feat: Update release notes and version to 0.8.8 with new features and bug fixes

Dev

README.md

@@ -20,6 +20,12 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 
 ## Release Notes
 
+### v0.8.8
+* **Real-time TriggerWord Updates** - Enhanced TriggerWord Toggle node to instantly update when connected Lora Loader or Lora Stacker nodes change, without requiring workflow execution
+* **Optimized Metadata Recovery** - Improved utilization of existing .civitai.info files for faster initialization and preservation of metadata from models deleted from CivitAI
+* **Migration Acceleration** - Further speed improvements for users transitioning from A1111/Forge environments
+* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
+
 ### v0.8.7
 * **Enhanced Context Menu** - Added comprehensive context menu functionality to Recipes and Checkpoints pages for improved workflow
 * **Interactive LoRA Strength Control** - Implemented drag functionality in LoRA Loader for intuitive strength adjustment

py/config.py

@@ -103,21 +103,29 @@ class Config:
 
     def _init_lora_paths(self) -> List[str]:
         """Initialize and validate LoRA paths from ComfyUI settings"""
-        paths = sorted(set(path.replace(os.sep, "/") 
+        raw_paths = folder_paths.get_folder_paths("loras")
-                for path in folder_paths.get_folder_paths("loras") 
-                if os.path.exists(path)), key=lambda p: p.lower())
-        print("Found LoRA roots:", "\n - " + "\n - ".join(paths))
         
-        if not paths:
+        # Normalize and resolve symlinks, store mapping from resolved -> original
+        path_map = {}
+        for path in raw_paths:
+            if os.path.exists(path):
+                real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
+                path_map[real_path] = path_map.get(real_path, path)  # preserve first seen
+        
+        # Now sort and use only the deduplicated real paths
+        unique_paths = sorted(path_map.values(), key=lambda p: p.lower())
+        print("Found LoRA roots:", "\n - " + "\n - ".join(unique_paths))
+        
+        if not unique_paths:
             raise ValueError("No valid loras folders found in ComfyUI configuration")
         
-        # 初始化路径映射
+        for original_path in unique_paths:
-        for path in paths:
+            real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
-            real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
+            if real_path != original_path:
-            if real_path != path:
+                self.add_path_mapping(original_path, real_path)
-                self.add_path_mapping(path, real_path)
+        
+        return unique_paths
 
-        return paths
 
     def _init_checkpoint_paths(self) -> List[str]:
         """Initialize and validate checkpoint paths from ComfyUI settings"""

py/lora_manager.py

@@ -5,6 +5,8 @@ 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 .routes.update_routes import UpdateRoutes
+from .routes.usage_stats_routes import UsageStatsRoutes
 from .services.service_registry import ServiceRegistry
 import logging
 
@@ -92,6 +94,8 @@ class LoraManager:
         checkpoints_routes.setup_routes(app)
         ApiRoutes.setup_routes(app)
         RecipeRoutes.setup_routes(app)
+        UpdateRoutes.setup_routes(app)  
+        UsageStatsRoutes.setup_routes(app)  # Register usage stats routes
         
         # Schedule service initialization 
         app.on_startup.append(lambda app: cls._initialize_services())

py/metadata_collector/constants.py

@@ -1,12 +1,14 @@
 """Constants used by the metadata collector"""
 
-# Individual category constants
+# Metadata collection constants
+
+# Metadata categories
 MODELS = "models"
 PROMPTS = "prompts"
 SAMPLING = "sampling"
 LORAS = "loras"
 SIZE = "size"
-IMAGES = "images"  # Added new category for image results
+IMAGES = "images"
 
-# Collection of categories for iteration
+# Complete list of categories to track
-METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES]  # Added IMAGES to categories
+METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES]

py/nodes/lora_loader.py

@@ -5,7 +5,7 @@ from ..services.lora_scanner import LoraScanner
 from ..config import config
 import asyncio
 import os
-from .utils import FlexibleOptionalInputType, any_type
+from .utils import FlexibleOptionalInputType, any_type, get_lora_info, extract_lora_name, get_loras_list
 
 logger = logging.getLogger(__name__)
 
@@ -33,48 +33,6 @@ class LoraManagerLoader:
     RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
     FUNCTION = "load_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 load_loras(self, model, text, **kwargs):
         """Loads multiple LoRAs based on the kwargs input and lora_stack."""
         loaded_loras = []
@@ -89,14 +47,14 @@ class LoraManagerLoader:
                 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)
+                lora_name = extract_lora_name(lora_path)
-                _, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+                _, trigger_words = asyncio.run(get_lora_info(lora_name))
                 
                 all_trigger_words.extend(trigger_words)
                 loaded_loras.append(f"{lora_name}: {model_strength}")
         
         # Then process loras from kwargs with support for both old and new formats
-        loras_list = self._get_loras_list(kwargs)
+        loras_list = get_loras_list(kwargs)
         for lora in loras_list:
             if not lora.get('active', False):
                 continue
@@ -105,7 +63,7 @@ class LoraManagerLoader:
             strength = float(lora['strength'])
             
             # Get lora path and trigger words
-            lora_path, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+            lora_path, trigger_words = asyncio.run(get_lora_info(lora_name))
             
             # Apply the LoRA using the resolved path
             model, clip = LoraLoader().load_lora(model, clip, lora_path, strength, strength)

py/nodes/lora_stacker.py

@@ -3,7 +3,7 @@ from ..services.lora_scanner import LoraScanner
 from ..config import config
 import asyncio
 import os
-from .utils import FlexibleOptionalInputType, any_type
+from .utils import FlexibleOptionalInputType, any_type, get_lora_info, extract_lora_name, get_loras_list
 import logging
 
 logger = logging.getLogger(__name__)
@@ -30,48 +30,6 @@ class LoraStacker:
     RETURN_NAMES = ("LORA_STACK", "trigger_words", "active_loras")
     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 = []
@@ -84,12 +42,12 @@ class LoraStacker:
             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)
+                lora_name = extract_lora_name(lora_path)
-                _, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+                _, trigger_words = asyncio.run(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)
+        loras_list = get_loras_list(kwargs)
         for lora in loras_list:
             if not lora.get('active', False):
                 continue
@@ -99,7 +57,7 @@ class LoraStacker:
             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))
+            lora_path, trigger_words = asyncio.run(get_lora_info(lora_name))
             
             # Add to stack without loading
             # replace '/' with os.sep to avoid different OS path format

py/nodes/save_image.py

@@ -5,6 +5,7 @@ import re
 import numpy as np
 import folder_paths # type: ignore
 from ..services.lora_scanner import LoraScanner
+from ..services.checkpoint_scanner import CheckpointScanner
 from ..metadata_collector.metadata_processor import MetadataProcessor
 from ..metadata_collector import get_metadata
 from PIL import Image, PngImagePlugin
@@ -53,18 +54,55 @@ class SaveImage:
     async def get_lora_hash(self, lora_name):
         """Get the lora hash from cache"""
         scanner = await LoraScanner.get_instance()
-        cache = await scanner.get_cached_data()
         
+        # Use the new direct filename lookup method
+        hash_value = scanner.get_hash_by_filename(lora_name)
+        if hash_value:
+            return hash_value
+            
+        # Fallback to old method for compatibility
+        cache = await scanner.get_cached_data()
         for item in cache.raw_data:
             if item.get('file_name') == lora_name:
                 return item.get('sha256')
         return None
 
