Bump version to v0.8.3

Dev

.gitignore

@@ -1,4 +1,5 @@
 __pycache__/
 settings.json
 output/*
-py/run_test.py
+py/run_test.py
+.vscode/

README.md

@@ -14,11 +14,33 @@ A comprehensive toolset that streamlines organizing, downloading, and applying L
 Watch this quick tutorial to learn how to use the new one-click LoRA integration feature:
 
 [![One-Click LoRA Integration Tutorial](https://img.youtube.com/vi/qS95OjX3e70/0.jpg)](https://youtu.be/qS95OjX3e70)
+[![LoRA Manager v0.8.0 - New Recipe Feature & Bulk Operations](https://img.youtube.com/vi/noN7f_ER7yo/0.jpg)](https://youtu.be/noN7f_ER7yo)
 
 ---
 
 ## Release Notes
 
+### v0.8.3
+* **Enhanced Workflow Parser** - Rebuilt workflow analysis engine with improved support for ComfyUI core nodes and easier extensibility
+* **Improved Recipe System** - Refined the experimental Save Recipe functionality with better workflow integration
+* **New Save Image Node** - Added experimental node with metadata support for perfect CivitAI compatibility
+  * Supports dynamic filename prefixes with variables [1](https://github.com/nkchocoai/ComfyUI-SaveImageWithMetaData?tab=readme-ov-file#filename_prefix)
+* **Default LoRA Root Setting** - Added configuration option for setting your preferred LoRA directory
+
+### v0.8.2  
+* **Faster Initialization for Forge Users** - Improved first-run efficiency by utilizing existing `.json` and `.civitai.info` files from Forge’s CivitAI helper extension, making migration smoother.  
+* **LoRA Filename Editing** - Added support for renaming LoRA files directly within LoRA Manager.  
+* **Recipe Editing** - Users can now edit recipe names and tags.  
+* **Retain Deleted LoRAs in Recipes** - Deleted LoRAs will remain listed in recipes, allowing future functionality to reconnect them once re-obtained.  
+* **Download Missing LoRAs from Recipes** - Easily fetch missing LoRAs associated with a recipe.
+
+### v0.8.1
+* **Base Model Correction** - Added support for modifying base model associations to fix incorrect metadata for non-CivitAI LoRAs
+* **LoRA Loader Flexibility** - Made CLIP input optional for model-only workflows like Hunyuan video generation
+* **Expanded Recipe Support** - Added compatibility with 3 additional recipe metadata formats
+* **Enhanced Showcase Images** - Generation parameters now displayed alongside LoRA preview images
+* **UI Improvements & Bug Fixes** - Various interface refinements and stability enhancements
+
 ### v0.8.0
 * **Introduced LoRA Recipes** - Create, import, save, and share your favorite LoRA combinations
 * **Recipe Management System** - Easily browse, search, and organize your LoRA recipes
@@ -27,52 +49,6 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 * **Enhanced UI & UX** - Improved interface design and user experience
 * **Bug Fixes & Stability** - Resolved various issues and enhanced overall performance
 
-### v0.7.37
-* Added NSFW content control settings (blur mature content and SFW-only filter)
-* Implemented intelligent blur effects for previews and showcase media
-* Added manual content rating option through context menu
-* Enhanced user experience with configurable content visibility
-* Fixed various bugs and improved stability
-
-### v0.7.36
-* Enhanced LoRA details view with model descriptions and tags display
-* Added tag filtering system for improved model discovery
-* Implemented editable trigger words functionality
-* Improved TriggerWord Toggle node with new group mode option for granular control
-* Added new Lora Stacker node with cross-compatibility support (works with efficiency nodes, ComfyRoll, easy-use, etc.)
-* Fixed several bugs
-
-### v0.7.35-beta
-* Added base model filtering
-* Implemented bulk operations (copy syntax, move multiple LoRAs)
-* Added ability to edit LoRA model names in details view
-* Added update checker with notification system
-* Added support modal for user feedback and community links
-
-### v0.7.33
-* Enhanced LoRA Loader node with visual strength adjustment widgets
-* Added toggle switches for LoRA enable/disable
-* Implemented image tooltips for LoRA preview
-* Added TriggerWord Toggle node with visual word selection
-* Fixed various bugs and improved stability
-
-### v0.7.3
-* Added "Lora Loader (LoraManager)" custom node for workflows
-* Implemented one-click LoRA integration
-* Added direct copying of LoRA syntax from manager interface
-* Added automatic preset strength value application
-* Added automatic trigger word loading
-
-### v0.7.0
-* Added direct CivitAI integration for downloading LoRAs
-* Implemented version selection for model downloads
-* Added target folder selection for downloads
-* Added context menu with quick actions
-* Added force refresh for CivitAI data
-* Implemented LoRA movement between folders
-* Added personal usage tips and notes for LoRAs
-* Improved performance for details window
-
 [View Update History](./update_logs.md)
 
 ---
@@ -156,6 +132,15 @@ pip install requirements.txt
 
 ---
 
+## Credits
+
+This project has been inspired by and benefited from other excellent ComfyUI extensions:
+
+- [ComfyUI-SaveImageWithMetaData](https://github.com/Comfy-Community/ComfyUI-SaveImageWithMetaData) - For the image metadata functionality
+- [rgthree-comfy](https://github.com/rgthree/rgthree-comfy) - For the lora loader functionality
+
+---
+
 ## Contributing
 
 If you have suggestions, bug reports, or improvements, feel free to open an issue or contribute directly to the codebase. Pull requests are always welcome!

__init__.py

@@ -2,13 +2,13 @@ from .py.lora_manager import LoraManager
 from .py.nodes.lora_loader import LoraManagerLoader
 from .py.nodes.trigger_word_toggle import TriggerWordToggle
 from .py.nodes.lora_stacker import LoraStacker
-# from .py.nodes.save_image import SaveImage
+from .py.nodes.save_image import SaveImage
 
 NODE_CLASS_MAPPINGS = {
     LoraManagerLoader.NAME: LoraManagerLoader,
     TriggerWordToggle.NAME: TriggerWordToggle,
     LoraStacker.NAME: LoraStacker,
-    # SaveImage.NAME: SaveImage
+    SaveImage.NAME: SaveImage
 }
 
 WEB_DIRECTORY = "./web/comfyui"

py/nodes/lora_loader.py

@@ -18,7 +18,7 @@ class LoraManagerLoader:
         return {
             "required": {
                 "model": ("MODEL",),
-                "clip": ("CLIP",),
+                # "clip": ("CLIP",),
                 "text": (IO.STRING, {
                     "multiline": True, 
                     "dynamicPrompts": True, 
@@ -75,11 +75,12 @@ class LoraManagerLoader:
             logger.warning(f"Unexpected loras format: {type(loras_data)}")
             return []
     
-    def load_loras(self, model, clip, text, **kwargs):
+    def load_loras(self, model, text, **kwargs):
         """Loads multiple LoRAs based on the kwargs input and lora_stack."""
         loaded_loras = []
         all_trigger_words = []
         
+        clip = kwargs.get('clip', None)
         lora_stack = kwargs.get('lora_stack', None)
         # First process lora_stack if available
         if lora_stack:

py/nodes/lora_stacker.py

@@ -26,8 +26,8 @@ class LoraStacker:
             "optional": FlexibleOptionalInputType(any_type),
         }
 
-    RETURN_TYPES = ("LORA_STACK", IO.STRING)
-    RETURN_NAMES = ("LORA_STACK", "trigger_words")
+    RETURN_TYPES = ("LORA_STACK", IO.STRING, IO.STRING)
+    RETURN_NAMES = ("LORA_STACK", "trigger_words", "active_loras")
     FUNCTION = "stack_loras"
 
     async def get_lora_info(self, lora_name):
@@ -75,6 +75,7 @@ class LoraStacker:
     def stack_loras(self, text, **kwargs):
         """Stacks multiple LoRAs based on the kwargs input without loading them."""
         stack = []
+        active_loras = []
         all_trigger_words = []
         
         # Process existing lora_stack if available
@@ -103,11 +104,15 @@ class LoraStacker:
             # Add to stack without loading
             # replace '/' with os.sep to avoid different OS path format
             stack.append((lora_path.replace('/', os.sep), model_strength, clip_strength))
+            active_loras.append((lora_name, model_strength))
             
             # Add trigger words to collection
             all_trigger_words.extend(trigger_words)
         
         # use ',, ' to separate trigger words for group mode
         trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+        # Format active_loras as <lora:lora_name:strength> separated by spaces
+        active_loras_text = " ".join([f"<lora:{name}:{str(strength).strip()}>" 
+                                  for name, strength in active_loras])
 
-        return (stack, trigger_words_text)
+        return (stack, trigger_words_text, active_loras_text)

py/nodes/save_image.py

@@ -1,16 +1,43 @@
 import json
-from server import PromptServer # type: ignore
+import os
+import asyncio
+import re
+import numpy as np
+import folder_paths # type: ignore
+from ..services.lora_scanner import LoraScanner
+from ..workflow.parser import WorkflowParser
+from PIL import Image, PngImagePlugin
+import piexif
+from io import BytesIO
 
 class SaveImage:
     NAME = "Save Image (LoraManager)"
     CATEGORY = "Lora Manager/utils"
-    DESCRIPTION = "Experimental node to display image preview and print prompt and extra_pnginfo"
+    DESCRIPTION = "Save images with embedded generation metadata in compatible format"
+
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+        self.compress_level = 4
+        self.counter = 0
+    
+    # Add pattern format regex for filename substitution
+    pattern_format = re.compile(r"(%[^%]+%)")
     
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "image": ("IMAGE",),
+                "images": ("IMAGE",),
+                "filename_prefix": ("STRING", {"default": "ComfyUI"}),
+                "file_format": (["png", "jpeg", "webp"],),
+            },
+            "optional": {
+                "lossless_webp": ("BOOLEAN", {"default": True}),
+                "quality": ("INT", {"default": 100, "min": 1, "max": 100}),
+                "embed_workflow": ("BOOLEAN", {"default": False}),
+                "add_counter_to_filename": ("BOOLEAN", {"default": True}),
             },
             "hidden": {
                 "prompt": "PROMPT",
@@ -19,23 +46,317 @@ class SaveImage:
         }
 
     RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image",)
+    RETURN_NAMES = ("images",)
     FUNCTION = "process_image"
+    OUTPUT_NODE = True
 
-    def process_image(self, image, prompt=None, extra_pnginfo=None):
-        # Print the prompt information
-        print("SaveImage Node - Prompt:")
+    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()
+        
+        for item in cache.raw_data:
+            if item.get('file_name') == lora_name:
+                return item.get('sha256')
+        return None
+
+    async def format_metadata(self, parsed_workflow):
+        """Format metadata in the requested format similar to userComment example"""
+        if not parsed_workflow:
+            return ""
+        
+        # Extract the prompt and negative prompt
+        prompt = parsed_workflow.get('prompt', '')
+        negative_prompt = parsed_workflow.get('negative_prompt', '')
+        
+        # Extract loras from the prompt if present
+        loras_text = parsed_workflow.get('loras', '')
+        lora_hashes = {}
+        
+        # If loras are found, add them on a new line after the prompt
+        if loras_text:
+            prompt_with_loras = f"{prompt}\n{loras_text}"
+            
+            # Extract lora names from the format <lora:name:strength>
+            lora_matches = re.findall(r'<lora:([^:]+):([^>]+)>', loras_text)
+            
+            # Get hash for each lora
+            for lora_name, strength in lora_matches:
+                hash_value = await self.get_lora_hash(lora_name)
+                if hash_value:
+                    lora_hashes[lora_name] = hash_value
+        else:
+            prompt_with_loras = prompt
+        
+        # Format the first part (prompt and loras)
+        metadata_parts = [prompt_with_loras]
+        
+        # Add negative prompt
+        if negative_prompt:
+            metadata_parts.append(f"Negative prompt: {negative_prompt}")
+        
+        # Format the second part (generation parameters)
+        params = []
+        
+        # Add standard parameters in the correct order
+        if 'steps' in parsed_workflow:
+            params.append(f"Steps: {parsed_workflow.get('steps')}")
+        
+        if 'sampler' in parsed_workflow:
+            sampler = parsed_workflow.get('sampler')
+            # Convert ComfyUI sampler names to user-friendly names
+            sampler_mapping = {
+                'euler': 'Euler',
+                'euler_ancestral': 'Euler a',
+                'dpm_2': 'DPM2',
+                'dpm_2_ancestral': 'DPM2 a',
+                'heun': 'Heun',
+                'dpm_fast': 'DPM fast',
+                'dpm_adaptive': 'DPM adaptive',
+                'lms': 'LMS',
+                'dpmpp_2s_ancestral': 'DPM++ 2S a',
+                'dpmpp_sde': 'DPM++ SDE',
+                'dpmpp_sde_gpu': 'DPM++ SDE',
+                'dpmpp_2m': 'DPM++ 2M',
+                'dpmpp_2m_sde': 'DPM++ 2M SDE',
+                'dpmpp_2m_sde_gpu': 'DPM++ 2M SDE',
+                'ddim': 'DDIM'
+            }
+            sampler_name = sampler_mapping.get(sampler, sampler)
+            params.append(f"Sampler: {sampler_name}")
+        
+        if 'scheduler' in parsed_workflow:
+            scheduler = parsed_workflow.get('scheduler')
+            scheduler_mapping = {
+                'normal': 'Simple',
+                'karras': 'Karras',
+                'exponential': 'Exponential',
+                'sgm_uniform': 'SGM Uniform',
+                'sgm_quadratic': 'SGM Quadratic'
+            }
+            scheduler_name = scheduler_mapping.get(scheduler, scheduler)
+            params.append(f"Schedule type: {scheduler_name}")
+        
+        # CFG scale (cfg in parsed_workflow)
+        if 'cfg_scale' in parsed_workflow:
+            params.append(f"CFG scale: {parsed_workflow.get('cfg_scale')}")
+        elif 'cfg' in parsed_workflow:
+            params.append(f"CFG scale: {parsed_workflow.get('cfg')}")
+        
+        # Seed
+        if 'seed' in parsed_workflow:
+            params.append(f"Seed: {parsed_workflow.get('seed')}")
+        
+        # Size
+        if 'size' in parsed_workflow:
+            params.append(f"Size: {parsed_workflow.get('size')}")
+        
+        # Model info
+        if 'checkpoint' in parsed_workflow:
+            # Extract basename without path
+            checkpoint = os.path.basename(parsed_workflow.get('checkpoint', ''))
+            # Remove extension if present
+            checkpoint = os.path.splitext(checkpoint)[0]
+            params.append(f"Model: {checkpoint}")
+        
+        # Add LoRA hashes if available
+        if lora_hashes:
+            lora_hash_parts = []
+            for lora_name, hash_value in lora_hashes.items():
+                lora_hash_parts.append(f"{lora_name}: {hash_value}")
+            
+            if lora_hash_parts:
+                params.append(f"Lora hashes: \"{', '.join(lora_hash_parts)}\"")
+        
+        # Combine all parameters with commas
+        metadata_parts.append(", ".join(params))
+        
+        # Join all parts with a new line
+        return "\n".join(metadata_parts)
+
+    # credit to nkchocoai
+    # Add format_filename method to handle pattern substitution
+    def format_filename(self, filename, parsed_workflow):
+        """Format filename with metadata values"""
+        if not parsed_workflow:
+            return filename
+            
+        result = re.findall(self.pattern_format, filename)
+        for segment in result:
+            parts = segment.replace("%", "").split(":")
+            key = parts[0]
+            
+            if key == "seed" and 'seed' in parsed_workflow:
+                filename = filename.replace(segment, str(parsed_workflow.get('seed', '')))
+            elif key == "width" and 'size' in parsed_workflow:
+                size = parsed_workflow.get('size', 'x')
+                w = size.split('x')[0] if isinstance(size, str) else size[0]
+                filename = filename.replace(segment, str(w))
+            elif key == "height" and 'size' in parsed_workflow:
+                size = parsed_workflow.get('size', 'x')
+                h = size.split('x')[1] if isinstance(size, str) else size[1]
+                filename = filename.replace(segment, str(h))
+            elif key == "pprompt" and 'prompt' in parsed_workflow:
+                prompt = parsed_workflow.get('prompt', '').replace("\n", " ")
+                if len(parts) >= 2:
+                    length = int(parts[1])
+                    prompt = prompt[:length]
+                filename = filename.replace(segment, prompt.strip())
+            elif key == "nprompt" and 'negative_prompt' in parsed_workflow:
+                prompt = parsed_workflow.get('negative_prompt', '').replace("\n", " ")
+                if len(parts) >= 2:
+                    length = int(parts[1])
+                    prompt = prompt[:length]
+                filename = filename.replace(segment, prompt.strip())
+            elif key == "model" and 'checkpoint' in parsed_workflow:
+                model = parsed_workflow.get('checkpoint', '')
+                model = os.path.splitext(os.path.basename(model))[0]
+                if len(parts) >= 2:
+                    length = int(parts[1])
+                    model = model[:length]
+                filename = filename.replace(segment, model)
+            elif key == "date":
+                from datetime import datetime
+                now = datetime.now()
+                date_table = {
+                    "yyyy": str(now.year),
+                    "MM": str(now.month).zfill(2),
+                    "dd": str(now.day).zfill(2),
+                    "hh": str(now.hour).zfill(2),
+                    "mm": str(now.minute).zfill(2),
+                    "ss": str(now.second).zfill(2),
+                }
+                if len(parts) >= 2:
+                    date_format = parts[1]
+                    for k, v in date_table.items():
+                        date_format = date_format.replace(k, v)
+                    filename = filename.replace(segment, date_format)
+                else:
+                    date_format = "yyyyMMddhhmmss"
+                    for k, v in date_table.items():
+                        date_format = date_format.replace(k, v)
+                    filename = filename.replace(segment, date_format)
+                    
+        return filename
+
+    def save_images(self, images, filename_prefix, file_format, prompt=None, extra_pnginfo=None, 
+                   lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
+        """Save images with metadata"""
+        results = []
+        
+        # Parse the workflow using the WorkflowParser
+        parser = WorkflowParser()
         if prompt:
-            print(json.dumps(prompt, indent=2))
+            parsed_workflow = parser.parse_workflow(prompt)
         else:
-            print("No prompt information available")
+            parsed_workflow = {}
+            
+        # Get or create metadata asynchronously
+        metadata = asyncio.run(self.format_metadata(parsed_workflow))
         
-        # Print the extra_pnginfo
-        print("\nSaveImage Node - Extra PNG Info:")
-        if extra_pnginfo:
-            print(json.dumps(extra_pnginfo, indent=2))
-        else:
-            print("No extra PNG info available")
+        # Process filename_prefix with pattern substitution
+        filename_prefix = self.format_filename(filename_prefix, parsed_workflow)
         
-        # Return the image unchanged
-        return (image,)
+        # Process each image
+        for i, image in enumerate(images):
+            # Convert the tensor image to numpy array
+            img = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
+            
+            # Create directory if filename_prefix contains path separators
+            output_path = os.path.join(self.output_dir, filename_prefix)
+            if not os.path.exists(os.path.dirname(output_path)):
+                os.makedirs(os.path.dirname(output_path), exist_ok=True)
+                
+            # Use folder_paths.get_save_image_path for better counter handling
+            full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
+                filename_prefix, self.output_dir, img.width, img.height
+            )
+            
+            # Generate filename with counter if needed
+            if add_counter_to_filename:
+                filename += f"_{counter:05}"
+                
+            # Set file extension and prepare saving parameters
+            if file_format == "png":
+                file = filename + ".png"
+                file_extension = ".png"
+                save_kwargs = {"optimize": True, "compress_level": self.compress_level}
+                pnginfo = PngImagePlugin.PngInfo()
+            elif file_format == "jpeg":
+                file = filename + ".jpg"
+                file_extension = ".jpg"
+                save_kwargs = {"quality": quality, "optimize": True}
+            elif file_format == "webp":
+                file = filename + ".webp" 
+                file_extension = ".webp"
+                save_kwargs = {"quality": quality, "lossless": lossless_webp}
+            
+            # Full save path
+            file_path = os.path.join(full_output_folder, file)
+            
+            # Save the image with metadata
+            try:
+                if file_format == "png":
+                    if metadata:
+                        pnginfo.add_text("parameters", metadata)
+                    if embed_workflow and extra_pnginfo is not None:
+                        workflow_json = json.dumps(extra_pnginfo["workflow"])
+                        pnginfo.add_text("workflow", workflow_json)
+                    save_kwargs["pnginfo"] = pnginfo
+                    img.save(file_path, format="PNG", **save_kwargs)
+                elif file_format == "jpeg":
+                    # For JPEG, use piexif
+                    if metadata:
+                        try:
+                            exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
+                            exif_bytes = piexif.dump(exif_dict)
+                            save_kwargs["exif"] = exif_bytes
+                        except Exception as e:
+                            print(f"Error adding EXIF data: {e}")
+                    img.save(file_path, format="JPEG", **save_kwargs)
+                elif file_format == "webp":
+                    # For WebP, also use piexif for metadata
+                    if metadata:
+                        try:
+                            exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
+                            exif_bytes = piexif.dump(exif_dict)
+                            save_kwargs["exif"] = exif_bytes
+                        except Exception as e:
+                            print(f"Error adding EXIF data: {e}")
+                    img.save(file_path, format="WEBP", **save_kwargs)
+                
+                results.append({
+                    "filename": file,
+                    "subfolder": subfolder,
+                    "type": self.type
+                })
+                
+            except Exception as e:
+                print(f"Error saving image: {e}")
+        
+        return results
+
+    def process_image(self, images, filename_prefix="ComfyUI", file_format="png", prompt=None, extra_pnginfo=None,
+                     lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
+        """Process and save image with metadata"""
+        # Make sure the output directory exists
+        os.makedirs(self.output_dir, exist_ok=True)
+        
+        # Convert single image to list for consistent processing
+        images = [images[0]] if len(images.shape) == 3 else [img for img in images]
+        
+        # Save all images
+        results = self.save_images(
+            images, 
+            filename_prefix, 
+            file_format, 
+            prompt, 
+            extra_pnginfo,
+            lossless_webp,
+            quality,
+            embed_workflow,
+            add_counter_to_filename
+        )
+        
+        return (images,)

py/routes/api_routes.py

@@ -42,6 +42,8 @@ class ApiRoutes:
         app.router.add_get('/api/lora-roots', routes.get_lora_roots)
         app.router.add_get('/api/folders', routes.get_folders)
         app.router.add_get('/api/civitai/versions/{model_id}', routes.get_civitai_versions)
+        app.router.add_get('/api/civitai/model/{modelVersionId}', routes.get_civitai_model)
+        app.router.add_get('/api/civitai/model/{hash}', routes.get_civitai_model)
         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)
@@ -52,6 +54,7 @@ class ApiRoutes:
         app.router.add_get('/api/loras/top-tags', routes.get_top_tags)  # Add new route for top tags
         app.router.add_get('/api/loras/base-models', routes.get_base_models)  # Add new route for base models
         app.router.add_get('/api/lora-civitai-url', routes.get_lora_civitai_url)  # Add new route for Civitai URL
+        app.router.add_post('/api/rename_lora', routes.rename_lora)  # Add new route for renaming LoRA files
 
         # Add update check routes
         UpdateRoutes.setup_routes(app)
@@ -565,6 +568,23 @@ class ApiRoutes:
         except Exception as e:
             logger.error(f"Error fetching model versions: {e}")
             return web.Response(status=500, text=str(e))
+        
+    async def get_civitai_model(self, request: web.Request) -> web.Response:
+        """Get CivitAI model details by model version ID or hash"""
+        try:
+            model_version_id = request.match_info['modelVersionId']
+            if not model_version_id:
+                hash = request.match_info['hash']
+                model = await self.civitai_client.get_model_by_hash(hash)
+                return web.json_response(model)
+            
+            # Get model details from Civitai API    
+            model = await self.civitai_client.get_model_version_info(model_version_id)
+            return web.json_response(model)
+        except Exception as e:
+            logger.error(f"Error fetching model details: {e}")
+            return web.Response(status=500, text=str(e))
+    
 
     async def download_lora(self, request: web.Request) -> web.Response:
         async with self._download_lock:
@@ -578,8 +598,22 @@ class ApiRoutes:
                         'progress': progress
                     })
                 
