Bump version to 0.8.0

- Added functionality to update the hash index by removing entries associated with the specified file path during cache management.
- Ensured that the cache is properly resorted after the removal of raw data items.

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,36 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+### **LoRA Manager Version**
+- Version: `vX.X.X`
+
+### **Environment Information**
+- **Operating System**: (e.g., Windows 11, macOS Ventura, Ubuntu 22.04)
+- **Browser & Version**: (e.g., Chrome 120.0.0, Edge 115.0.0)
+
+### **Issue Description**
+- Describe the issue in detail.
+
+### **Steps to Reproduce**
+1. Open LoRA Manager in [your browser].
+2. Perform [specific action].
+3. Observe the issue.
+
+### **Expected Behavior**
+- What did you expect to happen?
+
+### **Screenshots** *(If applicable)*
+- Upload screenshots or screen recordings.
+
+### **Logs**
+- Provide the **ComfyUI startup log** and any relevant error messages.
+- Check the browser developer console (F12 → Console tab) and attach any errors.
+
+### **Additional Context** *(Optional)*
+- Any other relevant details.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.gitignore

@@ -1,2 +1,4 @@
 __pycache__/
-settings.json
+settings.json
+output/*
+py/run_test.py

README.md

@@ -1,8 +1,14 @@
 # ComfyUI LoRA Manager
 
-A web-based management interface designed to help you organize and manage your local LoRA models in ComfyUI. Access the interface at: `http://localhost:8188/loras`
+> **Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!**
 
-![Interface Preview](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/static/images/Screenshot%202025-01-27%20172349.png)
+[![Discord](https://img.shields.io/discord/1346296675538571315?color=7289DA&label=Discord&logo=discord&logoColor=white)](https://discord.gg/vcqNrWVFvM)
+[![Release](https://img.shields.io/github/v/release/willmiao/ComfyUI-Lora-Manager?include_prereleases&color=blue&logo=github)](https://github.com/willmiao/ComfyUI-Lora-Manager/releases)
+[![Release Date](https://img.shields.io/github/release-date/willmiao/ComfyUI-Lora-Manager?color=green&logo=github)](https://github.com/willmiao/ComfyUI-Lora-Manager/releases)
+
+A comprehensive toolset that streamlines organizing, downloading, and applying LoRA models in ComfyUI. With powerful features like recipe management and one-click workflow integration, working with LoRAs becomes faster, smoother, and significantly easier. Access the interface at: `http://localhost:8188/loras`
+
+![Interface Preview](https://github.com/willmiao/ComfyUI-Lora-Manager/blob/main/static/images/screenshot.png)
 
 ## 📺 Tutorial: One-Click LoRA Integration
 Watch this quick tutorial to learn how to use the new one-click LoRA integration feature:
@@ -13,6 +19,29 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 
 ## Release Notes
 
+### v0.8.0
+* **Introduced LoRA Recipes** - Create, import, save, and share your favorite LoRA combinations
+* **Recipe Management System** - Easily browse, search, and organize your LoRA recipes
+* **Workflow Integration** - Save recipes directly from your workflow with generation parameters preserved
+* **Simplified Workflow Application** - Quickly apply saved recipes to new projects
+* **Enhanced UI & UX** - Improved interface design and user experience
+* **Bug Fixes & Stability** - Resolved various issues and enhanced overall performance
+
+### v0.7.37
+* Added NSFW content control settings (blur mature content and SFW-only filter)
+* Implemented intelligent blur effects for previews and showcase media
+* Added manual content rating option through context menu
+* Enhanced user experience with configurable content visibility
+* Fixed various bugs and improved stability
+
+### v0.7.36
+* Enhanced LoRA details view with model descriptions and tags display
+* Added tag filtering system for improved model discovery
+* Implemented editable trigger words functionality
+* Improved TriggerWord Toggle node with new group mode option for granular control
+* Added new Lora Stacker node with cross-compatibility support (works with efficiency nodes, ComfyRoll, easy-use, etc.)
+* Fixed several bugs
+
 ### v0.7.35-beta
 * Added base model filtering
 * Implemented bulk operations (copy syntax, move multiple LoRAs)
@@ -76,6 +105,12 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
   - Trigger words at a glance
   - One-click workflow integration with preset values
   
+- 🧩 **LoRA Recipes**
+  - Save and share favorite LoRA combinations
+  - Preserve generation parameters for future reference
+  - Quick application to workflows
+  - Import/export functionality for community sharing
+  
 - 💻 **User Friendly**
   - One-click access from ComfyUI menu
   - Context menu for quick actions
@@ -139,12 +174,3 @@ Join our Discord community for support, discussions, and updates:
 [Discord Server](https://discord.gg/vcqNrWVFvM)
 
 ---
-
-## 🗺️ Roadmap
-
-- ✅ One-click integration of LoRAs into ComfyUI workflows with preset strength values
-- 🤝 Improved usage tips retrieval from CivitAI model pages
-- 🔌 Integration with Power LoRA Loader and other management tools
-- 🛡️ Configurable NSFW level settings for content filtering
-
----

__init__.py

@@ -1,14 +1,18 @@
 from .py.lora_manager import LoraManager
 from .py.nodes.lora_loader import LoraManagerLoader
 from .py.nodes.trigger_word_toggle import TriggerWordToggle
+from .py.nodes.lora_stacker import LoraStacker
+# from .py.nodes.save_image import SaveImage
 
 NODE_CLASS_MAPPINGS = {
     LoraManagerLoader.NAME: LoraManagerLoader,
-    TriggerWordToggle.NAME: TriggerWordToggle
+    TriggerWordToggle.NAME: TriggerWordToggle,
+    LoraStacker.NAME: LoraStacker,
+    # SaveImage.NAME: SaveImage
 }
 
 WEB_DIRECTORY = "./web/comfyui"
 
 # Register routes on import
 LoraManager.add_routes()
-__all__ = ['NODE_CLASS_MAPPINGS', 'WEB_DIRECTORY']
+__all__ = ['NODE_CLASS_MAPPINGS', 'WEB_DIRECTORY']

py/config.py

@@ -17,6 +17,7 @@ class Config:
         # 静态路由映射字典, target to route mapping
         self._route_mappings = {}
         self.loras_roots = self._init_lora_paths()
+        self.temp_directory = folder_paths.get_temp_directory()
         # 在初始化时扫描符号链接
         self._scan_symbolic_links()
 
@@ -87,9 +88,9 @@ class Config:
 
     def _init_lora_paths(self) -> List[str]:
         """Initialize and validate LoRA paths from ComfyUI settings"""
-        paths = list(set(path.replace(os.sep, "/") 
+        paths = sorted(set(path.replace(os.sep, "/") 
                 for path in folder_paths.get_folder_paths("loras") 
-                if os.path.exists(path)))
+                if os.path.exists(path)), key=lambda p: p.lower())
         print("Found LoRA roots:", "\n - " + "\n - ".join(paths))
         
         if not paths:

py/lora_manager.py

@@ -4,9 +4,13 @@ from server import PromptServer # type: ignore
 from .config import config
 from .routes.lora_routes import LoraRoutes
 from .routes.api_routes import ApiRoutes
+from .routes.recipe_routes import RecipeRoutes
+from .routes.checkpoints_routes import CheckpointsRoutes
 from .services.lora_scanner import LoraScanner
+from .services.recipe_scanner import RecipeScanner
 from .services.file_monitor import LoraFileMonitor
 from .services.lora_cache import LoraCache
+from .services.recipe_cache import RecipeCache
 import logging
 
 logger = logging.getLogger(__name__)
@@ -56,36 +60,42 @@ class LoraManager:
         
         # Setup feature routes
         routes = LoraRoutes()
+        checkpoints_routes = CheckpointsRoutes()
         
         # Setup file monitoring
         monitor = LoraFileMonitor(routes.scanner, config.loras_roots)
         monitor.start()
         
         routes.setup_routes(app)
+        checkpoints_routes.setup_routes(app)
         ApiRoutes.setup_routes(app, monitor)
+        RecipeRoutes.setup_routes(app)
         
         # Store monitor in app for cleanup
         app['lora_monitor'] = monitor
         
         # Schedule cache initialization using the application's startup handler
-        app.on_startup.append(lambda app: cls._schedule_cache_init(routes.scanner))
+        app.on_startup.append(lambda app: cls._schedule_cache_init(routes.scanner, routes.recipe_scanner))
         
         # Add cleanup
         app.on_shutdown.append(cls._cleanup)
         app.on_shutdown.append(ApiRoutes.cleanup)
     
     @classmethod
-    async def _schedule_cache_init(cls, scanner: LoraScanner):
+    async def _schedule_cache_init(cls, scanner: LoraScanner, recipe_scanner: RecipeScanner):
         """Schedule cache initialization in the running event loop"""
         try:
             # 创建低优先级的初始化任务
-            asyncio.create_task(cls._initialize_cache(scanner), name='lora_cache_init')
+            lora_task = asyncio.create_task(cls._initialize_lora_cache(scanner), name='lora_cache_init')
+            
+            # Schedule recipe cache initialization with a delay to let lora scanner initialize first
+            recipe_task = asyncio.create_task(cls._initialize_recipe_cache(recipe_scanner, delay=2), name='recipe_cache_init')
         except Exception as e:
-            print(f"LoRA Manager: Error scheduling cache initialization: {e}")
+            logger.error(f"LoRA Manager: Error scheduling cache initialization: {e}")
     
     @classmethod
-    async def _initialize_cache(cls, scanner: LoraScanner):
-        """Initialize cache in background"""
+    async def _initialize_lora_cache(cls, scanner: LoraScanner):
+        """Initialize lora cache in background"""
         try:
             # 设置初始缓存占位
             scanner._cache = LoraCache(
@@ -98,10 +108,29 @@ class LoraManager:
             # 分阶段加载缓存
             await scanner.get_cached_data(force_refresh=True)
         except Exception as e:
-            print(f"LoRA Manager: Error initializing cache: {e}")
+            logger.error(f"LoRA Manager: Error initializing lora cache: {e}")
+    
+    @classmethod
+    async def _initialize_recipe_cache(cls, scanner: RecipeScanner, delay: float = 2.0):
+        """Initialize recipe cache in background with a delay"""
+        try:
+            # Wait for the specified delay to let lora scanner initialize first
+            await asyncio.sleep(delay)
+            
+            # Set initial empty cache
+            scanner._cache = RecipeCache(
+                raw_data=[],
+                sorted_by_name=[],
+                sorted_by_date=[]
+            )
+            
+            # Force refresh to load the actual data
+            await scanner.get_cached_data(force_refresh=True)
+        except Exception as e:
+            logger.error(f"LoRA Manager: Error initializing recipe cache: {e}")
     
     @classmethod
     async def _cleanup(cls, app):
         """Cleanup resources"""
         if 'lora_monitor' in app:
-            app['lora_monitor'].stop()
+            app['lora_monitor'].stop()

py/nodes/lora_loader.py

@@ -1,3 +1,4 @@
+import logging
 from nodes import LoraLoader
 from comfy.comfy_types import IO # type: ignore
 from ..services.lora_scanner import LoraScanner
@@ -6,9 +7,11 @@ import asyncio
 import os
 from .utils import FlexibleOptionalInputType, any_type
 
+logger = logging.getLogger(__name__)
+
 class LoraManagerLoader:
     NAME = "Lora Loader (LoraManager)"
-    CATEGORY = "loaders"
+    CATEGORY = "Lora Manager/loaders"
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -26,8 +29,8 @@ class LoraManagerLoader:
             "optional": FlexibleOptionalInputType(any_type),
         }
 
-    RETURN_TYPES = ("MODEL", "CLIP", IO.STRING)
-    RETURN_NAMES = ("MODEL", "CLIP", "trigger_words")
+    RETURN_TYPES = ("MODEL", "CLIP", IO.STRING, IO.STRING)
+    RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
     FUNCTION = "load_loras"
 
     async def get_lora_info(self, lora_name):
@@ -49,29 +52,72 @@ class LoraManagerLoader:
                             return relative_path, trigger_words
         return lora_name, []  # Fallback if not found
 
+    def extract_lora_name(self, lora_path):
+        """Extract the lora name from a lora path (e.g., 'IL\\aorunIllstrious.safetensors' -> 'aorunIllstrious')"""
+        # Get the basename without extension
+        basename = os.path.basename(lora_path)
+        return os.path.splitext(basename)[0]
+    
+    def _get_loras_list(self, kwargs):
+        """Helper to extract loras list from either old or new kwargs format"""
+        if 'loras' not in kwargs:
+            return []
+            
+        loras_data = kwargs['loras']
+        # Handle new format: {'loras': {'__value__': [...]}}
+        if isinstance(loras_data, dict) and '__value__' in loras_data:
+            return loras_data['__value__']
+        # Handle old format: {'loras': [...]}
+        elif isinstance(loras_data, list):
+            return loras_data
+        # Unexpected format
+        else:
+            logger.warning(f"Unexpected loras format: {type(loras_data)}")
+            return []
+    
     def load_loras(self, model, clip, text, **kwargs):
-        """Loads multiple LoRAs based on the kwargs input."""
+        """Loads multiple LoRAs based on the kwargs input and lora_stack."""
         loaded_loras = []
         all_trigger_words = []
         
-        if 'loras' in kwargs:
-            for lora in kwargs['loras']:
-                if not lora.get('active', False):
-                    continue
-                    
-                lora_name = lora['name']
-                strength = float(lora['strength'])
+        lora_stack = kwargs.get('lora_stack', None)
+        # First process lora_stack if available
+        if lora_stack:
+            for lora_path, model_strength, clip_strength in lora_stack:
+                # Apply the LoRA using the provided path and strengths
+                model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
                 
-                # Get lora path and trigger words
-                lora_path, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+                # Extract lora name for trigger words lookup
+                lora_name = self.extract_lora_name(lora_path)
+                _, trigger_words = asyncio.run(self.get_lora_info(lora_name))
                 
-                # Apply the LoRA using the resolved path
-                model, clip = LoraLoader().load_lora(model, clip, lora_path, strength, strength)
-                loaded_loras.append(f"{lora_name}: {strength}")
-                
-                # Add trigger words to collection
                 all_trigger_words.extend(trigger_words)
+                loaded_loras.append(f"{lora_name}: {model_strength}")
         
-        trigger_words_text = ", ".join(all_trigger_words) if all_trigger_words else ""
+        # Then process loras from kwargs with support for both old and new formats
+        loras_list = self._get_loras_list(kwargs)
+        for lora in loras_list:
+            if not lora.get('active', False):
+                continue
+                
+            lora_name = lora['name']
+            strength = float(lora['strength'])
+            
+            # Get lora path and trigger words
+            lora_path, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+            
+            # Apply the LoRA using the resolved path
+            model, clip = LoraLoader().load_lora(model, clip, lora_path, strength, strength)
+            loaded_loras.append(f"{lora_name}: {strength}")
+            
+            # Add trigger words to collection
+            all_trigger_words.extend(trigger_words)
+        
+        # use ',, ' to separate trigger words for group mode
+        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+        
+        # Format loaded_loras as <lora:lora_name:strength> separated by spaces
+        formatted_loras = " ".join([f"<lora:{name.split(':')[0].strip()}:{str(strength).strip()}>" 
+                                  for name, strength in [item.split(':') for item in loaded_loras]])
 
-        return (model, clip, trigger_words_text)
+        return (model, clip, trigger_words_text, formatted_loras)

py/nodes/lora_stacker.py

@@ -0,0 +1,113 @@
+from comfy.comfy_types import IO # type: ignore
+from ..services.lora_scanner import LoraScanner
+from ..config import config
+import asyncio
+import os
+from .utils import FlexibleOptionalInputType, any_type
+import logging
+
+logger = logging.getLogger(__name__)
+
+class LoraStacker:
+    NAME = "Lora Stacker (LoraManager)"
+    CATEGORY = "Lora Manager/stackers"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": (IO.STRING, {
+                    "multiline": True, 
+                    "dynamicPrompts": True, 
+                    "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
+                    "placeholder": "LoRA syntax input: <lora:name:strength>"
+                }),
+            },
+            "optional": FlexibleOptionalInputType(any_type),
+        }
+
+    RETURN_TYPES = ("LORA_STACK", IO.STRING)
+    RETURN_NAMES = ("LORA_STACK", "trigger_words")
+    FUNCTION = "stack_loras"
+
+    async def get_lora_info(self, lora_name):
+        """Get the lora path and trigger words from cache"""
+        scanner = await LoraScanner.get_instance()
+        cache = await scanner.get_cached_data()
+        
+        for item in cache.raw_data:
+            if item.get('file_name') == lora_name:
+                file_path = item.get('file_path')
+                if file_path:
+                    for root in config.loras_roots:
+                        root = root.replace(os.sep, '/')
+                        if file_path.startswith(root):
+                            relative_path = os.path.relpath(file_path, root).replace(os.sep, '/')
+                            # Get trigger words from civitai metadata
+                            civitai = item.get('civitai', {})
+                            trigger_words = civitai.get('trainedWords', []) if civitai else []
+                            return relative_path, trigger_words
+        return lora_name, []  # Fallback if not found
+
+    def extract_lora_name(self, lora_path):
+        """Extract the lora name from a lora path (e.g., 'IL\\aorunIllstrious.safetensors' -> 'aorunIllstrious')"""
+        # Get the basename without extension
+        basename = os.path.basename(lora_path)
+        return os.path.splitext(basename)[0]
+    
+    def _get_loras_list(self, kwargs):
+        """Helper to extract loras list from either old or new kwargs format"""
+        if 'loras' not in kwargs:
+            return []
+            
+        loras_data = kwargs['loras']
+        # Handle new format: {'loras': {'__value__': [...]}}
+        if isinstance(loras_data, dict) and '__value__' in loras_data:
+            return loras_data['__value__']
+        # Handle old format: {'loras': [...]}
+        elif isinstance(loras_data, list):
+            return loras_data
+        # Unexpected format
+        else:
+            logger.warning(f"Unexpected loras format: {type(loras_data)}")
+            return []
+    
+    def stack_loras(self, text, **kwargs):
+        """Stacks multiple LoRAs based on the kwargs input without loading them."""
+        stack = []
+        all_trigger_words = []
+        
+        # Process existing lora_stack if available
+        lora_stack = kwargs.get('lora_stack', None)
+        if lora_stack:
+            stack.extend(lora_stack)
+            # Get trigger words from existing stack entries
+            for lora_path, _, _ in lora_stack:
+                lora_name = self.extract_lora_name(lora_path)
+                _, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+                all_trigger_words.extend(trigger_words)
+        
+        # Process loras from kwargs with support for both old and new formats
+        loras_list = self._get_loras_list(kwargs)
+        for lora in loras_list:
+            if not lora.get('active', False):
+                continue
+                
+            lora_name = lora['name']
+            model_strength = float(lora['strength'])
+            clip_strength = model_strength  # Using same strength for both as in the original loader
+            
+            # Get lora path and trigger words
+            lora_path, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+            
+            # Add to stack without loading
+            # replace '/' with os.sep to avoid different OS path format
+            stack.append((lora_path.replace('/', os.sep), model_strength, clip_strength))
+            
+            # Add trigger words to collection
+            all_trigger_words.extend(trigger_words)
+        
+        # use ',, ' to separate trigger words for group mode
+        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+
+        return (stack, trigger_words_text)

py/nodes/save_image.py

@@ -0,0 +1,41 @@
+import json
+from server import PromptServer # type: ignore
+
+class SaveImage:
+    NAME = "Save Image (LoraManager)"
+    CATEGORY = "Lora Manager/utils"
+    DESCRIPTION = "Experimental node to display image preview and print prompt and extra_pnginfo"
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+            "hidden": {
+                "prompt": "PROMPT",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "process_image"
+
+    def process_image(self, image, prompt=None, extra_pnginfo=None):
+        # Print the prompt information
+        print("SaveImage Node - Prompt:")
+        if prompt:
+            print(json.dumps(prompt, indent=2))
+        else:
+            print("No prompt information available")
+        
+        # Print the extra_pnginfo
+        print("\nSaveImage Node - Extra PNG Info:")
+        if extra_pnginfo:
+            print(json.dumps(extra_pnginfo, indent=2))
+        else:
+            print("No extra PNG info available")
+        
+        # Return the image unchanged
+        return (image,)

py/nodes/trigger_word_toggle.py

@@ -1,17 +1,22 @@
 import json
+import re
 from server import PromptServer # type: ignore
 from .utils import FlexibleOptionalInputType, any_type
+import logging
+
+logger = logging.getLogger(__name__)
+
 
 class TriggerWordToggle:
     NAME = "TriggerWord Toggle (LoraManager)"
-    CATEGORY = "lora manager"
+    CATEGORY = "Lora Manager/utils"
     DESCRIPTION = "Toggle trigger words on/off"
     
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "trigger_words": ("STRING", {"defaultInput": True, "forceInput": True}),
+                "group_mode": ("BOOLEAN", {"default": True}),
             },
             "optional": FlexibleOptionalInputType(any_type),
             "hidden": {
@@ -23,7 +28,24 @@ class TriggerWordToggle:
     RETURN_NAMES = ("filtered_trigger_words",)
     FUNCTION = "process_trigger_words"
 
-    def process_trigger_words(self, trigger_words, id, **kwargs):
+    def _get_toggle_data(self, kwargs, key='toggle_trigger_words'):
+        """Helper to extract data from either old or new kwargs format"""
+        if key not in kwargs:
+            return None
+            
+        data = kwargs[key]
+        # Handle new format: {'key': {'__value__': ...}}
+        if isinstance(data, dict) and '__value__' in data:
+            return data['__value__']
+        # Handle old format: {'key': ...}
+        else:
+            return data
+
+    def process_trigger_words(self, id, group_mode, **kwargs):
+        # Handle both old and new formats for trigger_words
+        trigger_words_data = self._get_toggle_data(kwargs, 'trigger_words')
+        trigger_words = trigger_words_data if isinstance(trigger_words_data, str) else ""
+        
         # Send trigger words to frontend
         PromptServer.instance.send_sync("trigger_word_update", {
             "id": id,
@@ -32,31 +54,43 @@ class TriggerWordToggle:
         
         filtered_triggers = trigger_words
         
-        if 'toggle_trigger_words' in kwargs:
+        # Get toggle data with support for both formats
+        trigger_data = self._get_toggle_data(kwargs, 'toggle_trigger_words')
+        if trigger_data:
             try:
-                # Get trigger word toggle data
-                trigger_data = kwargs['toggle_trigger_words']
-                
                 # Convert to list if it's a JSON string
                 if isinstance(trigger_data, str):
                     trigger_data = json.loads(trigger_data)
                 
-                # Create dictionaries to track active state of words
+                # Create dictionaries to track active state of words or groups
                 active_state = {item['text']: item.get('active', False) for item in trigger_data}
                 
-                # Split original trigger words
-                original_words = [word.strip() for word in trigger_words.split(',')]
-                
-                # Filter words: keep those not in toggle_trigger_words or those that are active
-                filtered_words = [word for word in original_words if word not in active_state or active_state[word]]
-                
-                # Join them in the same format as input
-                if filtered_words:
-                    filtered_triggers = ', '.join(filtered_words)
+                if group_mode:
+                    # Split by two or more consecutive commas to get groups
+                    groups = re.split(r',{2,}', trigger_words)
+                    # Remove leading/trailing whitespace from each group
+                    groups = [group.strip() for group in groups]
+                    
+                    # Filter groups: keep those not in toggle_trigger_words or those that are active
+                    filtered_groups = [group for group in groups if group not in active_state or active_state[group]]
+                    
+                    if filtered_groups:
+                        filtered_triggers = ', '.join(filtered_groups)
+                    else:
+                        filtered_triggers = ""
                 else:
-                    filtered_triggers = ""
+                    # Original behavior for individual words mode
+                    original_words = [word.strip() for word in trigger_words.split(',')]
+                    # Filter out empty strings
+                    original_words = [word for word in original_words if word]
+                    filtered_words = [word for word in original_words if word not in active_state or active_state[word]]
+                    
+                    if filtered_words:
+                        filtered_triggers = ', '.join(filtered_words)
+                    else:
+                        filtered_triggers = ""
                     
             except Exception as e:
-                print(f"Error processing trigger words: {e}")
+                logger.error(f"Error processing trigger words: {e}")
             
         return (filtered_triggers,)

py/nodes/utils.py

@@ -4,6 +4,7 @@ class AnyType(str):
   def __ne__(self, __value: object) -> bool:
     return False
 
+# Credit to Regis Gaughan, III (rgthree)
 class FlexibleOptionalInputType(dict):
   """A special class to make flexible nodes that pass data to our python handlers.
 

py/routes/api_routes.py

@@ -4,6 +4,8 @@ import logging
 from aiohttp import web
 from typing import Dict, List
 
+from ..utils.model_utils import determine_base_model
+
 from ..services.file_monitor import LoraFileMonitor
 from ..services.download_manager import DownloadManager
 from ..services.civitai_client import CivitaiClient
@@ -14,6 +16,7 @@ from ..services.websocket_manager import ws_manager
 from ..services.settings_manager import settings
 import asyncio
 from .update_routes import UpdateRoutes
+from ..services.recipe_scanner import RecipeScanner
 
 logger = logging.getLogger(__name__)
 
@@ -37,13 +40,18 @@ class ApiRoutes:
         app.router.add_post('/api/fetch-all-civitai', routes.fetch_all_civitai)
         app.router.add_get('/ws/fetch-progress', ws_manager.handle_connection)
         app.router.add_get('/api/lora-roots', routes.get_lora_roots)
+        app.router.add_get('/api/folders', routes.get_folders)
         app.router.add_get('/api/civitai/versions/{model_id}', routes.get_civitai_versions)
         app.router.add_post('/api/download-lora', routes.download_lora)
         app.router.add_post('/api/settings', routes.update_settings)
         app.router.add_post('/api/move_model', routes.move_model)
+        app.router.add_get('/api/lora-model-description', routes.get_lora_model_description)  # Add new route
         app.router.add_post('/loras/api/save-metadata', routes.save_metadata)
         app.router.add_get('/api/lora-preview-url', routes.get_lora_preview_url)  # Add new route
         app.router.add_post('/api/move_models_bulk', routes.move_models_bulk)
+        app.router.add_get('/api/loras/top-tags', routes.get_top_tags)  # Add new route for top tags
+        app.router.add_get('/api/loras/base-models', routes.get_base_models)  # Add new route for base models
+        app.router.add_get('/api/lora-civitai-url', routes.get_lora_civitai_url)  # Add new route for Civitai URL
 
         # Add update check routes
         UpdateRoutes.setup_routes(app)
@@ -124,12 +132,17 @@ class ApiRoutes:
             folder = request.query.get('folder')
             search = request.query.get('search', '').lower()
             fuzzy = request.query.get('fuzzy', 'false').lower() == 'true'
-            recursive = request.query.get('recursive', 'false').lower() == 'true'
             
             # Parse base models filter parameter
             base_models = request.query.get('base_models', '').split(',')
             base_models = [model.strip() for model in base_models if model.strip()]
             
+            # Parse search options
+            search_filename = request.query.get('search_filename', 'true').lower() == 'true'
+            search_modelname = request.query.get('search_modelname', 'true').lower() == 'true'
+            search_tags = request.query.get('search_tags', 'false').lower() == 'true'
+            recursive = request.query.get('recursive', 'false').lower() == 'true'
+            
             # Validate parameters
             if page < 1 or page_size < 1 or page_size > 100:
                 return web.json_response({
@@ -141,6 +154,10 @@ class ApiRoutes:
                     'error': 'Invalid sort parameter'
                 }, status=400)
             
+            # Parse tags filter parameter
+            tags = request.query.get('tags', '').split(',')
+            tags = [tag.strip() for tag in tags if tag.strip()]
+            
             # Get paginated data with search and filters
             result = await self.scanner.get_paginated_data(
                 page=page,
@@ -149,8 +166,14 @@ class ApiRoutes:
                 folder=folder,
                 search=search,
                 fuzzy=fuzzy,
-                recursive=recursive,
-                base_models=base_models  # Pass base models filter
+                base_models=base_models,  # Pass base models filter
+                tags=tags,  # Add tags parameter
+                search_options={
+                    'filename': search_filename,
+                    'modelname': search_modelname,
+                    'tags': search_tags,
+                    'recursive': recursive
+                }
             )
             
             # Format the response data
@@ -183,12 +206,15 @@ class ApiRoutes:
             "model_name": lora["model_name"],
             "file_name": lora["file_name"],
             "preview_url": config.get_preview_static_url(lora["preview_url"]),
+            "preview_nsfw_level": lora.get("preview_nsfw_level", 0),
             "base_model": lora["base_model"],
             "folder": lora["folder"],
             "sha256": lora["sha256"],
             "file_path": lora["file_path"].replace(os.sep, "/"),
             "file_size": lora["size"],
             "modified": lora["modified"],
+            "tags": lora["tags"],
+            "modelDescription": lora["modelDescription"],
             "from_civitai": lora.get("from_civitai", True),
             "usage_tips": lora.get("usage_tips", ""),
             "notes": lora.get("notes", ""),
@@ -243,6 +269,9 @@ class ApiRoutes:
         cache = await self.scanner.get_cached_data()
         cache.raw_data = [item for item in cache.raw_data if item['file_path'] != main_path]
         await cache.resort()
+
+        # update hash index
+        self.scanner._hash_index.remove_by_path(main_path)
         
         # Delete optional files
         for pattern in patterns[1:]:
@@ -331,11 +360,19 @@ class ApiRoutes:
         
         # Update model name if available
         if 'model' in civitai_metadata:
-            local_metadata['model_name'] = civitai_metadata['model'].get('name', 
-                                                                       local_metadata.get('model_name'))
+            if civitai_metadata.get('model', {}).get('name'):
+                local_metadata['model_name'] = civitai_metadata['model']['name']
+        
+            # Fetch additional model metadata (description and tags) if we have model ID
+            model_id = civitai_metadata['modelId']
+            if model_id:
+                model_metadata, _ = await client.get_model_metadata(str(model_id))
+                if model_metadata:
+                    local_metadata['modelDescription'] = model_metadata.get('description', '')
+                    local_metadata['tags'] = model_metadata.get('tags', [])
         
         # Update base model
-        local_metadata['base_model'] = civitai_metadata.get('baseModel')
+        local_metadata['base_model'] = determine_base_model(civitai_metadata.get('baseModel'))
         
         # Update preview if needed
         if not local_metadata.get('preview_url') or not os.path.exists(local_metadata['preview_url']):
@@ -348,6 +385,7 @@ class ApiRoutes:
                 
                 if await client.download_preview_image(first_preview['url'], preview_path):
                     local_metadata['preview_url'] = preview_path.replace(os.sep, '/')
+                    local_metadata['preview_nsfw_level'] = first_preview.get('nsfwLevel', 0)
 
         # Save updated metadata
         with open(metadata_path, 'w', encoding='utf-8') as f:
@@ -367,7 +405,7 @@ class ApiRoutes:
             # 准备要处理的 loras
             to_process = [
                 lora for lora in cache.raw_data 
-                if lora.get('sha256') and not lora.get('civitai') and lora.get('from_civitai')
+                if lora.get('sha256') and (not lora.get('civitai') or 'id' not in lora.get('civitai')) and lora.get('from_civitai')  # TODO: for lora not from CivitAI but added traineWords
             ]
             total_to_process = len(to_process)
             
@@ -487,6 +525,13 @@ class ApiRoutes:
         return web.json_response({
             'roots': config.loras_roots
         })
+    
+    async def get_folders(self, request: web.Request) -> web.Response:
+        """Get all folders in the cache"""
+        cache = await self.scanner.get_cached_data()
+        return web.json_response({
+            'folders': cache.folders
+        })
 
     async def get_civitai_versions(self, request: web.Request) -> web.Response:
         """Get available versions for a Civitai model with local availability info"""
@@ -498,13 +543,24 @@ class ApiRoutes:
             
             # Check local availability for each version
             for version in versions:
-                for file in version.get('files', []):
-                    sha256 = file.get('hashes', {}).get('SHA256')
+                # Find the model file (type="Model") in the files list
+                model_file = next((file for file in version.get('files', []) 
+                                  if file.get('type') == 'Model'), None)
+                
+                if model_file:
+                    sha256 = model_file.get('hashes', {}).get('SHA256')
                     if sha256:
-                        file['existsLocally'] = self.scanner.has_lora_hash(sha256)
-                        if file['existsLocally']:
-                            file['localPath'] = self.scanner.get_lora_path_by_hash(sha256)
+                        # Set existsLocally and localPath at the version level
+                        version['existsLocally'] = self.scanner.has_lora_hash(sha256)
+                        if version['existsLocally']:
+                            version['localPath'] = self.scanner.get_lora_path_by_hash(sha256)
                         
+                        # Also set the model file size at the version level for easier access
+                        version['modelSizeKB'] = model_file.get('sizeKB')
+                else:
+                    # No model file found in this version
+                    version['existsLocally'] = False
+                    
             return web.json_response(versions)
         except Exception as e:
             logger.error(f"Error fetching model versions: {e}")
@@ -526,16 +582,36 @@ class ApiRoutes:
                     download_url=data.get('download_url'),
                     save_dir=data.get('lora_root'),
                     relative_path=data.get('relative_path'),
-                    progress_callback=progress_callback  # Add progress callback
+                    progress_callback=progress_callback
                 )
                 
                 if not result.get('success', False):
-                    return web.Response(status=500, text=result.get('error', 'Unknown error'))
+                    error_message = result.get('error', 'Unknown error')
+                    
+                    # Return 401 for early access errors
+                    if 'early access' in error_message.lower():
+                        logger.warning(f"Early access download failed: {error_message}")
+                        return web.Response(
+                            status=401,  # Use 401 status code to match Civitai's response
+                            text=f"Early Access Restriction: {error_message}"
+                        )
+                    
+                    return web.Response(status=500, text=error_message)
                 
                 return web.json_response(result)
             except Exception as e:
-                logger.error(f"Error downloading LoRA: {e}")
-                return web.Response(status=500, text=str(e))
+                error_message = str(e)
+                
+                # Check if this might be an early access error
+                if '401' in error_message:
+                    logger.warning(f"Early access error (401): {error_message}")
+                    return web.Response(
+                        status=401,
+                        text="Early Access Restriction: This LoRA requires purchase. Please buy early access on Civitai.com."
+                    )
+                
+                logger.error(f"Error downloading LoRA: {error_message}")
+                return web.Response(status=500, text=error_message)
 
     async def update_settings(self, request: web.Request) -> web.Response:
         """Update application settings"""
@@ -545,6 +621,8 @@ class ApiRoutes:
             # Validate and update settings
             if 'civitai_api_key' in data:
                 settings.set('civitai_api_key', data['civitai_api_key'])
+            if 'show_only_sfw' in data:
+                settings.set('show_only_sfw', data['show_only_sfw'])
             
             return web.json_response({'success': True})
         except Exception as e:
@@ -600,8 +678,15 @@ class ApiRoutes:
             else:
                 metadata = {}
 
-            # Update metadata with new values
-            metadata.update(metadata_updates)
+            # Handle nested updates (for civitai.trainedWords)
+            for key, value in metadata_updates.items():
+                if isinstance(value, dict) and key in metadata and isinstance(metadata[key], dict):
+                    # Deep update for nested dictionaries
+                    for nested_key, nested_value in value.items():
+                        metadata[key][nested_key] = nested_value
+                else:
+                    # Regular update for top-level keys
+                    metadata[key] = value
 
             # Save updated metadata
             with open(metadata_path, 'w', encoding='utf-8') as f:
@@ -656,6 +741,48 @@ class ApiRoutes:
             logger.error(f"Error getting lora preview URL: {e}", exc_info=True)
             return web.Response(text=str(e), status=500)
 
+    async def get_lora_civitai_url(self, request: web.Request) -> web.Response:
+        """Get the Civitai URL for a LoRA file"""
+        try:
+            # Get lora file name from query parameters
+            lora_name = request.query.get('name')
+            if not lora_name:
+                return web.Response(text='Lora file name is required', status=400)
+
+            # Get cache data
+            cache = await self.scanner.get_cached_data()
+            
+            # Search for the lora in cache data
+            for lora in cache.raw_data:
+                file_name = lora['file_name']
+                if file_name == lora_name:
+                    civitai_data = lora.get('civitai', {})
+                    model_id = civitai_data.get('modelId')
+                    version_id = civitai_data.get('id')
+                    
+                    if model_id:
+                        civitai_url = f"https://civitai.com/models/{model_id}"
+                        if version_id:
+                            civitai_url += f"?modelVersionId={version_id}"
+                            
+                        return web.json_response({
+                            'success': True,
+                            'civitai_url': civitai_url,
+                            'model_id': model_id,
+                            'version_id': version_id
+                        })
+                    break
+
+            # If no Civitai data found
+            return web.json_response({
+                'success': False,
+                'error': 'No Civitai data found for the specified lora'
+            }, status=404)
+
+        except Exception as e:
+            logger.error(f"Error getting lora Civitai URL: {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:
@@ -691,3 +818,118 @@ class ApiRoutes:
         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:
+            # Get parameters
+            model_id = request.query.get('model_id')
+            file_path = request.query.get('file_path')
+            
+            if not model_id:
+                return web.json_response({
+                    'success': False, 
+                    'error': 'Model ID is required'
+                }, status=400)
+            
+            # Check if we already have the description stored in metadata
+            description = None
+            tags = []
+            if file_path:
+                metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+                if os.path.exists(metadata_path):
+                    try:
+                        with open(metadata_path, 'r', encoding='utf-8') as f:
+                            metadata = json.load(f)
+                            description = metadata.get('modelDescription')
+                            tags = metadata.get('tags', [])
+                    except Exception as e:
+                        logger.error(f"Error loading metadata from {metadata_path}: {e}")
+            
+            # If description is not in metadata, fetch from CivitAI
+            if not description:
+                logger.info(f"Fetching model metadata for model ID: {model_id}")
+                model_metadata, _ = await self.civitai_client.get_model_metadata(model_id)
+                
+                if model_metadata:
+                    description = model_metadata.get('description')
+                    tags = model_metadata.get('tags', [])
+                
+                    # Save the metadata to file if we have a file path and got metadata
+                    if file_path:
+                        try:
+                            metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+                            if os.path.exists(metadata_path):
+                                with open(metadata_path, 'r', encoding='utf-8') as f:
+                                    metadata = json.load(f)
+                                
+                                metadata['modelDescription'] = description
+                                metadata['tags'] = tags
+                                
+                                with open(metadata_path, 'w', encoding='utf-8') as f:
+                                    json.dump(metadata, f, indent=2, ensure_ascii=False)
+                                    logger.info(f"Saved model metadata to file for {file_path}")
+                        except Exception as e:
+                            logger.error(f"Error saving model metadata: {e}")
+            
+            return web.json_response({
+                'success': True,
+                'description': description or "<p>No model description available.</p>",
+                'tags': tags
+            })
+            
+        except Exception as e:
+            logger.error(f"Error getting model metadata: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    async def get_top_tags(self, request: web.Request) -> web.Response:
+        """Handle request for top tags sorted by frequency"""
+        try:
+            # Parse query parameters
+            limit = int(request.query.get('limit', '20'))
+            
+            # Validate limit
+            if limit < 1 or limit > 100:
+                limit = 20  # Default to a reasonable limit
+                
+            # Get top tags
+            top_tags = await self.scanner.get_top_tags(limit)
+            
+            return web.json_response({
+                'success': True,
+                'tags': top_tags
+            })
+            
+        except Exception as e:
+            logger.error(f"Error getting top tags: {str(e)}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': 'Internal server error'
+            }, status=500)
+
+    async def get_base_models(self, request: web.Request) -> web.Response:
+        """Get base models used in loras"""
+        try:
+            # Parse query parameters
+            limit = int(request.query.get('limit', '20'))
+            
+            # Validate limit
+            if limit < 1 or limit > 100:
+                limit = 20  # Default to a reasonable limit
+                
+            # Get base models
+            base_models = await self.scanner.get_base_models(limit)
+            
+            return web.json_response({
+                'success': True,
+                'base_models': base_models
+            })
+        except Exception as e:
+            logger.error(f"Error retrieving base models: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/routes/checkpoints_routes.py

@@ -0,0 +1,44 @@
+import os
+from aiohttp import web
+import jinja2
+import logging
+from ..config import config
+from ..services.settings_manager import settings
+
+logger = logging.getLogger(__name__)
+logging.getLogger('asyncio').setLevel(logging.CRITICAL)
+
+class CheckpointsRoutes:
+    """Route handlers for Checkpoints management endpoints"""
+    
+    def __init__(self):
+        self.template_env = jinja2.Environment(
+            loader=jinja2.FileSystemLoader(config.templates_path),
+            autoescape=True
+        )
+
+    async def handle_checkpoints_page(self, request: web.Request) -> web.Response:
+        """Handle GET /checkpoints request"""
+        try:
+            template = self.template_env.get_template('checkpoints.html')
+            rendered = template.render(
+                is_initializing=False,
+                settings=settings,
+                request=request
+            )
+            
+            return web.Response(
+                text=rendered,
+                content_type='text/html'
+            )
+            
+        except Exception as e:
+            logger.error(f"Error handling checkpoints request: {e}", exc_info=True)
+            return web.Response(
+                text="Error loading checkpoints page",
+                status=500
+            )
+
+    def setup_routes(self, app: web.Application):
+        """Register routes with the application"""
+        app.router.add_get('/checkpoints', self.handle_checkpoints_page)

py/routes/lora_routes.py

@@ -4,6 +4,7 @@ import jinja2
 from typing import Dict, List
 import logging
 from ..services.lora_scanner import LoraScanner
+from ..services.recipe_scanner import RecipeScanner
 from ..config import config
 from ..services.settings_manager import settings  # Add this import
 
@@ -15,6 +16,7 @@ class LoraRoutes:
     
     def __init__(self):
         self.scanner = LoraScanner()
+        self.recipe_scanner = RecipeScanner(self.scanner)
         self.template_env = jinja2.Environment(
             loader=jinja2.FileSystemLoader(config.templates_path),
             autoescape=True
@@ -26,10 +28,16 @@ class LoraRoutes:
             "model_name": lora["model_name"],
             "file_name": lora["file_name"],   
             "preview_url": config.get_preview_static_url(lora["preview_url"]),
+            "preview_nsfw_level": lora.get("preview_nsfw_level", 0),
             "base_model": lora["base_model"],
             "folder": lora["folder"],
             "sha256": lora["sha256"],
             "file_path": lora["file_path"].replace(os.sep, "/"),
+            "size": lora["size"],
+            "tags": lora["tags"],
+            "modelDescription": lora["modelDescription"],
+            "usage_tips": lora["usage_tips"],
+            "notes": lora["notes"],
             "modified": lora["modified"],
             "from_civitai": lora.get("from_civitai", True),
             "civitai": self._filter_civitai_data(lora.get("civitai", {}))
@@ -63,7 +71,8 @@ class LoraRoutes:
                 rendered = template.render(
                     folders=[],  # 空文件夹列表
                     is_initializing=True,  # 新增标志
-                    settings=settings  # Pass settings to template
+                    settings=settings,  # Pass settings to template
+                    request=request  # Pass the request object to the template
                 )
             else:
                 # 正常流程
@@ -72,7 +81,8 @@ class LoraRoutes:
                 rendered = template.render(
                     folders=cache.folders,
                     is_initializing=False,
-                    settings=settings  # Pass settings to template
+                    settings=settings,  # Pass settings to template
+                    request=request  # Pass the request object to the template
                 )
             
             return web.Response(
@@ -87,6 +97,65 @@ class LoraRoutes:
                 status=500
             )
 
+    async def handle_recipes_page(self, request: web.Request) -> web.Response:
+        """Handle GET /loras/recipes request"""
+        try:
+            # Check cache initialization status
+            is_initializing = (
+                self.recipe_scanner._cache is None and 
+                (self.recipe_scanner._initialization_task is not None and 
+                not self.recipe_scanner._initialization_task.done())
+            )
+
+            if is_initializing:
+                # If initializing, return a loading page
+                template = self.template_env.get_template('recipes.html')
+                rendered = template.render(
+                    is_initializing=True,
+                    settings=settings,
+                    request=request  # Pass the request object to the template
+                )
+            else:
+                # return empty recipes
+                recipes_data = []
+                
+                template = self.template_env.get_template('recipes.html')
+                rendered = template.render(
+                    recipes=recipes_data,
+                    is_initializing=False,
+                    settings=settings,
+                    request=request  # Pass the request object to the template
+                )
+            
+            return web.Response(
+                text=rendered,
+                content_type='text/html'
+            )
+            
+        except Exception as e:
+            logger.error(f"Error handling recipes request: {e}", exc_info=True)
+            return web.Response(
+                text="Error loading recipes page",
+                status=500
+            )
+
+    def _format_recipe_file_url(self, file_path: str) -> str:
+        """Format file path for recipe image as a URL - same as in recipe_routes"""
+        try:
+            # Return the file URL directly for the first lora root's preview
+            recipes_dir = os.path.join(config.loras_roots[0], "recipes").replace(os.sep, '/')
+            if file_path.replace(os.sep, '/').startswith(recipes_dir):
+                relative_path = os.path.relpath(file_path, config.loras_roots[0]).replace(os.sep, '/')
+                return f"/loras_static/root1/preview/{relative_path}" 
+            
+            # If not in recipes dir, try to create a valid URL from the file path
+            file_name = os.path.basename(file_path)
+            return f"/loras_static/root1/preview/recipes/{file_name}"
+        except Exception as e:
+            logger.error(f"Error formatting recipe file URL: {e}", exc_info=True)
+            return '/loras_static/images/no-preview.png'  # Return default image on error
+
     def setup_routes(self, app: web.Application):
         """Register routes with the application"""
         app.router.add_get('/loras', self.handle_loras_page)
+        app.router.add_get('/loras/recipes', self.handle_recipes_page)

