Merge branch 'main' into dev

py/nodes/lora_loader.py

@@ -8,7 +8,7 @@ from .utils import FlexibleOptionalInputType, any_type
 
 class LoraManagerLoader:
     NAME = "Lora Loader (LoraManager)"
-    CATEGORY = "loaders"
+    CATEGORY = "Lora Manager/loaders"
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -49,11 +49,32 @@ 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 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 = []
         
+        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)
+                
+                # 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))
+                
+                all_trigger_words.extend(trigger_words)
+                loaded_loras.append(f"{lora_name}: {model_strength}")
+        
+        # Then process loras from kwargs
         if 'loras' in kwargs:
             for lora in kwargs['loras']:
                 if not lora.get('active', False):
@@ -72,6 +93,7 @@ class LoraManagerLoader:
                 # Add trigger words to collection
                 all_trigger_words.extend(trigger_words)
         
-        trigger_words_text = ", ".join(all_trigger_words) if all_trigger_words else ""
+        # use ',, ' to separate trigger words for group mode
+        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
 
         return (model, clip, trigger_words_text)

py/nodes/lora_stacker.py

@@ -0,0 +1,91 @@
+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
+
+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 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)
+        
+        if 'loras' in kwargs:
+            for lora in kwargs['loras']:
+                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
+                stack.append((lora_path, 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/trigger_word_toggle.py

@@ -1,17 +1,18 @@
 import json
+import re
 from server import PromptServer # type: ignore
 from .utils import FlexibleOptionalInputType, any_type
 
 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 +24,8 @@ class TriggerWordToggle:
     RETURN_NAMES = ("filtered_trigger_words",)
     FUNCTION = "process_trigger_words"
 
-    def process_trigger_words(self, trigger_words, id, **kwargs):
+    def process_trigger_words(self, id, group_mode, **kwargs):
+        trigger_words = kwargs.get("trigger_words", "")
         # Send trigger words to frontend
         PromptServer.instance.send_sync("trigger_word_update", {
             "id": id,
@@ -41,20 +43,33 @@ class TriggerWordToggle:
                 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}")

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
@@ -42,9 +44,11 @@ class ApiRoutes:
         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/top-tags', routes.get_top_tags)  # Add new route for top tags
         app.router.add_get('/api/recipes', cls.handle_get_recipes)
 
         # Add update check routes
@@ -132,6 +136,11 @@ class ApiRoutes:
             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'
+            
             # Validate parameters
             if page < 1 or page_size < 1 or page_size > 100:
                 return web.json_response({
@@ -143,6 +152,10 @@ 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,
@@ -152,7 +165,13 @@ class ApiRoutes:
                 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
+                }
             )
             
             # Format the response data
@@ -185,12 +204,15 @@ 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", ""),
@@ -333,8 +355,16 @@ 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'] = determine_base_model(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')
@@ -350,6 +380,7 @@ 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:
@@ -369,7 +400,7 @@ 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)
             
@@ -547,6 +578,8 @@ 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:
@@ -602,8 +635,15 @@ 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:
@@ -694,6 +734,97 @@ class ApiRoutes:
             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)
+
     @staticmethod
     async def handle_get_recipes(request):
         """API endpoint for getting paginated recipes"""

py/routes/lora_routes.py

@@ -28,10 +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", {}))

py/services/civitai_client.py

@@ -163,6 +163,53 @@ 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:

py/services/download_manager.py

@@ -51,6 +51,16 @@ class DownloadManager:
             # 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,
@@ -86,6 +96,7 @@ 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)
 

py/services/file_monitor.py

@@ -98,6 +98,10 @@ 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 +113,16 @@ 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_scanner.py

@@ -11,6 +11,8 @@ 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
 import sys
 
 logger = logging.getLogger(__name__)
@@ -35,6 +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"""
@@ -91,13 +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'])
+                
+                # 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(
@@ -159,7 +170,8 @@ class LoraScanner:
 
     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):
+                               recursive: bool = False, base_models: list = None, tags: list = None,
+                               search_options: dict = None) -> Dict:
         """Get paginated and filtered lora data
         
         Args:
@@ -171,22 +183,39 @@ class LoraScanner:
             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)
         """
         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
+            }
+
+        # 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
+                # 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
@@ -199,28 +228,27 @@ 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
-                    )
+                    if self._fuzzy_search_match(item, search, search_options)
                 ]
             else:
-                # Original exact search logic
                 filtered_data = [
                     item for item in filtered_data 
-                    if search in str(item.get('model_name', '')).lower()
+                    if self._exact_search_match(item, search, search_options)
                 ]
 
-        # 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)
@@ -235,6 +263,44 @@ class LoraScanner:
         
         return result
 
+    def _fuzzy_search_match(self, item: Dict, search: str, search_options: Dict) -> bool:
+        """Check if an item matches the search term using fuzzy matching with search options"""
+        # Check filename if enabled
+        if search_options.get('filename', True) and self.fuzzy_match(item.get('file_name', ''), search):
+            return True
+            
+        # Check model name if enabled
+        if search_options.get('modelname', True) and self.fuzzy_match(item.get('model_name', ''), search):
+            return True
+            
+        # Check tags if enabled
+        if search_options.get('tags', False) and item.get('tags'):
+            for tag in item['tags']:
+                if self.fuzzy_match(tag, search):
+                    return True
+                    
+        return False
+
+    def _exact_search_match(self, item: Dict, search: str, search_options: Dict) -> bool:
+        """Check if an item matches the search term using exact matching with search options"""
+        search = search.lower()
+        
+        # Check filename if enabled
+        if search_options.get('filename', True) and search in item.get('file_name', '').lower():
+            return True
+            
+        # Check model name if enabled
+        if search_options.get('modelname', True) and search in item.get('model_name', '').lower():
+            return True
+            
+        # Check tags if enabled
+        if search_options.get('tags', False) and item.get('tags'):
+            for tag in item['tags']:
+                if search in tag.lower():
+                    return True
+                    
+        return False
+
     def invalidate_cache(self):
         """Invalidate the current cache"""
         self._cache = None
@@ -312,12 +378,86 @@ 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
         
@@ -428,6 +568,15 @@ 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)
         
@@ -461,6 +610,11 @@ class LoraScanner:
             # 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()
@@ -506,6 +660,29 @@ class LoraScanner:
         """Get hash for a LoRA by its file path"""
         return self._hash_index.get_hash(file_path)
 
+    # 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 diagnose_hash_index(self):
         """Diagnostic method to verify hash index functionality"""
         print("\n\n*** DIAGNOSING LORA HASH INDEX ***\n\n", file=sys.stderr)

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/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

@@ -8,7 +8,8 @@ BASE_MODEL_MAPPING = {
     "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':
@@ -42,6 +50,7 @@ class LoraMetadata:
             sha256=file_info['hashes'].get('SHA256', ''),
             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
         )