+    async def get_checkpoint_hash(self, checkpoint_path):
+        """Get the checkpoint hash from cache"""
+        scanner = await CheckpointScanner.get_instance()
+        
+        if not checkpoint_path:
+            return None
+            
+        # Extract basename without extension
+        checkpoint_name = os.path.basename(checkpoint_path)
+        checkpoint_name = os.path.splitext(checkpoint_name)[0]
+        
+        # Try direct filename lookup first
+        hash_value = scanner.get_hash_by_filename(checkpoint_name)
+        if hash_value:
+            return hash_value
+            
+        # Fallback to old method for compatibility
+        cache = await scanner.get_cached_data()
+        normalized_path = checkpoint_path.replace('\\', '/')
+        
+        for item in cache.raw_data:
+            if item.get('file_name') == checkpoint_name and item.get('file_path').endswith(normalized_path):
+                return item.get('sha256')
+                
+        return None
+
     async def format_metadata(self, metadata_dict):
         """Format metadata in the requested format similar to userComment example"""
         if not metadata_dict:
             return ""
         
+        # Helper function to only add parameter if value is not None
+        def add_param_if_not_none(param_list, label, value):
+            if value is not None:
+                param_list.append(f"{label}: {value}")
+        
         # Extract the prompt and negative prompt
         prompt = metadata_dict.get('prompt', '')
         negative_prompt = metadata_dict.get('negative_prompt', '')
@@ -100,7 +138,11 @@ class SaveImage:
         
         # Add standard parameters in the correct order
         if 'steps' in metadata_dict:
-            params.append(f"Steps: {metadata_dict.get('steps')}")
+            add_param_if_not_none(params, "Steps", metadata_dict.get('steps'))
+        
+        # Combine sampler and scheduler information
+        sampler_name = None
+        scheduler_name = None
         
         if 'sampler' in metadata_dict:
             sampler = metadata_dict.get('sampler')
@@ -123,7 +165,6 @@ class SaveImage:
                 'ddim': 'DDIM'
             }
             sampler_name = sampler_mapping.get(sampler, sampler)
-            params.append(f"Sampler: {sampler_name}")
         
         if 'scheduler' in metadata_dict:
             scheduler = metadata_dict.get('scheduler')
@@ -135,38 +176,48 @@ class SaveImage:
                 'sgm_quadratic': 'SGM Quadratic'
             }
             scheduler_name = scheduler_mapping.get(scheduler, scheduler)
-            params.append(f"Schedule type: {scheduler_name}")
         
-        # CFG scale (cfg_scale in metadata_dict)
+        # Add combined sampler and scheduler information
-        if 'cfg_scale' in metadata_dict:
+        if sampler_name:
-            params.append(f"CFG scale: {metadata_dict.get('cfg_scale')}")
+            if scheduler_name:
+                params.append(f"Sampler: {sampler_name} {scheduler_name}")
+            else:
+                params.append(f"Sampler: {sampler_name}")
+        
+        # CFG scale (Use guidance if available, otherwise fall back to cfg_scale or cfg)
+        if 'guidance' in metadata_dict:
+            add_param_if_not_none(params, "CFG scale", metadata_dict.get('guidance'))
+        elif 'cfg_scale' in metadata_dict:
+            add_param_if_not_none(params, "CFG scale", metadata_dict.get('cfg_scale'))
         elif 'cfg' in metadata_dict:
-            params.append(f"CFG scale: {metadata_dict.get('cfg')}")
+            add_param_if_not_none(params, "CFG scale", metadata_dict.get('cfg'))
         
         # Seed
         if 'seed' in metadata_dict:
-            params.append(f"Seed: {metadata_dict.get('seed')}")
+            add_param_if_not_none(params, "Seed", metadata_dict.get('seed'))
         
         # Size
         if 'size' in metadata_dict:
-            params.append(f"Size: {metadata_dict.get('size')}")
+            add_param_if_not_none(params, "Size", metadata_dict.get('size'))
         
         # Model info
         if 'checkpoint' in metadata_dict:
             # Ensure checkpoint is a string before processing
             checkpoint = metadata_dict.get('checkpoint')
             if checkpoint is not None:
-                # Handle both string and other types safely
+                # Get model hash
-                if isinstance(checkpoint, str):
+                model_hash = await self.get_checkpoint_hash(checkpoint)
-                    # Extract basename without path
-                    checkpoint = os.path.basename(checkpoint)
-                    # Remove extension if present
-                    checkpoint = os.path.splitext(checkpoint)[0]
-                else:
-                    # Convert non-string to string
-                    checkpoint = str(checkpoint)
                 
-                params.append(f"Model: {checkpoint}")
+                # Extract basename without path
+                checkpoint_name = os.path.basename(checkpoint)
+                # Remove extension if present
+                checkpoint_name = os.path.splitext(checkpoint_name)[0]
+                
+                # Add model hash if available
+                if model_hash:
+                    params.append(f"Model hash: {model_hash[:10]}, Model: {checkpoint_name}")
+                else:
+                    params.append(f"Model: {checkpoint_name}")
         
         # Add LoRA hashes if available
         if lora_hashes:
@@ -284,7 +335,7 @@ class SaveImage:
             if add_counter_to_filename:
                 # Use counter + i to ensure unique filenames for all images in batch
                 current_counter = counter + i
-                base_filename += f"_{current_counter:05}"
+                base_filename += f"_{current_counter:05}_"
                 
             # Set file extension and prepare saving parameters
             if file_format == "png":

py/nodes/trigger_word_toggle.py

@@ -47,10 +47,10 @@ class TriggerWordToggle:
         trigger_words = trigger_words_data if isinstance(trigger_words_data, str) else ""
         
         # Send trigger words to frontend
-        PromptServer.instance.send_sync("trigger_word_update", {
+        # PromptServer.instance.send_sync("trigger_word_update", {
-            "id": id,
+        #     "id": id,
-            "message": trigger_words
+        #     "message": trigger_words
-        })
+        # })
         
         filtered_triggers = trigger_words
         

py/nodes/utils.py

@@ -31,3 +31,54 @@ class FlexibleOptionalInputType(dict):
 
 
 any_type = AnyType("*")
+
+# Common methods extracted from lora_loader.py and lora_stacker.py
+import os
+import logging
+import asyncio
+from ..services.lora_scanner import LoraScanner
+from ..config import config
+
+logger = logging.getLogger(__name__)
+
+async def get_lora_info(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(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(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 []

py/routes/api_routes.py

@@ -3,8 +3,10 @@ import json
 import logging
 from aiohttp import web
 from typing import Dict
+from server import PromptServer # type: ignore
 
 from ..utils.routes_common import ModelRouteUtils
+from ..nodes.utils import get_lora_info
 
 from ..config import config
 from ..services.websocket_manager import ws_manager
@@ -65,6 +67,9 @@ class ApiRoutes:
         app.router.add_post('/api/rename_lora', routes.rename_lora)  # Add new route for renaming LoRA files
         app.router.add_get('/api/loras/scan', routes.scan_loras)  # Add new route for scanning LoRA files
         
+        # Add the new trigger words route
+        app.router.add_post('/loramanager/get_trigger_words', routes.get_trigger_words)
+
         # Add update check routes
         UpdateRoutes.setup_routes(app)
 