py/routes/recipe_routes.py

@@ -0,0 +1,908 @@
+import os
+import time
+import logging
+from aiohttp import web
+from typing import Dict
+import tempfile
+import json
+import asyncio
+from ..utils.exif_utils import ExifUtils
+from ..utils.recipe_parsers import RecipeParserFactory
+from ..services.civitai_client import CivitaiClient
+
+from ..services.recipe_scanner import RecipeScanner
+from ..services.lora_scanner import LoraScanner
+from ..config import config
+from ..workflow.parser import WorkflowParser
+from ..utils.utils import download_twitter_image
+
+logger = logging.getLogger(__name__)
+
+class RecipeRoutes:
+    """API route handlers for Recipe management"""
+
+    def __init__(self):
+        self.recipe_scanner = RecipeScanner(LoraScanner())
+        self.civitai_client = CivitaiClient()
+        self.parser = WorkflowParser()
+        
+        # Pre-warm the cache
+        self._init_cache_task = None
+
+    @classmethod
+    def setup_routes(cls, app: web.Application):
+        """Register API routes"""
+        routes = cls()
+        app.router.add_get('/api/recipes', routes.get_recipes)
+        app.router.add_get('/api/recipe/{recipe_id}', routes.get_recipe_detail)
+        app.router.add_post('/api/recipes/analyze-image', routes.analyze_recipe_image)
+        app.router.add_post('/api/recipes/save', routes.save_recipe)
+        app.router.add_delete('/api/recipe/{recipe_id}', routes.delete_recipe)
+        
+        # Add new filter-related endpoints
+        app.router.add_get('/api/recipes/top-tags', routes.get_top_tags)
+        app.router.add_get('/api/recipes/base-models', routes.get_base_models)
+        
+        # Add new sharing endpoints
+        app.router.add_get('/api/recipe/{recipe_id}/share', routes.share_recipe)
+        app.router.add_get('/api/recipe/{recipe_id}/share/download', routes.download_shared_recipe)
+        
+        # Start cache initialization
+        app.on_startup.append(routes._init_cache)
+        
+        app.router.add_post('/api/recipes/save-from-widget', routes.save_recipe_from_widget)
+    
+    async def _init_cache(self, app):
+        """Initialize cache on startup"""
+        try:
+            # First, ensure the lora scanner is fully initialized
+            lora_scanner = self.recipe_scanner._lora_scanner
+            
+            # Get lora cache to ensure it's initialized
+            lora_cache = await lora_scanner.get_cached_data()
+            
+            # Verify hash index is built
+            if hasattr(lora_scanner, '_hash_index'):
+                hash_index_size = len(lora_scanner._hash_index._hash_to_path) if hasattr(lora_scanner._hash_index, '_hash_to_path') else 0
+            
+            # Now that lora scanner is initialized, initialize recipe cache
+            await self.recipe_scanner.get_cached_data(force_refresh=True)
+        except Exception as e:
+            logger.error(f"Error pre-warming recipe cache: {e}", exc_info=True)
+    
+    async def get_recipes(self, request: web.Request) -> web.Response:
+        """API endpoint for getting paginated recipes"""
+        try:
+            # Get query parameters with defaults
+            page = int(request.query.get('page', '1'))
+            page_size = int(request.query.get('page_size', '20'))
+            sort_by = request.query.get('sort_by', 'date')
+            search = request.query.get('search', None)
+            
+            # Get search options (renamed for better clarity)
+            search_title = request.query.get('search_title', 'true').lower() == 'true'
+            search_tags = request.query.get('search_tags', 'true').lower() == 'true'  
+            search_lora_name = request.query.get('search_lora_name', 'true').lower() == 'true'
+            search_lora_model = request.query.get('search_lora_model', 'true').lower() == 'true'
+            
+            # Get filter parameters
+            base_models = request.query.get('base_models', None)
+            tags = request.query.get('tags', None)
+            
+            # Parse filter parameters
+            filters = {}
+            if base_models:
+                filters['base_model'] = base_models.split(',')
+            if tags:
+                filters['tags'] = tags.split(',')
+            
+            # Add search options to filters
+            search_options = {
+                'title': search_title,
+                'tags': search_tags,
+                'lora_name': search_lora_name,
+                'lora_model': search_lora_model
+            }
+
+            # Get paginated data
+            result = await self.recipe_scanner.get_paginated_data(
+                page=page,
+                page_size=page_size,
+                sort_by=sort_by,
+                search=search,
+                filters=filters,
+                search_options=search_options
+            )
+            
+            # Format the response data with static URLs for file paths
+            for item in result['items']:
+                # Always ensure file_url is set
+                if 'file_path' in item:
+                    item['file_url'] = self._format_recipe_file_url(item['file_path'])
+                else:
+                    item['file_url'] = '/loras_static/images/no-preview.png'
+                
+                # 确保 loras 数组存在
+                if 'loras' not in item:
+                    item['loras'] = []
+                    
+                # 确保有 base_model 字段
+                if 'base_model' not in item:
+                    item['base_model'] = ""
+            
+            return web.json_response(result)
+        except Exception as e:
+            logger.error(f"Error retrieving recipes: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)
+
+    async def get_recipe_detail(self, request: web.Request) -> web.Response:
+        """Get detailed information about a specific recipe"""
+        try:
+            recipe_id = request.match_info['recipe_id']
+
+            # Get all recipes from cache
+            cache = await self.recipe_scanner.get_cached_data()
+            
+            # Find the specific recipe
+            recipe = next((r for r in cache.raw_data if str(r.get('id', '')) == recipe_id), None)
+            
+            if not recipe:
+                return web.json_response({"error": "Recipe not found"}, status=404)
+            
+            # Format recipe data
+            formatted_recipe = self._format_recipe_data(recipe)
+            
+            return web.json_response(formatted_recipe)
+        except Exception as e:
+            logger.error(f"Error retrieving recipe details: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)
+    
+    def _format_recipe_file_url(self, file_path: str) -> str:
+        """Format file path for recipe image as a URL"""
+        try:
+            # Return the file URL directly for the first lora root's preview
+            recipes_dir = os.path.join(config.loras_roots[0], "recipes").replace(os.sep, '/')
+            if file_path.replace(os.sep, '/').startswith(recipes_dir):
+                relative_path = os.path.relpath(file_path, config.loras_roots[0]).replace(os.sep, '/')
+                return f"/loras_static/root1/preview/{relative_path}" 
+            
+            # If not in recipes dir, try to create a valid URL from the file path
+            file_name = os.path.basename(file_path)
+            return f"/loras_static/root1/preview/recipes/{file_name}"
+        except Exception as e:
+            logger.error(f"Error formatting recipe file URL: {e}", exc_info=True)
+            return '/loras_static/images/no-preview.png'  # Return default image on error
+    
+    def _format_recipe_data(self, recipe: Dict) -> Dict:
+        """Format recipe data for API response"""
+        formatted = {**recipe}  # Copy all fields
+        
+        # Format file paths to URLs
+        if 'file_path' in formatted:
+            formatted['file_url'] = self._format_recipe_file_url(formatted['file_path'])
+        
+        # Format dates for display
+        for date_field in ['created_date', 'modified']:
+            if date_field in formatted:
+                formatted[f"{date_field}_formatted"] = self._format_timestamp(formatted[date_field])
+        
+        return formatted
+    
+    def _format_timestamp(self, timestamp: float) -> str:
+        """Format timestamp for display"""
+        from datetime import datetime
+        return datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S') 
+
+    async def analyze_recipe_image(self, request: web.Request) -> web.Response:
+        """Analyze an uploaded image or URL for recipe metadata"""
+        temp_path = None
+        try:
+            # Check if request contains multipart data (image) or JSON data (url)
+            content_type = request.headers.get('Content-Type', '')
+            
+            is_url_mode = False
+            
+            if 'multipart/form-data' in content_type:
+                # Handle image upload
+                reader = await request.multipart()
+                field = await reader.next()
+                
+                if field.name != 'image':
+                    return web.json_response({
+                        "error": "No image field found",
+                        "loras": []
+                    }, status=400)
+                
+                # Create a temporary file to store the uploaded image
+                with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
+                    while True:
+                        chunk = await field.read_chunk()
+                        if not chunk:
+                            break
+                        temp_file.write(chunk)
+                    temp_path = temp_file.name
+                    
+            elif 'application/json' in content_type:
+                # Handle URL input
+                data = await request.json()
+                url = data.get('url')
+                is_url_mode = True
+                
+                if not url:
+                    return web.json_response({
+                        "error": "No URL provided",
+                        "loras": []
+                    }, status=400)
+                
+                # Download image from URL
+                temp_path = download_twitter_image(url)
+                
+                if not temp_path:
+                    return web.json_response({
+                        "error": "Failed to download image from URL",
+                        "loras": []
+                    }, status=400)
+            
+            # Extract metadata from the image using ExifUtils
+            user_comment = ExifUtils.extract_user_comment(temp_path)
+            
+            # If no metadata found, return a more specific error
+            if not user_comment:
+                result = {
+                    "error": "No metadata found in this image",
+                    "loras": []  # Return empty loras array to prevent client-side errors
+                }
+                
+                # For URL mode, include the image data as base64
+                if is_url_mode and temp_path:
+                    import base64
+                    with open(temp_path, "rb") as image_file:
+                        result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
+                    
+                return web.json_response(result, status=200)
+            
+            # Use the parser factory to get the appropriate parser
+            parser = RecipeParserFactory.create_parser(user_comment)
+
+            if parser is None:
+                result = {
+                    "error": "No parser found for this image",
+                    "loras": []  # Return empty loras array to prevent client-side errors
+                }
+                
+                # For URL mode, include the image data as base64
+                if is_url_mode and temp_path:
+                    import base64
+                    with open(temp_path, "rb") as image_file:
+                        result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
+                    
+                return web.json_response(result, status=200)
+            
+            # Parse the metadata
+            result = await parser.parse_metadata(
+                user_comment, 
+                recipe_scanner=self.recipe_scanner, 
+                civitai_client=self.civitai_client
+            )
+            
+            # For URL mode, include the image data as base64
+            if is_url_mode and temp_path:
+                import base64
+                with open(temp_path, "rb") as image_file:
+                    result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
+            
+            # Check for errors
+            if "error" in result and not result.get("loras"):
+                return web.json_response(result, status=200)
+            
+            return web.json_response(result)
+            
+        except Exception as e:
+            logger.error(f"Error analyzing recipe image: {e}", exc_info=True)
+            return web.json_response({
+                "error": str(e),
+                "loras": []  # Return empty loras array to prevent client-side errors
+            }, status=500)
+        finally:
+            # Clean up the temporary file in the finally block
+            if temp_path and os.path.exists(temp_path):
+                try:
+                    os.unlink(temp_path)
+                except Exception as e:
+                    logger.error(f"Error deleting temporary file: {e}")
+
+
+    async def save_recipe(self, request: web.Request) -> web.Response:
+        """Save a recipe to the recipes folder"""
+        try:
+            reader = await request.multipart()
+            
+            # Process form data
+            image = None
+            image_base64 = None
+            image_url = None
+            name = None
+            tags = []
+            metadata = None
+            
+            while True:
+                field = await reader.next()
+                if field is None:
+                    break
+                
+                if field.name == 'image':
+                    # Read image data
+                    image_data = b''
+                    while True:
+                        chunk = await field.read_chunk()
+                        if not chunk:
+                            break
+                        image_data += chunk
+                    image = image_data
+                    
+                elif field.name == 'image_base64':
+                    # Get base64 image data
+                    image_base64 = await field.text()
+                    
+                elif field.name == 'image_url':
+                    # Get image URL
+                    image_url = await field.text()
+                    
+                elif field.name == 'name':
+                    name = await field.text()
+                    
+                elif field.name == 'tags':
+                    tags_text = await field.text()
+                    try:
+                        tags = json.loads(tags_text)
+                    except:
+                        tags = []
+                    
+                elif field.name == 'metadata':
+                    metadata_text = await field.text()
+                    try:
+                        metadata = json.loads(metadata_text)
+                    except:
+                        metadata = {}
+            
+            missing_fields = []
+            if not name:
+                missing_fields.append("name")
+            if not metadata:
+                missing_fields.append("metadata")
+            if missing_fields:
+                return web.json_response({"error": f"Missing required fields: {', '.join(missing_fields)}"}, status=400)
+            
+            # Handle different image sources
+            if not image:
+                if image_base64:
+                    # Convert base64 to binary
+                    import base64
+                    try:
+                        # Remove potential data URL prefix
+                        if ',' in image_base64:
+                            image_base64 = image_base64.split(',', 1)[1]
+                        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
+                    from ..utils.utils import download_twitter_image
+                    temp_path = download_twitter_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)
+            
+            # Create recipes directory if it doesn't exist
+            recipes_dir = self.recipe_scanner.recipes_dir
+            os.makedirs(recipes_dir, exist_ok=True)
+            
+            # Generate UUID for the recipe
+            import uuid
+            recipe_id = str(uuid.uuid4())
+            
+            # Optimize the image (resize and convert to WebP)
+            optimized_image, extension = ExifUtils.optimize_image(
+                image_data=image,
+                target_width=480,
+                format='webp',
+                quality=85,
+                preserve_metadata=True
+            )
+            
+            # Save the optimized image
+            image_filename = f"{recipe_id}{extension}"
+            image_path = os.path.join(recipes_dir, image_filename)
+            with open(image_path, 'wb') as f:
+                f.write(optimized_image)
+            
+            # Create the recipe JSON
+            current_time = time.time()
+            
+            # Format loras data according to the recipe.json format
+            loras_data = []
+            for lora in metadata.get("loras", []):
+                # Skip deleted LoRAs if they're marked to be excluded
+                if lora.get("isDeleted", False) and lora.get("exclude", False):
+                    continue
+                
+                # Convert frontend lora format to recipe format
+                lora_entry = {
+                    "file_name": lora.get("file_name", "") or os.path.splitext(os.path.basename(lora.get("localPath", "")))[0],
+                    "hash": lora.get("hash", "").lower() if lora.get("hash") else "",
+                    "strength": float(lora.get("weight", 1.0)),
+                    "modelVersionId": lora.get("id", ""),
+                    "modelName": lora.get("name", ""),
+                    "modelVersionName": lora.get("version", ""),
+                    "isDeleted": lora.get("isDeleted", False)  # Preserve deletion status in saved recipe
+                }
+                loras_data.append(lora_entry)
+            
+            # Format gen_params according to the recipe.json format
+            gen_params = metadata.get("gen_params", {})
+            if not gen_params and "raw_metadata" in metadata:
+                # Extract from raw metadata if available
+                raw_metadata = metadata.get("raw_metadata", {})
+                gen_params = {
+                    "prompt": raw_metadata.get("prompt", ""),
+                    "negative_prompt": raw_metadata.get("negative_prompt", ""),
+                    "checkpoint": raw_metadata.get("checkpoint", {}),
+                    "steps": raw_metadata.get("steps", ""),
+                    "sampler": raw_metadata.get("sampler", ""),
+                    "cfg_scale": raw_metadata.get("cfg_scale", ""),
+                    "seed": raw_metadata.get("seed", ""),
+                    "size": raw_metadata.get("size", ""),
+                    "clip_skip": raw_metadata.get("clip_skip", "")
+                }
+            
+            # Create the recipe data structure
+            recipe_data = {
+                "id": recipe_id,
+                "file_path": image_path,
+                "title": name,
+                "modified": current_time,
+                "created_date": current_time,
+                "base_model": metadata.get("base_model", ""),
+                "loras": loras_data,
+                "gen_params": gen_params
+            }
+            
+            # Add tags if provided
+            if tags:
+                recipe_data["tags"] = tags
+            
+            # Save the recipe JSON
+            json_filename = f"{recipe_id}.recipe.json"
+            json_path = os.path.join(recipes_dir, json_filename)
+            with open(json_path, 'w', encoding='utf-8') as f:
+                json.dump(recipe_data, f, indent=4, ensure_ascii=False)
+
+            # Add recipe metadata to the image
+            ExifUtils.append_recipe_metadata(image_path, recipe_data)
+            
+            # Simplified cache update approach
+            # Instead of trying to update the cache directly, just set it to None
+            # to force a refresh on the next get_cached_data call
+            if self.recipe_scanner._cache is not None:
+                # Add the recipe to the raw data if the cache exists
+                # This is a simple direct update without locks or timeouts
+                self.recipe_scanner._cache.raw_data.append(recipe_data)
+                # Schedule a background task to resort the cache
+                asyncio.create_task(self.recipe_scanner._cache.resort())
+                logger.info(f"Added recipe {recipe_id} to cache")
+            
+            return web.json_response({
+                'success': True,
+                'recipe_id': recipe_id,
+                'image_path': image_path,
+                'json_path': json_path
+            })
+            
+        except Exception as e:
+            logger.error(f"Error saving recipe: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500) 
+
+    async def delete_recipe(self, request: web.Request) -> web.Response:
+        """Delete a recipe by ID"""
+        try:
+            recipe_id = request.match_info['recipe_id']
+            
+            # Get recipes directory
+            recipes_dir = self.recipe_scanner.recipes_dir
+            if not recipes_dir or not os.path.exists(recipes_dir):
+                return web.json_response({"error": "Recipes directory not found"}, status=404)
+            
+            # Find recipe JSON file
+            recipe_json_path = os.path.join(recipes_dir, f"{recipe_id}.recipe.json")
+            if not os.path.exists(recipe_json_path):
+                return web.json_response({"error": "Recipe not found"}, status=404)
+            
+            # Load recipe data to get image path
+            with open(recipe_json_path, 'r', encoding='utf-8') as f:
+                recipe_data = json.load(f)
+            
+            # Get image path
+            image_path = recipe_data.get('file_path')
+            
+            # Delete recipe JSON file
+            os.remove(recipe_json_path)
+            logger.info(f"Deleted recipe JSON file: {recipe_json_path}")
+            
+            # Delete recipe image if it exists
+            if image_path and os.path.exists(image_path):
+                os.remove(image_path)
+                logger.info(f"Deleted recipe image: {image_path}")
+            
+            # Simplified cache update approach
+            if self.recipe_scanner._cache is not None:
+                # Remove the recipe from raw_data if it exists
+                self.recipe_scanner._cache.raw_data = [
+                    r for r in self.recipe_scanner._cache.raw_data 
+                    if str(r.get('id', '')) != recipe_id
+                ]
+                # Schedule a background task to resort the cache
+                asyncio.create_task(self.recipe_scanner._cache.resort())
+                logger.info(f"Removed recipe {recipe_id} from cache")
+            
+            return web.json_response({"success": True, "message": "Recipe deleted successfully"})
+        except Exception as e:
+            logger.error(f"Error deleting recipe: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500) 
+
+    async def get_top_tags(self, request: web.Request) -> web.Response:
+        """Get top tags used in recipes"""
+        try:
+            # Get limit parameter with default
+            limit = int(request.query.get('limit', '20'))
+            
+            # Get all recipes from cache
+            cache = await self.recipe_scanner.get_cached_data()
+            
+            # Count tag occurrences
+            tag_counts = {}
+            for recipe in cache.raw_data:
+                if 'tags' in recipe and recipe['tags']:
+                    for tag in recipe['tags']:
+                        tag_counts[tag] = tag_counts.get(tag, 0) + 1
+            
+            # Sort tags by count and limit results
+            sorted_tags = [{'tag': tag, 'count': count} for tag, count in tag_counts.items()]
+            sorted_tags.sort(key=lambda x: x['count'], reverse=True)
+            top_tags = sorted_tags[:limit]
+            
+            return web.json_response({
+                'success': True,
+                'tags': top_tags
+            })
+        except Exception as e:
+            logger.error(f"Error retrieving top tags: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def get_base_models(self, request: web.Request) -> web.Response:
+        """Get base models used in recipes"""
+        try:
+            # Get all recipes from cache
+            cache = await self.recipe_scanner.get_cached_data()
+            
+            # Count base model occurrences
+            base_model_counts = {}
+            for recipe in cache.raw_data:
+                if 'base_model' in recipe and recipe['base_model']:
+                    base_model = recipe['base_model']
+                    base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
+            
+            # Sort base models by count
+            sorted_models = [{'name': model, 'count': count} for model, count in base_model_counts.items()]
+            sorted_models.sort(key=lambda x: x['count'], reverse=True)
+            
+            return web.json_response({
+                'success': True,
+                'base_models': sorted_models
+            })
+        except Exception as e:
+            logger.error(f"Error retrieving base models: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500) 
+
+    async def share_recipe(self, request: web.Request) -> web.Response:
+        """Process a recipe image for sharing by adding metadata to EXIF"""
+        try:
+            recipe_id = request.match_info['recipe_id']
+            
+            # Get all recipes from cache
+            cache = await self.recipe_scanner.get_cached_data()
+            
+            # Find the specific recipe
+            recipe = next((r for r in cache.raw_data if str(r.get('id', '')) == recipe_id), None)
+            
+            if not recipe:
+                return web.json_response({"error": "Recipe not found"}, status=404)
+            
+            # Get the image path
+            image_path = recipe.get('file_path')
+            if not image_path or not os.path.exists(image_path):
+                return web.json_response({"error": "Recipe image not found"}, status=404)
+            
+            # Create a temporary copy of the image to modify
+            import tempfile
+            import shutil
+            
+            # Create temp file with same extension
+            ext = os.path.splitext(image_path)[1]
+            with tempfile.NamedTemporaryFile(suffix=ext, delete=False) as temp_file:
+                temp_path = temp_file.name
+            
+            # Copy the original image to temp file
+            shutil.copy2(image_path, temp_path)
+            processed_path = temp_path
+            
+            # Create a URL for the processed image
+            # Use a timestamp to prevent caching
+            timestamp = int(time.time())
+            url_path = f"/api/recipe/{recipe_id}/share/download?t={timestamp}"
+            
+            # Store the temp path in a dictionary to serve later
+            if not hasattr(self, '_shared_recipes'):
+                self._shared_recipes = {}
+            
+            self._shared_recipes[recipe_id] = {
+                'path': processed_path,
+                'timestamp': timestamp,
+                'expires': time.time() + 300  # Expire after 5 minutes
+            }
+            
+            # Clean up old entries
+            self._cleanup_shared_recipes()
+            
+            return web.json_response({
+                'success': True,
+                'download_url': url_path,
+                'filename': f"recipe_{recipe.get('title', '').replace(' ', '_').lower()}{ext}"
+            })
+        except Exception as e:
+            logger.error(f"Error sharing recipe: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)
+
+    async def download_shared_recipe(self, request: web.Request) -> web.Response:
+        """Serve a processed recipe image for download"""
+        try:
+            recipe_id = request.match_info['recipe_id']
+            
+            # Check if we have this shared recipe
+            if not hasattr(self, '_shared_recipes') or recipe_id not in self._shared_recipes:
+                return web.json_response({"error": "Shared recipe not found or expired"}, status=404)
+            
+            shared_info = self._shared_recipes[recipe_id]
+            file_path = shared_info['path']
+            
+            if not os.path.exists(file_path):
+                return web.json_response({"error": "Shared recipe file not found"}, status=404)
+            
+            # Get recipe to determine filename
+            cache = await self.recipe_scanner.get_cached_data()
+            recipe = next((r for r in cache.raw_data if str(r.get('id', '')) == recipe_id), None)
+            
+            # Set filename for download
+            filename = f"recipe_{recipe.get('title', '').replace(' ', '_').lower() if recipe else recipe_id}"
+            ext = os.path.splitext(file_path)[1]
+            download_filename = f"{filename}{ext}"
+            
+            # Serve the file
+            return web.FileResponse(
+                file_path,
+                headers={
+                    'Content-Disposition': f'attachment; filename="{download_filename}"'
+                }
+            )
+        except Exception as e:
+            logger.error(f"Error downloading shared recipe: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)
+
+    def _cleanup_shared_recipes(self):
+        """Clean up expired shared recipes"""
+        if not hasattr(self, '_shared_recipes'):
+            return
+        
+        current_time = time.time()
+        expired_ids = [rid for rid, info in self._shared_recipes.items() 
+                      if current_time > info.get('expires', 0)]
+        
+        for rid in expired_ids:
+            try:
+                # Delete the temporary file
+                file_path = self._shared_recipes[rid]['path']
+                if os.path.exists(file_path):
+                    os.unlink(file_path)
+                
+                # Remove from dictionary
+                del self._shared_recipes[rid]
+            except Exception as e:
+                logger.error(f"Error cleaning up shared recipe {rid}: {e}")
+
+    async def save_recipe_from_widget(self, request: web.Request) -> web.Response:
+        """Save a recipe from the LoRAs widget"""
+        try:
+            reader = await request.multipart()
+            
+            # Process form data
+            workflow_json = None
+            
+            while True:
+                field = await reader.next()
+                if field is None:
+                    break
+                
+                if field.name == 'workflow_json':
+                    workflow_text = await field.text()
+                    try:
+                        workflow_json = json.loads(workflow_text)
+                    except:
+                        return web.json_response({"error": "Invalid workflow JSON"}, status=400)
+            
+            if not workflow_json:
+                return web.json_response({"error": "Missing required workflow_json field"}, status=400)
+            
+            # Find the latest image in the temp directory
+            temp_dir = config.temp_directory
+            image_files = []
+            
+            for file in os.listdir(temp_dir):
+                if file.lower().endswith(('.png', '.jpg', '.jpeg', '.webp')):
+                    file_path = os.path.join(temp_dir, file)
+                    image_files.append((file_path, os.path.getmtime(file_path)))
+            
+            if not image_files:
+                return web.json_response({"error": "No recent images found to use for recipe"}, status=400)
+            
+            # Sort by modification time (newest first)
+            image_files.sort(key=lambda x: x[1], reverse=True)
+            latest_image_path = image_files[0][0]
+            
+            # Parse the workflow to extract generation parameters and loras
+            parsed_workflow = self.parser.parse_workflow(workflow_json)
+
+            if not parsed_workflow or not parsed_workflow.get("gen_params"):
+                return web.json_response({"error": "Could not extract generation parameters from workflow"}, status=400)
+            
+            # Get the lora stack from the parsed workflow
+            lora_stack = parsed_workflow.get("loras", "")
+            
+            # Parse the lora stack format: "<lora:name:strength> <lora:name2:strength2> ..."
+            import re
+            lora_matches = re.findall(r'<lora:([^:]+):([^>]+)>', lora_stack)
+            
+            # Check if any loras were found
+            if not lora_matches:
+                return web.json_response({"error": "No LoRAs found in the workflow"}, status=400)
+            
+            # Generate recipe name from the first 3 loras (or less if fewer are available)
+            loras_for_name = lora_matches[:3]  # Take at most 3 loras for the name
+            
+            recipe_name_parts = []
+            for lora_name, lora_strength in loras_for_name:
+                # Get the basename without path or extension
+                basename = os.path.basename(lora_name)
+                basename = os.path.splitext(basename)[0]
+                recipe_name_parts.append(f"{basename}:{lora_strength}")
+            
+            recipe_name = " ".join(recipe_name_parts)
+            
+            # Read the image
+            with open(latest_image_path, 'rb') as f:
+                image = f.read()
+            
+            # Create recipes directory if it doesn't exist
+            recipes_dir = self.recipe_scanner.recipes_dir
+            os.makedirs(recipes_dir, exist_ok=True)
+            
+            # Generate UUID for the recipe
+            import uuid
+            recipe_id = str(uuid.uuid4())
+            
+            # Optimize the image (resize and convert to WebP)
+            optimized_image, extension = ExifUtils.optimize_image(
+                image_data=image,
+                target_width=480,
+                format='webp',
+                quality=85,
+                preserve_metadata=True
+            )
+            
+            # Save the optimized image
+            image_filename = f"{recipe_id}{extension}"
+            image_path = os.path.join(recipes_dir, image_filename)
+            with open(image_path, 'wb') as f:
+                f.write(optimized_image)
+            
+            # Format loras data from the lora stack
+            loras_data = []
+            
+            for lora_name, lora_strength in lora_matches:
+                try:
+                    # Get lora info from scanner
+                    lora_info = await self.recipe_scanner._lora_scanner.get_lora_info_by_name(lora_name)
+                    
+                    # Create lora entry
+                    lora_entry = {
+                        "file_name": lora_name,
+                        "hash": lora_info.get("sha256", "").lower() if lora_info else "",
+                        "strength": float(lora_strength),
+                        "modelVersionId": lora_info.get("civitai", {}).get("id", "") if lora_info else "",
+                        "modelName": lora_info.get("civitai", {}).get("model", {}).get("name", "") if lora_info else lora_name,
+                        "modelVersionName": lora_info.get("civitai", {}).get("name", "") if lora_info else "",
+                        "isDeleted": False
+                    }
+                    loras_data.append(lora_entry)
+                except Exception as e:
+                    logger.warning(f"Error processing LoRA {lora_name}: {e}")
+            
+            # Get base model from lora scanner for the available loras
+            base_model_counts = {}
+            for lora in loras_data:
+                lora_info = await self.recipe_scanner._lora_scanner.get_lora_info_by_name(lora.get("file_name", ""))
+                if lora_info and "base_model" in lora_info:
+                    base_model = lora_info["base_model"]
+                    base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
+            
+            # Get most common base model
+            most_common_base_model = ""
+            if base_model_counts:
+                most_common_base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
+            
+            # Create the recipe data structure
+            recipe_data = {
+                "id": recipe_id,
+                "file_path": image_path,
+                "title": recipe_name,  # Use generated recipe name
+                "modified": time.time(),
+                "created_date": time.time(),
+                "base_model": most_common_base_model,
+                "loras": loras_data,
+                "gen_params": parsed_workflow.get("gen_params", {}),  # Use the parsed workflow parameters
+                "loras_stack": lora_stack  # Include the original lora stack string
+            }
+            
+            # Save the recipe JSON
+            json_filename = f"{recipe_id}.recipe.json"
+            json_path = os.path.join(recipes_dir, json_filename)
+            with open(json_path, 'w', encoding='utf-8') as f:
+                json.dump(recipe_data, f, indent=4, ensure_ascii=False)
+
+            # Add recipe metadata to the image
+            ExifUtils.append_recipe_metadata(image_path, recipe_data)
+            
+            # Update cache
+            if self.recipe_scanner._cache is not None:
+                # Add the recipe to the raw data if the cache exists
+                self.recipe_scanner._cache.raw_data.append(recipe_data)
+                # Schedule a background task to resort the cache
+                asyncio.create_task(self.recipe_scanner._cache.resort())
+                logger.info(f"Added recipe {recipe_id} to cache")
+            
+            return web.json_response({
+                'success': True,
+                'recipe_id': recipe_id,
+                'image_path': image_path,
+                'json_path': json_path,
+                'recipe_name': recipe_name  # Include the generated recipe name in the response
+            })
+            
+        except Exception as e:
+            logger.error(f"Error saving recipe from widget: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)

py/routes/update_routes.py

@@ -24,11 +24,9 @@ class UpdateRoutes:
         try:
             # Read local version from pyproject.toml
             local_version = UpdateRoutes._get_local_version()
-            logger.info(f"Local version: {local_version}")
-            
+
             # Fetch remote version from GitHub
             remote_version, changelog = await UpdateRoutes._get_remote_version()
-            logger.info(f"Remote version: {remote_version}")
             
             # Compare versions
             update_available = UpdateRoutes._compare_versions(
@@ -36,8 +34,6 @@ class UpdateRoutes:
                 remote_version.replace('v', '')
             )
             
-            logger.info(f"Update available: {update_available}")
-            
             return web.json_response({
                 'success': True,
                 'current_version': local_version,

py/services/civitai_client.py

@@ -76,6 +76,17 @@ class CivitaiClient:
             headers = self._get_request_headers()
             async with session.get(url, headers=headers, allow_redirects=True) as response:
                 if response.status != 200:
+                    # Handle early access 401 unauthorized responses
+                    if response.status == 401:
+                        logger.warning(f"Unauthorized access to resource: {url} (Status 401)")
+                        return False, "Early access restriction: You must purchase early access to download this LoRA."
+                    
+                    # Handle other client errors that might be permission-related
+                    if response.status == 403:
+                        logger.warning(f"Forbidden access to resource: {url} (Status 403)")
+                        return False, "Access forbidden: You don't have permission to download this file."
+                    
+                    # Generic error response for other status codes
                     return False, f"Download failed with status {response.status}"
 
                 # Get filename from content-disposition header
@@ -163,8 +174,81 @@ class CivitaiClient:
             logger.error(f"Error fetching model version info: {e}")
             return None
 
+    async def get_model_metadata(self, model_id: str) -> Tuple[Optional[Dict], int]:
+        """Fetch model metadata (description and tags) from Civitai API
+        
+        Args:
+            model_id: The Civitai model ID
+            
+        Returns:
+            Tuple[Optional[Dict], int]: A tuple containing:
+                - A dictionary with model metadata or None if not found
+                - The HTTP status code from the request
+        """
+        try:
+            session = await self.session
+            headers = self._get_request_headers()
+            url = f"{self.base_url}/models/{model_id}"
+            
+            async with session.get(url, headers=headers) as response:
+                status_code = response.status
+                
+                if status_code != 200:
+                    logger.warning(f"Failed to fetch model metadata: Status {status_code}")
+                    return None, status_code
+                
+                data = await response.json()
+                
+                # Extract relevant metadata
+                metadata = {
+                    "description": data.get("description") or "No model description available",
+                    "tags": data.get("tags", [])
+                }
+                
+                if metadata["description"] or metadata["tags"]:
+                    return metadata, status_code
+                else:
+                    logger.warning(f"No metadata found for model {model_id}")
+                    return None, status_code
+                
+        except Exception as e:
+            logger.error(f"Error fetching model metadata: {e}", exc_info=True)
+            return None, 0
+
+    # Keep old method for backward compatibility, delegating to the new one
+    async def get_model_description(self, model_id: str) -> Optional[str]:
+        """Fetch the model description from Civitai API (Legacy method)"""
+        metadata, _ = await self.get_model_metadata(model_id)
+        return metadata.get("description") if metadata else None
+
     async def close(self):
         """Close the session if it exists"""
         if self._session is not None:
             await self._session.close()
-            self._session = None
+            self._session = None
+
+    async def _get_hash_from_civitai(self, model_version_id: str) -> Optional[str]:
+        """Get hash from Civitai API"""
+        try:
+            if not self._session:
+                return None
+            
+            logger.info(f"Fetching model version info from Civitai for ID: {model_version_id}")
+            version_info = await self._session.get(f"{self.base_url}/model-versions/{model_version_id}")
+            
+            if not version_info or not version_info.json().get('files'):
+                logger.warning(f"No files found in version info for ID: {model_version_id}")
+                return None
+            
+            # Get hash from the first file
+            for file_info in version_info.json().get('files', []):
+                if file_info.get('hashes', {}).get('SHA256'):
+                    # Convert hash to lowercase to standardize
+                    hash_value = file_info['hashes']['SHA256'].lower()
+                    return hash_value
+                
+            logger.warning(f"No SHA256 hash found in version info for ID: {model_version_id}")
+            return None
+        except Exception as e:
+            logger.error(f"Error getting hash from Civitai: {e}")
+            return None

py/services/download_manager.py

@@ -28,6 +28,25 @@ class DownloadManager:
             if not version_info:
                 return {'success': False, 'error': 'Failed to fetch model metadata'}
 
+            # Check if this is an early access LoRA
+            if 'earlyAccessEndsAt' in version_info:
+                early_access_date = version_info.get('earlyAccessEndsAt', '')
+                # Convert to a readable date if possible
+                try:
+                    from datetime import datetime
+                    date_obj = datetime.fromisoformat(early_access_date.replace('Z', '+00:00'))
+                    formatted_date = date_obj.strftime('%Y-%m-%d')
+                    early_access_msg = f"This LoRA requires early access payment (until {formatted_date}). "
+                except:
+                    early_access_msg = "This LoRA requires early access payment. "
+                
+                early_access_msg += "Please ensure you have purchased early access and are logged in to Civitai."
+                logger.warning(f"Early access LoRA detected: {version_info.get('name', 'Unknown')}")
+                
+                # We'll still try to download, but log a warning and prepare for potential failure
+                if progress_callback:
+                    await progress_callback(1)  # Show minimal progress to indicate we're trying
+
             # Report initial progress
             if progress_callback:
                 await progress_callback(0)
@@ -42,15 +61,32 @@ class DownloadManager:
             save_path = os.path.join(save_dir, file_name)
             file_size = file_info.get('sizeKB', 0) * 1024
 
-            # 4. 通知文件监控系统
-            self.file_monitor.handler.add_ignore_path(
-                save_path.replace(os.sep, '/'),
-                file_size
-            )
+            # 4. 通知文件监控系统 - 使用规范化路径和文件大小
+            if self.file_monitor and self.file_monitor.handler:
+                # Add both the normalized path and potential alternative paths
+                normalized_path = save_path.replace(os.sep, '/')
+                self.file_monitor.handler.add_ignore_path(normalized_path, file_size)
+                
+                # Also add the path with file extension variations (.safetensors)
+                if not normalized_path.endswith('.safetensors'):
+                    safetensors_path = os.path.splitext(normalized_path)[0] + '.safetensors'
+                    self.file_monitor.handler.add_ignore_path(safetensors_path, file_size)
+                
+                logger.debug(f"Added download path to ignore list: {normalized_path} (size: {file_size} bytes)")
 
             # 5. 准备元数据
             metadata = LoraMetadata.from_civitai_info(version_info, file_info, save_path)
             
+            # 5.1 获取并更新模型标签和描述信息
+            model_id = version_info.get('modelId')
+            if model_id:
+                model_metadata, _ = await self.civitai_client.get_model_metadata(str(model_id))
+                if model_metadata:
+                    if model_metadata.get("tags"):
+                        metadata.tags = model_metadata.get("tags", [])
+                    if model_metadata.get("description"):
+                        metadata.modelDescription = model_metadata.get("description", "")
+            
             # 6. 开始下载流程
             result = await self._execute_download(
                 download_url=download_url,
@@ -65,6 +101,10 @@ class DownloadManager:
 
         except Exception as e:
             logger.error(f"Error in download_from_civitai: {e}", exc_info=True)
+            # Check if this might be an early access error
+            error_str = str(e).lower()
+            if "403" in error_str or "401" in error_str or "unauthorized" in error_str or "early access" in error_str:
+                return {'success': False, 'error': f"Early access restriction: {str(e)}. Please ensure you have purchased early access and are logged in to Civitai."}
             return {'success': False, 'error': str(e)}
 
     async def _execute_download(self, download_url: str, save_dir: str, 
@@ -86,6 +126,7 @@ class DownloadManager:
                 preview_path = os.path.splitext(save_path)[0] + '.preview' + preview_ext
                 if await self.civitai_client.download_preview_image(images[0]['url'], preview_path):
                     metadata.preview_url = preview_path.replace(os.sep, '/')
+                    metadata.preview_nsfw_level = images[0].get('nsfwLevel', 0)
                     with open(metadata_path, 'w', encoding='utf-8') as f:
                         json.dump(metadata.to_dict(), f, indent=2, ensure_ascii=False)
 
@@ -124,6 +165,12 @@ class DownloadManager:
             all_folders = set(cache.folders)
             all_folders.add(relative_path)
             cache.folders = sorted(list(all_folders), key=lambda x: x.lower())
+            
+            # Update the hash index with the new LoRA entry
+            self.file_monitor.scanner._hash_index.add_entry(metadata_dict['sha256'], metadata_dict['file_path'])
+
+            # Update the hash index with the new LoRA entry
+            self.file_monitor.scanner._hash_index.add_entry(metadata_dict['sha256'], metadata_dict['file_path'])
 
             # Report 100% completion
             if progress_callback:

py/services/file_monitor.py

@@ -20,29 +20,75 @@ class LoraFileHandler(FileSystemEventHandler):
         self.pending_changes = set()  # 待处理的变更
         self.lock = Lock()  # 线程安全锁
         self.update_task = None  # 异步更新任务
-        self._ignore_paths = set()  # Add ignore paths set
+        self._ignore_paths = {}  # Change to dictionary to store expiration times
         self._min_ignore_timeout = 5  # minimum timeout in seconds
         self._download_speed = 1024 * 1024  # assume 1MB/s as base speed
 
     def _should_ignore(self, path: str) -> bool:
         """Check if path should be ignored"""
         real_path = os.path.realpath(path)  # Resolve any symbolic links
-        return real_path.replace(os.sep, '/') in self._ignore_paths
+        normalized_path = real_path.replace(os.sep, '/')
+        
+        # Also check with backslashes for Windows compatibility
+        alt_path = real_path.replace('/', '\\')
+        
+        # 使用传入的事件循环而不是尝试获取当前线程的事件循环
+        current_time = self.loop.time()
+        
+        # Check if path is in ignore list and not expired
+        if normalized_path in self._ignore_paths and self._ignore_paths[normalized_path] > current_time:
+            return True
+        
+        # Also check alternative path format
+        if alt_path in self._ignore_paths and self._ignore_paths[alt_path] > current_time:
+            return True
+            
+        return False
 
     def add_ignore_path(self, path: str, file_size: int = 0):
         """Add path to ignore list with dynamic timeout based on file size"""
         real_path = os.path.realpath(path)  # Resolve any symbolic links
-        self._ignore_paths.add(real_path.replace(os.sep, '/'))
+        normalized_path = real_path.replace(os.sep, '/')
         
-        # Short timeout (e.g. 5 seconds) is sufficient to ignore the CREATE event
-        timeout = 5
+        # Calculate timeout based on file size
+        # For small files, use minimum timeout
+        # For larger files, estimate download time + buffer
+        if file_size > 0:
+            # Estimate download time in seconds (size / speed) + buffer
+            estimated_time = (file_size / self._download_speed) + 10
+            timeout = max(self._min_ignore_timeout, estimated_time)
+        else:
+            timeout = self._min_ignore_timeout
         
-        asyncio.get_event_loop().call_later(
+        current_time = self.loop.time()
+        expiration_time = current_time + timeout
+        
+        # Store both normalized and alternative path formats
+        self._ignore_paths[normalized_path] = expiration_time
+        
+        # Also store with backslashes for Windows compatibility
+        alt_path = real_path.replace('/', '\\')
+        self._ignore_paths[alt_path] = expiration_time
+        
+        logger.debug(f"Added ignore path: {normalized_path} (expires in {timeout:.1f}s)")
+        
+        self.loop.call_later(
             timeout,
-            self._ignore_paths.discard,
-            real_path.replace(os.sep, '/')
+            self._remove_ignore_path,
+            normalized_path
         )
+    
+    def _remove_ignore_path(self, path: str):
+        """Remove path from ignore list after timeout"""
+        if path in self._ignore_paths:
+            del self._ignore_paths[path]
+            logger.debug(f"Removed ignore path: {path}")
         
+        # Also remove alternative path format
+        alt_path = path.replace('/', '\\')
+        if alt_path in self._ignore_paths:
+            del self._ignore_paths[alt_path]
+
     def on_created(self, event):
         if event.is_directory or not event.src_path.endswith('.safetensors'):
             return
@@ -98,6 +144,10 @@ class LoraFileHandler(FileSystemEventHandler):
                         # Scan new file
                         lora_data = await self.scanner.scan_single_lora(file_path)
                         if lora_data:
+                            # Update tags count
+                            for tag in lora_data.get('tags', []):
+                                self.scanner._tags_count[tag] = self.scanner._tags_count.get(tag, 0) + 1
+                            
                             cache.raw_data.append(lora_data)
                             new_folders.add(lora_data['folder'])
                             # Update hash index
@@ -109,6 +159,16 @@ class LoraFileHandler(FileSystemEventHandler):
                             needs_resort = True
                             
                     elif action == 'remove':
+                        # Find the lora to remove so we can update tags count
+                        lora_to_remove = next((item for item in cache.raw_data if item['file_path'] == file_path), None)
+                        if lora_to_remove:
+                            # Update tags count by reducing counts
+                            for tag in lora_to_remove.get('tags', []):
+                                if tag in self.scanner._tags_count:
+                                    self.scanner._tags_count[tag] = max(0, self.scanner._tags_count[tag] - 1)
+                                    if self.scanner._tags_count[tag] == 0:
+                                        del self.scanner._tags_count[tag]
+                        
                         # Remove from cache and hash index
                         logger.info(f"Removing {file_path} from cache")
                         self.scanner._hash_index.remove_by_path(file_path)

py/services/lora_hash_index.py

@@ -15,11 +15,13 @@ class LoraHashIndex:
         """Add or update a hash -> path mapping"""
         if not sha256 or not file_path:
             return
-        self._hash_to_path[sha256] = file_path
+        # Always store lowercase hashes for consistency
+        self._hash_to_path[sha256.lower()] = file_path
         
     def remove_entry(self, sha256: str) -> None:
         """Remove a hash entry"""
-        self._hash_to_path.pop(sha256, None)
+        if sha256:
+            self._hash_to_path.pop(sha256.lower(), None)
         
     def remove_by_path(self, file_path: str) -> None:
         """Remove entry by file path"""
@@ -30,7 +32,9 @@ class LoraHashIndex:
                 
     def get_path(self, sha256: str) -> Optional[str]:
         """Get file path for a given hash"""
-        return self._hash_to_path.get(sha256)
+        if not sha256:
+            return None
+        return self._hash_to_path.get(sha256.lower())
         
     def get_hash(self, file_path: str) -> Optional[str]:
         """Get hash for a given file path"""
@@ -41,7 +45,9 @@ class LoraHashIndex:
         
     def has_hash(self, sha256: str) -> bool:
         """Check if hash exists in index"""
-        return sha256 in self._hash_to_path
+        if not sha256:
+            return False
+        return sha256.lower() in self._hash_to_path
         
     def clear(self) -> None:
         """Clear all entries"""

py/services/lora_scanner.py

@@ -9,8 +9,11 @@ from operator import itemgetter
 from ..config import config
 from ..utils.file_utils import load_metadata, get_file_info
 from .lora_cache import LoraCache
-from difflib import SequenceMatcher
 from .lora_hash_index import LoraHashIndex
+from .settings_manager import settings
+from ..utils.constants import NSFW_LEVELS
+from ..utils.utils import fuzzy_match
+import sys
 
 logger = logging.getLogger(__name__)
 
@@ -34,6 +37,7 @@ class LoraScanner:
             self._initialization_task: Optional[asyncio.Task] = None
             self._initialized = True
             self.file_monitor = None  # Add this line
+            self._tags_count = {}  # Add a dictionary to store tag counts
 
     def set_file_monitor(self, monitor):
         """Set file monitor instance"""
@@ -90,13 +94,21 @@ class LoraScanner:
             # Clear existing hash index
             self._hash_index.clear()
             
+            # Clear existing tags count
+            self._tags_count = {}
+            
             # Scan for new data
             raw_data = await self.scan_all_loras()
             
-            # Build hash index
+            # Build hash index and tags count
             for lora_data in raw_data:
                 if 'sha256' in lora_data and 'file_path' in lora_data:
-                    self._hash_index.add_entry(lora_data['sha256'], lora_data['file_path'])
+                    self._hash_index.add_entry(lora_data['sha256'].lower(), lora_data['file_path'])
+                
+                # Count tags
+                if 'tags' in lora_data and lora_data['tags']:
+                    for tag in lora_data['tags']:
+                        self._tags_count[tag] = self._tags_count.get(tag, 0) + 1
             
             # Update cache
             self._cache = LoraCache(
@@ -120,45 +132,10 @@ class LoraScanner:
                 folders=[]
             )
 
-    def fuzzy_match(self, text: str, pattern: str, threshold: float = 0.7) -> bool:
-        """
-        Check if text matches pattern using fuzzy matching.
-        Returns True if similarity ratio is above threshold.
-        """
-        if not pattern or not text:
-            return False
-        
-        # Convert both to lowercase for case-insensitive matching
-        text = text.lower()
-        pattern = pattern.lower()
-        
-        # Split pattern into words
-        search_words = pattern.split()
-        
-        # Check each word
-        for word in search_words:
-            # First check if word is a substring (faster)
-            if word in text:
-                continue
-            
-            # If not found as substring, try fuzzy matching
-            # Check if any part of the text matches this word
-            found_match = False
-            for text_part in text.split():
-                ratio = SequenceMatcher(None, text_part, word).ratio()
-                if ratio >= threshold:
-                    found_match = True
-                    break
-                
-            if not found_match:
-                return False
-        
-        # All words found either as substrings or fuzzy matches
-        return True
-
     async def get_paginated_data(self, page: int, page_size: int, sort_by: str = 'name', 
                                folder: str = None, search: str = None, fuzzy: bool = False,
-                               recursive: bool = False, base_models: list = None):
+                               base_models: list = None, tags: list = None,
+                               search_options: dict = None) -> Dict:
         """Get paginated and filtered lora data
         
         Args:
@@ -168,24 +145,41 @@ class LoraScanner:
             folder: Filter by folder path
             search: Search term
             fuzzy: Use fuzzy matching for search
-            recursive: Include subfolders when folder filter is applied
             base_models: List of base models to filter by
+            tags: List of tags to filter by
+            search_options: Dictionary with search options (filename, modelname, tags, recursive)
         """
         cache = await self.get_cached_data()
 
-        # 先获取基础数据集
+        # Get default search options if not provided
+        if search_options is None:
+            search_options = {
+                'filename': True,
+                'modelname': True,
+                'tags': False,
+                'recursive': False
+            }
+
+        # Get the base data set
         filtered_data = cache.sorted_by_date if sort_by == 'date' else cache.sorted_by_name
         
-        # 应用文件夹过滤
+        # Apply SFW filtering if enabled
+        if settings.get('show_only_sfw', False):
+            filtered_data = [
+                item for item in filtered_data
+                if not item.get('preview_nsfw_level') or item.get('preview_nsfw_level') < NSFW_LEVELS['R']
+            ]
+        
+        # Apply folder filtering
         if folder is not None:
-            if recursive:
-                # 递归模式：匹配所有以该文件夹开头的路径
+            if search_options.get('recursive', False):
+                # Recursive mode: match all paths starting with this folder
                 filtered_data = [
                     item for item in filtered_data 
                     if item['folder'].startswith(folder + '/') or item['folder'] == folder
                 ]
             else:
-                # 非递归模式：只匹配确切的文件夹
+                # Non-recursive mode: match exact folder
                 filtered_data = [
                     item for item in filtered_data 
                     if item['folder'] == folder
@@ -198,28 +192,58 @@ class LoraScanner:
                 if item.get('base_model') in base_models
             ]
         
-        # 应用搜索过滤
+        # Apply tag filtering
+        if tags and len(tags) > 0:
+            filtered_data = [
+                item for item in filtered_data
+                if any(tag in item.get('tags', []) for tag in tags)
+            ]
+        
+        # Apply search filtering
         if search:
-            if fuzzy:
-                filtered_data = [
-                    item for item in filtered_data 
-                    if any(
-                        self.fuzzy_match(str(value), search) 
-                        for value in [
-                            item.get('model_name', ''),
-                            item.get('base_model', '')
-                        ]
-                        if value
-                    )
-                ]
-            else:
-                # Original exact search logic
-                filtered_data = [
-                    item for item in filtered_data 
-                    if search in str(item.get('model_name', '')).lower()
-                ]
+            search_results = []
+            for item in filtered_data:
+                # Check filename if enabled
+                if search_options.get('filename', True):
+                    if fuzzy:
+                        if fuzzy_match(item.get('file_name', ''), search):
+                            search_results.append(item)
+                            continue
+                    else:
+                        if search.lower() in item.get('file_name', '').lower():
+                            search_results.append(item)
+                            continue
+                            
+                # Check model name if enabled
+                if search_options.get('modelname', True):
+                    if fuzzy:
+                        if fuzzy_match(item.get('model_name', ''), search):
+                            search_results.append(item)
+                            continue
+                    else:
+                        if search.lower() in item.get('model_name', '').lower():
+                            search_results.append(item)
+                            continue
+                            
+                # Check tags if enabled
+                if search_options.get('tags', False) and item.get('tags'):
+                    found_tag = False
+                    for tag in item['tags']:
+                        if fuzzy:
+                            if fuzzy_match(tag, search):
+                                found_tag = True
+                                break
+                        else:
+                            if search.lower() in tag.lower():
+                                found_tag = True
+                                break
+                    if found_tag:
+                        search_results.append(item)
+                        continue
+                        
+            filtered_data = search_results
 
-        # 计算分页
+        # Calculate pagination
         total_items = len(filtered_data)
         start_idx = (page - 1) * page_size
         end_idx = min(start_idx + page_size, total_items)
@@ -311,12 +335,86 @@ class LoraScanner:
         
         # Convert to dict and add folder info
         lora_data = metadata.to_dict()
+        # Try to fetch missing metadata from Civitai if needed
+        await self._fetch_missing_metadata(file_path, lora_data)
         rel_path = os.path.relpath(file_path, root_path)
         folder = os.path.dirname(rel_path)
         lora_data['folder'] = folder.replace(os.path.sep, '/')
         
         return lora_data
 
+    async def _fetch_missing_metadata(self, file_path: str, lora_data: Dict) -> None:
+        """Fetch missing description and tags from Civitai if needed
+        
+        Args:
+            file_path: Path to the lora file
+            lora_data: Lora metadata dictionary to update
+        """
+        try:
+            # Skip if already marked as deleted on Civitai
+            if lora_data.get('civitai_deleted', False):
+                logger.debug(f"Skipping metadata fetch for {file_path}: marked as deleted on Civitai")
+                return
+
+            # Check if we need to fetch additional metadata from Civitai
+            needs_metadata_update = False
+            model_id = None
+            
+            # Check if we have Civitai model ID but missing metadata
+            if lora_data.get('civitai'):
+                # Try to get model ID directly from the correct location
+                model_id = lora_data['civitai'].get('modelId')
+                
+                if model_id:
+                    model_id = str(model_id)
+                    # Check if tags are missing or empty
+                    tags_missing = not lora_data.get('tags') or len(lora_data.get('tags', [])) == 0
+                    
+                    # Check if description is missing or empty
+                    desc_missing = not lora_data.get('modelDescription') or lora_data.get('modelDescription') in (None, "")
+                    
+                    needs_metadata_update = tags_missing or desc_missing
+            
+            # Fetch missing metadata if needed
+            if needs_metadata_update and model_id:
+                logger.debug(f"Fetching missing metadata for {file_path} with model ID {model_id}")
+                from ..services.civitai_client import CivitaiClient
+                client = CivitaiClient()
+                
+                # Get metadata and status code
+                model_metadata, status_code = await client.get_model_metadata(model_id)
+                await client.close()
+                
+                # Handle 404 status (model deleted from Civitai)
+                if status_code == 404:
+                    logger.warning(f"Model {model_id} appears to be deleted from Civitai (404 response)")
+                    # Mark as deleted to avoid future API calls
+                    lora_data['civitai_deleted'] = True
+                    
+                    # Save the updated metadata back to file
+                    metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+                    with open(metadata_path, 'w', encoding='utf-8') as f:
+                        json.dump(lora_data, f, indent=2, ensure_ascii=False)
+                
+                # Process valid metadata if available
+                elif model_metadata:
+                    logger.debug(f"Updating metadata for {file_path} with model ID {model_id}")
+                    
+                    # Update tags if they were missing
+                    if model_metadata.get('tags') and (not lora_data.get('tags') or len(lora_data.get('tags', [])) == 0):
+                        lora_data['tags'] = model_metadata['tags']
+                    
+                    # Update description if it was missing
+                    if model_metadata.get('description') and (not lora_data.get('modelDescription') or lora_data.get('modelDescription') in (None, "")):
+                        lora_data['modelDescription'] = model_metadata['description']
+                    
+                    # Save the updated metadata back to file
+                    metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+                    with open(metadata_path, 'w', encoding='utf-8') as f:
+                        json.dump(lora_data, f, indent=2, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"Failed to update metadata from Civitai for {file_path}: {e}")
+
     async def update_preview_in_cache(self, file_path: str, preview_url: str) -> bool:
         """Update preview URL in cache for a specific lora
         
@@ -427,6 +525,15 @@ class LoraScanner:
     async def update_single_lora_cache(self, original_path: str, new_path: str, metadata: Dict) -> bool:
         cache = await self.get_cached_data()
         
+        # Find the existing item to remove its tags from count
+        existing_item = next((item for item in cache.raw_data if item['file_path'] == original_path), None)
+        if existing_item and 'tags' in existing_item:
+            for tag in existing_item.get('tags', []):
+                if tag in self._tags_count:
+                    self._tags_count[tag] = max(0, self._tags_count[tag] - 1)
+                    if self._tags_count[tag] == 0:
+                        del self._tags_count[tag]
+        
         # Remove old path from hash index if exists
         self._hash_index.remove_by_path(original_path)
         
@@ -455,11 +562,16 @@ class LoraScanner:
             
             # Update hash index with new path
             if 'sha256' in metadata:
-                self._hash_index.add_entry(metadata['sha256'], new_path)
+                self._hash_index.add_entry(metadata['sha256'].lower(), new_path)
             
             # Update folders list
             all_folders = set(item['folder'] for item in cache.raw_data)
             cache.folders = sorted(list(all_folders), key=lambda x: x.lower())
+            
+            # Update tags count with the new/updated tags
+            if 'tags' in metadata:
+                for tag in metadata.get('tags', []):
+                    self._tags_count[tag] = self._tags_count.get(tag, 0) + 1
         
         # Resort cache
         await cache.resort()
@@ -495,13 +607,133 @@ class LoraScanner:
     # Add new methods for hash index functionality
     def has_lora_hash(self, sha256: str) -> bool:
         """Check if a LoRA with given hash exists"""
-        return self._hash_index.has_hash(sha256)
+        return self._hash_index.has_hash(sha256.lower())
         
     def get_lora_path_by_hash(self, sha256: str) -> Optional[str]:
         """Get file path for a LoRA by its hash"""
-        return self._hash_index.get_path(sha256)
+        return self._hash_index.get_path(sha256.lower())
         
     def get_lora_hash_by_path(self, file_path: str) -> Optional[str]:
         """Get hash for a LoRA by its file path"""
-        return self._hash_index.get_hash(file_path)
+        return self._hash_index.get_hash(file_path) 
+
+    def get_preview_url_by_hash(self, sha256: str) -> Optional[str]:
+        """Get preview static URL for a LoRA by its hash"""
+        # Get the file path first
+        file_path = self._hash_index.get_path(sha256.lower())
+        if not file_path:
+            return None
+            
+        # Determine the preview file path (typically same name with different extension)
+        base_name = os.path.splitext(file_path)[0]
+        preview_extensions = ['.preview.png', '.preview.jpeg', '.preview.jpg', '.preview.mp4',
+                            '.png', '.jpeg', '.jpg', '.mp4']
+        
+        for ext in preview_extensions:
+            preview_path = f"{base_name}{ext}"
+            if os.path.exists(preview_path):
+                # Convert to static URL using config
+                return config.get_preview_static_url(preview_path)
+        
+        return None
+
+    # Add new method to get top tags
+    async def get_top_tags(self, limit: int = 20) -> List[Dict[str, any]]:
+        """Get top tags sorted by count
+        
+        Args:
+            limit: Maximum number of tags to return
+            
+        Returns:
+            List of dictionaries with tag name and count, sorted by count
+        """
+        # Make sure cache is initialized
+        await self.get_cached_data()
+        
+        # Sort tags by count in descending order
+        sorted_tags = sorted(
+            [{"tag": tag, "count": count} for tag, count in self._tags_count.items()],
+            key=lambda x: x['count'],
+            reverse=True
+        )
+        
+        # Return limited number
+        return sorted_tags[:limit]
+        
+    async def get_base_models(self, limit: int = 20) -> List[Dict[str, any]]:
+        """Get base models used in loras sorted by frequency
+        
+        Args:
+            limit: Maximum number of base models to return
+            
+        Returns:
+            List of dictionaries with base model name and count, sorted by count
+        """
+        # Make sure cache is initialized
+        cache = await self.get_cached_data()
+        
+        # Count base model occurrences
+        base_model_counts = {}
+        for lora in cache.raw_data:
+            if 'base_model' in lora and lora['base_model']:
+                base_model = lora['base_model']
+                base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
+        
+        # Sort base models by count
+        sorted_models = [{'name': model, 'count': count} for model, count in base_model_counts.items()]
+        sorted_models.sort(key=lambda x: x['count'], reverse=True)
+        
+        # Return limited number
+        return sorted_models[:limit]
+
+    async def diagnose_hash_index(self):
+        """Diagnostic method to verify hash index functionality"""
+        print("\n\n*** DIAGNOSING LORA HASH INDEX ***\n\n", file=sys.stderr)
+        
+        # First check if the hash index has any entries
+        if hasattr(self, '_hash_index'):
+            index_entries = len(self._hash_index._hash_to_path)
+            print(f"Hash index has {index_entries} entries", file=sys.stderr)
+            
+            # Print a few example entries if available
+            if index_entries > 0:
+                print("\nSample hash index entries:", file=sys.stderr)
+                count = 0
+                for hash_val, path in self._hash_index._hash_to_path.items():
+                    if count < 5:  # Just show the first 5
+                        print(f"Hash: {hash_val[:8]}... -> Path: {path}", file=sys.stderr)
+                        count += 1
+                    else:
+                        break
+        else:
+            print("Hash index not initialized", file=sys.stderr)
+        
+        # Try looking up by a known hash for testing
+        if not hasattr(self, '_hash_index') or not self._hash_index._hash_to_path:
+            print("No hash entries to test lookup with", file=sys.stderr)
+            return
+        
+        test_hash = next(iter(self._hash_index._hash_to_path.keys()))
+        test_path = self._hash_index.get_path(test_hash)
+        print(f"\nTest lookup by hash: {test_hash[:8]}... -> {test_path}", file=sys.stderr)
+        
+        # Also test reverse lookup
+        test_hash_result = self._hash_index.get_hash(test_path)
+        print(f"Test reverse lookup: {test_path} -> {test_hash_result[:8]}...\n\n", file=sys.stderr)
+
+    async def get_lora_info_by_name(self, name):
+        """Get LoRA information by name"""
+        try:
+            # Get cached data
+            cache = await self.get_cached_data()
+            
+            # Find the LoRA by name
+            for lora in cache.raw_data:
+                if lora.get("file_name") == name:
+                    return lora
+                    
+            return None
+        except Exception as e:
+            logger.error(f"Error getting LoRA info by name: {e}", exc_info=True)
+            return None
 

py/services/recipe_cache.py

@@ -0,0 +1,85 @@
+import asyncio
+from typing import List, Dict
+from dataclasses import dataclass
+from operator import itemgetter
+
+@dataclass
+class RecipeCache:
+    """Cache structure for Recipe data"""
+    raw_data: List[Dict]
+    sorted_by_name: List[Dict]
+    sorted_by_date: List[Dict]
+    
+    def __post_init__(self):
+        self._lock = asyncio.Lock()
+
+    async def resort(self, name_only: bool = False):
+        """Resort all cached data views"""
+        async with self._lock:
+            self.sorted_by_name = sorted(
+                self.raw_data, 
+                key=lambda x: x.get('title', '').lower()  # Case-insensitive sort
+            )
+            if not name_only:
+                self.sorted_by_date = sorted(
+                    self.raw_data, 
+                    key=itemgetter('created_date', 'file_path'), 
+                    reverse=True
+                )
+    
+    async def update_recipe_metadata(self, recipe_id: str, metadata: Dict) -> bool:
+        """Update metadata for a specific recipe in all cached data
+        
+        Args:
+            recipe_id: The ID of the recipe to update
+            metadata: The new metadata
+            
+        Returns:
+            bool: True if the update was successful, False if the recipe wasn't found
+        """
+        async with self._lock:
+            # Update in raw_data
+            for item in self.raw_data:
+                if item.get('id') == recipe_id:
+                    item.update(metadata)
+                    break
+            else:
+                return False  # Recipe not found
+                
+            # Resort to reflect changes
+            await self.resort()
+            return True
+            
+    async def add_recipe(self, recipe_data: Dict) -> None:
+        """Add a new recipe to the cache
+        
+        Args:
+            recipe_data: The recipe data to add
+        """
+        async with self._lock:
+            self.raw_data.append(recipe_data)
+            await self.resort() 
+
+    async def remove_recipe(self, recipe_id: str) -> bool:
+        """Remove a recipe from the cache by ID
+        
+        Args:
+            recipe_id: The ID of the recipe to remove
+            
+        Returns:
+            bool: True if the recipe was found and removed, False otherwise
+        """
+        # Find the recipe in raw_data
+        recipe_index = next((i for i, recipe in enumerate(self.raw_data) 
+                             if recipe.get('id') == recipe_id), None)
+        
+        if recipe_index is None:
+            return False
+        
+        # Remove from raw_data
+        self.raw_data.pop(recipe_index)
+        
+        # Resort to update sorted lists
+        await self.resort()
+        
+        return True 