+                # Check which identifier is provided
+                download_url = data.get('download_url')
+                model_hash = data.get('model_hash')
+                model_version_id = data.get('model_version_id')
+                
+                # Validate that at least one identifier is provided
+                if not any([download_url, model_hash, model_version_id]):
+                    return web.Response(
+                        status=400, 
+                        text="Missing required parameter: Please provide either 'download_url', 'hash', or 'modelVersionId'"
+                    )
+                
                 result = await self.download_manager.download_from_civitai(
-                    download_url=data.get('download_url'),
+                    download_url=download_url,
+                    model_hash=model_hash,
+                    model_version_id=model_version_id,
                     save_dir=data.get('lora_root'),
                     relative_path=data.get('relative_path'),
                     progress_callback=progress_callback
@@ -929,6 +963,149 @@ class ApiRoutes:
             })
         except Exception as e:
             logger.error(f"Error retrieving base models: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    def get_multipart_ext(self, filename):
+        parts = filename.split(".")
+        if len(parts) > 2:  # 如果包含多级扩展名
+            return "." + ".".join(parts[-2:])  # 取最后两部分，如 ".metadata.json"
+        return os.path.splitext(filename)[1]  # 否则取普通扩展名，如 ".safetensors"
+
+    async def rename_lora(self, request: web.Request) -> web.Response:
+        """Handle renaming a LoRA file and its associated files"""
+        try:
+            data = await request.json()
+            file_path = data.get('file_path')
+            new_file_name = data.get('new_file_name')
+            
+            if not file_path or not new_file_name:
+                return web.json_response({
+                    'success': False,
+                    'error': 'File path and new file name are required'
+                }, status=400)
+            
+            # Validate the new file name (no path separators or invalid characters)
+            invalid_chars = ['/', '\\', ':', '*', '?', '"', '<', '>', '|']
+            if any(char in new_file_name for char in invalid_chars):
+                return web.json_response({
+                    'success': False,
+                    'error': 'Invalid characters in file name'
+                }, status=400)
+            
+            # Get the directory and current file name
+            target_dir = os.path.dirname(file_path)
+            old_file_name = os.path.splitext(os.path.basename(file_path))[0]
+            
+            # Check if the target file already exists
+            new_file_path = os.path.join(target_dir, f"{new_file_name}.safetensors").replace(os.sep, '/')
+            if os.path.exists(new_file_path):
+                return web.json_response({
+                    'success': False,
+                    'error': 'A file with this name already exists'
+                }, status=400)
+            
+            # Define the patterns for associated files
+            patterns = [
+                f"{old_file_name}.safetensors",  # Required
+                f"{old_file_name}.metadata.json",
+                f"{old_file_name}.preview.png",
+                f"{old_file_name}.preview.jpg",
+                f"{old_file_name}.preview.jpeg",
+                f"{old_file_name}.preview.webp",
+                f"{old_file_name}.preview.mp4",
+                f"{old_file_name}.png",
+                f"{old_file_name}.jpg",
+                f"{old_file_name}.jpeg",
+                f"{old_file_name}.webp",
+                f"{old_file_name}.mp4"
+            ]
+            
+            # Find all matching files
+            existing_files = []
+            for pattern in patterns:
+                path = os.path.join(target_dir, pattern)
+                if os.path.exists(path):
+                    existing_files.append((path, pattern))
+            
+            # Get the hash from the main file to update hash index
+            hash_value = None
+            metadata = None
+            metadata_path = os.path.join(target_dir, f"{old_file_name}.metadata.json")
+            
+            if os.path.exists(metadata_path):
+                try:
+                    with open(metadata_path, 'r', encoding='utf-8') as f:
+                        metadata = json.load(f)
+                        hash_value = metadata.get('sha256')
+                except Exception as e:
+                    logger.error(f"Error loading metadata for rename: {e}")
+            
+            # Rename all files
+            renamed_files = []
+            new_metadata_path = None
+            
+            # Notify file monitor to ignore these events
+            main_file_path = os.path.join(target_dir, f"{old_file_name}.safetensors")
+            if os.path.exists(main_file_path) and self.download_manager.file_monitor:
+                # Add old and new paths to ignore list
+                file_size = os.path.getsize(main_file_path)
+                self.download_manager.file_monitor.handler.add_ignore_path(main_file_path, file_size)
+                self.download_manager.file_monitor.handler.add_ignore_path(new_file_path, file_size)
+            
+            for old_path, pattern in existing_files:
+                # Get the file extension like .safetensors or .metadata.json
+                ext = self.get_multipart_ext(pattern)
+
+                # Create the new path
+                new_path = os.path.join(target_dir, f"{new_file_name}{ext}").replace(os.sep, '/')
+                
+                # Rename the file
+                os.rename(old_path, new_path)
+                renamed_files.append(new_path)
+                
+                # Keep track of metadata path for later update
+                if ext == '.metadata.json':
+                    new_metadata_path = new_path
+            
+            # Update the metadata file with new file name and paths
+            if new_metadata_path and metadata:
+                # Update file_name, file_path and preview_url in metadata
+                metadata['file_name'] = new_file_name
+                metadata['file_path'] = new_file_path
+                
+                # Update preview_url if it exists
+                if 'preview_url' in metadata and metadata['preview_url']:
+                    old_preview = metadata['preview_url']
+                    ext = self.get_multipart_ext(old_preview)
+                    new_preview = os.path.join(target_dir, f"{new_file_name}{ext}").replace(os.sep, '/')
+                    metadata['preview_url'] = new_preview
+                
+                # Save updated metadata
+                with open(new_metadata_path, 'w', encoding='utf-8') as f:
+                    json.dump(metadata, f, indent=2, ensure_ascii=False)
+            
+            # Update the scanner cache
+            if metadata:
+                await self.scanner.update_single_lora_cache(file_path, new_file_path, metadata)
+                
+                # Update recipe files and cache if hash is available
+                if hash_value:
+                    recipe_scanner = RecipeScanner(self.scanner)
+                    recipes_updated, cache_updated = await recipe_scanner.update_lora_filename_by_hash(hash_value, new_file_name)
+                    logger.info(f"Updated {recipes_updated} recipe files and {cache_updated} cache entries for renamed LoRA")
+            
+            return web.json_response({
+                'success': True,
+                'new_file_path': new_file_path,
+                'renamed_files': renamed_files,
+                'reload_required': False
+            })
+            
+        except Exception as e:
+            logger.error(f"Error renaming LoRA: {e}", exc_info=True)
             return web.json_response({
                 'success': False,
                 'error': str(e)

py/routes/lora_routes.py

@@ -58,11 +58,13 @@ class LoraRoutes:
     async def handle_loras_page(self, request: web.Request) -> web.Response:
         """Handle GET /loras request"""
         try:
-            # 不等待缓存数据，直接检查缓存状态
+            # 检查缓存初始化状态，增强判断条件
             is_initializing = (
-                self.scanner._cache is None and 
+                self.scanner._cache is None or 
                 (self.scanner._initialization_task is not None and 
-                not self.scanner._initialization_task.done())
+                not self.scanner._initialization_task.done()) or
+                (self.scanner._cache is not None and len(self.scanner._cache.raw_data) == 0 and 
+                 self.scanner._initialization_task is not None)
             )
 
             if is_initializing:
@@ -74,16 +76,31 @@ class LoraRoutes:
                     settings=settings,  # Pass settings to template
                     request=request  # Pass the request object to the template
                 )
+                
+                logger.info("Loras page is initializing, returning loading page")
             else:
-                # 正常流程
-                cache = await self.scanner.get_cached_data()
-                template = self.template_env.get_template('loras.html')
-                rendered = template.render(
-                    folders=cache.folders,
-                    is_initializing=False,
-                    settings=settings,  # Pass settings to template
-                    request=request  # Pass the request object to the template
-                )
+                # 正常流程 - 但不要等待缓存刷新
+                try:
+                    cache = await self.scanner.get_cached_data(force_refresh=False)
+                    template = self.template_env.get_template('loras.html')
+                    rendered = template.render(
+                        folders=cache.folders,
+                        is_initializing=False,
+                        settings=settings,  # Pass settings to template
+                        request=request  # Pass the request object to the template
+                    )
+                    logger.info(f"Loras page loaded successfully with {len(cache.raw_data)} items")
+                except Exception as cache_error:
+                    logger.error(f"Error loading cache data: {cache_error}")
+                    # 如果获取缓存失败，也显示初始化页面
+                    template = self.template_env.get_template('loras.html')
+                    rendered = template.render(
+                        folders=[],
+                        is_initializing=True,
+                        settings=settings,
+                        request=request
+                    )
+                    logger.info("Cache error, returning initialization page")
             
             return web.Response(
                 text=rendered,

py/routes/recipe_routes.py

@@ -14,7 +14,7 @@ from ..services.recipe_scanner import RecipeScanner
 from ..services.lora_scanner import LoraScanner
 from ..config import config
 from ..workflow.parser import WorkflowParser
-from ..utils.utils import download_twitter_image
+from ..utils.utils import download_civitai_image
 
 logger = logging.getLogger(__name__)
 
@@ -47,6 +47,12 @@ class RecipeRoutes:
         app.router.add_get('/api/recipe/{recipe_id}/share', routes.share_recipe)
         app.router.add_get('/api/recipe/{recipe_id}/share/download', routes.download_shared_recipe)
         
+        # Add new endpoint for getting recipe syntax
+        app.router.add_get('/api/recipe/{recipe_id}/syntax', routes.get_recipe_syntax)
+        
+        # Add new endpoint for updating recipe metadata (name and tags)
+        app.router.add_put('/api/recipe/{recipe_id}/update', routes.update_recipe)
+        
         # Start cache initialization
         app.on_startup.append(routes._init_cache)
         
@@ -235,7 +241,7 @@ class RecipeRoutes:
                     }, status=400)
                 
                 # Download image from URL
-                temp_path = download_twitter_image(url)
+                temp_path = download_civitai_image(url)
                 
                 if not temp_path:
                     return web.json_response({
@@ -244,10 +250,10 @@ class RecipeRoutes:
                     }, status=400)
             
             # Extract metadata from the image using ExifUtils
-            user_comment = ExifUtils.extract_user_comment(temp_path)
+            metadata = ExifUtils.extract_image_metadata(temp_path)
             
             # If no metadata found, return a more specific error
-            if not user_comment:
+            if not metadata:
                 result = {
                     "error": "No metadata found in this image",
                     "loras": []  # Return empty loras array to prevent client-side errors
@@ -262,7 +268,7 @@ class RecipeRoutes:
                 return web.json_response(result, status=200)
             
             # Use the parser factory to get the appropriate parser
-            parser = RecipeParserFactory.create_parser(user_comment)
+            parser = RecipeParserFactory.create_parser(metadata)
 
             if parser is None:
                 result = {
@@ -280,7 +286,7 @@ class RecipeRoutes:
             
             # Parse the metadata
             result = await parser.parse_metadata(
-                user_comment, 
+                metadata, 
                 recipe_scanner=self.recipe_scanner, 
                 civitai_client=self.civitai_client
             )
@@ -387,8 +393,7 @@ class RecipeRoutes:
                         return web.json_response({"error": f"Invalid base64 image data: {str(e)}"}, status=400)
                 elif image_url:
                     # Download image from URL
-                    from ..utils.utils import download_twitter_image
-                    temp_path = download_twitter_image(image_url)
+                    temp_path = download_civitai_image(image_url)
                     if not temp_path:
                         return web.json_response({"error": "Failed to download image from URL"}, status=400)
                     
@@ -433,19 +438,20 @@ class RecipeRoutes:
             # Format loras data according to the recipe.json format
             loras_data = []
             for lora in metadata.get("loras", []):
-                # Skip deleted LoRAs if they're marked to be excluded
-                if lora.get("isDeleted", False) and lora.get("exclude", False):
-                    continue
+                # Modified: Always include deleted LoRAs in the recipe metadata
+                # Even if they're marked to be excluded, we still keep their identifying information
+                # The exclude flag will only be used to determine if they should be included in recipe syntax
                 
                 # Convert frontend lora format to recipe format
                 lora_entry = {
-                    "file_name": lora.get("file_name", "") or os.path.splitext(os.path.basename(lora.get("localPath", "")))[0],
+                    "file_name": lora.get("file_name", "") or os.path.splitext(os.path.basename(lora.get("localPath", "")))[0] if lora.get("localPath") else "",
                     "hash": lora.get("hash", "").lower() if lora.get("hash") else "",
                     "strength": float(lora.get("weight", 1.0)),
                     "modelVersionId": lora.get("id", ""),
                     "modelName": lora.get("name", ""),
                     "modelVersionName": lora.get("version", ""),
-                    "isDeleted": lora.get("isDeleted", False)  # Preserve deletion status in saved recipe
+                    "isDeleted": lora.get("isDeleted", False),  # Preserve deletion status in saved recipe
+                    "exclude": lora.get("exclude", False)  # Add exclude flag to the recipe
                 }
                 loras_data.append(lora_entry)
             
@@ -777,8 +783,8 @@ class RecipeRoutes:
             # Parse the workflow to extract generation parameters and loras
             parsed_workflow = self.parser.parse_workflow(workflow_json)
 
-            if not parsed_workflow or not parsed_workflow.get("gen_params"):
-                return web.json_response({"error": "Could not extract generation parameters from workflow"}, status=400)
+            if not parsed_workflow:
+                return web.json_response({"error": "Could not extract parameters from workflow"}, status=400)
             
             # Get the lora stack from the parsed workflow
             lora_stack = parsed_workflow.get("loras", "")
@@ -874,7 +880,9 @@ class RecipeRoutes:
                 "created_date": time.time(),
                 "base_model": most_common_base_model,
                 "loras": loras_data,
-                "gen_params": parsed_workflow.get("gen_params", {}),  # Use the parsed workflow parameters
+                "checkpoint": parsed_workflow.get("checkpoint", ""),
+                "gen_params": {key: value for key, value in parsed_workflow.items() 
+                               if key not in ['checkpoint', 'loras']},
                 "loras_stack": lora_stack  # Include the original lora stack string
             }
             
@@ -906,3 +914,110 @@ class RecipeRoutes:
         except Exception as e:
             logger.error(f"Error saving recipe from widget: {e}", exc_info=True)
             return web.json_response({"error": str(e)}, status=500)
+
+    async def get_recipe_syntax(self, request: web.Request) -> web.Response:
+        """Generate recipe syntax for LoRAs in the recipe, looking up proper file names using hash_index"""
+        try:
+            recipe_id = request.match_info['recipe_id']
+            
+            # Get all recipes from cache
+            cache = await self.recipe_scanner.get_cached_data()
+            
+            # Find the specific recipe
+            recipe = next((r for r in cache.raw_data if str(r.get('id', '')) == recipe_id), None)
+            
+            if not recipe:
+                return web.json_response({"error": "Recipe not found"}, status=404)
+            
+            # Get the loras from the recipe
+            loras = recipe.get('loras', [])
+            
+            if not loras:
+                return web.json_response({"error": "No LoRAs found in this recipe"}, status=400)
+            
+            # Generate recipe syntax for all LoRAs that:
+            # 1. Are in the library (not deleted) OR
+            # 2. Are deleted but not marked for exclusion
+            lora_syntax_parts = []
+            
+            # Access the hash_index from lora_scanner
+            hash_index = self.recipe_scanner._lora_scanner._hash_index
+            
+            for lora in loras:
+                # Skip loras that are deleted AND marked for exclusion
+                if lora.get("isDeleted", False):
+                    continue
+
+                if not self.recipe_scanner._lora_scanner.has_lora_hash(lora.get("hash", "")):
+                    continue
+                
+                # Get the strength
+                strength = lora.get("strength", 1.0)
+                
+                # Try to find the actual file name for this lora
+                file_name = None
+                hash_value = lora.get("hash", "").lower()
+                
+                if hash_value and hasattr(hash_index, "_hash_to_path"):
+                    # Look up the file path from the hash
+                    file_path = hash_index._hash_to_path.get(hash_value)
+                    
+                    if file_path:
+                        # Extract the file name without extension from the path
+                        file_name = os.path.splitext(os.path.basename(file_path))[0]
+                
+                # If hash lookup failed, fall back to modelVersionId lookup
+                if not file_name and lora.get("modelVersionId"):
+                    # Search for files with matching modelVersionId
+                    all_loras = await self.recipe_scanner._lora_scanner.get_cached_data()
+                    for cached_lora in all_loras.raw_data:
+                        if not cached_lora.get("civitai"):
+                            continue
+                        if cached_lora.get("civitai", {}).get("id") == lora.get("modelVersionId"):
+                            file_name = os.path.splitext(os.path.basename(cached_lora["path"]))[0]
+                            break
+                
+                # If all lookups failed, use the file_name from the recipe
+                if not file_name:
+                    file_name = lora.get("file_name", "unknown-lora")
+                
+                # Add to syntax parts
+                lora_syntax_parts.append(f"<lora:{file_name}:{strength}>")
+            
+            # Join the LoRA syntax parts
+            lora_syntax = " ".join(lora_syntax_parts)
+            
+            return web.json_response({
+                'success': True,
+                'syntax': lora_syntax
+            })
+        except Exception as e:
+            logger.error(f"Error generating recipe syntax: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)
+
+    async def update_recipe(self, request: web.Request) -> web.Response:
+        """Update recipe metadata (name and tags)"""
+        try:
+            recipe_id = request.match_info['recipe_id']
+            data = await request.json()
+            
+            # Validate required fields
+            if 'title' not in data and 'tags' not in data:
+                return web.json_response({
+                    "error": "At least one field to update must be provided (title or tags)"
+                }, status=400)
+            
+            # Use the recipe scanner's update method
+            success = await self.recipe_scanner.update_recipe_metadata(recipe_id, data)
+            
+            if not success:
+                return web.json_response({"error": "Recipe not found or update failed"}, status=404)
+            
+            return web.json_response({
+                "success": True,
+                "recipe_id": recipe_id,
+                "updates": data
+            })
+        except Exception as e:
+            logger.error(f"Error updating recipe: {e}", exc_info=True)
+            return web.json_response({"error": str(e)}, status=500)

py/services/civitai_client.py

@@ -76,10 +76,11 @@ class CivitaiClient:
             headers = self._get_request_headers()
             async with session.get(url, headers=headers, allow_redirects=True) as response:
                 if response.status != 200:
-                    # Handle early access 401 unauthorized responses
+                    # Handle 401 unauthorized responses
                     if response.status == 401:
                         logger.warning(f"Unauthorized access to resource: {url} (Status 401)")
-                        return False, "Early access restriction: You must purchase early access to download this LoRA."
+                        
+                        return False, "Invalid or missing CivitAI API key, or early access restriction."
                     
                     # Handle other client errors that might be permission-related
                     if response.status == 403:
@@ -233,11 +234,9 @@ class CivitaiClient:
             if not self._session:
                 return None
             
-            logger.info(f"Fetching model version info from Civitai for ID: {model_version_id}")
             version_info = await self._session.get(f"{self.base_url}/model-versions/{model_version_id}")
             
             if not version_info or not version_info.json().get('files'):
-                logger.warning(f"No files found in version info for ID: {model_version_id}")
                 return None
             
             # Get hash from the first file
@@ -247,8 +246,7 @@ class CivitaiClient:
                     hash_value = file_info['hashes']['SHA256'].lower()
                     return hash_value
                 
-            logger.warning(f"No SHA256 hash found in version info for ID: {model_version_id}")
             return None
         except Exception as e:
             logger.error(f"Error getting hash from Civitai: {e}")
-            return None
+            return None

py/services/download_manager.py

@@ -13,8 +13,9 @@ class DownloadManager:
         self.civitai_client = CivitaiClient()
         self.file_monitor = file_monitor
 
-    async def download_from_civitai(self, download_url: str, save_dir: str, relative_path: str = '', 
-                                  progress_callback=None) -> Dict:
+    async def download_from_civitai(self, download_url: str = None, model_hash: str = None, 
+                                  model_version_id: str = None, save_dir: str = None, 
+                                  relative_path: str = '', progress_callback=None) -> Dict:
         try:
             # Update save directory with relative path if provided
             if relative_path:
@@ -22,14 +23,26 @@ class DownloadManager:
                 # Create directory if it doesn't exist
                 os.makedirs(save_dir, exist_ok=True)
 
-            # Get version info
-            version_id = download_url.split('/')[-1]
-            version_info = await self.civitai_client.get_model_version_info(version_id)
+            # Get version info based on the provided identifier
+            version_info = None
+            
+            if download_url:
+                # Extract version ID from download URL
+                version_id = download_url.split('/')[-1]
+                version_info = await self.civitai_client.get_model_version_info(version_id)
+            elif model_version_id:
+                # Use model version ID directly
+                version_info = await self.civitai_client.get_model_version_info(model_version_id)
+            elif model_hash:
+                # Get model by hash
+                version_info = await self.civitai_client.get_model_by_hash(model_hash)
+
+            
             if not version_info:
                 return {'success': False, 'error': 'Failed to fetch model metadata'}
 
             # Check if this is an early access LoRA
-            if 'earlyAccessEndsAt' in version_info:
+            if version_info.get('earlyAccessEndsAt'):
                 early_access_date = version_info.get('earlyAccessEndsAt', '')
                 # Convert to a readable date if possible
                 try:
@@ -89,7 +102,7 @@ class DownloadManager:
             
             # 6. 开始下载流程
             result = await self._execute_download(
-                download_url=download_url,
+                download_url=file_info.get('downloadUrl', ''),
                 save_dir=save_dir,
                 metadata=metadata,
                 version_info=version_info,

py/services/lora_scanner.py

@@ -3,11 +3,13 @@ import os
 import logging
 import asyncio
 import shutil
+import time
 from typing import List, Dict, Optional
-from dataclasses import dataclass
-from operator import itemgetter
+
+from ..utils.models import LoraMetadata
 from ..config import config
-from ..utils.file_utils import load_metadata, get_file_info
+from ..utils.file_utils import load_metadata, get_file_info, normalize_path, find_preview_file, save_metadata
+from ..utils.lora_metadata import extract_lora_metadata
 from .lora_cache import LoraCache
 from .lora_hash_index import LoraHashIndex
 from .settings_manager import settings
@@ -91,6 +93,7 @@ class LoraScanner:
     async def _initialize_cache(self) -> None:
         """Initialize or refresh the cache"""
         try:
+            start_time = time.time()
             # Clear existing hash index
             self._hash_index.clear()
             
@@ -122,7 +125,7 @@ class LoraScanner:
             await self._cache.resort()
 
             self._initialization_task = None
-            logger.info("LoRA Manager: Cache initialization completed")
+            logger.info(f"LoRA Manager: Cache initialization completed in {time.time() - start_time:.2f} seconds, found {len(raw_data)} loras")
         except Exception as e:
             logger.error(f"LoRA Manager: Error initializing cache: {e}")
             self._cache = LoraCache(
@@ -330,8 +333,30 @@ class LoraScanner:
         metadata = await load_metadata(file_path)
         
         if metadata is None:
-            # Create new metadata if none exists
-            metadata = await get_file_info(file_path)
+            # Try to find and use .civitai.info file first
+            civitai_info_path = f"{os.path.splitext(file_path)[0]}.civitai.info"
+            if os.path.exists(civitai_info_path):
+                try:
+                    with open(civitai_info_path, 'r', encoding='utf-8') as f:
+                        version_info = json.load(f)
+                    
+                    file_info = next((f for f in version_info.get('files', []) if f.get('primary')), None)
+                    if file_info:
+                        # Create a minimal file_info with the required fields
+                        file_name = os.path.splitext(os.path.basename(file_path))[0]
+                        file_info['name'] = file_name
+                    
+                        # Use from_civitai_info to create metadata
+                        metadata = LoraMetadata.from_civitai_info(version_info, file_info, file_path)
+                        metadata.preview_url = find_preview_file(file_name, os.path.dirname(file_path))
+                        await save_metadata(file_path, metadata)
+                        logger.debug(f"Created metadata from .civitai.info for {file_path}")
+                except Exception as e:
+                    logger.error(f"Error creating metadata from .civitai.info for {file_path}: {e}")
+            
+            # If still no metadata, create new metadata using get_file_info
+            if metadata is None:
+                metadata = await get_file_info(file_path)
         
         # Convert to dict and add folder info
         lora_data = metadata.to_dict()
@@ -342,7 +367,7 @@ class LoraScanner:
         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
         

py/services/recipe_cache.py

@@ -37,18 +37,18 @@ class RecipeCache:
         Returns:
             bool: True if the update was successful, False if the recipe wasn't found
         """
-        async with self._lock:
-            # Update in raw_data
-            for item in self.raw_data:
-                if item.get('id') == recipe_id:
-                    item.update(metadata)
-                    break
-            else:
-                return False  # Recipe not found
-                
-            # Resort to reflect changes
-            await self.resort()
-            return True
+
+        # Update in raw_data
+        for item in self.raw_data:
+            if item.get('id') == recipe_id:
+                item.update(metadata)
+                break
+        else:
+            return False  # Recipe not found
+            
+        # Resort to reflect changes
+        await self.resort()
+        return True
             
     async def add_recipe(self, recipe_data: Dict) -> None:
         """Add a new recipe to the cache

py/services/recipe_scanner.py

@@ -2,9 +2,7 @@ import os
 import logging
 import asyncio
 import json
-import re
-from typing import List, Dict, Optional, Any
-from datetime import datetime
+from typing import List, Dict, Optional, Any, Tuple
 from ..config import config
 from .recipe_cache import RecipeCache
 from .lora_scanner import LoraScanner
@@ -64,61 +62,44 @@ class RecipeScanner:
 
         # Try to acquire the lock with a timeout to prevent deadlocks
         try:
-            # Use a timeout for acquiring the lock
-            async with asyncio.timeout(1.0):
-                async with self._initialization_lock:
-                    # Check again after acquiring the lock
-                    if self._cache is not None and not force_refresh:
-                        return self._cache
+            async with self._initialization_lock:
+                # Check again after acquiring the lock
+                if self._cache is not None and not force_refresh:
+                    return self._cache
+                
+                # Mark as initializing to prevent concurrent initializations
+                self._is_initializing = True
+                
+                try:
+                    # Remove dependency on lora scanner initialization
+                    # Scan for recipe data directly
+                    raw_data = await self.scan_all_recipes()
                     
-                    # Mark as initializing to prevent concurrent initializations
-                    self._is_initializing = True
+                    # Update cache
+                    self._cache = RecipeCache(
+                        raw_data=raw_data,
+                        sorted_by_name=[],
+                        sorted_by_date=[]
+                    )
                     
-                    try:
-                        # First ensure the lora scanner is initialized
-                        if self._lora_scanner:
-                            try:
-                                lora_cache = await asyncio.wait_for(
-                                    self._lora_scanner.get_cached_data(), 
-                                    timeout=10.0
-                                )
-                            except asyncio.TimeoutError:
-                                logger.error("Timeout waiting for lora scanner initialization")
-                            except Exception as e:
-                                logger.error(f"Error waiting for lora scanner: {e}")
-                        
-                        # Scan for recipe data
-                        raw_data = await self.scan_all_recipes()
-                        
-                        # Update cache
-                        self._cache = RecipeCache(
-                            raw_data=raw_data,
-                            sorted_by_name=[],
-                            sorted_by_date=[]
-                        )
-                        
-                        # Resort cache
-                        await self._cache.resort()
-                        
-                        return self._cache
+                    # Resort cache
+                    await self._cache.resort()
                     
-                    except Exception as e:
-                        logger.error(f"Recipe Manager: Error initializing cache: {e}", exc_info=True)
-                        # Create empty cache on error
-                        self._cache = RecipeCache(
-                            raw_data=[],
-                            sorted_by_name=[],
-                            sorted_by_date=[]
-                        )
-                        return self._cache
-                    finally:
-                        # Mark initialization as complete
-                        self._is_initializing = False
+                    return self._cache
+                
+                except Exception as e:
+                    logger.error(f"Recipe Manager: Error initializing cache: {e}", exc_info=True)
+                    # Create empty cache on error
+                    self._cache = RecipeCache(
+                        raw_data=[],
+                        sorted_by_name=[],
+                        sorted_by_date=[]
+                    )
+                    return self._cache
+                finally:
+                    # Mark initialization as complete
+                    self._is_initializing = False
         
-        except asyncio.TimeoutError:
-            # If we can't acquire the lock in time, return the current cache or an empty one
-            logger.warning("Timeout acquiring initialization lock - returning current cache state")
-            return self._cache or RecipeCache(raw_data=[], sorted_by_name=[], sorted_by_date=[])
         except Exception as e:
             logger.error(f"Unexpected error in get_cached_data: {e}")
             return self._cache or RecipeCache(raw_data=[], sorted_by_name=[], sorted_by_date=[])
@@ -230,7 +211,7 @@ class RecipeScanner:
                         lora['hash'] = hash_from_civitai
                         metadata_updated = True
                     else:
-                        logger.warning(f"Could not get hash for modelVersionId {model_version_id}")
+                        logger.debug(f"Could not get hash for modelVersionId {model_version_id}")
             