@@ -1022,3 +1027,34 @@ class ApiRoutes:
                 'success': False,
                 'error': str(e)
             }, status=500)
+
+    async def get_trigger_words(self, request: web.Request) -> web.Response:
+        """Get trigger words for specified LoRA models"""
+        try:
+            json_data = await request.json()
+            lora_names = json_data.get("lora_names", [])
+            node_ids = json_data.get("node_ids", [])
+            
+            all_trigger_words = []
+            for lora_name in lora_names:
+                _, trigger_words = await get_lora_info(lora_name)
+                all_trigger_words.extend(trigger_words)
+            
+            # Format the trigger words
+            trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+            
+            # Send update to all connected trigger word toggle nodes
+            for node_id in node_ids:
+                PromptServer.instance.send_sync("trigger_word_update", {
+                    "id": node_id,
+                    "message": trigger_words_text
+                })
+            
+            return web.json_response({"success": True})
+
+        except Exception as e:
+            logger.error(f"Error getting trigger words: {e}")
+            return web.json_response({
+                "success": False,
+                "error": str(e)
+            }, status=500)

py/routes/usage_stats_routes.py

@@ -0,0 +1,69 @@
+import logging
+from aiohttp import web
+from ..utils.usage_stats import UsageStats
+
+logger = logging.getLogger(__name__)
+
+class UsageStatsRoutes:
+    """Routes for handling usage statistics updates"""
+    
+    @staticmethod
+    def setup_routes(app):
+        """Register usage stats routes"""
+        app.router.add_post('/loras/api/update-usage-stats', UsageStatsRoutes.update_usage_stats)
+        app.router.add_get('/loras/api/get-usage-stats', UsageStatsRoutes.get_usage_stats)
+    
+    @staticmethod
+    async def update_usage_stats(request):
+        """
+        Update usage statistics based on a prompt_id
+        
+        Expects a JSON body with:
+        {
+            "prompt_id": "string"
+        }
+        """
+        try:
+            # Parse the request body
+            data = await request.json()
+            prompt_id = data.get('prompt_id')
+            
+            if not prompt_id:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing prompt_id'
+                }, status=400)
+            
+            # Call the UsageStats to process this prompt_id synchronously
+            usage_stats = UsageStats()
+            await usage_stats.process_execution(prompt_id)
+            
+            return web.json_response({
+                'success': True
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to update usage stats: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    @staticmethod
+    async def get_usage_stats(request):
+        """Get current usage statistics"""
+        try:
+            usage_stats = UsageStats()
+            stats = await usage_stats.get_stats()
+            
+            return web.json_response({
+                'success': True,
+                'data': stats
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to get usage stats: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/server_routes.py

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

py/services/lora_scanner.py

@@ -9,7 +9,7 @@ from typing import List, Dict, Optional, Set
 from ..utils.models import LoraMetadata
 from ..config import config
 from .model_scanner import ModelScanner
-from .lora_hash_index import LoraHashIndex
+from .model_hash_index import ModelHashIndex  # Changed from LoraHashIndex to ModelHashIndex
 from .settings_manager import settings
 from ..utils.constants import NSFW_LEVELS
 from ..utils.utils import fuzzy_match
@@ -35,12 +35,12 @@ class LoraScanner(ModelScanner):
             # Define supported file extensions
             file_extensions = {'.safetensors'}
             
-            # Initialize parent class
+            # Initialize parent class with ModelHashIndex
             super().__init__(
                 model_type="lora",
                 model_class=LoraMetadata, 
                 file_extensions=file_extensions,
-                hash_index=LoraHashIndex()
+                hash_index=ModelHashIndex()  # Changed from LoraHashIndex to ModelHashIndex
             )
             self._initialized = True
     

py/services/model_hash_index.py

@@ -1,11 +1,12 @@
 from typing import Dict, Optional, Set
+import os
 
 class ModelHashIndex:
     """Index for looking up models by hash or path"""
     
     def __init__(self):
         self._hash_to_path: Dict[str, str] = {}
-        self._path_to_hash: Dict[str, str] = {}
+        self._filename_to_hash: Dict[str, str] = {}  # Changed from path_to_hash to filename_to_hash
     
     def add_entry(self, sha256: str, file_path: str) -> None:
         """Add or update hash index entry"""
@@ -15,37 +16,47 @@ class ModelHashIndex:
         # Ensure hash is lowercase for consistency
         sha256 = sha256.lower()
         
+        # Extract filename without extension
+        filename = self._get_filename_from_path(file_path)
+        
         # Remove old path mapping if hash exists
         if sha256 in self._hash_to_path:
             old_path = self._hash_to_path[sha256]
-            if old_path in self._path_to_hash:
+            old_filename = self._get_filename_from_path(old_path)
-                del self._path_to_hash[old_path]
+            if old_filename in self._filename_to_hash:
+                del self._filename_to_hash[old_filename]
         
-        # Remove old hash mapping if path exists
+        # Remove old hash mapping if filename exists
-        if file_path in self._path_to_hash:
+        if filename in self._filename_to_hash:
-            old_hash = self._path_to_hash[file_path]
+            old_hash = self._filename_to_hash[filename]
             if old_hash in self._hash_to_path:
                 del self._hash_to_path[old_hash]
         
         # Add new mappings
         self._hash_to_path[sha256] = file_path
-        self._path_to_hash[file_path] = sha256
+        self._filename_to_hash[filename] = sha256
+    
+    def _get_filename_from_path(self, file_path: str) -> str:
+        """Extract filename without extension from path"""
+        return os.path.splitext(os.path.basename(file_path))[0]
     
     def remove_by_path(self, file_path: str) -> None:
         """Remove entry by file path"""
-        if file_path in self._path_to_hash:
+        filename = self._get_filename_from_path(file_path)
-            hash_val = self._path_to_hash[file_path]
+        if filename in self._filename_to_hash:
+            hash_val = self._filename_to_hash[filename]
             if hash_val in self._hash_to_path:
                 del self._hash_to_path[hash_val]
-            del self._path_to_hash[file_path]
+            del self._filename_to_hash[filename]
     
     def remove_by_hash(self, sha256: str) -> None:
         """Remove entry by hash"""
         sha256 = sha256.lower()
         if sha256 in self._hash_to_path:
             path = self._hash_to_path[sha256]
-            if path in self._path_to_hash:
+            filename = self._get_filename_from_path(path)
-                del self._path_to_hash[path]
+            if filename in self._filename_to_hash:
+                del self._filename_to_hash[filename]
             del self._hash_to_path[sha256]
     
     def has_hash(self, sha256: str) -> bool:
@@ -58,20 +69,27 @@ class ModelHashIndex:
     
     def get_hash(self, file_path: str) -> Optional[str]:
         """Get hash for a file path"""
-        return self._path_to_hash.get(file_path)
+        filename = self._get_filename_from_path(file_path)
+        return self._filename_to_hash.get(filename)
+    
+    def get_hash_by_filename(self, filename: str) -> Optional[str]:
+        """Get hash for a filename without extension"""
+        # Strip extension if present to make the function more flexible
+        filename = os.path.splitext(filename)[0]
+        return self._filename_to_hash.get(filename)
     
     def clear(self) -> None:
         """Clear all entries"""
         self._hash_to_path.clear()
-        self._path_to_hash.clear()
+        self._filename_to_hash.clear()
     
     def get_all_hashes(self) -> Set[str]:
         """Get all hashes in the index"""
         return set(self._hash_to_path.keys())
     
-    def get_all_paths(self) -> Set[str]:
+    def get_all_filenames(self) -> Set[str]:
-        """Get all file paths in the index"""
+        """Get all filenames in the index"""
-        return set(self._path_to_hash.keys())
+        return set(self._filename_to_hash.keys())
     
     def __len__(self) -> int:
         """Get number of entries"""

py/services/model_scanner.py

@@ -292,7 +292,7 @@ class ModelScanner:
             )
 