py/services/recipe_scanner.py

@@ -0,0 +1,451 @@
+import os
+import logging
+import asyncio
+import json
+import re
+from typing import List, Dict, Optional, Any
+from datetime import datetime
+from ..config import config
+from .recipe_cache import RecipeCache
+from .lora_scanner import LoraScanner
+from .civitai_client import CivitaiClient
+from ..utils.utils import fuzzy_match
+import sys
+
+logger = logging.getLogger(__name__)
+
+class RecipeScanner:
+    """Service for scanning and managing recipe images"""
+    
+    _instance = None
+    _lock = asyncio.Lock()
+    
+    def __new__(cls, lora_scanner: Optional[LoraScanner] = None):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._lora_scanner = lora_scanner
+            cls._instance._civitai_client = CivitaiClient()
+        return cls._instance
+    
+    def __init__(self, lora_scanner: Optional[LoraScanner] = None):
+        # Ensure initialization only happens once
+        if not hasattr(self, '_initialized'):
+            self._cache: Optional[RecipeCache] = None
+            self._initialization_lock = asyncio.Lock()
+            self._initialization_task: Optional[asyncio.Task] = None
+            self._is_initializing = False
+            if lora_scanner:
+                self._lora_scanner = lora_scanner
+            self._initialized = True
+            
+            # Initialization will be scheduled by LoraManager
+    
+    @property
+    def recipes_dir(self) -> str:
+        """Get path to recipes directory"""
+        if not config.loras_roots:
+            return ""
+        
+        # config.loras_roots already sorted case-insensitively, use the first one
+        recipes_dir = os.path.join(config.loras_roots[0], "recipes")
+        os.makedirs(recipes_dir, exist_ok=True)
+        
+        return recipes_dir
+    
+    async def get_cached_data(self, force_refresh: bool = False) -> RecipeCache:
+        """Get cached recipe data, refresh if needed"""
+        # If cache is already initialized and no refresh is needed, return it immediately
+        if self._cache is not None and not force_refresh:
+            return self._cache
+
+        # If another initialization is already in progress, wait for it to complete
+        if self._is_initializing and not force_refresh:
+            return self._cache or RecipeCache(raw_data=[], sorted_by_name=[], sorted_by_date=[])
+
+        # Try to acquire the lock with a timeout to prevent deadlocks
+        try:
+            # Use a timeout for acquiring the lock
+            async with asyncio.timeout(1.0):
+                async with self._initialization_lock:
+                    # Check again after acquiring the lock
+                    if self._cache is not None and not force_refresh:
+                        return self._cache
+                    
+                    # Mark as initializing to prevent concurrent initializations
+                    self._is_initializing = True
+                    
+                    try:
+                        # First ensure the lora scanner is initialized
+                        if self._lora_scanner:
+                            try:
+                                lora_cache = await asyncio.wait_for(
+                                    self._lora_scanner.get_cached_data(), 
+                                    timeout=10.0
+                                )
+                            except asyncio.TimeoutError:
+                                logger.error("Timeout waiting for lora scanner initialization")
+                            except Exception as e:
+                                logger.error(f"Error waiting for lora scanner: {e}")
+                        
+                        # Scan for recipe data
+                        raw_data = await self.scan_all_recipes()
+                        
+                        # Update cache
+                        self._cache = RecipeCache(
+                            raw_data=raw_data,
+                            sorted_by_name=[],
+                            sorted_by_date=[]
+                        )
+                        
+                        # Resort cache
+                        await self._cache.resort()
+                        
+                        return self._cache
+                    
+                    except Exception as e:
+                        logger.error(f"Recipe Manager: Error initializing cache: {e}", exc_info=True)
+                        # Create empty cache on error
+                        self._cache = RecipeCache(
+                            raw_data=[],
+                            sorted_by_name=[],
+                            sorted_by_date=[]
+                        )
+                        return self._cache
+                    finally:
+                        # Mark initialization as complete
+                        self._is_initializing = False
+        
+        except asyncio.TimeoutError:
+            # If we can't acquire the lock in time, return the current cache or an empty one
+            logger.warning("Timeout acquiring initialization lock - returning current cache state")
+            return self._cache or RecipeCache(raw_data=[], sorted_by_name=[], sorted_by_date=[])
+        except Exception as e:
+            logger.error(f"Unexpected error in get_cached_data: {e}")
+            return self._cache or RecipeCache(raw_data=[], sorted_by_name=[], sorted_by_date=[])
+    
+    async def scan_all_recipes(self) -> List[Dict]:
+        """Scan all recipe JSON files and return metadata"""
+        recipes = []
+        recipes_dir = self.recipes_dir
+        
+        if not recipes_dir or not os.path.exists(recipes_dir):
+            logger.warning(f"Recipes directory not found: {recipes_dir}")
+            return recipes
+        
+        # Get all recipe JSON files in the recipes directory
+        recipe_files = []
+        for root, _, files in os.walk(recipes_dir):
+            recipe_count = sum(1 for f in files if f.lower().endswith('.recipe.json'))
+            if recipe_count > 0:
+                for file in files:
+                    if file.lower().endswith('.recipe.json'):
+                        recipe_files.append(os.path.join(root, file))
+        
+        # Process each recipe file
+        for recipe_path in recipe_files:
+            recipe_data = await self._load_recipe_file(recipe_path)
+            if recipe_data:
+                recipes.append(recipe_data)
+        
+        return recipes
+    
+    async def _load_recipe_file(self, recipe_path: str) -> Optional[Dict]:
+        """Load recipe data from a JSON file"""
+        try:
+            with open(recipe_path, 'r', encoding='utf-8') as f:
+                recipe_data = json.load(f)
+            
+            # Validate recipe data
+            if not recipe_data or not isinstance(recipe_data, dict):
+                logger.warning(f"Invalid recipe data in {recipe_path}")
+                return None
+            
+            # Ensure required fields exist
+            required_fields = ['id', 'file_path', 'title']
+            for field in required_fields:
+                if field not in recipe_data:
+                    logger.warning(f"Missing required field '{field}' in {recipe_path}")
+                    return None
+            
+            # Ensure the image file exists
+            image_path = recipe_data.get('file_path')
+            if not os.path.exists(image_path):
+                logger.warning(f"Recipe image not found: {image_path}")
+                # Try to find the image in the same directory as the recipe
+                recipe_dir = os.path.dirname(recipe_path)
+                image_filename = os.path.basename(image_path)
+                alternative_path = os.path.join(recipe_dir, image_filename)
+                if os.path.exists(alternative_path):
+                    recipe_data['file_path'] = alternative_path
+                else:
+                    logger.warning(f"Could not find alternative image path for {image_path}")
+            
+            # Ensure loras array exists
+            if 'loras' not in recipe_data:
+                recipe_data['loras'] = []
+            
+            # Ensure gen_params exists
+            if 'gen_params' not in recipe_data:
+                recipe_data['gen_params'] = {}
+            
+            # Update lora information with local paths and availability
+            await self._update_lora_information(recipe_data)
+            
+            return recipe_data
+        except Exception as e:
+            logger.error(f"Error loading recipe file {recipe_path}: {e}")
+            import traceback
+            traceback.print_exc(file=sys.stderr)
+            return None
+    
+    async def _update_lora_information(self, recipe_data: Dict) -> bool:
+        """Update LoRA information with hash and file_name
+        
+        Returns:
+            bool: True if metadata was updated
+        """
+        if not recipe_data.get('loras'):
+            return False
+        
+        metadata_updated = False
+        
+        for lora in recipe_data['loras']:
+            # Skip if already has complete information
+            if 'hash' in lora and 'file_name' in lora and lora['file_name']:
+                continue
+                
+            # If has modelVersionId but no hash, look in lora cache first, then fetch from Civitai
+            if 'modelVersionId' in lora and not lora.get('hash'):
+                model_version_id = lora['modelVersionId']
+
+                # Try to find in lora cache first
+                hash_from_cache = await self._find_hash_in_lora_cache(model_version_id)
+                if hash_from_cache:
+                    lora['hash'] = hash_from_cache
+                    metadata_updated = True
+                else:
+                    # If not in cache, fetch from Civitai
+                    hash_from_civitai = await self._get_hash_from_civitai(model_version_id)
+                    if hash_from_civitai:
+                        lora['hash'] = hash_from_civitai
+                        metadata_updated = True
+                    else:
+                        logger.warning(f"Could not get hash for modelVersionId {model_version_id}")
+            
+            # If has hash but no file_name, look up in lora library
+            if 'hash' in lora and (not lora.get('file_name') or not lora['file_name']):
+                hash_value = lora['hash']
+                
+                if self._lora_scanner.has_lora_hash(hash_value):
+                    lora_path = self._lora_scanner.get_lora_path_by_hash(hash_value)
+                    if lora_path:
+                        file_name = os.path.splitext(os.path.basename(lora_path))[0]
+                        lora['file_name'] = file_name
+                        metadata_updated = True
+                else:
+                    # Lora not in library
+                    lora['file_name'] = ''
+                    metadata_updated = True
+        
+        return metadata_updated
+    
+    async def _find_hash_in_lora_cache(self, model_version_id: str) -> Optional[str]:
+        """Find hash in lora cache based on modelVersionId"""
+        try:
+            # Get all loras from cache
+            if not self._lora_scanner:
+                return None
+                
+            cache = await self._lora_scanner.get_cached_data()
+            if not cache or not cache.raw_data:
+                return None
+                
+            # Find lora with matching civitai.id
+            for lora in cache.raw_data:
+                civitai_data = lora.get('civitai', {})
+                if civitai_data and str(civitai_data.get('id', '')) == str(model_version_id):
+                    return lora.get('sha256')
+                    
+            return None
+        except Exception as e:
+            logger.error(f"Error finding hash in lora cache: {e}")
+            return None
+    
+    async def _get_hash_from_civitai(self, model_version_id: str) -> Optional[str]:
+        """Get hash from Civitai API"""
+        try:
+            if not self._civitai_client:
+                return None
+                
+            version_info = await self._civitai_client.get_model_version_info(model_version_id)
+            
+            if not version_info or not version_info.get('files'):
+                logger.warning(f"No files found in version info for ID: {model_version_id}")
+                return None
+                
+            # Get hash from the first file
+            for file_info in version_info.get('files', []):
+                if file_info.get('hashes', {}).get('SHA256'):
+                    return file_info['hashes']['SHA256']
+                    
+            logger.warning(f"No SHA256 hash found in version info for ID: {model_version_id}")
+            return None
+        except Exception as e:
+            logger.error(f"Error getting hash from Civitai: {e}")
+            return None
+
+    async def _get_model_version_name(self, model_version_id: str) -> Optional[str]:
+        """Get model version name from Civitai API"""
+        try:
+            if not self._civitai_client:
+                return None
+                
+            version_info = await self._civitai_client.get_model_version_info(model_version_id)
+            
+            if version_info and 'name' in version_info:
+                return version_info['name']
+                    
+            logger.warning(f"No version name found for modelVersionId {model_version_id}")
+            return None
+        except Exception as e:
+            logger.error(f"Error getting model version name from Civitai: {e}")
+            return None
+
+    async def _determine_base_model(self, loras: List[Dict]) -> Optional[str]:
+        """Determine the most common base model among LoRAs"""
+        base_models = {}
+        
+        # Count occurrences of each base model
+        for lora in loras:
+            if 'hash' in lora:
+                lora_path = self._lora_scanner.get_lora_path_by_hash(lora['hash'])
+                if lora_path:
+                    base_model = await self._get_base_model_for_lora(lora_path)
+                    if base_model:
+                        base_models[base_model] = base_models.get(base_model, 0) + 1
+        
+        # Return the most common base model
+        if base_models:
+            return max(base_models.items(), key=lambda x: x[1])[0]
+        return None
+
+    async def _get_base_model_for_lora(self, lora_path: str) -> Optional[str]:
+        """Get base model for a LoRA from cache"""
+        try:
+            if not self._lora_scanner:
+                return None
+            
+            cache = await self._lora_scanner.get_cached_data()
+            if not cache or not cache.raw_data:
+                return None
+            
+            # Find matching lora in cache
+            for lora in cache.raw_data:
+                if lora.get('file_path') == lora_path:
+                    return lora.get('base_model')
+                
+            return None
+        except Exception as e:
+            logger.error(f"Error getting base model for lora: {e}")
+            return None
+
+    async def get_paginated_data(self, page: int, page_size: int, sort_by: str = 'date', search: str = None, filters: dict = None, search_options: dict = None):
+        """Get paginated and filtered recipe data
+        
+        Args:
+            page: Current page number (1-based)
+            page_size: Number of items per page
+            sort_by: Sort method ('name' or 'date')
+            search: Search term
+            filters: Dictionary of filters to apply
+            search_options: Dictionary of search options to apply
+        """
+        cache = await self.get_cached_data()
+
+        # Get base dataset
+        filtered_data = cache.sorted_by_date if sort_by == 'date' else cache.sorted_by_name
+        
+        # Apply search filter
+        if search:
+            # Default search options if none provided
+            if not search_options:
+                search_options = {
+                    'title': True,
+                    'tags': True,
+                    'lora_name': True,
+                    'lora_model': True
+                }
+            
+            # Build the search predicate based on search options
+            def matches_search(item):
+                # Search in title if enabled
+                if search_options.get('title', True):
+                    if fuzzy_match(str(item.get('title', '')), search):
+                        return True
+                
+                # Search in tags if enabled
+                if search_options.get('tags', True) and 'tags' in item:
+                    for tag in item['tags']:
+                        if fuzzy_match(tag, search):
+                            return True
+                
+                # Search in lora file names if enabled
+                if search_options.get('lora_name', True) and 'loras' in item:
+                    for lora in item['loras']:
+                        if fuzzy_match(str(lora.get('file_name', '')), search):
+                            return True
+                
+                # Search in lora model names if enabled
+                if search_options.get('lora_model', True) and 'loras' in item:
+                    for lora in item['loras']:
+                        if fuzzy_match(str(lora.get('modelName', '')), search):
+                            return True
+                
+                # No match found
+                return False
+            
+            # Filter the data using the search predicate
+            filtered_data = [item for item in filtered_data if matches_search(item)]
+        
+        # Apply additional filters
+        if filters:
+            # Filter by base model
+            if 'base_model' in filters and filters['base_model']:
+                filtered_data = [
+                    item for item in filtered_data
+                    if item.get('base_model', '') in filters['base_model']
+                ]
+            
+            # Filter by tags
+            if 'tags' in filters and filters['tags']:
+                filtered_data = [
+                    item for item in filtered_data
+                    if any(tag in item.get('tags', []) for tag in filters['tags'])
+                ]
+
+        # Calculate pagination
+        total_items = len(filtered_data)
+        start_idx = (page - 1) * page_size
+        end_idx = min(start_idx + page_size, total_items)
+        
+        # Get paginated items
+        paginated_items = filtered_data[start_idx:end_idx]
+        
+        # Add inLibrary information for each lora
+        for item in paginated_items:
+            if 'loras' in item:
+                for lora in item['loras']:
+                    if 'hash' in lora and lora['hash']:
+                        lora['inLibrary'] = self._lora_scanner.has_lora_hash(lora['hash'].lower())
+                        lora['preview_url'] = self._lora_scanner.get_preview_url_by_hash(lora['hash'].lower())
+                        lora['localPath'] = self._lora_scanner.get_lora_path_by_hash(lora['hash'].lower())
+        
+        result = {
+            'items': paginated_items,
+            'total': total_items,
+            'page': page,
+            'page_size': page_size,
+            'total_pages': (total_items + page_size - 1) // page_size
+        }
+        
+        return result 

py/services/settings_manager.py

@@ -37,7 +37,8 @@ class SettingsManager:
     def _get_default_settings(self) -> Dict[str, Any]:
         """Return default settings"""
         return {
-            "civitai_api_key": ""
+            "civitai_api_key": "",
+            "show_only_sfw": False
         }
 
     def get(self, key: str, default: Any = None) -> Any:

py/utils/constants.py

@@ -0,0 +1,8 @@
+NSFW_LEVELS = {
+    "PG": 1,
+    "PG13": 2,
+    "R": 4,
+    "X": 8,
+    "XXX": 16,
+    "Blocked": 32, # Probably not actually visible through the API without being logged in on model owner account?
+}

py/utils/exif_utils.py

@@ -0,0 +1,486 @@
+import piexif
+import json
+import logging
+from typing import Dict, Optional, Any
+from io import BytesIO
+import os
+from PIL import Image
+import re
+
+logger = logging.getLogger(__name__)
+
+class ExifUtils:
+    """Utility functions for working with EXIF data in images"""
+    
+    @staticmethod
+    def extract_user_comment(image_path: str) -> Optional[str]:
+        """Extract UserComment field from image EXIF data"""
+        try:
+            # First try to open as image to check format
+            with Image.open(image_path) as img:
+                if img.format not in ['JPEG', 'TIFF', 'WEBP']:
+                    # For non-JPEG/TIFF/WEBP images, try to get EXIF through PIL
+                    exif = img._getexif()
+                    if exif and piexif.ExifIFD.UserComment in exif:
+                        user_comment = exif[piexif.ExifIFD.UserComment]
+                        if isinstance(user_comment, bytes):
+                            if user_comment.startswith(b'UNICODE\0'):
+                                return user_comment[8:].decode('utf-16be')
+                            return user_comment.decode('utf-8', errors='ignore')
+                        return user_comment
+                    return None
+                
+                # For JPEG/TIFF/WEBP, use piexif
+                exif_dict = piexif.load(image_path)
+                
+                if piexif.ExifIFD.UserComment in exif_dict.get('Exif', {}):
+                    user_comment = exif_dict['Exif'][piexif.ExifIFD.UserComment]
+                    if isinstance(user_comment, bytes):
+                        if user_comment.startswith(b'UNICODE\0'):
+                            user_comment = user_comment[8:].decode('utf-16be')
+                        else:
+                            user_comment = user_comment.decode('utf-8', errors='ignore')
+                    return user_comment
+                return None
+                
+        except Exception as e:
+            return None
+    
+    @staticmethod
+    def update_user_comment(image_path: str, user_comment: str) -> str:
+        """Update UserComment field in image EXIF data"""
+        try:
+            # Load the image and its EXIF data
+            with Image.open(image_path) as img:
+                # Get original format
+                img_format = img.format
+                
+                # For WebP format, we need a different approach
+                if img_format == 'WEBP':
+                    # WebP doesn't support standard EXIF through piexif
+                    # We'll use PIL's exif parameter directly
+                    exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + user_comment.encode('utf-16be')}}
+                    exif_bytes = piexif.dump(exif_dict)
+                    
+                    # Save with the exif data
+                    img.save(image_path, format='WEBP', exif=exif_bytes, quality=85)
+                    return image_path
+                
+                # For other formats, use the standard approach
+                try:
+                    exif_dict = piexif.load(img.info.get('exif', b''))
+                except:
+                    exif_dict = {'0th':{}, 'Exif':{}, 'GPS':{}, 'Interop':{}, '1st':{}}
+                
+                # If no Exif dictionary exists, create one
+                if 'Exif' not in exif_dict:
+                    exif_dict['Exif'] = {}
+                
+                # Update the UserComment field - use UNICODE format
+                unicode_bytes = user_comment.encode('utf-16be')
+                user_comment_bytes = b'UNICODE\0' + unicode_bytes
+                
+                exif_dict['Exif'][piexif.ExifIFD.UserComment] = user_comment_bytes
+                
+                # Convert EXIF dict back to bytes
+                exif_bytes = piexif.dump(exif_dict)
+                
+                # Save the image with updated EXIF data
+                img.save(image_path, exif=exif_bytes)
+                
+            return image_path
+        except Exception as e:
+            logger.error(f"Error updating EXIF data in {image_path}: {e}")
+            return image_path
+            
+    @staticmethod
+    def append_recipe_metadata(image_path, recipe_data) -> str:
+        """Append recipe metadata to an image's EXIF data"""
+        try:
+            # First, extract existing user comment
+            user_comment = ExifUtils.extract_user_comment(image_path)
+            
+            # Check if there's already recipe metadata in the user comment
+            if user_comment:
+                # Remove any existing recipe metadata
+                user_comment = ExifUtils.remove_recipe_metadata(user_comment)
+            
+            # Prepare simplified loras data
+            simplified_loras = []
+            for lora in recipe_data.get("loras", []):
+                simplified_lora = {
+                    "file_name": lora.get("file_name", ""),
+                    "hash": lora.get("hash", "").lower() if lora.get("hash") else "",
+                    "strength": float(lora.get("strength", 1.0)),
+                    "modelVersionId": lora.get("modelVersionId", ""),
+                    "modelName": lora.get("modelName", ""),
+                    "modelVersionName": lora.get("modelVersionName", ""),
+                }
+                simplified_loras.append(simplified_lora)            
+            
+            # Create recipe metadata JSON
+            recipe_metadata = {
+                'title': recipe_data.get('title', ''),
+                'base_model': recipe_data.get('base_model', ''),
+                'loras': simplified_loras,
+                'gen_params': recipe_data.get('gen_params', {}),
+                'tags': recipe_data.get('tags', [])
+            }
+            
+            # Convert to JSON string
+            recipe_metadata_json = json.dumps(recipe_metadata)
+            
+            # Create the recipe metadata marker
+            recipe_metadata_marker = f"Recipe metadata: {recipe_metadata_json}"
+            
+            # Append to existing user comment or create new one
+            new_user_comment = f"{user_comment} \n {recipe_metadata_marker}" if user_comment else recipe_metadata_marker
+            
+            # Write back to the image
+            return ExifUtils.update_user_comment(image_path, new_user_comment)
+        except Exception as e:
+            logger.error(f"Error appending recipe metadata: {e}", exc_info=True)
+            return image_path
+
+    @staticmethod
+    def remove_recipe_metadata(user_comment):
+        """Remove recipe metadata from user comment"""
+        if not user_comment:
+            return ""
+        
+        # Find the recipe metadata marker
+        recipe_marker_index = user_comment.find("Recipe metadata: ")
+        if recipe_marker_index == -1:
+            return user_comment
+        
+        # If recipe metadata is not at the start, remove the preceding ", "
+        if recipe_marker_index >= 2 and user_comment[recipe_marker_index-2:recipe_marker_index] == ", ":
+            recipe_marker_index -= 2
+        
+        # Remove the recipe metadata part
+        # First, find where the metadata ends (next line or end of string)
+        next_line_index = user_comment.find("\n", recipe_marker_index)
+        if next_line_index == -1:
+            # Metadata is at the end of the string
+            return user_comment[:recipe_marker_index].rstrip()
+        else:
+            # Metadata is in the middle of the string
+            return user_comment[:recipe_marker_index] + user_comment[next_line_index:]
+            
+    @staticmethod
+    def optimize_image(image_data, target_width=250, format='webp', quality=85, preserve_metadata=True):
+        """
+        Optimize an image by resizing and converting to WebP format
+        
+        Args:
+            image_data: Binary image data or path to image file
+            target_width: Width to resize the image to (preserves aspect ratio)
+            format: Output format (default: webp)
+            quality: Output quality (0-100)
+            preserve_metadata: Whether to preserve EXIF metadata
+            
+        Returns:
+            Tuple of (optimized_image_data, extension)
+        """
+        try:
+            # Extract metadata if needed
+            user_comment = None
+            if preserve_metadata:
+                if isinstance(image_data, str) and os.path.exists(image_data):
+                    # It's a file path
+                    user_comment = ExifUtils.extract_user_comment(image_data)
+                    img = Image.open(image_data)
+                else:
+                    # It's binary data
+                    temp_img = BytesIO(image_data)
+                    img = Image.open(temp_img)
+                    # Save to a temporary file to extract metadata
+                    import tempfile
+                    with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as temp_file:
+                        temp_path = temp_file.name
+                        temp_file.write(image_data)
+                    user_comment = ExifUtils.extract_user_comment(temp_path)
+                    os.unlink(temp_path)
+            else:
+                # Just open the image without extracting metadata
+                if isinstance(image_data, str) and os.path.exists(image_data):
+                    img = Image.open(image_data)
+                else:
+                    img = Image.open(BytesIO(image_data))
+            
+            # Calculate new height to maintain aspect ratio
+            width, height = img.size
+            new_height = int(height * (target_width / width))
+            
+            # Resize the image
+            resized_img = img.resize((target_width, new_height), Image.LANCZOS)
+            
+            # Save to BytesIO in the specified format
+            output = BytesIO()
+            
+            # WebP format
+            if format.lower() == 'webp':
+                resized_img.save(output, format='WEBP', quality=quality)
+                extension = '.webp'
+            # JPEG format
+            elif format.lower() in ('jpg', 'jpeg'):
+                resized_img.save(output, format='JPEG', quality=quality)
+                extension = '.jpg'
+            # PNG format
+            elif format.lower() == 'png':
+                resized_img.save(output, format='PNG', optimize=True)
+                extension = '.png'
+            else:
+                # Default to WebP
+                resized_img.save(output, format='WEBP', quality=quality)
+                extension = '.webp'
+            
+            # Get the optimized image data
+            optimized_data = output.getvalue()
+            
+            # If we need to preserve metadata, write it to a temporary file
+            if preserve_metadata and user_comment:
+                # For WebP format, we'll directly save with metadata
+                if format.lower() == 'webp':
+                    # Create a new BytesIO with metadata
+                    output_with_metadata = BytesIO()
+                    
+                    # Create EXIF data with user comment
+                    exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + user_comment.encode('utf-16be')}}
+                    exif_bytes = piexif.dump(exif_dict)
+                    
+                    # Save with metadata
+                    resized_img.save(output_with_metadata, format='WEBP', exif=exif_bytes, quality=quality)
+                    optimized_data = output_with_metadata.getvalue()
+                else:
+                    # For other formats, use the temporary file approach
+                    import tempfile
+                    with tempfile.NamedTemporaryFile(suffix=extension, delete=False) as temp_file:
+                        temp_path = temp_file.name
+                        temp_file.write(optimized_data)
+                    
+                    # Add the metadata back
+                    ExifUtils.update_user_comment(temp_path, user_comment)
+                    
+                    # Read the file with metadata
+                    with open(temp_path, 'rb') as f:
+                        optimized_data = f.read()
+                    
+                    # Clean up
+                    os.unlink(temp_path)
+            
+            return optimized_data, extension
+            
+        except Exception as e:
+            logger.error(f"Error optimizing image: {e}", exc_info=True)
+            # Return original data if optimization fails
+            if isinstance(image_data, str) and os.path.exists(image_data):
+                with open(image_data, 'rb') as f:
+                    return f.read(), os.path.splitext(image_data)[1]
+            return image_data, '.jpg'
+
+    @staticmethod
+    def _parse_comfyui_workflow(workflow_data: Any) -> Dict[str, Any]:
+        """
+        Parse ComfyUI workflow data and extract relevant generation parameters
+        
+        Args:
+            workflow_data: Raw workflow data (string or dict)
+            
+        Returns:
+            Formatted generation parameters dictionary
+        """
+        try:
+            # If workflow_data is a string, try to parse it as JSON
+            if isinstance(workflow_data, str):
+                try:
+                    workflow_data = json.loads(workflow_data)
+                except json.JSONDecodeError:
+                    logger.error("Failed to parse workflow data as JSON")
+                    return {}
+            
+            # Now workflow_data should be a dictionary
+            if not isinstance(workflow_data, dict):
+                logger.error(f"Workflow data is not a dictionary: {type(workflow_data)}")
+                return {}
+                
+            # Initialize parameters dictionary with only the required fields
+            gen_params = {
+                "prompt": "",
+                "negative_prompt": "",
+                "steps": "",
+                "sampler": "",
+                "cfg_scale": "",
+                "seed": "",
+                "size": "",
+                "clip_skip": ""
+            }
+            
+            # First pass: find the KSampler node to get basic parameters and node references
+            # Store node references to follow for prompts
+            positive_ref = None
+            negative_ref = None
+            
+            for node_id, node_data in workflow_data.items():
+                if not isinstance(node_data, dict):
+                    continue
+                    
+                # Extract node inputs if available
+                inputs = node_data.get("inputs", {})
+                if not inputs:
+                    continue
+                
+                # KSampler nodes contain most generation parameters and references to prompt nodes
+                if "KSampler" in node_data.get("class_type", ""):
+                    # Extract basic sampling parameters
+                    gen_params["steps"] = inputs.get("steps", "")
+                    gen_params["cfg_scale"] = inputs.get("cfg", "")
+                    gen_params["sampler"] = inputs.get("sampler_name", "")
+                    gen_params["seed"] = inputs.get("seed", "")
+                    if isinstance(gen_params["seed"], list) and len(gen_params["seed"]) > 1:
+                        gen_params["seed"] = gen_params["seed"][1]  # Use the actual value if it's a list
+                    
+                    # Get references to positive and negative prompt nodes
+                    positive_ref = inputs.get("positive", "")
+                    negative_ref = inputs.get("negative", "")
+                    
+                # CLIPSetLastLayer contains clip_skip information
+                elif "CLIPSetLastLayer" in node_data.get("class_type", ""):
+                    gen_params["clip_skip"] = inputs.get("stop_at_clip_layer", "")
+                    if isinstance(gen_params["clip_skip"], int) and gen_params["clip_skip"] < 0:
+                        # Convert negative layer index to positive clip skip value
+                        gen_params["clip_skip"] = abs(gen_params["clip_skip"])
+                
+                # Look for resolution information
+                elif "LatentImage" in node_data.get("class_type", "") or "Empty" in node_data.get("class_type", ""):
+                    width = inputs.get("width", 0)
+                    height = inputs.get("height", 0)
+                    if width and height:
+                        gen_params["size"] = f"{width}x{height}"
+                    
+                    # Some nodes have resolution as a string like "832x1216 (0.68)"
+                    resolution = inputs.get("resolution", "")
+                    if isinstance(resolution, str) and "x" in resolution:
+                        gen_params["size"] = resolution.split(" ")[0]  # Extract just the dimensions
+            
+            # Helper function to follow node references and extract text content
+            def get_text_from_node_ref(node_ref, workflow_data):
+                if not node_ref or not isinstance(node_ref, list) or len(node_ref) < 2:
+                    return ""
+                
+                node_id, slot_idx = node_ref
+                
+                # If we can't find the node, return empty string
+                if node_id not in workflow_data:
+                    return ""
+                
+                node = workflow_data[node_id]
+                inputs = node.get("inputs", {})
+                
+                # Direct text input in CLIP Text Encode nodes
+                if "CLIPTextEncode" in node.get("class_type", ""):
+                    text = inputs.get("text", "")
+                    if isinstance(text, str):
+                        return text
+                    elif isinstance(text, list) and len(text) >= 2:
+                        # If text is a reference to another node, follow it
+                        return get_text_from_node_ref(text, workflow_data)
+                
+                # Other nodes might have text input with different field names
+                for field_name, field_value in inputs.items():
+                    if field_name == "text" and isinstance(field_value, str):
+                        return field_value
+                    elif isinstance(field_value, list) and len(field_value) >= 2 and field_name in ["text"]:
+                        # If it's a reference to another node, follow it
+                        return get_text_from_node_ref(field_value, workflow_data)
+                
+                return ""
+            
+            # Extract prompts by following references from KSampler node
+            if positive_ref:
+                gen_params["prompt"] = get_text_from_node_ref(positive_ref, workflow_data)
+            
+            if negative_ref:
+                gen_params["negative_prompt"] = get_text_from_node_ref(negative_ref, workflow_data)
+            
+            # Fallback: if we couldn't extract prompts via references, use the traditional method
+            if not gen_params["prompt"] or not gen_params["negative_prompt"]:
+                for node_id, node_data in workflow_data.items():
+                    if not isinstance(node_data, dict):
+                        continue
+                        
+                    inputs = node_data.get("inputs", {})
+                    if not inputs:
+                        continue
+                    
+                    if "CLIPTextEncode" in node_data.get("class_type", ""):
+                        # Check for negative prompt nodes
+                        title = node_data.get("_meta", {}).get("title", "").lower()
+                        prompt_text = inputs.get("text", "")
+                        
+                        if isinstance(prompt_text, str):
+                            if "negative" in title and not gen_params["negative_prompt"]:
+                                gen_params["negative_prompt"] = prompt_text
+                            elif prompt_text and not "negative" in title and not gen_params["prompt"]:
+                                gen_params["prompt"] = prompt_text
+            
+            return gen_params
+            
+        except Exception as e:
+            logger.error(f"Error parsing ComfyUI workflow: {e}", exc_info=True)
+            return {}
+
+    @staticmethod
+    def extract_comfyui_gen_params(image_path: str) -> Dict[str, Any]:
+        """
+        Extract ComfyUI workflow data from PNG images and format for recipe data
+        Only extracts the specific generation parameters needed for recipes.
+        
+        Args:
+            image_path: Path to the ComfyUI-generated PNG image
+            
+        Returns:
+            Dictionary containing formatted generation parameters
+        """
+        try:
+            # Check if the file exists and is accessible
+            if not os.path.exists(image_path):
+                logger.error(f"Image file not found: {image_path}")
+                return {}
+                
+            # Open the image to extract embedded workflow data
+            with Image.open(image_path) as img:
+                workflow_data = None
+                
+                # For PNG images, look for the ComfyUI workflow data in PNG chunks
+                if img.format == 'PNG':
+                    # Check standard metadata fields that might contain workflow
+                    if 'parameters' in img.info:
+                        workflow_data = img.info['parameters']
+                    elif 'prompt' in img.info:
+                        workflow_data = img.info['prompt']
+                    else:
+                        # Look for other potential field names that might contain workflow data
+                        for key in img.info:
+                            if isinstance(key, str) and ('workflow' in key.lower() or 'comfy' in key.lower()):
+                                workflow_data = img.info[key]
+                                break
+                
+                # If no workflow data found in PNG chunks, try EXIF as fallback
+                if not workflow_data:
+                    user_comment = ExifUtils.extract_user_comment(image_path)
+                    if user_comment and '{' in user_comment and '}' in user_comment:
+                        # Try to extract JSON part
+                        json_start = user_comment.find('{')
+                        json_end = user_comment.rfind('}') + 1
+                        workflow_data = user_comment[json_start:json_end]
+                
+                # Parse workflow data if found
+                if workflow_data:
+                    return ExifUtils._parse_comfyui_workflow(workflow_data)
+                
+                return {}
+                
+        except Exception as e:
+            logger.error(f"Error extracting ComfyUI gen params from {image_path}: {e}", exc_info=True)
+            return {}

py/utils/file_utils.py

@@ -4,6 +4,8 @@ import hashlib
 import json
 from typing import Dict, Optional
 
+from .model_utils import determine_base_model
+
 from .lora_metadata import extract_lora_metadata
 from .models import LoraMetadata
 
@@ -69,6 +71,8 @@ async def get_file_info(file_path: str) -> Optional[LoraMetadata]:
             notes="",
             from_civitai=True,
             preview_url=normalize_path(preview_url),
+            tags=[],
+            modelDescription=""
         )
 
         # create metadata file
@@ -103,9 +107,18 @@ async def load_metadata(file_path: str) -> Optional[LoraMetadata]:
                 data = json.load(f)
                 
                 needs_update = False
+
+                # Check and normalize base model name
+                normalized_base_model = determine_base_model(data['base_model'])
+                if data['base_model'] != normalized_base_model:
+                    data['base_model'] = normalized_base_model
+                    needs_update = True
                 
-                if data['file_path'] != normalize_path(data['file_path']):
-                    data['file_path'] = normalize_path(data['file_path'])
+                # Compare paths without extensions
+                stored_path_base = os.path.splitext(data['file_path'])[0]
+                current_path_base = os.path.splitext(normalize_path(file_path))[0]
+                if stored_path_base != current_path_base:
+                    data['file_path'] = normalize_path(file_path)
                     needs_update = True
                 
                 preview_url = data.get('preview_url', '')
@@ -116,8 +129,21 @@ async def load_metadata(file_path: str) -> Optional[LoraMetadata]:
                     if new_preview_url != preview_url:
                         data['preview_url'] = new_preview_url
                         needs_update = True
-                elif preview_url != normalize_path(preview_url):
-                    data['preview_url'] = normalize_path(preview_url)
+                else:
+                    # Compare preview paths without extensions
+                    stored_preview_base = os.path.splitext(preview_url)[0]
+                    current_preview_base = os.path.splitext(normalize_path(preview_url))[0]
+                    if stored_preview_base != current_preview_base:
+                        data['preview_url'] = normalize_path(preview_url)
+                        needs_update = True
+
+                # Ensure all fields are present
+                if 'tags' not in data:
+                    data['tags'] = []
+                    needs_update = True
+                    
+                if 'modelDescription' not in data:
+                    data['modelDescription'] = ""
                     needs_update = True
                 
                 if needs_update:

py/utils/model_utils.py

@@ -2,13 +2,15 @@ from typing import Optional
 
 # Base model mapping based on version string
 BASE_MODEL_MAPPING = {
+    "sd_1.5": "SD 1.5",
     "sd-v1-5": "SD 1.5",
     "sd-v2-1": "SD 2.1",
     "sdxl": "SDXL 1.0",
     "sd-v2": "SD 2.0",
     "flux1": "Flux.1 D",
     "flux.1 d": "Flux.1 D",
-    "illustrious": "IL",
+    "illustrious": "Illustrious",
+    "il": "Illustrious",
     "pony": "Pony",
     "Hunyuan Video": "Hunyuan Video"
 }

py/utils/models.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass, asdict
-from typing import Dict, Optional
+from typing import Dict, Optional, List
 from datetime import datetime
 import os
 from .model_utils import determine_base_model
@@ -15,10 +15,18 @@ class LoraMetadata:
     sha256: str                 # SHA256 hash of the file
     base_model: str             # Base model (SD1.5/SD2.1/SDXL/etc.)
     preview_url: str            # Preview image URL
+    preview_nsfw_level: int = 0 # NSFW level of the preview image
     usage_tips: str = "{}"      # Usage tips for the model, json string
     notes: str = ""             # Additional notes
-    from_civitai: bool = True  # Whether the lora is from Civitai
+    from_civitai: bool = True   # Whether the lora is from Civitai
     civitai: Optional[Dict] = None  # Civitai API data if available
+    tags: List[str] = None      # Model tags
+    modelDescription: str = ""  # Full model description
+
+    def __post_init__(self):
+        # Initialize empty lists to avoid mutable default parameter issue
+        if self.tags is None:
+            self.tags = []
 
     @classmethod
     def from_dict(cls, data: Dict) -> 'LoraMetadata':
@@ -39,9 +47,10 @@ class LoraMetadata:
             file_path=save_path.replace(os.sep, '/'),
             size=file_info.get('sizeKB', 0) * 1024,
             modified=datetime.now().timestamp(),
-            sha256=file_info['hashes'].get('SHA256', ''),
+            sha256=file_info['hashes'].get('SHA256', '').lower(),
             base_model=base_model,
             preview_url=None,  # Will be updated after preview download
+            preview_nsfw_level=0, # Will be updated after preview download, it is decided by the nsfw level of the preview image
             from_civitai=True,
             civitai=version_info
         )

py/utils/recipe_parsers.py

@@ -0,0 +1,547 @@
+import json
+import logging
+import os
+import re
+from typing import Dict, List, Any, Optional, Tuple
+from abc import ABC, abstractmethod
+from ..config import config
+
+logger = logging.getLogger(__name__)
+
+# Constants for generation parameters
+GEN_PARAM_KEYS = [
+    'prompt',
+    'negative_prompt', 
+    'steps',
+    'sampler',
+    'cfg_scale',
+    'seed',
+    'size',
+    'clip_skip',
+]
+
+class RecipeMetadataParser(ABC):
+    """Interface for parsing recipe metadata from image user comments"""
+
+    METADATA_MARKER = None
+
+    @abstractmethod
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the metadata format"""
+        pass
+    
+    @abstractmethod
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """
+        Parse metadata from user comment and return structured recipe data
+        
+        Args:
+            user_comment: The EXIF UserComment string from the image
+            recipe_scanner: Optional recipe scanner instance for local LoRA lookup
+            civitai_client: Optional Civitai client for fetching model information
+            
+        Returns:
+            Dict containing parsed recipe data with standardized format
+        """
+        pass
+
+
+class RecipeFormatParser(RecipeMetadataParser):
+    """Parser for images with dedicated recipe metadata format"""
+    
+    # Regular expression pattern for extracting recipe metadata
+    METADATA_MARKER = r'Recipe metadata: (\{.*\})'
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the metadata format"""
+        return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from images with dedicated recipe metadata format"""
+        try:
+            # Extract recipe metadata from user comment
+            try:
+                # Look for recipe metadata section
+                recipe_match = re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL)
+                if not recipe_match:
+                    recipe_metadata = None
+                else:
+                    recipe_json = recipe_match.group(1)
+                    recipe_metadata = json.loads(recipe_json)
+            except Exception as e:
+                logger.error(f"Error extracting recipe metadata: {e}")
+                recipe_metadata = None
+            if not recipe_metadata:
+                return {"error": "No recipe metadata found", "loras": []}
+                
+            # Process the recipe metadata
+            loras = []
+            for lora in recipe_metadata.get('loras', []):
+                # Convert recipe lora format to frontend format
+                lora_entry = {
+                    'id': lora.get('modelVersionId', ''),
+                    'name': lora.get('modelName', ''),
+                    'version': lora.get('modelVersionName', ''),
+                    'type': 'lora',
+                    'weight': lora.get('strength', 1.0),
+                    'file_name': lora.get('file_name', ''),
+                    'hash': lora.get('hash', '')
+                }
+                
+                # Check if this LoRA exists locally by SHA256 hash
+                if lora.get('hash') and recipe_scanner:
+                    lora_scanner = recipe_scanner._lora_scanner
+                    exists_locally = lora_scanner.has_lora_hash(lora['hash'])
+                    if exists_locally:
+                        lora_cache = await lora_scanner.get_cached_data()
+                        lora_item = next((item for item in lora_cache.raw_data if item['sha256'].lower() == lora['hash'].lower()), None)
+                        if lora_item:
+                            lora_entry['existsLocally'] = True
+                            lora_entry['localPath'] = lora_item['file_path']
+                            lora_entry['file_name'] = lora_item['file_name']
+                            lora_entry['size'] = lora_item['size']
+                            lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
+                            
+                    else:
+                        lora_entry['existsLocally'] = False
+                        lora_entry['localPath'] = None
+                        
+                        # Try to get additional info from Civitai if we have a model version ID
+                        if lora.get('modelVersionId') and civitai_client:
+                            try:
+                                civitai_info = await civitai_client.get_model_version_info(lora['modelVersionId'])
+                                if civitai_info and civitai_info.get("error") != "Model not found":
+                                    # Check if this is an early access lora
+                                    if civitai_info.get('earlyAccessEndsAt'):
+                                        # Convert earlyAccessEndsAt to a human-readable date
+                                        early_access_date = civitai_info.get('earlyAccessEndsAt', '')
+                                        lora_entry['isEarlyAccess'] = True
+                                        lora_entry['earlyAccessEndsAt'] = early_access_date
+                                    
+                                    # Get thumbnail URL from first image
+                                    if 'images' in civitai_info and civitai_info['images']:
+                                        lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
+                                    
+                                    # Get base model
+                                    lora_entry['baseModel'] = civitai_info.get('baseModel', '')
+                                    
+                                    # Get download URL
+                                    lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
+                                    
+                                    # Get size from files if available
+                                    if 'files' in civitai_info:
+                                        model_file = next((file for file in civitai_info.get('files', []) 
+                                                        if file.get('type') == 'Model'), None)
+                                        if model_file:
+                                            lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
+                                else:
+                                    lora_entry['isDeleted'] = True
+                                    lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for LoRA: {e}")
+                                lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                
+                loras.append(lora_entry)
+
+            logger.info(f"Found {len(loras)} loras in recipe metadata")
+            
+            # Filter gen_params to only include recognized keys
+            filtered_gen_params = {}
+            if 'gen_params' in recipe_metadata:
+                for key, value in recipe_metadata['gen_params'].items():
+                    if key in GEN_PARAM_KEYS:
+                        filtered_gen_params[key] = value
+            
+            return {
+                'base_model': recipe_metadata.get('base_model', ''),
+                'loras': loras,
+                'gen_params': filtered_gen_params,
+                'tags': recipe_metadata.get('tags', []),
+                'title': recipe_metadata.get('title', ''),
+                'from_recipe_metadata': True
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing recipe format metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}
+
+
+class StandardMetadataParser(RecipeMetadataParser):
+    """Parser for images with standard civitai metadata format (prompt, negative prompt, etc.)"""
+
+    METADATA_MARKER = r'Civitai resources: '
+
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the metadata format"""
+        return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from images with standard metadata format"""
+        try:
+            # Parse the standard metadata
+            metadata = self._parse_recipe_metadata(user_comment)
+            
+            # Look for Civitai resources in the metadata
+            civitai_resources = metadata.get('loras', [])
+            checkpoint = metadata.get('checkpoint')
+            
+            if not civitai_resources and not checkpoint:
+                return {
+                    "error": "No LoRA information found in this image",
+                    "loras": []
+                }
+            
+            # Process LoRAs and collect base models
+            base_model_counts = {}
+            loras = []
+            
+            # Process LoRAs
+            for resource in civitai_resources:
+                # Get model version ID
+                model_version_id = resource.get('modelVersionId')
+                if not model_version_id:
+                    continue
+                
+                # Initialize lora entry with default values
+                lora_entry = {
+                    'id': model_version_id,
+                    'name': resource.get('modelName', ''),
+                    'version': resource.get('modelVersionName', ''),
+                    'type': resource.get('type', 'lora'),
+                    'weight': resource.get('weight', 1.0),
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': '',
+                    'hash': '',
+                    'thumbnailUrl': '',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+                
+                # Get additional info from Civitai if client is available
+                if civitai_client:
+                    civitai_info = await civitai_client.get_model_version_info(model_version_id)
+
+                    # Check if this LoRA exists locally by SHA256 hash
+                    if civitai_info and civitai_info.get("error") != "Model not found":
+                        # Check if this is an early access lora
+                        if civitai_info.get('earlyAccessEndsAt'):
+                            # Convert earlyAccessEndsAt to a human-readable date
+                            early_access_date = civitai_info.get('earlyAccessEndsAt', '')
+                            lora_entry['isEarlyAccess'] = True
+                            lora_entry['earlyAccessEndsAt'] = early_access_date
+                        
+                        # LoRA exists on Civitai, process its information
+                        if 'files' in civitai_info:
+                            # Find the model file (type="Model") in the files list
+                            model_file = next((file for file in civitai_info.get('files', []) 
+                                            if file.get('type') == 'Model'), None)
+                            
+                            if model_file and recipe_scanner:
+                                sha256 = model_file.get('hashes', {}).get('SHA256', '')
+                                if sha256:
+                                    lora_scanner = recipe_scanner._lora_scanner
+                                    exists_locally = lora_scanner.has_lora_hash(sha256)
+                                    if exists_locally:
+                                        local_path = lora_scanner.get_lora_path_by_hash(sha256)
+                                        lora_entry['existsLocally'] = True
+                                        lora_entry['localPath'] = local_path
+                                        lora_entry['file_name'] = os.path.splitext(os.path.basename(local_path))[0]
+                                    else:
+                                        # For missing LoRAs, get file_name from model_file.name
+                                        file_name = model_file.get('name', '')
+                                        lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else ''
+                                
+                                lora_entry['hash'] = sha256
+                                lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
+                        
+                        # Get thumbnail URL from first image
+                        if 'images' in civitai_info and civitai_info['images']:
+                            lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
+                        
+                        # Get base model and update counts
+                        current_base_model = civitai_info.get('baseModel', '')
+                        lora_entry['baseModel'] = current_base_model
+                        if current_base_model:
+                            base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
+                        
+                        # Get download URL
+                        lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
+                    else:
+                        # LoRA is deleted from Civitai or not found
+                        lora_entry['isDeleted'] = True
+                        lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                
+                loras.append(lora_entry)
+            
+            # Set base_model to the most common one from civitai_info
+            base_model = None
+            if base_model_counts:
+                base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
+            
+            # Extract generation parameters for recipe metadata
+            gen_params = {}
+            for key in GEN_PARAM_KEYS:
+                if key in metadata:
+                    gen_params[key] = metadata.get(key, '')
+            
+            return {
+                'base_model': base_model,
+                'loras': loras,
+                'gen_params': gen_params,
+                'raw_metadata': metadata
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing standard metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}
+        
+    def _parse_recipe_metadata(self, user_comment: str) -> Dict[str, Any]:
+        """Parse recipe metadata from UserComment"""
+        try:
+            # Split by 'Negative prompt:' to get the prompt
+            parts = user_comment.split('Negative prompt:', 1)
+            prompt = parts[0].strip()
+            
+            # Initialize metadata with prompt
+            metadata = {"prompt": prompt, "loras": [], "checkpoint": None}
+            
+            # Extract additional fields if available
+            if len(parts) > 1:
+                negative_and_params = parts[1]
+                
+                # Extract negative prompt
+                if "Steps:" in negative_and_params:
+                    neg_prompt = negative_and_params.split("Steps:", 1)[0].strip()
+                    metadata["negative_prompt"] = neg_prompt
+                
+                # Extract key-value parameters (Steps, Sampler, CFG scale, etc.)
+                param_pattern = r'([A-Za-z ]+): ([^,]+)'
+                params = re.findall(param_pattern, negative_and_params)
+                for key, value in params:
+                    clean_key = key.strip().lower().replace(' ', '_')
+                    metadata[clean_key] = value.strip()
+            
+            # Extract Civitai resources
+            if 'Civitai resources:' in user_comment:
+                resources_part = user_comment.split('Civitai resources:', 1)[1]
+                if '],' in resources_part:
+                    resources_json = resources_part.split('],', 1)[0] + ']'
+                    try:
+                        resources = json.loads(resources_json)
+                        # Filter loras and checkpoints
+                        for resource in resources:
+                            if resource.get('type') == 'lora':
+                                # 确保 weight 字段被正确保留
+                                lora_entry = resource.copy()
+                                # 如果找不到 weight，默认为 1.0
+                                if 'weight' not in lora_entry:
+                                    lora_entry['weight'] = 1.0
+                                # Ensure modelVersionName is included
+                                if 'modelVersionName' not in lora_entry:
+                                    lora_entry['modelVersionName'] = ''
+                                metadata['loras'].append(lora_entry)
+                            elif resource.get('type') == 'checkpoint':
+                                metadata['checkpoint'] = resource
+                    except json.JSONDecodeError:
+                        pass
+            
+            return metadata
+        except Exception as e:
+            logger.error(f"Error parsing recipe metadata: {e}")
+            return {"prompt": user_comment, "loras": [], "checkpoint": None}
+
+
+class A1111MetadataParser(RecipeMetadataParser):
+    """Parser for images with A1111 metadata format (Lora hashes)"""
+    
+    METADATA_MARKER = r'Lora hashes:'
+    LORA_PATTERN = r'<lora:([^:]+):([^>]+)>'
+    LORA_HASH_PATTERN = r'([^:]+): ([a-f0-9]+)'
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the A1111 metadata format"""
+        return 'Lora hashes:' in user_comment
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from images with A1111 metadata format"""
+        try:
+            # Extract prompt and negative prompt
+            parts = user_comment.split('Negative prompt:', 1)
+            prompt = parts[0].strip()
+            
+            # Initialize metadata
+            metadata = {"prompt": prompt, "loras": []}
+            
+            # Extract negative prompt and parameters
+            if len(parts) > 1:
+                negative_and_params = parts[1]
+                
+                # Extract negative prompt
+                if "Steps:" in negative_and_params:
+                    neg_prompt = negative_and_params.split("Steps:", 1)[0].strip()
+                    metadata["negative_prompt"] = neg_prompt
+                
+                # Extract key-value parameters (Steps, Sampler, CFG scale, etc.)
+                param_pattern = r'([A-Za-z ]+): ([^,]+)'
+                params = re.findall(param_pattern, negative_and_params)
+                for key, value in params:
+                    clean_key = key.strip().lower().replace(' ', '_')
+                    metadata[clean_key] = value.strip()
+            
+            # Extract LoRA information from prompt
+            lora_weights = {}
+            lora_matches = re.findall(self.LORA_PATTERN, prompt)
+            for lora_name, weight in lora_matches:
+                lora_weights[lora_name.strip()] = float(weight.strip())
+            
+            # Remove LoRA patterns from prompt
+            metadata["prompt"] = re.sub(self.LORA_PATTERN, '', prompt).strip()
+            
+            # Extract LoRA hashes
+            lora_hashes = {}
+            if 'Lora hashes:' in user_comment:
+                lora_hash_section = user_comment.split('Lora hashes:', 1)[1].strip()
+                if lora_hash_section.startswith('"'):
+                    lora_hash_section = lora_hash_section[1:].split('"', 1)[0]
+                hash_matches = re.findall(self.LORA_HASH_PATTERN, lora_hash_section)
+                for lora_name, hash_value in hash_matches:
+                    # Remove any leading comma and space from lora name
+                    clean_name = lora_name.strip().lstrip(',').strip()
+                    lora_hashes[clean_name] = hash_value.strip()
+            
+            # Process LoRAs and collect base models
+            base_model_counts = {}
+            loras = []
+            
+            # Process each LoRA with hash and weight
+            for lora_name, hash_value in lora_hashes.items():
+                weight = lora_weights.get(lora_name, 1.0)
+                
+                # Initialize lora entry with default values
+                lora_entry = {
+                    'name': lora_name,
+                    'type': 'lora',
+                    'weight': weight,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': lora_name,
+                    'hash': hash_value,
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+                
+                # Get info from Civitai by hash
+                if civitai_client:
+                    try:
+                        civitai_info = await civitai_client.get_model_by_hash(hash_value)
+                        if civitai_info and civitai_info.get("error") != "Model not found":
+                            # Check if this is an early access lora
+                            if civitai_info.get('earlyAccessEndsAt'):
+                                # Convert earlyAccessEndsAt to a human-readable date
+                                early_access_date = civitai_info.get('earlyAccessEndsAt', '')
+                                lora_entry['isEarlyAccess'] = True
+                                lora_entry['earlyAccessEndsAt'] = early_access_date
+                            
+                            # Get model version ID
+                            lora_entry['id'] = civitai_info.get('id', '')
+                            
+                            # Get model name and version
+                            lora_entry['name'] = civitai_info.get('model', {}).get('name', lora_name)
+                            lora_entry['version'] = civitai_info.get('name', '')
+                            
+                            # Get thumbnail URL
+                            if 'images' in civitai_info and civitai_info['images']:
+                                lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
+                            
+                            # Get base model and update counts
+                            current_base_model = civitai_info.get('baseModel', '')
+                            lora_entry['baseModel'] = current_base_model
+                            if current_base_model:
+                                base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
+                            
+                            # Get download URL
+                            lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
+                            
+                            # Get file name and size from Civitai
+                            if 'files' in civitai_info:
+                                model_file = next((file for file in civitai_info.get('files', []) 
+                                                if file.get('type') == 'Model'), None)
+                                if model_file:
+                                    file_name = model_file.get('name', '')
+                                    lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else lora_name
+                                    lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
+                                    # Update hash to sha256
+                                    lora_entry['hash'] = model_file.get('hashes', {}).get('SHA256', hash_value).lower()
+                                    
+                            # Check if exists locally with sha256 hash
+                            if recipe_scanner and lora_entry['hash']:
+                                lora_scanner = recipe_scanner._lora_scanner
+                                exists_locally = lora_scanner.has_lora_hash(lora_entry['hash'])
+                                if exists_locally:
+                                    lora_cache = await lora_scanner.get_cached_data()
+                                    lora_item = next((item for item in lora_cache.raw_data if item['sha256'] == lora_entry['hash']), None)
+                                    if lora_item:
+                                        lora_entry['existsLocally'] = True
+                                        lora_entry['localPath'] = lora_item['file_path']
+                                        lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
+                                        
+                    except Exception as e:
+                        logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}")
+                
+                loras.append(lora_entry)
+
+            # Set base_model to the most common one from civitai_info
+            base_model = None
+            if base_model_counts:
+                base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
+            
+            # Extract generation parameters for recipe metadata
+            gen_params = {}
+            for key in GEN_PARAM_KEYS:
+                if key in metadata:
+                    gen_params[key] = metadata.get(key, '')
+            
+            # Add model information if available
+            if 'model' in metadata:
+                gen_params['checkpoint'] = metadata['model']
+            
+            return {
+                'base_model': base_model,
+                'loras': loras,
+                'gen_params': gen_params,
+                'raw_metadata': metadata
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing A1111 metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}
+
+
+class RecipeParserFactory:
+    """Factory for creating recipe metadata parsers"""
+    
+    @staticmethod
+    def create_parser(user_comment: str) -> RecipeMetadataParser:
+        """
+        Create appropriate parser based on the user comment content
+        
+        Args:
+            user_comment: The EXIF UserComment string from the image
+            
+        Returns:
+            Appropriate RecipeMetadataParser implementation
+        """
+        if RecipeFormatParser().is_metadata_matching(user_comment):
+            return RecipeFormatParser()
+        elif StandardMetadataParser().is_metadata_matching(user_comment):
+            return StandardMetadataParser()
+        elif A1111MetadataParser().is_metadata_matching(user_comment):
+            return A1111MetadataParser()
+        else:
+            return None

py/utils/utils.py

@@ -0,0 +1,78 @@
+from difflib import SequenceMatcher
+import requests
+import tempfile
+import re
+from bs4 import BeautifulSoup
+
+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 fuzzy_match(text: str, pattern: str, threshold: float = 0.7) -> bool:
+        """
+        Check if text matches pattern using fuzzy matching.
+        Returns True if similarity ratio is above threshold.
+        """
+        if not pattern or not text:
+            return False
+        
+        # Convert both to lowercase for case-insensitive matching
+        text = text.lower()
+        pattern = pattern.lower()
+        
+        # Split pattern into words
+        search_words = pattern.split()
+        
+        # Check each word
+        for word in search_words:
+            # First check if word is a substring (faster)
+            if word in text:
+                continue
+            
+            # If not found as substring, try fuzzy matching
+            # Check if any part of the text matches this word
+            found_match = False
+            for text_part in text.split():
+                ratio = SequenceMatcher(None, text_part, word).ratio()
+                if ratio >= threshold:
+                    found_match = True
+                    break
+                
+            if not found_match:
+                return False
+        
+        # All words found either as substrings or fuzzy matches
+        return True