             # If has hash but no file_name, look up in lora library
             if 'hash' in lora and (not lora.get('file_name') or not lora['file_name']):
@@ -280,7 +261,7 @@ class RecipeScanner:
             version_info = await self._civitai_client.get_model_version_info(model_version_id)
             
             if not version_info or not version_info.get('files'):
-                logger.warning(f"No files found in version info for ID: {model_version_id}")
+                logger.debug(f"No files found in version info for ID: {model_version_id}")
                 return None
                 
             # Get hash from the first file
@@ -288,7 +269,7 @@ class RecipeScanner:
                 if file_info.get('hashes', {}).get('SHA256'):
                     return file_info['hashes']['SHA256']
                     
-            logger.warning(f"No SHA256 hash found in version info for ID: {model_version_id}")
+            logger.debug(f"No SHA256 hash found in version info for ID: {model_version_id}")
             return None
         except Exception as e:
             logger.error(f"Error getting hash from Civitai: {e}")
@@ -305,7 +286,7 @@ class RecipeScanner:
             if version_info and 'name' in version_info:
                 return version_info['name']
                     
-            logger.warning(f"No version name found for modelVersionId {model_version_id}")
+            logger.debug(f"No version name found for modelVersionId {model_version_id}")
             return None
         except Exception as e:
             logger.error(f"Error getting model version name from Civitai: {e}")
@@ -448,4 +429,136 @@ class RecipeScanner:
             'total_pages': (total_items + page_size - 1) // page_size
         }
         
-        return result 
+        return result
+
+    async def update_recipe_metadata(self, recipe_id: str, metadata: dict) -> bool:
+        """Update recipe metadata (like title and tags) in both file system and cache
+        
+        Args:
+            recipe_id: The ID of the recipe to update
+            metadata: Dictionary containing metadata fields to update (title, tags, etc.)
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        import os
+        import json
+        
+        # First, find the recipe JSON file path
+        recipe_json_path = os.path.join(self.recipes_dir, f"{recipe_id}.recipe.json")
+        
+        if not os.path.exists(recipe_json_path):
+            return False
+            
+        try:
+            # Load existing recipe data
+            with open(recipe_json_path, 'r', encoding='utf-8') as f:
+                recipe_data = json.load(f)
+                
+            # Update fields
+            for key, value in metadata.items():
+                recipe_data[key] = value
+                
+            # Save updated recipe
+            with open(recipe_json_path, 'w', encoding='utf-8') as f:
+                json.dump(recipe_data, f, indent=4, ensure_ascii=False)
+                
+            # Update the cache if it exists
+            if self._cache is not None:
+                await self._cache.update_recipe_metadata(recipe_id, metadata)
+                
+            # If the recipe has an image, update its EXIF metadata
+            from ..utils.exif_utils import ExifUtils
+            image_path = recipe_data.get('file_path')
+            if image_path and os.path.exists(image_path):
+                ExifUtils.append_recipe_metadata(image_path, recipe_data)
+                
+            return True
+        except Exception as e:
+            import logging
+            logging.getLogger(__name__).error(f"Error updating recipe metadata: {e}", exc_info=True)
+            return False
+
+    async def update_lora_filename_by_hash(self, hash_value: str, new_file_name: str) -> Tuple[int, int]:
+        """Update file_name in all recipes that contain a LoRA with the specified hash.
+        
+        Args:
+            hash_value: The SHA256 hash value of the LoRA
+            new_file_name: The new file_name to set
+            
+        Returns:
+            Tuple[int, int]: (number of recipes updated in files, number of recipes updated in cache)
+        """
+        if not hash_value or not new_file_name:
+            return 0, 0
+            
+        # Always use lowercase hash for consistency
+        hash_value = hash_value.lower()
+        
+        # Get recipes directory
+        recipes_dir = self.recipes_dir
+        if not recipes_dir or not os.path.exists(recipes_dir):
+            logger.warning(f"Recipes directory not found: {recipes_dir}")
+            return 0, 0
+            
+        # Check if cache is initialized
+        cache_initialized = self._cache is not None
+        cache_updated_count = 0
+        file_updated_count = 0
+        
+        # Get all recipe JSON files in the recipes directory
+        recipe_files = []
+        for root, _, files in os.walk(recipes_dir):
+            for file in files:
+                if file.lower().endswith('.recipe.json'):
+                    recipe_files.append(os.path.join(root, file))
+        
+        # Process each recipe file
+        for recipe_path in recipe_files:
+            try:
+                # Load the recipe data
+                with open(recipe_path, 'r', encoding='utf-8') as f:
+                    recipe_data = json.load(f)
+                
+                # Skip if no loras or invalid structure
+                if not recipe_data or not isinstance(recipe_data, dict) or 'loras' not in recipe_data:
+                    continue
+                
+                # Check if any lora has matching hash
+                file_updated = False
+                for lora in recipe_data.get('loras', []):
+                    if 'hash' in lora and lora['hash'].lower() == hash_value:
+                        # Update file_name
+                        old_file_name = lora.get('file_name', '')
+                        lora['file_name'] = new_file_name
+                        file_updated = True
+                        logger.info(f"Updated file_name in recipe {recipe_path}: {old_file_name} -> {new_file_name}")
+                
+                # If updated, save the file
+                if file_updated:
+                    with open(recipe_path, 'w', encoding='utf-8') as f:
+                        json.dump(recipe_data, f, indent=4, ensure_ascii=False)
+                    file_updated_count += 1
+                    
+                    # Also update in cache if it exists
+                    if cache_initialized:
+                        recipe_id = recipe_data.get('id')
+                        if recipe_id:
+                            for cache_item in self._cache.raw_data:
+                                if cache_item.get('id') == recipe_id:
+                                    # Replace loras array with updated version
+                                    cache_item['loras'] = recipe_data['loras']
+                                    cache_updated_count += 1
+                                    break
+            
+            except Exception as e:
+                logger.error(f"Error updating recipe file {recipe_path}: {e}")
+                import traceback
+                traceback.print_exc(file=sys.stderr)
+        
+        # Resort cache if updates were made
+        if cache_initialized and cache_updated_count > 0:
+            await self._cache.resort()
+            logger.info(f"Resorted recipe cache after updating {cache_updated_count} items")
+            
+        return file_updated_count, cache_updated_count

py/utils/exif_utils.py

@@ -1,11 +1,10 @@
 import piexif
 import json
 import logging
-from typing import Dict, Optional, Any
+from typing import Optional
 from io import BytesIO
 import os
 from PIL import Image
-import re
 
 logger = logging.getLogger(__name__)
 
@@ -13,11 +12,23 @@ class ExifUtils:
     """Utility functions for working with EXIF data in images"""
     
     @staticmethod
-    def extract_user_comment(image_path: str) -> Optional[str]:
-        """Extract UserComment field from image EXIF data"""
+    def extract_image_metadata(image_path: str) -> Optional[str]:
+        """Extract metadata from image including UserComment or parameters field
+        
+        Args:
+            image_path (str): Path to the image file
+            
+        Returns:
+            Optional[str]: Extracted metadata or None if not found
+        """
         try:
-            # First try to open as image to check format
+            # First try to open the image
             with Image.open(image_path) as img:
+                # Method 1: Check for parameters in image info
+                if hasattr(img, 'info') and 'parameters' in img.info:
+                    return img.info['parameters']
+                
+                # Method 2: Check EXIF UserComment field
                 if img.format not in ['JPEG', 'TIFF', 'WEBP']:
                     # For non-JPEG/TIFF/WEBP images, try to get EXIF through PIL
                     exif = img._getexif()
@@ -28,82 +39,106 @@ class ExifUtils:
                                 return user_comment[8:].decode('utf-16be')
                             return user_comment.decode('utf-8', errors='ignore')
                         return user_comment
-                    return None
                 
                 # For JPEG/TIFF/WEBP, use piexif
-                exif_dict = piexif.load(image_path)
+                try:
+                    exif_dict = piexif.load(image_path)
+                    
+                    if piexif.ExifIFD.UserComment in exif_dict.get('Exif', {}):
+                        user_comment = exif_dict['Exif'][piexif.ExifIFD.UserComment]
+                        if isinstance(user_comment, bytes):
+                            if user_comment.startswith(b'UNICODE\0'):
+                                user_comment = user_comment[8:].decode('utf-16be')
+                            else:
+                                user_comment = user_comment.decode('utf-8', errors='ignore')
+                        return user_comment
+                except Exception as e:
+                    logger.debug(f"Error loading EXIF data: {e}")
+                
+                # Method 3: Check PNG metadata for workflow info (for ComfyUI images)
+                if img.format == 'PNG':
+                    # Look for workflow or prompt metadata in PNG chunks
+                    for key in img.info:
+                        if key in ['workflow', 'prompt', 'parameters']:
+                            return img.info[key]
                 
-                if piexif.ExifIFD.UserComment in exif_dict.get('Exif', {}):
-                    user_comment = exif_dict['Exif'][piexif.ExifIFD.UserComment]
-                    if isinstance(user_comment, bytes):
-                        if user_comment.startswith(b'UNICODE\0'):
-                            user_comment = user_comment[8:].decode('utf-16be')
-                        else:
-                            user_comment = user_comment.decode('utf-8', errors='ignore')
-                    return user_comment
                 return None
                 
         except Exception as e:
+            logger.error(f"Error extracting image metadata: {e}", exc_info=True)
             return None
     
     @staticmethod
-    def update_user_comment(image_path: str, user_comment: str) -> str:
-        """Update UserComment field in image EXIF data"""
+    def update_image_metadata(image_path: str, metadata: str) -> str:
+        """Update metadata in image's EXIF data or parameters fields
+        
+        Args:
+            image_path (str): Path to the image file
+            metadata (str): Metadata string to save
+            
+        Returns:
+            str: Path to the updated image
+        """
         try:
-            # Load the image and its EXIF data
+            # Load the image and check its format
             with Image.open(image_path) as img:
-                # Get original format
                 img_format = img.format
                 
-                # For WebP format, we need a different approach
-                if img_format == 'WEBP':
-                    # WebP doesn't support standard EXIF through piexif
-                    # We'll use PIL's exif parameter directly
-                    exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + user_comment.encode('utf-16be')}}
+                # For PNG, try to update parameters directly
+                if img_format == 'PNG':
+                    # We'll save with parameters in the PNG info
+                    info_dict = {'parameters': metadata}
+                    img.save(image_path, format='PNG', pnginfo=info_dict)
+                    return image_path
+                
+                # For WebP format, use PIL's exif parameter directly
+                elif img_format == 'WEBP':
+                    exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
                     exif_bytes = piexif.dump(exif_dict)
                     
                     # Save with the exif data
                     img.save(image_path, format='WEBP', exif=exif_bytes, quality=85)
                     return image_path
                 
-                # For other formats, use the standard approach
-                try:
-                    exif_dict = piexif.load(img.info.get('exif', b''))
-                except:
-                    exif_dict = {'0th':{}, 'Exif':{}, 'GPS':{}, 'Interop':{}, '1st':{}}
-                
-                # If no Exif dictionary exists, create one
-                if 'Exif' not in exif_dict:
-                    exif_dict['Exif'] = {}
-                
-                # Update the UserComment field - use UNICODE format
-                unicode_bytes = user_comment.encode('utf-16be')
-                user_comment_bytes = b'UNICODE\0' + unicode_bytes
-                
-                exif_dict['Exif'][piexif.ExifIFD.UserComment] = user_comment_bytes
-                
-                # Convert EXIF dict back to bytes
-                exif_bytes = piexif.dump(exif_dict)
-                
-                # Save the image with updated EXIF data
-                img.save(image_path, exif=exif_bytes)
-                
+                # For other formats, use standard EXIF approach
+                else:
+                    try:
+                        exif_dict = piexif.load(img.info.get('exif', b''))
+                    except:
+                        exif_dict = {'0th':{}, 'Exif':{}, 'GPS':{}, 'Interop':{}, '1st':{}}
+                    
+                    # If no Exif dictionary exists, create one
+                    if 'Exif' not in exif_dict:
+                        exif_dict['Exif'] = {}
+                    
+                    # Update the UserComment field - use UNICODE format
+                    unicode_bytes = metadata.encode('utf-16be')
+                    metadata_bytes = b'UNICODE\0' + unicode_bytes
+                    
+                    exif_dict['Exif'][piexif.ExifIFD.UserComment] = metadata_bytes
+                    
+                    # Convert EXIF dict back to bytes
+                    exif_bytes = piexif.dump(exif_dict)
+                    
+                    # Save the image with updated EXIF data
+                    img.save(image_path, exif=exif_bytes)
+                    
             return image_path
         except Exception as e:
-            logger.error(f"Error updating EXIF data in {image_path}: {e}")
+            logger.error(f"Error updating metadata in {image_path}: {e}")
             return image_path
             
     @staticmethod
     def append_recipe_metadata(image_path, recipe_data) -> str:
         """Append recipe metadata to an image's EXIF data"""
         try:
-            # First, extract existing user comment
-            user_comment = ExifUtils.extract_user_comment(image_path)
+            # First, extract existing metadata
+            metadata = ExifUtils.extract_image_metadata(image_path)
             
-            # Check if there's already recipe metadata in the user comment
-            if user_comment:
+            # Check if there's already recipe metadata
+            if metadata:
                 # Remove any existing recipe metadata
-                user_comment = ExifUtils.remove_recipe_metadata(user_comment)
+                metadata = ExifUtils.remove_recipe_metadata(metadata)
             
             # Prepare simplified loras data
             simplified_loras = []
@@ -133,11 +168,11 @@ class ExifUtils:
             # Create the recipe metadata marker
             recipe_metadata_marker = f"Recipe metadata: {recipe_metadata_json}"
             
-            # Append to existing user comment or create new one
-            new_user_comment = f"{user_comment} \n {recipe_metadata_marker}" if user_comment else recipe_metadata_marker
+            # Append to existing metadata or create new one
+            new_metadata = f"{metadata} \n {recipe_metadata_marker}" if metadata else recipe_metadata_marker
             
             # Write back to the image
-            return ExifUtils.update_user_comment(image_path, new_user_comment)
+            return ExifUtils.update_image_metadata(image_path, new_metadata)
         except Exception as e:
             logger.error(f"Error appending recipe metadata: {e}", exc_info=True)
             return image_path
@@ -184,11 +219,11 @@ class ExifUtils:
         """
         try:
             # Extract metadata if needed
-            user_comment = None
+            metadata = None
             if preserve_metadata:
                 if isinstance(image_data, str) and os.path.exists(image_data):
                     # It's a file path
-                    user_comment = ExifUtils.extract_user_comment(image_data)
+                    metadata = ExifUtils.extract_image_metadata(image_data)
                     img = Image.open(image_data)
                 else:
                     # It's binary data
@@ -199,7 +234,7 @@ class ExifUtils:
                     with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as temp_file:
                         temp_path = temp_file.name
                         temp_file.write(image_data)
-                    user_comment = ExifUtils.extract_user_comment(temp_path)
+                    metadata = ExifUtils.extract_image_metadata(temp_path)
                     os.unlink(temp_path)
             else:
                 # Just open the image without extracting metadata
@@ -239,14 +274,14 @@ class ExifUtils:
             optimized_data = output.getvalue()
             
             # If we need to preserve metadata, write it to a temporary file
-            if preserve_metadata and user_comment:
+            if preserve_metadata and metadata:
                 # For WebP format, we'll directly save with metadata
                 if format.lower() == 'webp':
                     # Create a new BytesIO with metadata
                     output_with_metadata = BytesIO()
                     
                     # Create EXIF data with user comment
-                    exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + user_comment.encode('utf-16be')}}
+                    exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
                     exif_bytes = piexif.dump(exif_dict)
                     
                     # Save with metadata
@@ -260,7 +295,7 @@ class ExifUtils:
                         temp_file.write(optimized_data)
                     
                     # Add the metadata back
-                    ExifUtils.update_user_comment(temp_path, user_comment)
+                    ExifUtils.update_image_metadata(temp_path, metadata)
                     
                     # Read the file with metadata
                     with open(temp_path, 'rb') as f:
@@ -277,210 +312,4 @@ class ExifUtils:
             if isinstance(image_data, str) and os.path.exists(image_data):
                 with open(image_data, 'rb') as f:
                     return f.read(), os.path.splitext(image_data)[1]
-            return image_data, '.jpg'
-
-    @staticmethod
-    def _parse_comfyui_workflow(workflow_data: Any) -> Dict[str, Any]:
-        """
-        Parse ComfyUI workflow data and extract relevant generation parameters
-        
-        Args:
-            workflow_data: Raw workflow data (string or dict)
-            
-        Returns:
-            Formatted generation parameters dictionary
-        """
-        try:
-            # If workflow_data is a string, try to parse it as JSON
-            if isinstance(workflow_data, str):
-                try:
-                    workflow_data = json.loads(workflow_data)
-                except json.JSONDecodeError:
-                    logger.error("Failed to parse workflow data as JSON")
-                    return {}
-            
-            # Now workflow_data should be a dictionary
-            if not isinstance(workflow_data, dict):
-                logger.error(f"Workflow data is not a dictionary: {type(workflow_data)}")
-                return {}
-                
-            # Initialize parameters dictionary with only the required fields
-            gen_params = {
-                "prompt": "",
-                "negative_prompt": "",
-                "steps": "",
-                "sampler": "",
-                "cfg_scale": "",
-                "seed": "",
-                "size": "",
-                "clip_skip": ""
-            }
-            
-            # First pass: find the KSampler node to get basic parameters and node references
-            # Store node references to follow for prompts
-            positive_ref = None
-            negative_ref = None
-            
-            for node_id, node_data in workflow_data.items():
-                if not isinstance(node_data, dict):
-                    continue
-                    
-                # Extract node inputs if available
-                inputs = node_data.get("inputs", {})
-                if not inputs:
-                    continue
-                
-                # KSampler nodes contain most generation parameters and references to prompt nodes
-                if "KSampler" in node_data.get("class_type", ""):
-                    # Extract basic sampling parameters
-                    gen_params["steps"] = inputs.get("steps", "")
-                    gen_params["cfg_scale"] = inputs.get("cfg", "")
-                    gen_params["sampler"] = inputs.get("sampler_name", "")
-                    gen_params["seed"] = inputs.get("seed", "")
-                    if isinstance(gen_params["seed"], list) and len(gen_params["seed"]) > 1:
-                        gen_params["seed"] = gen_params["seed"][1]  # Use the actual value if it's a list
-                    
-                    # Get references to positive and negative prompt nodes
-                    positive_ref = inputs.get("positive", "")
-                    negative_ref = inputs.get("negative", "")
-                    
-                # CLIPSetLastLayer contains clip_skip information
-                elif "CLIPSetLastLayer" in node_data.get("class_type", ""):
-                    gen_params["clip_skip"] = inputs.get("stop_at_clip_layer", "")
-                    if isinstance(gen_params["clip_skip"], int) and gen_params["clip_skip"] < 0:
-                        # Convert negative layer index to positive clip skip value
-                        gen_params["clip_skip"] = abs(gen_params["clip_skip"])
-                
-                # Look for resolution information
-                elif "LatentImage" in node_data.get("class_type", "") or "Empty" in node_data.get("class_type", ""):
-                    width = inputs.get("width", 0)
-                    height = inputs.get("height", 0)
-                    if width and height:
-                        gen_params["size"] = f"{width}x{height}"
-                    
-                    # Some nodes have resolution as a string like "832x1216 (0.68)"
-                    resolution = inputs.get("resolution", "")
-                    if isinstance(resolution, str) and "x" in resolution:
-                        gen_params["size"] = resolution.split(" ")[0]  # Extract just the dimensions
-            
-            # Helper function to follow node references and extract text content
-            def get_text_from_node_ref(node_ref, workflow_data):
-                if not node_ref or not isinstance(node_ref, list) or len(node_ref) < 2:
-                    return ""
-                
-                node_id, slot_idx = node_ref
-                
-                # If we can't find the node, return empty string
-                if node_id not in workflow_data:
-                    return ""
-                
-                node = workflow_data[node_id]
-                inputs = node.get("inputs", {})
-                
-                # Direct text input in CLIP Text Encode nodes
-                if "CLIPTextEncode" in node.get("class_type", ""):
-                    text = inputs.get("text", "")
-                    if isinstance(text, str):
-                        return text
-                    elif isinstance(text, list) and len(text) >= 2:
-                        # If text is a reference to another node, follow it
-                        return get_text_from_node_ref(text, workflow_data)
-                
-                # Other nodes might have text input with different field names
-                for field_name, field_value in inputs.items():
-                    if field_name == "text" and isinstance(field_value, str):
-                        return field_value
-                    elif isinstance(field_value, list) and len(field_value) >= 2 and field_name in ["text"]:
-                        # If it's a reference to another node, follow it
-                        return get_text_from_node_ref(field_value, workflow_data)
-                
-                return ""
-            
-            # Extract prompts by following references from KSampler node
-            if positive_ref:
-                gen_params["prompt"] = get_text_from_node_ref(positive_ref, workflow_data)
-            
-            if negative_ref:
-                gen_params["negative_prompt"] = get_text_from_node_ref(negative_ref, workflow_data)
-            
-            # Fallback: if we couldn't extract prompts via references, use the traditional method
-            if not gen_params["prompt"] or not gen_params["negative_prompt"]:
-                for node_id, node_data in workflow_data.items():
-                    if not isinstance(node_data, dict):
-                        continue
-                        
-                    inputs = node_data.get("inputs", {})
-                    if not inputs:
-                        continue
-                    
-                    if "CLIPTextEncode" in node_data.get("class_type", ""):
-                        # Check for negative prompt nodes
-                        title = node_data.get("_meta", {}).get("title", "").lower()
-                        prompt_text = inputs.get("text", "")
-                        
-                        if isinstance(prompt_text, str):
-                            if "negative" in title and not gen_params["negative_prompt"]:
-                                gen_params["negative_prompt"] = prompt_text
-                            elif prompt_text and not "negative" in title and not gen_params["prompt"]:
-                                gen_params["prompt"] = prompt_text
-            
-            return gen_params
-            
-        except Exception as e:
-            logger.error(f"Error parsing ComfyUI workflow: {e}", exc_info=True)
-            return {}
-
-    @staticmethod
-    def extract_comfyui_gen_params(image_path: str) -> Dict[str, Any]:
-        """
-        Extract ComfyUI workflow data from PNG images and format for recipe data
-        Only extracts the specific generation parameters needed for recipes.
-        
-        Args:
-            image_path: Path to the ComfyUI-generated PNG image
-            
-        Returns:
-            Dictionary containing formatted generation parameters
-        """
-        try:
-            # Check if the file exists and is accessible
-            if not os.path.exists(image_path):
-                logger.error(f"Image file not found: {image_path}")
-                return {}
-                
-            # Open the image to extract embedded workflow data
-            with Image.open(image_path) as img:
-                workflow_data = None
-                
-                # For PNG images, look for the ComfyUI workflow data in PNG chunks
-                if img.format == 'PNG':
-                    # Check standard metadata fields that might contain workflow
-                    if 'parameters' in img.info:
-                        workflow_data = img.info['parameters']
-                    elif 'prompt' in img.info:
-                        workflow_data = img.info['prompt']
-                    else:
-                        # Look for other potential field names that might contain workflow data
-                        for key in img.info:
-                            if isinstance(key, str) and ('workflow' in key.lower() or 'comfy' in key.lower()):
-                                workflow_data = img.info[key]
-                                break
-                
-                # If no workflow data found in PNG chunks, try EXIF as fallback
-                if not workflow_data:
-                    user_comment = ExifUtils.extract_user_comment(image_path)
-                    if user_comment and '{' in user_comment and '}' in user_comment:
-                        # Try to extract JSON part
-                        json_start = user_comment.find('{')
-                        json_end = user_comment.rfind('}') + 1
-                        workflow_data = user_comment[json_start:json_end]
-                
-                # Parse workflow data if found
-                if workflow_data:
-                    return ExifUtils._parse_comfyui_workflow(workflow_data)
-                
-                return {}
-                
-        except Exception as e:
-            logger.error(f"Error extracting ComfyUI gen params from {image_path}: {e}", exc_info=True)
-            return {}
+            return image_data, '.jpg'

py/utils/file_utils.py

@@ -19,7 +19,7 @@ async def calculate_sha256(file_path: str) -> str:
             sha256_hash.update(byte_block)
     return sha256_hash.hexdigest()
 
-def _find_preview_file(base_name: str, dir_path: str) -> str:
+def find_preview_file(base_name: str, dir_path: str) -> str:
     """Find preview file for given base name in directory"""
     preview_patterns = [
         f"{base_name}.preview.png",
@@ -56,16 +56,33 @@ async def get_file_info(file_path: str) -> Optional[LoraMetadata]:
     base_name = os.path.splitext(os.path.basename(file_path))[0]
     dir_path = os.path.dirname(file_path)
     