         # If force refresh is requested, initialize the cache directly
-        if force_refresh:
+        if (force_refresh):
             if self._cache is None:
                 # For initial creation, do a full initialization
                 await self._initialize_cache()
@@ -553,9 +553,36 @@ class ModelScanner:
                         logger.debug(f"Created metadata from .civitai.info for {file_path}")
                 except Exception as e:
                     logger.error(f"Error creating metadata from .civitai.info for {file_path}: {e}")
+        else:
+            # Check if metadata exists but civitai field is empty - try to restore from civitai.info
+            if metadata.civitai is None or metadata.civitai == {}:
+                civitai_info_path = f"{os.path.splitext(file_path)[0]}.civitai.info"
+                if os.path.exists(civitai_info_path):
+                    try:
+                        with open(civitai_info_path, 'r', encoding='utf-8') as f:
+                            version_info = json.load(f)
                         
-            if metadata is None:
+                        logger.debug(f"Restoring missing civitai data from .civitai.info for {file_path}")
-                metadata = await self._get_file_info(file_path)
+                        metadata.civitai = version_info
+                        
+                        # Ensure tags are also updated if they're missing
+                        if (not metadata.tags or len(metadata.tags) == 0) and 'model' in version_info:
+                            if 'tags' in version_info['model']:
+                                metadata.tags = version_info['model']['tags']
+                        
+                        # Also restore description if missing
+                        if (not metadata.modelDescription or metadata.modelDescription == "") and 'model' in version_info:
+                            if 'description' in version_info['model']:
+                                metadata.modelDescription = version_info['model']['description']
+                        
+                        # Save the updated metadata
+                        await save_metadata(file_path, metadata)
+                        logger.debug(f"Updated metadata with civitai info for {file_path}")
+                    except Exception as e:
+                        logger.error(f"Error restoring civitai data from .civitai.info for {file_path}: {e}")
+            
+        if metadata is None:
+            metadata = await self._get_file_info(file_path)
         
         model_data = metadata.to_dict()
         
@@ -806,6 +833,10 @@ class ModelScanner:
         """Get hash for a model by its file path"""
         return self._hash_index.get_hash(file_path)
         
+    def get_hash_by_filename(self, filename: str) -> Optional[str]:
+        """Get hash for a model by its filename without path"""
+        return self._hash_index.get_hash_by_filename(filename)
+
     # TODO: Adjust this method to use metadata instead of finding the file    
     def get_preview_url_by_hash(self, sha256: str) -> Optional[str]:
         """Get preview static URL for a model by its hash"""

py/utils/models.py

@@ -21,6 +21,7 @@ class BaseModelMetadata:
     civitai: Optional[Dict] = None  # Civitai API data if available
     tags: List[str] = None      # Model tags
     modelDescription: str = ""  # Full model description
+    civitai_deleted: bool = False  # Whether deleted from Civitai
 
     def __post_init__(self):
         # Initialize empty lists to avoid mutable default parameter issue
@@ -64,6 +65,15 @@ class LoraMetadata(BaseModelMetadata):
         file_name = file_info['name']
         base_model = determine_base_model(version_info.get('baseModel', ''))
         
+        # Extract tags and description if available
+        tags = []
+        description = ""
+        if 'model' in version_info:
+            if 'tags' in version_info['model']:
+                tags = version_info['model']['tags']
+            if 'description' in version_info['model']:
+                description = version_info['model']['description']
+        
         return cls(
             file_name=os.path.splitext(file_name)[0],
             model_name=version_info.get('model').get('name', os.path.splitext(file_name)[0]),
@@ -75,7 +85,9 @@ class LoraMetadata(BaseModelMetadata):
             preview_url=None,  # Will be updated after preview download
             preview_nsfw_level=0, # Will be updated after preview download
             from_civitai=True,
-            civitai=version_info
+            civitai=version_info,
+            tags=tags,
+            modelDescription=description
         )
 
 @dataclass
@@ -90,6 +102,15 @@ class CheckpointMetadata(BaseModelMetadata):
         base_model = determine_base_model(version_info.get('baseModel', ''))
         model_type = version_info.get('type', 'checkpoint')
         
+        # Extract tags and description if available
+        tags = []
+        description = ""
+        if 'model' in version_info:
+            if 'tags' in version_info['model']:
+                tags = version_info['model']['tags']
+            if 'description' in version_info['model']:
+                description = version_info['model']['description']
+        
         return cls(
             file_name=os.path.splitext(file_name)[0],
             model_name=version_info.get('model').get('name', os.path.splitext(file_name)[0]),
@@ -102,6 +123,8 @@ class CheckpointMetadata(BaseModelMetadata):
             preview_nsfw_level=0,
             from_civitai=True,
             civitai=version_info,
-            model_type=model_type
+            model_type=model_type,
+            tags=tags,
+            modelDescription=description
         )
 

py/utils/recipe_parsers.py

@@ -45,14 +45,14 @@ class RecipeMetadataParser(ABC):
         """
         pass
     
-    async def populate_lora_from_civitai(self, lora_entry: Dict[str, Any], civitai_info: Dict[str, Any], 
+    async def populate_lora_from_civitai(self, lora_entry: Dict[str, Any], civitai_info_tuple: Tuple[Dict[str, Any], Optional[str]], 
                                          recipe_scanner=None, base_model_counts=None, hash_value=None) -> Dict[str, Any]:
         """
         Populate a lora entry with information from Civitai API response
         
         Args:
             lora_entry: The lora entry to populate
-            civitai_info: The response from Civitai API
+            civitai_info_tuple: The response tuple from Civitai API (data, error_msg)
             recipe_scanner: Optional recipe scanner for local file lookup
             base_model_counts: Optional dict to track base model counts
             hash_value: Optional hash value to use if not available in civitai_info
@@ -61,6 +61,9 @@ class RecipeMetadataParser(ABC):
             The populated lora_entry dict
         """
         try:
+            # Unpack the tuple to get the actual data
+            civitai_info, error_msg = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
+            
             if civitai_info and civitai_info.get("error") != "Model not found":
                 # Check if this is an early access lora
                 if civitai_info.get('earlyAccessEndsAt'):
@@ -241,11 +244,11 @@ class RecipeFormatParser(RecipeMetadataParser):
                         # 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'])
+                                civitai_info_tuple = await civitai_client.get_model_version_info(lora['modelVersionId'])
                                 # Populate lora entry with Civitai info
                                 lora_entry = await self.populate_lora_from_civitai(
                                     lora_entry, 
-                                    civitai_info, 
+                                    civitai_info_tuple, 
                                     recipe_scanner,
                                     None,  # No need to track base model counts
                                     lora['hash']
@@ -336,12 +339,13 @@ class StandardMetadataParser(RecipeMetadataParser):
                 # Get additional info from Civitai if client is available
                 if civitai_client:
                     try:
-                        civitai_info = await civitai_client.get_model_version_info(model_version_id)
+                        civitai_info_tuple = await civitai_client.get_model_version_info(model_version_id)
                         # Populate lora entry with Civitai info
                         lora_entry = await self.populate_lora_from_civitai(
                             lora_entry, 
-                            civitai_info, 
+                            civitai_info_tuple, 
-                            recipe_scanner
+                            recipe_scanner,
+                            base_model_counts
                         )
                     except Exception as e:
                         logger.error(f"Error fetching Civitai info for LoRA: {e}")
@@ -621,11 +625,11 @@ class ComfyMetadataParser(RecipeMetadataParser):
                 # Get additional info from Civitai if client is available
                 if civitai_client:
                     try:
-                        civitai_info = await civitai_client.get_model_version_info(model_version_id)
+                        civitai_info_tuple = await civitai_client.get_model_version_info(model_version_id)
                         # Populate lora entry with Civitai info
                         lora_entry = await self.populate_lora_from_civitai(
                             lora_entry, 
-                            civitai_info, 
+                            civitai_info_tuple, 
                             recipe_scanner
                         )
                     except Exception as e:
@@ -660,7 +664,8 @@ class ComfyMetadataParser(RecipeMetadataParser):
                         # Get additional checkpoint info from Civitai
                         if civitai_client:
                             try:
-                                civitai_info = await civitai_client.get_model_version_info(checkpoint_version_id)
+                                civitai_info_tuple = await civitai_client.get_model_version_info(checkpoint_version_id)
+                                civitai_info, _ = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
                                 # Populate checkpoint with Civitai info
                                 checkpoint = await self.populate_checkpoint_from_civitai(checkpoint, civitai_info)
                             except Exception as e:

py/utils/usage_stats.py

@@ -0,0 +1,267 @@
+import os
+import json
+import time
+import asyncio
+import logging
+from typing import Dict, Set
+
+from ..config import config
+from ..services.service_registry import ServiceRegistry
+from ..metadata_collector.metadata_registry import MetadataRegistry
+from ..metadata_collector.constants import MODELS, LORAS
+
+logger = logging.getLogger(__name__)
+
+class UsageStats:
+    """Track usage statistics for models and save to JSON"""
+    
+    _instance = None
+    _lock = asyncio.Lock()  # For thread safety
+    
+    # Default stats file name
+    STATS_FILENAME = "lora_manager_stats.json"
+    
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._initialized = False
+        return cls._instance
+    
+    def __init__(self):
+        if self._initialized:
+            return
+            
+        # Initialize stats storage
+        self.stats = {
+            "checkpoints": {},  # sha256 -> count
+            "loras": {},        # sha256 -> count
+            "total_executions": 0,
+            "last_save_time": 0
+        }
+        
+        # Queue for prompt_ids to process
+        self.pending_prompt_ids = set()
+        
+        # Load existing stats if available
+        self._stats_file_path = self._get_stats_file_path()
+        self._load_stats()
+        
+        # Save interval in seconds
+        self.save_interval = 90  # 1.5 minutes
+        
+        # Start background task to process queued prompt_ids
+        self._bg_task = asyncio.create_task(self._background_processor())
+        
+        self._initialized = True
+        logger.info("Usage statistics tracker initialized")
+    
+    def _get_stats_file_path(self) -> str:
+        """Get the path to the stats JSON file"""
+        if not config.loras_roots or len(config.loras_roots) == 0:
+            # Fallback to temporary directory if no lora roots
+            return os.path.join(config.temp_directory, self.STATS_FILENAME)
+        
+        # Use the first lora root
+        return os.path.join(config.loras_roots[0], self.STATS_FILENAME)
+    
+    def _load_stats(self):
+        """Load existing statistics from file"""
+        try:
+            if os.path.exists(self._stats_file_path):
+                with open(self._stats_file_path, 'r', encoding='utf-8') as f:
+                    loaded_stats = json.load(f)
+                
+                # Update our stats with loaded data
+                if isinstance(loaded_stats, dict):
+                    # Update individual sections to maintain structure
+                    if "checkpoints" in loaded_stats and isinstance(loaded_stats["checkpoints"], dict):
+                        self.stats["checkpoints"] = loaded_stats["checkpoints"]
+                    
+                    if "loras" in loaded_stats and isinstance(loaded_stats["loras"], dict):
+                        self.stats["loras"] = loaded_stats["loras"]
+                    
+                    if "total_executions" in loaded_stats:
+                        self.stats["total_executions"] = loaded_stats["total_executions"]
+                    
+                logger.info(f"Loaded usage statistics from {self._stats_file_path}")
+        except Exception as e:
+            logger.error(f"Error loading usage statistics: {e}")
+    
+    async def save_stats(self, force=False):
+        """Save statistics to file"""
+        try:
+            # Only save if it's been at least save_interval since last save or force is True
+            current_time = time.time()
+            if not force and (current_time - self.stats.get("last_save_time", 0)) < self.save_interval:
+                return False
+                
+            # Use a lock to prevent concurrent writes
+            async with self._lock:
+                # Update last save time
+                self.stats["last_save_time"] = current_time
+                
+                # Create directory if it doesn't exist
+                os.makedirs(os.path.dirname(self._stats_file_path), exist_ok=True)
+                
+                # Write to a temporary file first, then move it to avoid corruption
+                temp_path = f"{self._stats_file_path}.tmp"
+                with open(temp_path, 'w', encoding='utf-8') as f:
+                    json.dump(self.stats, f, indent=2, ensure_ascii=False)
+                
+                # Replace the old file with the new one
+                os.replace(temp_path, self._stats_file_path)
+                
+                logger.debug(f"Saved usage statistics to {self._stats_file_path}")
+                return True
+        except Exception as e:
+            logger.error(f"Error saving usage statistics: {e}", exc_info=True)
+            return False
+    
+    def register_execution(self, prompt_id):
+        """Register a completed execution by prompt_id for later processing"""
+        if prompt_id:
+            self.pending_prompt_ids.add(prompt_id)
+    
+    async def _background_processor(self):
+        """Background task to process queued prompt_ids"""
+        try:
+            while True:
+                # Wait a short interval before checking for new prompt_ids
+                await asyncio.sleep(5)  # Check every 5 seconds
+                
+                # Process any pending prompt_ids
+                if self.pending_prompt_ids:
+                    async with self._lock:
+                        # Get a copy of the set and clear original
+                        prompt_ids = self.pending_prompt_ids.copy()
+                        self.pending_prompt_ids.clear()
+                    
+                    # Process each prompt_id
+                    registry = MetadataRegistry()
+                    for prompt_id in prompt_ids:
+                        try:
+                            metadata = registry.get_metadata(prompt_id)
+                            await self._process_metadata(metadata)
+                        except Exception as e:
+                            logger.error(f"Error processing prompt_id {prompt_id}: {e}")
+                
+                # Periodically save stats
+                await self.save_stats()
+        except asyncio.CancelledError:
+            # Task was cancelled, clean up
+            await self.save_stats(force=True)
+        except Exception as e:
+            logger.error(f"Error in background processing task: {e}", exc_info=True)
+            # Restart the task after a delay if it fails
+            asyncio.create_task(self._restart_background_task())
+    
+    async def _restart_background_task(self):
+        """Restart the background task after a delay"""
+        await asyncio.sleep(30)  # Wait 30 seconds before restarting
+        self._bg_task = asyncio.create_task(self._background_processor())
+    
+    async def _process_metadata(self, metadata):
+        """Process metadata from an execution"""
+        if not metadata or not isinstance(metadata, dict):
+            return
+            
+        # Increment total executions count
+        self.stats["total_executions"] += 1
+        
+        # Process checkpoints
+        if MODELS in metadata and isinstance(metadata[MODELS], dict):
+            await self._process_checkpoints(metadata[MODELS])
+        
+        # Process loras
+        if LORAS in metadata and isinstance(metadata[LORAS], dict):
+            await self._process_loras(metadata[LORAS])
+    
+    async def _process_checkpoints(self, models_data):
+        """Process checkpoint models from metadata"""
+        try:
+            # Get checkpoint scanner service
+            checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+            if not checkpoint_scanner:
+                logger.warning("Checkpoint scanner not available for usage tracking")
+                return
+            
+            for node_id, model_info in models_data.items():
+                if not isinstance(model_info, dict):
+                    continue
+                    
+                # Check if this is a checkpoint model
+                model_type = model_info.get("type")
+                if model_type == "checkpoint":
+                    model_name = model_info.get("name")
+                    if not model_name:
+                        continue
+                    
+                    # Clean up filename (remove extension if present)
+                    model_filename = os.path.splitext(os.path.basename(model_name))[0]
+                    
+                    # Get hash for this checkpoint
+                    model_hash = checkpoint_scanner.get_hash_by_filename(model_filename)
+                    if model_hash:
+                        # Update stats for this checkpoint
+                        self.stats["checkpoints"][model_hash] = self.stats["checkpoints"].get(model_hash, 0) + 1
+        except Exception as e:
+            logger.error(f"Error processing checkpoint usage: {e}", exc_info=True)
+    
+    async def _process_loras(self, loras_data):
+        """Process LoRA models from metadata"""
+        try:
+            # Get LoRA scanner service
+            lora_scanner = await ServiceRegistry.get_lora_scanner()
+            if not lora_scanner:
+                logger.warning("LoRA scanner not available for usage tracking")
+                return
+            
+            for node_id, lora_info in loras_data.items():
+                if not isinstance(lora_info, dict):
+                    continue
+                
+                # Get the list of LoRAs from standardized format
+                lora_list = lora_info.get("lora_list", [])
+                for lora in lora_list:
+                    if not isinstance(lora, dict):
+                        continue
+                        
+                    lora_name = lora.get("name")
+                    if not lora_name:
+                        continue
+                    
+                    # Get hash for this LoRA
+                    lora_hash = lora_scanner.get_hash_by_filename(lora_name)
+                    if lora_hash:
+                        # Update stats for this LoRA
+                        self.stats["loras"][lora_hash] = self.stats["loras"].get(lora_hash, 0) + 1
+        except Exception as e:
+            logger.error(f"Error processing LoRA usage: {e}", exc_info=True)
+    
+    async def get_stats(self):
+        """Get current usage statistics"""
+        return self.stats
+    
+    async def get_model_usage_count(self, model_type, sha256):
+        """Get usage count for a specific model by hash"""
+        if model_type == "checkpoint":
+            return self.stats["checkpoints"].get(sha256, 0)
+        elif model_type == "lora":
+            return self.stats["loras"].get(sha256, 0)
+        return 0
+    
+    async def process_execution(self, prompt_id):
+        """Process a prompt execution immediately (synchronous approach)"""
+        if not prompt_id:
+            return
+            
+        try:
+            # Process metadata for this prompt_id
+            registry = MetadataRegistry()
+            metadata = registry.get_metadata(prompt_id)
+            if metadata:
+                await self._process_metadata(metadata)
+                # Save stats if needed
+                await self.save_stats()
+        except Exception as e:
+            logger.error(f"Error processing prompt_id {prompt_id}: {e}", exc_info=True)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-lora-manager"
 description = "LoRA Manager for ComfyUI - Access it at http://localhost:8188/loras for managing LoRA models with previews and metadata integration."
-version = "0.8.7"
+version = "0.8.8"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",