py/workflow/README.md

@@ -0,0 +1,149 @@
+# ComfyUI Workflow Parser
+
+本模块提供了一个灵活的解析系统，可以从ComfyUI工作流中提取生成参数和LoRA信息。
+
+## 设计理念
+
+工作流解析器基于以下设计原则：
+
+1. **模块化**: 每种节点类型由独立的mapper处理
+2. **可扩展性**: 通过扩展系统轻松添加新的节点类型支持
+3. **回溯**: 通过工作流图的模型输入路径跟踪LoRA节点
+4. **灵活性**: 适应不同的ComfyUI工作流结构
+
+## 主要组件
+
+### 1. NodeMapper
+
+`NodeMapper`是所有节点映射器的基类，定义了如何从工作流中提取节点信息:
+
+```python
+class NodeMapper:
+    def __init__(self, node_type: str, inputs_to_track: List[str]):
+        self.node_type = node_type
+        self.inputs_to_track = inputs_to_track
+    
+    def process(self, node_id: str, node_data: Dict, workflow: Dict, parser) -> Any:
+        # 处理节点的通用逻辑
+        ...
+    
+    def transform(self, inputs: Dict) -> Any:
+        # 由子类覆盖以提供特定转换
+        return inputs
+```
+
+### 2. WorkflowParser
+
+主要解析类，通过跟踪工作流图来提取参数:
+
+```python
+parser = WorkflowParser()
+result = parser.parse_workflow("workflow.json")
+```
+
+### 3. 扩展系统
+
+允许通过添加新的自定义mapper来扩展支持的节点类型:
+
+```python
+# 在py/workflow/ext/中添加自定义mapper模块
+load_extensions()  # 自动加载所有扩展
+```
+
+## 使用方法
+
+### 基本用法
+
+```python
+from workflow.parser import parse_workflow
+
+# 解析工作流并保存结果
+result = parse_workflow("workflow.json", "output.json")
+```
+
+### 自定义解析
+
+```python
+from workflow.parser import WorkflowParser
+from workflow.mappers import register_mapper, load_extensions
+
+# 加载扩展
+load_extensions()
+
+# 创建解析器
+parser = WorkflowParser(load_extensions_on_init=False)  # 不自动加载扩展
+
+# 解析工作流
+result = parser.parse_workflow(workflow_data)
+```
+
+## 扩展系统
+
+### 添加新的节点映射器
+
+在`py/workflow/ext/`目录中创建Python文件，定义从`NodeMapper`继承的类:
+
+```python
+# example_mapper.py
+from ..mappers import NodeMapper
+
+class MyCustomNodeMapper(NodeMapper):
+    def __init__(self):
+        super().__init__(
+            node_type="MyCustomNode",  # 节点的class_type
+            inputs_to_track=["param1", "param2"]  # 要提取的参数
+        )
+    
+    def transform(self, inputs: Dict) -> Any:
+        # 处理提取的参数
+        return {
+            "custom_param": inputs.get("param1", "default")
+        }
+```
+
+扩展系统会自动加载和注册这些映射器。
+
+### LoraManager节点说明
+
+LoraManager相关节点的处理方式:
+
+1. **Lora Loader**: 处理`loras`数组，过滤出`active=true`的条目，和`lora_stack`输入
+2. **Lora Stacker**: 处理`loras`数组和已有的`lora_stack`，构建叠加的LoRA
+3. **TriggerWord Toggle**: 从`toggle_trigger_words`中提取`active=true`的条目
+
+## 输出格式
+
+解析器生成的输出格式如下:
+
+```json
+{
+    "gen_params": {
+        "prompt": "...",
+        "negative_prompt": "",
+        "steps": "25",
+        "sampler": "dpmpp_2m",
+        "scheduler": "beta",
+        "cfg": "1",
+        "seed": "48",
+        "guidance": 3.5,
+        "size": "896x1152",
+        "clip_skip": "2"
+    },
+    "loras": "<lora:name1:0.9> <lora:name2:0.8>"
+}
+```
+
+## 高级用法
+
+### 直接注册映射器
+
+```python
+from workflow.mappers import register_mapper
+from workflow.mappers import NodeMapper
+
+# 创建自定义映射器
+class CustomMapper(NodeMapper):
+    # ...实现映射器
+
+# 注册映射器
+register_mapper(CustomMapper()) 

py/workflow/__init__.py

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

py/workflow/cli.py

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

py/workflow/ext/__init__.py

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

py/workflow/ext/example_mapper.py

@@ -0,0 +1,54 @@
+"""
+Example extension mapper for demonstrating the extension system
+"""
+from typing import Dict, Any
+from ..mappers import NodeMapper
+
+class ExampleNodeMapper(NodeMapper):
+    """Example mapper for custom nodes"""
+    
+    def __init__(self):
+        super().__init__(
+            node_type="ExampleCustomNode",
+            inputs_to_track=["param1", "param2", "image"]
+        )
+    
+    def transform(self, inputs: Dict) -> Dict:
+        """Transform extracted inputs into the desired output format"""
+        result = {}
+        
+        # Extract interesting parameters
+        if "param1" in inputs:
+            result["example_param1"] = inputs["param1"]
+        
+        if "param2" in inputs:
+            result["example_param2"] = inputs["param2"]
+        
+        # You can process the data in any way needed
+        return result
+
+
+class VAEMapperExtension(NodeMapper):
+    """Extension mapper for VAE nodes"""
+    
+    def __init__(self):
+        super().__init__(
+            node_type="VAELoader",
+            inputs_to_track=["vae_name"]
+        )
+    
+    def transform(self, inputs: Dict) -> Dict:
+        """Extract VAE information"""
+        vae_name = inputs.get("vae_name", "")
+        
+        # Remove path prefix if present
+        if "/" in vae_name or "\\" in vae_name:
+            # Get just the filename without path or extension
+            vae_name = vae_name.replace("\\", "/").split("/")[-1]
+            vae_name = vae_name.split(".")[0]  # Remove extension
+            
+        return {"vae": vae_name}
+
+
+# Note: No need to register manually - extensions are automatically registered
+# when the extension system loads this file 

py/workflow/main.py

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

py/workflow/mappers.py

@@ -0,0 +1,428 @@
+"""
+Node mappers for ComfyUI workflow parsing
+"""
+import logging
+import os
+import importlib.util
+import inspect
+from typing import Dict, List, Any, Optional, Union, Type, Callable
+
+logger = logging.getLogger(__name__)
+
+# Global mapper registry
+_MAPPER_REGISTRY: Dict[str, 'NodeMapper'] = {}
+
+class NodeMapper:
+    """Base class for node mappers that define how to extract information from a specific node type"""
+    
+    def __init__(self, node_type: str, inputs_to_track: List[str]):
+        self.node_type = node_type
+        self.inputs_to_track = inputs_to_track
+    
+    def process(self, node_id: str, node_data: Dict, workflow: Dict, parser: 'WorkflowParser') -> Any: # type: ignore
+        """Process the node and extract relevant information"""
+        result = {}
+        for input_name in self.inputs_to_track:
+            if input_name in node_data.get("inputs", {}):
+                input_value = node_data["inputs"][input_name]
+                # Check if input is a reference to another node's output
+                if isinstance(input_value, list) and len(input_value) == 2:
+                    # Format is [node_id, output_slot]
+                    try:
+                        ref_node_id, output_slot = input_value
+                        # Convert node_id to string if it's an integer
+                        if isinstance(ref_node_id, int):
+                            ref_node_id = str(ref_node_id)
+                        
+                        # Recursively process the referenced node
+                        ref_value = parser.process_node(ref_node_id, workflow)
+                        
+                        # Store the processed value
+                        if ref_value is not None:
+                            result[input_name] = ref_value
+                        else:
+                            # If we couldn't get a value from the reference, store the raw value
+                            result[input_name] = input_value
+                    except Exception as e:
+                        logger.error(f"Error processing reference in node {node_id}, input {input_name}: {e}")
+                        # If we couldn't process the reference, store the raw value
+                        result[input_name] = input_value
+                else:
+                    # Direct value
+                    result[input_name] = input_value
+        
+        # Apply any transformations
+        return self.transform(result)
+    
+    def transform(self, inputs: Dict) -> Any:
+        """Transform the extracted inputs - override in subclasses"""
+        return inputs
+
+
+class KSamplerMapper(NodeMapper):
+    """Mapper for KSampler nodes"""
+    
+    def __init__(self):
+        super().__init__(
+            node_type="KSampler",
+            inputs_to_track=["seed", "steps", "cfg", "sampler_name", "scheduler", 
+                             "denoise", "positive", "negative", "latent_image",
+                             "model", "clip_skip"]
+        )
+    
+    def transform(self, inputs: Dict) -> Dict:
+        result = {
+            "seed": str(inputs.get("seed", "")),
+            "steps": str(inputs.get("steps", "")),
+            "cfg": str(inputs.get("cfg", "")),
+            "sampler": inputs.get("sampler_name", ""),
+            "scheduler": inputs.get("scheduler", ""),
+        }
+        
+        # Process positive prompt
+        if "positive" in inputs:
+            result["prompt"] = inputs["positive"]
+        
+        # Process negative prompt
+        if "negative" in inputs:
+            result["negative_prompt"] = inputs["negative"]
+        
+        # Get dimensions from latent image
+        if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
+            width = inputs["latent_image"].get("width", 0)
+            height = inputs["latent_image"].get("height", 0)
+            if width and height:
+                result["size"] = f"{width}x{height}"
+        
+        # Add clip_skip if present
+        if "clip_skip" in inputs:
+            result["clip_skip"] = str(inputs.get("clip_skip", ""))
+            
+        return result
+
+
+class EmptyLatentImageMapper(NodeMapper):
+    """Mapper for EmptyLatentImage nodes"""
+    
+    def __init__(self):
+        super().__init__(
+            node_type="EmptyLatentImage",
+            inputs_to_track=["width", "height", "batch_size"]
+        )
+    
+    def transform(self, inputs: Dict) -> Dict:
+        width = inputs.get("width", 0)
+        height = inputs.get("height", 0)
+        return {"width": width, "height": height, "size": f"{width}x{height}"}
+
+
+class EmptySD3LatentImageMapper(NodeMapper):
+    """Mapper for EmptySD3LatentImage nodes"""
+    
+    def __init__(self):
+        super().__init__(
+            node_type="EmptySD3LatentImage",
+            inputs_to_track=["width", "height", "batch_size"]
+        )
+    
+    def transform(self, inputs: Dict) -> Dict:
+        width = inputs.get("width", 0)
+        height = inputs.get("height", 0)
+        return {"width": width, "height": height, "size": f"{width}x{height}"}
+
+
+class CLIPTextEncodeMapper(NodeMapper):
+    """Mapper for CLIPTextEncode nodes"""
+    
+    def __init__(self):
+        super().__init__(
+            node_type="CLIPTextEncode",
+            inputs_to_track=["text", "clip"]
+        )
+    
+    def transform(self, inputs: Dict) -> Any:
+        # Simply return the text
+        return inputs.get("text", "")
+
+
+class LoraLoaderMapper(NodeMapper):
+    """Mapper for LoraLoader nodes"""
+    
+    def __init__(self):
+        super().__init__(
+            node_type="Lora Loader (LoraManager)",
+            inputs_to_track=["loras", "lora_stack"]
+        )
+    
+    def transform(self, inputs: Dict) -> Dict:
+        # Fallback to loras array if text field doesn't exist or is invalid
+        loras_data = inputs.get("loras", [])
+        lora_stack = inputs.get("lora_stack", {}).get("lora_stack", [])
+        
+        # Process loras array - filter active entries
+        lora_texts = []
+        
+        # Check if loras_data is a list or a dict with __value__ key (new format)
+        if isinstance(loras_data, dict) and "__value__" in loras_data:
+            loras_list = loras_data["__value__"]
+        elif isinstance(loras_data, list):
+            loras_list = loras_data
+        else:
+            loras_list = []
+            
+        # Process each active lora entry
+        for lora in loras_list:
+            logger.info(f"Lora: {lora}, active: {lora.get('active')}")
+            if isinstance(lora, dict) and lora.get("active", False):
+                lora_name = lora.get("name", "")
+                strength = lora.get("strength", 1.0)
+                lora_texts.append(f"<lora:{lora_name}:{strength}>")
+        
+        # Process lora_stack if it exists and is a valid format (list of tuples)
+        if lora_stack and isinstance(lora_stack, list):
+            # If lora_stack is a reference to another node ([node_id, output_slot]),
+            # we don't process it here as it's already been processed recursively
+            if len(lora_stack) == 2 and isinstance(lora_stack[0], (str, int)) and isinstance(lora_stack[1], int):
+                # This is a reference to another node, already processed
+                pass
+            else:
+                # Format each entry from the stack (assuming it's a list of tuples)
+                for stack_entry in lora_stack:
+                    lora_name = stack_entry[0]
+                    strength = stack_entry[1]
+                    lora_texts.append(f"<lora:{lora_name}:{strength}>")
+        
+        # Join with spaces
+        combined_text = " ".join(lora_texts)
+            
+        return {"loras": combined_text}
+
+
+class LoraStackerMapper(NodeMapper):
+    """Mapper for LoraStacker nodes"""
+    
+    def __init__(self):
+        super().__init__(
+            node_type="Lora Stacker (LoraManager)",
+            inputs_to_track=["loras", "lora_stack"]
+        )
+    
+    def transform(self, inputs: Dict) -> Dict:
+        loras_data = inputs.get("loras", [])
+        result_stack = []
+        
+        # Handle existing stack entries
+        existing_stack = []
+        lora_stack_input = inputs.get("lora_stack", [])
+        
+        # Handle different formats of lora_stack
+        if isinstance(lora_stack_input, dict) and "lora_stack" in lora_stack_input:
+            # Format from another LoraStacker node
+            existing_stack = lora_stack_input["lora_stack"]
+        elif isinstance(lora_stack_input, list):
+            # Direct list format or reference format [node_id, output_slot]
+            if len(lora_stack_input) == 2 and isinstance(lora_stack_input[0], (str, int)) and isinstance(lora_stack_input[1], int):
+                # This is likely a reference that was already processed
+                pass
+            else:
+                # Regular list of tuples/entries
+                existing_stack = lora_stack_input
+        
+        # Add existing entries first
+        if existing_stack:
+            result_stack.extend(existing_stack)
+        
+        # Process loras array - filter active entries
+        # Check if loras_data is a list or a dict with __value__ key (new format)
+        if isinstance(loras_data, dict) and "__value__" in loras_data:
+            loras_list = loras_data["__value__"]
+        elif isinstance(loras_data, list):
+            loras_list = loras_data
+        else:
+            loras_list = []
+            
+        # Process each active lora entry
+        for lora in loras_list:
+            if isinstance(lora, dict) and lora.get("active", False):
+                lora_name = lora.get("name", "")
+                strength = float(lora.get("strength", 1.0))
+                result_stack.append((lora_name, strength))
+        
+        return {"lora_stack": result_stack}
+
+
+class JoinStringsMapper(NodeMapper):
+    """Mapper for JoinStrings nodes"""
+    
+    def __init__(self):
+        super().__init__(
+            node_type="JoinStrings",
+            inputs_to_track=["string1", "string2", "delimiter"]
+        )
+    
+    def transform(self, inputs: Dict) -> str:
+        string1 = inputs.get("string1", "")
+        string2 = inputs.get("string2", "")
+        delimiter = inputs.get("delimiter", "")
+        return f"{string1}{delimiter}{string2}"
+
+
+class StringConstantMapper(NodeMapper):
+    """Mapper for StringConstant and StringConstantMultiline nodes"""
+    
+    def __init__(self):
+        super().__init__(
+            node_type="StringConstantMultiline",
+            inputs_to_track=["string"]
+        )
+    
+    def transform(self, inputs: Dict) -> str:
+        return inputs.get("string", "")
+
+
+class TriggerWordToggleMapper(NodeMapper):
+    """Mapper for TriggerWordToggle nodes"""
+    
+    def __init__(self):
+        super().__init__(
+            node_type="TriggerWord Toggle (LoraManager)",
+            inputs_to_track=["toggle_trigger_words"]
+        )
+    
+    def transform(self, inputs: Dict) -> str:
+        toggle_data = inputs.get("toggle_trigger_words", [])
+
+        # check if toggle_words is a list or a dict with __value__ key (new format)
+        if isinstance(toggle_data, dict) and "__value__" in toggle_data:
+            toggle_words = toggle_data["__value__"]
+        elif isinstance(toggle_data, list):
+            toggle_words = toggle_data
+        else:
+            toggle_words = []
+        
+        # Filter active trigger words
+        active_words = []
+        for item in toggle_words:
+            if isinstance(item, dict) and item.get("active", False):
+                word = item.get("text", "")
+                if word and not word.startswith("__dummy"):
+                    active_words.append(word)
+        
+        # Join with commas
+        result = ", ".join(active_words)
+        return result
+
+
+class FluxGuidanceMapper(NodeMapper):
+    """Mapper for FluxGuidance nodes"""
+    
+    def __init__(self):
+        super().__init__(
+            node_type="FluxGuidance",
+            inputs_to_track=["guidance", "conditioning"]
+        )
+    
+    def transform(self, inputs: Dict) -> Dict:
+        result = {}
+        
+        # Handle guidance parameter
+        if "guidance" in inputs:
+            result["guidance"] = inputs["guidance"]
+        
+        # Handle conditioning (the prompt text)
+        if "conditioning" in inputs:
+            conditioning = inputs["conditioning"]
+            if isinstance(conditioning, str):
+                result["prompt"] = conditioning
+            else:
+                result["prompt"] = "Unknown prompt"
+        
+        return result
+
+
+# =============================================================================
+# Mapper Registry Functions
+# =============================================================================
+
+def register_mapper(mapper: NodeMapper) -> None:
+    """Register a node mapper in the global registry"""
+    _MAPPER_REGISTRY[mapper.node_type] = mapper
+    logger.debug(f"Registered mapper for node type: {mapper.node_type}")
+
+def get_mapper(node_type: str) -> Optional[NodeMapper]:
+    """Get a mapper for the specified node type"""
+    return _MAPPER_REGISTRY.get(node_type)
+
+def get_all_mappers() -> Dict[str, NodeMapper]:
+    """Get all registered mappers"""
+    return _MAPPER_REGISTRY.copy()
+
+def register_default_mappers() -> None:
+    """Register all default mappers"""
+    default_mappers = [
+        KSamplerMapper(),
+        EmptyLatentImageMapper(),
+        EmptySD3LatentImageMapper(),
+        CLIPTextEncodeMapper(),
+        LoraLoaderMapper(),
+        LoraStackerMapper(),
+        JoinStringsMapper(),
+        StringConstantMapper(),
+        TriggerWordToggleMapper(),
+        FluxGuidanceMapper()
+    ]
+    
+    for mapper in default_mappers:
+        register_mapper(mapper)
+
+# =============================================================================
+# Extension Loading
+# =============================================================================
+
+def load_extensions(ext_dir: str = None) -> None:
+    """
+    Load mapper extensions from the specified directory
+    
+    Each Python file in the directory will be loaded, and any NodeMapper subclasses
+    defined in those files will be automatically registered.
+    """
+    # Use default path if none provided
+    if ext_dir is None:
+        # Get the directory of this file
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        ext_dir = os.path.join(current_dir, 'ext')
+    
+    # Ensure the extension directory exists
+    if not os.path.exists(ext_dir):
+        os.makedirs(ext_dir, exist_ok=True)
+        logger.info(f"Created extension directory: {ext_dir}")
+        return
+    
+    # Load each Python file in the extension directory
+    for filename in os.listdir(ext_dir):
+        if filename.endswith('.py') and not filename.startswith('_'):
+            module_path = os.path.join(ext_dir, filename)
+            module_name = f"workflow.ext.{filename[:-3]}"  # Remove .py
+            
+            try:
+                # Load the module
+                spec = importlib.util.spec_from_file_location(module_name, module_path)
+                if spec and spec.loader:
+                    module = importlib.util.module_from_spec(spec)
+                    spec.loader.exec_module(module)
+                    
+                    # Find all NodeMapper subclasses in the module
+                    for name, obj in inspect.getmembers(module):
+                        if (inspect.isclass(obj) and issubclass(obj, NodeMapper) 
+                                and obj != NodeMapper and hasattr(obj, 'node_type')):
+                            # Instantiate and register the mapper
+                            mapper = obj()
+                            register_mapper(mapper)
+                            logger.info(f"Loaded extension mapper: {mapper.node_type} from {filename}")
+            
+            except Exception as e:
+                logger.warning(f"Error loading extension {filename}: {e}")
+
+
+# Initialize the registry with default mappers
+register_default_mappers() 

py/workflow/parser.py

@@ -0,0 +1,196 @@
+"""
+Main workflow parser implementation for ComfyUI
+"""
+import json
+import logging
+from typing import Dict, List, Any, Optional, Union, Set
+from .mappers import get_mapper, get_all_mappers, load_extensions
+from .utils import (
+    load_workflow, save_output, find_node_by_type,
+    trace_model_path
+)
+
+logger = logging.getLogger(__name__)
+
+class WorkflowParser:
+    """Parser for ComfyUI workflows"""
+    
+    def __init__(self, load_extensions_on_init: bool = True):
+        """Initialize the parser with mappers"""
+        self.processed_nodes: Set[str] = set()  # Track processed nodes to avoid cycles
+        self.node_results_cache: Dict[str, Any] = {}  # Cache for processed node results
+        
+        # Load extensions if requested
+        if load_extensions_on_init:
+            load_extensions()
+    
+    def process_node(self, node_id: str, workflow: Dict) -> Any:
+        """Process a single node and extract relevant information"""
+        # Return cached result if available
+        if node_id in self.node_results_cache:
+            return self.node_results_cache[node_id]
+            
+        # Check if we're in a cycle
+        if node_id in self.processed_nodes:
+            return None
+        
+        # Mark this node as being processed (to detect cycles)
+        self.processed_nodes.add(node_id)
+        
+        if node_id not in workflow:
+            self.processed_nodes.remove(node_id)
+            return None
+        
+        node_data = workflow[node_id]
+        node_type = node_data.get("class_type")
+        
+        result = None
+        mapper = get_mapper(node_type)
+        if mapper:
+            try:
+                result = mapper.process(node_id, node_data, workflow, self)
+                # Cache the result
+                self.node_results_cache[node_id] = result
+            except Exception as e:
+                logger.error(f"Error processing node {node_id} of type {node_type}: {e}", exc_info=True)
+                # Return a partial result or None depending on how we want to handle errors
+                result = {}
+        
+        # Remove node from processed set to allow it to be processed again in a different context
+        self.processed_nodes.remove(node_id)
+        return result
+    
+    def collect_loras_from_model(self, model_input: List, workflow: Dict) -> str:
+        """Collect loras information from the model node chain"""
+        if not isinstance(model_input, list) or len(model_input) != 2:
+            return ""
+            
+        model_node_id, _ = model_input
+        # Convert node_id to string if it's an integer
+        if isinstance(model_node_id, int):
+            model_node_id = str(model_node_id)
+            
+        # Process the model node
+        model_result = self.process_node(model_node_id, workflow)
+        
+        # If this is a Lora Loader node, return the loras text
+        if model_result and isinstance(model_result, dict) and "loras" in model_result:
+            return model_result["loras"]
+            
+        # If not a lora loader, check the node's inputs for a model connection
+        node_data = workflow.get(model_node_id, {})
+        inputs = node_data.get("inputs", {})
+        
+        # If this node has a model input, follow that path
+        if "model" in inputs and isinstance(inputs["model"], list):
+            return self.collect_loras_from_model(inputs["model"], workflow)
+            
+        return ""
+    
+    def parse_workflow(self, workflow_data: Union[str, Dict], output_path: Optional[str] = None) -> Dict:
+        """
+        Parse the workflow and extract generation parameters
+        
+        Args:
+            workflow_data: The workflow data as a dictionary or a file path
+            output_path: Optional path to save the output JSON
+            
+        Returns:
+            Dictionary containing extracted parameters
+        """
+        # Load workflow from file if needed
+        if isinstance(workflow_data, str):
+            workflow = load_workflow(workflow_data)
+        else:
+            workflow = workflow_data
+            
+        # Reset the processed nodes tracker and cache
+        self.processed_nodes = set()
+        self.node_results_cache = {}
+        
+        # Find the KSampler node
+        ksampler_node_id = find_node_by_type(workflow, "KSampler")
+        if not ksampler_node_id:
+            logger.warning("No KSampler node found in workflow")
+            return {}
+        
+        # Start parsing from the KSampler node
+        result = {
+            "gen_params": {},
+            "loras": ""
+        }
+        
+        # Process KSampler node to extract parameters
+        ksampler_result = self.process_node(ksampler_node_id, workflow)
+        if ksampler_result:
+            # Process the result
+            for key, value in ksampler_result.items():
+                # Special handling for the positive prompt from FluxGuidance
+                if key == "positive" and isinstance(value, dict):
+                    # Extract guidance value
+                    if "guidance" in value:
+                        result["gen_params"]["guidance"] = value["guidance"]
+                    
+                    # Extract prompt
+                    if "prompt" in value:
+                        result["gen_params"]["prompt"] = value["prompt"]
+                else:
+                    # Normal handling for other values
+                    result["gen_params"][key] = value
+        
+        # Process the positive prompt node if it exists and we don't have a prompt yet
+        if "prompt" not in result["gen_params"] and "positive" in ksampler_result:
+            positive_value = ksampler_result.get("positive")
+            if isinstance(positive_value, str):
+                result["gen_params"]["prompt"] = positive_value
+        
+        # Manually check for FluxGuidance if we don't have guidance value
+        if "guidance" not in result["gen_params"]:
+            flux_node_id = find_node_by_type(workflow, "FluxGuidance")
+            if flux_node_id:
+                # Get the direct input from the node
+                node_inputs = workflow[flux_node_id].get("inputs", {})
+                if "guidance" in node_inputs:
+                    result["gen_params"]["guidance"] = node_inputs["guidance"]
+        
+        # Extract loras from the model input of KSampler
+        ksampler_node = workflow.get(ksampler_node_id, {})
+        ksampler_inputs = ksampler_node.get("inputs", {})
+        if "model" in ksampler_inputs and isinstance(ksampler_inputs["model"], list):
+            loras_text = self.collect_loras_from_model(ksampler_inputs["model"], workflow)
+            if loras_text:
+                result["loras"] = loras_text
+        
+        # Handle standard ComfyUI names vs our output format
+        if "cfg" in result["gen_params"]:
+            result["gen_params"]["cfg_scale"] = result["gen_params"].pop("cfg")
+            
+        # Add clip_skip = 2 to match reference output if not already present
+        if "clip_skip" not in result["gen_params"]:
+            result["gen_params"]["clip_skip"] = "2"
+        
+        # Ensure the prompt is a string and not a nested dictionary
+        if "prompt" in result["gen_params"] and isinstance(result["gen_params"]["prompt"], dict):
+            if "prompt" in result["gen_params"]["prompt"]:
+                result["gen_params"]["prompt"] = result["gen_params"]["prompt"]["prompt"]
+        
+        # Save the result if requested
+        if output_path:
+            save_output(result, output_path)
+            
+        return result
+
+
+def parse_workflow(workflow_path: str, output_path: Optional[str] = None) -> Dict:
+    """
+    Parse a ComfyUI workflow file and extract generation parameters
+    
+    Args:
+        workflow_path: Path to the workflow JSON file
+        output_path: Optional path to save the output JSON
+        
+    Returns:
+        Dictionary containing extracted parameters
+    """
+    parser = WorkflowParser()
+    return parser.parse_workflow(workflow_path, output_path) 

py/workflow/test.py

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

py/workflow/utils.py

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

pyproject.toml

@@ -1,13 +1,17 @@
 [project]
 name = "comfyui-lora-manager"
 description = "LoRA Manager for ComfyUI - Access it at http://localhost:8188/loras for managing LoRA models with previews and metadata integration."
-version = "0.7.35-beta"
+version = "0.8.0"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",
     "jinja2", 
     "safetensors",
-    "watchdog"
+    "watchdog",
+    "beautifulsoup4",
+    "piexif",
+    "Pillow",
+    "requests"
 ]
 
 [project.urls]

refs/civitai_api_model_by_versionId.json

@@ -0,0 +1,101 @@
+{
+    "id": 1387174,
+    "modelId": 1231067,
+    "name": "v1.0",
+    "createdAt": "2025-02-08T11:15:47.197Z",
+    "updatedAt": "2025-02-08T11:29:04.526Z",
+    "status": "Published",
+    "publishedAt": "2025-02-08T11:29:04.487Z",
+    "trainedWords": [
+        "ppstorybook"
+    ],
+    "trainingStatus": null,
+    "trainingDetails": null,
+    "baseModel": "Flux.1 D",
+    "baseModelType": null,
+    "earlyAccessEndsAt": null,
+    "earlyAccessConfig": null,
+    "description": null,
+    "uploadType": "Created",
+    "usageControl": "Download",
+    "air": "urn:air:flux1:lora:civitai:1231067@1387174",
+    "stats": {
+        "downloadCount": 1436,
+        "ratingCount": 0,
+        "rating": 0,
+        "thumbsUpCount": 316
+    },
+    "model": {
+        "name": "Vivid Impressions Storybook Style",
+        "type": "LORA",
+        "nsfw": false,
+        "poi": false
+    },
+    "files": [
+        {
+            "id": 1289799,
+            "sizeKB": 18829.1484375,
+            "name": "pp-storybook_rank2_bf16.safetensors",
+            "type": "Model",
+            "pickleScanResult": "Success",
+            "pickleScanMessage": "No Pickle imports",
+            "virusScanResult": "Success",
+            "virusScanMessage": null,
+            "scannedAt": "2025-02-08T11:21:04.247Z",
+            "metadata": {
+                "format": "SafeTensor",
+                "size": null,
+                "fp": null
+            },
+            "hashes": {
+                "AutoV1": "F414C813",
+                "AutoV2": "9753338AB6",
+                "SHA256": "9753338AB693CA82BF89ED77A5D1912879E40051463EC6E330FB9866CE798668",
+                "CRC32": "A65AE7B3",
+                "BLAKE3": "A5F8AB95AC2486345E4ACCAE541FF19D97ED53EFB0A7CC9226636975A0437591",
+                "AutoV3": "34A22376739D"
+            },
+            "primary": true,
+            "downloadUrl": "https://civitai.com/api/download/models/1387174"
+        }
+    ],
+    "images": [
+        {
+            "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/42b875cf-c62b-41fa-a349-383b7f074351/width=832/56547310.jpeg",
+            "nsfwLevel": 1,
+            "width": 832,
+            "height": 1216,
+            "hash": "U5IiO6s-4Vn+0~EO^5xa00VsL#IU_O?E7yWC",
+            "type": "image",
+            "metadata": {
+                "hash": "U5IiO6s-4Vn+0~EO^5xa00VsL#IU_O?E7yWC",
+                "size": 1361590,
+                "width": 832,
+                "height": 1216
+            },
+            "meta": {
+                "Size": "832x1216",
+                "seed": 1116375220995209,
+                "Model": "flux_dev_fp8",
+                "steps": 23,
+                "hashes": {
+                    "model": ""
+                },
+                "prompt": "ppstorybook,A dreamy bunny hopping across a rainbow bridge, with fluffy clouds surrounding it and tiny birds flying alongside, rendered in a magical, soft-focus style with pastel hues and glowing accents.",
+                "Version": "ComfyUI",
+                "sampler": "DPM++ 2M",
+                "cfgScale": 3.5,
+                "clipSkip": 1,
+                "resources": [],
+                "Model hash": ""
+            },
+            "availability": "Public",
+            "hasMeta": true,
+            "hasPositivePrompt": true,
+            "onSite": false,
+            "remixOfId": null
+        }
+        // more images here
+    ],
+    "downloadUrl": "https://civitai.com/api/download/models/1387174"
+}

refs/flux_output.json

@@ -0,0 +1,15 @@
+{
+    "loras": "<lora:pp-enchanted-whimsy:0.9> <lora:ral-frctlgmtry_flux:1> <lora:pp-storybook_rank2_bf16:0.8>",
+    "gen_params": {
+        "prompt": "in the style of ppWhimsy, ral-frctlgmtry, ppstorybook,Stylized geek cat artist with glasses and a paintbrush, smiling at the viewer while holding a sign that reads 'Stay tuned!', solid white background",
+        "negative_prompt": "",
+        "steps": "25",
+        "sampler": "dpmpp_2m",
+        "scheduler": "beta",
+        "cfg": "1",
+        "seed": "48",
+        "guidance": 3.5,
+        "size": "896x1152",
+        "clip_skip": "2"
+    }
+}

refs/flux_prompt.json

@@ -0,0 +1,314 @@
+{
+    "6": {
+        "inputs": {
+            "text": [
+                "46",
+                0
+            ],
+            "clip": [
+                "58",
+                1
+            ]
+        },
+        "class_type": "CLIPTextEncode",
+        "_meta": {
+            "title": "CLIP Text Encode (Positive Prompt)"
+        }
+    },
+    "8": {
+        "inputs": {
+            "samples": [
+                "31",
+                0
+            ],
+            "vae": [
+                "39",
+                0
+            ]
+        },
+        "class_type": "VAEDecode",
+        "_meta": {
+            "title": "VAE Decode"
+        }
+    },
+    "27": {
+        "inputs": {
+            "width": 896,
+            "height": 1152,
+            "batch_size": 1
+        },
+        "class_type": "EmptySD3LatentImage",
+        "_meta": {
+            "title": "EmptySD3LatentImage"
+        }
+    },
+    "31": {
+        "inputs": {
+            "seed": 44,
+            "steps": 25,
+            "cfg": 1,
+            "sampler_name": "dpmpp_2m",
+            "scheduler": "beta",
+            "denoise": 1,
+            "model": [
+                "58",
+                0
+            ],
+            "positive": [
+                "35",
+                0
+            ],
+            "negative": [
+                "33",
+                0
+            ],
+            "latent_image": [
+                "27",
+                0
+            ]
+        },
+        "class_type": "KSampler",
+        "_meta": {
+            "title": "KSampler"
+        }
+    },
+    "33": {
+        "inputs": {
+            "text": "",
+            "clip": [
+                "58",
+                1
+            ]
+        },
+        "class_type": "CLIPTextEncode",
+        "_meta": {
+            "title": "CLIP Text Encode (Negative Prompt)"
+        }
+    },
+    "35": {
+        "inputs": {
+            "guidance": 3.5,
+            "conditioning": [
+                "6",
+                0
+            ]
+        },
+        "class_type": "FluxGuidance",
+        "_meta": {
+            "title": "FluxGuidance"
+        }
+    },
+    "37": {
+        "inputs": {
+            "unet_name": "flux\\flux1-dev-fp8-e4m3fn.safetensors",
+            "weight_dtype": "fp8_e4m3fn_fast"
+        },
+        "class_type": "UNETLoader",
+        "_meta": {
+            "title": "Load Diffusion Model"
+        }
+    },
+    "38": {
+        "inputs": {
+            "clip_name1": "t5xxl_fp8_e4m3fn.safetensors",
+            "clip_name2": "clip_l.safetensors",
+            "type": "flux",
+            "device": "default"
+        },
+        "class_type": "DualCLIPLoader",
+        "_meta": {
+            "title": "DualCLIPLoader"
+        }
+    },
+    "39": {
+        "inputs": {
+            "vae_name": "flux1\\ae.safetensors"
+        },
+        "class_type": "VAELoader",
+        "_meta": {
+            "title": "Load VAE"
+        }
+    },
+    "46": {
+        "inputs": {
+            "string1": [
+                "59",
+                0
+            ],
+            "string2": [
+                "51",
+                0
+            ],
+            "delimiter": ","
+        },
+        "class_type": "JoinStrings",
+        "_meta": {
+            "title": "Join Strings"
+        }
+    },
+    "50": {
+        "inputs": {
+            "images": [
+                "8",
+                0
+            ]
+        },
+        "class_type": "PreviewImage",
+        "_meta": {
+            "title": "Preview Image"
+        }
+    },
+    "51": {
+        "inputs": {
+            "string": "Stylized geek cat artist with glasses and a paintbrush, smiling at the viewer while holding a sign that reads 'Stay tuned!', solid white background",
+            "strip_newlines": true
+        },
+        "class_type": "StringConstantMultiline",
+        "_meta": {
+            "title": "positive"
+        }
+    },
+    "58": {
+        "inputs": {
+            "text": "<lora:pp-enchanted-whimsy:0.9><lora:ral-frctlgmtry_flux:1><lora:pp-storybook_rank2_bf16:0.8>",
+            "loras": [
+                {
+                    "name": "pp-enchanted-whimsy",
+                    "strength": "0.90",
+                    "active": false
+                },
+                {
+                    "name": "ral-frctlgmtry_flux",
+                    "strength": "0.85",
+                    "active": false
+                },
+                {
+                    "name": "pp-storybook_rank2_bf16",
+                    "strength": 0.8,
+                    "active": true
+                },
+                {
+                    "name": "__dummy_item1__",
+                    "strength": 0,
+                    "active": false,
+                    "_isDummy": true
+                },
+                {
+                    "name": "__dummy_item2__",
+                    "strength": 0,
+                    "active": false,
+                    "_isDummy": true
+                }
+            ],
+            "model": [
+                "37",
+                0
+            ],
+            "clip": [
+                "38",
+                0
+            ]
+        },
+        "class_type": "Lora Loader (LoraManager)",
+        "_meta": {
+            "title": "Lora Loader (LoraManager)"
+        }
+    },
+    "59": {
+        "inputs": {
+            "group_mode": "",
+            "toggle_trigger_words": [
+                {
+                    "text": "ppstorybook",
+                    "active": false
+                },
+                {
+                    "text": "__dummy_item__",
+                    "active": false,
+                    "_isDummy": true
+                },
+                {
+                    "text": "__dummy_item__",
+                    "active": false,
+                    "_isDummy": true
+                }
+            ],
+            "orinalMessage": "ppstorybook",
+            "trigger_words": [
+                "58",
+                2
+            ]
+        },
+        "class_type": "TriggerWord Toggle (LoraManager)",
+        "_meta": {
+            "title": "TriggerWord Toggle (LoraManager)"
+        }
+    },
+    "61": {
+        "inputs": {
+            "add_noise": "enable",
+            "noise_seed": 1111423448930884,
+            "steps": 20,
+            "cfg": 8,
+            "sampler_name": "euler",
+            "scheduler": "normal",
+            "start_at_step": 0,
+            "end_at_step": 10000,
+            "return_with_leftover_noise": "disable"
+        },
+        "class_type": "KSamplerAdvanced",
+        "_meta": {
+            "title": "KSampler (Advanced)"
+        }
+    },
+    "62": {
+        "inputs": {
+            "sigmas": [
+                "63",
+                0
+            ]
+        },
+        "class_type": "SamplerCustomAdvanced",
+        "_meta": {
+            "title": "SamplerCustomAdvanced"
+        }
+    },
+    "63": {
+        "inputs": {
+            "scheduler": "normal",
+            "steps": 20,
+            "denoise": 1
+        },
+        "class_type": "BasicScheduler",
+        "_meta": {
+            "title": "BasicScheduler"
+        }
+    },
+    "64": {
+        "inputs": {
+            "seed": 1089899258710474,
+            "steps": 20,
+            "cfg": 8,
+            "sampler_name": "euler",
+            "scheduler": "normal",
+            "denoise": 1
+        },
+        "class_type": "KSampler",
+        "_meta": {
+            "title": "KSampler"
+        }
+    },
+    "65": {
+        "inputs": {
+            "text": ",Stylized geek cat artist with glasses and a paintbrush, smiling at the viewer while holding a sign that reads 'Stay tuned!', solid white background",
+            "anything": [
+                "46",
+                0
+            ]
+        },
+        "class_type": "easy showAnything",
+        "_meta": {
+            "title": "Show Any"
+        }
+    }
+}

refs/jpeg_civitai_exif_userComment_example

@@ -0,0 +1,29 @@
+a dynamic and dramatic digital artwork featuring a stylized anthropomorphic white tiger with striking yellow eyes. The tiger is depicted in a powerful stance, wielding a katana with one hand raised above its head. Its fur is detailed with black stripes, and its mane flows wildly, blending with the stormy background. The scene is set amidst swirling dark clouds and flashes of lightning, enhancing the sense of movement and energy. The composition is vertical, with the tiger positioned centrally, creating a sense of depth and intensity. The color palette is dominated by shades of blue, gray, and white, with bright highlights from the lightning. The overall style is reminiscent of fantasy or manga art, with a focus on dynamic action and dramatic lighting.
+Negative prompt:
+Steps: 30, Sampler: Undefined, CFG scale: 3.5, Seed: 90300501, Size: 832x1216, Clip skip: 2, Created Date: 2025-03-05T13:51:18.1770234Z, Civitai resources: [{"type":"checkpoint","modelVersionId":691639,"modelName":"FLUX","modelVersionName":"Dev"},{"type":"lora","weight":0.4,"modelVersionId":1202162,"modelName":"Velvet\u0027s Mythic Fantasy Styles | Flux \u002B Pony \u002B illustrious","modelVersionName":"Flux Gothic Lines"},{"type":"lora","weight":0.8,"modelVersionId":1470588,"modelName":"Velvet\u0027s Mythic Fantasy Styles | Flux \u002B Pony \u002B illustrious","modelVersionName":"Flux Retro"},{"type":"lora","weight":0.75,"modelVersionId":746484,"modelName":"Elden Ring - Yoshitaka Amano","modelVersionName":"V1"},{"type":"lora","weight":0.2,"modelVersionId":914935,"modelName":"Ink-style","modelVersionName":"ink-dynamic"},{"type":"lora","weight":0.2,"modelVersionId":1189379,"modelName":"Painterly Fantasy by ChronoKnight - [FLUX \u0026 IL]","modelVersionName":"FLUX"},{"type":"lora","weight":0.2,"modelVersionId":757030,"modelName":"Mezzotint Artstyle for Flux - by Ethanar","modelVersionName":"V1"}], Civitai metadata: {}
+
+<lora:ck-shadow-circuit-IL:0.78>,
+masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject,
+dynamic angle, dutch angle, from below, epic half body portrait, gritty, wabi sabi, looking at viewer, woman is a geisha, parted lips,
+holographic skin, holofoil glitter, faint, glowing, ethereal, neon hair, glowing hair, otherworldly glow, she is dangerous,
+<lora:ck-nc-cyberpunk-IL-000011:0.4>
+<lora:ck-neon-retrowave-IL:0.2>
+ <lora:ck-yoneyama-mai-IL-000014:0.4>
+Negative prompt: score_6, score_5, score_4, bad quality, worst quality, worst detail, sketch, censorship, furry, window, headphones,
+Steps: 30, Sampler: Euler a, Schedule type: Simple, CFG scale: 7, Seed: 1405717592, Size: 832x1216, Model hash: 1ad6ca7f70, Model: waiNSFWIllustrious_v100, Denoising strength: 0.35, Hires CFG Scale: 5, Hires upscale: 1.3, Hires steps: 20, Hires upscaler: 4x-AnimeSharp, Lora hashes: "ck-shadow-circuit-IL: 88e247aa8c3d, ck-nc-cyberpunk-IL-000011: 935e6755554c, ck-neon-retrowave-IL: edafb9df7da1, ck-yoneyama-mai-IL-000014: 1b9305692a2e", Version: f2.0.1v1.10.1-1.10.1, Diffusion in Low Bits: Automatic (fp16 LoRA)
+
+masterpiece, best quality,high quality, newest, highres,8K,HDR,absurdres, 1girl, solo, steampunk aesthetic, mechanical monocle, long trench coat, leather gloves, brass accessories, intricate clockwork rifle, aiming at viewer, wind-blown scarf, high boots, fingerless gloves, pocket watch, corset, brown and gold color scheme, industrial cityscape, smoke and gears, atmospheric lighting, depth of field, dynamic pose, dramatic composition, detailed background, foreshortening, detailed background, dynamic pose, dynamic composition,dutch angle, detailed backgroud,foreshortening,blurry edges <lora:iLLMythAn1m3Style:1> MythAn1m3
+Negative prompt: worst quality, normal quality, anatomical nonsense, bad anatomy,interlocked fingers, extra fingers,watermark,simple background, loli,
+Steps: 35, Sampler: DPM++ 2M SDE, Schedule type: Karras, CFG scale: 4, Seed: 3537159932, Size: 1072x1376, Model hash: c364bbdae9, Model: waiNSFWIllustrious_v110, Clip skip: 2, ADetailer model: face_yolov8n.pt, ADetailer confidence: 0.3, ADetailer dilate erode: 4, ADetailer mask blur: 4, ADetailer denoising strength: 0.4, ADetailer inpaint only masked: True, ADetailer inpaint padding: 32, ADetailer version: 24.8.0, Lora hashes: "iLLMythAn1m3Style: d3480076057b", Version: f2.0.1v1.10.1-previous-519-g44eb4ea8, Module 1: sdxl.vae
+
+Masterpiece, best quality, high quality, newest, highres, 8K, HDR, absurdres, 1girl, solo, futuristic warrior, sleek exosuit with glowing energy cores, long braided hair flowing behind, gripping a high-tech bow with an energy arrow drawn, standing on a floating platform overlooking a massive space station, planets and nebulae in the distance, soft glow from distant stars, cinematic depth, foreshortening, dynamic pose, dramatic sci-fi lighting.
+Negative prompt: worst quality, normal quality, anatomical nonsense, bad anatomy,interlocked fingers, extra fingers,watermark,simple background, loli,
+Steps: 20, Sampler: euler_ancestral_karras, CFG scale: 8.0, Seed: 691121152183439, Model: il\waiNSFWIllustrious_v110.safetensors, Model hash: c3688ee04c, Lora_0 Model name: iLLMythAn1m3Style.safetensors, Lora_0 Model hash: ba7a040786, Lora_0 Strength model: 1.0, Lora_0 Strength clip: 1.0, Hashes: {"model": "c3688ee04c", "lora:iLLMythAn1m3Style": "ba7a040786"}
+
+Masterpiece, best quality, high quality, newest, highres, 8K, HDR, absurdres, 1boy, solo, gothic horror, pale vampire lord in regal, intricately detailed robes, crimson eyes glowing under the dim candlelight of a grand but decayed castle hall, holding a silver goblet filled with an unknown substance, a massive stained-glass window shattered behind him, cold mist rolling in, dramatic lighting, dark yet elegant aesthetic, foreshortening, cinematic perspective.
+Negative prompt: worst quality, normal quality, anatomical nonsense, bad anatomy,interlocked fingers, extra fingers,watermark,simple background, loli,
+Steps: 20, Sampler: euler_ancestral_karras, CFG scale: 8.0, Seed: 290117945770094, Model: il\waiNSFWIllustrious_v110.safetensors, Model hash: c3688ee04c, Lora_0 Model name: iLLMythAn1m3Style.safetensors, Lora_0 Model hash: ba7a040786, Lora_0 Strength model: 0.6, Lora_0 Strength clip: 0.7000000000000001, Hashes: {"model": "c3688ee04c", "lora:iLLMythAn1m3Style": "ba7a040786"}
+
+bo-exposure, An impressionistic oil painting in the style of J.M.W. Turner, depicting a ghostly ship sailing through a sea of swirling golden mist. The waves crash and dissolve into abstract, fiery strokes of orange and deep indigo, blurring the line between ocean and sky. The ship appears almost ethereal, as if drifting between worlds, lost in the ever-changing tides of memory and myth. The dynamic brushstrokes capture the relentless power of nature and the fleeting essence of time.
+Negative prompt:
+Steps: 25, Sampler: DPM++ 2M, CFG scale: 3.5, Seed: 1024252061321625, Size: 832x1216, Clip skip: 1, Model hash: , Model: flux_dev, Hashes: {"model": ""}, Version: ComfyUI

refs/output.json

@@ -0,0 +1,13 @@
+{
+    "loras": "<lora:ck-neon-retrowave-IL-000012:0.8> <lora:aorunIllstrious:1> <lora:ck-shadow-circuit-IL-000012:0.78> <lora:MoriiMee_Gothic_Niji_Style_Illustrious_r1:0.45> <lora:ck-nc-cyberpunk-IL-000011:0.4>",
+    "gen_params": {
+        "prompt": "in the style of ck-rw, aorun, scales, makeup, bare shoulders, pointy ears, dress, claws, in the style of cksc, artist:moriimee, in the style of cknc, masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject, close up, stylized, in gold and neon shades, wabi sabi, 1girl, rainbow angel wings, looking at viewer, dynamic angle, from below, from side, relaxing",
+        "negative_prompt": "bad quality, worst quality, worst detail, sketch ,signature, watermark, patreon logo, nsfw",
+        "steps": "20",
+        "sampler": "euler_ancestral",
+        "cfg_scale": "8",
+        "seed": "241",
+        "size": "832x1216",
+        "clip_skip": "2"
+    }
+}

refs/prompt.json

@@ -0,0 +1,297 @@
+{
+    "3": {
+        "inputs": {
+            "seed": 241,
+            "steps": 20,
+            "cfg": 8,
+            "sampler_name": "euler_ancestral",
+            "scheduler": "karras",
+            "denoise": 1,
+            "model": [
+                "56",
+                0
+            ],
+            "positive": [
+                "6",
+                0
+            ],
+            "negative": [
+                "7",
+                0
+            ],
+            "latent_image": [
+                "5",
+                0
+            ]
+        },
+        "class_type": "KSampler",
+        "_meta": {
+            "title": "KSampler"
+        }
+    },
+    "4": {
+        "inputs": {
+            "ckpt_name": "il\\waiNSFWIllustrious_v110.safetensors"
+        },
+        "class_type": "CheckpointLoaderSimple",
+        "_meta": {
+            "title": "Load Checkpoint"
+        }
+    },
+    "5": {
+        "inputs": {
+            "width": 832,
+            "height": 1216,
+            "batch_size": 1
+        },
+        "class_type": "EmptyLatentImage",
+        "_meta": {
+            "title": "Empty Latent Image"
+        }
+    },
+    "6": {
+        "inputs": {
+            "text": [
+                "22",
+                0
+            ],
+            "clip": [
+                "56",
+                1
+            ]
+        },
+        "class_type": "CLIPTextEncode",
+        "_meta": {
+            "title": "CLIP Text Encode (Prompt)"
+        }
+    },
+    "7": {
+        "inputs": {
+            "text": "bad quality, worst quality, worst detail, sketch ,signature, watermark, patreon logo, nsfw",
+            "clip": [
+                "56",
+                1
+            ]
+        },
+        "class_type": "CLIPTextEncode",
+        "_meta": {
+            "title": "CLIP Text Encode (Prompt)"
+        }
+    },
+    "8": {
+        "inputs": {
+            "samples": [
+                "3",
+                0
+            ],
+            "vae": [
+                "4",
+                2
+            ]
+        },
+        "class_type": "VAEDecode",
+        "_meta": {
+            "title": "VAE Decode"
+        }
+    },
+    "14": {
+        "inputs": {
+            "images": [
+                "8",
+                0
+            ]
+        },
+        "class_type": "PreviewImage",
+        "_meta": {
+            "title": "Preview Image"
+        }
+    },
+    "19": {
+        "inputs": {
+            "stop_at_clip_layer": -2,
+            "clip": [
+                "4",
+                1
+            ]
+        },
+        "class_type": "CLIPSetLastLayer",
+        "_meta": {
+            "title": "CLIP Set Last Layer"
+        }
+    },
+    "21": {
+        "inputs": {
+            "string": "masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject, close up, stylized, in gold and neon shades, wabi sabi, 1girl, rainbow angel wings, looking at viewer, dynamic angle, from below, from side, relaxing",
+            "strip_newlines": false
+        },
+        "class_type": "StringConstantMultiline",
+        "_meta": {
+            "title": "positive"
+        }
+    },
+    "22": {
+        "inputs": {
+            "string1": [
+                "55",
+                0
+            ],
+            "string2": [
+                "21",
+                0
+            ],
+            "delimiter": ", "
+        },
+        "class_type": "JoinStrings",
+        "_meta": {
+            "title": "Join Strings"
+        }
+    },
+    "55": {
+        "inputs": {
+            "group_mode": true,
+            "toggle_trigger_words": [
+                {
+                    "text": "in the style of ck-rw",
+                    "active": true
+                },
+                {
+                    "text": "in the style of cksc",
+                    "active": true
+                },
+                {
+                    "text": "artist:moriimee",
+                    "active": true
+                },
+                {
+                    "text": "__dummy_item__",
+                    "active": false,
+                    "_isDummy": true
+                },
+                {
+                    "text": "__dummy_item__",
+                    "active": false,
+                    "_isDummy": true
+                }
+            ],
+            "orinalMessage": "in the style of ck-rw,, in the style of cksc,, artist:moriimee",
+            "trigger_words": [
+                "56",
+                2
+            ]
+        },
+        "class_type": "TriggerWord Toggle (LoraManager)",
+        "_meta": {
+            "title": "TriggerWord Toggle (LoraManager)"
+        }
+    },
+    "56": {
+        "inputs": {
+            "text": "<lora:ck-shadow-circuit-IL-000012:0.78> <lora:MoriiMee_Gothic_Niji_Style_Illustrious_r1:0.45> <lora:ck-nc-cyberpunk-IL-000011:0.4>",
+            "loras": [
+                {
+                    "name": "ck-shadow-circuit-IL-000012",
+                    "strength": 0.78,
+                    "active": true
+                },
+                {
+                    "name": "MoriiMee_Gothic_Niji_Style_Illustrious_r1",
+                    "strength": 0.45,
+                    "active": true
+                },
+                {
+                    "name": "ck-nc-cyberpunk-IL-000011",
+                    "strength": 0.4,
+                    "active": false
+                },
+                {
+                    "name": "__dummy_item1__",
+                    "strength": 0,
+                    "active": false,
+                    "_isDummy": true
+                },
+                {
+                    "name": "__dummy_item2__",
+                    "strength": 0,
+                    "active": false,
+                    "_isDummy": true
+                }
+            ],
+            "model": [
+                "4",
+                0
+            ],
+            "clip": [
+                "4",
+                1
+            ],
+            "lora_stack": [
+                "57",
+                0
+            ]
+        },
+        "class_type": "Lora Loader (LoraManager)",
+        "_meta": {
+            "title": "Lora Loader (LoraManager)"
+        }
+    },
+    "57": {
+        "inputs": {
+            "text": "<lora:aorunIllstrious:1>",
+            "loras": [
+                {
+                    "name": "aorunIllstrious",
+                    "strength": "0.90",
+                    "active": false
+                },
+                {
+                    "name": "__dummy_item1__",
+                    "strength": 0,
+                    "active": false,
+                    "_isDummy": true
+                },
+                {
+                    "name": "__dummy_item2__",
+                    "strength": 0,
+                    "active": false,
+                    "_isDummy": true
+                }
+            ],
+            "lora_stack": [
+                "59",
+                0
+            ]
+        },
+        "class_type": "Lora Stacker (LoraManager)",
+        "_meta": {
+            "title": "Lora Stacker (LoraManager)"
+        }
+    },
+    "59": {
+        "inputs": {
+            "text": "<lora:ck-neon-retrowave-IL-000012:0.8>",
+            "loras": [
+                {
+                    "name": "ck-neon-retrowave-IL-000012",
+                    "strength": 0.8,
+                    "active": true
+                },
+                {
+                    "name": "__dummy_item1__",
+                    "strength": 0,
+                    "active": false,
+                    "_isDummy": true
+                },
+                {
+                    "name": "__dummy_item2__",
+                    "strength": 0,
+                    "active": false,
+                    "_isDummy": true
+                }
+            ]
+        },
+        "class_type": "Lora Stacker (LoraManager)",
+        "_meta": {
+            "title": "Lora Stacker (LoraManager)"
+        }
+    }
+}