-    preview_url = _find_preview_file(base_name, dir_path)
+    preview_url = find_preview_file(base_name, dir_path)
+    
+    # Check if a .json file exists with SHA256 hash to avoid recalculation
+    json_path = f"{os.path.splitext(file_path)[0]}.json"
+    sha256 = None
+    if os.path.exists(json_path):
+        try:
+            with open(json_path, 'r', encoding='utf-8') as f:
+                json_data = json.load(f)
+                if 'sha256' in json_data:
+                    sha256 = json_data['sha256'].lower()
+                    logger.debug(f"Using SHA256 from .json file for {file_path}")
+        except Exception as e:
+            logger.error(f"Error reading .json file for {file_path}: {e}")
 
     try:
+        # If we didn't get SHA256 from the .json file, calculate it
+        if not sha256:
+            sha256 = await calculate_sha256(real_path)
+            
         metadata = LoraMetadata(
             file_name=base_name,
             model_name=base_name,
             file_path=normalize_path(file_path),
             size=os.path.getsize(real_path),
             modified=os.path.getmtime(real_path),
-            sha256=await calculate_sha256(real_path),
+            sha256=sha256,
             base_model="Unknown",  # Will be updated later
             usage_tips="",
             notes="",
@@ -125,7 +142,7 @@ async def load_metadata(file_path: str) -> Optional[LoraMetadata]:
                 if not preview_url or not os.path.exists(preview_url):
                     base_name = os.path.splitext(os.path.basename(file_path))[0]
                     dir_path = os.path.dirname(file_path)
-                    new_preview_url = normalize_path(_find_preview_file(base_name, dir_path))
+                    new_preview_url = normalize_path(find_preview_file(base_name, dir_path))
                     if new_preview_url != preview_url:
                         data['preview_url'] = new_preview_url
                         needs_update = True

py/utils/recipe_parsers.py

@@ -44,6 +44,138 @@ class RecipeMetadataParser(ABC):
             Dict containing parsed recipe data with standardized format
         """
         pass
+    
+    async def populate_lora_from_civitai(self, lora_entry: Dict[str, Any], civitai_info: Dict[str, Any], 
+                                         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
+            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
+            
+        Returns:
+            The populated lora_entry dict
+        """
+        try:
+            if civitai_info and civitai_info.get("error") != "Model not found":
+                # Check if this is an early access lora
+                if civitai_info.get('earlyAccessEndsAt'):
+                    # Convert earlyAccessEndsAt to a human-readable date
+                    early_access_date = civitai_info.get('earlyAccessEndsAt', '')
+                    lora_entry['isEarlyAccess'] = True
+                    lora_entry['earlyAccessEndsAt'] = early_access_date
+                
+                # Update model name if available
+                if 'model' in civitai_info and 'name' in civitai_info['model']:
+                    lora_entry['name'] = civitai_info['model']['name']
+                
+                # Update version if available
+                if 'name' in civitai_info:
+                    lora_entry['version'] = civitai_info.get('name', '')
+                
+                # Get thumbnail URL from first image
+                if 'images' in civitai_info and civitai_info['images']:
+                    lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
+                
+                # Get base model
+                current_base_model = civitai_info.get('baseModel', '')
+                lora_entry['baseModel'] = current_base_model
+                
+                # Update base model counts if tracking them
+                if base_model_counts is not None and current_base_model:
+                    base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
+                
+                # Get download URL
+                lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
+                
+                # Process file information if available
+                if 'files' in civitai_info:
+                    model_file = next((file for file in civitai_info.get('files', []) 
+                                    if file.get('type') == 'Model'), None)
+                    
+                    if model_file:
+                        # Get size
+                        lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
+                        
+                        # Get SHA256 hash
+                        sha256 = model_file.get('hashes', {}).get('SHA256', hash_value)
+                        if sha256:
+                            lora_entry['hash'] = sha256.lower()
+                        
+                        # Check if exists locally
+                        if recipe_scanner and lora_entry['hash']:
+                            lora_scanner = recipe_scanner._lora_scanner
+                            exists_locally = lora_scanner.has_lora_hash(lora_entry['hash'])
+                            if exists_locally:
+                                try:
+                                    local_path = lora_scanner.get_lora_path_by_hash(lora_entry['hash'])
+                                    lora_entry['existsLocally'] = True
+                                    lora_entry['localPath'] = local_path
+                                    lora_entry['file_name'] = os.path.splitext(os.path.basename(local_path))[0]
+                                    
+                                    # Get thumbnail from local preview if available
+                                    lora_cache = await lora_scanner.get_cached_data()
+                                    lora_item = next((item for item in lora_cache.raw_data 
+                                                     if item['sha256'].lower() == lora_entry['hash'].lower()), None)
+                                    if lora_item and 'preview_url' in lora_item:
+                                        lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
+                                except Exception as e:
+                                    logger.error(f"Error getting local lora path: {e}")
+                            else:
+                                # For missing LoRAs, get file_name from model_file.name
+                                file_name = model_file.get('name', '')
+                                lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else ''
+            else:
+                # Model not found or deleted
+                lora_entry['isDeleted'] = True
+                lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                
+        except Exception as e:
+            logger.error(f"Error populating lora from Civitai info: {e}")
+            
+        return lora_entry
+        
+    async def populate_checkpoint_from_civitai(self, checkpoint: Dict[str, Any], civitai_info: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Populate checkpoint information from Civitai API response
+        
+        Args:
+            checkpoint: The checkpoint entry to populate
+            civitai_info: The response from Civitai API
+            
+        Returns:
+            The populated checkpoint dict
+        """
+        try:
+            if civitai_info and civitai_info.get("error") != "Model not found":
+                # Update model name if available
+                if 'model' in civitai_info and 'name' in civitai_info['model']:
+                    checkpoint['name'] = civitai_info['model']['name']
+                
+                # Update version if available
+                if 'name' in civitai_info:
+                    checkpoint['version'] = civitai_info.get('name', '')
+                
+                # Get thumbnail URL from first image
+                if 'images' in civitai_info and civitai_info['images']:
+                    checkpoint['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
+                
+                # Get base model
+                checkpoint['baseModel'] = civitai_info.get('baseModel', '')
+                
+                # Get download URL
+                checkpoint['downloadUrl'] = civitai_info.get('downloadUrl', '')
+            else:
+                # Model not found or deleted
+                checkpoint['isDeleted'] = True
+        except Exception as e:
+            logger.error(f"Error populating checkpoint from Civitai info: {e}")
+            
+        return checkpoint
 
 
 class RecipeFormatParser(RecipeMetadataParser):
@@ -110,33 +242,14 @@ class RecipeFormatParser(RecipeMetadataParser):
                         if lora.get('modelVersionId') and civitai_client:
                             try:
                                 civitai_info = await civitai_client.get_model_version_info(lora['modelVersionId'])
-                                if civitai_info and civitai_info.get("error") != "Model not found":
-                                    # Check if this is an early access lora
-                                    if civitai_info.get('earlyAccessEndsAt'):
-                                        # Convert earlyAccessEndsAt to a human-readable date
-                                        early_access_date = civitai_info.get('earlyAccessEndsAt', '')
-                                        lora_entry['isEarlyAccess'] = True
-                                        lora_entry['earlyAccessEndsAt'] = early_access_date
-                                    
-                                    # Get thumbnail URL from first image
-                                    if 'images' in civitai_info and civitai_info['images']:
-                                        lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
-                                    
-                                    # Get base model
-                                    lora_entry['baseModel'] = civitai_info.get('baseModel', '')
-                                    
-                                    # Get download URL
-                                    lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
-                                    
-                                    # Get size from files if available
-                                    if 'files' in civitai_info:
-                                        model_file = next((file for file in civitai_info.get('files', []) 
-                                                        if file.get('type') == 'Model'), None)
-                                        if model_file:
-                                            lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
-                                else:
-                                    lora_entry['isDeleted'] = True
-                                    lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                                # Populate lora entry with Civitai info
+                                lora_entry = await self.populate_lora_from_civitai(
+                                    lora_entry, 
+                                    civitai_info, 
+                                    recipe_scanner,
+                                    None,  # No need to track base model counts
+                                    lora['hash']
+                                )
                             except Exception as e:
                                 logger.error(f"Error fetching Civitai info for LoRA: {e}")
                                 lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
@@ -222,57 +335,16 @@ class StandardMetadataParser(RecipeMetadataParser):
                 
                 # Get additional info from Civitai if client is available
                 if civitai_client:
-                    civitai_info = await civitai_client.get_model_version_info(model_version_id)
-
-                    # Check if this LoRA exists locally by SHA256 hash
-                    if civitai_info and civitai_info.get("error") != "Model not found":
-                        # Check if this is an early access lora
-                        if civitai_info.get('earlyAccessEndsAt'):
-                            # Convert earlyAccessEndsAt to a human-readable date
-                            early_access_date = civitai_info.get('earlyAccessEndsAt', '')
-                            lora_entry['isEarlyAccess'] = True
-                            lora_entry['earlyAccessEndsAt'] = early_access_date
-                        
-                        # LoRA exists on Civitai, process its information
-                        if 'files' in civitai_info:
-                            # Find the model file (type="Model") in the files list
-                            model_file = next((file for file in civitai_info.get('files', []) 
-                                            if file.get('type') == 'Model'), None)
-                            
-                            if model_file and recipe_scanner:
-                                sha256 = model_file.get('hashes', {}).get('SHA256', '')
-                                if sha256:
-                                    lora_scanner = recipe_scanner._lora_scanner
-                                    exists_locally = lora_scanner.has_lora_hash(sha256)
-                                    if exists_locally:
-                                        local_path = lora_scanner.get_lora_path_by_hash(sha256)
-                                        lora_entry['existsLocally'] = True
-                                        lora_entry['localPath'] = local_path
-                                        lora_entry['file_name'] = os.path.splitext(os.path.basename(local_path))[0]
-                                    else:
-                                        # For missing LoRAs, get file_name from model_file.name
-                                        file_name = model_file.get('name', '')
-                                        lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else ''
-                                
-                                lora_entry['hash'] = sha256
-                                lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
-                        
-                        # Get thumbnail URL from first image
-                        if 'images' in civitai_info and civitai_info['images']:
-                            lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
-                        
-                        # Get base model and update counts
-                        current_base_model = civitai_info.get('baseModel', '')
-                        lora_entry['baseModel'] = current_base_model
-                        if current_base_model:
-                            base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
-                        
-                        # Get download URL
-                        lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
-                    else:
-                        # LoRA is deleted from Civitai or not found
-                        lora_entry['isDeleted'] = True
-                        lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                    try:
+                        civitai_info = 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, 
+                            recipe_scanner
+                        )
+                    except Exception as e:
+                        logger.error(f"Error fetching Civitai info for LoRA: {e}")
                 
                 loras.append(lora_entry)
             
@@ -394,7 +466,9 @@ class A1111MetadataParser(RecipeMetadataParser):
             # Extract LoRA information from prompt
             lora_weights = {}
             lora_matches = re.findall(self.LORA_PATTERN, prompt)
-            for lora_name, weight in lora_matches:
+            for lora_name, weights in lora_matches:
+                # Take only the first strength value (before the colon)
+                weight = weights.split(':')[0]
                 lora_weights[lora_name.strip()] = float(weight.strip())
             
             # Remove LoRA patterns from prompt
@@ -436,61 +510,18 @@ class A1111MetadataParser(RecipeMetadataParser):
                     'isDeleted': False
                 }
                 
-                # Get info from Civitai by hash
-                if civitai_client:
+                # Get info from Civitai by hash if available
+                if civitai_client and hash_value:
                     try:
                         civitai_info = await civitai_client.get_model_by_hash(hash_value)
-                        if civitai_info and civitai_info.get("error") != "Model not found":
-                            # Check if this is an early access lora
-                            if civitai_info.get('earlyAccessEndsAt'):
-                                # Convert earlyAccessEndsAt to a human-readable date
-                                early_access_date = civitai_info.get('earlyAccessEndsAt', '')
-                                lora_entry['isEarlyAccess'] = True
-                                lora_entry['earlyAccessEndsAt'] = early_access_date
-                            
-                            # Get model version ID
-                            lora_entry['id'] = civitai_info.get('id', '')
-                            
-                            # Get model name and version
-                            lora_entry['name'] = civitai_info.get('model', {}).get('name', lora_name)
-                            lora_entry['version'] = civitai_info.get('name', '')
-                            
-                            # Get thumbnail URL
-                            if 'images' in civitai_info and civitai_info['images']:
-                                lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
-                            
-                            # Get base model and update counts
-                            current_base_model = civitai_info.get('baseModel', '')
-                            lora_entry['baseModel'] = current_base_model
-                            if current_base_model:
-                                base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
-                            
-                            # Get download URL
-                            lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
-                            
-                            # Get file name and size from Civitai
-                            if 'files' in civitai_info:
-                                model_file = next((file for file in civitai_info.get('files', []) 
-                                                if file.get('type') == 'Model'), None)
-                                if model_file:
-                                    file_name = model_file.get('name', '')
-                                    lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else lora_name
-                                    lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
-                                    # Update hash to sha256
-                                    lora_entry['hash'] = model_file.get('hashes', {}).get('SHA256', hash_value).lower()
-                                    
-                            # Check if exists locally with sha256 hash
-                            if recipe_scanner and lora_entry['hash']:
-                                lora_scanner = recipe_scanner._lora_scanner
-                                exists_locally = lora_scanner.has_lora_hash(lora_entry['hash'])
-                                if exists_locally:
-                                    lora_cache = await lora_scanner.get_cached_data()
-                                    lora_item = next((item for item in lora_cache.raw_data if item['sha256'] == lora_entry['hash']), None)
-                                    if lora_item:
-                                        lora_entry['existsLocally'] = True
-                                        lora_entry['localPath'] = lora_item['file_path']
-                                        lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
-                                        
+                        # Populate lora entry with Civitai info
+                        lora_entry = await self.populate_lora_from_civitai(
+                            lora_entry, 
+                            civitai_info, 
+                            recipe_scanner,
+                            base_model_counts,
+                            hash_value
+                        )
                     except Exception as e:
                         logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}")
                 
@@ -523,6 +554,499 @@ class A1111MetadataParser(RecipeMetadataParser):
             return {"error": str(e), "loras": []}
 
 
+class ComfyMetadataParser(RecipeMetadataParser):
+    """Parser for Civitai ComfyUI metadata JSON format"""
+    
+    METADATA_MARKER = r"class_type"
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the ComfyUI metadata format"""
+        try:
+            data = json.loads(user_comment)
+            # Check if it contains class_type nodes typical of ComfyUI workflow
+            return isinstance(data, dict) and any(isinstance(v, dict) and 'class_type' in v for v in data.values())
+        except (json.JSONDecodeError, TypeError):
+            return False
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from Civitai ComfyUI metadata format"""
+        try:
+            data = json.loads(user_comment)
+            loras = []
+            
+            # Find all LoraLoader nodes
+            lora_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'LoraLoader'}
+            
+            if not lora_nodes:
+                return {"error": "No LoRA information found in this ComfyUI workflow", "loras": []}
+            
+            # Process each LoraLoader node
+            for node_id, node in lora_nodes.items():
+                if 'inputs' not in node or 'lora_name' not in node['inputs']:
+                    continue
+                    
+                lora_name = node['inputs'].get('lora_name', '')
+                
+                # Parse the URN to extract model ID and version ID
+                # Format: "urn:air:sdxl:lora:civitai:1107767@1253442"
+                lora_id_match = re.search(r'civitai:(\d+)@(\d+)', lora_name)
+                if not lora_id_match:
+                    continue
+                    
+                model_id = lora_id_match.group(1)
+                model_version_id = lora_id_match.group(2)
+                
+                # Get strength from node inputs
+                weight = node['inputs'].get('strength_model', 1.0)
+                
+                # Initialize lora entry with default values
+                lora_entry = {
+                    'id': model_version_id,
+                    'modelId': model_id,
+                    'name': f"Lora {model_id}",  # Default name
+                    'version': '',
+                    'type': 'lora',
+                    'weight': weight,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': '',
+                    'hash': '',
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+                
+                # 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)
+                        # Populate lora entry with Civitai info
+                        lora_entry = await self.populate_lora_from_civitai(
+                            lora_entry, 
+                            civitai_info, 
+                            recipe_scanner
+                        )
+                    except Exception as e:
+                        logger.error(f"Error fetching Civitai info for LoRA: {e}")
+                
+                loras.append(lora_entry)
+            
+            # Find checkpoint info
+            checkpoint_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'CheckpointLoaderSimple'}
+            checkpoint = None
+            checkpoint_id = None
+            checkpoint_version_id = None
+            
+            if checkpoint_nodes:
+                # Get the first checkpoint node
+                checkpoint_node = next(iter(checkpoint_nodes.values()))
+                if 'inputs' in checkpoint_node and 'ckpt_name' in checkpoint_node['inputs']:
+                    checkpoint_name = checkpoint_node['inputs']['ckpt_name']
+                    # Parse checkpoint URN
+                    checkpoint_match = re.search(r'civitai:(\d+)@(\d+)', checkpoint_name)
+                    if checkpoint_match:
+                        checkpoint_id = checkpoint_match.group(1)
+                        checkpoint_version_id = checkpoint_match.group(2)
+                        checkpoint = {
+                            'id': checkpoint_version_id,
+                            'modelId': checkpoint_id,
+                            'name': f"Checkpoint {checkpoint_id}",
+                            'version': '',
+                            'type': 'checkpoint'
+                        }
+                        
+                        # Get additional checkpoint info from Civitai
+                        if civitai_client:
+                            try:
+                                civitai_info = await civitai_client.get_model_version_info(checkpoint_version_id)
+                                # Populate checkpoint with Civitai info
+                                checkpoint = await self.populate_checkpoint_from_civitai(checkpoint, civitai_info)
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for checkpoint: {e}")
+            
+            # Extract generation parameters
+            gen_params = {}
+            
+            # First try to get from extraMetadata
+            if 'extraMetadata' in data:
+                try:
+                    # extraMetadata is a JSON string that needs to be parsed
+                    extra_metadata = json.loads(data['extraMetadata'])
+                    
+                    # Map fields from extraMetadata to our standard format
+                    mapping = {
+                        'prompt': 'prompt',
+                        'negativePrompt': 'negative_prompt',
+                        'steps': 'steps',
+                        'sampler': 'sampler',
+                        'cfgScale': 'cfg_scale',
+                        'seed': 'seed'
+                    }
+                    
+                    for src_key, dest_key in mapping.items():
+                        if src_key in extra_metadata:
+                            gen_params[dest_key] = extra_metadata[src_key]
+                    
+                    # If size info is available, format as "width x height"
+                    if 'width' in extra_metadata and 'height' in extra_metadata:
+                        gen_params['size'] = f"{extra_metadata['width']}x{extra_metadata['height']}"
+                    
+                except Exception as e:
+                    logger.error(f"Error parsing extraMetadata: {e}")
+            
+            # If extraMetadata doesn't have all the info, try to get from nodes
+            if not gen_params or len(gen_params) < 3:  # At least we want prompt, negative_prompt, and steps
+                # Find positive prompt node
+                positive_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and 
+                                v.get('class_type', '').endswith('CLIPTextEncode') and 
+                                v.get('_meta', {}).get('title') == 'Positive'}
+                
+                if positive_nodes:
+                    positive_node = next(iter(positive_nodes.values()))
+                    if 'inputs' in positive_node and 'text' in positive_node['inputs']:
+                        gen_params['prompt'] = positive_node['inputs']['text']
+                
+                # Find negative prompt node
+                negative_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and 
+                                v.get('class_type', '').endswith('CLIPTextEncode') and 
+                                v.get('_meta', {}).get('title') == 'Negative'}
+                
+                if negative_nodes:
+                    negative_node = next(iter(negative_nodes.values()))
+                    if 'inputs' in negative_node and 'text' in negative_node['inputs']:
+                        gen_params['negative_prompt'] = negative_node['inputs']['text']
+                
+                # Find KSampler node for other parameters
+                ksampler_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'KSampler'}
+                
+                if ksampler_nodes:
+                    ksampler_node = next(iter(ksampler_nodes.values()))
+                    if 'inputs' in ksampler_node:
+                        inputs = ksampler_node['inputs']
+                        if 'sampler_name' in inputs:
+                            gen_params['sampler'] = inputs['sampler_name']
+                        if 'steps' in inputs:
+                            gen_params['steps'] = inputs['steps']
+                        if 'cfg' in inputs:
+                            gen_params['cfg_scale'] = inputs['cfg']
+                        if 'seed' in inputs:
+                            gen_params['seed'] = inputs['seed']
+            
+            # Determine base model from loras info
+            base_model = None
+            if loras:
+                # Use the most common base model from loras
+                base_models = [lora['baseModel'] for lora in loras if lora.get('baseModel')]
+                if base_models:
+                    from collections import Counter
+                    base_model_counts = Counter(base_models)
+                    base_model = base_model_counts.most_common(1)[0][0]
+            
+            return {
+                'base_model': base_model,
+                'loras': loras,
+                'checkpoint': checkpoint,
+                'gen_params': gen_params,
+                'from_comfy_metadata': True
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing ComfyUI metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}
+
+
+class MetaFormatParser(RecipeMetadataParser):
+    """Parser for images with meta format metadata (Lora_N Model hash format)"""
+    
+    METADATA_MARKER = r'Lora_\d+ Model hash:'
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the metadata format"""
+        return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from images with meta format metadata"""
+        try:
+            # Extract prompt and negative prompt
+            parts = user_comment.split('Negative prompt:', 1)
+            prompt = parts[0].strip()
+            
+            # Initialize metadata
+            metadata = {"prompt": prompt, "loras": []}
+            
+            # Extract negative prompt and parameters if available
+            if len(parts) > 1:
+                negative_and_params = parts[1]
+                
+                # Extract negative prompt - everything until the first parameter (usually "Steps:")
+                param_start = re.search(r'([A-Za-z]+): ', negative_and_params)
+                if param_start:
+                    neg_prompt = negative_and_params[:param_start.start()].strip()
+                    metadata["negative_prompt"] = neg_prompt
+                    params_section = negative_and_params[param_start.start():]
+                else:
+                    params_section = negative_and_params
+                
+                # Extract key-value parameters (Steps, Sampler, Seed, etc.)
+                param_pattern = r'([A-Za-z_0-9 ]+): ([^,]+)'
+                params = re.findall(param_pattern, params_section)
+                for key, value in params:
+                    clean_key = key.strip().lower().replace(' ', '_')
+                    metadata[clean_key] = value.strip()
+            
+            # Extract LoRA information
+            # Pattern to match lora entries: Lora_0 Model name: ArtVador I.safetensors, Lora_0 Model hash: 08f7133a58, etc.
+            lora_pattern = r'Lora_(\d+) Model name: ([^,]+), Lora_\1 Model hash: ([^,]+), Lora_\1 Strength model: ([^,]+), Lora_\1 Strength clip: ([^,]+)'
+            lora_matches = re.findall(lora_pattern, user_comment)
+            
+            # If the regular pattern doesn't match, try a more flexible approach
+            if not lora_matches:
+                # First find all Lora indices
+                lora_indices = set(re.findall(r'Lora_(\d+)', user_comment))
+                
+                # For each index, extract the information
+                for idx in lora_indices:
+                    lora_info = {}
+                    
+                    # Extract model name
+                    name_match = re.search(f'Lora_{idx} Model name: ([^,]+)', user_comment)
+                    if name_match:
+                        lora_info['name'] = name_match.group(1).strip()
+                    
+                    # Extract model hash
+                    hash_match = re.search(f'Lora_{idx} Model hash: ([^,]+)', user_comment)
+                    if hash_match:
+                        lora_info['hash'] = hash_match.group(1).strip()
+                    
+                    # Extract strength model
+                    strength_model_match = re.search(f'Lora_{idx} Strength model: ([^,]+)', user_comment)
+                    if strength_model_match:
+                        lora_info['strength_model'] = float(strength_model_match.group(1).strip())
+                    
+                    # Extract strength clip
+                    strength_clip_match = re.search(f'Lora_{idx} Strength clip: ([^,]+)', user_comment)
+                    if strength_clip_match:
+                        lora_info['strength_clip'] = float(strength_clip_match.group(1).strip())
+                    
+                    # Only add if we have at least name and hash
+                    if 'name' in lora_info and 'hash' in lora_info:
+                        lora_matches.append((idx, lora_info['name'], lora_info['hash'], 
+                                            str(lora_info.get('strength_model', 1.0)), 
+                                            str(lora_info.get('strength_clip', 1.0))))
+            
+            # Process LoRAs
+            base_model_counts = {}
+            loras = []
+            
+            for match in lora_matches:
+                if len(match) == 5:  # Regular pattern match
+                    idx, name, hash_value, strength_model, strength_clip = match
+                else:  # Flexible approach match
+                    continue  # Should not happen now
+                
+                # Clean up the values
+                name = name.strip()
+                if name.endswith('.safetensors'):
+                    name = name[:-12]  # Remove .safetensors extension
+                    
+                hash_value = hash_value.strip()
+                weight = float(strength_model)  # Use model strength as weight
+                
+                # Initialize lora entry with default values
+                lora_entry = {
+                    'name': name,
+                    'type': 'lora',
+                    'weight': weight,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': name,
+                    'hash': hash_value,
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+                
+                # Get info from Civitai by hash if available
+                if civitai_client and hash_value:
+                    try:
+                        civitai_info = await civitai_client.get_model_by_hash(hash_value)
+                        # Populate lora entry with Civitai info
+                        lora_entry = await self.populate_lora_from_civitai(
+                            lora_entry, 
+                            civitai_info, 
+                            recipe_scanner,
+                            base_model_counts,
+                            hash_value
+                        )
+                    except Exception as e:
+                        logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}")
+                
+                loras.append(lora_entry)
+            
+            # Extract model information
+            model = None
+            if 'model' in metadata:
+                model = metadata['model']
+            
+            # Set base_model to the most common one from civitai_info
+            base_model = None
+            if base_model_counts:
+                base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
+            
+            # Extract generation parameters for recipe metadata
+            gen_params = {}
+            for key in GEN_PARAM_KEYS:
+                if key in metadata:
+                    gen_params[key] = metadata.get(key, '')
+            
+            # Try to extract size information if available
+            if 'width' in metadata and 'height' in metadata:
+                gen_params['size'] = f"{metadata['width']}x{metadata['height']}"
+            
+            return {
+                'base_model': base_model,
+                'loras': loras,
+                'gen_params': gen_params,
+                'raw_metadata': metadata,
+                'from_meta_format': True
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing meta format metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}
+
+
+class ImageSaverMetadataParser(RecipeMetadataParser):
+    """Parser for ComfyUI Image Saver plugin metadata format"""
+    
+    METADATA_MARKER = r'Hashes: \{"LORA:'
+    LORA_PATTERN = r'<lora:([^:]+):([^>]+)>'
+    HASH_PATTERN = r'Hashes: (\{.*?\})'
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the Image Saver metadata format"""
+        return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from Image Saver plugin format"""
+        try:
+            # Extract prompt and negative prompt
+            parts = user_comment.split('Negative prompt:', 1)
+            prompt = parts[0].strip()
+            
+            # Initialize metadata
+            metadata = {"prompt": prompt, "loras": []}
+            
+            # Extract negative prompt and parameters
+            if len(parts) > 1:
+                negative_and_params = parts[1]
+                
+                # Extract negative prompt
+                if "Steps:" in negative_and_params:
+                    neg_prompt = negative_and_params.split("Steps:", 1)[0].strip()
+                    metadata["negative_prompt"] = neg_prompt
+                
+                # Extract key-value parameters (Steps, Sampler, CFG scale, etc.)
+                param_pattern = r'([A-Za-z ]+): ([^,]+)'
+                params = re.findall(param_pattern, negative_and_params)
+                for key, value in params:
+                    clean_key = key.strip().lower().replace(' ', '_')
+                    metadata[clean_key] = value.strip()
+            
+            # Extract LoRA information from prompt
+            lora_weights = {}
+            lora_matches = re.findall(self.LORA_PATTERN, prompt)
+            for lora_name, weight in lora_matches:
+                lora_weights[lora_name.strip()] = float(weight.split(':')[0].strip())
+            
+            # Remove LoRA patterns from prompt
+            metadata["prompt"] = re.sub(self.LORA_PATTERN, '', prompt).strip()
+            
+            # Extract LoRA hashes from Hashes section
+            lora_hashes = {}
+            hash_match = re.search(self.HASH_PATTERN, user_comment)
+            if hash_match:
+                try:
+                    hashes = json.loads(hash_match.group(1))
+                    for key, hash_value in hashes.items():
+                        if key.startswith('LORA:'):
+                            lora_name = key[5:]  # Remove 'LORA:' prefix
+                            lora_hashes[lora_name] = hash_value.strip()
+                except json.JSONDecodeError:
+                    pass
+            
+            # Process LoRAs and collect base models
+            base_model_counts = {}
+            loras = []
+            
+            # Process each LoRA with hash and weight
+            for lora_name, hash_value in lora_hashes.items():
+                weight = lora_weights.get(lora_name, 1.0)
+                
+                # Initialize lora entry with default values
+                lora_entry = {
+                    'name': lora_name,
+                    'type': 'lora',
+                    'weight': weight,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': lora_name,
+                    'hash': hash_value,
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+                
+                # Get info from Civitai by hash if available
+                if civitai_client and hash_value:
+                    try:
+                        civitai_info = await civitai_client.get_model_by_hash(hash_value)
+                        # Populate lora entry with Civitai info
+                        lora_entry = await self.populate_lora_from_civitai(
+                            lora_entry, 
+                            civitai_info, 
+                            recipe_scanner,
+                            base_model_counts,
+                            hash_value
+                        )
+                    except Exception as e:
+                        logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}")
+                
+                loras.append(lora_entry)
+
+            # Set base_model to the most common one from civitai_info
+            base_model = None
+            if base_model_counts:
+                base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
+            
+            # Extract generation parameters for recipe metadata
+            gen_params = {}
+            for key in GEN_PARAM_KEYS:
+                if key in metadata:
+                    gen_params[key] = metadata.get(key, '')
+            
+            # Add model information if available
+            if 'model' in metadata:
+                gen_params['checkpoint'] = metadata['model']
+            
+            return {
+                'base_model': base_model,
+                'loras': loras,
+                'gen_params': gen_params,
+                'raw_metadata': metadata
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing Image Saver metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}
+
+
 class RecipeParserFactory:
     """Factory for creating recipe metadata parsers"""
     
