight}px`);
-  container.style.setProperty('--comfy-widget-max-height', `${defaultHeight * 2}px`);
-  container.style.setProperty('--comfy-widget-height', `${defaultHeight}px`);
   
   Object.assign(container.style, {
     display: "flex",
@@ -199,23 +196,8 @@ export function addTagsWidget(node, name, opts, callback) {
       widgetValue = v;
       renderTags(widgetValue, widget);
     },
-    getMinHeight: function() {
-      return parseInt(container.style.getPropertyValue('--comfy-widget-min-height')) || defaultHeight;
-    },
-    getMaxHeight: function() {
-      return parseInt(container.style.getPropertyValue('--comfy-widget-max-height')) || defaultHeight * 2;
-    },
-    getHeight: function() {
-      return parseInt(container.style.getPropertyValue('--comfy-widget-height')) || defaultHeight;
-    },
     hideOnZoom: true,
-    selectOn: ['click', 'focus'],
-    afterResize: function(node) {
-      // Re-render tags after node resize
-      if (this.value && this.value.length > 0) {
-        renderTags(this.value, this);
-      }
-    }
+    selectOn: ['click', 'focus']
   });
 
   // Set initial value

web/comfyui/utils.js

@@ -183,4 +183,47 @@ export function updateConnectedTriggerWords(node, loraNames) {
             })
         }).catch(err => console.error("Error fetching trigger words:", err));
     }
+}
+
+export function mergeLoras(lorasText, lorasArr) {
+  // Parse lorasText into a map: name -> {strength, clipStrength}
+  const parsedLoras = {};
+  let match;
+  LORA_PATTERN.lastIndex = 0;
+  while ((match = LORA_PATTERN.exec(lorasText)) !== null) {
+    const name = match[1];
+    const modelStrength = Number(match[2]);
+    const clipStrength = match[3] ? Number(match[3]) : modelStrength;
+    parsedLoras[name] = { strength: modelStrength, clipStrength };
+  }
+
+  // Build result array in the order of lorasArr
+  const result = [];
+  const usedNames = new Set();
+
+  for (const lora of lorasArr) {
+    if (parsedLoras[lora.name]) {
+      result.push({
+        name: lora.name,
+        strength: lora.strength !== undefined ? lora.strength : parsedLoras[lora.name].strength,
+        active: lora.active !== undefined ? lora.active : true,
+        clipStrength: lora.clipStrength !== undefined ? lora.clipStrength : parsedLoras[lora.name].clipStrength,
+      });
+      usedNames.add(lora.name);
+    }
+  }
+
+  // Add any new loras from lorasText that are not in lorasArr, in their text order
+  for (const name in parsedLoras) {
+    if (!usedNames.has(name)) {
+      result.push({
+        name,
+        strength: parsedLoras[name].strength,
+        active: true,
+        clipStrength: parsedLoras[name].clipStrength,
+      });
+    }
+  }
+
+  return result;
 }

web/comfyui/wanvideo_lora_select.js

@@ -2,41 +2,12 @@ import { app } from "../../scripts/app.js";
 import { 
     LORA_PATTERN, 
     getActiveLorasFromNode,
-    collectActiveLorasFromChain,
     updateConnectedTriggerWords,
-    chainCallback
+    chainCallback,
+    mergeLoras
 } from "./utils.js";
 import { addLorasWidget } from "./loras_widget.js";
 
-function mergeLoras(lorasText, lorasArr) {
-    const result = [];
-    let match;
-
-    // Reset pattern index before using
-    LORA_PATTERN.lastIndex = 0;
-    
-    // Parse text input and create initial entries
-    while ((match = LORA_PATTERN.exec(lorasText)) !== null) {
-        const name = match[1];
-        const modelStrength = Number(match[2]);
-        // Extract clip strength if provided, otherwise use model strength
-        const clipStrength = match[3] ? Number(match[3]) : modelStrength;
-        
-        // Find if this lora exists in the array data
-        const existingLora = lorasArr.find(l => l.name === name);
-        
-        result.push({
-            name: name,
-            // Use existing strength if available, otherwise use input strength
-            strength: existingLora ? existingLora.strength : modelStrength,
-            active: existingLora ? existingLora.active : true,
-            clipStrength: existingLora ? existingLora.clipStrength : clipStrength,
-        });
-    }
-
-    return result;
-}
-
 app.registerExtension({
     name: "LoraManager.WanVideoLoraSelect",
     
@@ -107,6 +78,7 @@ app.registerExtension({
 
                 // Update input widget callback
                 const inputWidget = this.widgets[1];
+                inputWidget.options.getMaxHeight = () => 100;
                 this.inputWidget = inputWidget;
                 inputWidget.callback = (value) => {
                     if (isUpdating) return;