refs/recipe.json

@@ -0,0 +1,82 @@
+{
+    "id": "0448c06d-de1b-46ab-975c-c5aa60d90dbc",
+    "file_path": "D:/Workspace/ComfyUI/models/loras/recipes/0448c06d-de1b-46ab-975c-c5aa60d90dbc.jpg",
+    "title": "a mysterious, steampunk-inspired character standing in a dramatic pose",
+    "modified": 1741837612.3931093,
+    "created_date": 1741492786.5581934,
+    "base_model": "Flux.1 D",
+    "loras": [
+        {
+            "file_name": "ChronoDivinitiesFlux_r1",
+            "hash": "ddbc5abd00db46ad464f5e3ca85f8f7121bc14b594d6785f441d9b002fffe66a",
+            "strength": 0.8,
+            "modelVersionId": 1438879,
+            "modelName": "Chrono Divinities - By HailoKnight",
+            "modelVersionName": "Flux"
+        },
+        {
+            "file_name": "flux.1_lora_flyway_ink-dynamic",
+            "hash": "4b4f3b469a0d5d3a04a46886abfa33daa37a905db070ccfbd10b345c6fb00eff",
+            "strength": 0.2,
+            "modelVersionId": 914935,
+            "modelName": "Ink-style",
+            "modelVersionName": "ink-dynamic"
+        },
+        {
+            "file_name": "ck-painterly-fantasy-000017",
+            "hash": "48c67064e2936aec342580a2a729d91d75eb818e45ecf993b9650cc66c94c420",
+            "strength": 0.2,
+            "modelVersionId": 1189379,
+            "modelName": "Painterly Fantasy by ChronoKnight - [FLUX & IL]",
+            "modelVersionName": "FLUX"
+        },
+        {
+            "file_name": "RetroAnimeFluxV1",
+            "hash": "8f43c31b6c3238ac44195c970d511d759c5893bddd00f59f42b8fe51e8e76fa0",
+            "strength": 0.8,
+            "modelVersionId": 806265,
+            "modelName": "Retro Anime Flux - Style",
+            "modelVersionName": "v1.0"
+        },
+        {
+            "file_name": "Mezzotint_Artstyle_for_Flux_-_by_Ethanar",
+            "hash": "e6961502769123bf23a66c5c5298d76264fd6b9610f018319a0ccb091bfc308e",
+            "strength": 0.2,
+            "modelVersionId": 757030,
+            "modelName": "Mezzotint Artstyle for Flux - by Ethanar",
+            "modelVersionName": "V1"
+        },
+        {
+            "file_name": "FluxMythG0thicL1nes",
+            "hash": "ecb03595de62bd6183a0dd2b38bea35669fd4d509f4bbae5aa0572cfb7ef4279",
+            "strength": 0.4,
+            "modelVersionId": 1202162,
+            "modelName": "Velvet's Mythic Fantasy Styles | Flux + Pony + illustrious",
+            "modelVersionName": "Flux Gothic Lines"
+        },
+        {
+            "file_name": "Elden_Ring_-_Yoshitaka_Amano",
+            "hash": "c660c4c55320be7206cb6a917c59d8da3953cc07169fe10bda833a54ec0024f9",
+            "strength": 0.75,
+            "modelVersionId": 746484,
+            "modelName": "Elden Ring - Yoshitaka Amano",
+            "modelVersionName": "V1"
+        }
+    ],
+    "gen_params": {
+        "prompt": "a mysterious, steampunk-inspired character standing in a dramatic pose. The character is dressed in a long, intricately detailed dark coat with ornate patterns, a wide-brimmed hat, and leather boots. The face is partially obscured by the hat's shadow, adding to the enigmatic aura. The background showcases a large, antique clock with Roman numerals, surrounded by dynamic lightning and ethereal white birds, enhancing the fantastical atmosphere. The color palette is dominated by dark tones with striking contrasts of white and blue lightning, creating a sense of tension and energy. The overall composition is vertical, with the character centrally positioned, exuding a sense of power and mystery. hkchrono",
+        "negative_prompt": "",
+        "checkpoint": {
+            "type": "checkpoint",
+            "modelVersionId": 691639,
+            "modelName": "FLUX",
+            "modelVersionName": "Dev"
+        },
+        "steps": "30",
+        "sampler": "Undefined",
+        "cfg_scale": "3.5",
+        "seed": "1472903449",
+        "size": "832x1216",
+        "clip_skip": "2"
+    }
+}

refs/test_output.txt

@@ -0,0 +1,294 @@
+Loading workflow from D:\Workspace\ComfyUI\custom_nodes\ComfyUI-Lora-Manager\refs\prompt.json
+Expected output from D:\Workspace\ComfyUI\custom_nodes\ComfyUI-Lora-Manager\refs\output.json
+
+Expected output:
+{
+  "loras": "<lora:ck-neon-retrowave-IL-000012:0.8> <lora:aorunIllstrious:1> <lora:ck-shadow-circuit-IL-000012:0.78> <lora:MoriiMee_Gothic_Niji_Style_Illustrious_r1:0.45> <lora:ck-nc-cyberpunk-IL-000011:0.4>",
+  "gen_params": {
+    "prompt": "in the style of ck-rw, aorun, scales, makeup, bare shoulders, pointy ears, dress, claws, in the style of cksc, artist:moriimee, in the style of cknc, masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject, close up, stylized, in gold and neon shades, wabi sabi, 1girl, rainbow angel wings, looking at viewer, dynamic angle, from below, from side, relaxing",
+    "negative_prompt": "bad quality, worst quality, worst detail, sketch ,signature, watermark, patreon logo, nsfw",
+    "steps": "20",
+    "sampler": "euler_ancestral",
+    "cfg_scale": "8",
+    "seed": "241",
+    "size": "832x1216",
+    "clip_skip": "2"
+  }
+}
+
+Sampler node:
+{
+  "inputs": {
+    "seed": 241,
+    "steps": 20,
+    "cfg": 8,
+    "sampler_name": "euler_ancestral",
+    "scheduler": "karras",
+    "denoise": 1,
+    "model": [
+      "56",
+      0
+    ],
+    "positive": [
+      "6",
+      0
+    ],
+    "negative": [
+      "7",
+      0
+    ],
+    "latent_image": [
+      "5",
+      0
+    ]
+  },
+  "class_type": "KSampler",
+  "_meta": {
+    "title": "KSampler"
+  }
+}
+
+Extracted parameters:
+seed: 241
+steps: 20
+cfg_scale: 8
+
+Positive node (6):
+{
+  "inputs": {
+    "text": [
+      "22",
+      0
+    ],
+    "clip": [
+      "56",
+      1
+    ]
+  },
+  "class_type": "CLIPTextEncode",
+  "_meta": {
+    "title": "CLIP Text Encode (Prompt)"
+  }
+}
+
+Text node (22):
+{
+  "inputs": {
+    "string1": [
+      "55",
+      0
+    ],
+    "string2": [
+      "21",
+      0
+    ],
+    "delimiter": ", "
+  },
+  "class_type": "JoinStrings",
+  "_meta": {
+    "title": "Join Strings"
+  }
+}
+
+String1 node (55):
+{
+  "inputs": {
+    "group_mode": true,
+    "toggle_trigger_words": [
+      {
+        "text": "in the style of ck-rw",
+        "active": true
+      },
+      {
+        "text": "aorun, scales, makeup, bare shoulders, pointy ears",
+        "active": true
+      },
+      {
+        "text": "dress",
+        "active": true
+      },
+      {
+        "text": "claws",
+        "active": true
+      },
+      {
+        "text": "in the style of cksc",
+        "active": true
+      },
+      {
+        "text": "artist:moriimee",
+        "active": true
+      },
+      {
+        "text": "in the style of cknc",
+        "active": true
+      },
+      {
+        "text": "__dummy_item__",
+        "active": false,
+        "_isDummy": true
+      },
+      {
+        "text": "__dummy_item__",
+        "active": false,
+        "_isDummy": true
+      }
+    ],
+    "orinalMessage": "in the style of ck-rw,, aorun, scales, makeup, bare shoulders, pointy ears,, dress,, claws,, in the style of cksc,, artist:moriimee,, in the style of cknc",
+    "trigger_words": [
+      "56",
+      2
+    ]
+  },
+  "class_type": "TriggerWord Toggle (LoraManager)",
+  "_meta": {
+    "title": "TriggerWord Toggle (LoraManager)"
+  }
+}
+
+String2 node (21):
+{
+  "inputs": {
+    "string": "masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject, close up, stylized, in gold and neon shades, wabi sabi, 1girl, rainbow angel wings, looking at viewer, dynamic angle, from below, from side, relaxing",
+    "strip_newlines": false
+  },
+  "class_type": "StringConstantMultiline",
+  "_meta": {
+    "title": "positive"
+  }
+}
+
+Negative node (7):
+{
+  "inputs": {
+    "text": "bad quality, worst quality, worst detail, sketch ,signature, watermark, patreon logo, nsfw",
+    "clip": [
+      "56",
+      1
+    ]
+  },
+  "class_type": "CLIPTextEncode",
+  "_meta": {
+    "title": "CLIP Text Encode (Prompt)"
+  }
+}
+
+LoRA nodes (3):
+
+LoRA node 56:
+{
+  "inputs": {
+    "text": "<lora:ck-shadow-circuit-IL-000012:0.78> <lora:MoriiMee_Gothic_Niji_Style_Illustrious_r1:0.45> <lora:ck-nc-cyberpunk-IL-000011:0.4>",
+    "loras": [
+      {
+        "name": "ck-shadow-circuit-IL-000012",
+        "strength": 0.78,
+        "active": true
+      },
+      {
+        "name": "MoriiMee_Gothic_Niji_Style_Illustrious_r1",
+        "strength": 0.45,
+        "active": true
+      },
+      {
+        "name": "ck-nc-cyberpunk-IL-000011",
+        "strength": 0.4,
+        "active": true
+      },
+      {
+        "name": "__dummy_item1__",
+        "strength": 0,
+        "active": false,
+        "_isDummy": true
+      },
+      {
+        "name": "__dummy_item2__",
+        "strength": 0,
+        "active": false,
+        "_isDummy": true
+      }
+    ],
+    "model": [
+      "4",
+      0
+    ],
+    "clip": [
+      "4",
+      1
+    ],
+    "lora_stack": [
+      "57",
+      0
+    ]
+  },
+  "class_type": "Lora Loader (LoraManager)",
+  "_meta": {
+    "title": "Lora Loader (LoraManager)"
+  }
+}
+
+LoRA node 57:
+{
+  "inputs": {
+    "text": "<lora:aorunIllstrious:1>",
+    "loras": [
+      {
+        "name": "aorunIllstrious",
+        "strength": "0.90",
+        "active": true
+      },
+      {
+        "name": "__dummy_item1__",
+        "strength": 0,
+        "active": false,
+        "_isDummy": true
+      },
+      {
+        "name": "__dummy_item2__",
+        "strength": 0,
+        "active": false,
+        "_isDummy": true
+      }
+    ],
+    "lora_stack": [
+      "59",
+      0
+    ]
+  },
+  "class_type": "Lora Stacker (LoraManager)",
+  "_meta": {
+    "title": "Lora Stacker (LoraManager)"
+  }
+}
+
+LoRA node 59:
+{
+  "inputs": {
+    "text": "<lora:ck-neon-retrowave-IL-000012:0.8>",
+    "loras": [
+      {
+        "name": "ck-neon-retrowave-IL-000012",
+        "strength": 0.8,
+        "active": true
+      },
+      {
+        "name": "__dummy_item1__",
+        "strength": 0,
+        "active": false,
+        "_isDummy": true
+      },
+      {
+        "name": "__dummy_item2__",
+        "strength": 0,
+        "active": false,
+        "_isDummy": true
+      }
+    ]
+  },
+  "class_type": "Lora Stacker (LoraManager)",
+  "_meta": {
+    "title": "Lora Stacker (LoraManager)"
+  }
+}
+
+Test completed.

requirements.txt

@@ -1,4 +1,8 @@
 aiohttp
 jinja2
 safetensors
-watchdog
+watchdog
+beautifulsoup4
+piexif
+Pillow
+requests

simple_test.py

@@ -0,0 +1,25 @@
+import json
+from py.workflow.parser import WorkflowParser
+
+# Load workflow data
+with open('refs/prompt.json', 'r') as f:
+    workflow_data = json.load(f)
+
+# Parse workflow
+parser = WorkflowParser()
+try:
+    # Parse the workflow
+    result = parser.parse_workflow(workflow_data)
+    print("Parsing successful!")
+    
+    # Print each component separately
+    print("\nGeneration Parameters:")
+    for k, v in result.get("gen_params", {}).items():
+        print(f"  {k}: {v}")
+    
+    print("\nLoRAs:")
+    print(result.get("loras", ""))
+except Exception as e:
+    print(f"Error parsing workflow: {e}")
+    import traceback
+    traceback.print_exc() 

static/css/base.css

@@ -1,6 +1,8 @@
-/* 强制显示滚动条，防止页面跳动 */
-html {
-    overflow-y: scroll;
+html, body {
+    margin: 0;
+    padding: 0;
+    height: 100%;
+    overflow: hidden; /* Disable default scrolling */
 }
 
 /* 针对Firefox */
@@ -16,6 +18,7 @@ html {
 
 ::-webkit-scrollbar-track {
     background: transparent;
+    margin-top: 0;
 }
 
 ::-webkit-scrollbar-thumb {
@@ -35,6 +38,7 @@ html {
     --lora-border: oklch(90% 0.02 256 / 0.15);
     --lora-text: oklch(95% 0.02 256);
     --lora-error: oklch(75% 0.32 29);
+    --lora-warning: oklch(75% 0.25 80); /* Add warning color for deleted LoRAs */
     
     /* Spacing Scale */
     --space-1: calc(8px * 1);
@@ -43,6 +47,7 @@ html {
     
     /* Z-index Scale */
     --z-base: 10;
+    --z-header: 100;
     --z-modal: 1000;
     --z-overlay: 2000;
 
@@ -64,11 +69,14 @@ html {
     --lora-surface: oklch(25% 0.02 256 / 0.98);
     --lora-border: oklch(90% 0.02 256 / 0.15);
     --lora-text: oklch(98% 0.02 256);
+    --lora-warning: oklch(75% 0.25 80); /* Add warning color for dark theme too */
 }
 
 body {
-    margin: 0;
     font-family: 'Segoe UI', sans-serif;
     background: var(--bg-color);
     color: var(--text-color);
+    display: flex;
+    flex-direction: column;
+    padding-top: 0; /* Remove the padding-top */
 }

static/css/components/bulk.css

@@ -262,6 +262,83 @@
     background: var(--lora-accent);
 }
 
+/* NSFW Level Selector */
+.nsfw-level-selector {
+    position: fixed;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-base);
+    padding: 16px;
+    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.2);
+    z-index: var(--z-modal);
+    width: 300px;
+    display: none;
+}
+
+.nsfw-level-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 16px;
+}
+
+.nsfw-level-header h3 {
+    margin: 0;
+    font-size: 16px;
+    font-weight: 500;
+}
+
+.close-nsfw-selector {
+    background: transparent;
+    border: none;
+    color: var(--text-color);
+    cursor: pointer;
+    padding: 4px;
+    border-radius: var(--border-radius-xs);
+}
+
+.close-nsfw-selector:hover {
+    background: var(--border-color);
+}
+
+.current-level {
+    margin-bottom: 12px;
+    padding: 8px;
+    background: var(--bg-color);
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+}
+
+.nsfw-level-options {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 8px;
+}
+
+.nsfw-level-btn {
+    flex: 1 0 calc(33% - 8px);
+    padding: 8px;
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    border: 1px solid var(--border-color);
+    color: var(--text-color);
+    cursor: pointer;
+    transition: all 0.2s ease;
+}
+
+.nsfw-level-btn:hover {
+    background: var(--lora-border);
+}
+
+.nsfw-level-btn.active {
+    background: var(--lora-accent);
+    color: white;
+    border-color: var(--lora-accent);
+}
+
 /* Mobile optimizations */
 @media (max-width: 768px) {
     .selected-thumbnails-strip {

static/css/components/card.css

@@ -20,6 +20,9 @@
     aspect-ratio: 896/1152;
     max-width: 260px; /* Adjusted from 320px to fit 5 cards */
     margin: 0 auto;
+    cursor: pointer; /* Added from recipe-card */
+    display: flex; /* Added from recipe-card */
+    flex-direction: column; /* Added from recipe-card */
 }
 
 .lora-card:hover {
@@ -57,6 +60,97 @@
     width: 100%;
     height: 100%;
     object-fit: cover;
+    object-position: center top; /* Align the top of the image with the top of the container */
+}
+
+/* NSFW Content Blur */
+.card-preview.blurred img,
+.card-preview.blurred video {
+    filter: blur(25px);
+}
+
+.nsfw-overlay {
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    z-index: 2;
+    pointer-events: none;
+}
+
+.nsfw-warning {
+    text-align: center;
+    color: white;
+    background: rgba(0, 0, 0, 0.6);
+    padding: var(--space-2);
+    border-radius: var(--border-radius-base);
+    backdrop-filter: blur(4px);
+    max-width: 80%;
+    pointer-events: auto;
+}
+
+.nsfw-warning p {
+    margin: 0 0 var(--space-1);
+    font-weight: bold;
+    font-size: 1.1em;
+    text-shadow: 1px 1px 1px rgba(0, 0, 0, 0.5);
+}
+
+.toggle-blur-btn {
+    position: absolute;
+    left: var(--space-1);
+    top: var(--space-1);
+    background: rgba(0, 0, 0, 0.5);
+    border: none;
+    border-radius: 50%;
+    width: 24px;
+    height: 24px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    color: white;
+    cursor: pointer;
+    z-index: 3;
+    transition: background-color 0.2s, transform 0.2s;
+}
+
+.toggle-blur-btn:hover {
+    background: rgba(0, 0, 0, 0.7);
+    transform: scale(1.1);
+}
+
+.toggle-blur-btn i {
+    font-size: 0.9em;
+}
+
+.show-content-btn {
+    background: var(--lora-accent);
+    color: white;
+    border: none;
+    border-radius: var(--border-radius-xs);
+    padding: 4px var(--space-1);
+    cursor: pointer;
+    font-size: 0.9em;
+    transition: background-color 0.2s, transform 0.2s;
+}
+
+.show-content-btn:hover {
+    background: oklch(58% 0.28 256);
+    transform: scale(1.05);
+}
+
+/* Adjust base model label positioning when toggle button is present */
+.base-model-label.with-toggle {
+    margin-left: 28px; /* Make room for the toggle button */
+}
+
+/* Ensure card actions remain clickable */
+.card-header .card-actions {
+    z-index: 3;
 }
 
 .card-footer {
@@ -183,4 +277,55 @@
     border-radius: var(--border-radius-xs);
     backdrop-filter: blur(2px);
     font-size: 0.85em;
+}
+
+/* Recipe specific elements - migrated from recipe-card.css */
+.recipe-indicator {
+    position: absolute;
+    top: 6px;
+    left: 8px;
+    width: 24px;
+    height: 24px;
+    background: var(--lora-primary);
+    border-radius: 50%;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    color: white;
+    font-weight: bold;
+    z-index: 2;
+}
+
+.base-model-wrapper {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    margin-left: 32px; /* For accommodating the recipe indicator */
+}
+
+.lora-count {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+    background: rgba(255, 255, 255, 0.2);
+    padding: 2px 8px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.85em;
+    position: relative;
+}
+
+.lora-count.ready {
+    background: rgba(46, 204, 113, 0.3);
+}
+
+.lora-count.missing {
+    background: rgba(231, 76, 60, 0.3);
+}
+
+.placeholder-message {
+    grid-column: 1 / -1;
+    text-align: center;
+    padding: 2rem;
+    background: var(--lora-surface-alt);
+    border-radius: var(--border-radius-base);
 }

static/css/components/download-modal.css

@@ -23,12 +23,6 @@
     color: var(--text-color);
 }
 
-.error-message {
-    color: var(--lora-error);
-    font-size: 0.9em;
-    margin-top: 4px;
-}
-
 /* Version List Styles */
 .version-list {
     max-height: 400px;
@@ -104,6 +98,7 @@
 .version-info {
     display: flex;
     flex-wrap: wrap;
+    flex-direction: row !important;
     gap: 8px;
     align-items: center;
     font-size: 0.9em;
@@ -130,50 +125,6 @@
     gap: 4px;
 }
 
-/* Local Version Badge */
-.local-badge {
-    display: inline-flex;
-    align-items: center;
-    background: var(--lora-accent);
-    color: var(--lora-text);
-    padding: 4px 8px;
-    border-radius: var(--border-radius-xs);
-    font-size: 0.8em;
-    font-weight: 500;
-    white-space: nowrap;
-    flex-shrink: 0;
-    position: relative;
-}
-
-.local-badge i {
-    margin-right: 4px;
-    font-size: 0.9em;
-}
-
-.local-path {
-    display: none;
-    position: absolute;
-    top: 100%;
-    right: 0;
-    background: var(--card-bg);
-    border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-xs);
-    padding: var(--space-1);
-    margin-top: 4px;
-    font-size: 0.9em;
-    color: var(--text-color);
-    white-space: normal;
-    word-break: break-all;
-    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
-    z-index: 1;
-    min-width: 200px;
-    max-width: 300px;
-}
-
-.local-badge:hover .local-path {
-    display: block;
-}
-
 /* Folder Browser Styles */
 .folder-browser {
     border: 1px solid var(--border-color);
@@ -251,47 +202,4 @@
 .version-item.exists-locally {
     background: oklch(var(--lora-accent) / 0.05);
     border-left: 4px solid var(--lora-accent);
-}
-
-.local-badge {
-    display: inline-flex;
-    align-items: center;
-    background: var(--lora-accent);
-    color: var(--lora-text);
-    padding: 4px 8px;
-    border-radius: var(--border-radius-xs);
-    font-size: 0.8em;
-    font-weight: 500;
-    white-space: nowrap;
-    flex-shrink: 0;
-    position: relative;
-}
-
-.local-badge i {
-    margin-right: 4px;
-    font-size: 0.9em;
-}
-
-.local-path {
-    display: none;
-    position: absolute;
-    top: 100%;
-    right: 0;
-    background: var(--card-bg);
-    border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-xs);
-    padding: var(--space-1);
-    margin-top: 4px;
-    font-size: 0.9em;
-    color: var(--text-color);
-    white-space: normal;
-    word-break: break-all;
-    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
-    z-index: 1;
-    min-width: 200px;
-    max-width: 300px;
-}
-
-.local-badge:hover .local-path {
-    display: block;
-} 
+}

static/css/components/header.css

@@ -0,0 +1,177 @@
+.app-header {
+  background: var(--card-bg);
+  border-bottom: 1px solid var(--border-color);
+  position: fixed;
+  top: 0;
+  z-index: var(--z-header);
+  height: 48px; /* Reduced height */
+  width: 100%;
+  box-shadow: 0 1px 3px rgba(0,0,0,0.05);
+}
+
+.header-container {
+  max-width: 1400px;
+  margin: 0 auto;
+  padding: 0 15px;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  height: 100%;
+}
+
+/* Logo and title styling */
+.header-branding {
+  display: flex;
+  align-items: center;
+  flex-shrink: 0;
+}
+
+.logo-link {
+  display: flex;
+  align-items: center;
+  text-decoration: none;
+  color: var(--text-color);
+  gap: 8px;
+}
+
+.app-logo {
+  width: 24px;
+  height: 24px;
+}
+
+.app-title {
+  font-size: 1rem;
+  font-weight: 600;
+  margin: 0;
+}
+
+/* Navigation styling */
+.main-nav {
+  display: flex;
+  gap: 0.5rem;
+  flex-shrink: 0;
+  margin-right: 1rem;
+}
+
+.nav-item {
+  padding: 0.25rem 0.75rem;
+  border-radius: var(--border-radius-xs);
+  color: var(--text-color);
+  text-decoration: none;
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  transition: all 0.2s ease;
+  font-size: 0.9rem;
+}
+
+.nav-item:hover {
+  background-color: var(--lora-surface-hover, oklch(95% 0.02 256));
+}
+
+.nav-item.active {
+  background-color: var(--lora-accent);
+  color: white;
+}
+
+/* Header search */
+.header-search {
+  flex: 1;
+  max-width: 400px;
+  margin: 0 1rem;
+}
+
+/* Header controls (formerly corner controls) */
+.header-controls {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  flex-shrink: 0;
+}
+
+.header-controls > div {
+  width: 32px;
+  height: 32px;
+  border-radius: 50%;
+  background: var(--card-bg);
+  border: 1px solid var(--border-color);
+  color: var(--text-color);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  cursor: pointer;
+  transition: all 0.2s ease;
+  position: relative;
+}
+
+.header-controls > div:hover {
+  background: var(--lora-accent);
+  color: white;
+  transform: translateY(-2px);
+}
+
+.theme-toggle {
+  position: relative; /* Ensure relative positioning for the container */
+}
+
+.theme-toggle .light-icon,
+.theme-toggle .dark-icon {
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%); /* Center perfectly */
+  opacity: 0;
+  transition: opacity 0.3s ease;
+}
+
+.theme-toggle .dark-icon {
+  opacity: 1;
+}
+
+[data-theme="light"] .theme-toggle .light-icon {
+  opacity: 1;
+}
+
+[data-theme="light"] .theme-toggle .dark-icon {
+  opacity: 0;
+}
+
+/* Mobile adjustments */
+@media (max-width: 768px) {
+  .app-title {
+    display: none; /* Hide text title on mobile */
+  }
+  
+  .header-controls {
+    gap: 4px;
+  }
+  
+  .header-controls > div {
+    width: 28px;
+    height: 28px;
+  }
+  
+  .header-search {
+    max-width: none;
+    margin: 0 0.5rem;
+  }
+  
+  .main-nav {
+    margin-right: 0.5rem;
+  }
+}
+
+/* For very small screens */
+@media (max-width: 600px) {
+  .header-container {
+    padding: 0 8px;
+  }
+  
+  .main-nav {
+    display: none; /* Hide navigation on very small screens */
+  }
+  
+  .header-search {
+    flex: 1;
+  }
+}

static/css/components/import-modal.css

@@ -0,0 +1,735 @@
+/* Import Modal Styles */
+.import-step {
+    margin: var(--space-2) 0;
+    transition: none !important; /* Disable any transitions that might affect display */
+}
+
+/* Import Mode Toggle */
+.import-mode-toggle {
+    display: flex;
+    margin-bottom: var(--space-3);
+    border-radius: var(--border-radius-sm);
+    overflow: hidden;
+    border: 1px solid var(--border-color);
+}
+
+.toggle-btn {
+    flex: 1;
+    padding: 10px 16px;
+    background: var(--bg-color);
+    color: var(--text-color);
+    border: none;
+    cursor: pointer;
+    font-weight: 500;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 8px;
+    transition: background-color 0.2s, color 0.2s;
+}
+
+.toggle-btn:first-child {
+    border-right: 1px solid var(--border-color);
+}
+
+.toggle-btn.active {
+    background: var(--lora-accent);
+    color: var(--lora-text);
+}
+
+.toggle-btn:hover:not(.active) {
+    background: var(--lora-surface);
+}
+
+.import-section {
+    margin-bottom: var(--space-3);
+}
+
+/* File Input Styles */
+.file-input-wrapper {
+    position: relative;
+    margin-bottom: var(--space-1);
+}
+
+.file-input-wrapper input[type="file"] {
+    position: absolute;
+    width: 100%;
+    height: 100%;
+    opacity: 0;
+    cursor: pointer;
+    z-index: 2;
+}
+
+.file-input-button {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 8px;
+    padding: 10px 16px;
+    background: var(--lora-accent);
+    color: var(--lora-text);
+    border-radius: var(--border-radius-xs);
+    font-weight: 500;
+    cursor: pointer;
+    transition: background-color 0.2s;
+}
+
+.file-input-button:hover {
+    background: oklch(from var(--lora-accent) l c h / 0.9);
+}
+
+.file-input-wrapper:hover .file-input-button {
+    background: oklch(from var(--lora-accent) l c h / 0.9);
+}
+
+/* Recipe Details Layout */
+.recipe-details-layout {
+    display: grid;
+    grid-template-columns: 200px 1fr;
+    gap: var(--space-3);
+    margin-bottom: var(--space-3);
+}
+
+.recipe-image-container {
+    width: 100%;
+    height: 200px;
+    border-radius: var(--border-radius-sm);
+    overflow: hidden;
+    background: var(--lora-surface);
+    border: 1px solid var(--border-color);
+}
+
+.recipe-image {
+    width: 100%;
+    height: 100%;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.recipe-image img {
+    max-width: 100%;
+    max-height: 100%;
+    object-fit: contain;
+}
+
+.recipe-form-container {
+    display: flex;
+    flex-direction: column;
+    gap: var(--space-2);
+}
+
+/* Tags Input Styles */
+.tag-input-container {
+    display: flex;
+    gap: 8px;
+    margin-bottom: var(--space-1);
+}
+
+.tag-input-container input {
+    flex: 1;
+    padding: 8px;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    color: var(--text-color);
+}
+
+.tags-container {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 8px;
+    margin-top: var(--space-1);
+    min-height: 32px;
+}
+
+.recipe-tag {
+    display: inline-flex;
+    align-items: center;
+    gap: 6px;
+    padding: 4px 10px;
+    background: var(--lora-surface);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    font-size: 0.9em;
+}
+
+.recipe-tag i {
+    cursor: pointer;
+    opacity: 0.7;
+    transition: opacity 0.2s;
+}
+
+.recipe-tag i:hover {
+    opacity: 1;
+    color: var(--lora-error);
+}
+
+.empty-tags {
+    color: var(--text-color);
+    opacity: 0.6;
+    font-size: 0.9em;
+    font-style: italic;
+}
+
+/* LoRAs List Styles */
+.loras-list {
+    max-height: 300px;
+    overflow-y: auto;
+    margin: var(--space-2) 0;
+    display: flex;
+    flex-direction: column;
+    gap: 12px;
+    padding: 1px;
+}
+
+.lora-item {
+    display: flex;
+    gap: var(--space-2);
+    padding: var(--space-2);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    background: var(--bg-color);
+    margin: 1px;
+}
+
+.lora-item.exists-locally {
+    background: oklch(var(--lora-accent) / 0.05);
+    border-left: 4px solid var(--lora-accent);
+}
+
+.lora-item.missing-locally {
+    border-left: 4px solid var(--lora-error);
+}
+
+.lora-item.is-deleted {
+    background: oklch(var(--lora-warning) / 0.05);
+    border-left: 4px solid var(--lora-warning);
+}
+
+.lora-item.is-early-access {
+    background: rgba(0, 184, 122, 0.05);
+    border-left: 4px solid #00B87A;
+}
+
+.lora-item.missing-locally {
+    border-left: 4px solid var(--lora-error);
+}
+
+.lora-thumbnail {
+    width: 80px;
+    height: 80px;
+    flex-shrink: 0;
+    border-radius: var(--border-radius-xs);
+    overflow: hidden;
+    background: var(--bg-color);
+}
+
+.lora-thumbnail img {
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+.lora-content {
+    display: flex;
+    flex-direction: column;
+    gap: 8px;
+    flex: 1;
+    min-width: 0;
+}
+
+.lora-header {
+    display: flex;
+    align-items: flex-start;
+    justify-content: space-between;
+    gap: var(--space-2);
+}
+
+.lora-content h3 {
+    margin: 0;
+    font-size: 1.1em;
+    color: var(--text-color);
+    flex: 1;
+}
+
+.lora-info {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 8px;
+    align-items: center;
+    font-size: 0.9em;
+}
+
+.lora-info .base-model {
+    background: oklch(var(--lora-accent) / 0.1);
+    color: var(--lora-accent);
+    padding: 2px 8px;
+    border-radius: var(--border-radius-xs);
+}
+
+.lora-version {
+    font-size: 0.9em;
+    color: var(--text-color);
+    opacity: 0.7;
+}
+
+.weight-badge {
+    background: var(--lora-surface);
+    padding: 2px 8px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.85em;
+}
+
+/* Missing LoRAs List */
+.missing-loras-list {
+    max-height: 200px;
+    overflow-y: auto;
+    margin: var(--space-2) 0;
+    display: flex;
+    flex-direction: column;
+    gap: 8px;
+    padding: var(--space-1);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    background: var(--lora-surface);
+}
+
+.missing-lora-item {
+    display: flex;
+    gap: var(--space-2);
+    padding: var(--space-1);
+    border-bottom: 1px solid var(--border-color);
+}
+
+.missing-lora-item:last-child {
+    border-bottom: none;
+}
+
+.missing-lora-item.is-early-access {
+    background: rgba(0, 184, 122, 0.05);
+    border-left: 3px solid #00B87A;
+    padding-left: 10px;
+}
+
+.missing-badge {
+    display: inline-flex;
+    align-items: center;
+    background: var(--lora-error);
+    color: white;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.8em;
+    font-weight: 500;
+    white-space: nowrap;
+    flex-shrink: 0;
+}
+
+.missing-badge i {
+    margin-right: 4px;
+    font-size: 0.9em;
+}
+
+.lora-count-info {
+    font-size: 0.85em;
+    opacity: 0.8;
+    font-weight: normal;
+    margin-left: 8px;
+}
+
+/* Location Selection Styles */
+.location-selection {
+    margin: var(--space-2) 0;
+    padding: var(--space-2);
+    background: var(--lora-surface);
+    border-radius: var(--border-radius-sm);
+}
+
+/* Reuse folder browser and path preview styles from download-modal.css */
+.folder-browser {
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    padding: var(--space-1);
+    max-height: 200px;
+    overflow-y: auto;
+}
+
+.folder-item {
+    padding: 8px;
+    cursor: pointer;
+    border-radius: var(--border-radius-xs);
+    transition: background-color 0.2s;
+}
+
+.folder-item:hover {
+    background: var(--lora-surface);
+}
+
+.folder-item.selected {
+    background: oklch(var(--lora-accent) / 0.1);
+    border: 1px solid var(--lora-accent);
+}
+
+.path-preview {
+    margin-bottom: var(--space-3);
+    padding: var(--space-2);
+    background: var(--bg-color);
+    border-radius: var(--border-radius-sm);
+    border: 1px dashed var(--border-color);
+}
+
+.path-preview label {
+    display: block;
+    margin-bottom: 8px;
+    color: var(--text-color);
+    font-size: 0.9em;
+    opacity: 0.8;
+}
+
+.path-display {
+    padding: var(--space-1);
+    color: var(--text-color);
+    font-family: monospace;
+    font-size: 0.9em;
+    line-height: 1.4;
+    white-space: pre-wrap;
+    word-break: break-all;
+    opacity: 0.85;
+    background: var(--lora-surface);
+    border-radius: var(--border-radius-xs);
+}
+
+/* Input Group Styles */
+.input-group {
+    margin-bottom: var(--space-2);
+}
+
+.input-with-button {
+    display: flex;
+    gap: 8px;
+}
+
+.input-with-button input {
+    flex: 1;
+    min-width: 0;
+}
+
+.input-with-button button {
+    flex-shrink: 0;
+    white-space: nowrap;
+    padding: 8px 16px;
+    background: var(--lora-accent);
+    color: var(--lora-text);
+    border: none;
+    border-radius: var(--border-radius-xs);
+    cursor: pointer;
+    transition: background-color 0.2s;
+}
+
+.input-with-button button:hover {
+    background: oklch(from var(--lora-accent) l c h / 0.9);
+}
+
+.input-group label {
+    display: block;
+    margin-bottom: 8px;
+    color: var(--text-color);
+}
+
+.input-group input,
+.input-group select {
+    width: 100%;
+    padding: 8px;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    color: var(--text-color);
+}
+
+/* Dark theme adjustments */
+[data-theme="dark"] .lora-item {
+    background: var(--lora-surface);
+}
+
+[data-theme="dark"] .recipe-tag {
+    background: var(--card-bg);
+}
+
+/* Responsive adjustments */
+@media (max-width: 768px) {
+    .recipe-details-layout {
+        grid-template-columns: 1fr;
+    }
+    
+    .recipe-image-container {
+        height: 150px;
+    }
+}
+
+/* Size badge for LoRA items */
+.size-badge {
+    background: var(--lora-surface);
+    padding: 2px 8px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.85em;
+    color: var(--text-color);
+    opacity: 0.8;
+}
+
+/* Improved Missing LoRAs summary section */
+.missing-loras-summary {
+    margin-bottom: var(--space-3);
+    padding: var(--space-2);
+    background: var(--bg-color);
+    border-radius: var(--border-radius-sm);
+    border: 1px solid var(--border-color);
+}
+
+.summary-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 0;
+}
+
+.summary-header h3 {
+    margin: 0;
+    font-size: 1.1em;
+    color: var(--text-color);
+    display: flex;
+    align-items: center;
+    gap: var(--space-1);
+}
+
+.lora-count-badge {
+    font-size: 0.9em;
+    font-weight: normal;
+    opacity: 0.7;
+}
+
+.total-size-badge {
+    font-size: 0.85em;
+    font-weight: normal;
+    background: var(--lora-surface);
+    padding: 2px 8px;
+    border-radius: var(--border-radius-xs);
+    margin-left: var(--space-1);
+}
+
+.toggle-list-btn {
+    background: none;
+    border: none;
+    cursor: pointer;
+    color: var(--text-color);
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+}
+
+.toggle-list-btn:hover {
+    background: var(--lora-surface);
+}
+
+.missing-loras-list {
+    max-height: 200px;
+    overflow-y: auto;
+    transition: max-height 0.3s ease, margin-top 0.3s ease, padding-top 0.3s ease;
+    margin-top: 0;
+    padding-top: 0;
+}
+
+.missing-loras-list.collapsed {
+    max-height: 0;
+    overflow: hidden;
+    padding-top: 0;
+}
+
+.missing-loras-list:not(.collapsed) {
+    margin-top: var(--space-1);
+    padding-top: var(--space-1);
+    border-top: 1px solid var(--border-color);
+}
+
+.missing-lora-item {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 8px;
+    border-bottom: 1px solid var(--border-color);
+}
+
+.missing-lora-item:last-child {
+    border-bottom: none;
+}
+
+.missing-lora-info {
+    display: flex;
+    flex-direction: column;
+    gap: 4px;
+}
+
+.missing-lora-name {
+    font-weight: 500;
+}
+
+.lora-base-model {
+    font-size: 0.85em;
+    color: var(--lora-accent);
+    background: oklch(var(--lora-accent) / 0.1);
+    padding: 2px 6px;
+    border-radius: var(--border-radius-xs);
+    display: inline-block;
+}
+
+.missing-lora-size {
+    font-size: 0.9em;
+    color: var(--text-color);
+    opacity: 0.8;
+}
+
+/* Recipe name input select-all behavior */
+#recipeName:focus {
+    outline: 2px solid var(--lora-accent);
+}
+
+/* Prevent layout shift with scrollbar */
+.modal-content {
+    overflow-y: scroll; /* Always show scrollbar */
+    scrollbar-gutter: stable; /* Reserve space for scrollbar */
+}
+
+/* For browsers that don't support scrollbar-gutter */
+@supports not (scrollbar-gutter: stable) {
+    .modal-content {
+        padding-right: calc(var(--space-2) + var(--scrollbar-width)); /* Add extra padding for scrollbar */
+    }
+}
+
+/* Deleted LoRA styles - Fix layout issues */
+.lora-item.is-deleted {
+    background: oklch(var(--lora-warning) / 0.05);
+    border-left: 4px solid var(--lora-warning);
+}
+
+.deleted-badge {
+    display: inline-flex;
+    align-items: center;
+    background: var(--lora-warning);
+    color: white;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.8em;
+    font-weight: 500;
+    white-space: nowrap;
+    flex-shrink: 0;
+}
+
+.deleted-badge i {
+    margin-right: 4px;
+    font-size: 0.9em;
+}
+
+.exclude-lora-checkbox {
+    display: none;
+}
+
+/* Deleted LoRAs warning - redesigned to not interfere with modal buttons */
+.deleted-loras-warning {
+    display: flex;
+    align-items: flex-start;
+    gap: 12px;
+    padding: 12px 16px;
+    background: oklch(var(--lora-warning) / 0.1);
+    border: 1px solid var(--lora-warning);
+    border-radius: var(--border-radius-sm);
+    color: var(--text-color);
+    margin-bottom: var(--space-2);
+}
+
+.warning-icon {
+    color: var(--lora-warning);
+    font-size: 1.2em;
+    padding-top: 2px;
+}
+
+.warning-content {
+    flex: 1;
+}
+
+.warning-title {
+    font-weight: 600;
+    margin-bottom: 4px;
+}
+
+.warning-text {
+    font-size: 0.9em;
+    line-height: 1.4;
+}
+
+/* Remove the old warning-message styles that were causing layout issues */
+.warning-message {
+    display: none; /* Hide the old style */
+}
+
+/* Update deleted badge to be more prominent */
+.deleted-badge {
+    display: inline-flex;
+    align-items: center;
+    background: var(--lora-warning);
+    color: white;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.8em;
+    font-weight: 500;
+    white-space: nowrap;
+    flex-shrink: 0;
+}
+
+.deleted-badge i {
+    margin-right: 4px;
+    font-size: 0.9em;
+}
+
+/* Error message styling */
+.error-message {
+    color: var(--lora-error);
+    font-size: 0.9em;
+    margin-top: 8px;
+    min-height: 20px; /* Ensure there's always space for the error message */
+    font-weight: 500;
+}
+
+.early-access-warning {
+    display: flex;
+    align-items: flex-start;
+    gap: 12px;
+    padding: 12px 16px;
+    background: rgba(0, 184, 122, 0.1);
+    border: 1px solid #00B87A;
+    border-radius: var(--border-radius-sm);
+    color: var(--text-color);
+    margin-bottom: var(--space-2);
+}
+
+/* Add special styling for early access badge in the missing loras list */
+.missing-lora-item .early-access-badge {
+    padding: 2px 6px;
+    font-size: 0.75em;
+    margin-top: 4px;
+    display: inline-flex;
+}
+
+/* Specific styling for the early access warning container in import modal */
+.early-access-warning .warning-icon {
+    color: #00B87A;
+    font-size: 1.2em;
+}
+
+.early-access-warning .warning-title {
+    font-weight: 600;
+    margin-bottom: 4px;
+}
+
+.early-access-warning .warning-text {
+    font-size: 0.9em;
+    line-height: 1.4;
+}

static/css/components/loading.css

@@ -56,6 +56,53 @@
     transition: width 200ms ease-out;
 }
 
+/* Enhanced progress display */
+.progress-details-container {
+    margin-top: var(--space-3);
+    width: 100%;
+    text-align: left;
+}
+
+.overall-progress-label {
+    font-size: 0.9rem;
+    margin-bottom: var(--space-1);
+    color: var(--text-color);
+}
+
+.current-item-progress {
+    margin-top: var(--space-2);
+}
+
+.current-item-label {
+    font-size: 0.9rem;
+    margin-bottom: var(--space-1);
+    color: var(--text-color);
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+
+.current-item-bar-container {
+    height: 8px;
+    background-color: var(--lora-border);
+    border-radius: 4px;
+    overflow: hidden;
+    margin-bottom: var(--space-1);
+}
+
+.current-item-bar {
+    height: 100%;
+    background-color: var(--lora-accent);
+    transition: width 200ms ease-out;
+    width: 0%;
+}
+
+.current-item-percent {
+    font-size: 0.8rem;
+    color: var(--text-color-secondary, var(--text-color));
+    opacity: 0.7;
+}
+
 @keyframes spin {
     0% { transform: rotate(0deg); }
     100% { transform: rotate(360deg); }
@@ -63,7 +110,8 @@
 
 @media (prefers-reduced-motion: reduce) {
     .lora-card,
-    .progress-bar {
+    .progress-bar,
+    .current-item-bar {
         transition: none;
     }
 }

static/css/components/lora-modal.css

@@ -1,8 +1,9 @@
 /* Lora Modal Header */
 .modal-header {
     display: flex;
-    justify-content: space-between;
-    align-items: center;
+    flex-direction: column;
+    justify-content: flex-start;
+    align-items: flex-start;
     margin-bottom: var(--space-3);
     padding-bottom: var(--space-2);
     border-bottom: 1px solid var(--lora-border);
@@ -162,12 +163,57 @@
     border: 1px solid var(--lora-border);
 }
 
+/* New header style for trigger words */
+.trigger-words-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 6px;
+}
+
+.edit-trigger-words-btn {
+    background: transparent;
+    border: none;
+    color: var(--text-color);
+    opacity: 0.5;
+    cursor: pointer;
+    padding: 2px 5px;
+    border-radius: var(--border-radius-xs);
+    transition: all 0.2s ease;
+}
+
+.edit-trigger-words-btn:hover {
+    opacity: 0.8;
+    background: rgba(0, 0, 0, 0.05);
+}
+
+[data-theme="dark"] .edit-trigger-words-btn:hover {
+    background: rgba(255, 255, 255, 0.05);
+}
+
+/* Edit mode active state */
+.trigger-words.edit-mode .edit-trigger-words-btn {
+    opacity: 0.8;
+    color: var(--lora-accent);
+}
+
+.trigger-words-content {
+    margin-bottom: var(--space-1);
+}
+
 .trigger-words-tags {
     display: flex;
     flex-wrap: wrap;
     gap: 8px;
     align-items: flex-start;
-    margin-top: var(--space-1);
+}
+
+/* No trigger words message */
+.no-trigger-words {
+    color: var(--text-color);
+    opacity: 0.7;
+    font-style: italic;
+    font-size: 0.9em;
 }
 
 /* Update Trigger Words styles */
@@ -181,6 +227,7 @@
     cursor: pointer;
     transition: all 0.2s ease;
     gap: 6px;
+    position: relative;
 }
 
 /* Update trigger word content color to use theme accent */
@@ -206,6 +253,123 @@
     transition: opacity 0.2s;
 }
 
+/* Delete button for trigger word */
+.delete-trigger-word-btn {
+    position: absolute;
+    top: -5px;
+    right: -5px;
+    width: 16px;
+    height: 16px;
+    background: var(--lora-error);
+    color: white;
+    border: none;
+    border-radius: 50%;
+    cursor: pointer;
+    padding: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    font-size: 9px;
+    transition: transform 0.2s ease;
+}
+
+.delete-trigger-word-btn:hover {
+    transform: scale(1.1);
+}
+
+/* Edit controls */
+.trigger-words-edit-controls {
+    display: flex;
+    justify-content: flex-end;
+    gap: var(--space-2);
+    margin-top: var(--space-2);
+}
+
+.trigger-words-edit-controls button {
+    padding: 3px 8px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    background: var(--bg-color);
+    color: var(--text-color);
+    font-size: 0.85em;
+    cursor: pointer;
+    display: flex;
+    align-items: center;
+    gap: 4px;
+    transition: all 0.2s ease;
+}
+
+.trigger-words-edit-controls button:hover {
+    background: oklch(var(--lora-accent) / 0.1);
+    border-color: var(--lora-accent);
+}
+
+.trigger-words-edit-controls button i {
+    font-size: 0.8em;
+}
+
+.save-trigger-words-btn {
+    background: var(--lora-accent) !important;
+    color: white !important;
+    border-color: var(--lora-accent) !important;
+}
+
+.save-trigger-words-btn:hover {
+    opacity: 0.9;
+}
+
+/* Add trigger word form */
+.add-trigger-word-form {
+    margin-top: var(--space-2);
+    display: flex;
+    gap: var(--space-1);
+}
+
+.new-trigger-word-input {
+    flex: 1;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    background: var(--bg-color);
+    color: var(--text-color);
+    font-size: 0.9em;
+}
+
+.new-trigger-word-input:focus {
+    border-color: var(--lora-accent);
+    outline: none;
+}
+
+.confirm-add-trigger-word-btn,
+.cancel-add-trigger-word-btn {
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    background: var(--bg-color);
+    color: var(--text-color);
+    font-size: 0.85em;
+    cursor: pointer;
+    transition: all 0.2s ease;
+}
+
+.confirm-add-trigger-word-btn {
+    background: var(--lora-accent);
+    color: white;
+    border-color: var(--lora-accent);
+}
+
+.confirm-add-trigger-word-btn:hover {
+    opacity: 0.9;
+}
+
+.cancel-add-trigger-word-btn:hover {
+    background: rgba(0, 0, 0, 0.05);
+}
+
+[data-theme="dark"] .cancel-add-trigger-word-btn:hover {
+    background: rgba(255, 255, 255, 0.05);
+}
+
 /* Editable Fields */
 .editable-field {
     position: relative;
@@ -429,54 +593,441 @@
 
 /* Model name field styles - complete replacement */
 .model-name-field {
-    display: flex;
-    align-items: center;
-    gap: var(--space-2);
-    width: calc(100% - 40px); /* Reduce width to avoid overlap with close button */
-    position: relative; /* Add position relative for absolute positioning of save button */
+    display: none;
 }
 
-.model-name-field h2 {
+/* New Model Name Header Styles */
+.model-name-header {
+    display: flex;
+    align-items: center;
+    width: calc(100% - 40px); /* Avoid overlap with close button */
+    position: relative;
+}
+
+.model-name-content {
     margin: 0;
     padding: var(--space-1);
     border-radius: var(--border-radius-xs);
-    transition: background-color 0.2s;
-    flex: 1;
-    font-size: 1.5em !important; /* Increased and forced size */
-    font-weight: 600; /* Make it bolder */
-    min-height: 1.5em;
-    box-sizing: border-box;
-    border: 1px solid transparent;
+    font-size: 1.5em !important;
+    font-weight: 600;
     line-height: 1.2;
-    color: var(--text-color); /* Ensure correct color */
-}
-
-.model-name-field h2:hover {
-    background: oklch(var(--lora-accent) / 0.1);
-    cursor: text;
-}
-
-.model-name-field h2:focus {
+    color: var(--text-color);
+    border: 1px solid transparent;
     outline: none;
-    background: var(--bg-color);
+    flex: 1;
+}
+
+.model-name-content:focus {
     border: 1px solid var(--lora-accent);
+    background: var(--bg-color);
 }
 
-.model-name-field .save-btn {
-    position: absolute;
-    right: 10px; /* Position closer to the end of the field */
-    top: 50%;
-    transform: translateY(-50%);
+.edit-model-name-btn {
+    background: transparent;
+    border: none;
+    color: var(--text-color);
     opacity: 0;
-    transition: opacity 0.2s;
+    cursor: pointer;
+    padding: 2px 5px;
+    border-radius: var(--border-radius-xs);
+    transition: all 0.2s ease;
+    margin-left: var(--space-1);
 }
 
-.model-name-field:hover .save-btn,
-.model-name-field h2:focus ~ .save-btn {
+.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;
+    border-bottom: 1px solid var(--lora-border);
+    margin-bottom: var(--space-2);
+    position: relative;
+    z-index: 2;
+}
+
+.tab-btn {
+    padding: var(--space-1) var(--space-2);
+    background: transparent;
+    border: none;
+    border-bottom: 2px solid transparent;
+    color: var(--text-color);
+    cursor: pointer;
+    font-size: 0.95em;
+    transition: all 0.2s;
+    opacity: 0.7;
+    position: relative;
+}
+
+.tab-btn:hover {
     opacity: 1;
+    background: oklch(var(--lora-accent) / 0.05);
 }
 
-/* Ensure close button is accessible */
-.modal-content .close {
-    z-index: 10; /* Ensure close button is above other elements */
+.tab-btn.active {
+    border-bottom: 2px solid var(--lora-accent);
+    opacity: 1;
+    font-weight: 600;
+}
+
+.tab-content {
+    position: relative;
+    min-height: 100px;
+}
+
+.tab-pane {
+    display: none;
+}
+
+.tab-pane.active {
+    display: block;
+}
+
+/* Model Description Styling */
+.model-description-container {
+    background: var(--lora-surface);
+    border-radius: var(--border-radius-sm);
+    overflow: hidden;
+    min-height: 200px;
+    position: relative;
+    /* Remove the max-height and overflow-y to allow content to expand naturally */
+}
+
+.model-description-loading {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    padding: var(--space-3);
+    color: var(--text-color);
+    opacity: 0.7;
+    font-size: 0.9em;
+}
+
+.model-description-loading .fa-spinner {
+    margin-right: var(--space-1);
+}
+
+.model-description-content {
+    padding: var(--space-2);
+    line-height: 1.5;
+    overflow-wrap: break-word;
+    font-size: 0.95em;
+}
+
+.model-description-content h1,
+.model-description-content h2,
+.model-description-content h3,
+.model-description-content h4,
+.model-description-content h5,
+.model-description-content h6 {
+    margin-top: 1em;
+    margin-bottom: 0.5em;
+    font-weight: 600;
+}
+
+.model-description-content p {
+    margin-bottom: 1em;
+}
+
+.model-description-content img {
+    max-width: 100%;
+    height: auto;
+    border-radius: var(--border-radius-xs);
+    display: block;
+    margin: 1em 0;
+}
+
+.model-description-content pre {
+    background: rgba(0, 0, 0, 0.05);
+    border-radius: var(--border-radius-xs);
+    padding: var(--space-1);
+    white-space: pre-wrap;
+    margin: 1em 0;
+    overflow-x: auto;
+}
+
+.model-description-content code {
+    font-family: monospace;
+    font-size: 0.9em;
+    background: rgba(0, 0, 0, 0.05);
+    padding: 0.1em 0.3em;
+    border-radius: 3px;
+}
+
+.model-description-content pre code {
+    background: transparent;
+    padding: 0;
+}
+
+.model-description-content ul,
+.model-description-content ol {
+    margin-left: 1.5em;
+    margin-bottom: 1em;
+}
+
+.model-description-content li {
+    margin-bottom: 0.5em;
+}
+
+.model-description-content blockquote {
+    border-left: 3px solid var(--lora-accent);
+    padding-left: 1em;
+    margin-left: 0;
+    margin-right: 0;
+    font-style: italic;
+    opacity: 0.8;
+}
+
+/* Adjust dark mode for model description */
+[data-theme="dark"] .model-description-content pre,
+[data-theme="dark"] .model-description-content code {
+    background: rgba(255, 255, 255, 0.05);
+}
+
+.hidden {
+    display: none !important;
+}
+
+.no-examples {
+    text-align: center;
+    padding: var(--space-3);
+    color: var(--text-color);
+    opacity: 0.7;
+}
+
+/* Adjust the media wrapper for tab system */
+#showcase-tab .carousel-container {
+    margin-top: var(--space-2);
+}
+
+/* Enhanced Model Description Styling */
+.model-description-container {
+    background: var(--lora-surface);
+    border-radius: var(--border-radius-sm);
+    overflow: hidden;
+    min-height: 200px;
+    position: relative;
+    /* Remove the max-height and overflow-y to allow content to expand naturally */
+}
+
+.model-description-content {
+    padding: var(--space-2);
+    line-height: 1.5;
+    overflow-wrap: break-word;
+    font-size: 0.95em;
+}
+
+.model-description-content h1,
+.model-description-content h2,
+.model-description-content h3,
+.model-description-content h4,
+.model-description-content h5,
+.model-description-content h6 {
+    margin-top: 1em;
+    margin-bottom: 0.5em;
+    font-weight: 600;
+}
+
+.model-description-content p {
+    margin-bottom: 1em;
+}
+
+.model-description-content img {
+    max-width: 100%;
+    height: auto;
+    border-radius: var(--border-radius-xs);
+    display: block;
+    margin: 1em 0;
+}
+
+.model-description-content pre {
+    background: rgba(0, 0, 0, 0.05);
+    border-radius: var(--border-radius-xs);
+    padding: var(--space-1);
+    white-space: pre-wrap;
+    margin: 1em 0;
+    overflow-x: auto;
+}
+
+.model-description-content code {
+    font-family: monospace;
+    font-size: 0.9em;
+    background: rgba(0, 0, 0, 0.05);
+    padding: 0.1em 0.3em;
+    border-radius: 3px;
+}
+
+.model-description-content pre code {
+    background: transparent;
+    padding: 0;
+}
+
+.model-description-content ul,
+.model-description-content ol {
+    margin-left: 1.5em;
+    margin-bottom: 1em;
+}
+
+.model-description-content li {
+    margin-bottom: 0.5em;
+}
+
+.model-description-content blockquote {
+    border-left: 3px solid var (--lora-accent);
+    padding-left: 1em;
+    margin-left: 0;
+    margin-right: 0;
+    font-style: italic;
+    opacity: 0.8;
+}
+
+/* Adjust dark mode for model description */
+[data-theme="dark"] .model-description-content pre,
+[data-theme="dark"] .model-description-content code {
+    background: rgba(255, 255, 255, 0.05);
+}
+
+/* Model Tags styles */
+.model-tags {
+    display: none;
+}
+
+.model-tag {
+    display: none;
+}
+
+/* Updated Model Tags styles - improved visibility in light theme */
+.model-tags-container {
+    position: relative;
+}
+
+.model-tags-compact {
+    display: flex;
+    flex-wrap: nowrap;
+    gap: 6px;
+    align-items: center;
+}
+
+.model-tag-compact {
+    /* Updated styles to match info-item appearance */
+    background: rgba(0, 0, 0, 0.03);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: var(--border-radius-xs);
+    padding: 2px 8px;
+    font-size: 0.75em;
+    color: var(--text-color);
+    white-space: nowrap;
+}
+
+/* Adjust dark theme tag styles */
+[data-theme="dark"] .model-tag-compact {
+    background: rgba(255, 255, 255, 0.03);
+    border: 1px solid var(--lora-border);
+}
+
+.model-tag-more {
+    background: var(--lora-accent);
+    color: var(--lora-text);
+    border-radius: var(--border-radius-xs);
+    padding: 2px 8px;
+    font-size: 0.75em;
+    cursor: pointer;
+    white-space: nowrap;
+    font-weight: 500;
+}
+
+.model-tags-tooltip {
+    position: absolute;
+    top: calc(100% + 8px);
+    left: 0;
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    box-shadow: 0 3px 8px rgba(0, 0, 0, 0.15); /* Enhanced shadow for better visibility */
+    padding: 10px 14px;
+    max-width: 400px;
+    z-index: 10;
+    opacity: 0;
+    visibility: hidden;
+    transform: translateY(-4px);
+    transition: all 0.2s ease;
+    pointer-events: none;
+}
+
+.model-tags-tooltip.visible {
+    opacity: 1;
+    visibility: visible;
+    transform: translateY(0);
+    pointer-events: auto; /* Enable interactions when visible */
+}
+
+.tooltip-content {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 6px;
+    max-height: 200px;
+    overflow-y: auto;
+}
+
+.tooltip-tag {
+    /* Updated styles to match info-item appearance */
+    background: rgba(0, 0, 0, 0.03);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: var(--border-radius-xs);
+    padding: 3px 8px;
+    font-size: 0.75em;
+    color: var(--text-color);
+}
+
+/* Adjust dark theme tooltip tag styles */
+[data-theme="dark"] .tooltip-tag {
+    background: rgba(255, 255, 255, 0.03);
+    border: 1px solid var(--lora-border);
+}
+
+/* Add styles for blurred showcase content */
+.nsfw-media-wrapper {
+    position: relative;
+}
+
+.media-wrapper img.blurred,
+.media-wrapper video.blurred {
+    filter: blur(25px);
+}
+
+.media-wrapper .nsfw-overlay {
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    z-index: 2;
+    pointer-events: none;
+}
+
+/* Position the toggle button at the top left of showcase media */
+.showcase-toggle-btn {
+    position: absolute;
+    left: var(--space-1);
+    top: var(--space-1);
+    z-index: 3;
+}
+
+/* Make sure media wrapper maintains position: relative for absolute positioning of children */
+.carousel .media-wrapper {
+    position: relative;
 }

static/css/components/modal.css

@@ -2,13 +2,13 @@
 .modal {
     display: none;
     position: fixed;
-    top: 0;
+    top: 48px; /* Start below the header */
     left: 0;
     width: 100%;
-    height: 100%;
+    height: calc(100% - 48px); /* Adjust height to exclude header */
     background: rgba(0, 0, 0, 0.2); /* 调整为更淡的半透明黑色 */
     z-index: var(--z-modal);
-    overflow: hidden; /* 改为 hidden，防止双滚动条 */
+    overflow: auto; /* Change from hidden to auto to allow scrolling */
 }
 