static/js/components/CheckpointCard.js

@@ -1,4 +1,4 @@
-import { showToast } from '../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { showCheckpointModal } from './checkpointModal/index.js';
 import { NSFW_LEVELS } from '../utils/constants.js';
@@ -204,21 +204,7 @@ export function createCheckpointCard(checkpoint) {
         const checkpointName = card.dataset.file_name;
         
         try {
-            // Modern clipboard API
+            await copyToClipboard(checkpointName, 'Checkpoint name copied');
-            if (navigator.clipboard && window.isSecureContext) {
-                await navigator.clipboard.writeText(checkpointName);
-            } else {
-                // Fallback for older browsers
-                const textarea = document.createElement('textarea');
-                textarea.value = checkpointName;
-                textarea.style.position = 'absolute';
-                textarea.style.left = '-99999px';
-                document.body.appendChild(textarea);
-                textarea.select();
-                document.execCommand('copy');
-                document.body.removeChild(textarea);
-            }
-            showToast('Checkpoint name copied', 'success');
         } catch (err) {
             console.error('Copy failed:', err);
             showToast('Copy failed', 'error');

static/js/components/LoraCard.js

@@ -1,4 +1,4 @@
-import { showToast, openCivitai } from '../utils/uiHelpers.js';
+import { showToast, openCivitai, copyToClipboard } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { showLoraModal } from './loraModal/index.js';
 import { bulkManager } from '../managers/BulkManager.js';
@@ -205,26 +205,7 @@ export function createLoraCard(lora) {
         const strength = usageTips.strength || 1;
         const loraSyntax = `<lora:${card.dataset.file_name}:${strength}>`;
         
-        try {
+        await copyToClipboard(loraSyntax, 'LoRA syntax copied');
-            // Modern clipboard API
-            if (navigator.clipboard && window.isSecureContext) {
-                await navigator.clipboard.writeText(loraSyntax);
-            } else {
-                // Fallback for older browsers
-                const textarea = document.createElement('textarea');
-                textarea.value = loraSyntax;
-                textarea.style.position = 'absolute';
-                textarea.style.left = '-99999px';
-                document.body.appendChild(textarea);
-                textarea.select();
-                document.execCommand('copy');
-                document.body.removeChild(textarea);
-            }
-            showToast('LoRA syntax copied', 'success');
-        } catch (err) {
-            console.error('Copy failed:', err);
-            showToast('Copy failed', 'error');
-        }
     });
 
     // Civitai button click event

static/js/components/RecipeCard.js

@@ -1,5 +1,5 @@
 // Recipe Card Component
-import { showToast } from '../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
 import { modalManager } from '../managers/ModalManager.js';
 
 class RecipeCard {
@@ -109,14 +109,11 @@ class RecipeCard {
                 .then(response => response.json())
                 .then(data => {
                     if (data.success && data.syntax) {
-                        return navigator.clipboard.writeText(data.syntax);
+                        return copyToClipboard(data.syntax, 'Recipe syntax copied to clipboard');
                     } else {
                         throw new Error(data.error || 'No syntax returned');
                     }
                 })
-                .then(() => {
-                    showToast('Recipe syntax copied to clipboard', 'success');
-                })
                 .catch(err => {
                     console.error('Failed to copy: ', err);
                     showToast('Failed to copy recipe syntax', 'error');

static/js/components/RecipeModal.js

@@ -1,5 +1,5 @@
 // Recipe Modal Component
-import { showToast } from '../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { setSessionItem, removeSessionItem } from '../utils/storageHelpers.js';
 
@@ -747,9 +747,8 @@ class RecipeModal {
             const data = await response.json();
             
             if (data.success && data.syntax) {
-                // Copy to clipboard
+                // Use the centralized copyToClipboard utility function
-                await navigator.clipboard.writeText(data.syntax);
+                await copyToClipboard(data.syntax, 'Recipe syntax copied to clipboard');
-                showToast('Recipe syntax copied to clipboard', 'success');
             } else {
                 throw new Error(data.error || 'No syntax returned from server');
             }
@@ -761,12 +760,7 @@ class RecipeModal {
     
     // Helper method to copy text to clipboard
     copyToClipboard(text, successMessage) {
-        navigator.clipboard.writeText(text).then(() => {
+        copyToClipboard(text, successMessage);
-            showToast(successMessage, 'success');
-        }).catch(err => {
-            console.error('Failed to copy text: ', err);
-            showToast('Failed to copy text', 'error');
-        });
     }
 
     // Add new method to handle downloading missing LoRAs

static/js/components/checkpointModal/ShowcaseView.js

@@ -2,7 +2,7 @@
  * ShowcaseView.js
  * Handles showcase content (images, videos) display for checkpoint modal
  */
-import { showToast } from '../../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
 import { state } from '../../state/index.js';
 import { NSFW_LEVELS } from '../../utils/constants.js';
 