@@ -537,11 +1061,23 @@ class RecipeParserFactory:
         Returns:
             Appropriate RecipeMetadataParser implementation
         """
+        # Try ComfyMetadataParser first since it requires valid JSON
+        try:
+            if ComfyMetadataParser().is_metadata_matching(user_comment):
+                return ComfyMetadataParser()
+        except Exception:
+            # If JSON parsing fails, move on to other parsers
+            pass
+            
         if RecipeFormatParser().is_metadata_matching(user_comment):
             return RecipeFormatParser()
         elif StandardMetadataParser().is_metadata_matching(user_comment):
             return StandardMetadataParser()
         elif A1111MetadataParser().is_metadata_matching(user_comment):
             return A1111MetadataParser()
+        elif MetaFormatParser().is_metadata_matching(user_comment):
+            return MetaFormatParser()
+        elif ImageSaverMetadataParser().is_metadata_matching(user_comment):
+            return ImageSaverMetadataParser()
         else:
-            return None
+            return None

py/utils/utils.py

@@ -40,7 +40,45 @@ def download_twitter_image(url):
     except Exception as e:
         print(f"Error downloading twitter image: {e}")
         return None
+
+def download_civitai_image(url):
+    """Download image from a URL containing avatar image with specific class and style attributes
     
+    Args:
+        url (str): The URL to download image from
+        
+    Returns:
+        str: Path to downloaded temporary image file
+    """
+    try:
+        # Download page content
+        response = requests.get(url)
+        response.raise_for_status()
+        
+        # Parse HTML
+        soup = BeautifulSoup(response.text, 'html.parser')
+        
+        # Find image with specific class and style attributes
+        image = soup.select_one('img.EdgeImage_image__iH4_q.max-h-full.w-auto.max-w-full')
+        
+        if not image or 'src' not in image.attrs:
+            return None
+            
+        image_url = image['src']
+        
+        # Download image
+        image_response = requests.get(image_url)
+        image_response.raise_for_status()
+        
+        # Save to temp file
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
+            temp_file.write(image_response.content)
+            return temp_file.name
+            
+    except Exception as e:
+        print(f"Error downloading civitai avatar: {e}")
+        return None
+
 def fuzzy_match(text: str, pattern: str, threshold: float = 0.7) -> bool:
         """
         Check if text matches pattern using fuzzy matching.

py/workflow/README.md

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

py/workflow/ext/comfyui_core.py

@@ -0,0 +1,285 @@
+"""
+ComfyUI Core nodes mappers extension for workflow parsing
+"""
+import logging
+from typing import Dict, Any, List
+
+logger = logging.getLogger(__name__)
+
+# =============================================================================
+# Transform Functions
+# =============================================================================
+
+def transform_random_noise(inputs: Dict) -> Dict:
+    """Transform function for RandomNoise node"""
+    return {"seed": str(inputs.get("noise_seed", ""))}
+
+def transform_ksampler_select(inputs: Dict) -> Dict:
+    """Transform function for KSamplerSelect node"""
+    return {"sampler": inputs.get("sampler_name", "")}
+
+def transform_basic_scheduler(inputs: Dict) -> Dict:
+    """Transform function for BasicScheduler node"""
+    result = {
+        "scheduler": inputs.get("scheduler", ""),
+        "denoise": str(inputs.get("denoise", "1.0"))
+    }
+    
+    # Get steps from inputs or steps input
+    if "steps" in inputs:
+        if isinstance(inputs["steps"], str):
+            result["steps"] = inputs["steps"]
+        elif isinstance(inputs["steps"], dict) and "value" in inputs["steps"]:
+            result["steps"] = str(inputs["steps"]["value"])
+        else:
+            result["steps"] = str(inputs["steps"])
+    
+    return result
+
+def transform_basic_guider(inputs: Dict) -> Dict:
+    """Transform function for BasicGuider node"""
+    result = {}
+    
+    # Process conditioning
+    if "conditioning" in inputs:
+        if isinstance(inputs["conditioning"], str):
+            result["prompt"] = inputs["conditioning"]
+        elif isinstance(inputs["conditioning"], dict):
+            result["conditioning"] = inputs["conditioning"]
+    
+    # Get model information if needed
+    if "model" in inputs and isinstance(inputs["model"], dict):
+        result["model"] = inputs["model"]
+    
+    return result
+
+def transform_model_sampling_flux(inputs: Dict) -> Dict:
+    """Transform function for ModelSamplingFlux - mostly a pass-through node"""
+    # This node is primarily used for routing, so we mostly pass through values
+    
+    return inputs["model"]
+
+def transform_sampler_custom_advanced(inputs: Dict) -> Dict:
+    """Transform function for SamplerCustomAdvanced node"""
+    result = {}
+    
+    # Extract seed from noise
+    if "noise" in inputs and isinstance(inputs["noise"], dict):
+        result["seed"] = str(inputs["noise"].get("seed", ""))
+    
+    # Extract sampler info
+    if "sampler" in inputs and isinstance(inputs["sampler"], dict):
+        sampler = inputs["sampler"].get("sampler", "")
+        if sampler:
+            result["sampler"] = sampler
+    
+    # Extract scheduler, steps, denoise from sigmas
+    if "sigmas" in inputs and isinstance(inputs["sigmas"], dict):
+        sigmas = inputs["sigmas"]
+        result["scheduler"] = sigmas.get("scheduler", "")
+        result["steps"] = str(sigmas.get("steps", ""))
+        result["denoise"] = str(sigmas.get("denoise", "1.0"))
+    
+    # Extract prompt and guidance from guider
+    if "guider" in inputs and isinstance(inputs["guider"], dict):
+        guider = inputs["guider"]
+        
+        # Get prompt from conditioning
+        if "conditioning" in guider and isinstance(guider["conditioning"], str):
+            result["prompt"] = guider["conditioning"]
+        elif "conditioning" in guider and isinstance(guider["conditioning"], dict):
+            result["guidance"] = guider["conditioning"].get("guidance", "")
+            result["prompt"] = guider["conditioning"].get("prompt", "")
+
+        if "model" in guider and isinstance(guider["model"], dict):
+            result["checkpoint"] = guider["model"].get("checkpoint", "")
+            result["loras"] = guider["model"].get("loras", "")
+            result["clip_skip"] = str(int(guider["model"].get("clip_skip", "-1")) * -1)
+    
+    # Extract dimensions from latent_image
+    if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
+        latent = inputs["latent_image"]
+        width = latent.get("width", 0)
+        height = latent.get("height", 0)
+        if width and height:
+            result["width"] = width
+            result["height"] = height
+            result["size"] = f"{width}x{height}"
+    
+    return result
+
+def transform_ksampler(inputs: Dict) -> Dict:
+    """Transform function for KSampler nodes"""
+    result = {
+        "seed": str(inputs.get("seed", "")),
+        "steps": str(inputs.get("steps", "")),
+        "cfg": str(inputs.get("cfg", "")),
+        "sampler": inputs.get("sampler_name", ""),
+        "scheduler": inputs.get("scheduler", ""),
+    }
+    
+    # Process positive prompt
+    if "positive" in inputs:
+        result["prompt"] = inputs["positive"]
+    
+    # Process negative prompt
+    if "negative" in inputs:
+        result["negative_prompt"] = inputs["negative"]
+    
+    # Get dimensions from latent image
+    if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
+        width = inputs["latent_image"].get("width", 0)
+        height = inputs["latent_image"].get("height", 0)
+        if width and height:
+            result["size"] = f"{width}x{height}"
+    
+    # Add clip_skip if present
+    if "clip_skip" in inputs:
+        result["clip_skip"] = str(inputs.get("clip_skip", ""))
+
+    # Add guidance if present
+    if "guidance" in inputs:
+        result["guidance"] = str(inputs.get("guidance", ""))
+
+    # Add model if present
+    if "model" in inputs:
+        result["checkpoint"] = inputs.get("model", {}).get("checkpoint", "")
+        result["loras"] = inputs.get("model", {}).get("loras", "")
+        result["clip_skip"] = str(inputs.get("model", {}).get("clip_skip", -1) * -1)
+        
+    return result
+
+def transform_empty_latent(inputs: Dict) -> Dict:
+    """Transform function for EmptyLatentImage nodes"""
+    width = inputs.get("width", 0)
+    height = inputs.get("height", 0)
+    return {"width": width, "height": height, "size": f"{width}x{height}"}
+
+def transform_clip_text(inputs: Dict) -> Any:
+    """Transform function for CLIPTextEncode nodes"""
+    return inputs.get("text", "")
+
+def transform_flux_guidance(inputs: Dict) -> Dict:
+    """Transform function for FluxGuidance nodes"""
+    result = {}
+    
+    if "guidance" in inputs:
+        result["guidance"] = inputs["guidance"]
+    
+    if "conditioning" in inputs:
+        conditioning = inputs["conditioning"]
+        if isinstance(conditioning, str):
+            result["prompt"] = conditioning
+        else:
+            result["prompt"] = "Unknown prompt"
+    
+    return result
+
+def transform_unet_loader(inputs: Dict) -> Dict:
+    """Transform function for UNETLoader node"""
+    unet_name = inputs.get("unet_name", "")
+    return {"checkpoint": unet_name} if unet_name else {}
+
+def transform_checkpoint_loader(inputs: Dict) -> Dict:
+    """Transform function for CheckpointLoaderSimple node"""
+    ckpt_name = inputs.get("ckpt_name", "")
+    return {"checkpoint": ckpt_name} if ckpt_name else {}
+
+def transform_latent_upscale_by(inputs: Dict) -> Dict:
+    """Transform function for LatentUpscaleBy node"""
+    result = {}
+    
+    width = inputs["samples"].get("width", 0) * inputs["scale_by"]
+    height = inputs["samples"].get("height", 0) * inputs["scale_by"]
+    result["width"] = width
+    result["height"] = height
+    result["size"] = f"{width}x{height}"
+    
+    return result
+
+def transform_clip_set_last_layer(inputs: Dict) -> Dict:
+    """Transform function for CLIPSetLastLayer node"""
+    result = {}
+    
+    if "stop_at_clip_layer" in inputs:
+        result["clip_skip"] = inputs["stop_at_clip_layer"]
+    
+    return result
+
+# =============================================================================
+# Node Mapper Definitions
+# =============================================================================
+
+# Define the mappers for ComfyUI core nodes not in main mapper
+NODE_MAPPERS_EXT = {
+    # KSamplers
+    "SamplerCustomAdvanced": {
+        "inputs_to_track": ["noise", "guider", "sampler", "sigmas", "latent_image"],
+        "transform_func": transform_sampler_custom_advanced
+    },
+    "KSampler": {
+        "inputs_to_track": [
+            "seed", "steps", "cfg", "sampler_name", "scheduler", 
+            "denoise", "positive", "negative", "latent_image",
+            "model", "clip_skip"
+        ],
+        "transform_func": transform_ksampler
+    },
+    # ComfyUI core nodes
+    "EmptyLatentImage": {
+        "inputs_to_track": ["width", "height", "batch_size"],
+        "transform_func": transform_empty_latent
+    },
+    "EmptySD3LatentImage": {
+        "inputs_to_track": ["width", "height", "batch_size"],
+        "transform_func": transform_empty_latent
+    },
+    "CLIPTextEncode": {
+        "inputs_to_track": ["text", "clip"],
+        "transform_func": transform_clip_text
+    },
+    "FluxGuidance": {
+        "inputs_to_track": ["guidance", "conditioning"],
+        "transform_func": transform_flux_guidance
+    },
+    "RandomNoise": {
+        "inputs_to_track": ["noise_seed"],
+        "transform_func": transform_random_noise
+    },
+    "KSamplerSelect": {
+        "inputs_to_track": ["sampler_name"],
+        "transform_func": transform_ksampler_select
+    },
+    "BasicScheduler": {
+        "inputs_to_track": ["scheduler", "steps", "denoise", "model"],
+        "transform_func": transform_basic_scheduler
+    },
+    "BasicGuider": {
+        "inputs_to_track": ["model", "conditioning"],
+        "transform_func": transform_basic_guider
+    },
+    "ModelSamplingFlux": {
+        "inputs_to_track": ["max_shift", "base_shift", "width", "height", "model"],
+        "transform_func": transform_model_sampling_flux
+    },
+    "UNETLoader": {
+        "inputs_to_track": ["unet_name"],
+        "transform_func": transform_unet_loader
+    },
+    "CheckpointLoaderSimple": {
+        "inputs_to_track": ["ckpt_name"],
+        "transform_func": transform_checkpoint_loader
+    },
+    "LatentUpscale": {
+        "inputs_to_track": ["width", "height"],
+        "transform_func": transform_empty_latent
+    },
+    "LatentUpscaleBy": {
+        "inputs_to_track": ["samples", "scale_by"],
+        "transform_func": transform_latent_upscale_by
+    },
+    "CLIPSetLastLayer": {
+        "inputs_to_track": ["clip", "stop_at_clip_layer"],
+        "transform_func": transform_clip_set_last_layer
+    }
+} 

py/workflow/ext/example_mapper.py

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

py/workflow/ext/kjnodes.py

@@ -0,0 +1,74 @@
+"""
+KJNodes mappers extension for ComfyUI workflow parsing
+"""
+import logging
+import re
+from typing import Dict, Any
+
+logger = logging.getLogger(__name__)
+
+# =============================================================================
+# Transform Functions
+# =============================================================================
+
+def transform_join_strings(inputs: Dict) -> str:
+    """Transform function for JoinStrings nodes"""
+    string1 = inputs.get("string1", "")
+    string2 = inputs.get("string2", "")
+    delimiter = inputs.get("delimiter", "")
+    return f"{string1}{delimiter}{string2}"
+
+def transform_string_constant(inputs: Dict) -> str:
+    """Transform function for StringConstant nodes"""
+    return inputs.get("string", "")
+
+def transform_empty_latent_presets(inputs: Dict) -> Dict:
+    """Transform function for EmptyLatentImagePresets nodes"""
+    dimensions = inputs.get("dimensions", "")
+    invert = inputs.get("invert", False)
+    
+    # Extract width and height from dimensions string
+    # Expected format: "width x height (ratio)" or similar
+    width = 0
+    height = 0
+    
+    if dimensions:
+        # Try to extract dimensions using regex
+        match = re.search(r'(\d+)\s*x\s*(\d+)', dimensions)
+        if match:
+            width = int(match.group(1))
+            height = int(match.group(2))
+    
+    # If invert is True, swap width and height
+    if invert and width and height:
+        width, height = height, width
+    
+    return {"width": width, "height": height, "size": f"{width}x{height}"}
+
+def transform_int_constant(inputs: Dict) -> int:
+    """Transform function for INTConstant nodes"""
+    return inputs.get("value", 0)
+
+# =============================================================================
+# Node Mapper Definitions
+# =============================================================================
+
+# Define the mappers for KJNodes
+NODE_MAPPERS_EXT = {
+    "JoinStrings": {
+        "inputs_to_track": ["string1", "string2", "delimiter"],
+        "transform_func": transform_join_strings
+    },
+    "StringConstantMultiline": {
+        "inputs_to_track": ["string"],
+        "transform_func": transform_string_constant
+    },
+    "EmptyLatentImagePresets": {
+        "inputs_to_track": ["dimensions", "invert", "batch_size"],
+        "transform_func": transform_empty_latent_presets
+    },
+    "INTConstant": {
+        "inputs_to_track": ["value"],
+        "transform_func": transform_int_constant
+    }
+} 

py/workflow/mappers.py

@@ -5,375 +5,229 @@ import logging
 import os
 import importlib.util
 import inspect
-from typing import Dict, List, Any, Optional, Union, Type, Callable
+from typing import Dict, List, Any, Optional, Union, Type, Callable, Tuple
 
 logger = logging.getLogger(__name__)
 
 # Global mapper registry
-_MAPPER_REGISTRY: Dict[str, 'NodeMapper'] = {}
-
-class NodeMapper:
-    """Base class for node mappers that define how to extract information from a specific node type"""
-    
-    def __init__(self, node_type: str, inputs_to_track: List[str]):
-        self.node_type = node_type
-        self.inputs_to_track = inputs_to_track
-    
-    def process(self, node_id: str, node_data: Dict, workflow: Dict, parser: 'WorkflowParser') -> Any: # type: ignore
-        """Process the node and extract relevant information"""
-        result = {}
-        for input_name in self.inputs_to_track:
-            if input_name in node_data.get("inputs", {}):
-                input_value = node_data["inputs"][input_name]
-                # Check if input is a reference to another node's output
-                if isinstance(input_value, list) and len(input_value) == 2:
-                    # Format is [node_id, output_slot]
-                    try:
-                        ref_node_id, output_slot = input_value
-                        # Convert node_id to string if it's an integer
-                        if isinstance(ref_node_id, int):
-                            ref_node_id = str(ref_node_id)
-                        
-                        # Recursively process the referenced node
-                        ref_value = parser.process_node(ref_node_id, workflow)
-                        
-                        # Store the processed value
-                        if ref_value is not None:
-                            result[input_name] = ref_value
-                        else:
-                            # If we couldn't get a value from the reference, store the raw value
-                            result[input_name] = input_value
-                    except Exception as e:
-                        logger.error(f"Error processing reference in node {node_id}, input {input_name}: {e}")
-                        # If we couldn't process the reference, store the raw value
-                        result[input_name] = input_value
-                else:
-                    # Direct value
-                    result[input_name] = input_value
-        
-        # Apply any transformations
-        return self.transform(result)
-    
-    def transform(self, inputs: Dict) -> Any:
-        """Transform the extracted inputs - override in subclasses"""
-        return inputs
-
-
-class KSamplerMapper(NodeMapper):
-    """Mapper for KSampler nodes"""
-    
-    def __init__(self):
-        super().__init__(
-            node_type="KSampler",
-            inputs_to_track=["seed", "steps", "cfg", "sampler_name", "scheduler", 
-                             "denoise", "positive", "negative", "latent_image",
-                             "model", "clip_skip"]
-        )
-    
-    def transform(self, inputs: Dict) -> Dict:
-        result = {
-            "seed": str(inputs.get("seed", "")),
-            "steps": str(inputs.get("steps", "")),
-            "cfg": str(inputs.get("cfg", "")),
-            "sampler": inputs.get("sampler_name", ""),
-            "scheduler": inputs.get("scheduler", ""),
-        }
-        
-        # Process positive prompt
-        if "positive" in inputs:
-            result["prompt"] = inputs["positive"]
-        
-        # Process negative prompt
-        if "negative" in inputs:
-            result["negative_prompt"] = inputs["negative"]
-        
-        # Get dimensions from latent image
-        if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
-            width = inputs["latent_image"].get("width", 0)
-            height = inputs["latent_image"].get("height", 0)
-            if width and height:
-                result["size"] = f"{width}x{height}"
-        
-        # Add clip_skip if present
-        if "clip_skip" in inputs:
-            result["clip_skip"] = str(inputs.get("clip_skip", ""))
-            
-        return result
-
-
-class EmptyLatentImageMapper(NodeMapper):
-    """Mapper for EmptyLatentImage nodes"""
-    
-    def __init__(self):
-        super().__init__(
-            node_type="EmptyLatentImage",
-            inputs_to_track=["width", "height", "batch_size"]
-        )
-    
-    def transform(self, inputs: Dict) -> Dict:
-        width = inputs.get("width", 0)
-        height = inputs.get("height", 0)
-        return {"width": width, "height": height, "size": f"{width}x{height}"}
-
-
-class EmptySD3LatentImageMapper(NodeMapper):
-    """Mapper for EmptySD3LatentImage nodes"""
-    
-    def __init__(self):
-        super().__init__(
-            node_type="EmptySD3LatentImage",
-            inputs_to_track=["width", "height", "batch_size"]
-        )
-    
-    def transform(self, inputs: Dict) -> Dict:
-        width = inputs.get("width", 0)
-        height = inputs.get("height", 0)
-        return {"width": width, "height": height, "size": f"{width}x{height}"}
-
-
-class CLIPTextEncodeMapper(NodeMapper):
-    """Mapper for CLIPTextEncode nodes"""
-    
-    def __init__(self):
-        super().__init__(
-            node_type="CLIPTextEncode",
-            inputs_to_track=["text", "clip"]
-        )
-    
-    def transform(self, inputs: Dict) -> Any:
-        # Simply return the text
-        return inputs.get("text", "")
-
-
-class LoraLoaderMapper(NodeMapper):
-    """Mapper for LoraLoader nodes"""
-    
-    def __init__(self):
-        super().__init__(
-            node_type="Lora Loader (LoraManager)",
-            inputs_to_track=["loras", "lora_stack"]
-        )
-    
-    def transform(self, inputs: Dict) -> Dict:
-        # Fallback to loras array if text field doesn't exist or is invalid
-        loras_data = inputs.get("loras", [])
-        lora_stack = inputs.get("lora_stack", {}).get("lora_stack", [])
-        
-        # Process loras array - filter active entries
-        lora_texts = []
-        
-        # Check if loras_data is a list or a dict with __value__ key (new format)
-        if isinstance(loras_data, dict) and "__value__" in loras_data:
-            loras_list = loras_data["__value__"]
-        elif isinstance(loras_data, list):
-            loras_list = loras_data
-        else:
-            loras_list = []
-            
-        # Process each active lora entry
-        for lora in loras_list:
-            logger.info(f"Lora: {lora}, active: {lora.get('active')}")
-            if isinstance(lora, dict) and lora.get("active", False):
-                lora_name = lora.get("name", "")
-                strength = lora.get("strength", 1.0)
-                lora_texts.append(f"<lora:{lora_name}:{strength}>")
-        
-        # Process lora_stack if it exists and is a valid format (list of tuples)
-        if lora_stack and isinstance(lora_stack, list):
-            # If lora_stack is a reference to another node ([node_id, output_slot]),
-            # we don't process it here as it's already been processed recursively
-            if len(lora_stack) == 2 and isinstance(lora_stack[0], (str, int)) and isinstance(lora_stack[1], int):
-                # This is a reference to another node, already processed
-                pass
-            else:
-                # Format each entry from the stack (assuming it's a list of tuples)
-                for stack_entry in lora_stack:
-                    lora_name = stack_entry[0]
-                    strength = stack_entry[1]
-                    lora_texts.append(f"<lora:{lora_name}:{strength}>")
-        
-        # Join with spaces
-        combined_text = " ".join(lora_texts)
-            
-        return {"loras": combined_text}
-
-
-class LoraStackerMapper(NodeMapper):
-    """Mapper for LoraStacker nodes"""
-    
-    def __init__(self):
-        super().__init__(
-            node_type="Lora Stacker (LoraManager)",
-            inputs_to_track=["loras", "lora_stack"]
-        )
-    
-    def transform(self, inputs: Dict) -> Dict:
-        loras_data = inputs.get("loras", [])
-        result_stack = []
-        
-        # Handle existing stack entries
-        existing_stack = []
-        lora_stack_input = inputs.get("lora_stack", [])
-        
-        # Handle different formats of lora_stack
-        if isinstance(lora_stack_input, dict) and "lora_stack" in lora_stack_input:
-            # Format from another LoraStacker node
-            existing_stack = lora_stack_input["lora_stack"]
-        elif isinstance(lora_stack_input, list):
-            # Direct list format or reference format [node_id, output_slot]
-            if len(lora_stack_input) == 2 and isinstance(lora_stack_input[0], (str, int)) and isinstance(lora_stack_input[1], int):
-                # This is likely a reference that was already processed
-                pass
-            else:
-                # Regular list of tuples/entries
-                existing_stack = lora_stack_input
-        
-        # Add existing entries first
-        if existing_stack:
-            result_stack.extend(existing_stack)
-        
-        # Process loras array - filter active entries
-        # Check if loras_data is a list or a dict with __value__ key (new format)
-        if isinstance(loras_data, dict) and "__value__" in loras_data:
-            loras_list = loras_data["__value__"]
-        elif isinstance(loras_data, list):
-            loras_list = loras_data
-        else:
-            loras_list = []
-            
-        # Process each active lora entry
-        for lora in loras_list:
-            if isinstance(lora, dict) and lora.get("active", False):
-                lora_name = lora.get("name", "")
-                strength = float(lora.get("strength", 1.0))
-                result_stack.append((lora_name, strength))
-        
-        return {"lora_stack": result_stack}
-
-
-class JoinStringsMapper(NodeMapper):
-    """Mapper for JoinStrings nodes"""
-    
-    def __init__(self):
-        super().__init__(
-            node_type="JoinStrings",
-            inputs_to_track=["string1", "string2", "delimiter"]
-        )
-    
-    def transform(self, inputs: Dict) -> str:
-        string1 = inputs.get("string1", "")
-        string2 = inputs.get("string2", "")
-        delimiter = inputs.get("delimiter", "")
-        return f"{string1}{delimiter}{string2}"
-
-
-class StringConstantMapper(NodeMapper):
-    """Mapper for StringConstant and StringConstantMultiline nodes"""
-    
-    def __init__(self):
-        super().__init__(
-            node_type="StringConstantMultiline",
-            inputs_to_track=["string"]
-        )
-    
-    def transform(self, inputs: Dict) -> str:
-        return inputs.get("string", "")
-
-
-class TriggerWordToggleMapper(NodeMapper):
-    """Mapper for TriggerWordToggle nodes"""
-    
-    def __init__(self):
-        super().__init__(
-            node_type="TriggerWord Toggle (LoraManager)",
-            inputs_to_track=["toggle_trigger_words"]
-        )
-    
-    def transform(self, inputs: Dict) -> str:
-        toggle_data = inputs.get("toggle_trigger_words", [])
-
-        # check if toggle_words is a list or a dict with __value__ key (new format)
-        if isinstance(toggle_data, dict) and "__value__" in toggle_data:
-            toggle_words = toggle_data["__value__"]
-        elif isinstance(toggle_data, list):
-            toggle_words = toggle_data
-        else:
-            toggle_words = []
-        
-        # Filter active trigger words
-        active_words = []
-        for item in toggle_words:
-            if isinstance(item, dict) and item.get("active", False):
-                word = item.get("text", "")
-                if word and not word.startswith("__dummy"):
-                    active_words.append(word)
-        
-        # Join with commas
-        result = ", ".join(active_words)
-        return result
-
-
-class FluxGuidanceMapper(NodeMapper):
-    """Mapper for FluxGuidance nodes"""
-    
-    def __init__(self):
-        super().__init__(
-            node_type="FluxGuidance",
-            inputs_to_track=["guidance", "conditioning"]
-        )
-    
-    def transform(self, inputs: Dict) -> Dict:
-        result = {}
-        
-        # Handle guidance parameter
-        if "guidance" in inputs:
-            result["guidance"] = inputs["guidance"]
-        
-        # Handle conditioning (the prompt text)
-        if "conditioning" in inputs:
-            conditioning = inputs["conditioning"]
-            if isinstance(conditioning, str):
-                result["prompt"] = conditioning
-            else:
-                result["prompt"] = "Unknown prompt"
-        
-        return result
-
+_MAPPER_REGISTRY: Dict[str, Dict] = {}
 