 /* 当模态窗口打开时，禁止body滚动 */
@@ -23,8 +23,8 @@ body.modal-open {
     position: relative;
     max-width: 800px;
     height: auto;
-    max-height: 90vh;
-    margin: 2rem auto;
+    max-height: calc(90vh - 48px); /* Adjust to account for header height */
+    margin: 1rem auto; /* Keep reduced top margin */
     background: var(--lora-surface);
     border-radius: var(--border-radius-base);
     padding: var(--space-3);
@@ -323,4 +323,163 @@ body.modal-open {
 [data-theme="dark"] .path-preview {
     background: rgba(255, 255, 255, 0.03);
     border: 1px solid var(--lora-border);
+}
+
+/* Settings Styles */
+.settings-section {
+    margin-top: var(--space-3);
+    border-top: 1px solid var(--lora-border);
+    padding-top: var(--space-2);
+}
+
+.settings-section h3 {
+    font-size: 1.1em;
+    margin-bottom: var(--space-2);
+    color: var(--text-color);
+    opacity: 0.9;
+}
+
+.setting-item {
+    display: flex;
+    justify-content: space-between;
+    align-items: flex-start;
+    margin-bottom: var(--space-2);
+    padding: var(--space-1);
+    border-radius: var(--border-radius-xs);
+}
+
+.setting-item:hover {
+    background: rgba(0, 0, 0, 0.02);
+}
+
+[data-theme="dark"] .setting-item:hover {
+    background: rgba(255, 255, 255, 0.05);
+}
+
+.setting-info {
+    flex: 1;
+}
+
+.setting-info label {
+    display: block;
+    margin-bottom: 4px;
+    font-weight: 500;
+}
+
+.setting-control {
+    padding-left: var(--space-2);
+}
+
+/* Toggle Switch */
+.toggle-switch {
+    position: relative;
+    display: inline-block;
+    width: 50px;
+    height: 24px;
+    cursor: pointer;
+}
+
+.toggle-switch input {
+    opacity: 0;
+    width: 0;
+    height: 0;
+}
+
+.toggle-slider {
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background-color: var(--border-color);
+    transition: .3s;
+    border-radius: 24px;
+}
+
+.toggle-slider:before {
+    position: absolute;
+    content: "";
+    height: 18px;
+    width: 18px;
+    left: 3px;
+    bottom: 3px;
+    background-color: white;
+    transition: .3s;
+    border-radius: 50%;
+}
+
+input:checked + .toggle-slider {
+    background-color: var(--lora-accent);
+}
+
+input:checked + .toggle-slider:before {
+    transform: translateX(26px);
+}
+
+.toggle-label {
+    margin-left: 60px;
+    line-height: 24px;
+}
+
+/* Add small animation for the toggle */
+.toggle-slider:active:before {
+    width: 22px;
+}
+
+/* Update input help styles */
+.input-help {
+    font-size: 0.85em;
+    color: var(--text-color);
+    opacity: 0.7;
+    margin-top: 4px;
+    line-height: 1.4;
+}
+
+/* Blur effect for NSFW content */
+.nsfw-blur {
+    filter: blur(12px);
+    transition: filter 0.3s ease;
+}
+
+.nsfw-blur:hover {
+    filter: blur(8px);
+}
+
+/* Add styles for delete preview image */
+.delete-preview {
+    max-width: 150px;
+    margin: 0 auto var(--space-2);
+    overflow: hidden;
+}
+
+.delete-preview img {
+    width: 100%;
+    height: auto;
+    max-height: 150px;
+    object-fit: contain;
+    border-radius: var(--border-radius-sm);
+}
+
+.delete-info {
+    text-align: center;
+}
+
+.delete-info h3 {
+    margin-bottom: var(--space-1);
+    word-break: break-word;
+}
+
+.delete-info p {
+    margin: var(--space-1) 0;
+    font-size: 0.9em;
+    opacity: 0.8;
+}
+
+.delete-note {
+    font-size: 0.85em;
+    color: var(--text-color);
+    opacity: 0.7;
+    font-style: italic;
+    margin-top: var(--space-1);
+    text-align: center;
 }

static/css/components/recipe-card.css

@@ -0,0 +1,184 @@
+.recipe-tag-container {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 0.5rem;
+    margin-bottom: 1rem;
+}
+
+.recipe-tag {
+    background: var(--lora-surface-hover);
+    color: var(--lora-text-secondary);
+    padding: 0.25rem 0.5rem;
+    border-radius: var(--border-radius-sm);
+    font-size: 0.8rem;
+    cursor: pointer;
+    transition: all 0.2s ease;
+}
+
+.recipe-tag:hover, .recipe-tag.active {
+    background: var(--lora-primary);
+    color: var(--lora-text-on-primary);
+}
+
+.recipe-card {
+    position: relative;
+    background: var(--lora-surface);
+    border-radius: var(--border-radius-base);
+    overflow: hidden;
+    box-shadow: var(--shadow-sm);
+    transition: all 0.2s ease;
+    aspect-ratio: 896/1152;
+    cursor: pointer;
+    display: flex;
+    flex-direction: column;
+}
+
+.recipe-card:hover {
+    transform: translateY(-3px);
+    box-shadow: var(--shadow-md);
+}
+
+.recipe-card:focus-visible {
+    outline: 2px solid var(--lora-accent);
+    outline-offset: 2px;
+}
+
+.recipe-indicator {
+    position: absolute;
+    top: 6px;
+    left: 8px;
+    width: 24px;
+    height: 24px;
+    background: var(--lora-primary);
+    border-radius: 50%;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    color: white;
+    font-weight: bold;
+    z-index: 2;
+}
+
+.recipe-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(250px, 1fr));
+    gap: 1.5rem;
+    margin-top: 1.5rem;
+}
+
+.placeholder-message {
+    grid-column: 1 / -1;
+    text-align: center;
+    padding: 2rem;
+    background: var(--lora-surface-alt);
+    border-radius: var(--border-radius-base);
+}
+
+.card-preview {
+    position: relative;
+    width: 100%;
+    height: 100%;
+    border-radius: var(--border-radius-base);
+    overflow: hidden;
+}
+
+.card-preview img {
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+    object-position: center top;
+}
+
+.card-header {
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    background: linear-gradient(oklch(0% 0 0 / 0.75), transparent 85%);
+    backdrop-filter: blur(8px);
+    color: white;
+    padding: var(--space-1);
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    z-index: 1;
+    min-height: 20px;
+}
+
+.base-model-wrapper {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    margin-left: 32px;
+}
+
+.card-actions {
+    display: flex;
+    gap: 8px;
+}
+
+.card-actions i {
+    cursor: pointer;
+    opacity: 0.8;
+    transition: opacity 0.2s ease;
+}
+
+.card-actions i:hover {
+    opacity: 1;
+}
+
+.card-footer {
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    right: 0;
+    background: linear-gradient(transparent 15%, oklch(0% 0 0 / 0.75));
+    backdrop-filter: blur(8px);
+    color: white;
+    padding: var(--space-1);
+    display: flex;
+    justify-content: space-between;
+    align-items: flex-start;
+    min-height: 32px;
+    gap: var(--space-1);
+}
+
+.lora-count {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+    background: rgba(255, 255, 255, 0.2);
+    padding: 2px 8px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.85em;
+    position: relative;
+}
+
+.lora-count.ready {
+    background: rgba(46, 204, 113, 0.3);
+}
+
+.lora-count.missing {
+    background: rgba(231, 76, 60, 0.3);
+}
+
+/* 响应式设计 */
+@media (max-width: 1400px) {
+    .recipe-grid {
+        grid-template-columns: repeat(auto-fill, minmax(240px, 1fr));
+    }
+    
+    .recipe-card {
+        max-width: 240px;
+    }
+}
+
+@media (max-width: 768px) {
+    .recipe-grid {
+        grid-template-columns: minmax(260px, 1fr);
+    }
+    
+    .recipe-card {
+        max-width: 100%;
+    }
+} 

static/css/components/recipe-modal.css

@@ -0,0 +1,497 @@
+.recipe-modal-header {
+    display: flex;
+    flex-direction: column;
+    justify-content: flex-start;
+    align-items: flex-start;
+    border-bottom: 1px solid var(--lora-border);
+    padding-bottom: 10px;
+    margin-bottom: 10px;
+}
+
+.recipe-modal-header h2 {
+    font-size: 1.4em; /* Reduced from default h2 size */
+    line-height: 1.3;
+    margin: 0;
+    max-height: 2.6em; /* Limit to 2 lines */
+    overflow: hidden;
+    text-overflow: ellipsis;
+    display: -webkit-box;
+    -webkit-line-clamp: 2;
+    -webkit-box-orient: vertical;
+}
+
+/* Recipe Tags styles */
+.recipe-tags-container {
+    position: relative;
+    margin-top: 6px;
+    margin-bottom: 10px;
+}
+
+.recipe-tags-compact {
+    display: flex;
+    flex-wrap: nowrap;
+    gap: 6px;
+    align-items: center;
+}
+
+.recipe-tag-compact {
+    background: rgba(0, 0, 0, 0.03);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: var(--border-radius-xs);
+    padding: 2px 8px;
+    font-size: 0.75em;
+    color: var(--text-color);
+    white-space: nowrap;
+}
+
+[data-theme="dark"] .recipe-tag-compact {
+    background: rgba(255, 255, 255, 0.03);
+    border: 1px solid var(--lora-border);
+}
+
+.recipe-tag-more {
+    background: var(--lora-accent);
+    color: var(--lora-text);
+    border-radius: var(--border-radius-xs);
+    padding: 2px 8px;
+    font-size: 0.75em;
+    cursor: pointer;
+    white-space: nowrap;
+    font-weight: 500;
+}
+
+.recipe-tags-tooltip {
+    position: absolute;
+    top: calc(100% + 8px);
+    left: 0;
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    box-shadow: 0 3px 8px rgba(0, 0, 0, 0.15);
+    padding: 10px 14px;
+    max-width: 400px;
+    z-index: 10;
+    opacity: 0;
+    visibility: hidden;
+    transform: translateY(-4px);
+    transition: all 0.2s ease;
+    pointer-events: none;
+}
+
+.recipe-tags-tooltip.visible {
+    opacity: 1;
+    visibility: visible;
+    transform: translateY(0);
+    pointer-events: auto;
+}
+
+.tooltip-content {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 6px;
+    max-height: 200px;
+    overflow-y: auto;
+}
+
+.tooltip-tag {
+    background: rgba(0, 0, 0, 0.03);
+    border: 1px solid rgba(0, 0, 0, 0.1);
+    border-radius: var(--border-radius-xs);
+    padding: 3px 8px;
+    font-size: 0.75em;
+    color: var(--text-color);
+}
+
+[data-theme="dark"] .tooltip-tag {
+    background: rgba(255, 255, 255, 0.03);
+    border: 1px solid var(--lora-border);
+}
+
+/* Top Section: Preview and Gen Params */
+.recipe-top-section {
+    display: grid;
+    grid-template-columns: 280px 1fr;
+    gap: var(--space-2);
+    flex-shrink: 0;
+    margin-bottom: var(--space-2);
+}
+
+/* Recipe Preview */
+.recipe-preview-container {
+    width: 100%;
+    height: 360px;
+    border-radius: var(--border-radius-sm);
+    overflow: hidden;
+    background: var(--lora-surface);
+    border: 1px solid var(--border-color);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.recipe-preview-container img {
+    max-width: 100%;
+    max-height: 100%;
+    object-fit: contain;
+}
+
+/* Generation Parameters */
+.recipe-gen-params {
+    height: 360px;
+    display: flex;
+    flex-direction: column;
+}
+
+.recipe-gen-params h3 {
+    margin-top: 0;
+    margin-bottom: var(--space-2);
+    font-size: 1.2em;
+    color: var(--text-color);
+    padding-bottom: var(--space-1);
+    border-bottom: 1px solid var(--border-color);
+    flex-shrink: 0;
+}
+
+.gen-params-container {
+    display: flex;
+    flex-direction: column;
+    gap: var(--space-2);
+    overflow-y: auto;
+    flex: 1;
+}
+
+.param-group {
+    display: flex;
+    flex-direction: column;
+    gap: 8px;
+}
+
+.param-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+}
+
+.param-header label {
+    font-weight: 500;
+    color: var(--text-color);
+}
+
+.copy-btn {
+    background: none;
+    border: none;
+    color: var(--text-color);
+    opacity: 0.6;
+    cursor: pointer;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+    transition: all 0.2s;
+}
+
+.copy-btn:hover {
+    opacity: 1;
+    background: var(--lora-surface);
+}
+
+.param-content {
+    background: var(--lora-surface);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    padding: var(--space-2);
+    color: var(--text-color);
+    font-size: 0.9em;
+    line-height: 1.5;
+    max-height: 150px;
+    overflow-y: auto;
+    white-space: pre-wrap;
+    word-break: break-word;
+}
+
+/* Other Parameters */
+.other-params {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 8px;
+    margin-top: var(--space-1);
+}
+
+.param-tag {
+    background: var(--lora-surface);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    padding: 4px 8px;
+    font-size: 0.85em;
+    color: var(--text-color);
+    display: flex;
+    align-items: center;
+    gap: 6px;
+}
+
+.param-tag .param-name {
+    font-weight: 500;
+    opacity: 0.8;
+}
+
+/* Bottom Section: Resources */
+.recipe-bottom-section {
+    max-height: 320px;
+    display: flex;
+    flex-direction: column;
+    border-top: 1px solid var(--border-color);
+    padding-top: var(--space-2);
+}
+
+.recipe-section-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: var(--space-2);
+    padding-bottom: var(--space-1);
+    border-bottom: 1px solid var(--border-color);
+    flex-shrink: 0;
+}
+
+.recipe-section-header h3 {
+    margin: 0;
+    font-size: 1.2em;
+    color: var(--text-color);
+    display: flex;
+    align-items: center;
+    gap: 8px;
+}
+
+.recipe-status {
+    display: inline-flex;
+    align-items: center;
+    font-size: 0.85em;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+    margin-left: var(--space-1);
+}
+
+.recipe-status.ready {
+    background: oklch(var(--lora-accent) / 0.1);
+    color: var(--lora-accent);
+}
+
+.recipe-status.missing {
+    background: oklch(var(--lora-error) / 0.1);
+    color: var(--lora-error);
+}
+
+.recipe-status i {
+    margin-right: 4px;
+}
+
+.recipe-section-actions {
+    display: flex;
+    align-items: center;
+    gap: var(--space-1);
+}
+
+#recipeLorasCount {
+    font-size: 0.9em;
+    color: var(--text-color);
+    opacity: 0.8;
+    display: flex;
+    align-items: center;
+    gap: 6px;
+}
+
+#recipeLorasCount i {
+    font-size: 1em;
+}
+
+/* LoRAs List */
+.recipe-loras-list {
+    display: flex;
+    flex-direction: column;
+    gap: 10px;
+    overflow-y: auto;
+    flex: 1;
+}
+
+.recipe-lora-item {
+    display: flex;
+    gap: var(--space-2);
+    padding: 10px var(--space-2);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    background: var(--bg-color);
+    /* Add will-change to create a new stacking context and force hardware acceleration */
+    will-change: transform;
+    /* Create a new containing block for absolutely positioned descendants */
+    transform: translateZ(0);
+}
+
+.recipe-lora-item.exists-locally {
+    background: oklch(var(--lora-accent) / 0.05);
+    border-left: 4px solid var(--lora-accent);
+}
+
+.recipe-lora-item.missing-locally {
+    border-left: 4px solid var(--lora-error);
+}
+
+.recipe-lora-thumbnail {
+    width: 46px;
+    height: 46px;
+    flex-shrink: 0;
+    border-radius: var(--border-radius-xs);
+    overflow: hidden;
+    background: var(--bg-color);
+}
+
+.recipe-lora-thumbnail img {
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+.recipe-lora-content {
+    display: flex;
+    flex-direction: column;
+    gap: 3px;
+    flex: 1;
+    min-width: 0;
+}
+
+.recipe-lora-header {
+    display: flex;
+    align-items: flex-start;
+    justify-content: space-between;
+    gap: var(--space-2);
+    position: relative;
+    min-height: 28px;
+    /* Ensure badges don't move during scroll in Chrome */
+    transform: translateZ(0);
+}
+
+.recipe-lora-content h4 {
+    margin: 0;
+    font-size: 1em;
+    color: var(--text-color);
+    flex: 1;
+    max-width: calc(100% - 120px); /* Make room for the badge */
+    overflow: hidden;
+    text-overflow: ellipsis;
+    display: -webkit-box;
+    -webkit-line-clamp: 2; /* Limit to 2 lines */
+    -webkit-box-orient: vertical;
+    line-height: 1.3;
+}
+
+.recipe-lora-info {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 8px;
+    align-items: center;
+    font-size: 0.85em;
+    margin-top: 4px;
+    padding-right: 4px;
+}
+
+.recipe-lora-info .base-model {
+    background: oklch(var(--lora-accent) / 0.1);
+    color: var(--lora-accent);
+    padding: 2px 8px;
+    border-radius: var(--border-radius-xs);
+}
+
+.recipe-lora-version {
+    font-size: 0.85em;
+    color: var(--text-color);
+    opacity: 0.7;
+}
+
+.recipe-lora-weight {
+    background: var(--lora-surface);
+    padding: 2px 8px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.85em;
+    color: var(--lora-accent);
+}
+
+.local-badge, 
+.missing-badge {
+    position: absolute;
+    right: 0;
+    top: 0;
+    /* Force hardware acceleration for Chrome */
+    transform: translateZ(0);
+    backface-visibility: hidden;
+}
+
+/* Specific styles for recipe modal badges - update z-index */
+.recipe-lora-header .local-badge,
+.recipe-lora-header .missing-badge {
+    z-index: 2; /* Ensure the badge is above other elements */
+    backface-visibility: hidden;
+}
+
+/* Ensure local-path tooltip is properly positioned and won't move during scroll */
+.recipe-lora-header .local-badge .local-path {
+    z-index: 3;
+    top: calc(100% + 4px); /* Position tooltip below the badge */
+    right: -4px; /* Align with the badge */
+    max-width: 250px;
+    /* Force hardware acceleration for Chrome */
+    transform: translateZ(0);
+}
+
+.missing-badge {
+    display: inline-flex;
+    align-items: center;
+    background: var(--lora-error);
+    color: white;
+    padding: 3px 6px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.75em;
+    font-weight: 500;
+    white-space: nowrap;
+    flex-shrink: 0;
+}
+
+.missing-badge i {
+    margin-right: 4px;
+    font-size: 0.9em;
+}
+
+/* Responsive adjustments */
+@media (max-width: 768px) {
+    .recipe-top-section {
+        grid-template-columns: 1fr;
+    }
+    
+    .recipe-preview-container {
+        height: 200px;
+    }
+    
+    .recipe-gen-params {
+        height: auto;
+        max-height: 300px;
+    }
+}
+
+.badge-container {
+    position: relative;
+    display: flex;
+    align-items: center;
+    justify-content: flex-end;
+    flex-shrink: 0;
+    min-width: 110px;
+    z-index: 2;
+}
+
+/* Update the local-badge and missing-badge to be positioned within the badge-container */
+.badge-container .local-badge, 
+.badge-container .missing-badge {
+    position: static; /* Override absolute positioning */
+    transform: none; /* Remove the transform */
+}
+
+/* Ensure the tooltip is still properly positioned */
+.badge-container .local-badge .local-path {
+    position: fixed; /* Keep as fixed for Chrome */
+    z-index: 100;
+}

static/css/components/search-filter.css

@@ -1,9 +1,7 @@
 /* Search Container Styles */
 .search-container {
     position: relative;
-    width: 250px;
-    margin-left: auto;
-    flex-shrink: 0; /* 防止搜索框被压缩 */
+    width: 100%;
     display: flex;
     align-items: center;
     gap: 4px;
@@ -12,14 +10,14 @@
 /* 调整搜索框样式以匹配其他控件 */
 .search-container input {
     width: 100%;
-    padding: 6px 75px 6px 12px; /* Increased right padding to accommodate both buttons */
-    border: 1px solid oklch(65% 0.02 256); /* 更深的边框颜色，提高对比度 */
+    padding: 6px 35px 6px 12px; /* Reduced right padding */
+    border: 1px solid oklch(65% 0.02 256);
     border-radius: var(--border-radius-sm);
     background: var(--lora-surface);
     color: var(--text-color);
     font-size: 0.9em;
     height: 32px;
-    box-sizing: border-box; /* 确保padding不会增加总宽度 */
+    box-sizing: border-box;
 }
 
 .search-container input:focus {
@@ -34,7 +32,7 @@
     transform: translateY(-50%);
     color: oklch(var(--text-color) / 0.5);
     pointer-events: none;
-    line-height: 1; /* 防止图标影响容器高度 */
+    line-height: 1;
 }
 
 /* 修改清空按钮样式 */
@@ -47,8 +45,8 @@
     cursor: pointer;
     border: none;
     background: none;
-    padding: 4px 8px; /* 增加点击区域 */
-    display: none; /* 默认隐藏 */
+    padding: 4px 8px;
+    display: none;
     line-height: 1;
     transition: color 0.2s ease;
 }
@@ -144,19 +142,19 @@
 
 /* Filter Panel Styles */
 .filter-panel {
-    position: absolute;
-    top: 140px; /* Adjust to be closer to the filter button */
+    position: fixed;
     right: 20px;
-    width: 300px;
+    top: 50px; /* Position below header */
+    width: 320px;
     background-color: var(--card-bg);
     border: 1px solid var(--border-color);
     border-radius: var(--border-radius-base);
     box-shadow: 0 4px 10px rgba(0, 0, 0, 0.1);
-    z-index: var(--z-overlay); /* Increase z-index to be above cards */
+    z-index: var(--z-overlay);
     padding: 16px;
     transition: transform 0.3s ease, opacity 0.3s ease;
     transform-origin: top right;
-    max-height: calc(100vh - 160px);
+    max-height: calc(100vh - 70px); /* Adjusted for header height */
     overflow-y: auto;
 }
 
@@ -237,6 +235,44 @@
     border-color: var(--lora-accent);
 }
 
+/* Tag filter styles */
+.tag-filter {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    min-width: 60px;
+}
+
+.tag-count {
+    background: rgba(0, 0, 0, 0.1);
+    padding: 1px 6px;
+    border-radius: 10px;
+    font-size: 0.8em;
+    margin-left: 4px;
+}
+
+[data-theme="dark"] .tag-count {
+    background: rgba(255, 255, 255, 0.1);
+}
+
+.tag-filter.active .tag-count {
+    background: rgba(255, 255, 255, 0.3);
+    color: white;
+}
+
+.tags-loading, .tags-error, .no-tags {
+    width: 100%;
+    padding: 8px;
+    text-align: center;
+    font-size: 0.9em;
+    color: var(--text-color);
+    opacity: 0.7;
+}
+
+.tags-error {
+    color: var(--lora-error);
+}
+
 /* Filter actions */
 .filter-actions {
     display: flex;
@@ -274,6 +310,211 @@
         width: calc(100% - 40px);
         left: 20px;
         right: 20px;
-        top: 140px;
+        top: 160px; /* Adjusted for mobile layout */
     }
-} 
+}
+
+/* Search Options Toggle */
+.search-options-toggle {
+    background: var(--lora-surface);
+    border: 1px solid oklch(65% 0.02 256);
+    border-radius: var(--border-radius-sm);
+    color: var(--text-color);
+    width: 32px;
+    height: 32px;
+    padding: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    cursor: pointer;
+    transition: all 0.2s ease;
+    flex-shrink: 0;
+}
+
+.search-options-toggle:hover {
+    background-color: var(--lora-surface-hover, oklch(95% 0.02 256));
+    color: var(--lora-accent);
+    border-color: var(--lora-accent);
+}
+
+.search-options-toggle.active {
+    background-color: oklch(95% 0.05 256);
+    color: var(--lora-accent);
+    border-color: var(--lora-accent);
+}
+
+.search-options-toggle i {
+    font-size: 0.9em;
+}
+
+/* Search Options Panel */
+.search-options-panel {
+    position: fixed;
+    right: 20px;
+    top: 50px; /* Position below header */
+    width: 280px;
+    background-color: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-base);
+    box-shadow: 0 4px 10px rgba(0, 0, 0, 0.1);
+    z-index: var(--z-overlay);
+    padding: 16px;
+    transition: transform 0.3s ease, opacity 0.3s ease;
+    transform-origin: top right;
+    display: block; /* Ensure it's block by default */
+}
+
+.search-options-panel.hidden {
+    opacity: 0;
+    transform: scale(0.95);
+    pointer-events: none;
+}
+
+.options-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 16px;
+}
+
+.options-header h3 {
+    margin: 0;
+    font-size: 16px;
+    color: var(--text-color);
+}
+
+.close-options-btn {
+    background: none;
+    border: none;
+    color: var(--text-color);
+    cursor: pointer;
+    font-size: 16px;
+    padding: 4px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.close-options-btn:hover {
+    color: var(--lora-accent);
+}
+
+.options-section {
+    margin-bottom: 16px;
+}
+
+.options-section h4 {
+    margin: 0 0 8px 0;
+    font-size: 14px;
+    color: var(--text-color);
+    opacity: 0.8;
+}
+
+.search-option-tags {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 8px; /* Increased gap for better spacing */
+}
+
+.search-option-tag {
+    padding: 6px 8px; /* Adjusted padding for better text display */
+    border-radius: var(--border-radius-sm);
+    background-color: var(--lora-surface);
+    border: 1px solid var(--border-color);
+    color: var(--text-color);
+    font-size: 13px; /* Slightly smaller font size */
+    cursor: pointer;
+    transition: all 0.2s ease;
+    user-select: none;
+    flex: 1;
+    text-align: center;
+    white-space: nowrap; /* Prevent text wrapping */
+    min-width: 80px; /* Ensure minimum width for each tag */
+    display: inline-flex; /* Better control over layout */
+    justify-content: center;
+    align-items: center;
+}
+
+.search-option-tag:hover {
+    background-color: var(--lora-surface-hover);
+}
+
+.search-option-tag.active {
+    background-color: var(--lora-accent);
+    color: white;
+    border-color: var(--lora-accent);
+}
+
+/* Switch styles */
+.search-option-switch {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 4px 0;
+}
+
+.switch {
+    position: relative;
+    display: inline-block;
+    width: 46px;
+    height: 24px;
+}
+
+.switch input {
+    opacity: 0;
+    width: 0;
+    height: 0;
+}
+
+.slider {
+    position: absolute;
+    cursor: pointer;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background-color: #ccc;
+    transition: .4s;
+}
+
+.slider:before {
+    position: absolute;
+    content: "";
+    height: 18px;
+    width: 18px;
+    left: 3px;
+    bottom: 3px;
+    background-color: white;
+    transition: .4s;
+}
+
+input:checked + .slider {
+    background-color: var(--lora-accent);
+}
+
+input:focus + .slider {
+    box-shadow: 0 0 1px var(--lora-accent);
+}
+
+input:checked + .slider:before {
+    transform: translateX(22px);
+}
+
+.slider.round {
+    border-radius: 34px;
+}
+
+.slider.round:before {
+    border-radius: 50%;
+}
+
+/* Mobile adjustments */
+@media (max-width: 768px) {
+    .search-options-panel,
+    .filter-panel {
+        width: calc(100% - 40px);
+        left: 20px;
+        right: 20px;
+        top: 160px; /* Adjusted for mobile layout */
+    }
+}

static/css/components/shared.css

@@ -0,0 +1,111 @@
+/* Local Version Badge */
+.local-badge {
+    display: inline-flex;
+    align-items: center;
+    background: var(--lora-accent);
+    color: var(--lora-text);
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.8em;
+    font-weight: 500;
+    white-space: nowrap;
+    flex-shrink: 0;
+    position: relative;
+    /* Force hardware acceleration to prevent Chrome scroll issues */
+    transform: translateZ(0);
+    will-change: transform;
+}
+
+.local-badge i {
+    margin-right: 4px;
+    font-size: 0.9em;
+}
+
+/* Early Access Badge */
+.early-access-badge {
+    display: inline-flex;
+    align-items: center;
+    background: #00B87A; /* Green for early access */
+    color: white;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.8em;
+    font-weight: 500;
+    white-space: nowrap;
+    flex-shrink: 0;
+    position: relative;
+    /* Force hardware acceleration to prevent Chrome scroll issues */
+    transform: translateZ(0);
+    will-change: transform;
+}
+
+.early-access-badge i {
+    margin-right: 4px;
+    font-size: 0.9em;
+}
+
+.early-access-info {
+    display: none;
+    position: absolute;
+    top: 100%;
+    right: 0;
+    background: var(--card-bg);
+    border: 1px solid #00B87A;
+    border-radius: var(--border-radius-xs);
+    padding: var(--space-1);
+    margin-top: 4px;
+    font-size: 0.9em;
+    color: var(--text-color);
+    white-space: normal;
+    word-break: break-all;
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+    z-index: 100; /* Higher z-index to ensure it's above other elements */
+    min-width: 300px;
+    max-width: 300px;
+    /* Create a separate layer with hardware acceleration */
+    transform: translateZ(0);
+    /* Use a fixed position to ensure it's in a separate layer from scrollable content */
+    position: fixed;
+    pointer-events: none; /* Don't block mouse events */
+}
+
+.early-access-badge:hover .early-access-info {
+    display: block;
+    pointer-events: auto; /* Allow interaction with the tooltip when visible */
+}
+
+.local-path {
+    display: none;
+    position: absolute;
+    top: 100%;
+    right: 0;
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    padding: var(--space-1);
+    margin-top: 4px;
+    font-size: 0.9em;
+    color: var(--text-color);
+    white-space: normal;
+    word-break: break-all;
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+    z-index: 100; /* Higher z-index to ensure it's above other elements */
+    min-width: 200px;
+    max-width: 300px;
+    /* Create a separate layer with hardware acceleration */
+    transform: translateZ(0);
+    /* Use a fixed position to ensure it's in a separate layer from scrollable content */
+    position: fixed;
+    pointer-events: none; /* Don't block mouse events */
+}
+
+.local-badge:hover .local-path {
+    display: block;
+    pointer-events: auto; /* Allow interaction with the tooltip when visible */
+}
+
+.error-message {
+    color: var(--lora-error);
+    font-size: 0.9em;
+    margin-top: 4px;
+}

static/css/components/support-modal.css

@@ -1,6 +1,6 @@
 /* Support Modal Styles */
 .support-modal {
-    max-width: 550px;
+    max-width: 570px;
 }
 
 .support-header {
@@ -141,7 +141,7 @@
 
 .support-toggle:hover {
     background: var(--lora-accent);
-    color: white;
+    color: var(--lora-error) !important;
     transform: translateY(-2px);
 }
 

static/css/components/toast.css

@@ -120,4 +120,63 @@
 
 .tooltip:hover::after {
     opacity: 1;
+}
+
+/* Toast Container for stacked notifications */
+.toast-container {
+    position: fixed;
+    top: 0;
+    right: 0;
+    z-index: calc(var(--z-overlay) + 10);
+    display: flex;
+    flex-direction: column;
+    gap: 10px;
+    padding: 20px;
+    pointer-events: none; /* Allow clicking through the container */
+    width: 400px;
+    max-width: 100%;
+}
+
+/* Ensure each toast has pointer events */
+.toast-container .toast {
+    pointer-events: auto;
+    position: relative; /* Override fixed positioning */
+    top: 0 !important; /* Let the container handle positioning */
+    right: 0 !important;
+    margin-bottom: 10px;
+}
+
+/* Add missing warning toast style */
+.toast-warning {
+    border-left: 4px solid var(--lora-warning);
+}
+
+.toast-warning::before {
+    background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24' fill='%23ff9800'%3E%3Cpath d='M1 21h22L12 2 1 21zm12-3h-2v-2h2v2zm0-4h-2v-4h2v4z'/%3E%3C/svg%3E");
+}
+
+/* Improve toast animation */
+.toast {
+    transform: translateX(120%);
+    opacity: 0;
+    transition: transform 0.3s cubic-bezier(0.4, 0, 0.2, 1), 
+                opacity 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+.toast.show {
+    transform: translateX(0);
+    opacity: 1;
+}
+
+/* Responsive adjustments */
+@media (max-width: 480px) {
+    .toast-container {
+        width: 100%;
+        padding: 10px;
+    }
+    
+    .toast {
+        width: 100%;
+        max-width: none;
+    }
 }

static/css/components/update-modal.css

@@ -153,56 +153,43 @@
     border-top: 1px solid var(--lora-border);
     margin-top: var(--space-2);
     padding-top: var(--space-2);
-}
-
-/* Toggle switch styles */
-.toggle-switch {
     display: flex;
     align-items: center;
-    gap: 12px;
+    justify-content: flex-start;
+}
+
+/* Override toggle switch styles for update preferences */
+.update-preferences .toggle-switch {
+    position: relative;
+    display: inline-flex;
+    align-items: center;
+    width: auto;
+    height: 24px;
     cursor: pointer;
-    user-select: none;
 }
 
-.toggle-switch input {
-    opacity: 0;
-    width: 0;
-    height: 0;
-    position: absolute;
-}
-
-.toggle-slider {
+.update-preferences .toggle-slider {
     position: relative;
     display: inline-block;
-    width: 40px;
-    height: 20px;
-    background-color: var(--border-color);
-    border-radius: 20px;
-    transition: .4s;
+    width: 50px;
+    height: 24px;
     flex-shrink: 0;
+    margin-right: 10px;
 }
 
-.toggle-slider:before {
-    position: absolute;
-    content: "";
-    height: 16px;
-    width: 16px;
-    left: 2px;
-    bottom: 2px;
-    background-color: white;
-    border-radius: 50%;
-    transition: .4s;
+.update-preferences .toggle-label {
+    margin-left: 0;
+    white-space: nowrap;
+    line-height: 24px;
 }
 
-input:checked + .toggle-slider {
-    background-color: var(--lora-accent);
-}
-
-input:checked + .toggle-slider:before {
-    transform: translateX(20px);
-}
-
-.toggle-label {
-    font-size: 0.9em;
-    color: var(--text-color);
+@media (max-width: 480px) {
+    .update-preferences {
+        flex-direction: row;
+        flex-wrap: wrap;
+    }
+    
+    .update-preferences .toggle-label {
+        margin-top: 5px;
+    }
 }

static/css/layout.css

@@ -1,7 +1,18 @@
+.page-content {
+    height: calc(100vh - 48px); /* Full height minus header */
+    margin-top: 48px; /* Push down below header */
+    overflow-y: auto; /* Enable scrolling here */
+    width: 100%;
+    position: relative;
+    overflow-y: scroll;
+}
+
 .container {
     max-width: 1400px;
     margin: 20px auto;
     padding: 0 15px;
+    position: relative;
+    z-index: var(--z-base);
 }
 
 .controls {
@@ -14,69 +25,17 @@
 .actions {
     display: flex;
     align-items: center;
+    justify-content: space-between;
     gap: var(--space-2);
     flex-wrap: nowrap;
     width: 100%;
 }
 
-/* Search and filter styles moved to components/search-filter.css */
-
-/* Update corner-controls for collapsible behavior */
-.corner-controls {
-    position: fixed;
-    top: 20px;
-    right: 20px;
-    z-index: var(--z-overlay);
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    transition: all 0.3s ease;
-}
-
-.corner-controls-toggle {
-    width: 36px;
-    height: 36px;
-    border-radius: 50%;
-    background: var(--card-bg);
-    border: 1px solid var(--border-color);
-    color: var(--text-color);
+.action-buttons {
     display: flex;
     align-items: center;
-    justify-content: center;
-    cursor: pointer;
-    transition: all 0.2s ease;
-    z-index: 2;
-    margin-bottom: 10px;
-}
-
-.corner-controls-toggle:hover {
-    background: var(--lora-accent);
-    color: white;
-    transform: translateY(-2px);
-}
-
-.corner-controls-items {
-    display: flex;
-    flex-direction: column;
-    gap: 10px;
-    opacity: 0;
-    transform: translateY(-10px) scale(0.9);
-    transition: all 0.3s ease;
-    pointer-events: none;
-}
-
-/* Expanded state */
-.corner-controls.expanded .corner-controls-items {
-    opacity: 1;
-    transform: translateY(0) scale(1);
-    pointer-events: all;
-}
-
-/* Expanded state - only expand on hover if not already expanded by click */
-.corner-controls:hover:not(.expanded) .corner-controls-items {
-    opacity: 1;
-    transform: translateY(0) scale(1);
-    pointer-events: all;
+    gap: var(--space-2);
+    flex-wrap: nowrap;
 }
 
 /* Ensure hidden class works properly */
@@ -84,46 +43,6 @@
     display: none !important;
 }
 
-/* Update toggle button styles */
-.update-toggle {
-    width: 36px;
-    height: 36px;
-    border-radius: 50%;
-    background: var(--card-bg);
-    border: 1px solid var(--border-color);
-    color: var(--text-color); /* Changed from var(--lora-accent) to match other toggles */
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    cursor: pointer;
-    transition: all 0.2s ease;
-    position: relative;
-}
-
-.update-toggle:hover {
-    background: var(--lora-accent);
-    color: white;
-    transform: translateY(-2px);
-}
-
-/* Update badge styles */
-.update-badge {
-    position: absolute;
-    top: -3px;
-    right: -3px;
-    background-color: var(--lora-error);
-    width: 8px;
-    height: 8px;
-    border-radius: 50%;
-    box-shadow: 0 0 0 2px var(--card-bg);
-}
-
-/* Badge on corner toggle */
-.corner-badge {
-    top: 0;
-    right: 0;
-}
-
 .folder-tags-container {
     position: relative;
     width: 100%;
@@ -131,11 +50,14 @@
 }
 
 .folder-tags {
+    display: flex;
+    gap: 4px;
+    padding: 2px 0;
+    flex-wrap: wrap;
     transition: max-height 0.3s ease, opacity 0.2s ease;
     max-height: 150px; /* Limit height to prevent overflow */
     opacity: 1;
     overflow-y: auto; /* Enable vertical scrolling */
-    padding-right: 40px; /* Make space for the toggle button */
     margin-bottom: 5px; /* Add margin below the tags */
 }
 
@@ -144,13 +66,15 @@
     opacity: 0;
     margin: 0;
     padding-bottom: 0;
+    overflow: hidden;
+}
+
+.toggle-folders-container {
+    margin-left: auto;
 }
 
 /* Toggle Folders Button */
 .toggle-folders-btn {
-    position: absolute;
-    bottom: 0;      /* 固定在容器底部 */
-    right: 0;       /* 固定在容器右侧 */
     width: 36px;
     height: 36px;
     border-radius: 50%;
@@ -162,7 +86,6 @@
     justify-content: center;
     cursor: pointer;
     transition: all 0.3s ease;
-    z-index: 2;
 }
 
 .toggle-folders-btn:hover {
@@ -175,25 +98,18 @@
     transition: transform 0.3s ease;
 }
 
-/* 折叠状态样式 */
-.folder-tags.collapsed + .toggle-folders-btn {
-    position: static;
-    margin-right: auto; /* 确保按钮在左侧 */
-    transform: translateY(0);
+/* Icon-only button style */
+.icon-only {
+    min-width: unset !important;
+    width: 36px !important;
+    padding: 0 !important;
 }
 
-.folder-tags.collapsed + .toggle-folders-btn i {
+/* Rotate icon when folders are collapsed */
+.folder-tags.collapsed ~ .actions .toggle-folders-btn i {
     transform: rotate(180deg);
 }
 
-/* 文件夹标签样式 */
-.folder-tags {
-    display: flex;
-    gap: 4px;
-    padding: 2px 0;
-    flex-wrap: wrap;
-}
-
 /* Add custom scrollbar for better visibility */
 .folder-tags::-webkit-scrollbar {
     width: 6px;
@@ -263,124 +179,32 @@
     transform: translateY(-2px);
 }
 
-.theme-toggle {
-    width: 36px;
-    height: 36px;
-    border-radius: 50%;
-    background: var(--card-bg);
-    border: 1px solid var(--border-color);
-    color: var(--text-color);
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    cursor: pointer;
-    transition: all 0.2s ease;
-}
-
-.theme-toggle:hover {
-    background: var(--lora-accent);
-    color: white;
-    transform: translateY(-2px);
-}
-
-.support-toggle {
-    width: 36px;
-    height: 36px;
-    border-radius: 50%;
-    background: var(--card-bg);
-    border: 1px solid var(--border-color);
-    color: var(--lora-error);
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    cursor: pointer;
-    transition: all 0.2s ease;
-}
-
-.support-toggle:hover {
-    background: var(--lora-error);
-    color: white;
-    transform: translateY(-2px);
-}
-
-.support-toggle i {
-    font-size: 1.1em;
-    position: relative;
-    top: 1px;
-    left: -0.5px;
-}
-
-.theme-toggle img {
-    width: 20px;
-    height: 20px;
-}
-
-.theme-toggle .theme-icon {
-    width: 20px;
-    height: 20px;
-    position: absolute;
-    transition: opacity 0.2s ease;
-}
-
-.theme-toggle .light-icon {
-    opacity: 0;
-}
-
-.theme-toggle .dark-icon {
-    opacity: 1;
-}
-
-[data-theme="light"] .theme-toggle .light-icon {
-    opacity: 1;
-}
-
-[data-theme="light"] .theme-toggle .dark-icon {
-    opacity: 0;
-}
-
 @media (max-width: 768px) {
     .actions {
         flex-wrap: wrap;
         gap: var(--space-1);
     }
-
-    .controls {
-        flex-direction: column;
-        gap: 15px;
+    
+    .action-buttons {
+        flex-wrap: wrap;
+        gap: var(--space-1);
+        width: 100%;
+    }
+    
+    .toggle-folders-container {
+        margin-left: 0;
+        width: 100%;
+        display: flex;
+        justify-content: flex-end;
     }
     
     .folder-tags-container {
         order: -1;
     }
     
-    .toggle-folders-btn {
-        position: absolute;
-        bottom: 0;
-        right: 0;
-        transform: none; /* 移除transform，防止hover时的位移 */
-    }
-    
     .toggle-folders-btn:hover {
         transform: none; /* 移动端下禁用hover效果 */
     }
-    
-    .folder-tags.collapsed + .toggle-folders-btn {
-        position: relative;
-        transform: none;
-    }
-
-    .corner-controls {
-        top: 10px;
-        right: 10px;
-    }
-    
-    .corner-controls-items {
-        display: none;
-    }
-    
-    .corner-controls.expanded .corner-controls-items {
-        display: flex;
-    }
 
     .back-to-top {
         bottom: 60px; /* Give some extra space from bottom on mobile */

static/css/style.css

@@ -5,6 +5,7 @@
 @import 'layout.css';
 
 /* Import Components */
+@import 'components/header.css';
 @import 'components/card.css';
 @import 'components/modal.css';
 @import 'components/download-modal.css';
@@ -16,6 +17,7 @@
 @import 'components/support-modal.css';
 @import 'components/search-filter.css';
 @import 'components/bulk.css';
+@import 'components/shared.css';
 
 .initialization-notice {
     display: flex;

static/images/site.webmanifest

@@ -1 +1 @@
-{"name":"","short_name":"","icons":[{"src":"/android-chrome-192x192.png","sizes":"192x192","type":"image/png"},{"src":"/android-chrome-512x512.png","sizes":"512x512","type":"image/png"}],"theme_color":"#ffffff","background_color":"#ffffff","display":"standalone"}
+{"name":"","short_name":"","icons":[{"src":"/loras_static/images/android-chrome-192x192.png","sizes":"192x192","type":"image/png"},{"src":"/loras_static/images/android-chrome-512x512.png","sizes":"512x512","type":"image/png"}],"theme_color":"#ffffff","background_color":"#ffffff","display":"standalone"}

static/js/api/loraApi.js

@@ -1,59 +1,76 @@
-import { state } from '../state/index.js';
+import { state, getCurrentPageState } from '../state/index.js';
 import { showToast } from '../utils/uiHelpers.js';
 import { createLoraCard } from '../components/LoraCard.js';
 import { initializeInfiniteScroll } from '../utils/infiniteScroll.js';
 import { showDeleteModal } from '../utils/modalUtils.js';
 import { toggleFolder } from '../utils/uiHelpers.js';
 
-export async function loadMoreLoras(boolUpdateFolders = false) {
-    if (state.isLoading || !state.hasMore) return;
+export async function loadMoreLoras(resetPage = false, updateFolders = false) {
+    const pageState = getCurrentPageState();
     
-    state.isLoading = true;
+    if (pageState.isLoading || (!pageState.hasMore && !resetPage)) return;
+    
+    pageState.isLoading = true;
     try {
+        // Reset to first page if requested
+        if (resetPage) {
+            pageState.currentPage = 1;
+            // Clear grid if resetting
+            const grid = document.getElementById('loraGrid');
+            if (grid) grid.innerHTML = '';
+            initializeInfiniteScroll();
+        }
+        
         const params = new URLSearchParams({
-            page: state.currentPage,
+            page: pageState.currentPage,
             page_size: 20,
-            sort_by: state.sortBy
+            sort_by: pageState.sortBy
         });
         
-        // 使用 state 中的 searchManager 获取递归搜索状态
-        const isRecursiveSearch = state.searchManager?.isRecursiveSearch ?? false;
-        
-        if (state.activeFolder !== null) {
-            params.append('folder', state.activeFolder);
-            params.append('recursive', isRecursiveSearch.toString());
+        if (pageState.activeFolder !== null) {
+            params.append('folder', pageState.activeFolder);
         }
 
         // Add search parameters if there's a search term
-        const searchInput = document.getElementById('searchInput');
-        if (searchInput && searchInput.value.trim()) {
-            params.append('search', searchInput.value.trim());
+        if (pageState.filters?.search) {
+            params.append('search', pageState.filters.search);
             params.append('fuzzy', 'true');
+            
+            // Add search option parameters if available
+            if (pageState.searchOptions) {
+                params.append('search_filename', pageState.searchOptions.filename.toString());
+                params.append('search_modelname', pageState.searchOptions.modelname.toString());
+                params.append('search_tags', (pageState.searchOptions.tags || false).toString());
+                params.append('recursive', (pageState.searchOptions?.recursive ?? false).toString());
+            }
         }
         
         // Add filter parameters if active
-        if (state.filters && state.filters.baseModel && state.filters.baseModel.length > 0) {
-            // Convert the array of base models to a comma-separated string
-            params.append('base_models', state.filters.baseModel.join(','));
+        if (pageState.filters) {
+            if (pageState.filters.tags && pageState.filters.tags.length > 0) {
+                // Convert the array of tags to a comma-separated string
+                params.append('tags', pageState.filters.tags.join(','));
+            }
+            if (pageState.filters.baseModel && pageState.filters.baseModel.length > 0) {
+                // Convert the array of base models to a comma-separated string
+                params.append('base_models', pageState.filters.baseModel.join(','));
+            }
         }
 
-        console.log('Loading loras with params:', params.toString());
-
         const response = await fetch(`/api/loras?${params}`);
         if (!response.ok) {
             throw new Error(`Failed to fetch loras: ${response.statusText}`);
         }
         
         const data = await response.json();
-        console.log('Received data:', data);
         
-        if (data.items.length === 0 && state.currentPage === 1) {
+        if (data.items.length === 0 && pageState.currentPage === 1) {
             const grid = document.getElementById('loraGrid');
             grid.innerHTML = '<div class="no-results">No loras found in this folder</div>';
-            state.hasMore = false;
+            pageState.hasMore = false;
         } else if (data.items.length > 0) {
-            state.hasMore = state.currentPage < data.total_pages;
-            state.currentPage++;
+            pageState.hasMore = pageState.currentPage < data.total_pages;
+            pageState.currentPage++;
             appendLoraCards(data.items);
             
             const sentinel = document.getElementById('scroll-sentinel');
@@ -61,10 +78,10 @@ export async function loadMoreLoras(boolUpdateFolders = false) {
                 state.observer.observe(sentinel);
             }
         } else {
-            state.hasMore = false;
+            pageState.hasMore = false;
         }
 
-        if (boolUpdateFolders && data.folders) {
+        if (updateFolders && data.folders) {
             updateFolderTags(data.folders);
         }
         
@@ -72,7 +89,7 @@ export async function loadMoreLoras(boolUpdateFolders = false) {
         console.error('Error loading loras:', error);
         showToast('Failed to load loras: ' + error.message, 'error');
     } finally {
-        state.isLoading = false;
+        pageState.isLoading = false;
     }
 }
 
@@ -81,7 +98,8 @@ function updateFolderTags(folders) {
     if (!folderTagsContainer) return;
 
     // Keep track of currently selected folder
-    const currentFolder = state.activeFolder;
+    const pageState = getCurrentPageState();
+    const currentFolder = pageState.activeFolder;
 
     // Create HTML for folder tags
     const tagsHTML = folders.map(folder => {
@@ -107,7 +125,8 @@ export async function fetchCivitai() {
     
     await state.loadingManager.showWithProgress(async (loading) => {
         try {
-            ws = new WebSocket(`ws://${window.location.host}/ws/fetch-progress`);
+            const wsProtocol = window.location.protocol === 'https:' ? 'wss://' : 'ws://';
+            const ws = new WebSocket(`${wsProtocol}${window.location.host}/ws/fetch-progress`);
             
             const operationComplete = new Promise((resolve, reject) => {
                 ws.onmessage = (event) => {
@@ -253,31 +272,19 @@ export function appendLoraCards(loras) {
     
     loras.forEach(lora => {
         const card = createLoraCard(lora);
-        if (sentinel) {
-            grid.insertBefore(card, sentinel);
-        } else {
-            grid.appendChild(card);
-        }
+        grid.appendChild(card);
     });
 }
 
-export async function resetAndReload(boolUpdateFolders = false) {
-    console.log('Resetting with state:', { ...state });
-    
-    state.currentPage = 1;
-    state.hasMore = true;
-    state.isLoading = false;
-    
-    const grid = document.getElementById('loraGrid');
-    grid.innerHTML = '';
-    
-    const sentinel = document.createElement('div');
-    sentinel.id = 'scroll-sentinel';
-    grid.appendChild(sentinel);
+export async function resetAndReload(updateFolders = false) {
+    const pageState = getCurrentPageState();
+    console.log('Resetting with state:', { ...pageState });
     
+    // Initialize infinite scroll - will reset the observer
     initializeInfiniteScroll();
     
-    await loadMoreLoras(boolUpdateFolders);
+    // Load more loras with reset flag
+    await loadMoreLoras(true, updateFolders);
 }
 
 export async function refreshLoras() {
@@ -325,4 +332,19 @@ export async function refreshSingleLoraMetadata(filePath) {
         state.loadingManager.hide();
         state.loadingManager.restoreProgressBar();
     }
+}
+
+export async function fetchModelDescription(modelId, filePath) {
+    try {
+        const response = await fetch(`/api/lora-model-description?model_id=${modelId}&file_path=${encodeURIComponent(filePath)}`);
+        
+        if (!response.ok) {
+            throw new Error(`Failed to fetch model description: ${response.statusText}`);
+        }
+        
+        return await response.json();
+    } catch (error) {
+        console.error('Error fetching model description:', error);
+        throw error;
+    }
 }

static/js/checkpoints.js

@@ -0,0 +1,36 @@
+import { appCore } from './core.js';
+import { state, initPageState } from './state/index.js';
+
+// Initialize the Checkpoints page
+class CheckpointsPageManager {
+    constructor() {
+        // Initialize any necessary state
+        this.initialized = false;
+    }
+    
+    async initialize() {
+        if (this.initialized) return;
+        
+        // Initialize page state
+        initPageState('checkpoints');
+        
+        // Initialize core application
+        await appCore.initialize();
+        
+        // Initialize page-specific components
+        this._initializeWorkInProgress();
+        
+        this.initialized = true;
+    }
+    
+    _initializeWorkInProgress() {
+        // Add any work-in-progress specific initialization here
+        console.log('Checkpoints Manager is under development');
+    }
+}
+
+// Initialize everything when DOM is ready
+document.addEventListener('DOMContentLoaded', async () => {
+    const checkpointsPage = new CheckpointsPageManager();
+    await checkpointsPage.initialize();
+});

static/js/components/ContextMenu.js

@@ -1,9 +1,13 @@
 import { refreshSingleLoraMetadata } from '../api/loraApi.js';
+import { showToast, getNSFWLevelName } from '../utils/uiHelpers.js';
+import { NSFW_LEVELS } from '../utils/constants.js';
+import { getStorageItem } from '../utils/storageHelpers.js';
 
 export class LoraContextMenu {
     constructor() {
         this.menu = document.getElementById('loraContextMenu');
         this.currentCard = null;
+        this.nsfwSelector = document.getElementById('nsfwLevelSelector');
         this.init();
     }
 