@@ -307,8 +307,7 @@ function initMetadataPanelHandlers(container) {
                 if (!promptElement) return;
                 
                 try {
-                    await navigator.clipboard.writeText(promptElement.textContent);
+                    await copyToClipboard(promptElement.textContent, 'Prompt copied to clipboard');
-                    showToast('Prompt copied to clipboard', 'success');
                 } catch (err) {
                     console.error('Copy failed:', err);
                     showToast('Copy failed', 'error');

static/js/components/loraModal/RecipeTab.js

@@ -1,7 +1,7 @@
 /**
  * RecipeTab - Handles the recipes tab in the Lora Modal
  */
-import { showToast } from '../../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
 import { setSessionItem, removeSessionItem } from '../../utils/storageHelpers.js';
 
 /**
@@ -172,14 +172,11 @@ function copyRecipeSyntax(recipeId) {
         .then(response => response.json())
         .then(data => {
             if (data.success && data.syntax) {
-                return navigator.clipboard.writeText(data.syntax);
+                return copyToClipboard(data.syntax, 'Recipe syntax copied to clipboard');
             } else {
                 throw new Error(data.error || 'No syntax returned');
             }
         })
-        .then(() => {
-            showToast('Recipe syntax copied to clipboard', 'success');
-        })
         .catch(err => {
             console.error('Failed to copy: ', err);
             showToast('Failed to copy recipe syntax', 'error');

static/js/components/loraModal/ShowcaseView.js

@@ -2,7 +2,7 @@
  * ShowcaseView.js
  * 处理LoRA模型展示内容（图片、视频）的功能模块
  */
-import { showToast } from '../../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
 import { state } from '../../state/index.js';
 import { NSFW_LEVELS } from '../../utils/constants.js';
 
@@ -311,8 +311,7 @@ function initMetadataPanelHandlers(container) {
                 if (!promptElement) return;
                 
                 try {
-                    await navigator.clipboard.writeText(promptElement.textContent);
+                    await copyToClipboard(promptElement.textContent, 'Prompt copied to clipboard');
-                    showToast('Prompt copied to clipboard', 'success');
                 } catch (err) {
                     console.error('Copy failed:', err);
                     showToast('Copy failed', 'error');

static/js/components/loraModal/TriggerWords.js

@@ -2,7 +2,7 @@
  * TriggerWords.js
  * 处理LoRA模型触发词相关的功能模块
  */
-import { showToast } from '../../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
 import { saveModelMetadata } from './ModelMetadata.js';
 
 /**
@@ -336,23 +336,7 @@ async function saveTriggerWords() {
  */
 window.copyTriggerWord = async function(word) {
     try {
-        // Modern clipboard API - with fallback for non-secure contexts
+        await copyToClipboard(word, 'Trigger word copied');
-        if (navigator.clipboard && window.isSecureContext) {
-            await navigator.clipboard.writeText(word);
-        } else {
-            // Fallback for older browsers or non-secure contexts
-            const textarea = document.createElement('textarea');
-            textarea.value = word;
-            textarea.style.position = 'absolute';
-            textarea.style.left = '-99999px';
-            document.body.appendChild(textarea);
-            textarea.select();
-            const success = document.execCommand('copy');
-            document.body.removeChild(textarea);
-            
-            if (!success) throw new Error('Copy command failed');
-        }
-        showToast('Trigger word copied', 'success');
     } catch (err) {
         console.error('Copy failed:', err);
         showToast('Copy failed', 'error');

static/js/components/loraModal/index.js

@@ -3,8 +3,7 @@
  * 
  * 将原始的LoraModal.js拆分成多个功能模块后的主入口文件
  */
-import { showToast } from '../../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
-import { state } from '../../state/index.js';
 import { modalManager } from '../../managers/ModalManager.js';
 import { renderShowcaseContent, toggleShowcase, setupShowcaseScroll, scrollToTop } from './ShowcaseView.js';
 import { setupTabSwitching, loadModelDescription } from './ModelDescription.js';
@@ -174,8 +173,7 @@ export function showLoraModal(lora) {
 // Copy file name function
 window.copyFileName = async function(fileName) {
     try {
-        await navigator.clipboard.writeText(fileName);
+        await copyToClipboard(fileName, 'File name copied');
-        showToast('File name copied', 'success');
     } catch (err) {
         console.error('Copy failed:', err);
         showToast('Copy failed', 'error');

static/js/managers/BulkManager.js

@@ -1,5 +1,5 @@
 import { state } from '../state/index.js';
-import { showToast } from '../utils/uiHelpers.js';
+import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
 import { updateCardsForBulkMode } from '../components/LoraCard.js';
 
 export class BulkManager {
@@ -205,13 +205,7 @@ export class BulkManager {
             return;
         }
         
-        try {
+        await copyToClipboard(loraSyntaxes.join(', '), `Copied ${loraSyntaxes.length} LoRA syntaxes to clipboard`);
-            await navigator.clipboard.writeText(loraSyntaxes.join(', '));
-            showToast(`Copied ${loraSyntaxes.length} LoRA syntaxes to clipboard`, 'success');
-        } catch (err) {
-            console.error('Copy failed:', err);
-            showToast('Copy failed', 'error');
-        }
     }
     
     // Create and show the thumbnail strip of selected LoRAs

static/js/utils/uiHelpers.js

@@ -2,6 +2,40 @@ import { state } from '../state/index.js';
 import { resetAndReload } from '../api/loraApi.js';
 import { getStorageItem, setStorageItem } from './storageHelpers.js';
 
+/**
+ * Utility function to copy text to clipboard with fallback for older browsers
+ * @param {string} text - The text to copy to clipboard
+ * @param {string} successMessage - Optional success message to show in toast
+ * @returns {Promise<boolean>} - Promise that resolves to true if copy was successful
+ */
+export async function copyToClipboard(text, successMessage = 'Copied to clipboard') {
+    try {
+        // Modern clipboard API
+        if (navigator.clipboard && window.isSecureContext) {
+            await navigator.clipboard.writeText(text);
+        } else {
+            // Fallback for older browsers
+            const textarea = document.createElement('textarea');
+            textarea.value = text;
+            textarea.style.position = 'absolute';
+            textarea.style.left = '-99999px';
+            document.body.appendChild(textarea);
+            textarea.select();
+            document.execCommand('copy');
+            document.body.removeChild(textarea);
+        }
+        
+        if (successMessage) {
+            showToast(successMessage, 'success');
+        }
+        return true;
+    } catch (err) {
+        console.error('Copy failed:', err);
+        showToast('Copy failed', 'error');
+        return false;
+    }
+}
+
 export function showToast(message, type = 'info') {
     const toast = document.createElement('div');
     toast.className = `toast toast-${type}`;
@@ -108,12 +142,6 @@ export function toggleFolder(tag) {
     resetAndReload();
 }
 
-export function copyTriggerWord(word) {
-    navigator.clipboard.writeText(word).then(() => {
-        showToast('Trigger word copied', 'success');
-    });
-}
-
 function filterByFolder(folderPath) {
     document.querySelectorAll('.lora-card').forEach(card => {
         card.style.display = card.dataset.folder === folderPath ? '' : 'none';

web/comfyui/legacy_loras_widget.js

@@ -927,10 +927,6 @@ export function addLorasWidget(node, name, opts, callback) {
 // Function to directly save the recipe without dialog
 async function saveRecipeDirectly(widget) {
   try {
-    // Get the workflow data from the ComfyUI app
-    const prompt = await app.graphToPrompt();
-    console.log('Prompt:', prompt);
-    
     // Show loading toast
     if (app && app.extensionManager && app.extensionManager.toast) {
       app.extensionManager.toast.add({
@@ -941,14 +937,9 @@ async function saveRecipeDirectly(widget) {
       });
     }
     