 # =============================================================================
-# Mapper Registry Functions
+# Mapper Definition Functions
 # =============================================================================
 
-def register_mapper(mapper: NodeMapper) -> None:
+def create_mapper(
+    node_type: str,
+    inputs_to_track: List[str],
+    transform_func: Callable[[Dict], Any] = None
+) -> Dict:
+    """Create a mapper definition for a node type"""
+    mapper = {
+        "node_type": node_type,
+        "inputs_to_track": inputs_to_track,
+        "transform": transform_func or (lambda inputs: inputs)
+    }
+    return mapper
+
+def register_mapper(mapper: Dict) -> None:
     """Register a node mapper in the global registry"""
-    _MAPPER_REGISTRY[mapper.node_type] = mapper
-    logger.debug(f"Registered mapper for node type: {mapper.node_type}")
+    _MAPPER_REGISTRY[mapper["node_type"]] = mapper
+    logger.debug(f"Registered mapper for node type: {mapper['node_type']}")
 
-def get_mapper(node_type: str) -> Optional[NodeMapper]:
+def get_mapper(node_type: str) -> Optional[Dict]:
     """Get a mapper for the specified node type"""
     return _MAPPER_REGISTRY.get(node_type)
 
-def get_all_mappers() -> Dict[str, NodeMapper]:
+def get_all_mappers() -> Dict[str, Dict]:
     """Get all registered mappers"""
     return _MAPPER_REGISTRY.copy()
 
-def register_default_mappers() -> None:
-    """Register all default mappers"""
-    default_mappers = [
-        KSamplerMapper(),
-        EmptyLatentImageMapper(),
-        EmptySD3LatentImageMapper(),
-        CLIPTextEncodeMapper(),
-        LoraLoaderMapper(),
-        LoraStackerMapper(),
-        JoinStringsMapper(),
-        StringConstantMapper(),
-        TriggerWordToggleMapper(),
-        FluxGuidanceMapper()
-    ]
+# =============================================================================
+# Node Processing Function
+# =============================================================================
+
+def process_node(node_id: str, node_data: Dict, workflow: Dict, parser: 'WorkflowParser') -> Any: # type: ignore
+    """Process a node using its mapper and extract relevant information"""
+    node_type = node_data.get("class_type")
+    mapper = get_mapper(node_type)
     
-    for mapper in default_mappers:
+    if not mapper:
+        logger.warning(f"No mapper found for node type: {node_type}")
+        return None
+        
+    result = {}
+    
+    # Extract inputs based on the mapper's tracked inputs
+    for input_name in mapper["inputs_to_track"]:
+        if input_name in node_data.get("inputs", {}):
+            input_value = node_data["inputs"][input_name]
+            
+            # Check if input is a reference to another node's output
+            if isinstance(input_value, list) and len(input_value) == 2:
+                try:
+                    # Format is [node_id, output_slot]
+                    ref_node_id, output_slot = input_value
+                    # Convert node_id to string if it's an integer
+                    if isinstance(ref_node_id, int):
+                        ref_node_id = str(ref_node_id)
+                    
+                    # Recursively process the referenced node
+                    ref_value = parser.process_node(ref_node_id, workflow)
+                    
+                    if ref_value is not None:
+                        result[input_name] = ref_value
+                    else:
+                        # If we couldn't get a value from the reference, store the raw value
+                        result[input_name] = input_value
+                except Exception as e:
+                    logger.error(f"Error processing reference in node {node_id}, input {input_name}: {e}")
+                    result[input_name] = input_value
+            else:
+                # Direct value
+                result[input_name] = input_value
+    
+    # Apply the transform function
+    try:
+        return mapper["transform"](result)
+    except Exception as e:
+        logger.error(f"Error in transform function for node {node_id} of type {node_type}: {e}")
+        return result
+
+# =============================================================================
+# Transform Functions
+# =============================================================================
+
+
+
+def transform_lora_loader(inputs: Dict) -> Dict:
+    """Transform function for LoraLoader nodes"""
+    loras_data = inputs.get("loras", [])
+    lora_stack = inputs.get("lora_stack", {}).get("lora_stack", [])
+    
+    lora_texts = []
+    
+    # Process loras array
+    if isinstance(loras_data, dict) and "__value__" in loras_data:
+        loras_list = loras_data["__value__"]
+    elif isinstance(loras_data, list):
+        loras_list = loras_data
+    else:
+        loras_list = []
+        
+    # Process each active lora entry
+    for lora in loras_list:
+        if isinstance(lora, dict) and lora.get("active", False):
+            lora_name = lora.get("name", "")
+            strength = lora.get("strength", 1.0)
+            lora_texts.append(f"<lora:{lora_name}:{strength}>")
+    
+    # Process lora_stack if valid
+    if lora_stack and isinstance(lora_stack, list):
+        if not (len(lora_stack) == 2 and isinstance(lora_stack[0], (str, int)) and isinstance(lora_stack[1], int)):
+            for stack_entry in lora_stack:
+                lora_name = stack_entry[0]
+                strength = stack_entry[1]
+                lora_texts.append(f"<lora:{lora_name}:{strength}>")
+
+    result = {
+        "checkpoint": inputs.get("model", {}).get("checkpoint", ""),
+        "loras": " ".join(lora_texts)
+    }
+
+    if "clip" in inputs:
+        result["clip_skip"] = inputs["clip"].get("clip_skip", "-1")
+    
+    return result
+
+def transform_lora_stacker(inputs: Dict) -> Dict:
+    """Transform function for LoraStacker nodes"""
+    loras_data = inputs.get("loras", [])
+    result_stack = []
+    
+    # Handle existing stack entries
+    existing_stack = []
+    lora_stack_input = inputs.get("lora_stack", [])
+    
+    if isinstance(lora_stack_input, dict) and "lora_stack" in lora_stack_input:
+        existing_stack = lora_stack_input["lora_stack"]
+    elif isinstance(lora_stack_input, list):
+        if not (len(lora_stack_input) == 2 and isinstance(lora_stack_input[0], (str, int)) and 
+               isinstance(lora_stack_input[1], int)):
+            existing_stack = lora_stack_input
+    
+    # Add existing entries
+    if existing_stack:
+        result_stack.extend(existing_stack)
+    
+    # Process new loras
+    if isinstance(loras_data, dict) and "__value__" in loras_data:
+        loras_list = loras_data["__value__"]
+    elif isinstance(loras_data, list):
+        loras_list = loras_data
+    else:
+        loras_list = []
+        
+    for lora in loras_list:
+        if isinstance(lora, dict) and lora.get("active", False):
+            lora_name = lora.get("name", "")
+            strength = float(lora.get("strength", 1.0))
+            result_stack.append((lora_name, strength))
+    
+    return {"lora_stack": result_stack}
+
+def transform_trigger_word_toggle(inputs: Dict) -> str:
+    """Transform function for TriggerWordToggle nodes"""
+    toggle_data = inputs.get("toggle_trigger_words", [])
+
+    if isinstance(toggle_data, dict) and "__value__" in toggle_data:
+        toggle_words = toggle_data["__value__"]
+    elif isinstance(toggle_data, list):
+        toggle_words = toggle_data
+    else:
+        toggle_words = []
+    
+    # Filter active trigger words
+    active_words = []
+    for item in toggle_words:
+        if isinstance(item, dict) and item.get("active", False):
+            word = item.get("text", "")
+            if word and not word.startswith("__dummy"):
+                active_words.append(word)
+    
+    return ", ".join(active_words)
+
+# =============================================================================
+# Node Mapper Definitions
+# =============================================================================
+
+# Central definition of all supported node types and their configurations
+NODE_MAPPERS = {
+    
+    # LoraManager nodes
+    "Lora Loader (LoraManager)": {
+        "inputs_to_track": ["model", "clip", "loras", "lora_stack"],
+        "transform_func": transform_lora_loader
+    },
+    "Lora Stacker (LoraManager)": {
+        "inputs_to_track": ["loras", "lora_stack"],
+        "transform_func": transform_lora_stacker
+    },
+    "TriggerWord Toggle (LoraManager)": {
+        "inputs_to_track": ["toggle_trigger_words"],
+        "transform_func": transform_trigger_word_toggle
+    }   
+}
+
+def register_all_mappers() -> None:
+    """Register all mappers from the NODE_MAPPERS dictionary"""
+    for node_type, config in NODE_MAPPERS.items():
+        mapper = create_mapper(
+            node_type=node_type,
+            inputs_to_track=config["inputs_to_track"],
+            transform_func=config["transform_func"]
+        )
         register_mapper(mapper)
+    logger.info(f"Registered {len(NODE_MAPPERS)} node mappers")
 
 # =============================================================================
 # Extension Loading
@@ -383,8 +237,8 @@ def load_extensions(ext_dir: str = None) -> None:
     """
     Load mapper extensions from the specified directory
     
-    Each Python file in the directory will be loaded, and any NodeMapper subclasses
-    defined in those files will be automatically registered.
+    Extension files should define a NODE_MAPPERS_EXT dictionary containing mapper configurations.
+    These will be added to the global NODE_MAPPERS dictionary and registered automatically.
     """
     # Use default path if none provided
     if ext_dir is None:
@@ -411,18 +265,18 @@ def load_extensions(ext_dir: str = None) -> None:
                     module = importlib.util.module_from_spec(spec)
                     spec.loader.exec_module(module)
                     
-                    # Find all NodeMapper subclasses in the module
-                    for name, obj in inspect.getmembers(module):
-                        if (inspect.isclass(obj) and issubclass(obj, NodeMapper) 
-                                and obj != NodeMapper and hasattr(obj, 'node_type')):
-                            # Instantiate and register the mapper
-                            mapper = obj()
-                            register_mapper(mapper)
-                            logger.info(f"Loaded extension mapper: {mapper.node_type} from {filename}")
-            
+                    # Check if the module defines NODE_MAPPERS_EXT
+                    if hasattr(module, 'NODE_MAPPERS_EXT'):
+                        # Add the extension mappers to the global NODE_MAPPERS dictionary
+                        NODE_MAPPERS.update(module.NODE_MAPPERS_EXT)
+                        logger.info(f"Added {len(module.NODE_MAPPERS_EXT)} mappers from extension: {filename}")
+                    else:
+                        logger.warning(f"Extension {filename} does not define NODE_MAPPERS_EXT dictionary")
             except Exception as e:
                 logger.warning(f"Error loading extension {filename}: {e}")
-
+    
+    # Re-register all mappers after loading extensions
+    register_all_mappers()
 
 # Initialize the registry with default mappers
-register_default_mappers() 
+# register_default_mappers() 

py/workflow/parser.py

@@ -4,7 +4,7 @@ Main workflow parser implementation for ComfyUI
 import json
 import logging
 from typing import Dict, List, Any, Optional, Union, Set
-from .mappers import get_mapper, get_all_mappers, load_extensions
+from .mappers import get_mapper, get_all_mappers, load_extensions, process_node
 from .utils import (
     load_workflow, save_output, find_node_by_type,
     trace_model_path
@@ -15,14 +15,13 @@ logger = logging.getLogger(__name__)
 class WorkflowParser:
     """Parser for ComfyUI workflows"""
     
-    def __init__(self, load_extensions_on_init: bool = True):
+    def __init__(self):
         """Initialize the parser with mappers"""
         self.processed_nodes: Set[str] = set()  # Track processed nodes to avoid cycles
         self.node_results_cache: Dict[str, Any] = {}  # Cache for processed node results
         
-        # Load extensions if requested
-        if load_extensions_on_init:
-            load_extensions()
+        # Load extensions
+        load_extensions()
     
     def process_node(self, node_id: str, workflow: Dict) -> Any:
         """Process a single node and extract relevant information"""
@@ -45,10 +44,9 @@ class WorkflowParser:
         node_type = node_data.get("class_type")
         
         result = None
-        mapper = get_mapper(node_type)
-        if mapper:
+        if get_mapper(node_type):
             try:
-                result = mapper.process(node_id, node_data, workflow, self)
+                result = process_node(node_id, node_data, workflow, self)
                 # Cache the result
                 self.node_results_cache[node_id] = result
             except Exception as e:
@@ -60,32 +58,58 @@ class WorkflowParser:
         self.processed_nodes.remove(node_id)
         return result
     
-    def collect_loras_from_model(self, model_input: List, workflow: Dict) -> str:
-        """Collect loras information from the model node chain"""
-        if not isinstance(model_input, list) or len(model_input) != 2:
-            return ""
-            
-        model_node_id, _ = model_input
-        # Convert node_id to string if it's an integer
-        if isinstance(model_node_id, int):
-            model_node_id = str(model_node_id)
-            
-        # Process the model node
-        model_result = self.process_node(model_node_id, workflow)
+    def find_primary_sampler_node(self, workflow: Dict) -> Optional[str]:
+        """
+        Find the primary sampler node in the workflow.
         
-        # If this is a Lora Loader node, return the loras text
-        if model_result and isinstance(model_result, dict) and "loras" in model_result:
-            return model_result["loras"]
-            
-        # If not a lora loader, check the node's inputs for a model connection
-        node_data = workflow.get(model_node_id, {})
-        inputs = node_data.get("inputs", {})
+        Priority:
+        1. First try to find a SamplerCustomAdvanced node
+        2. If not found, look for KSampler nodes with denoise=1.0
+        3. If still not found, use the first KSampler node
         
-        # If this node has a model input, follow that path
-        if "model" in inputs and isinstance(inputs["model"], list):
-            return self.collect_loras_from_model(inputs["model"], workflow)
+        Args:
+            workflow: The workflow data as a dictionary
             
-        return ""
+        Returns:
+            The node ID of the primary sampler node, or None if not found
+        """
+        # First check for SamplerCustomAdvanced nodes
+        sampler_advanced_nodes = []
+        ksampler_nodes = []
+        
+        # Scan workflow for sampler nodes
+        for node_id, node_data in workflow.items():
+            node_type = node_data.get("class_type")
+            
+            if node_type == "SamplerCustomAdvanced":
+                sampler_advanced_nodes.append(node_id)
+            elif node_type == "KSampler":
+                ksampler_nodes.append(node_id)
+        
+        # If we found SamplerCustomAdvanced nodes, return the first one
+        if sampler_advanced_nodes:
+            logger.debug(f"Found SamplerCustomAdvanced node: {sampler_advanced_nodes[0]}")
+            return sampler_advanced_nodes[0]
+        
+        # If we have KSampler nodes, look for one with denoise=1.0
+        if ksampler_nodes:
+            for node_id in ksampler_nodes:
+                node_data = workflow[node_id]
+                inputs = node_data.get("inputs", {})
+                denoise = inputs.get("denoise", 0)
+                
+                # Check if denoise is 1.0 (allowing for small floating point differences)
+                if abs(float(denoise) - 1.0) < 0.001:
+                    logger.debug(f"Found KSampler node with denoise=1.0: {node_id}")
+                    return node_id
+            
+            # If no KSampler with denoise=1.0 found, use the first one
+            logger.debug(f"No KSampler with denoise=1.0 found, using first KSampler: {ksampler_nodes[0]}")
+            return ksampler_nodes[0]
+        
+        # No sampler nodes found
+        logger.warning("No sampler nodes found in workflow")
+        return None
     
     def parse_workflow(self, workflow_data: Union[str, Dict], output_path: Optional[str] = None) -> Dict:
         """
@@ -108,77 +132,38 @@ class WorkflowParser:
         self.processed_nodes = set()
         self.node_results_cache = {}
         
-        # Find the KSampler node
-        ksampler_node_id = find_node_by_type(workflow, "KSampler")
-        if not ksampler_node_id:
-            logger.warning("No KSampler node found in workflow")
+        # Find the primary sampler node
+        sampler_node_id = self.find_primary_sampler_node(workflow)
+        if not sampler_node_id:
+            logger.warning("No suitable sampler node found in workflow")
             return {}
         
-        # Start parsing from the KSampler node
-        result = {
-            "gen_params": {},
-            "loras": ""
-        }
+        # Process sampler node to extract parameters
+        sampler_result = self.process_node(sampler_node_id, workflow)
+        if not sampler_result:
+            return {}
         
-        # Process KSampler node to extract parameters
-        ksampler_result = self.process_node(ksampler_node_id, workflow)
-        if ksampler_result:
-            # Process the result
-            for key, value in ksampler_result.items():
-                # Special handling for the positive prompt from FluxGuidance
-                if key == "positive" and isinstance(value, dict):
-                    # Extract guidance value
-                    if "guidance" in value:
-                        result["gen_params"]["guidance"] = value["guidance"]
-                    
-                    # Extract prompt
-                    if "prompt" in value:
-                        result["gen_params"]["prompt"] = value["prompt"]
-                else:
-                    # Normal handling for other values
-                    result["gen_params"][key] = value
-        
-        # Process the positive prompt node if it exists and we don't have a prompt yet
-        if "prompt" not in result["gen_params"] and "positive" in ksampler_result:
-            positive_value = ksampler_result.get("positive")
-            if isinstance(positive_value, str):
-                result["gen_params"]["prompt"] = positive_value
-        
-        # Manually check for FluxGuidance if we don't have guidance value
-        if "guidance" not in result["gen_params"]:
-            flux_node_id = find_node_by_type(workflow, "FluxGuidance")
-            if flux_node_id:
-                # Get the direct input from the node
-                node_inputs = workflow[flux_node_id].get("inputs", {})
-                if "guidance" in node_inputs:
-                    result["gen_params"]["guidance"] = node_inputs["guidance"]
-        
-        # Extract loras from the model input of KSampler
-        ksampler_node = workflow.get(ksampler_node_id, {})
-        ksampler_inputs = ksampler_node.get("inputs", {})
-        if "model" in ksampler_inputs and isinstance(ksampler_inputs["model"], list):
-            loras_text = self.collect_loras_from_model(ksampler_inputs["model"], workflow)
-            if loras_text:
-                result["loras"] = loras_text
+        # Return the sampler result directly - it's already in the format we need
+        # This simplifies the structure and makes it easier to use in recipe_routes.py
         
         # Handle standard ComfyUI names vs our output format
-        if "cfg" in result["gen_params"]:
-            result["gen_params"]["cfg_scale"] = result["gen_params"].pop("cfg")
+        if "cfg" in sampler_result:
+            sampler_result["cfg_scale"] = sampler_result.pop("cfg")
             
-        # Add clip_skip = 2 to match reference output if not already present
-        if "clip_skip" not in result["gen_params"]:
-            result["gen_params"]["clip_skip"] = "2"
+        # Add clip_skip = 1 to match reference output if not already present
+        if "clip_skip" not in sampler_result:
+            sampler_result["clip_skip"] = "1"
         
         # Ensure the prompt is a string and not a nested dictionary
-        if "prompt" in result["gen_params"] and isinstance(result["gen_params"]["prompt"], dict):
-            if "prompt" in result["gen_params"]["prompt"]:
-                result["gen_params"]["prompt"] = result["gen_params"]["prompt"]["prompt"]
+        if "prompt" in sampler_result and isinstance(sampler_result["prompt"], dict):
+            if "prompt" in sampler_result["prompt"]:
+                sampler_result["prompt"] = sampler_result["prompt"]["prompt"]
         
         # Save the result if requested
         if output_path:
-            save_output(result, output_path)
+            save_output(sampler_result, output_path)
             
-        return result
+        return sampler_result
 
 
 def parse_workflow(workflow_path: str, output_path: Optional[str] = None) -> Dict:
@@ -193,4 +178,4 @@ def parse_workflow(workflow_path: str, output_path: Optional[str] = None) -> Dic
         Dictionary containing extracted parameters
     """
     parser = WorkflowParser()
-    return parser.parse_workflow(workflow_path, output_path) 
+    return parser.parse_workflow(workflow_path, output_path)