@@ -58,10 +62,274 @@ export class LoraContextMenu {
                 case 'refresh-metadata':
                     refreshSingleLoraMetadata(this.currentCard.dataset.filepath);
                     break;
+                case 'set-nsfw':
+                    this.showNSFWLevelSelector(null, null, this.currentCard);
+                    break;
             }
             
             this.hideMenu();
         });
+
+        // Initialize NSFW Level Selector events
+        this.initNSFWSelector();
+    }
+
+    initNSFWSelector() {
+        // Close button
+        const closeBtn = this.nsfwSelector.querySelector('.close-nsfw-selector');
+        closeBtn.addEventListener('click', () => {
+            this.nsfwSelector.style.display = 'none';
+        });
+
+        // Level buttons
+        const levelButtons = this.nsfwSelector.querySelectorAll('.nsfw-level-btn');
+        levelButtons.forEach(btn => {
+            btn.addEventListener('click', async () => {
+                const level = parseInt(btn.dataset.level);
+                const filePath = this.nsfwSelector.dataset.cardPath;
+                
+                if (!filePath) return;
+                
+                try {
+                    await this.saveModelMetadata(filePath, { preview_nsfw_level: level });
+                    
+                    // Update card data
+                    const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
+                    if (card) {
+                        let metaData = {};
+                        try {
+                            metaData = JSON.parse(card.dataset.meta || '{}');
+                        } catch (err) {
+                            console.error('Error parsing metadata:', err);
+                        }
+                        
+                        metaData.preview_nsfw_level = level;
+                        card.dataset.meta = JSON.stringify(metaData);
+                        card.dataset.nsfwLevel = level.toString();
+                        
+                        // Apply blur effect immediately
+                        this.updateCardBlurEffect(card, level);
+                    }
+                    
+                    showToast(`Content rating set to ${getNSFWLevelName(level)}`, 'success');
+                    this.nsfwSelector.style.display = 'none';
+                } catch (error) {
+                    showToast(`Failed to set content rating: ${error.message}`, 'error');
+                }
+            });
+        });
+        
+        // Close when clicking outside
+        document.addEventListener('click', (e) => {
+            if (this.nsfwSelector.style.display === 'block' && 
+                !this.nsfwSelector.contains(e.target) && 
+                !e.target.closest('.context-menu-item[data-action="set-nsfw"]')) {
+                this.nsfwSelector.style.display = 'none';
+            }
+        });
+    }
+
+    async saveModelMetadata(filePath, data) {
+        const response = await fetch('/loras/api/save-metadata', {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_path: filePath,
+                ...data
+            })
+        });
+    
+        if (!response.ok) {
+            throw new Error('Failed to save metadata');
+        }
+        
+        return await response.json();
+    }
+
+    updateCardBlurEffect(card, level) {
+        // Get user settings for blur threshold
+        const blurThreshold = parseInt(getStorageItem('nsfwBlurLevel') || '4');
+        
+        // Get card preview container
+        const previewContainer = card.querySelector('.card-preview');
+        if (!previewContainer) return;
+        
+        // Get preview media element
+        const previewMedia = previewContainer.querySelector('img') || previewContainer.querySelector('video');
+        if (!previewMedia) return;
+        
+        // Check if blur should be applied
+        if (level >= blurThreshold) {
+            // Add blur class to the preview container
+            previewContainer.classList.add('blurred');
+            
+            // Get or create the NSFW overlay
+            let nsfwOverlay = previewContainer.querySelector('.nsfw-overlay');
+            if (!nsfwOverlay) {
+                // Create new overlay
+                nsfwOverlay = document.createElement('div');
+                nsfwOverlay.className = 'nsfw-overlay';
+                
+                // Create and configure the warning content
+                const warningContent = document.createElement('div');
+                warningContent.className = 'nsfw-warning';
+                
+                // Determine NSFW warning text based on level
+                let nsfwText = "Mature Content";
+                if (level >= NSFW_LEVELS.XXX) {
+                    nsfwText = "XXX-rated Content";
+                } else if (level >= NSFW_LEVELS.X) {
+                    nsfwText = "X-rated Content";
+                } else if (level >= NSFW_LEVELS.R) {
+                    nsfwText = "R-rated Content";
+                }
+                
+                // Add warning text and show button
+                warningContent.innerHTML = `
+                    <p>${nsfwText}</p>
+                    <button class="show-content-btn">Show</button>
+                `;
+                
+                // Add click event to the show button
+                const showBtn = warningContent.querySelector('.show-content-btn');
+                showBtn.addEventListener('click', (e) => {
+                    e.stopPropagation();
+                    previewContainer.classList.remove('blurred');
+                    nsfwOverlay.style.display = 'none';
+                    
+                    // Update toggle button icon if it exists
+                    const toggleBtn = card.querySelector('.toggle-blur-btn');
+                    if (toggleBtn) {
+                        toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
+                    }
+                });
+                
+                nsfwOverlay.appendChild(warningContent);
+                previewContainer.appendChild(nsfwOverlay);
+            } else {
+                // Update existing overlay
+                const warningText = nsfwOverlay.querySelector('p');
+                if (warningText) {
+                    let nsfwText = "Mature Content";
+                    if (level >= NSFW_LEVELS.XXX) {
+                        nsfwText = "XXX-rated Content";
+                    } else if (level >= NSFW_LEVELS.X) {
+                        nsfwText = "X-rated Content";
+                    } else if (level >= NSFW_LEVELS.R) {
+                        nsfwText = "R-rated Content";
+                    }
+                    warningText.textContent = nsfwText;
+                }
+                nsfwOverlay.style.display = 'flex';
+            }
+            
+            // Get or create the toggle button in the header
+            const cardHeader = previewContainer.querySelector('.card-header');
+            if (cardHeader) {
+                let toggleBtn = cardHeader.querySelector('.toggle-blur-btn');
+                
+                if (!toggleBtn) {
+                    toggleBtn = document.createElement('button');
+                    toggleBtn.className = 'toggle-blur-btn';
+                    toggleBtn.title = 'Toggle blur';
+                    toggleBtn.innerHTML = '<i class="fas fa-eye"></i>';
+                    
+                    // Add click event to toggle button
+                    toggleBtn.addEventListener('click', (e) => {
+                        e.stopPropagation();
+                        const isBlurred = previewContainer.classList.toggle('blurred');
+                        const icon = toggleBtn.querySelector('i');
+                        
+                        // Update icon and overlay visibility
+                        if (isBlurred) {
+                            icon.className = 'fas fa-eye';
+                            nsfwOverlay.style.display = 'flex';
+                        } else {
+                            icon.className = 'fas fa-eye-slash';
+                            nsfwOverlay.style.display = 'none';
+                        }
+                    });
+                    
+                    // Add to the beginning of header
+                    cardHeader.insertBefore(toggleBtn, cardHeader.firstChild);
+                    
+                    // Update base model label class
+                    const baseModelLabel = cardHeader.querySelector('.base-model-label');
+                    if (baseModelLabel && !baseModelLabel.classList.contains('with-toggle')) {
+                        baseModelLabel.classList.add('with-toggle');
+                    }
+                } else {
+                    // Update existing toggle button
+                    toggleBtn.querySelector('i').className = 'fas fa-eye';
+                }
+            }
+        } else {
+            // Remove blur
+            previewContainer.classList.remove('blurred');
+            
+            // Hide overlay if it exists
+            const overlay = previewContainer.querySelector('.nsfw-overlay');
+            if (overlay) overlay.style.display = 'none';
+            
+            // Update or remove toggle button
+            const toggleBtn = card.querySelector('.toggle-blur-btn');
+            if (toggleBtn) {
+                // We'll leave the button but update the icon
+                toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
+            }
+        }
+    }
+
+    showNSFWLevelSelector(x, y, card) {
+        const selector = document.getElementById('nsfwLevelSelector');
+        const currentLevelEl = document.getElementById('currentNSFWLevel');
+        
+        // Get current NSFW level
+        let currentLevel = 0;
+        try {
+            const metaData = JSON.parse(card.dataset.meta || '{}');
+            currentLevel = metaData.preview_nsfw_level || 0;
+            
+            // Update if we have no recorded level but have a dataset attribute
+            if (!currentLevel && card.dataset.nsfwLevel) {
+                currentLevel = parseInt(card.dataset.nsfwLevel) || 0;
+            }
+        } catch (err) {
+            console.error('Error parsing metadata:', err);
+        }
+        
+        currentLevelEl.textContent = getNSFWLevelName(currentLevel);
+        
+        // Position the selector
+        if (x && y) {
+            const viewportWidth = document.documentElement.clientWidth;
+            const viewportHeight = document.documentElement.clientHeight;
+            const selectorRect = selector.getBoundingClientRect();
+            
+            // Center the selector if no coordinates provided
+            let finalX = (viewportWidth - selectorRect.width) / 2;
+            let finalY = (viewportHeight - selectorRect.height) / 2;
+            
+            selector.style.left = `${finalX}px`;
+            selector.style.top = `${finalY}px`;
+        }
+        
+        // Highlight current level button
+        document.querySelectorAll('.nsfw-level-btn').forEach(btn => {
+            if (parseInt(btn.dataset.level) === currentLevel) {
+                btn.classList.add('active');
+            } else {
+                btn.classList.remove('active');
+            }
+        });
+        
+        // Store reference to current card
+        selector.dataset.cardPath = card.dataset.filepath;
+        
+        // Show selector
+        selector.style.display = 'block';
     }
 
     showMenu(x, y, card) {

static/js/components/Header.js

@@ -0,0 +1,82 @@
+import { updateService } from '../managers/UpdateService.js';
+import { toggleTheme } from '../utils/uiHelpers.js';
+import { SearchManager } from '../managers/SearchManager.js';
+import { FilterManager } from '../managers/FilterManager.js';
+import { initPageState } from '../state/index.js';
+
+/**
+ * Header.js - Manages the application header behavior across different pages
+ * Handles initialization of appropriate search and filter managers based on current page
+ */
+export class HeaderManager {
+    constructor() {
+      this.currentPage = this.detectCurrentPage();
+      initPageState(this.currentPage);
+      this.searchManager = null;
+      this.filterManager = null;
+      
+      // Initialize appropriate managers based on current page
+      this.initializeManagers();
+      
+      // Set up common header functionality
+      this.initializeCommonElements();
+    }
+    
+    detectCurrentPage() {
+      const path = window.location.pathname;
+      if (path.includes('/loras/recipes')) return 'recipes';
+      if (path.includes('/checkpoints')) return 'checkpoints';
+      if (path.includes('/loras')) return 'loras';
+      return 'unknown';
+    }
+    
+    initializeManagers() {
+      // Initialize SearchManager for all page types
+      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;
+      }
+    }
+    
+    initializeCommonElements() {
+      // Handle theme toggle
+      const themeToggle = document.querySelector('.theme-toggle');
+      if (themeToggle) {
+        themeToggle.addEventListener('click', () => {
+          if (typeof toggleTheme === 'function') {
+            toggleTheme();
+          }
+        });
+      }
+      
+      // Handle settings toggle
+      const settingsToggle = document.querySelector('.settings-toggle');
+      if (settingsToggle) {
+        settingsToggle.addEventListener('click', () => {
+          if (window.settingsManager) {
+            window.settingsManager.toggleSettings();
+          }
+        });
+      }
+      
+      // Handle update toggle
+      const updateToggle = document.getElementById('updateToggleBtn');
+      if (updateToggle) {
+        updateToggle.addEventListener('click', () => {
+          updateService.toggleUpdateModal();
+        });
+      }
+      
+      // Handle support toggle
+      const supportToggle = document.getElementById('supportToggleBtn');
+      if (supportToggle) {
+        supportToggle.addEventListener('click', () => {
+          // Handle support panel logic
+        });
+      }
+    }
+}

static/js/components/LoraCard.js

@@ -2,6 +2,7 @@ import { showToast } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { showLoraModal } from './LoraModal.js';
 import { bulkManager } from '../managers/BulkManager.js';
+import { NSFW_LEVELS } from '../utils/constants.js';
 
 export function createLoraCard(lora) {
     const card = document.createElement('div');
@@ -18,6 +19,24 @@ export function createLoraCard(lora) {
     card.dataset.usage_tips = lora.usage_tips;
     card.dataset.notes = lora.notes;
     card.dataset.meta = JSON.stringify(lora.civitai || {});
+    
+    // Store tags and model description
+    if (lora.tags && Array.isArray(lora.tags)) {
+        card.dataset.tags = JSON.stringify(lora.tags);
+    }
+    if (lora.modelDescription) {
+        card.dataset.modelDescription = lora.modelDescription;
+    }
+
+    // Store NSFW level if available
+    const nsfwLevel = lora.preview_nsfw_level !== undefined ? lora.preview_nsfw_level : 0;
+    card.dataset.nsfwLevel = nsfwLevel;
+    
+    // Determine if the preview should be blurred based on NSFW level and user settings
+    const shouldBlur = state.settings.blurMatureContent && nsfwLevel > NSFW_LEVELS.PG13;
+    if (shouldBlur) {
+        card.classList.add('nsfw-content');
+    }
 
     // Apply selection state if in bulk mode and this card is in the selected set
     if (state.bulkMode && state.selectedLoras.has(lora.file_path)) {
@@ -28,8 +47,18 @@ export function createLoraCard(lora) {
     const previewUrl = lora.preview_url || '/loras_static/images/no-preview.png';
     const versionedPreviewUrl = version ? `${previewUrl}?t=${version}` : previewUrl;
 
+    // Determine NSFW warning text based on level
+    let nsfwText = "Mature Content";
+    if (nsfwLevel >= NSFW_LEVELS.XXX) {
+        nsfwText = "XXX-rated Content";
+    } else if (nsfwLevel >= NSFW_LEVELS.X) {
+        nsfwText = "X-rated Content";
+    } else if (nsfwLevel >= NSFW_LEVELS.R) {
+        nsfwText = "R-rated Content";
+    }
+
     card.innerHTML = `
-        <div class="card-preview">
+        <div class="card-preview ${shouldBlur ? 'blurred' : ''}">
             ${previewUrl.endsWith('.mp4') ? 
                 `<video controls autoplay muted loop>
                     <source src="${versionedPreviewUrl}" type="video/mp4">
@@ -37,7 +66,11 @@ export function createLoraCard(lora) {
                 `<img src="${versionedPreviewUrl}" alt="${lora.model_name}">`
             }
             <div class="card-header">
-                <span class="base-model-label" title="${lora.base_model}">
+                ${shouldBlur ? 
+                  `<button class="toggle-blur-btn" title="Toggle blur">
+                      <i class="fas fa-eye"></i>
+                  </button>` : ''}
+                <span class="base-model-label ${shouldBlur ? 'with-toggle' : ''}" title="${lora.base_model}">
                     ${lora.base_model}
                 </span>
                 <div class="card-actions">
@@ -53,6 +86,14 @@ export function createLoraCard(lora) {
                     </i>
                 </div>
             </div>
+            ${shouldBlur ? `
+                <div class="nsfw-overlay">
+                    <div class="nsfw-warning">
+                        <p>${nsfwText}</p>
+                        <button class="show-content-btn">Show</button>
+                    </div>
+                </div>
+            ` : ''}
             <div class="card-footer">
                 <div class="model-info">
                     <span class="model-name">${lora.model_name}</span>
@@ -86,12 +127,69 @@ export function createLoraCard(lora) {
                 base_model: card.dataset.base_model,
                 usage_tips: card.dataset.usage_tips,
                 notes: card.dataset.notes,
-                civitai: JSON.parse(card.dataset.meta || '{}')
+                // Parse civitai metadata from the card's dataset
+                civitai: (() => {
+                    try {
+                        // Attempt to parse the JSON string
+                        return JSON.parse(card.dataset.meta || '{}');
+                    } catch (e) {
+                        console.error('Failed to parse civitai metadata:', e);
+                        return {}; // Return empty object on error
+                    }
+                })(),
+                tags: JSON.parse(card.dataset.tags || '[]'),
+                modelDescription: card.dataset.modelDescription || ''
             };
             showLoraModal(loraMeta);
         }
     });
 
+    // Toggle blur button functionality
+    const toggleBlurBtn = card.querySelector('.toggle-blur-btn');
+    if (toggleBlurBtn) {
+        toggleBlurBtn.addEventListener('click', (e) => {
+            e.stopPropagation();
+            const preview = card.querySelector('.card-preview');
+            const isBlurred = preview.classList.toggle('blurred');
+            const icon = toggleBlurBtn.querySelector('i');
+            
+            // Update the icon based on blur state
+            if (isBlurred) {
+                icon.className = 'fas fa-eye';
+            } else {
+                icon.className = 'fas fa-eye-slash';
+            }
+            
+            // Toggle the overlay visibility
+            const overlay = card.querySelector('.nsfw-overlay');
+            if (overlay) {
+                overlay.style.display = isBlurred ? 'flex' : 'none';
+            }
+        });
+    }
+
+    // Show content button functionality
+    const showContentBtn = card.querySelector('.show-content-btn');
+    if (showContentBtn) {
+        showContentBtn.addEventListener('click', (e) => {
+            e.stopPropagation();
+            const preview = card.querySelector('.card-preview');
+            preview.classList.remove('blurred');
+            
+            // Update the toggle button icon
+            const toggleBtn = card.querySelector('.toggle-blur-btn');
+            if (toggleBtn) {
+                toggleBtn.querySelector('i').className = 'fas fa-eye-slash';
+            }
+            
+            // Hide the overlay
+            const overlay = card.querySelector('.nsfw-overlay');
+            if (overlay) {
+                overlay.style.display = 'none';
+            }
+        });
+    }
+
     // Copy button click event
     card.querySelector('.fa-copy')?.addEventListener('click', async e => {
         e.stopPropagation();
@@ -159,11 +257,26 @@ export function updateCardsForBulkMode(isBulkMode) {
     
     document.body.classList.toggle('bulk-mode', isBulkMode);
     
-    document.querySelectorAll('.lora-card').forEach(card => {
+    // Get all lora cards
+    const loraCards = document.querySelectorAll('.lora-card');
+    
+    loraCards.forEach(card => {
+        // Get all action containers for this card
         const actions = card.querySelectorAll('.card-actions');
-        actions.forEach(actionGroup => {
-            actionGroup.style.display = isBulkMode ? 'none' : 'flex';
-        });
+        
+        // Handle display property based on mode
+        if (isBulkMode) {
+            // Hide actions when entering bulk mode
+            actions.forEach(actionGroup => {
+                actionGroup.style.display = 'none';
+            });
+        } else {
+            // Ensure actions are visible when exiting bulk mode
+            actions.forEach(actionGroup => {
+                // We need to reset to default display style which is flex
+                actionGroup.style.display = 'flex';
+            });
+        }
     });
     
     // Apply selection state to cards if entering bulk mode

static/js/components/RecipeCard.js

@@ -0,0 +1,280 @@
+// Recipe Card Component
+import { showToast } from '../utils/uiHelpers.js';
+import { modalManager } from '../managers/ModalManager.js';
+
+class RecipeCard {
+    constructor(recipe, clickHandler) {
+        this.recipe = recipe;
+        this.clickHandler = clickHandler;
+        this.element = this.createCardElement();
+    }
+    
+    createCardElement() {
+        const card = document.createElement('div');
+        card.className = 'lora-card';
+        card.dataset.filePath = this.recipe.file_path;
+        card.dataset.title = this.recipe.title;
+        card.dataset.created = this.recipe.created_date;
+        card.dataset.id = this.recipe.id || '';
+        
+        // Get base model
+        const baseModel = this.recipe.base_model || '';
+        
+        // Ensure loras array exists
+        const loras = this.recipe.loras || [];
+        const lorasCount = loras.length;
+        
+        // Check if all LoRAs are available in the library
+        const missingLorasCount = loras.filter(lora => !lora.inLibrary).length;
+        const allLorasAvailable = missingLorasCount === 0 && lorasCount > 0;
+        
+        // Ensure file_url exists, fallback to file_path if needed
+        const imageUrl = this.recipe.file_url || 
+                         (this.recipe.file_path ? `/loras_static/root1/preview/${this.recipe.file_path.split('/').pop()}` : 
+                         '/loras_static/images/no-preview.png');
+
+        card.innerHTML = `
+            <div class="recipe-indicator" title="Recipe">R</div>
+            <div class="card-preview">
+                <img src="${imageUrl}" alt="${this.recipe.title}">
+                <div class="card-header">
+                    <div class="base-model-wrapper">
+                        ${baseModel ? `<span class="base-model-label" title="${baseModel}">${baseModel}</span>` : ''}
+                    </div>
+                    <div class="card-actions">
+                        <i class="fas fa-share-alt" title="Share Recipe"></i>
+                        <i class="fas fa-copy" title="Copy Recipe Syntax"></i>
+                        <i class="fas fa-trash" title="Delete Recipe"></i>
+                    </div>
+                </div>
+                <div class="card-footer">
+                    <div class="model-info">
+                        <span class="model-name">${this.recipe.title}</span>
+                    </div>
+                    <div class="lora-count ${allLorasAvailable ? 'ready' : (lorasCount > 0 ? 'missing' : '')}" 
+                         title="${this.getLoraStatusTitle(lorasCount, missingLorasCount)}">
+                        <i class="fas fa-layer-group"></i> ${lorasCount}
+                    </div>
+                </div>
+            </div>
+        `;
+        
+        this.attachEventListeners(card);
+        return card;
+    }
+    
+    getLoraStatusTitle(totalCount, missingCount) {
+        if (totalCount === 0) return "No LoRAs in this recipe";
+        if (missingCount === 0) return "All LoRAs available - Ready to use";
+        return `${missingCount} of ${totalCount} LoRAs missing`;
+    }
+    
+    attachEventListeners(card) {
+        // Recipe card click event
+        card.addEventListener('click', () => {
+            this.clickHandler(this.recipe);
+        });
+        
+        // Share button click event - prevent propagation to card
+        card.querySelector('.fa-share-alt')?.addEventListener('click', (e) => {
+            e.stopPropagation();
+            this.shareRecipe();
+        });
+        
+        // Copy button click event - prevent propagation to card
+        card.querySelector('.fa-copy')?.addEventListener('click', (e) => {
+            e.stopPropagation();
+            this.copyRecipeSyntax();
+        });
+        
+        // Delete button click event - prevent propagation to card
+        card.querySelector('.fa-trash')?.addEventListener('click', (e) => {
+            e.stopPropagation();
+            this.showDeleteConfirmation();
+        });
+    }
+    
+    copyRecipeSyntax() {
+        try {
+            // Generate recipe syntax in the format <lora:file_name:strength> separated by spaces
+            const loras = this.recipe.loras || [];
+            if (loras.length === 0) {
+                showToast('No LoRAs in this recipe to copy', 'warning');
+                return;
+            }
+            
+            const syntax = loras.map(lora => {
+                // Use file_name if available, otherwise use empty placeholder
+                const fileName = lora.file_name || '[missing-lora]';
+                const strength = lora.strength || 1.0;
+                return `<lora:${fileName}:${strength}>`;
+            }).join(' ');
+            
+            // Copy to clipboard
+            navigator.clipboard.writeText(syntax)
+                .then(() => {
+                    showToast('Recipe syntax copied to clipboard', 'success');
+                })
+                .catch(err => {
+                    console.error('Failed to copy: ', err);
+                    showToast('Failed to copy recipe syntax', 'error');
+                });
+        } catch (error) {
+            console.error('Error copying recipe syntax:', error);
+            showToast('Error copying recipe syntax', 'error');
+        }
+    }
+    
+    showDeleteConfirmation() {
+        try {
+            // Get recipe ID
+            const recipeId = this.recipe.id;
+            if (!recipeId) {
+                showToast('Cannot delete recipe: Missing recipe ID', 'error');
+                return;
+            }
+            
+            // Create delete modal content
+            const deleteModalContent = `
+                <div class="modal-content delete-modal-content">
+                    <h2>Delete Recipe</h2>
+                    <p class="delete-message">Are you sure you want to delete this recipe?</p>
+                    <div class="delete-model-info">
+                        <div class="delete-preview">
+                            <img src="${this.recipe.file_url || '/loras_static/images/no-preview.png'}" alt="${this.recipe.title}">
+                        </div>
+                        <div class="delete-info">
+                            <h3>${this.recipe.title}</h3>
+                            <p>This action cannot be undone.</p>
+                        </div>
+                    </div>
+                    <p class="delete-note">Note: Deleting this recipe will not affect the LoRA files used in it.</p>
+                    <div class="modal-actions">
+                        <button class="cancel-btn" onclick="closeDeleteModal()">Cancel</button>
+                        <button class="delete-btn" onclick="confirmDelete()">Delete</button>
+                    </div>
+                </div>
+            `;
+            
+            // Show the modal with custom content and setup callbacks
+            modalManager.showModal('deleteModal', deleteModalContent, () => {
+                // This is the onClose callback
+                const deleteModal = document.getElementById('deleteModal');
+                const deleteBtn = deleteModal.querySelector('.delete-btn');
+                deleteBtn.textContent = 'Delete';
+                deleteBtn.disabled = false;
+            });
+            
+            // Set up the delete and cancel buttons with proper event handlers
+            const deleteModal = document.getElementById('deleteModal');
+            const cancelBtn = deleteModal.querySelector('.cancel-btn');
+            const deleteBtn = deleteModal.querySelector('.delete-btn');
+            
+            // Store recipe ID in the modal for the delete confirmation handler
+            deleteModal.dataset.recipeId = recipeId;
+            
+            // Update button event handlers
+            cancelBtn.onclick = () => modalManager.closeModal('deleteModal');
+            deleteBtn.onclick = () => this.confirmDeleteRecipe();
+            
+        } catch (error) {
+            console.error('Error showing delete confirmation:', error);
+            showToast('Error showing delete confirmation', 'error');
+        }
+    }
+
+    confirmDeleteRecipe() {
+        const deleteModal = document.getElementById('deleteModal');
+        const recipeId = deleteModal.dataset.recipeId;
+        
+        if (!recipeId) {
+            showToast('Cannot delete recipe: Missing recipe ID', 'error');
+            modalManager.closeModal('deleteModal');
+            return;
+        }
+        
+        // Show loading state
+        const deleteBtn = deleteModal.querySelector('.delete-btn');
+        const originalText = deleteBtn.textContent;
+        deleteBtn.textContent = 'Deleting...';
+        deleteBtn.disabled = true;
+        
+        // Call API to delete the recipe
+        fetch(`/api/recipe/${recipeId}`, {
+            method: 'DELETE',
+            headers: {
+                'Content-Type': 'application/json'
+            }
+        })
+        .then(response => {
+            if (!response.ok) {
+                throw new Error('Failed to delete recipe');
+            }
+            return response.json();
+        })
+        .then(data => {
+            showToast('Recipe deleted successfully', 'success');
+            
+            window.recipeManager.loadRecipes();
+            
+            modalManager.closeModal('deleteModal');
+        })
+        .catch(error => {
+            console.error('Error deleting recipe:', error);
+            showToast('Error deleting recipe: ' + error.message, 'error');
+            
+            // Reset button state
+            deleteBtn.textContent = originalText;
+            deleteBtn.disabled = false;
+        });
+    }
+
+    shareRecipe() {
+        try {
+            // Get recipe ID
+            const recipeId = this.recipe.id;
+            if (!recipeId) {
+                showToast('Cannot share recipe: Missing recipe ID', 'error');
+                return;
+            }
+            
+            // Show loading toast
+            showToast('Preparing recipe for sharing...', 'info');
+            
+            // Call the API to process the image with metadata
+            fetch(`/api/recipe/${recipeId}/share`)
+                .then(response => {
+                    if (!response.ok) {
+                        throw new Error('Failed to prepare recipe for sharing');
+                    }
+                    return response.json();
+                })
+                .then(data => {
+                    if (!data.success) {
+                        throw new Error(data.error || 'Unknown error');
+                    }
+                    
+                    // Create a temporary anchor element for download
+                    const downloadLink = document.createElement('a');
+                    downloadLink.href = data.download_url;
+                    downloadLink.download = data.filename;
+                    
+                    // Append to body, click and remove
+                    document.body.appendChild(downloadLink);
+                    downloadLink.click();
+                    document.body.removeChild(downloadLink);
+                    
+                    showToast('Recipe download started', 'success');
+                })
+                .catch(error => {
+                    console.error('Error sharing recipe:', error);
+                    showToast('Error sharing recipe: ' + error.message, 'error');
+                });
+        } catch (error) {
+            console.error('Error sharing recipe:', error);
+            showToast('Error preparing recipe for sharing', 'error');
+        }
+    }
+}
+
+export { RecipeCard }; 

static/js/components/RecipeModal.js

@@ -0,0 +1,287 @@
+// Recipe Modal Component
+import { showToast } from '../utils/uiHelpers.js';
+
+class RecipeModal {
+    constructor() {
+        this.init();
+    }
+    
+    init() {
+        this.setupCopyButtons();
+        // Set up tooltip positioning handlers after DOM is ready
+        document.addEventListener('DOMContentLoaded', () => {
+            this.setupTooltipPositioning();
+        });
+    }
+    
+    // Add tooltip positioning handler to ensure correct positioning of fixed tooltips
+    setupTooltipPositioning() {
+        document.addEventListener('mouseover', (event) => {
+            // Check if we're hovering over a local-badge
+            if (event.target.closest('.local-badge')) {
+                const badge = event.target.closest('.local-badge');
+                const tooltip = badge.querySelector('.local-path');
+                
+                if (tooltip) {
+                    // Get badge position
+                    const badgeRect = badge.getBoundingClientRect();
+                    
+                    // Position the tooltip
+                    tooltip.style.top = (badgeRect.bottom + 4) + 'px';
+                    tooltip.style.left = (badgeRect.right - tooltip.offsetWidth) + 'px';
+                }
+            }
+        }, true);
+    }
+    
+    showRecipeDetails(recipe) {
+        // Set modal title
+        const modalTitle = document.getElementById('recipeModalTitle');
+        if (modalTitle) {
+            modalTitle.textContent = recipe.title || 'Recipe Details';
+        }
+        
+        // Set recipe tags if they exist
+        const tagsCompactElement = document.getElementById('recipeTagsCompact');
+        const tagsTooltipContent = document.getElementById('recipeTagsTooltipContent');
+        
+        if (tagsCompactElement && tagsTooltipContent && recipe.tags && recipe.tags.length > 0) {
+            // Clear previous tags
+            tagsCompactElement.innerHTML = '';
+            tagsTooltipContent.innerHTML = '';
+            
+            // Limit displayed tags to 5, show a "+X more" button if needed
+            const maxVisibleTags = 5;
+            const visibleTags = recipe.tags.slice(0, maxVisibleTags);
+            const remainingTags = recipe.tags.length > maxVisibleTags ? recipe.tags.slice(maxVisibleTags) : [];
+            
+            // Add visible tags
+            visibleTags.forEach(tag => {
+                const tagElement = document.createElement('div');
+                tagElement.className = 'recipe-tag-compact';
+                tagElement.textContent = tag;
+                tagsCompactElement.appendChild(tagElement);
+            });
+            
+            // Add "more" button if needed
+            if (remainingTags.length > 0) {
+                const moreButton = document.createElement('div');
+                moreButton.className = 'recipe-tag-more';
+                moreButton.textContent = `+${remainingTags.length} more`;
+                tagsCompactElement.appendChild(moreButton);
+                
+                // Add tooltip functionality
+                moreButton.addEventListener('mouseenter', () => {
+                    document.getElementById('recipeTagsTooltip').classList.add('visible');
+                });
+                
+                moreButton.addEventListener('mouseleave', () => {
+                    setTimeout(() => {
+                        if (!document.getElementById('recipeTagsTooltip').matches(':hover')) {
+                            document.getElementById('recipeTagsTooltip').classList.remove('visible');
+                        }
+                    }, 300);
+                });
+                
+                document.getElementById('recipeTagsTooltip').addEventListener('mouseleave', () => {
+                    document.getElementById('recipeTagsTooltip').classList.remove('visible');
+                });
+                
+                // Add all tags to tooltip
+                recipe.tags.forEach(tag => {
+                    const tooltipTag = document.createElement('div');
+                    tooltipTag.className = 'tooltip-tag';
+                    tooltipTag.textContent = tag;
+                    tagsTooltipContent.appendChild(tooltipTag);
+                });
+            }
+        } else if (tagsCompactElement) {
+            // No tags to display
+            tagsCompactElement.innerHTML = '';
+        }
+        
+        // Set recipe image
+        const modalImage = document.getElementById('recipeModalImage');
+        if (modalImage) {
+            // Ensure file_url exists, fallback to file_path if needed
+            const imageUrl = recipe.file_url || 
+                            (recipe.file_path ? `/loras_static/root1/preview/${recipe.file_path.split('/').pop()}` : 
+                            '/loras_static/images/no-preview.png');
+            modalImage.src = imageUrl;
+            modalImage.alt = recipe.title || 'Recipe Preview';
+        }
+        
+        // Set generation parameters
+        const promptElement = document.getElementById('recipePrompt');
+        const negativePromptElement = document.getElementById('recipeNegativePrompt');
+        const otherParamsElement = document.getElementById('recipeOtherParams');
+        
+        if (recipe.gen_params) {
+            // Set prompt
+            if (promptElement && recipe.gen_params.prompt) {
+                promptElement.textContent = recipe.gen_params.prompt;
+            } else if (promptElement) {
+                promptElement.textContent = 'No prompt information available';
+            }
+            
+            // Set negative prompt
+            if (negativePromptElement && recipe.gen_params.negative_prompt) {
+                negativePromptElement.textContent = recipe.gen_params.negative_prompt;
+            } else if (negativePromptElement) {
+                negativePromptElement.textContent = 'No negative prompt information available';
+            }
+            
+            // Set other parameters
+            if (otherParamsElement) {
+                // Clear previous params
+                otherParamsElement.innerHTML = '';
+                
+                // Add all other parameters except prompt and negative_prompt
+                const excludedParams = ['prompt', 'negative_prompt'];
+                
+                for (const [key, value] of Object.entries(recipe.gen_params)) {
+                    if (!excludedParams.includes(key) && value !== undefined && value !== null) {
+                        const paramTag = document.createElement('div');
+                        paramTag.className = 'param-tag';
+                        paramTag.innerHTML = `
+                            <span class="param-name">${key}:</span>
+                            <span class="param-value">${value}</span>
+                        `;
+                        otherParamsElement.appendChild(paramTag);
+                    }
+                }
+                
+                // If no other params, show a message
+                if (otherParamsElement.children.length === 0) {
+                    otherParamsElement.innerHTML = '<div class="no-params">No additional parameters available</div>';
+                }
+            }
+        } else {
+            // No generation parameters available
+            if (promptElement) promptElement.textContent = 'No prompt information available';
+            if (negativePromptElement) negativePromptElement.textContent = 'No negative prompt information available';
+            if (otherParamsElement) otherParamsElement.innerHTML = '<div class="no-params">No parameters available</div>';
+        }
+        
+        // Set LoRAs list and count
+        const lorasListElement = document.getElementById('recipeLorasList');
+        const lorasCountElement = document.getElementById('recipeLorasCount');
+        
+        // 检查所有 LoRAs 是否都在库中
+        let allLorasAvailable = true;
+        let missingLorasCount = 0;
+        
+        if (recipe.loras && recipe.loras.length > 0) {
+            recipe.loras.forEach(lora => {
+                if (!lora.inLibrary) {
+                    allLorasAvailable = false;
+                    missingLorasCount++;
+                }
+            });
+        }
+        
+        // 设置 LoRAs 计数和状态
+        if (lorasCountElement && recipe.loras) {
+            const totalCount = recipe.loras.length;
+            
+            // 创建状态指示器
+            let statusHTML = '';
+            if (totalCount > 0) {
+                if (allLorasAvailable) {
+                    statusHTML = `<div class="recipe-status ready"><i class="fas fa-check-circle"></i> Ready to use</div>`;
+                } else {
+                    statusHTML = `<div class="recipe-status missing"><i class="fas fa-exclamation-triangle"></i> ${missingLorasCount} missing</div>`;
+                }
+            }
+            
+            lorasCountElement.innerHTML = `<i class="fas fa-layer-group"></i> ${totalCount} LoRAs ${statusHTML}`;
+        }
+        
+        if (lorasListElement && recipe.loras && recipe.loras.length > 0) {
+            lorasListElement.innerHTML = recipe.loras.map(lora => {
+                const existsLocally = lora.inLibrary;
+                const localPath = lora.localPath || '';
+                
+                // Create local status badge with a more stable structure
+                const localStatus = existsLocally ? 
+                    `<div class="local-badge">
+                        <i class="fas fa-check"></i> In Library
+                        <div class="local-path">${localPath}</div>
+                     </div>` : 
+                    `<div class="missing-badge">
+                        <i class="fas fa-exclamation-triangle"></i> Not in Library
+                     </div>`;
+
+                return `
+                    <div class="recipe-lora-item ${existsLocally ? 'exists-locally' : 'missing-locally'}">
+                        <div class="recipe-lora-thumbnail">
+                            <img src="${lora.preview_url || '/loras_static/images/no-preview.png'}" alt="LoRA preview">
+                        </div>
+                        <div class="recipe-lora-content">
+                            <div class="recipe-lora-header">
+                                <h4>${lora.modelName}</h4>
+                                <div class="badge-container">${localStatus}</div>
+                            </div>
+                            <div class="recipe-lora-info">
+                                ${lora.modelVersionName ? `<div class="recipe-lora-version">${lora.modelVersionName}</div>` : ''}
+                                <div class="recipe-lora-weight">Weight: ${lora.strength || 1.0}</div>
+                                ${lora.baseModel ? `<div class="base-model">${lora.baseModel}</div>` : ''}
+                            </div>
+                        </div>
+                    </div>
+                `;
+            }).join('');
+            
+            // Generate recipe syntax for copy button
+            this.recipeLorasSyntax = recipe.loras.map(lora => 
+                `<lora:${lora.file_name}:${lora.strength || 1.0}>`
+            ).join(' ');
+            
+        } else if (lorasListElement) {
+            lorasListElement.innerHTML = '<div class="no-loras">No LoRAs associated with this recipe</div>';
+            this.recipeLorasSyntax = '';
+        }
+        
+        // Show the modal
+        modalManager.showModal('recipeModal');
+    }
+    
+    // Setup copy buttons for prompts and recipe syntax
+    setupCopyButtons() {
+        const copyPromptBtn = document.getElementById('copyPromptBtn');
+        const copyNegativePromptBtn = document.getElementById('copyNegativePromptBtn');
+        const copyRecipeSyntaxBtn = document.getElementById('copyRecipeSyntaxBtn');
+        
+        if (copyPromptBtn) {
+            copyPromptBtn.addEventListener('click', () => {
+                const promptText = document.getElementById('recipePrompt').textContent;
+                this.copyToClipboard(promptText, 'Prompt copied to clipboard');
+            });
+        }
+        
+        if (copyNegativePromptBtn) {
+            copyNegativePromptBtn.addEventListener('click', () => {
+                const negativePromptText = document.getElementById('recipeNegativePrompt').textContent;
+                this.copyToClipboard(negativePromptText, 'Negative prompt copied to clipboard');
+            });
+        }
+        
+        if (copyRecipeSyntaxBtn) {
+            copyRecipeSyntaxBtn.addEventListener('click', () => {
+                this.copyToClipboard(this.recipeLorasSyntax, 'Recipe syntax copied to clipboard');
+            });
+        }
+    }
+    
+    // Helper method to copy text to clipboard
+    copyToClipboard(text, successMessage) {
+        navigator.clipboard.writeText(text).then(() => {
+            showToast(successMessage, 'success');
+        }).catch(err => {
+            console.error('Failed to copy text: ', err);
+            showToast('Failed to copy text', 'error');
+        });
+    }
+}
+
+export { RecipeModal }; 

static/js/core.js

@@ -0,0 +1,79 @@
+// Core application functionality
+import { state } from './state/index.js';
+import { LoadingManager } from './managers/LoadingManager.js';
+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 { showToast, initTheme, initBackToTop, lazyLoadImages } from './utils/uiHelpers.js';
+import { initializeInfiniteScroll } from './utils/infiniteScroll.js';
+import { migrateStorageItems } from './utils/storageHelpers.js';
+
+// Core application class
+export class AppCore {
+    constructor() {
+        this.initialized = false;
+    }
+    
+    // Initialize core functionality
+    async initialize() {
+        if (this.initialized) return;
+
+        console.log('AppCore: Initializing...');
+        
+        // Initialize managers
+        state.loadingManager = new LoadingManager();
+        modalManager.initialize();
+        updateService.initialize();
+        window.modalManager = modalManager;
+        window.settingsManager = new SettingsManager();
+        
+        // Initialize UI components
+        window.headerManager = new HeaderManager();
+        initTheme();
+        initBackToTop();
+        
+        // Mark as initialized
+        this.initialized = true;
+        
+        // Return the core instance for chaining
+        return this;
+    }
+    
+    // Get the current page type
+    getPageType() {
+        const body = document.body;
+        return body.dataset.page || 'unknown';
+    }
+    
+    // Show toast messages
+    showToast(message, type = 'info') {
+        showToast(message, type);
+    }
+    
+    // Initialize common UI features based on page type
+    initializePageFeatures() {
+        const pageType = this.getPageType();
+        
+        // Initialize lazy loading for images on all pages
+        lazyLoadImages();
+        
+        // Initialize infinite scroll for pages that need it
+        if (['loras', 'recipes', 'checkpoints'].includes(pageType)) {
+            initializeInfiniteScroll(pageType);
+        }
+        
+        return this;
+    }
+}
+
+document.addEventListener('DOMContentLoaded', () => {
+    // Migrate localStorage items to use the namespace prefix
+    migrateStorageItems();
+});
+
+// Create and export a singleton instance
+export const appCore = new AppCore();
+
+// Export common utilities for global use
+export { showToast, lazyLoadImages, initializeInfiniteScroll }; 

static/js/loras.js

@@ -0,0 +1,106 @@
+import { appCore } from './core.js';
+import { state } from './state/index.js';
+import { showLoraModal, toggleShowcase, scrollToTop } from './components/LoraModal.js';
+import { loadMoreLoras, fetchCivitai, deleteModel, replacePreview, resetAndReload, refreshLoras } from './api/loraApi.js';
+import { 
+    restoreFolderFilter, 
+    toggleFolder,
+    copyTriggerWord,
+    openCivitai,
+    toggleFolderTags,
+    initFolderTagsVisibility,
+} from './utils/uiHelpers.js';
+import { confirmDelete, closeDeleteModal } from './utils/modalUtils.js';
+import { DownloadManager } from './managers/DownloadManager.js';
+import { toggleApiKeyVisibility } from './managers/SettingsManager.js';
+import { LoraContextMenu } from './components/ContextMenu.js';
+import { moveManager } from './managers/MoveManager.js';
+import { updateCardsForBulkMode } from './components/LoraCard.js';
+import { bulkManager } from './managers/BulkManager.js';
+
+// Initialize the LoRA page
+class LoraPageManager {
+    constructor() {
+        // Add bulk mode to state
+        state.bulkMode = false;
+        state.selectedLoras = new Set();
+        
+        // Initialize managers
+        this.downloadManager = new DownloadManager();
+        
+        // Expose necessary functions to the page
+        this._exposeGlobalFunctions();
+    }
+    
+    _exposeGlobalFunctions() {
+        // Only expose what's needed for the page
+        window.loadMoreLoras = loadMoreLoras;
+        window.fetchCivitai = fetchCivitai;
+        window.deleteModel = deleteModel;
+        window.replacePreview = replacePreview;
+        window.toggleFolder = toggleFolder;
+        window.copyTriggerWord = copyTriggerWord;
+        window.showLoraModal = showLoraModal;
+        window.confirmDelete = confirmDelete;
+        window.closeDeleteModal = closeDeleteModal;
+        window.refreshLoras = refreshLoras;
+        window.openCivitai = openCivitai;
+        window.toggleFolderTags = toggleFolderTags;
+        window.toggleApiKeyVisibility = toggleApiKeyVisibility;
+        window.downloadManager = this.downloadManager;
+        window.moveManager = moveManager;
+        window.toggleShowcase = toggleShowcase;
+        window.scrollToTop = scrollToTop;
+        
+        // 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;
+    }
+    
+    async initialize() {
+        // Initialize page-specific components
+        initializeEventListeners();
+        restoreFolderFilter();
+        initFolderTagsVisibility();
+        new LoraContextMenu();
+        
+        // Initialize cards for current bulk mode state (should be false initially)
+        updateCardsForBulkMode(state.bulkMode);
+        
+        // Initialize the bulk manager
+        bulkManager.initialize();
+        
+        // Initialize common page features (lazy loading, infinite scroll)
+        appCore.initializePageFeatures();
+    }
+}
+
+// Initialize event listeners
+function initializeEventListeners() {
+    const sortSelect = document.getElementById('sortSelect');
+    if (sortSelect) {
+        sortSelect.value = state.sortBy;
+        sortSelect.addEventListener('change', async (e) => {
+            state.sortBy = e.target.value;
+            await resetAndReload();
+        });
+    }
+
+    document.querySelectorAll('.folder-tags .tag').forEach(tag => {
+        tag.addEventListener('click', toggleFolder);
+    });
+}
+
+// Initialize everything when DOM is ready
+document.addEventListener('DOMContentLoaded', async () => {
+    // Initialize core application
+    await appCore.initialize();
+    
+    // Initialize page-specific functionality
+    const loraPage = new LoraPageManager();
+    await loraPage.initialize();
+});

static/js/main.js

@@ -1,112 +0,0 @@
-import { debounce } from './utils/debounce.js';
-import { LoadingManager } from './managers/LoadingManager.js';
-import { modalManager } from './managers/ModalManager.js';
-import { updateService } from './managers/UpdateService.js';
-import { state } from './state/index.js';
-import { showLoraModal } from './components/LoraModal.js';
-import { toggleShowcase, scrollToTop } from './components/LoraModal.js';
-import { loadMoreLoras, fetchCivitai, deleteModel, replacePreview, resetAndReload, refreshLoras } from './api/loraApi.js';
-import { 
-    showToast, 
-    lazyLoadImages, 
-    restoreFolderFilter, 
-    initTheme,
-    toggleTheme,
-    toggleFolder,
-    copyTriggerWord,
-    openCivitai,
-    toggleFolderTags,
-    initFolderTagsVisibility,
-    initBackToTop
-} from './utils/uiHelpers.js';
-import { initializeInfiniteScroll } from './utils/infiniteScroll.js';
-import { showDeleteModal, confirmDelete, closeDeleteModal } from './utils/modalUtils.js';
-import { SearchManager } from './utils/search.js';
-import { DownloadManager } from './managers/DownloadManager.js';
-import { SettingsManager, toggleApiKeyVisibility } from './managers/SettingsManager.js';
-import { LoraContextMenu } from './components/ContextMenu.js';
-import { moveManager } from './managers/MoveManager.js';
-import { FilterManager } from './managers/FilterManager.js';
-import { createLoraCard, updateCardsForBulkMode } from './components/LoraCard.js';
-import { bulkManager } from './managers/BulkManager.js';
-
-// Add bulk mode to state
-state.bulkMode = false;
-state.selectedLoras = new Set();
-
-// Export functions to global window object
-window.loadMoreLoras = loadMoreLoras;
-window.fetchCivitai = fetchCivitai;
-window.deleteModel = deleteModel;
-window.replacePreview = replacePreview;
-window.toggleTheme = toggleTheme;
-window.toggleFolder = toggleFolder;
-window.copyTriggerWord = copyTriggerWord;
-window.showLoraModal = showLoraModal;
-window.modalManager = modalManager;
-window.state = state;
-window.confirmDelete = confirmDelete;
-window.closeDeleteModal = closeDeleteModal;
-window.refreshLoras = refreshLoras;
-window.openCivitai = openCivitai;
-window.showToast = showToast
-window.toggleFolderTags = toggleFolderTags;
-window.settingsManager = new SettingsManager();
-window.toggleApiKeyVisibility = toggleApiKeyVisibility;
-window.moveManager = moveManager;
-window.toggleShowcase = toggleShowcase;
-window.scrollToTop = scrollToTop;
-
-// Export bulk manager methods to window
-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;
-
-// Initialize everything when DOM is ready
-document.addEventListener('DOMContentLoaded', async () => {
-    state.loadingManager = new LoadingManager();
-    modalManager.initialize();  // Initialize modalManager after DOM is loaded
-    updateService.initialize(); // Initialize updateService after modalManager
-    window.downloadManager = new DownloadManager();  // Move this after modalManager initialization
-    window.filterManager = new FilterManager(); // Initialize filter manager
-    
-    // Initialize state filters from filterManager if available
-    if (window.filterManager && window.filterManager.filters) {
-        state.filters = { ...window.filterManager.filters };
-    }
-    
-    initializeInfiniteScroll();
-    initializeEventListeners();
-    lazyLoadImages();
-    restoreFolderFilter();
-    initTheme();
-    initFolderTagsVisibility();
-    initBackToTop();
-    window.searchManager = new SearchManager();
-    new LoraContextMenu();
-    
-    // Initialize cards for current bulk mode state (should be false initially)
-    updateCardsForBulkMode(state.bulkMode);
-    
-    // Initialize the bulk manager
-    bulkManager.initialize();
-});
-
-// Initialize event listeners
-function initializeEventListeners() {
-    const sortSelect = document.getElementById('sortSelect');
-    if (sortSelect) {
-        sortSelect.value = state.sortBy;
-        sortSelect.addEventListener('change', async (e) => {
-            state.sortBy = e.target.value;
-            await resetAndReload();
-        });
-    }
-
-    document.querySelectorAll('.folder-tags .tag').forEach(tag => {
-        tag.addEventListener('click', toggleFolder);
-    });
-}

static/js/managers/BulkManager.js

@@ -56,12 +56,20 @@ export class BulkManager {
             this.hideThumbnailStrip();
         }
         
-        // Update all cards
+        // First update all cards' visual state before clearing selection
         updateCardsForBulkMode(state.bulkMode);
         
-        // Clear selection if exiting bulk mode
+        // Clear selection if exiting bulk mode - do this after updating cards
         if (!state.bulkMode) {
             this.clearSelection();
+            
+            // Force a lightweight refresh of the cards to ensure proper display
+            // This is less disruptive than a full resetAndReload()
+            document.querySelectorAll('.lora-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');
+            });
         }
     }
 
@@ -83,16 +91,17 @@ export class BulkManager {
             // Set text content without the icon
             countElement.textContent = `${state.selectedLoras.size} selected `;
             
-            // Re-add the caret icon with proper direction
-            const caretIcon = document.createElement('i');
-            // Use down arrow if strip is visible, up arrow if not
-            caretIcon.className = `fas fa-caret-${this.isStripVisible ? 'down' : 'up'} dropdown-caret`;
-            caretIcon.style.visibility = state.selectedLoras.size > 0 ? 'visible' : 'hidden';
-            countElement.appendChild(caretIcon);
-            
-            // If there are no selections, hide the thumbnail strip
-            if (state.selectedLoras.size === 0) {
-                this.hideThumbnailStrip();
+            // Update caret icon if it exists
+            const existingCaret = countElement.querySelector('.dropdown-caret');
+            if (existingCaret) {
+                existingCaret.className = `fas fa-caret-${this.isStripVisible ? 'down' : 'up'} dropdown-caret`;
+                existingCaret.style.visibility = state.selectedLoras.size > 0 ? 'visible' : 'hidden';
+            } 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';
+                countElement.appendChild(caretIcon);
             }
         }
     }
@@ -244,12 +253,20 @@ export class BulkManager {
     
     hideThumbnailStrip() {
         const strip = document.querySelector('.selected-thumbnails-strip');
-        if (strip) {
+        if (strip && this.isStripVisible) {  // Only hide if actually visible
             strip.classList.remove('visible');
             
-            // Update strip visibility state and caret direction
+            // Update strip visibility state
             this.isStripVisible = false;
-            this.updateSelectedCount(); // Update caret
+            
+            // Update caret without triggering another hide
+            const countElement = document.getElementById('selectedCount');
+            if (countElement) {
+                const caret = countElement.querySelector('.dropdown-caret');
+                if (caret) {
+                    caret.className = 'fas fa-caret-up dropdown-caret';
+                }
+            }
             
             // Wait for animation to complete before removing
             setTimeout(() => {
@@ -332,4 +349,4 @@ export class BulkManager {
 }
 
 // Create a singleton instance
-export const bulkManager = new BulkManager(); 
+export const bulkManager = new BulkManager();

static/js/managers/CheckpointSearchManager.js

@@ -0,0 +1,150 @@
+/**
+ * CheckpointSearchManager - Specialized search manager for the Checkpoints page
+ * Extends the base SearchManager with checkpoint-specific functionality
+ */
+import { SearchManager } from './SearchManager.js';
+import { state } from '../state/index.js';
+import { showToast } from '../utils/uiHelpers.js';
+
+export class CheckpointSearchManager extends SearchManager {
+  constructor(options = {}) {
+    super({
+      page: 'checkpoints',
+      ...options
+    });
+    
+    this.currentSearchTerm = '';
+    
+    // Store this instance in the state
+    if (state) {
+      state.searchManager = this;
+    }
+  }
+  
+  async performSearch() {
+    const searchTerm = this.searchInput.value.trim().toLowerCase();
+    
+    if (searchTerm === this.currentSearchTerm && !this.isSearching) {
+      return; // Avoid duplicate searches
+    }
+    
+    this.currentSearchTerm = searchTerm;
+    
+    const grid = document.getElementById('checkpointGrid');
+    
+    if (!searchTerm) {
+      if (state) {
+        state.currentPage = 1;
+      }
+      this.resetAndReloadCheckpoints();
+      return;
+    }
+
+    try {
+      this.isSearching = true;
+      if (state && state.loadingManager) {
+        state.loadingManager.showSimpleLoading('Searching checkpoints...');
+      }
+
+      // Store current scroll position
+      const scrollPosition = window.pageYOffset || document.documentElement.scrollTop;
+      
+      if (state) {
+        state.currentPage = 1;
+        state.hasMore = true;
+      }
+
+      const url = new URL('/api/checkpoints', window.location.origin);
+      url.searchParams.set('page', '1');
+      url.searchParams.set('page_size', '20');
+      url.searchParams.set('sort_by', state ? state.sortBy : 'name');
+      url.searchParams.set('search', searchTerm);
+      url.searchParams.set('fuzzy', 'true');
+      
+      // Add search options
+      const searchOptions = this.getActiveSearchOptions();
+      url.searchParams.set('search_filename', searchOptions.filename.toString());
+      url.searchParams.set('search_modelname', searchOptions.modelname.toString());
+
+      // Always send folder parameter if there is an active folder
+      if (state && state.activeFolder) {
+        url.searchParams.set('folder', state.activeFolder);
+        // Add recursive parameter when recursive search is enabled
+        const recursive = this.recursiveSearchToggle ? this.recursiveSearchToggle.checked : false;
+        url.searchParams.set('recursive', recursive.toString());
+      }
+
+      const response = await fetch(url);
+      
+      if (!response.ok) {
+        throw new Error('Search failed');
+      }
+
+      const data = await response.json();
+      
+      if (searchTerm === this.currentSearchTerm && grid) {
+        grid.innerHTML = '';
+        
+        if (data.items.length === 0) {
+          grid.innerHTML = '<div class="no-results">No matching checkpoints found</div>';
+          if (state) {
+            state.hasMore = false;
+          }
+        } else {
+          this.appendCheckpointCards(data.items);
+          if (state) {
+            state.hasMore = state.currentPage < data.total_pages;
+            state.currentPage++;
+          }
+        }
+        
+        // Restore scroll position after content is loaded
+        setTimeout(() => {
+          window.scrollTo({
+            top: scrollPosition,
+            behavior: 'instant' // Use 'instant' to prevent animation
+          });
+        }, 10);
+      }
+    } catch (error) {
+      console.error('Checkpoint search error:', error);
+      showToast('Checkpoint search failed', 'error');
+    } finally {
+      this.isSearching = false;
+      if (state && state.loadingManager) {
+        state.loadingManager.hide();
+      }
+    }
+  }
+  
+  resetAndReloadCheckpoints() {
+    // This function would be implemented in the checkpoints page
+    if (typeof window.loadCheckpoints === 'function') {
+      window.loadCheckpoints();
+    } else {
+      // Fallback to reloading the page
+      window.location.reload();
+    }
+  }
+  
+  appendCheckpointCards(checkpoints) {
+    // This function would be implemented in the checkpoints page
+    const grid = document.getElementById('checkpointGrid');
+    if (!grid) return;
+    
+    if (typeof window.appendCheckpointCards === 'function') {
+      window.appendCheckpointCards(checkpoints);
+    } else {
+      // Fallback implementation
+      checkpoints.forEach(checkpoint => {
+        const card = document.createElement('div');
+        card.className = 'checkpoint-card';
+        card.innerHTML = `
+          <h3>${checkpoint.name}</h3>
+          <p>${checkpoint.filename || 'No filename'}</p>
+        `;
+        grid.appendChild(card);
+      });
+    }
+  }
+} 

static/js/managers/DownloadManager.js

@@ -120,20 +120,42 @@ export class DownloadManager {
         versionList.innerHTML = this.versions.map(version => {
             const firstImage = version.images?.find(img => !img.url.endsWith('.mp4'));
             const thumbnailUrl = firstImage ? firstImage.url : '/loras_static/images/no-preview.png';
-            const fileSize = (version.files[0]?.sizeKB / 1024).toFixed(2);
             
-            const existsLocally = version.files[0]?.existsLocally;
-            const localPath = version.files[0]?.localPath;
+            // Use version-level size or fallback to first file
+            const fileSize = version.modelSizeKB ? 
+                (version.modelSizeKB / 1024).toFixed(2) : 
+                (version.files[0]?.sizeKB / 1024).toFixed(2);
             
-            // 更新本地状态指示器为badge样式
+            // Use version-level existsLocally flag
+            const existsLocally = version.existsLocally;
+            const localPath = version.localPath;
+            
+            // Check if this is an early access version
+            const isEarlyAccess = version.availability === 'EarlyAccess';
+            
+            // Create early access badge if needed
+            let earlyAccessBadge = '';
+            if (isEarlyAccess) {
+                earlyAccessBadge = `
+                    <div class="early-access-badge" title="Early access required">
+                        <i class="fas fa-clock"></i> Early Access
+                    </div>
+                `;
+            }
+
+            console.log(earlyAccessBadge);
+            
+            // Status badge for local models
             const localStatus = existsLocally ? 
                 `<div class="local-badge">
                     <i class="fas fa-check"></i> In Library
-                    <div class="local-path">${localPath}</div>
+                    <div class="local-path">${localPath || ''}</div>
                  </div>` : '';
 
             return `
-                <div class="version-item ${this.currentVersion?.id === version.id ? 'selected' : ''} ${existsLocally ? 'exists-locally' : ''}"
+                <div class="version-item ${this.currentVersion?.id === version.id ? 'selected' : ''} 
+                     ${existsLocally ? 'exists-locally' : ''} 
+                     ${isEarlyAccess ? 'is-early-access' : ''}"
                      onclick="downloadManager.selectVersion('${version.id}')">
                     <div class="version-thumbnail">
                         <img src="${thumbnailUrl}" alt="Version preview">
@@ -145,6 +167,7 @@ export class DownloadManager {
                         </div>
                         <div class="version-info">
                             ${version.baseModel ? `<div class="base-model">${version.baseModel}</div>` : ''}
+                            ${earlyAccessBadge}
                         </div>
                         <div class="version-meta">
                             <span><i class="fas fa-calendar"></i> ${new Date(version.createdAt).toLocaleDateString()}</span>
@@ -177,12 +200,12 @@ export class DownloadManager {
         this.updateNextButtonState();
     }
     