-    // Prepare the data - only send workflow JSON
-    const formData = new FormData();
-    formData.append('workflow_json', JSON.stringify(prompt.output));
-    
     // Send the request
     const response = await fetch('/api/recipes/save-from-widget', {
-      method: 'POST',
+      method: 'POST'
-      body: formData
     });
     
     const result = await response.json();

web/comfyui/lora_loader.js

@@ -9,6 +9,54 @@ async function getLorasWidgetModule() {
     return await dynamicImportByVersion("./loras_widget.js", "./legacy_loras_widget.js");
 }
 
+// Function to get connected trigger toggle nodes
+function getConnectedTriggerToggleNodes(node) {
+    const connectedNodes = [];
+    
+    // Check if node has outputs
+    if (node.outputs && node.outputs.length > 0) {
+        // For each output slot
+        for (const output of node.outputs) {
+            // Check if this output has any links
+            if (output.links && output.links.length > 0) {
+                // For each link, get the target node
+                for (const linkId of output.links) {
+                    const link = app.graph.links[linkId];
+                    if (link) {
+                        const targetNode = app.graph.getNodeById(link.target_id);
+                        if (targetNode && targetNode.comfyClass === "TriggerWord Toggle (LoraManager)") {
+                            connectedNodes.push(targetNode.id);
+                        }
+                    }
+                }
+            }
+        }
+    }
+    return connectedNodes;
+}
+
+// Function to update trigger words for connected toggle nodes
+function updateConnectedTriggerWords(node, text) {
+    const connectedNodeIds = getConnectedTriggerToggleNodes(node);
+    if (connectedNodeIds.length > 0) {
+        const loraNames = new Set();
+        let match;
+        LORA_PATTERN.lastIndex = 0;
+        while ((match = LORA_PATTERN.exec(text)) !== null) {
+            loraNames.add(match[1]);
+        }
+        
+        fetch("/loramanager/get_trigger_words", {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({
+                lora_names: Array.from(loraNames),
+                node_ids: connectedNodeIds
+            })
+        }).catch(err => console.error("Error fetching trigger words:", err));
+    }
+}
+
 function mergeLoras(lorasText, lorasArr) {
     const result = [];
     let match;
@@ -99,6 +147,9 @@ app.registerExtension({
                         newText = newText.replace(/\s+/g, ' ').trim();
                         
                         inputWidget.value = newText;
+                        
+                        // Add this line to update trigger words when lorasWidget changes cause inputWidget value to change
+                        updateConnectedTriggerWords(node, newText);
                     } finally {
                         isUpdating = false;
                     }
@@ -117,6 +168,9 @@ app.registerExtension({
                         const mergedLoras = mergeLoras(value, currentLoras);
                         
                         node.lorasWidget.value = mergedLoras;
+                        
+                        // Replace the existing trigger word update code with the new function
+                        updateConnectedTriggerWords(node, value);
                     } finally {
                         isUpdating = false;
                     }

web/comfyui/lora_stacker.js

@@ -1,9 +1,58 @@
 import { app } from "../../scripts/app.js";
-import { addLorasWidget } from "./loras_widget.js";
+import { dynamicImportByVersion } from "./utils.js";
 
 // Extract pattern into a constant for consistent use
 const LORA_PATTERN = /<lora:([^:]+):([-\d\.]+)>/g;
 
+// Function to get the appropriate loras widget based on ComfyUI version
+async function getLorasWidgetModule() {
+    return await dynamicImportByVersion("./loras_widget.js", "./legacy_loras_widget.js");
+}
+
+// Function to get connected trigger toggle nodes
+function getConnectedTriggerToggleNodes(node) {
+    const connectedNodes = [];
+    
+    if (node.outputs && node.outputs.length > 0) {
+        for (const output of node.outputs) {
+            if (output.links && output.links.length > 0) {
+                for (const linkId of output.links) {
+                    const link = app.graph.links[linkId];
+                    if (link) {
+                        const targetNode = app.graph.getNodeById(link.target_id);
+                        if (targetNode && targetNode.comfyClass === "TriggerWord Toggle (LoraManager)") {
+                            connectedNodes.push(targetNode.id);
+                        }
+                    }
+                }
+            }
+        }
+    }
+    return connectedNodes;
+}
+
+// Function to update trigger words for connected toggle nodes
+function updateConnectedTriggerWords(node, text) {
+    const connectedNodeIds = getConnectedTriggerToggleNodes(node);
+    if (connectedNodeIds.length > 0) {
+        const loraNames = new Set();
+        let match;
+        LORA_PATTERN.lastIndex = 0;
+        while ((match = LORA_PATTERN.exec(text)) !== null) {
+            loraNames.add(match[1]);
+        }
+        
+        fetch("/loramanager/get_trigger_words", {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({
+                lora_names: Array.from(loraNames),
+                node_ids: connectedNodeIds
+            })
+        }).catch(err => console.error("Error fetching trigger words:", err));
+    }
+}
+
 function mergeLoras(lorasText, lorasArr) {
     const result = [];
     let match;
@@ -40,7 +89,7 @@ app.registerExtension({
             });
 
             // Wait for node to be properly initialized
-            requestAnimationFrame(() => {               
+            requestAnimationFrame(async () => {               
                 // Restore saved value if exists
                 let existingLoras = [];
                 if (node.widgets_values && node.widgets_values.length > 0) {
@@ -64,7 +113,10 @@ app.registerExtension({
                 // Add flag to prevent callback loops
                 let isUpdating = false;
                  
-                // Get the widget object directly from the returned object
+                // Dynamically load the appropriate widget module
+                const lorasModule = await getLorasWidgetModule();
+                const { addLorasWidget } = lorasModule;
+                
                 const result = addLorasWidget(node, "loras", {
                     defaultVal: mergedLoras  // Pass object directly
                 }, (value) => {
@@ -86,6 +138,9 @@ app.registerExtension({
                         newText = newText.replace(/\s+/g, ' ').trim();
                         
                         inputWidget.value = newText;
+                        
+                        // Update trigger words when lorasWidget changes
+                        updateConnectedTriggerWords(node, newText);
                     } finally {
                         isUpdating = false;
                     }
@@ -104,6 +159,9 @@ app.registerExtension({
                         const mergedLoras = mergeLoras(value, currentLoras);
                         
                         node.lorasWidget.value = mergedLoras;
+                        
+                        // Update trigger words when input changes
+                        updateConnectedTriggerWords(node, value);
                     } finally {
                         isUpdating = false;
                     }

web/comfyui/usage_stats.js

@@ -0,0 +1,36 @@
+// ComfyUI extension to track model usage statistics
+import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js";
+
+// Register the extension
+app.registerExtension({
+    name: "ComfyUI-Lora-Manager.UsageStats",
+    
+    init() {
+        // Listen for successful executions
+        api.addEventListener("execution_success", ({ detail }) => {
+            if (detail && detail.prompt_id) {
+                this.updateUsageStats(detail.prompt_id);
+            }
+        });
+    },
+    
+    async updateUsageStats(promptId) {
+        try {
+            // Call backend endpoint with the prompt_id
+            const response = await fetch(`/loras/api/update-usage-stats`, {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({ prompt_id: promptId }),
+            });
+            
+            if (!response.ok) {
+                console.warn("Failed to update usage statistics:", response.statusText);
+            }
+        } catch (error) {
+            console.error("Error updating usage statistics:", error);
+        }
+    }
+});