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 = "0.8.0"
+version = "0.8.3"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",

refs/civitai_api_model_by_versionId.json

@@ -95,7 +95,6 @@
             "onSite": false,
             "remixOfId": null
         }
-        // more images here
     ],
     "downloadUrl": "https://civitai.com/api/download/models/1387174"
 }

refs/civitai_comfy_metadata.json

@@ -0,0 +1,153 @@
+{
+    "resource-stack": {
+      "class_type": "CheckpointLoaderSimple",
+      "inputs": { "ckpt_name": "urn:air:sdxl:checkpoint:civitai:827184@1410435" }
+    },
+    "resource-stack-1": {
+      "class_type": "LoraLoader",
+      "inputs": {
+        "lora_name": "urn:air:sdxl:lora:civitai:1107767@1253442",
+        "strength_model": 1,
+        "strength_clip": 1,
+        "model": ["resource-stack", 0],
+        "clip": ["resource-stack", 1]
+      }
+    },
+    "resource-stack-2": {
+      "class_type": "LoraLoader",
+      "inputs": {
+        "lora_name": "urn:air:sdxl:lora:civitai:1342708@1516344",
+        "strength_model": 1,
+        "strength_clip": 1,
+        "model": ["resource-stack-1", 0],
+        "clip": ["resource-stack-1", 1]
+      }
+    },
+    "resource-stack-3": {
+      "class_type": "LoraLoader",
+      "inputs": {
+        "lora_name": "urn:air:sdxl:lora:civitai:122359@135867",
+        "strength_model": 1.55,
+        "strength_clip": 1,
+        "model": ["resource-stack-2", 0],
+        "clip": ["resource-stack-2", 1]
+      }
+    },
+    "6": {
+      "class_type": "smZ CLIPTextEncode",
+      "inputs": {
+        "text": "masterpiece, best quality, amazing quality, detailed setting, detailed background, 1girl, yunyun (konosuba), nude, red eyes, hair ornament, braid, hair between eyes,low twintails, pink ribbon, bow, hair bow, pussy, frilled skirt, layered skirt, belt, pink thighhighs, (pussy juice), large insertion, vaginal tugging, pussy grip, detailed skin, detailed soles, stretched pussy, feet in stockings, ass, nipples, medium breasts, french kiss, anus, shocked, nervous, penis awe,  BREAK Professor\u0027s office, college student, pornographic, 1boy, close eyes, (musscular male, detailed large cock), vaginal sex, college office setting, ass grab, fucking, riding, cowgirl, erotic, side view, deep fucking",
+        "parser": "comfy",
+        "text_g": "",
+        "text_l": "",
+        "ascore": 2.5,
+        "width": 0,
+        "height": 0,
+        "crop_w": 0,
+        "crop_h": 0,
+        "target_width": 0,
+        "target_height": 0,
+        "smZ_steps": 1,
+        "mean_normalization": true,
+        "multi_conditioning": true,
+        "use_old_emphasis_implementation": false,
+        "with_SDXL": false,
+        "clip": ["resource-stack-3", 1]
+      },
+      "_meta": { "title": "Positive" }
+    },
+    "7": {
+      "class_type": "smZ CLIPTextEncode",
+      "inputs": {
+        "text": "bad quality,worst quality,worst detail,sketch,censor",
+        "parser": "comfy",
+        "text_g": "",
+        "text_l": "",
+        "ascore": 2.5,
+        "width": 0,
+        "height": 0,
+        "crop_w": 0,
+        "crop_h": 0,
+        "target_width": 0,
+        "target_height": 0,
+        "smZ_steps": 1,
+        "mean_normalization": true,
+        "multi_conditioning": true,
+        "use_old_emphasis_implementation": false,
+        "with_SDXL": false,
+        "clip": ["resource-stack-3", 1]
+      },
+      "_meta": { "title": "Negative" }
+    },
+    "20": {
+      "class_type": "UpscaleModelLoader",
+      "inputs": { "model_name": "urn:air:other:upscaler:civitai:147759@164821" },
+      "_meta": { "title": "Load Upscale Model" }
+    },
+    "17": {
+      "class_type": "LoadImage",
+      "inputs": {
+        "image": "https://orchestration.civitai.com/v2/consumer/blobs/5KZ6358TW8CNEGPZKD08NVDB30",
+        "upload": "image"
+      },
+      "_meta": { "title": "Image Load" }
+    },
+    "19": {
+      "class_type": "ImageUpscaleWithModel",
+      "inputs": { "upscale_model": ["20", 0], "image": ["17", 0] },
+      "_meta": { "title": "Upscale Image (using Model)" }
+    },
+    "23": {
+      "class_type": "ImageScale",
+      "inputs": {
+        "upscale_method": "nearest-exact",
+        "crop": "disabled",
+        "width": 1280,
+        "height": 1856,
+        "image": ["19", 0]
+      },
+      "_meta": { "title": "Upscale Image" }
+    },
+    "21": {
+      "class_type": "VAEEncode",
+      "inputs": { "pixels": ["23", 0], "vae": ["resource-stack", 2] },
+      "_meta": { "title": "VAE Encode" }
+    },
+    "11": {
+      "class_type": "KSampler",
+      "inputs": {
+        "sampler_name": "euler_ancestral",
+        "scheduler": "normal",
+        "seed": 2088370631,
+        "steps": 47,
+        "cfg": 6.5,
+        "denoise": 0.3,
+        "model": ["resource-stack-3", 0],
+        "positive": ["6", 0],
+        "negative": ["7", 0],
+        "latent_image": ["21", 0]
+      },
+      "_meta": { "title": "KSampler" }
+    },
+    "13": {
+      "class_type": "VAEDecode",
+      "inputs": { "samples": ["11", 0], "vae": ["resource-stack", 2] },
+      "_meta": { "title": "VAE Decode" }
+    },
+    "12": {
+      "class_type": "SaveImage",
+      "inputs": { "filename_prefix": "ComfyUI", "images": ["13", 0] },
+      "_meta": { "title": "Save Image" }
+    },
+    "extra": {
+      "airs": [
+        "urn:air:other:upscaler:civitai:147759@164821",
+        "urn:air:sdxl:checkpoint:civitai:827184@1410435",
+        "urn:air:sdxl:lora:civitai:1107767@1253442",
+        "urn:air:sdxl:lora:civitai:1342708@1516344",
+        "urn:air:sdxl:lora:civitai:122359@135867"
+      ]
+    },
+    "extraMetadata": "{\u0022prompt\u0022:\u0022masterpiece, best quality, amazing quality, detailed setting, detailed background, 1girl, yunyun (konosuba), nude, red eyes, hair ornament, braid, hair between eyes,low twintails, pink ribbon, bow, hair bow, pussy, frilled skirt, layered skirt, belt, pink thighhighs, (pussy juice), large insertion, vaginal tugging, pussy grip, detailed skin, detailed soles, stretched pussy, feet in stockings, ass, nipples, medium breasts, french kiss, anus, shocked, nervous, penis awe,  BREAK Professor\u0027s office, college student, pornographic, 1boy, close eyes, (musscular male, detailed large cock), vaginal sex, college office setting, ass grab, fucking, riding, cowgirl, erotic, side view, deep fucking\u0022,\u0022negativePrompt\u0022:\u0022bad quality,worst quality,worst detail,sketch,censor\u0022,\u0022steps\u0022:47,\u0022cfgScale\u0022:6.5,\u0022sampler\u0022:\u0022euler_ancestral\u0022,\u0022workflowId\u0022:\u0022img2img-hires\u0022,\u0022resources\u0022:[{\u0022modelVersionId\u0022:1410435,\u0022strength\u0022:1},{\u0022modelVersionId\u0022:1410435,\u0022strength\u0022:1},{\u0022modelVersionId\u0022:1253442,\u0022strength\u0022:1},{\u0022modelVersionId\u0022:1516344,\u0022strength\u0022:1},{\u0022modelVersionId\u0022:135867,\u0022strength\u0022:1.55}],\u0022remixOfId\u0022:32140259}"
+  }
+  

refs/jpeg_civitai_exif_userComment_example

@@ -2,28 +2,17 @@ a dynamic and dramatic digital artwork featuring a stylized anthropomorphic whit
 Negative prompt:
 Steps: 30, Sampler: Undefined, CFG scale: 3.5, Seed: 90300501, Size: 832x1216, Clip skip: 2, Created Date: 2025-03-05T13:51:18.1770234Z, Civitai resources: [{"type":"checkpoint","modelVersionId":691639,"modelName":"FLUX","modelVersionName":"Dev"},{"type":"lora","weight":0.4,"modelVersionId":1202162,"modelName":"Velvet\u0027s Mythic Fantasy Styles | Flux \u002B Pony \u002B illustrious","modelVersionName":"Flux Gothic Lines"},{"type":"lora","weight":0.8,"modelVersionId":1470588,"modelName":"Velvet\u0027s Mythic Fantasy Styles | Flux \u002B Pony \u002B illustrious","modelVersionName":"Flux Retro"},{"type":"lora","weight":0.75,"modelVersionId":746484,"modelName":"Elden Ring - Yoshitaka Amano","modelVersionName":"V1"},{"type":"lora","weight":0.2,"modelVersionId":914935,"modelName":"Ink-style","modelVersionName":"ink-dynamic"},{"type":"lora","weight":0.2,"modelVersionId":1189379,"modelName":"Painterly Fantasy by ChronoKnight - [FLUX \u0026 IL]","modelVersionName":"FLUX"},{"type":"lora","weight":0.2,"modelVersionId":757030,"modelName":"Mezzotint Artstyle for Flux - by Ethanar","modelVersionName":"V1"}], Civitai metadata: {}
 
-<lora:ck-shadow-circuit-IL:0.78>,
 masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject,
 dynamic angle, dutch angle, from below, epic half body portrait, gritty, wabi sabi, looking at viewer, woman is a geisha, parted lips,
-holographic skin, holofoil glitter, faint, glowing, ethereal, neon hair, glowing hair, otherworldly glow, she is dangerous,
-<lora:ck-nc-cyberpunk-IL-000011:0.4>
-<lora:ck-neon-retrowave-IL:0.2>
- <lora:ck-yoneyama-mai-IL-000014:0.4>
+holographic skin, holofoil glitter, faint, glowing, ethereal, neon hair, glowing hair, otherworldly glow, she is dangerous
+<lora:ck-shadow-circuit-IL:0.78>, <lora:ck-nc-cyberpunk-IL-000011:0.4>, <lora:ck-neon-retrowave-IL:0.2>, <lora:ck-yoneyama-mai-IL-000014:0.4>
 Negative prompt: score_6, score_5, score_4, bad quality, worst quality, worst detail, sketch, censorship, furry, window, headphones,
 Steps: 30, Sampler: Euler a, Schedule type: Simple, CFG scale: 7, Seed: 1405717592, Size: 832x1216, Model hash: 1ad6ca7f70, Model: waiNSFWIllustrious_v100, Denoising strength: 0.35, Hires CFG Scale: 5, Hires upscale: 1.3, Hires steps: 20, Hires upscaler: 4x-AnimeSharp, Lora hashes: "ck-shadow-circuit-IL: 88e247aa8c3d, ck-nc-cyberpunk-IL-000011: 935e6755554c, ck-neon-retrowave-IL: edafb9df7da1, ck-yoneyama-mai-IL-000014: 1b9305692a2e", Version: f2.0.1v1.10.1-1.10.1, Diffusion in Low Bits: Automatic (fp16 LoRA)
 
-masterpiece, best quality,high quality, newest, highres,8K,HDR,absurdres, 1girl, solo, steampunk aesthetic, mechanical monocle, long trench coat, leather gloves, brass accessories, intricate clockwork rifle, aiming at viewer, wind-blown scarf, high boots, fingerless gloves, pocket watch, corset, brown and gold color scheme, industrial cityscape, smoke and gears, atmospheric lighting, depth of field, dynamic pose, dramatic composition, detailed background, foreshortening, detailed background, dynamic pose, dynamic composition,dutch angle, detailed backgroud,foreshortening,blurry edges <lora:iLLMythAn1m3Style:1> MythAn1m3
-Negative prompt: worst quality, normal quality, anatomical nonsense, bad anatomy,interlocked fingers, extra fingers,watermark,simple background, loli,
-Steps: 35, Sampler: DPM++ 2M SDE, Schedule type: Karras, CFG scale: 4, Seed: 3537159932, Size: 1072x1376, Model hash: c364bbdae9, Model: waiNSFWIllustrious_v110, Clip skip: 2, ADetailer model: face_yolov8n.pt, ADetailer confidence: 0.3, ADetailer dilate erode: 4, ADetailer mask blur: 4, ADetailer denoising strength: 0.4, ADetailer inpaint only masked: True, ADetailer inpaint padding: 32, ADetailer version: 24.8.0, Lora hashes: "iLLMythAn1m3Style: d3480076057b", Version: f2.0.1v1.10.1-previous-519-g44eb4ea8, Module 1: sdxl.vae
-
 Masterpiece, best quality, high quality, newest, highres, 8K, HDR, absurdres, 1girl, solo, futuristic warrior, sleek exosuit with glowing energy cores, long braided hair flowing behind, gripping a high-tech bow with an energy arrow drawn, standing on a floating platform overlooking a massive space station, planets and nebulae in the distance, soft glow from distant stars, cinematic depth, foreshortening, dynamic pose, dramatic sci-fi lighting.
 Negative prompt: worst quality, normal quality, anatomical nonsense, bad anatomy,interlocked fingers, extra fingers,watermark,simple background, loli,
 Steps: 20, Sampler: euler_ancestral_karras, CFG scale: 8.0, Seed: 691121152183439, Model: il\waiNSFWIllustrious_v110.safetensors, Model hash: c3688ee04c, Lora_0 Model name: iLLMythAn1m3Style.safetensors, Lora_0 Model hash: ba7a040786, Lora_0 Strength model: 1.0, Lora_0 Strength clip: 1.0, Hashes: {"model": "c3688ee04c", "lora:iLLMythAn1m3Style": "ba7a040786"}
 
-Masterpiece, best quality, high quality, newest, highres, 8K, HDR, absurdres, 1boy, solo, gothic horror, pale vampire lord in regal, intricately detailed robes, crimson eyes glowing under the dim candlelight of a grand but decayed castle hall, holding a silver goblet filled with an unknown substance, a massive stained-glass window shattered behind him, cold mist rolling in, dramatic lighting, dark yet elegant aesthetic, foreshortening, cinematic perspective.
-Negative prompt: worst quality, normal quality, anatomical nonsense, bad anatomy,interlocked fingers, extra fingers,watermark,simple background, loli,
-Steps: 20, Sampler: euler_ancestral_karras, CFG scale: 8.0, Seed: 290117945770094, Model: il\waiNSFWIllustrious_v110.safetensors, Model hash: c3688ee04c, Lora_0 Model name: iLLMythAn1m3Style.safetensors, Lora_0 Model hash: ba7a040786, Lora_0 Strength model: 0.6, Lora_0 Strength clip: 0.7000000000000001, Hashes: {"model": "c3688ee04c", "lora:iLLMythAn1m3Style": "ba7a040786"}
-
-bo-exposure, An impressionistic oil painting in the style of J.M.W. Turner, depicting a ghostly ship sailing through a sea of swirling golden mist. The waves crash and dissolve into abstract, fiery strokes of orange and deep indigo, blurring the line between ocean and sky. The ship appears almost ethereal, as if drifting between worlds, lost in the ever-changing tides of memory and myth. The dynamic brushstrokes capture the relentless power of nature and the fleeting essence of time.
+Immerse yourself in the enchanting journey, where harmonious transmutation of Bauhaus art unites photographic precision and contemporary illustration, capturing an enthralling blend between vivid abstract nature and urban landscapes. Let your eyes be captivated by a kaleidoscope of rich, deep reds and yellows, entwined with intriguing shades that beckon a somber atmosphere. As your spirit ventures along this haunting path, witness the mysterious, high-angle perspective dominated by scattered clouds – granting you a mesmerizing glimpse into the ever-transforming realm of metamorphosing environments. ,<lora:flux/fav/ck-charcoal-drawing-000014.safetensors:1.0:1.0>   
 Negative prompt:
-Steps: 25, Sampler: DPM++ 2M, CFG scale: 3.5, Seed: 1024252061321625, Size: 832x1216, Clip skip: 1, Model hash: , Model: flux_dev, Hashes: {"model": ""}, Version: ComfyUI
+Steps: 20, Sampler: Euler, CFG scale: 3.5, Seed: 885491426361006, Size: 832x1216, Model hash: 4610115bb0, Model: flux_dev, Hashes: {"LORA:flux/fav/ck-charcoal-drawing-000014.safetensors": "34d36c17c1", "model": "4610115bb0"}, Version: ComfyUI

refs/meta_format.txt

@@ -0,0 +1,3 @@
+In this ethereal masterpiece, metallic sculptures juxtapose effortlessly against a subtle backdrop of misty neutral hues. Exquisite curvatures and geometric shapes converge harmoniously, creating an illuminating realm of polished metallic surfaces. Shimmering copper, gleaming silver, and lustrous gold hues dance in perfect balance, highlighting the intricate play of light and shadow cast upon these celestial forms.  A halo of diffused radiance envelops each piece, enhancing their textured depths and metallic brilliance while allowing delicate details to emerge from obscurity. The composition conveys a serene yet mesmerizing atmosphere, as if suspended in a dreamlike limbo between reality and fantasy. The tantalizing interplay of colors within this transcendent realm creates a profound sense of depth and grandeur that invites the viewer into an enchanting voyage through abstract metallic beauty.  This captivating artwork evokes emotions of boundless curiosity and reverence reminiscent of the timeless works by artists such as Giorgio de Chirico or Paul Klee, while asserting a unique, modern artistic sensibility. With every observation, a new nuance unfolds, as if a never-ending story waiting to be discovered through the lens of metallic artistry.
+Negative prompt:
+Steps: 25, Sampler: dpmpp_2m_sgm_uniform, Seed: 471889513588087, Model: Fluxmania V5P.safetensors, Model hash: 8ae0583b06, VAE: ae.sft, VAE hash: afc8e28272, Lora_0 Model name: ArtVador I.safetensors, Lora_0 Model hash: 08f7133a58, Lora_0 Strength model: 0.65, Lora_0 Strength clip: 0.65, Lora_1 Model name: Kaoru Yamada.safetensors, Lora_1 Model hash: d4893f7202, Lora_1 Strength model: 0.75, Lora_1 Strength clip: 0.75, Hashes: {"model": "8ae0583b06", "vae": "afc8e28272", "lora:ArtVador I": "08f7133a58", "lora:Kaoru Yamada": "d4893f7202"}

refs/output.json

@@ -1,13 +1,11 @@
 {
     "loras": "<lora:ck-neon-retrowave-IL-000012:0.8> <lora:aorunIllstrious:1> <lora:ck-shadow-circuit-IL-000012:0.78> <lora:MoriiMee_Gothic_Niji_Style_Illustrious_r1:0.45> <lora:ck-nc-cyberpunk-IL-000011:0.4>",
-    "gen_params": {
-        "prompt": "in the style of ck-rw, aorun, scales, makeup, bare shoulders, pointy ears, dress, claws, in the style of cksc, artist:moriimee, in the style of cknc, masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject, close up, stylized, in gold and neon shades, wabi sabi, 1girl, rainbow angel wings, looking at viewer, dynamic angle, from below, from side, relaxing",
-        "negative_prompt": "bad quality, worst quality, worst detail, sketch ,signature, watermark, patreon logo, nsfw",
-        "steps": "20",
-        "sampler": "euler_ancestral",
-        "cfg_scale": "8",
-        "seed": "241",
-        "size": "832x1216",
-        "clip_skip": "2"
-    }
+    "prompt": "in the style of ck-rw, aorun, scales, makeup, bare shoulders, pointy ears, dress, claws, in the style of cksc, artist:moriimee, in the style of cknc, masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject, close up, stylized, in gold and neon shades, wabi sabi, 1girl, rainbow angel wings, looking at viewer, dynamic angle, from below, from side, relaxing",
+    "negative_prompt": "bad quality, worst quality, worst detail, sketch ,signature, watermark, patreon logo, nsfw",
+    "steps": "20",
+    "sampler": "euler_ancestral",
+    "cfg_scale": "8",
+    "seed": "241",
+    "size": "832x1216",
+    "clip_skip": "2"
 }

refs/prompt.json

@@ -1,75 +1,12 @@
 {
-    "3": {
-        "inputs": {
-            "seed": 241,
-            "steps": 20,
-            "cfg": 8,
-            "sampler_name": "euler_ancestral",
-            "scheduler": "karras",
-            "denoise": 1,
-            "model": [
-                "56",
-                0
-            ],
-            "positive": [
-                "6",
-                0
-            ],
-            "negative": [
-                "7",
-                0
-            ],
-            "latent_image": [
-                "5",
-                0
-            ]
-        },
-        "class_type": "KSampler",
-        "_meta": {
-            "title": "KSampler"
-        }
-    },
-    "4": {
-        "inputs": {
-            "ckpt_name": "il\\waiNSFWIllustrious_v110.safetensors"
-        },
-        "class_type": "CheckpointLoaderSimple",
-        "_meta": {
-            "title": "Load Checkpoint"
-        }
-    },
-    "5": {
-        "inputs": {
-            "width": 832,
-            "height": 1216,
-            "batch_size": 1
-        },
-        "class_type": "EmptyLatentImage",
-        "_meta": {
-            "title": "Empty Latent Image"
-        }
-    },
     "6": {
         "inputs": {
             "text": [
-                "22",
+                "301",
                 0
             ],
             "clip": [
-                "56",
-                1
-            ]
-        },
-        "class_type": "CLIPTextEncode",
-        "_meta": {
-            "title": "CLIP Text Encode (Prompt)"
-        }
-    },
-    "7": {
-        "inputs": {
-            "text": "bad quality, worst quality, worst detail, sketch ,signature, watermark, patreon logo, nsfw",
-            "clip": [
-                "56",
+                "299",
                 1
             ]
         },
@@ -81,12 +18,12 @@
     "8": {
         "inputs": {
             "samples": [
-                "3",
-                0
+                "13",
+                1
             ],
             "vae": [
-                "4",
-                2
+                "10",
+                0
             ]
         },
         "class_type": "VAEDecode",
@@ -94,7 +31,230 @@
             "title": "VAE Decode"
         }
     },
-    "14": {
+    "10": {
+        "inputs": {
+            "vae_name": "flux1\\ae.safetensors"
+        },
+        "class_type": "VAELoader",
+        "_meta": {
+            "title": "Load VAE"
+        }
+    },
+    "11": {
+        "inputs": {
+            "clip_name1": "t5xxl_fp8_e4m3fn.safetensors",
+            "clip_name2": "ViT-L-14-TEXT-detail-improved-hiT-GmP-TE-only-HF.safetensors",
+            "type": "flux",
+            "device": "default"
+        },
+        "class_type": "DualCLIPLoader",
+        "_meta": {
+            "title": "DualCLIPLoader"
+        }
+    },
+    "13": {
+        "inputs": {
+            "noise": [
+                "147",
+                0
+            ],
+            "guider": [
+                "22",
+                0
+            ],
+            "sampler": [
+                "16",
+                0
+            ],
+            "sigmas": [
+                "17",
+                0
+            ],
+            "latent_image": [
+                "48",
+                0
+            ]
+        },
+        "class_type": "SamplerCustomAdvanced",
+        "_meta": {
+            "title": "SamplerCustomAdvanced"
+        }
+    },
+    "16": {
+        "inputs": {
+            "sampler_name": "dpmpp_2m"
+        },
+        "class_type": "KSamplerSelect",
+        "_meta": {
+            "title": "KSamplerSelect"
+        }
+    },
+    "17": {
+        "inputs": {
+            "scheduler": "beta",
+            "steps": [
+                "246",
+                0
+            ],
+            "denoise": 1,
+            "model": [
+                "28",
+                0
+            ]
+        },
+        "class_type": "BasicScheduler",
+        "_meta": {
+            "title": "BasicScheduler"
+        }
+    },
+    "22": {
+        "inputs": {
+            "model": [
+                "28",
+                0
+            ],
+            "conditioning": [
+                "29",
+                0
+            ]
+        },
+        "class_type": "BasicGuider",
+        "_meta": {
+            "title": "BasicGuider"
+        }
+    },
+    "28": {
+        "inputs": {
+            "max_shift": 1.1500000000000001,
+            "base_shift": 0.5,
+            "width": [
+                "48",
+                1
+            ],
+            "height": [
+                "48",
+                2
+            ],
+            "model": [
+                "299",
+                0
+            ]
+        },
+        "class_type": "ModelSamplingFlux",
+        "_meta": {
+            "title": "ModelSamplingFlux"
+        }
+    },
+    "29": {
+        "inputs": {
+            "guidance": 3.5,
+            "conditioning": [
+                "6",
+                0
+            ]
+        },
+        "class_type": "FluxGuidance",
+        "_meta": {
+            "title": "FluxGuidance"
+        }
+    },
+    "48": {
+        "inputs": {
+            "resolution": "832x1216 (0.68)",
+            "batch_size": 1,
+            "width_override": 0,
+            "height_override": 0
+        },
+        "class_type": "SDXLEmptyLatentSizePicker+",
+        "_meta": {
+            "title": "🔧 SDXL Empty Latent Size Picker"
+        }
+    },
+    "65": {
+        "inputs": {
+            "unet_name": "flux\\flux1-dev-fp8-e4m3fn.safetensors",
+            "weight_dtype": "fp8_e4m3fn_fast"
+        },
+        "class_type": "UNETLoader",
+        "_meta": {
+            "title": "Load Diffusion Model"
+        }
+    },
+    "147": {
+        "inputs": {
+            "noise_seed": 651532572596956
+        },
+        "class_type": "RandomNoise",
+        "_meta": {
+            "title": "RandomNoise"
+        }
+    },
+    "148": {
+        "inputs": {
+            "wildcard_text": "__some-prompts__",
+            "populated_text": "A surreal digital artwork showcases a forward-thinking inventor captivated by his intricate mechanical creation through a large magnifying glass. Viewed from an unconventional perspective, the scene reveals an eccentric assembly of gears, springs, and brass instruments within his workshop. Soft, ethereal light radiates from the invention, casting enigmatic shadows on the walls as time appears to bend around its metallic form, invoking a sense of curiosity, wonder, and exhilaration in discovery.",
+            "mode": "fixed",
+            "seed": 553084268162351,
+            "Select to add Wildcard": "Select the Wildcard to add to the text"
+        },
+        "class_type": "ImpactWildcardProcessor",
+        "_meta": {
+            "title": "ImpactWildcardProcessor"
+        }
+    },
+    "151": {
+        "inputs": {
+            "text": "A hyper-realistic close-up portrait of a young woman with shoulder-length black hair styled in edgy, futuristic layers, adorned with glowing tips. She wears mecha eyewear with a neon green visor that transitions into iridescent shades of teal and gold. The frame is sleek, with angular edges and fine mechanical detailing. Her expression is fierce and confident, with flawless skin highlighted by the neon reflections. She wears a high-tech bodysuit with integrated LED lines and metallic panels. The background depicts a hazy rendition of The Great Wave off Kanagawa by Hokusai, its powerful waves blending seamlessly with the neon tones, amplifying her intense, defiant aura."
+        },
+        "class_type": "Text Multiline",
+        "_meta": {
+            "title": "Text Multiline"
+        }
+    },
+    "191": {
+        "inputs": {
+            "text": "A cinematic, oil painting masterpiece captures the essence of impressionistic surrealism, inspired by Claude Monet. A mysterious woman in a flowing crimson dress stands at the edge of a tranquil lake, where lily pads shimmer under an ethereal, golden twilight. The water’s surface reflects a dreamlike sky, its swirling hues of violet and sapphire melting together like liquid light. The thick, expressive brushstrokes lend depth to the scene, evoking a sense of nostalgia and quiet longing, as if the world itself is caught between reality and a fleeting dream.  \nA mesmerizing oil painting masterpiece inspired by Salvador Dalí, blending surrealism with post-impressionist texture. A lone violinist plays atop a melting clock tower, his form distorted by the passage of time. The sky is a cascade of swirling, liquid oranges and deep blues, where floating staircases spiral endlessly into the horizon. The impasto technique gives depth and movement to the surreal elements, making time itself feel fluid, as if the world is dissolving into a dream.  \nA stunning impressionistic oil painting evokes the spirit of Edvard Munch, capturing a solitary figure standing on a rain-soaked street, illuminated by the glow of flickering gas lamps. The swirling, chaotic strokes of deep blues and fiery reds reflect the turbulence of emotion, while the blurred reflections in the wet cobblestone suggest a merging of past and present. The faceless figure, draped in a dark overcoat, seems lost in thought, embodying the ephemeral nature of memory and time.  \nA breathtaking oil painting masterpiece, inspired by Gustav Klimt, presents a celestial ballroom where faceless dancers swirl in an eternal waltz beneath a gilded, star-speckled sky. Their golden garments shimmer with intricate patterns, blending into the opulent mosaic floor that seems to stretch into infinity. The dreamlike composition, rich in warm amber and deep sapphire hues, captures an otherworldly elegance, as if the dancers are suspended in a moment that transcends time.  \nA visionary oil painting inspired by Marc Chagall depicts a dreamlike cityscape where gravity ceases to exist. A couple floats above a crimson-tinted town, their forms dissolving into the swirling strokes of a vast, cerulean sky. The buildings below twist and bend in rhythmic motion, their windows glowing like tiny stars. The thick, textured brushwork conveys a sense of weightlessness and wonder, as if love itself has defied the laws of the universe.  \nAn impressionistic oil painting in the style of J.M.W. Turner, depicting a ghostly ship sailing through a sea of swirling golden mist. The waves crash and dissolve into abstract, fiery strokes of orange and deep indigo, blurring the line between ocean and sky. The ship appears almost ethereal, as if drifting between worlds, lost in the ever-changing tides of memory and myth. The dynamic brushstrokes capture the relentless power of nature and the fleeting essence of time.  \nA captivating oil painting masterpiece, infused with surrealist impressionism, portrays a grand library where books float midair, their pages unraveling into ribbons of light. The towering shelves twist into the heavens, vanishing into an infinite, starry void. A lone scholar, illuminated by the glow of a suspended lantern, reaches for a book that seems to pulse with life. The scene pulses with mystery, where the impasto textures bring depth to the interplay between knowledge and dreams.  \nA luminous impressionistic oil painting captures the melancholic beauty of an abandoned carnival, its faded carousel horses frozen mid-gallop beneath a sky of swirling lavender and gold. The wind carries fragments of forgotten laughter through the empty fairground, where scattered ticket stubs and crumbling banners whisper tales of joy long past. The thick, textured brushstrokes blend nostalgia with an eerie dreamlike quality, as if the carnival exists only in the echoes of memory.  \nA surreal oil painting in the spirit of René Magritte, featuring a towering lighthouse that emits not light, but cascading waterfalls from its peak. The swirling sky, painted in deep midnight blues, is punctuated by glowing, crescent moons that defy gravity. A lone figure stands at the water’s edge, gazing up in quiet contemplation, as if caught between wonder and the unknown. The painting’s rich textures and luminous colors create an enigmatic, dreamlike landscape.  \nA striking impressionistic oil painting, reminiscent of Van Gogh, portrays a lone traveler on a winding cobblestone path, their silhouette bathed in the golden glow of lantern-lit cherry blossoms. The petals swirl through the night air like glowing embers, blending with the deep, rhythmic strokes of a star-filled indigo sky. The scene captures a feeling of wistful solitude, as if the traveler is walking not only through the city, but through the fleeting nature of time itself."
+        },
+        "class_type": "Text Multiline",
+        "_meta": {
+            "title": "Text Multiline"
+        }
+    },
+    "203": {
+        "inputs": {
+            "string1": [
+                "289",
+                0
+            ],
+            "string2": [
+                "293",
+                0
+            ],
+            "delimiter": ", "
+        },
+        "class_type": "JoinStrings",
+        "_meta": {
+            "title": "Join Strings"
+        }
+    },
+    "208": {
+        "inputs": {
+            "file_path": "",
+            "dictionary_name": "[filename]",
+            "label": "TextBatch",
+            "mode": "automatic",
+            "index": 0,
+            "multiline_text": [
+                "191",
+                0
+            ]
+        },
+        "class_type": "Text Load Line From File",
+        "_meta": {
+            "title": "Text Load Line From File"
+        }
+    },
+    "226": {
         "inputs": {
             "images": [
                 "8",
@@ -106,60 +266,21 @@
             "title": "Preview Image"
         }
     },
-    "19": {
+    "246": {
         "inputs": {
-            "stop_at_clip_layer": -2,
-            "clip": [
-                "4",
-                1
-            ]
+            "value": 25
         },
-        "class_type": "CLIPSetLastLayer",
+        "class_type": "INTConstant",
         "_meta": {
-            "title": "CLIP Set Last Layer"
+            "title": "Steps"
         }
     },
-    "21": {
-        "inputs": {
-            "string": "masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject, close up, stylized, in gold and neon shades, wabi sabi, 1girl, rainbow angel wings, looking at viewer, dynamic angle, from below, from side, relaxing",
-            "strip_newlines": false
-        },
-        "class_type": "StringConstantMultiline",
-        "_meta": {
-            "title": "positive"
-        }
-    },
-    "22": {
-        "inputs": {
-            "string1": [
-                "55",
-                0
-            ],
-            "string2": [
-                "21",
-                0
-            ],
-            "delimiter": ", "
-        },
-        "class_type": "JoinStrings",
-        "_meta": {
-            "title": "Join Strings"
-        }
-    },
-    "55": {
+    "289": {
         "inputs": {
             "group_mode": true,
             "toggle_trigger_words": [
                 {
-                    "text": "in the style of ck-rw",
-                    "active": true
-                },
-                {
-                    "text": "in the style of cksc",
-                    "active": true
-                },
-                {
-                    "text": "artist:moriimee",
+                    "text": "bo-exposure",
                     "active": true
                 },
                 {
@@ -173,9 +294,9 @@
                     "_isDummy": true
                 }
             ],
-            "orinalMessage": "in the style of ck-rw,, in the style of cksc,, artist:moriimee",
+            "orinalMessage": "bo-exposure",
             "trigger_words": [
-                "56",
+                "299",
                 2
             ]
         },
@@ -184,25 +305,58 @@
             "title": "TriggerWord Toggle (LoraManager)"
         }
     },
-    "56": {
+    "293": {
         "inputs": {
-            "text": "<lora:ck-shadow-circuit-IL-000012:0.78> <lora:MoriiMee_Gothic_Niji_Style_Illustrious_r1:0.45> <lora:ck-nc-cyberpunk-IL-000011:0.4>",
+            "input": 1,
+            "text1": [
+                "208",
+                0
+            ],
+            "text2": [
+                "151",
+                0
+            ]
+        },
+        "class_type": "easy textSwitch",
+        "_meta": {
+            "title": "Text Switch"
+        }
+    },
+    "297": {
+        "inputs": {
+            "text": ""
+        },
+        "class_type": "Lora Stacker (LoraManager)",
+        "_meta": {
+            "title": "Lora Stacker (LoraManager)"
+        }
+    },
+    "298": {
+        "inputs": {
+            "anything": [
+                "297",
+                0
+            ]
+        },
+        "class_type": "easy showAnything",
+        "_meta": {
+            "title": "Show Any"
+        }
+    },
+    "299": {
+        "inputs": {
+            "text": "<lora:boFLUX Double Exposure Magic v2:0.8> <lora:FluxDFaeTasticDetails:0.65>",
             "loras": [
                 {
-                    "name": "ck-shadow-circuit-IL-000012",
-                    "strength": 0.78,
+                    "name": "boFLUX Double Exposure Magic v2",
+                    "strength": 0.8,
                     "active": true
                 },
                 {
-                    "name": "MoriiMee_Gothic_Niji_Style_Illustrious_r1",
-                    "strength": 0.45,
+                    "name": "FluxDFaeTasticDetails",
+                    "strength": 0.65,
                     "active": true
                 },
-                {
-                    "name": "ck-nc-cyberpunk-IL-000011",
-                    "strength": 0.4,
-                    "active": false
-                },
                 {
                     "name": "__dummy_item1__",
                     "strength": 0,
@@ -217,15 +371,15 @@
                 }
             ],
             "model": [
-                "4",
+                "65",
                 0
             ],
             "clip": [
-                "4",
-                1
+                "11",
+                0
             ],
             "lora_stack": [
-                "57",
+                "297",
                 0
             ]
         },
@@ -234,64 +388,14 @@
             "title": "Lora Loader (LoraManager)"
         }
     },
-    "57": {
+    "301": {
         "inputs": {
-            "text": "<lora:aorunIllstrious:1>",
-            "loras": [
-                {
-                    "name": "aorunIllstrious",
-                    "strength": "0.90",
-                    "active": false
-                },
-                {
-                    "name": "__dummy_item1__",
-                    "strength": 0,
-                    "active": false,
-                    "_isDummy": true
-                },
-                {
-                    "name": "__dummy_item2__",
-                    "strength": 0,
-                    "active": false,
-                    "_isDummy": true
-                }
-            ],
-            "lora_stack": [
-                "59",
-                0
-            ]
+            "string": "A hyper-realistic close-up portrait of a young woman with shoulder-length black hair styled in edgy, futuristic layers, adorned with glowing tips. She wears mecha eyewear with a neon green visor that transitions into iridescent shades of teal and gold. The frame is sleek, with angular edges and fine mechanical detailing. Her expression is fierce and confident, with flawless skin highlighted by the neon reflections. She wears a high-tech bodysuit with integrated LED lines and metallic panels. The background depicts a hazy rendition of The Great Wave off Kanagawa by Hokusai, its powerful waves blending seamlessly with the neon tones, amplifying her intense, defiant aura.",
+            "strip_newlines": true
         },
-        "class_type": "Lora Stacker (LoraManager)",
+        "class_type": "StringConstantMultiline",
         "_meta": {
-            "title": "Lora Stacker (LoraManager)"
-        }
-    },
-    "59": {
-        "inputs": {
-            "text": "<lora:ck-neon-retrowave-IL-000012:0.8>",
-            "loras": [
-                {
-                    "name": "ck-neon-retrowave-IL-000012",
-                    "strength": 0.8,
-                    "active": true
-                },
-                {
-                    "name": "__dummy_item1__",
-                    "strength": 0,
-                    "active": false,
-                    "_isDummy": true
-                },
-                {
-                    "name": "__dummy_item2__",
-                    "strength": 0,
-                    "active": false,
-                    "_isDummy": true
-                }
-            ]
-        },
-        "class_type": "Lora Stacker (LoraManager)",
-        "_meta": {
-            "title": "Lora Stacker (LoraManager)"
+            "title": "String Constant Multiline"
         }
     }
 }