-    // Add new method to update Next button state
+    // Update this method to use version-level existsLocally
     updateNextButtonState() {
         const nextButton = document.querySelector('#versionStep .primary-btn');
         if (!nextButton) return;
         
-        const existsLocally = this.currentVersion?.files[0]?.existsLocally;
+        const existsLocally = this.currentVersion?.existsLocally;
         
         if (existsLocally) {
             nextButton.disabled = true;
@@ -202,7 +225,7 @@ export class DownloadManager {
         }
         
         // Double-check if the version exists locally
-        const existsLocally = this.currentVersion.files[0]?.existsLocally;
+        const existsLocally = this.currentVersion.existsLocally;
         if (existsLocally) {
             showToast('This version already exists in your library', 'info');
             return;
@@ -265,18 +288,37 @@ export class DownloadManager {
                 throw new Error('No download URL available');
             }
 
-            // Show loading with progress bar for download
-            this.loadingManager.show('Downloading LoRA...', 0);
+            // Show enhanced loading with progress details
+            const updateProgress = this.loadingManager.showDownloadProgress(1);
+            updateProgress(0, 0, this.currentVersion.name);
 
             // Setup WebSocket for progress updates
-            const ws = new WebSocket(`ws://${window.location.host}/ws/fetch-progress`);
+            const wsProtocol = window.location.protocol === 'https:' ? 'wss://' : 'ws://';
+            const ws = new WebSocket(`${wsProtocol}${window.location.host}/ws/fetch-progress`);
+            
             ws.onmessage = (event) => {
                 const data = JSON.parse(event.data);
                 if (data.status === 'progress') {
-                    this.loadingManager.setProgress(data.progress);
-                    this.loadingManager.setStatus(`Downloading: ${data.progress}%`);
+                    // Update progress display with current progress
+                    updateProgress(data.progress, 0, this.currentVersion.name);
+                    
+                    // Add more detailed status messages based on progress
+                    if (data.progress < 3) {
+                        this.loadingManager.setStatus(`Preparing download...`);
+                    } else if (data.progress === 3) {
+                        this.loadingManager.setStatus(`Downloaded preview image`);
+                    } else if (data.progress > 3 && data.progress < 100) {
+                        this.loadingManager.setStatus(`Downloading LoRA file`);
+                    } else {
+                        this.loadingManager.setStatus(`Finalizing download...`);
+                    }
                 }
             };
+            
+            ws.onerror = (error) => {
+                console.error('WebSocket error:', error);
+                // Continue with download even if WebSocket fails
+            };
 
             // Start download
             const response = await fetch('/api/download-lora', {

static/js/managers/FilterManager.js

@@ -1,28 +1,52 @@
 import { BASE_MODELS, BASE_MODEL_CLASSES } from '../utils/constants.js';
-import { state } from '../state/index.js';
-import { showToast } from '../utils/uiHelpers.js';
-import { resetAndReload } from '../api/loraApi.js';
+import { state, getCurrentPageState } from '../state/index.js';
+import { showToast, updatePanelPositions } from '../utils/uiHelpers.js';
+import { loadMoreLoras } from '../api/loraApi.js';
+import { removeStorageItem, setStorageItem, getStorageItem } from '../utils/storageHelpers.js';
 
 export class FilterManager {
-    constructor() {
-        this.filters = {
-            baseModel: []
+    constructor(options = {}) {
+        this.options = {
+            ...options
+        };
+        
+        this.currentPage = options.page || document.body.dataset.page || 'loras';
+        const pageState = getCurrentPageState();
+        
+        this.filters = pageState.filters || {
+            baseModel: [],
+            tags: []
         };
         
         this.filterPanel = document.getElementById('filterPanel');
         this.filterButton = document.getElementById('filterButton');
         this.activeFiltersCount = document.getElementById('activeFiltersCount');
+        this.tagsLoaded = false;
         
         this.initialize();
+        
+        // Store this instance in the state
+        if (pageState) {
+            pageState.filterManager = this;
+        }
     }
     
     initialize() {
-        // Create base model filter tags
-        this.createBaseModelTags();
+        // Create base model filter tags if they exist
+        if (document.getElementById('baseModelTags')) {
+            this.createBaseModelTags();
+        }
+
+        // Add click handler for filter button
+        if (this.filterButton) {
+            this.filterButton.addEventListener('click', () => {
+                this.toggleFilterPanel();
+            });
+        }
         
         // Close filter panel when clicking outside
         document.addEventListener('click', (e) => {
-            if (!this.filterPanel.contains(e.target) && 
+            if (this.filterPanel && !this.filterPanel.contains(e.target) && 
                 e.target !== this.filterButton &&
                 !this.filterButton.contains(e.target) &&
                 !this.filterPanel.classList.contains('hidden')) {
@@ -34,28 +58,69 @@ export class FilterManager {
         this.loadFiltersFromStorage();
     }
     
-    createBaseModelTags() {
-        const baseModelTagsContainer = document.getElementById('baseModelTags');
-        if (!baseModelTagsContainer) return;
+    async loadTopTags() {
+        try {
+            // Show loading state
+            const tagsContainer = document.getElementById('modelTagsFilter');
+            if (!tagsContainer) return;
+            
+            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';
+            }
+            
+            const response = await fetch(tagsEndpoint);
+            if (!response.ok) throw new Error('Failed to fetch tags');
+            
+            const data = await response.json();
+            if (data.success && data.tags) {
+                this.createTagFilterElements(data.tags);
+                
+                // After creating tag elements, mark any previously selected ones
+                this.updateTagSelections();
+            } else {
+                throw new Error('Invalid response format');
+            }
+        } catch (error) {
+            console.error('Error loading top tags:', error);
+            const tagsContainer = document.getElementById('modelTagsFilter');
+            if (tagsContainer) {
+                tagsContainer.innerHTML = '<div class="tags-error">Failed to load tags</div>';
+            }
+        }
+    }
+    
+    createTagFilterElements(tags) {
+        const tagsContainer = document.getElementById('modelTagsFilter');
+        if (!tagsContainer) return;
         
-        baseModelTagsContainer.innerHTML = '';
+        tagsContainer.innerHTML = '';
         
-        Object.entries(BASE_MODELS).forEach(([key, value]) => {
-            const tag = document.createElement('div');
-            tag.className = `filter-tag base-model-tag ${BASE_MODEL_CLASSES[value]}`;
-            tag.dataset.baseModel = value;
-            tag.innerHTML = value;
+        if (!tags.length) {
+            tagsContainer.innerHTML = `<div class="no-tags">No ${this.currentPage === 'recipes' ? 'recipe ' : ''}tags available</div>`;
+            return;
+        }
+        
+        tags.forEach(tag => {
+            const tagEl = document.createElement('div');
+            tagEl.className = 'filter-tag tag-filter';
+            const tagName = tag.tag;
+            tagEl.dataset.tag = tagName;
+            tagEl.innerHTML = `${tagName} <span class="tag-count">${tag.count}</span>`;
             
             // Add click handler to toggle selection and automatically apply
-            tag.addEventListener('click', async () => {
-                tag.classList.toggle('active');
+            tagEl.addEventListener('click', async () => {
+                tagEl.classList.toggle('active');
                 
-                if (tag.classList.contains('active')) {
-                    if (!this.filters.baseModel.includes(value)) {
-                        this.filters.baseModel.push(value);
+                if (tagEl.classList.contains('active')) {
+                    if (!this.filters.tags.includes(tagName)) {
+                        this.filters.tags.push(tagName);
                     }
                 } else {
-                    this.filters.baseModel = this.filters.baseModel.filter(model => model !== value);
+                    this.filters.tags = this.filters.tags.filter(t => t !== tagName);
                 }
                 
                 this.updateActiveFiltersCount();
@@ -64,21 +129,101 @@ export class FilterManager {
                 await this.applyFilters(false);
             });
             
-            baseModelTagsContainer.appendChild(tag);
+            tagsContainer.appendChild(tagEl);
         });
     }
     
-    toggleFilterPanel() {
-        this.filterPanel.classList.toggle('hidden');
+    createBaseModelTags() {
+        const baseModelTagsContainer = document.getElementById('baseModelTags');
+        if (!baseModelTagsContainer) return;
         
-        // Mark selected filters
-        if (!this.filterPanel.classList.contains('hidden')) {
-            this.updateTagSelections();
+        // 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 {
+            return; // No API endpoint for other pages
+        }
+        
+        // Fetch base models
+        fetch(apiEndpoint)
+            .then(response => response.json())
+            .then(data => {
+                if (data.success && data.base_models) {
+                    baseModelTagsContainer.innerHTML = '';
+                    
+                    data.base_models.forEach(model => {
+                        const tag = document.createElement('div');
+                        // Add base model classes only for the loras page
+                        const baseModelClass = (this.currentPage === 'loras' && BASE_MODEL_CLASSES[model.name]) 
+                            ? BASE_MODEL_CLASSES[model.name] 
+                            : '';
+                        tag.className = `filter-tag base-model-tag ${baseModelClass}`;
+                        tag.dataset.baseModel = model.name;
+                        tag.innerHTML = `${model.name} <span class="tag-count">${model.count}</span>`;
+                        
+                        // Add click handler to toggle selection and automatically apply
+                        tag.addEventListener('click', async () => {
+                            tag.classList.toggle('active');
+                            
+                            if (tag.classList.contains('active')) {
+                                if (!this.filters.baseModel.includes(model.name)) {
+                                    this.filters.baseModel.push(model.name);
+                                }
+                            } else {
+                                this.filters.baseModel = this.filters.baseModel.filter(m => m !== model.name);
+                            }
+                            
+                            this.updateActiveFiltersCount();
+                            
+                            // Auto-apply filter when tag is clicked
+                            await this.applyFilters(false);
+                        });
+                        
+                        baseModelTagsContainer.appendChild(tag);
+                    });
+                    
+                    // Update selections based on stored filters
+                    this.updateTagSelections();
+                }
+            })
+            .catch(error => {
+                console.error(`Error fetching base models for ${this.currentPage}:`, error);
+                baseModelTagsContainer.innerHTML = '<div class="tags-error">Failed to load base models</div>';
+            });
+    }
+    
+    toggleFilterPanel() {       
+        if (this.filterPanel) {
+            const isHidden = this.filterPanel.classList.contains('hidden');
+            
+            if (isHidden) {
+                // Update panel positions before showing
+                updatePanelPositions();
+                
+                this.filterPanel.classList.remove('hidden');
+                this.filterButton.classList.add('active');
+                
+                // Load tags if they haven't been loaded yet
+                if (!this.tagsLoaded) {
+                    this.loadTopTags();
+                    this.tagsLoaded = true;
+                }
+            } else {
+                this.closeFilterPanel();
+            }
         }
     }
     
     closeFilterPanel() {
-        this.filterPanel.classList.add('hidden');
+        if (this.filterPanel) {
+            this.filterPanel.classList.add('hidden');
+        }
+        if (this.filterButton) {
+            this.filterButton.classList.remove('active');
+        }
     }
     
     updateTagSelections() {
@@ -92,34 +237,69 @@ export class FilterManager {
                 tag.classList.remove('active');
             }
         });
+        
+        // Update model tags
+        const modelTags = document.querySelectorAll('.tag-filter');
+        modelTags.forEach(tag => {
+            const tagName = tag.dataset.tag;
+            if (this.filters.tags.includes(tagName)) {
+                tag.classList.add('active');
+            } else {
+                tag.classList.remove('active');
+            }
+        });
     }
     
     updateActiveFiltersCount() {
-        const totalActiveFilters = this.filters.baseModel.length;
+        const totalActiveFilters = this.filters.baseModel.length + this.filters.tags.length;
         
-        if (totalActiveFilters > 0) {
-            this.activeFiltersCount.textContent = totalActiveFilters;
-            this.activeFiltersCount.style.display = 'inline-flex';
-        } else {
-            this.activeFiltersCount.style.display = 'none';
+        if (this.activeFiltersCount) {
+            if (totalActiveFilters > 0) {
+                this.activeFiltersCount.textContent = totalActiveFilters;
+                this.activeFiltersCount.style.display = 'inline-flex';
+            } else {
+                this.activeFiltersCount.style.display = 'none';
+            }
         }
     }
     
     async applyFilters(showToastNotification = true) {
+        const pageState = getCurrentPageState();
+        const storageKey = `${this.currentPage}_filters`;
+        
         // Save filters to localStorage
-        localStorage.setItem('loraFilters', JSON.stringify(this.filters));
+        setStorageItem(storageKey, this.filters);
         
         // Update state with current filters
-        state.filters = { ...this.filters };
+        pageState.filters = { ...this.filters };
         
-        // Reload loras with filters applied
-        await resetAndReload();
+        // Call the appropriate manager's load method based on page type
+        if (this.currentPage === 'recipes' && window.recipeManager) {
+            await window.recipeManager.loadRecipes(true);
+        } else if (this.currentPage === 'loras') {
+            // For loras page, reset the page and reload
+            await loadMoreLoras(true, true);
+        } else if (this.currentPage === 'checkpoints' && window.checkpointManager) {
+            await window.checkpointManager.loadCheckpoints(true);
+        }
         
         // Update filter button to show active state
         if (this.hasActiveFilters()) {
             this.filterButton.classList.add('active');
             if (showToastNotification) {
-                showToast(`Filtering by ${this.filters.baseModel.length} base models`, 'success');
+                const baseModelCount = this.filters.baseModel.length;
+                const tagsCount = this.filters.tags.length;
+                
+                let message = '';
+                if (baseModelCount > 0 && tagsCount > 0) {
+                    message = `Filtering by ${baseModelCount} base model${baseModelCount > 1 ? 's' : ''} and ${tagsCount} tag${tagsCount > 1 ? 's' : ''}`;
+                } else if (baseModelCount > 0) {
+                    message = `Filtering by ${baseModelCount} base model${baseModelCount > 1 ? 's' : ''}`;
+                } else if (tagsCount > 0) {
+                    message = `Filtering by ${tagsCount} tag${tagsCount > 1 ? 's' : ''}`;
+                }
+                
+                showToast(message, 'success');
             }
         } else {
             this.filterButton.classList.remove('active');
@@ -132,29 +312,53 @@ export class FilterManager {
     async clearFilters() {
         // Clear all filters
         this.filters = {
-            baseModel: []
+            baseModel: [],
+            tags: []
         };
         
         // Update state
-        state.filters = { ...this.filters };
+        const pageState = getCurrentPageState();
+        pageState.filters = { ...this.filters };
         
         // Update UI
         this.updateTagSelections();
         this.updateActiveFiltersCount();
         
-        // Remove from localStorage
-        localStorage.removeItem('loraFilters');
+        // Remove from local Storage
+        const storageKey = `${this.currentPage}_filters`;
+        removeStorageItem(storageKey);
         
-        // Update UI and reload data
+        // Update UI
         this.filterButton.classList.remove('active');
-        await resetAndReload();
+        
+        // Reload data using the appropriate method for the current page
+        if (this.currentPage === 'recipes' && window.recipeManager) {
+            await window.recipeManager.loadRecipes(true);
+        } else if (this.currentPage === 'loras') {
+            await loadMoreLoras(true, true);
+        } else if (this.currentPage === 'checkpoints' && window.checkpointManager) {
+            await window.checkpointManager.loadCheckpoints(true);
+        }
+        
+        showToast(`Filters cleared`, 'info');
     }
     
     loadFiltersFromStorage() {
-        const savedFilters = localStorage.getItem('loraFilters');
+        const storageKey = `${this.currentPage}_filters`;
+        const savedFilters = getStorageItem(storageKey);
+        
         if (savedFilters) {
             try {
-                this.filters = JSON.parse(savedFilters);
+                // Ensure backward compatibility with older filter format
+                this.filters = {
+                    baseModel: savedFilters.baseModel || [],
+                    tags: savedFilters.tags || []
+                };
+                
+                // Update state with loaded filters
+                const pageState = getCurrentPageState();
+                pageState.filters = { ...this.filters };
+
                 this.updateTagSelections();
                 this.updateActiveFiltersCount();
                 
@@ -162,12 +366,12 @@ export class FilterManager {
                     this.filterButton.classList.add('active');
                 }
             } catch (error) {
-                console.error('Error loading filters from storage:', error);
+                console.error(`Error loading ${this.currentPage} filters from storage:`, error);
             }
         }
     }
     
     hasActiveFilters() {
-        return this.filters.baseModel.length > 0;
+        return this.filters.baseModel.length > 0 || this.filters.tags.length > 0;
     }
 }

static/js/managers/LoadingManager.js

@@ -4,17 +4,22 @@ export class LoadingManager {
         this.overlay = document.getElementById('loading-overlay');
         this.progressBar = this.overlay.querySelector('.progress-bar');
         this.statusText = this.overlay.querySelector('.loading-status');
+        this.detailsContainer = null; // Will be created when needed
     }
 
     show(message = 'Loading...', progress = 0) {
         this.overlay.style.display = 'flex';
         this.setProgress(progress);
         this.setStatus(message);
+        
+        // Remove any existing details container
+        this.removeDetailsContainer();
     }
 
     hide() {
         this.overlay.style.display = 'none';
         this.reset();
+        this.removeDetailsContainer();
     }
 
     setProgress(percent) {
@@ -29,6 +34,101 @@ export class LoadingManager {
     reset() {
         this.setProgress(0);
         this.setStatus('');
+        this.removeDetailsContainer();
+    }
+
+    // Create a details container for enhanced progress display
+    createDetailsContainer() {
+        // Remove existing container if any
+        this.removeDetailsContainer();
+        
+        // Create new container
+        this.detailsContainer = document.createElement('div');
+        this.detailsContainer.className = 'progress-details-container';
+        
+        // Insert after the main progress bar
+        const loadingContent = this.overlay.querySelector('.loading-content');
+        if (loadingContent) {
+            loadingContent.appendChild(this.detailsContainer);
+        }
+        
+        return this.detailsContainer;
+    }
+    
+    // Remove details container
+    removeDetailsContainer() {
+        if (this.detailsContainer) {
+            this.detailsContainer.remove();
+            this.detailsContainer = null;
+        }
+    }
+    
+    // Show enhanced progress for downloads
+    showDownloadProgress(totalItems = 1) {
+        this.show('Preparing download...', 0);
+        
+        // Create details container
+        const detailsContainer = this.createDetailsContainer();
+        
+        // Create current item progress
+        const currentItemContainer = document.createElement('div');
+        currentItemContainer.className = 'current-item-progress';
+        
+        const currentItemLabel = document.createElement('div');
+        currentItemLabel.className = 'current-item-label';
+        currentItemLabel.textContent = 'Current file:';
+        
+        const currentItemBar = document.createElement('div');
+        currentItemBar.className = 'current-item-bar-container';
+        
+        const currentItemProgress = document.createElement('div');
+        currentItemProgress.className = 'current-item-bar';
+        currentItemProgress.style.width = '0%';
+        
+        const currentItemPercent = document.createElement('span');
+        currentItemPercent.className = 'current-item-percent';
+        currentItemPercent.textContent = '0%';
+        
+        currentItemBar.appendChild(currentItemProgress);
+        currentItemContainer.appendChild(currentItemLabel);
+        currentItemContainer.appendChild(currentItemBar);
+        currentItemContainer.appendChild(currentItemPercent);
+        
+        // Create overall progress elements if multiple items
+        let overallLabel = null;
+        if (totalItems > 1) {
+            overallLabel = document.createElement('div');
+            overallLabel.className = 'overall-progress-label';
+            overallLabel.textContent = `Overall progress (0/${totalItems} complete):`;
+            detailsContainer.appendChild(overallLabel);
+        }
+        
+        // Add current item progress to container
+        detailsContainer.appendChild(currentItemContainer);
+        
+        // Return update function
+        return (currentProgress, currentIndex = 0, currentName = '') => {
+            // Update current item progress
+            currentItemProgress.style.width = `${currentProgress}%`;
+            currentItemPercent.textContent = `${Math.floor(currentProgress)}%`;
+            
+            // Update current item label if name provided
+            if (currentName) {
+                currentItemLabel.textContent = `Downloading: ${currentName}`;
+            }
+            
+            // Update overall label if multiple items
+            if (totalItems > 1 && overallLabel) {
+                overallLabel.textContent = `Overall progress (${currentIndex}/${totalItems} complete):`;
+                
+                // Calculate and update overall progress
+                const overallProgress = Math.floor((currentIndex + currentProgress/100) / totalItems * 100);
+                this.setProgress(overallProgress);
+            } else {
+                // Single item, just update main progress
+                this.setProgress(currentProgress);
+            }
+        };
     }
 
     async showWithProgress(callback, options = {}) {

static/js/managers/ModalManager.js

@@ -10,67 +10,114 @@ export class ModalManager {
         
         this.boundHandleEscape = this.handleEscape.bind(this);
         
-        // Register all modals
-        this.registerModal('loraModal', {
-            element: document.getElementById('loraModal'),
-            onClose: () => {
-                this.getModal('loraModal').element.style.display = 'none';
-                document.body.classList.remove('modal-open');
-            }
-        });
+        // Register all modals - only if they exist in the current page
+        const loraModal = document.getElementById('loraModal');
+        if (loraModal) {
+            this.registerModal('loraModal', {
+                element: loraModal,
+                onClose: () => {
+                    this.getModal('loraModal').element.style.display = 'none';
+                    document.body.classList.remove('modal-open');
+                },
+                closeOnOutsideClick: true
+            });
+        }
         
-        this.registerModal('deleteModal', {
-            element: document.getElementById('deleteModal'),
-            onClose: () => {
-                this.getModal('deleteModal').element.classList.remove('show');
-                document.body.classList.remove('modal-open');
-            }
-        });
+        const deleteModal = document.getElementById('deleteModal');
+        if (deleteModal) {
+            this.registerModal('deleteModal', {
+                element: deleteModal,
+                onClose: () => {
+                    this.getModal('deleteModal').element.classList.remove('show');
+                    document.body.classList.remove('modal-open');
+                }
+            });
+        }
 
         // Add downloadModal registration
-        this.registerModal('downloadModal', {
-            element: document.getElementById('downloadModal'),
-            onClose: () => {
-                this.getModal('downloadModal').element.style.display = 'none';
-                document.body.classList.remove('modal-open');
-            }
-        });
+        const downloadModal = document.getElementById('downloadModal');
+        if (downloadModal) {
+            this.registerModal('downloadModal', {
+                element: downloadModal,
+                onClose: () => {
+                    this.getModal('downloadModal').element.style.display = 'none';
+                    document.body.classList.remove('modal-open');
+                }
+            });
+        }
 
         // Add settingsModal registration
-        this.registerModal('settingsModal', {
-            element: document.getElementById('settingsModal'),
-            onClose: () => {
-                this.getModal('settingsModal').element.style.display = 'none';
-                document.body.classList.remove('modal-open');
-            }
-        });
+        const settingsModal = document.getElementById('settingsModal');
+        if (settingsModal) {
+            this.registerModal('settingsModal', {
+                element: settingsModal,
+                onClose: () => {
+                    this.getModal('settingsModal').element.style.display = 'none';
+                    document.body.classList.remove('modal-open');
+                }
+            });
+        }
 
         // Add moveModal registration
-        this.registerModal('moveModal', {
-            element: document.getElementById('moveModal'),
-            onClose: () => {
-                this.getModal('moveModal').element.style.display = 'none';
-                document.body.classList.remove('modal-open');
-            }
-        });
+        const moveModal = document.getElementById('moveModal');
+        if (moveModal) {
+            this.registerModal('moveModal', {
+                element: moveModal,
+                onClose: () => {
+                    this.getModal('moveModal').element.style.display = 'none';
+                    document.body.classList.remove('modal-open');
+                }
+            });
+        }
         
         // Add supportModal registration
-        this.registerModal('supportModal', {
-            element: document.getElementById('supportModal'),
-            onClose: () => {
-                this.getModal('supportModal').element.style.display = 'none';
-                document.body.classList.remove('modal-open');
-            }
-        });
+        const supportModal = document.getElementById('supportModal');
+        if (supportModal) {
+            this.registerModal('supportModal', {
+                element: supportModal,
+                onClose: () => {
+                    this.getModal('supportModal').element.style.display = 'none';
+                    document.body.classList.remove('modal-open');
+                }
+            });
+        }
 
         // Add updateModal registration
-        this.registerModal('updateModal', {
-            element: document.getElementById('updateModal'),
-            onClose: () => {
-                this.getModal('updateModal').element.style.display = 'none';
-                document.body.classList.remove('modal-open');
-            }
-        });
+        const updateModal = document.getElementById('updateModal');
+        if (updateModal) {
+            this.registerModal('updateModal', {
+                element: updateModal,
+                onClose: () => {
+                    this.getModal('updateModal').element.style.display = 'none';
+                    document.body.classList.remove('modal-open');
+                }
+            });
+        }
+
+        // Add importModal registration
+        const importModal = document.getElementById('importModal');
+        if (importModal) {
+            this.registerModal('importModal', {
+                element: importModal,
+                onClose: () => {
+                    this.getModal('importModal').element.style.display = 'none';
+                    document.body.classList.remove('modal-open');   
+                }
+            });
+        }
+
+        // Add recipeModal registration
+        const recipeModal = document.getElementById('recipeModal');
+        if (recipeModal) {
+            this.registerModal('recipeModal', {
+                element: recipeModal,
+                onClose: () => {
+                    this.getModal('recipeModal').element.style.display = 'none';
+                    document.body.classList.remove('modal-open');
+                },
+                closeOnOutsideClick: true
+            });
+        }
 
         // Set up event listeners for modal toggles
         const supportToggle = document.getElementById('supportToggleBtn');
@@ -89,8 +136,8 @@ export class ModalManager {
             isOpen: false
         });
 
-        // Only add click outside handler if it's the lora modal
-        if (id == 'loraModal') {
+        // Add click outside handler if specified in config
+        if (config.closeOnOutsideClick) {
             config.element.addEventListener('click', (e) => {
                 if (e.target === config.element) {
                     this.closeModal(id);

static/js/managers/MoveManager.js

@@ -1,4 +1,5 @@
 import { showToast } from '../utils/uiHelpers.js';
+import { state } from '../state/index.js';
 import { resetAndReload } from '../api/loraApi.js';
 import { modalManager } from './ModalManager.js';
 

static/js/managers/SearchManager.js

@@ -0,0 +1,324 @@
+import { updatePanelPositions } from "../utils/uiHelpers.js";
+import { getCurrentPageState } from "../state/index.js";
+import { setStorageItem, getStorageItem } from "../utils/storageHelpers.js";
+/**
+ * SearchManager - Handles search functionality across different pages
+ * Each page can extend or customize this base functionality
+ */
+export class SearchManager {
+    constructor(options = {}) {
+      this.options = {
+        searchDelay: 300,
+        minSearchLength: 2,
+        ...options
+      };
+      
+      this.searchInput = document.getElementById('searchInput');
+      this.searchOptionsToggle = document.getElementById('searchOptionsToggle');
+      this.searchOptionsPanel = document.getElementById('searchOptionsPanel');
+      this.closeSearchOptions = document.getElementById('closeSearchOptions');
+      this.searchOptionTags = document.querySelectorAll('.search-option-tag');
+      this.recursiveSearchToggle = document.getElementById('recursiveSearchToggle');
+      
+      this.searchTimeout = null;
+      this.currentPage = options.page || document.body.dataset.page || 'loras';
+      this.isSearching = false;
+      
+      // Create clear button for search input
+      this.createClearButton();
+      
+      this.initEventListeners();
+      this.loadSearchPreferences();
+      
+      updatePanelPositions();
+      
+      // Add resize listener
+      window.addEventListener('resize', updatePanelPositions);
+    }
+    
+    initEventListeners() {
+      // Search input event
+      if (this.searchInput) {
+        this.searchInput.addEventListener('input', () => {
+          clearTimeout(this.searchTimeout);
+          this.searchTimeout = setTimeout(() => this.performSearch(), this.options.searchDelay);
+          this.updateClearButtonVisibility();
+        });
+        
+        // Clear search with Escape key
+        this.searchInput.addEventListener('keydown', (e) => {
+          if (e.key === 'Escape') {
+            this.searchInput.value = '';
+            this.updateClearButtonVisibility();
+            this.performSearch();
+          }
+        });
+      }
+      
+      // Search options toggle
+      if (this.searchOptionsToggle) {
+        this.searchOptionsToggle.addEventListener('click', () => {
+          this.toggleSearchOptionsPanel();
+        });
+      }
+      
+      // Close search options
+      if (this.closeSearchOptions) {
+        this.closeSearchOptions.addEventListener('click', () => {
+          this.closeSearchOptionsPanel();
+        });
+      }
+      
+      // Search option tags
+      if (this.searchOptionTags) {
+        this.searchOptionTags.forEach(tag => {
+          tag.addEventListener('click', () => {
+            // Check if clicking would deselect the last active option
+            const activeOptions = document.querySelectorAll('.search-option-tag.active');
+            if (activeOptions.length === 1 && activeOptions[0] === tag) {
+              // Don't allow deselecting the last option
+              if (typeof showToast === 'function') {
+                showToast('At least one search option must be selected', 'info');
+              }
+              return;
+            }
+            
+            tag.classList.toggle('active');
+            this.saveSearchPreferences();
+            this.performSearch();
+          });
+        });
+      }
+      
+      // Recursive search toggle
+      if (this.recursiveSearchToggle) {
+        this.recursiveSearchToggle.addEventListener('change', () => {
+          this.saveSearchPreferences();
+          this.performSearch();
+        });
+      }
+      
+      // Add global click handler to close panels when clicking outside
+      document.addEventListener('click', (e) => {
+        // Close search options panel when clicking outside
+        if (this.searchOptionsPanel && 
+            !this.searchOptionsPanel.contains(e.target) && 
+            e.target !== this.searchOptionsToggle &&
+            !this.searchOptionsToggle.contains(e.target)) {
+          this.closeSearchOptionsPanel();
+        }
+        
+        // Close filter panel when clicking outside (if filterManager exists)
+        const filterPanel = document.getElementById('filterPanel');
+        const filterButton = document.getElementById('filterButton');
+        if (filterPanel && 
+            !filterPanel.contains(e.target) && 
+            e.target !== filterButton &&
+            !filterButton.contains(e.target) &&
+            window.filterManager) {
+          window.filterManager.closeFilterPanel();
+        }
+      });
+    }
+    
+    createClearButton() {
+      // Create clear button if it doesn't exist
+      if (!this.searchInput) return;
+      
+      // Check if clear button already exists
+      let clearButton = this.searchInput.parentNode.querySelector('.search-clear');
+      
+      if (!clearButton) {
+        // Create clear button
+        clearButton = document.createElement('button');
+        clearButton.className = 'search-clear';
+        clearButton.innerHTML = '<i class="fas fa-times"></i>';
+        clearButton.title = 'Clear search';
+        
+        // Add click handler
+        clearButton.addEventListener('click', () => {
+          this.searchInput.value = '';
+          this.updateClearButtonVisibility();
+          this.performSearch();
+        });
+        
+        // Insert after search input
+        this.searchInput.parentNode.appendChild(clearButton);
+      }
+      
+      this.clearButton = clearButton;
+      
+      // Set initial visibility
+      this.updateClearButtonVisibility();
+    }
+    
+    updateClearButtonVisibility() {
+      if (this.clearButton) {
+        this.clearButton.classList.toggle('visible', this.searchInput.value.length > 0);
+      }
+    }
+    
+    toggleSearchOptionsPanel() {
+      if (this.searchOptionsPanel) {
+        const isHidden = this.searchOptionsPanel.classList.contains('hidden');
+        if (isHidden) {
+          // Update position before showing
+          updatePanelPositions();
+          this.searchOptionsPanel.classList.remove('hidden');
+          this.searchOptionsToggle.classList.add('active');
+          
+          // Ensure the panel is visible
+          this.searchOptionsPanel.style.display = 'block';
+        } else {
+          this.closeSearchOptionsPanel();
+        }
+      }
+    }
+    
+    closeSearchOptionsPanel() {
+      if (this.searchOptionsPanel) {
+        this.searchOptionsPanel.classList.add('hidden');
+        this.searchOptionsToggle.classList.remove('active');
+      }
+    }
+    
+    loadSearchPreferences() {
+      try {
+        const preferences = getStorageItem(`${this.currentPage}_search_prefs`) || {};
+        
+        // Apply search options
+        if (preferences.options) {
+          this.searchOptionTags.forEach(tag => {
+            const option = tag.dataset.option;
+            if (preferences.options[option] !== undefined) {
+              tag.classList.toggle('active', preferences.options[option]);
+            }
+          });
+        }
+        
+        // Apply recursive search - only if the toggle exists
+        if (this.recursiveSearchToggle && preferences.recursive !== undefined) {
+          this.recursiveSearchToggle.checked = preferences.recursive;
+        }
+        
+        // Ensure at least one search option is selected
+        this.validateSearchOptions();
+      } catch (error) {
+        console.error('Error loading search preferences:', error);
+        // Set default options if loading fails
+        this.setDefaultSearchOptions();
+      }
+    }
+    
+    validateSearchOptions() {
+      // Check if at least one search option is active
+      const hasActiveOption = Array.from(this.searchOptionTags).some(tag => 
+        tag.classList.contains('active')
+      );
+      
+      // If no search options are active, activate default options
+      if (!hasActiveOption) {
+        this.setDefaultSearchOptions();
+      }
+    }
+    
+    setDefaultSearchOptions() {
+      // Default to filename search option if available
+      const filenameOption = Array.from(this.searchOptionTags).find(tag => 
+        tag.dataset.option === 'filename'
+      );
+      
+      if (filenameOption) {
+        filenameOption.classList.add('active');
+      } else if (this.searchOptionTags.length > 0) {
+        // Otherwise, select the first option
+        this.searchOptionTags[0].classList.add('active');
+      }
+      
+      // Save the default preferences
+      this.saveSearchPreferences();
+    }
+    
+    saveSearchPreferences() {
+      try {
+        const options = {};
+        this.searchOptionTags.forEach(tag => {
+          options[tag.dataset.option] = tag.classList.contains('active');
+        });
+        
+        const preferences = {
+          options
+        };
+        
+        // Only add recursive option if the toggle exists
+        if (this.recursiveSearchToggle) {
+          preferences.recursive = this.recursiveSearchToggle.checked;
+        }
+        
+        setStorageItem(`${this.currentPage}_search_prefs`, preferences);
+      } catch (error) {
+        console.error('Error saving search preferences:', error);
+      }
+    }
+    
+    getActiveSearchOptions() {
+      const options = {};
+      this.searchOptionTags.forEach(tag => {
+        options[tag.dataset.option] = tag.classList.contains('active');
+      });
+      return options;
+    }
+    
+    performSearch() {
+      const query = this.searchInput.value.trim();
+      const options = this.getActiveSearchOptions();
+      const recursive = this.recursiveSearchToggle ? this.recursiveSearchToggle.checked : false;
+      
+      // Update the state with search parameters
+      const pageState = getCurrentPageState();
+      
+      // Set search query in filters
+      if (pageState && pageState.filters) {
+        pageState.filters.search = query;
+      }
+      
+      // Update search options based on page type
+      if (pageState && pageState.searchOptions) {
+        if (this.currentPage === 'recipes') {
+          pageState.searchOptions = {
+            title: options.title || false,
+            tags: options.tags || false,
+            loraName: options.loraName || false,
+            loraModel: options.loraModel || false
+          };
+        } else if (this.currentPage === 'loras') {
+          pageState.searchOptions = {
+            filename: options.filename || false,
+            modelname: options.modelname || false,
+            tags: options.tags || false,
+            recursive: recursive
+          };
+        } else if (this.currentPage === 'checkpoints') {
+          pageState.searchOptions = {
+            filename: options.filename || false,
+            modelname: options.modelname || false,
+            recursive: recursive
+          };
+        }
+      }
+      
+      // Call the appropriate manager's load method based on page type
+      if (this.currentPage === 'recipes' && window.recipeManager) {
+        window.recipeManager.loadRecipes(true); // true to reset pagination
+      } else if (this.currentPage === 'loras' && window.loadMoreLoras) {
+        // Reset loras page and reload
+        if (pageState) {
+          pageState.currentPage = 1;
+          pageState.hasMore = true;
+        }
+        window.loadMoreLoras(true); // true to reset pagination
+      } else if (this.currentPage === 'checkpoints' && window.checkpointManager) {
+        window.checkpointManager.loadCheckpoints(true); // true to reset pagination
+      }
+    }
+  }

static/js/managers/SettingsManager.js

@@ -1,5 +1,8 @@
 import { modalManager } from './ModalManager.js';
 import { showToast } from '../utils/uiHelpers.js';
+import { state } from '../state/index.js';
+import { resetAndReload } from '../api/loraApi.js';
+import { setStorageItem, getStorageItem } from '../utils/storageHelpers.js';
 
 export class SettingsManager {
     constructor() {
@@ -7,9 +10,24 @@ export class SettingsManager {
         this.isOpen = false;
         
         // Add initialization to sync with modal state
+        this.currentPage = document.body.dataset.page || 'loras';
+        
+        // Ensure settings are loaded from localStorage
+        this.loadSettingsFromStorage();
+        
         this.initialize();
     }
 
+    loadSettingsFromStorage() {
+        // Get saved settings from localStorage
+        const savedSettings = getStorageItem('settings');
+        
+        // Apply saved settings to state if available
+        if (savedSettings) {
+            state.global.settings = { ...state.global.settings, ...savedSettings };
+        }
+    }
+
     initialize() {
         if (this.initialized) return;
         
@@ -20,6 +38,11 @@ export class SettingsManager {
                 mutations.forEach((mutation) => {
                     if (mutation.type === 'attributes' && mutation.attributeName === 'style') {
                         this.isOpen = settingsModal.style.display === 'block';
+                        
+                        // When modal is opened, update checkbox state from current settings
+                        if (this.isOpen) {
+                            this.loadSettingsToUI();
+                        }
                     }
                 });
             });
@@ -30,6 +53,22 @@ export class SettingsManager {
         this.initialized = true;
     }
 
+    loadSettingsToUI() {
+        // Set frontend settings from state
+        const blurMatureContentCheckbox = document.getElementById('blurMatureContent');
+        if (blurMatureContentCheckbox) {
+            blurMatureContentCheckbox.checked = state.global.settings.blurMatureContent;
+        }
+        
+        const showOnlySFWCheckbox = document.getElementById('showOnlySFW');
+        if (showOnlySFWCheckbox) {
+            // Sync with state (backend will set this via template)
+            state.global.settings.show_only_sfw = showOnlySFWCheckbox.checked;
+        }
+        
+        // Backend settings are loaded from the template directly
+    }
+
     toggleSettings() {
         if (this.isOpen) {
             modalManager.closeModal('settingsModal');
@@ -40,16 +79,30 @@ export class SettingsManager {
     }
 
     async saveSettings() {
+        // Get frontend settings from UI
+        const blurMatureContent = document.getElementById('blurMatureContent').checked;
+        
+        // Get backend settings
         const apiKey = document.getElementById('civitaiApiKey').value;
+        const showOnlySFW = document.getElementById('showOnlySFW').checked;
+        
+        // Update frontend state and save to localStorage
+        state.global.settings.blurMatureContent = blurMatureContent;
+        state.global.settings.show_only_sfw = showOnlySFW;
+        
+        // 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
+                    civitai_api_key: apiKey,
+                    show_only_sfw: showOnlySFW
                 })
             });
 
@@ -59,10 +112,39 @@ export class SettingsManager {
 
             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');
         }
     }
+
+    applyFrontendSettings() {
+        // Apply blur setting to existing content
+        const blurSetting = state.global.settings.blurMatureContent;
+        document.querySelectorAll('.lora-card[data-nsfw="true"] .card-image').forEach(img => {
+            if (blurSetting) {
+                img.classList.add('nsfw-blur');
+            } else {
+                img.classList.remove('nsfw-blur');
+            }
+        });
+        
+        // For show_only_sfw, there's no immediate action needed as it affects content loading
+        // The setting will take effect on next reload
+    }
 }
 
 // Helper function for toggling API key visibility

static/js/managers/UpdateService.js

@@ -1,4 +1,5 @@
 import { modalManager } from './ModalManager.js';
+import { getStorageItem, setStorageItem } from '../utils/storageHelpers.js';
 
 export class UpdateService {
     constructor() {
@@ -7,34 +8,33 @@ export class UpdateService {
         this.latestVersion = "v0.0.0";   // Initialize with default values
         this.updateInfo = null;
         this.updateAvailable = false;
-        this.updateNotificationsEnabled = localStorage.getItem('show_update_notifications') !== 'false';
-        this.lastCheckTime = parseInt(localStorage.getItem('last_update_check') || '0');
+        this.updateNotificationsEnabled = getStorageItem('show_update_notifications');
+        this.lastCheckTime = parseInt(getStorageItem('last_update_check') || '0');
     }
 
     initialize() {
-        // Initialize update preferences from localStorage
-        const showUpdates = localStorage.getItem('show_update_notifications');
-        this.updateNotificationsEnabled = showUpdates === null || showUpdates === 'true';
-        
         // Register event listener for update notification toggle
         const updateCheckbox = document.getElementById('updateNotifications');
         if (updateCheckbox) {
             updateCheckbox.checked = this.updateNotificationsEnabled;
             updateCheckbox.addEventListener('change', (e) => {
                 this.updateNotificationsEnabled = e.target.checked;
-                localStorage.setItem('show_update_notifications', e.target.checked);
+                setStorageItem('show_update_notifications', e.target.checked);
                 this.updateBadgeVisibility();
             });
         }
         
         // Perform update check if needed
-        this.checkForUpdates();
+        this.checkForUpdates().then(() => {
+            // Ensure badges are updated after checking
+            this.updateBadgeVisibility();
+        });
         
         // Set up event listener for update button
-        const updateToggle = document.getElementById('updateToggleBtn');
-        if (updateToggle) {
-            updateToggle.addEventListener('click', () => this.toggleUpdateModal());
-        }
+        // const updateToggle = document.getElementById('updateToggleBtn');
+        // if (updateToggle) {
+        //     updateToggle.addEventListener('click', () => this.toggleUpdateModal());
+        // }
 
         // Immediately update modal content with current values (even if from default)
         this.updateModalContent();
@@ -43,7 +43,9 @@ export class UpdateService {
     async checkForUpdates() {
         // Check if we should perform an update check
         const now = Date.now();
-        if (now - this.lastCheckTime < this.updateCheckInterval) {
+        const forceCheck = this.lastCheckTime === 0;
+        
+        if (!forceCheck && now - this.lastCheckTime < this.updateCheckInterval) {
             // If we already have update info, just update the UI
             if (this.updateAvailable) {
                 this.updateBadgeVisibility();
@@ -61,12 +63,12 @@ export class UpdateService {
                 this.latestVersion = data.latest_version || "v0.0.0";
                 this.updateInfo = data;
                 
-                // Determine if update is available
-                this.updateAvailable = data.update_available;
+                // Explicitly set update availability based on version comparison
+                this.updateAvailable = this.isNewerVersion(this.latestVersion, this.currentVersion);
                 
                 // Update last check time
                 this.lastCheckTime = now;
-                localStorage.setItem('last_update_check', now.toString());
+                setStorageItem('last_update_check', now.toString());
                 
                 // Update UI
                 this.updateBadgeVisibility();
@@ -83,6 +85,37 @@ export class UpdateService {
         }
     }
     
+    // Helper method to compare version strings
+    isNewerVersion(latestVersion, currentVersion) {
+        if (!latestVersion || !currentVersion) return false;
+        
+        // Remove 'v' prefix if present
+        const latest = latestVersion.replace(/^v/, '');
+        const current = currentVersion.replace(/^v/, '');
+        
+        // Split version strings into components
+        const latestParts = latest.split(/[-\.]/);
+        const currentParts = current.split(/[-\.]/);
+        
+        // Compare major, minor, patch versions
+        for (let i = 0; i < 3; i++) {
+            const latestNum = parseInt(latestParts[i] || '0', 10);
+            const currentNum = parseInt(currentParts[i] || '0', 10);
+            
+            if (latestNum > currentNum) return true;
+            if (latestNum < currentNum) return false;
+        }
+        
+        // If numeric versions are the same, check for beta/alpha status
+        const latestIsBeta = latest.includes('beta') || latest.includes('alpha');
+        const currentIsBeta = current.includes('beta') || current.includes('alpha');
+        
+        // Release version is newer than beta/alpha
+        if (!latestIsBeta && currentIsBeta) return true;
+        
+        return false;
+    }
+    
     updateBadgeVisibility() {
         const updateToggle = document.querySelector('.update-toggle');
         const updateBadge = document.querySelector('.update-toggle .update-badge');
@@ -94,14 +127,17 @@ export class UpdateService {
                 : "Check Updates";
         }
         
+        // Force updating badges visibility based on current state
+        const shouldShow = this.updateNotificationsEnabled && this.updateAvailable;
+        
         if (updateBadge) {
-            const shouldShow = this.updateNotificationsEnabled && this.updateAvailable;
             updateBadge.classList.toggle('hidden', !shouldShow);
+            console.log("Update badge visibility:", !shouldShow ? "hidden" : "visible");
         }
         
         if (cornerBadge) {
-            const shouldShow = this.updateNotificationsEnabled && this.updateAvailable;
             cornerBadge.classList.toggle('hidden', !shouldShow);
+            console.log("Corner badge visibility:", !shouldShow ? "hidden" : "visible");
         }
     }
     
@@ -187,13 +223,11 @@ export class UpdateService {
         });
     }
     
-    showUpdateModal() {
-        this.toggleUpdateModal();
-    }
-    
     async manualCheckForUpdates() {
         this.lastCheckTime = 0; // Reset last check time to force check
         await this.checkForUpdates();
+        // Ensure badge visibility is updated after manual check
+        this.updateBadgeVisibility();
     }
 }
 

static/js/recipes.js

@@ -0,0 +1,185 @@
+// Recipe manager module
+import { appCore } from './core.js';
+import { ImportManager } from './managers/ImportManager.js';
+import { RecipeCard } from './components/RecipeCard.js';
+import { RecipeModal } from './components/RecipeModal.js';
+import { getCurrentPageState } from './state/index.js';
+
+class RecipeManager {
+    constructor() {
+        // Get page state
+        this.pageState = getCurrentPageState();
+        
+        // Initialize ImportManager
+        this.importManager = new ImportManager();
+        
+        // Initialize RecipeModal
+        this.recipeModal = new RecipeModal();
+        
+        // Add state tracking for infinite scroll
+        this.pageState.isLoading = false;
+        this.pageState.hasMore = true;
+    }
+    
+    async initialize() {
+        // Initialize event listeners
+        this.initEventListeners();
+        
+        // Set default search options if not already defined
+        this._initSearchOptions();
+        
+        // Load initial set of recipes
+        await this.loadRecipes();
+        
+        // Expose necessary functions to the page
+        this._exposeGlobalFunctions();
+        
+        // Initialize common page features (lazy loading, infinite scroll)
+        appCore.initializePageFeatures();
+    }
+    
+    _initSearchOptions() {
+        // Ensure recipes search options are properly initialized
+        if (!this.pageState.searchOptions) {
+            this.pageState.searchOptions = {
+                title: true,       // Recipe title
+                tags: true,        // Recipe tags
+                loraName: true,    // LoRA file name
+                loraModel: true    // LoRA model name
+            };
+        }
+    }
+    
+    _exposeGlobalFunctions() {
+        // Only expose what's needed for the page
+        window.recipeManager = this;
+        window.importManager = this.importManager;
+    }
+    
+    initEventListeners() {
+        // Sort select
+        const sortSelect = document.getElementById('sortSelect');
+        if (sortSelect) {
+            sortSelect.addEventListener('change', () => {
+                this.pageState.sortBy = sortSelect.value;
+                this.loadRecipes();
+            });
+        }
+    }
+    
+    async loadRecipes(resetPage = true) {
+        try {
+            // Show loading indicator
+            document.body.classList.add('loading');
+            this.pageState.isLoading = true;
+            
+            // Reset to first page if requested
+            if (resetPage) {
+                this.pageState.currentPage = 1;
+                // Clear grid if resetting
+                const grid = document.getElementById('recipeGrid');
+                if (grid) grid.innerHTML = '';
+            }
+            
+            // Build query parameters
+            const params = new URLSearchParams({
+                page: this.pageState.currentPage,
+                page_size: this.pageState.pageSize || 20,
+                sort_by: this.pageState.sortBy
+            });
+            
+            // Add search filter if present
+            if (this.pageState.filters.search) {
+                params.append('search', this.pageState.filters.search);
+                
+                // Add search option parameters
+                if (this.pageState.searchOptions) {
+                    params.append('search_title', this.pageState.searchOptions.title.toString());
+                    params.append('search_tags', this.pageState.searchOptions.tags.toString());
+                    params.append('search_lora_name', this.pageState.searchOptions.loraName.toString());
+                    params.append('search_lora_model', this.pageState.searchOptions.loraModel.toString());
+                    params.append('fuzzy', 'true');
+                }
+            }
+            
+            // Add base model filters
+            if (this.pageState.filters.baseModel && this.pageState.filters.baseModel.length) {
+                params.append('base_models', this.pageState.filters.baseModel.join(','));
+            }
+            
+            // Add tag filters
+            if (this.pageState.filters.tags && this.pageState.filters.tags.length) {
+                params.append('tags', this.pageState.filters.tags.join(','));
+            }
+
+            // Fetch recipes
+            const response = await fetch(`/api/recipes?${params.toString()}`);
+            
+            if (!response.ok) {
+                throw new Error(`Failed to load recipes: ${response.statusText}`);
+            }
+            
+            const data = await response.json();
+
+            // Update recipes grid
+            this.updateRecipesGrid(data, resetPage);
+            
+            // Update pagination state based on current page and total pages
+            this.pageState.hasMore = data.page < data.total_pages;
+            
+        } catch (error) {
+            console.error('Error loading recipes:', error);
+            appCore.showToast('Failed to load recipes', 'error');
+        } finally {
+            // Hide loading indicator
+            document.body.classList.remove('loading');
+            this.pageState.isLoading = false;
+        }
+    }
+    
+    updateRecipesGrid(data, resetGrid = true) {
+        const grid = document.getElementById('recipeGrid');
+        if (!grid) return;
+        
+        // Check if data exists and has items
+        if (!data.items || data.items.length === 0) {
+            if (resetGrid) {
+                grid.innerHTML = `
+                    <div class="placeholder-message">
+                        <p>No recipes found</p>
+                        <p>Add recipe images to your recipes folder to see them here.</p>
+                    </div>
+                `;
+            }
+            return;
+        }
+        
+        // Clear grid if resetting
+        if (resetGrid) {
+            grid.innerHTML = '';
+        }
+        
+        // Create recipe cards
+        data.items.forEach(recipe => {
+            const recipeCard = new RecipeCard(recipe, (recipe) => this.showRecipeDetails(recipe));
+            grid.appendChild(recipeCard.element);
+        });
+    }
+    
+    showRecipeDetails(recipe) {
+        this.recipeModal.showRecipeDetails(recipe);
+    }
+}
+
+// Initialize components
+document.addEventListener('DOMContentLoaded', async () => {
+    // Initialize core application
+    await appCore.initialize();
+    
+    // Initialize recipe manager
+    const recipeManager = new RecipeManager();
+    await recipeManager.initialize();
+});
+
+// Export for use in other modules
+export { RecipeManager };

static/js/state/index.js

@@ -1,17 +1,154 @@
+// Create the new hierarchical state structure
+import { getStorageItem } from '../utils/storageHelpers.js';
+
+// Load settings from localStorage or use defaults
+const savedSettings = getStorageItem('settings', {
+    blurMatureContent: true,
+    show_only_sfw: false
+});
+
 export const state = {
-    currentPage: 1,
-    isLoading: false,
-    hasMore: true,
-    sortBy: 'name',
-    activeFolder: null,
-    loadingManager: null,
-    observer: null,
-    previewVersions: new Map(),
-    searchManager: null,
-    filters: {
-        baseModel: []
+    // Global state
+    global: {
+        settings: savedSettings,
+        loadingManager: null,
+        observer: null,
     },
-    bulkMode: false,
-    selectedLoras: new Set(),
-    loraMetadataCache: new Map()
-};
+    
+    // Page-specific states
+    pages: {
+        loras: {
+            currentPage: 1,
+            isLoading: false,
+            hasMore: true,
+            sortBy: 'name',
+            activeFolder: null,
+            previewVersions: new Map(),
+            searchManager: null,
+            searchOptions: {
+                filename: true,
+                modelname: true,
+                tags: false,
+                recursive: false
+            },
+            filters: {
+                baseModel: [],
+                tags: []
+            },
+            bulkMode: false,
+            selectedLoras: new Set(),
+            loraMetadataCache: new Map(),
+        },
+        
+        recipes: {
+            currentPage: 1,
+            isLoading: false,
+            hasMore: true,
+            sortBy: 'date',
+            searchManager: null,
+            searchOptions: {
+                title: true,
+                tags: true,
+                loraName: true,
+                loraModel: true
+            },
+            filters: {
+                baseModel: [],
+                tags: [],
+                search: ''
+            },
+            pageSize: 20
+        },
+        
+        checkpoints: {
+            currentPage: 1,
+            isLoading: false,
+            hasMore: true,
+            sortBy: 'name',
+            activeFolder: null,
+            searchManager: null,
+            searchOptions: {
+                filename: true,
+                modelname: true,
+                recursive: false
+            },
+            filters: {
+                baseModel: [],
+                tags: []
+            }
+        }
+    },
+    
+    // Current active page
+    currentPageType: 'loras',
+    
+    // Backward compatibility - proxy properties
+    get currentPage() { return this.pages[this.currentPageType].currentPage; },
+    set currentPage(value) { this.pages[this.currentPageType].currentPage = value; },
+    
+    get isLoading() { return this.pages[this.currentPageType].isLoading; },
+    set isLoading(value) { this.pages[this.currentPageType].isLoading = value; },
+    
+    get hasMore() { return this.pages[this.currentPageType].hasMore; },
+    set hasMore(value) { this.pages[this.currentPageType].hasMore = value; },
+    
+    get sortBy() { return this.pages[this.currentPageType].sortBy; },
+    set sortBy(value) { this.pages[this.currentPageType].sortBy = value; },
+    
+    get activeFolder() { return this.pages[this.currentPageType].activeFolder; },
+    set activeFolder(value) { this.pages[this.currentPageType].activeFolder = value; },
+    
+    get loadingManager() { return this.global.loadingManager; },
+    set loadingManager(value) { this.global.loadingManager = value; },
+    
+    get observer() { return this.global.observer; },
+    set observer(value) { this.global.observer = value; },
+    
+    get previewVersions() { return this.pages.loras.previewVersions; },
+    set previewVersions(value) { this.pages.loras.previewVersions = value; },
+    
+    get searchManager() { return this.pages[this.currentPageType].searchManager; },
+    set searchManager(value) { this.pages[this.currentPageType].searchManager = value; },
+    
+    get searchOptions() { return this.pages[this.currentPageType].searchOptions; },
+    set searchOptions(value) { this.pages[this.currentPageType].searchOptions = value; },
+    
+    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 selectedLoras() { return this.pages.loras.selectedLoras; },
+    set selectedLoras(value) { this.pages.loras.selectedLoras = value; },
+    
+    get loraMetadataCache() { return this.pages.loras.loraMetadataCache; },
+    set loraMetadataCache(value) { this.pages.loras.loraMetadataCache = value; },
+    
+    get settings() { return this.global.settings; },
+    set settings(value) { this.global.settings = value; }
+};
+
+// Get the current page state
+export function getCurrentPageState() {
+    return state.pages[state.currentPageType];
+}
+
+// Set the current page type
+export function setCurrentPageType(pageType) {
+    if (state.pages[pageType]) {
+        state.currentPageType = pageType;
+        return true;
+    }
+    console.warn(`Unknown page type: ${pageType}`);
+    return false;
+}
+
+// Initialize page state when a page loads
+export function initPageState(pageType) {
+    if (setCurrentPageType(pageType)) {
+        console.log(`Initialized state for page: ${pageType}`);
+        return getCurrentPageState();
+    }
+    return null;
+}