simple_test.py

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

static/css/components/lora-modal.css

@@ -99,6 +99,7 @@
     width: 100%;
     background: var(--lora-surface);
     margin-bottom: var(--space-2);
+    overflow: hidden; /* Ensure metadata panel is contained */
 }
 
 .media-wrapper:last-child {
@@ -542,25 +543,53 @@
     display: flex;
     align-items: center;
     gap: 8px;
-    cursor: pointer;
     padding: 4px;
     border-radius: var(--border-radius-xs);
     transition: background-color 0.2s;
+    position: relative;
 }
 
 .file-name-wrapper:hover {
     background: oklch(var(--lora-accent) / 0.1);
 }
 
-.file-name-wrapper i {
-    color: var(--text-color);
-    opacity: 0.5;
-    transition: opacity 0.2s;
+.file-name-content {
+    padding: 2px 4px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid transparent;
+    flex: 1;
 }
 
-.file-name-wrapper:hover i {
-    opacity: 1;
-    color: var(--lora-accent);
+.file-name-wrapper.editing .file-name-content {
+    border: 1px solid var(--lora-accent);
+    background: var(--bg-color);
+    outline: none;
+}
+
+.edit-file-name-btn {
+    background: transparent;
+    border: none;
+    color: var(--text-color);
+    opacity: 0;
+    cursor: pointer;
+    padding: 2px 5px;
+    border-radius: var(--border-radius-xs);
+    transition: all 0.2s ease;
+    margin-left: var(--space-1);
+}
+
+.edit-file-name-btn.visible,
+.file-name-wrapper:hover .edit-file-name-btn {
+    opacity: 0.5;
+}
+
+.edit-file-name-btn:hover {
+    opacity: 0.8 !important;
+    background: rgba(0, 0, 0, 0.05);
+}
+
+[data-theme="dark"] .edit-file-name-btn:hover {
+    background: rgba(255, 255, 255, 0.05);
 }
 
 /* Base Model and Size combined styles */
@@ -573,6 +602,59 @@
     flex: 2;  /* 分配更多空间给base model */
 }
 
+/* Base model display and editing styles */
+.base-model-display {
+    display: flex;
+    align-items: center;
+    position: relative;
+}
+
+.base-model-content {
+    padding: 2px 4px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid transparent;
+    color: var(--text-color);
+    flex: 1;
+}
+
+.edit-base-model-btn {
+    background: transparent;
+    border: none;
+    color: var(--text-color);
+    opacity: 0;
+    cursor: pointer;
+    padding: 2px 5px;
+    border-radius: var(--border-radius-xs);
+    transition: all 0.2s ease;
+    margin-left: var(--space-1);
+}
+
+.edit-base-model-btn.visible,
+.base-model-display:hover .edit-base-model-btn {
+    opacity: 0.5;
+}
+
+.edit-base-model-btn:hover {
+    opacity: 0.8 !important;
+    background: rgba(0, 0, 0, 0.05);
+}
+
+[data-theme="dark"] .edit-base-model-btn:hover {
+    background: rgba(255, 255, 255, 0.05);
+}
+
+.base-model-selector {
+    width: 100%;
+    padding: 3px 5px;
+    background: var(--bg-color);
+    border: 1px solid var(--lora-accent);
+    border-radius: var(--border-radius-xs);
+    color: var(--text-color);
+    font-size: 0.9em;
+    outline: none;
+    margin-right: var(--space-1);
+}
+
 .size-wrapper {
     flex: 1;
     border-left: 1px solid var(--lora-border);
@@ -1030,4 +1112,172 @@
 /* Make sure media wrapper maintains position: relative for absolute positioning of children */
 .carousel .media-wrapper {
     position: relative;
+}
+
+/* Image Metadata Panel Styles */
+.image-metadata-panel {
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    right: 0;
+    background: var(--bg-color);
+    border-top: 1px solid var(--border-color);
+    padding: var(--space-2);
+    transform: translateY(100%);
+    transition: transform 0.3s cubic-bezier(0.175, 0.885, 0.32, 1.275), opacity 0.25s ease;
+    z-index: 5;
+    max-height: 50%; /* Reduced to take less space */
+    overflow-y: auto;
+    box-shadow: 0 -2px 8px rgba(0, 0, 0, 0.1);
+    opacity: 0;
+    pointer-events: none;
+}
+
+/* Show metadata panel only on hover */
+.media-wrapper:hover .image-metadata-panel {
+    transform: translateY(0);
+    opacity: 0.98;
+    pointer-events: auto;
+}
+
+/* Adjust to dark theme */
+[data-theme="dark"] .image-metadata-panel {
+    background: var(--card-bg);
+    box-shadow: 0 -2px 8px rgba(0, 0, 0, 0.3);
+}
+
+.metadata-content {
+    display: flex;
+    flex-direction: column;
+    gap: 10px;
+}
+
+/* Styling for parameters tags */
+.params-tags {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 6px;
+    margin-bottom: var(--space-1);
+    padding-bottom: var(--space-1);
+    border-bottom: 1px solid var(--lora-border);
+}
+
+.param-tag {
+    display: inline-flex;
+    align-items: center;
+    background: var(--lora-surface);
+    border: 1px solid var(--lora-border);
+    border-radius: var(--border-radius-xs);
+    padding: 2px 6px;
+    font-size: 0.8em;
+    line-height: 1.2;
+    white-space: nowrap;
+}
+
+.param-tag .param-name {
+    font-weight: 600;
+    color: var(--text-color);
+    margin-right: 4px;
+    opacity: 0.8;
+}
+
+.param-tag .param-value {
+    color: var(--lora-accent);
+}
+
+/* Special styling for prompt row */
+.metadata-row.prompt-row {
+    flex-direction: column;
+    padding-top: 0;
+}
+
+.metadata-row.prompt-row + .metadata-row.prompt-row {
+    margin-top: var(--space-2);
+}
+
+.metadata-label {
+    font-weight: 600;
+    color: var(--text-color);
+    opacity: 0.8;
+    font-size: 0.85em;
+    display: block;
+    margin-bottom: 4px;
+}
+
+.metadata-prompt-wrapper {
+    position: relative;
+    background: var(--lora-surface);
+    border: 1px solid var(--lora-border);
+    border-radius: var(--border-radius-xs);
+    padding: 6px 30px 6px 8px;
+    margin-top: 2px;
+    max-height: 80px; /* Reduced from 120px */
+    overflow-y: auto;
+    word-break: break-word;
+    width: 100%;
+    box-sizing: border-box;
+}
+
+.metadata-prompt {
+    color: var(--text-color);
+    font-family: monospace;
+    font-size: 0.85em;
+    white-space: pre-wrap;
+}
+
+.copy-prompt-btn {
+    position: absolute;
+    top: 6px;
+    right: 6px;
+    background: transparent;
+    border: none;
+    color: var(--text-color);
+    opacity: 0.6;
+    cursor: pointer;
+    padding: 3px;
+    transition: all 0.2s ease;
+}
+
+.copy-prompt-btn:hover {
+    opacity: 1;
+    color: var(--lora-accent);
+}
+
+/* Scrollbar styling for metadata panel */
+.image-metadata-panel::-webkit-scrollbar {
+    width: 6px;
+}
+
+.image-metadata-panel::-webkit-scrollbar-track {
+    background: transparent;
+}
+
+.image-metadata-panel::-webkit-scrollbar-thumb {
+    background-color: var(--border-color);
+    border-radius: 3px;
+}
+
+/* For Firefox */
+.image-metadata-panel {
+    scrollbar-width: thin;
+    scrollbar-color: var(--border-color) transparent;
+}
+
+/* No metadata message styling */
+.no-metadata-message {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    padding: var(--space-2);
+    color: var(--text-color);
+    opacity: 0.7;
+    text-align: center;
+    font-style: italic;
+    gap: 8px;
+}
+
+.no-metadata-message i {
+    font-size: 1.1em;
+    color: var(--lora-accent);
+    opacity: 0.8;
 }

static/css/components/modal.css

@@ -341,8 +341,7 @@ body.modal-open {
 
 .setting-item {
     display: flex;
-    justify-content: space-between;
-    align-items: flex-start;
+    flex-direction: column;
     margin-bottom: var(--space-2);
     padding: var(--space-1);
     border-radius: var(--border-radius-xs);
@@ -357,7 +356,8 @@ body.modal-open {
 }
 
 .setting-info {
-    flex: 1;
+    margin-bottom: var(--space-1);
+    width: 100%;
 }
 
 .setting-info label {
@@ -367,7 +367,39 @@ body.modal-open {
 }
 
 .setting-control {
-    padding-left: var(--space-2);
+    width: 100%;
+    margin-bottom: var(--space-1);
+}
+
+/* Select Control Styles */
+.select-control {
+    width: 100%;
+}
+
+.select-control select {
+    width: 100%;
+    padding: 6px 10px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    background-color: var(--lora-surface);
+    color: var(--text-color);
+    font-size: 0.95em;
+}
+
+/* Fix dark theme select dropdown text color */
+[data-theme="dark"] .select-control select {
+    background-color: rgba(30, 30, 30, 0.9);
+    color: var(--text-color);
+}
+
+[data-theme="dark"] .select-control select option {
+    background-color: #2d2d2d;
+    color: var(--text-color);
+}
+
+.select-control select:focus {
+    border-color: var(--lora-accent);
+    outline: none;
 }
 
 /* Toggle Switch */
@@ -482,4 +514,44 @@ input:checked + .toggle-slider:before {
     font-style: italic;
     margin-top: var(--space-1);
     text-align: center;
+}
+
+/* Add styles for markdown elements in changelog */
+.changelog-item ul {
+    padding-left: 20px;
+    margin-top: 8px;
+}
+
+.changelog-item li {
+    margin-bottom: 6px;
+    line-height: 1.4;
+}
+
+.changelog-item strong {
+    font-weight: 600;
+}
+
+.changelog-item em {
+    font-style: italic;
+}
+
+.changelog-item code {
+    background: rgba(0, 0, 0, 0.05);
+    padding: 2px 4px;
+    border-radius: 3px;
+    font-family: monospace;
+    font-size: 0.9em;
+}
+
+[data-theme="dark"] .changelog-item code {
+    background: rgba(255, 255, 255, 0.1);
+}
+
+.changelog-item a {
+    color: var(--lora-accent);
+    text-decoration: none;
+}
+
+.changelog-item a:hover {
+    text-decoration: underline;
 }

static/css/components/recipe-modal.css

@@ -18,6 +18,110 @@
     display: -webkit-box;
     -webkit-line-clamp: 2;
     -webkit-box-orient: vertical;
+    width: calc(100% - 20px);
+}
+
+/* Editable content styles */
+.editable-content {
+    position: relative;
+    width: 100%;
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+}
+
+.editable-content.hide {
+    display: none;
+}
+
+.editable-content .content-text {
+    flex: 1;
+    min-width: 0;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+
+.edit-icon {
+    background: none;
+    border: none;
+    color: var(--text-color);
+    opacity: 0;
+    cursor: pointer;
+    padding: 4px 8px;
+    margin-left: 8px;
+    border-radius: var(--border-radius-xs);
+    transition: all 0.2s;
+    flex-shrink: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.editable-content:hover .edit-icon {
+    opacity: 0.6;
+}
+
+.edit-icon:hover {
+    opacity: 1 !important;
+    background: var(--lora-surface);
+}
+
+/* Content editor styles */
+.content-editor {
+    display: none;
+    width: 100%;
+    padding: 4px 0;
+}
+
+.content-editor.active {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+}
+
+.content-editor input {
+    flex: 1;
+    background: var(--bg-color);
+    border: 1px solid var(--lora-border);
+    border-radius: var(--border-radius-xs);
+    padding: 6px 8px;
+    font-size: 1em;
+    color: var(--text-color);
+    min-width: 0;
+}
+
+.content-editor.tags-editor input {
+    font-size: 0.9em;
+}
+
+/* 删除不再需要的按钮样式 */
+.editor-actions {
+    display: none;
+}
+
+/* Special styling for tags content */
+.tags-content {
+    display: flex;
+    align-items: center;
+    flex-wrap: nowrap;
+    gap: 8px;
+}
+
+.tags-display {
+    display: flex;
+    flex-wrap: nowrap;
+    gap: 6px;
+    align-items: center;
+    flex: 1;
+    min-width: 0;
+    overflow: hidden;
+}
+
+.no-tags {
+    font-size: 0.85em;
+    color: var(--text-color);
+    opacity: 0.6;
+    font-style: italic;
 }
 
 /* Recipe Tags styles */
@@ -129,7 +233,14 @@
     justify-content: center;
 }
 
-.recipe-preview-container img {
+.recipe-preview-container img,
+.recipe-preview-container video {
+    max-width: 100%;
+    max-height: 100%;
+    object-fit: contain;
+}
+
+.recipe-preview-media {
     max-width: 100%;
     max-height: 100%;
     object-fit: contain;
@@ -333,6 +444,12 @@
     border-left: 4px solid var(--lora-error);
 }
 
+.recipe-lora-item.is-deleted {
+    background: rgba(127, 127, 127, 0.05);
+    border-left: 4px solid #777;
+    opacity: 0.8;
+}
+
 .recipe-lora-thumbnail {
     width: 46px;
     height: 46px;
@@ -340,9 +457,19 @@
     border-radius: var(--border-radius-xs);
     overflow: hidden;
     background: var(--bg-color);
+    display: flex;
+    align-items: center;
+    justify-content: center;
 }
 
-.recipe-lora-thumbnail img {
+.recipe-lora-thumbnail img,
+.recipe-lora-thumbnail video {
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+.thumbnail-video {
     width: 100%;
     height: 100%;
     object-fit: cover;
@@ -457,6 +584,38 @@
     font-size: 0.9em;
 }
 
+/* Deleted badge */
+.deleted-badge {
+    display: inline-flex;
+    align-items: center;
+    background: #777;
+    color: white;
+    padding: 3px 6px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.75em;
+    font-weight: 500;
+    white-space: nowrap;
+    flex-shrink: 0;
+}
+
+.deleted-badge i {
+    margin-right: 4px;
+    font-size: 0.9em;
+}
+
+/* Recipe status partial state */
+.recipe-status.partial {
+    background: rgba(127, 127, 127, 0.1);
+    color: #777;
+}
+
+/* 标题输入框特定的样式 */
+.title-input {
+    font-size: 1.2em !important; /* 调整为更合适的大小 */
+    line-height: 1.2;
+    font-weight: 500;
+}
+
 /* Responsive adjustments */
 @media (max-width: 768px) {
     .recipe-top-section {
@@ -485,7 +644,8 @@
 
 /* Update the local-badge and missing-badge to be positioned within the badge-container */
 .badge-container .local-badge, 
-.badge-container .missing-badge {
+.badge-container .missing-badge,
+.badge-container .deleted-badge {
     position: static; /* Override absolute positioning */
     transform: none; /* Remove the transform */
 }
@@ -495,3 +655,46 @@
     position: fixed; /* Keep as fixed for Chrome */
     z-index: 100;
 }
+
+/* Add styles for missing LoRAs download feature */
+.recipe-status.missing {
+    position: relative;
+    cursor: pointer;
+    transition: background-color 0.2s ease;
+}
+
+.recipe-status.missing:hover {
+    background-color: rgba(var(--lora-warning-rgb, 255, 165, 0), 0.2);
+}
+
+.recipe-status.missing .missing-tooltip {
+    position: absolute;
+    display: none;
+    background-color: var(--card-bg);
+    color: var(--text-color);
+    padding: 8px 12px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+    z-index: var(--z-overlay);
+    width: max-content;
+    max-width: 200px;
+    font-size: 0.85rem;
+    font-weight: normal;
+    margin-left: -100px;
+    margin-top: -65px;
+}
+
+.recipe-status.missing:hover .missing-tooltip {
+    display: block;
+}
+
+.recipe-status.clickable {
+    cursor: pointer;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+}
+
+.recipe-status.clickable:hover {
+    background-color: rgba(var(--lora-warning-rgb, 255, 165, 0), 0.2);
+}

static/js/components/LoraModal.js

@@ -1,7 +1,7 @@
 import { showToast } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { modalManager } from '../managers/ModalManager.js';
-import { NSFW_LEVELS } from '../utils/constants.js';
+import { NSFW_LEVELS, BASE_MODELS } from '../utils/constants.js';
 
 export function showLoraModal(lora) {
     const escapedWords = lora.civitai?.trainedWords?.length ? 
@@ -29,9 +29,11 @@ export function showLoraModal(lora) {
                         </div>
                         <div class="info-item">
                             <label>File Name</label>
-                            <div class="file-name-wrapper" onclick="copyFileName('${lora.file_name}')">
-                                <span id="file-name">${lora.file_name || 'N/A'}</span>
-                                <i class="fas fa-copy" title="Copy file name"></i>
+                            <div class="file-name-wrapper">
+                                <span id="file-name" class="file-name-content">${lora.file_name || 'N/A'}</span>
+                                <button class="edit-file-name-btn" title="Edit file name">
+                                    <i class="fas fa-pencil-alt"></i>
+                                </button>
                             </div>
                         </div>
                         <div class="info-item location-size">
@@ -43,7 +45,12 @@ export function showLoraModal(lora) {
                         <div class="info-item base-size">
                             <div class="base-wrapper">
                                 <label>Base Model</label>
-                                <span>${lora.base_model || 'N/A'}</span>
+                                <div class="base-model-display">
+                                    <span class="base-model-content">${lora.base_model || 'N/A'}</span>
+                                    <button class="edit-base-model-btn" title="Edit base model">
+                                        <i class="fas fa-pencil-alt"></i>
+                                    </button>
+                                </div>
                             </div>
                             <div class="size-wrapper">
                                 <label>Size</label>
@@ -124,6 +131,8 @@ export function showLoraModal(lora) {
     setupTagTooltip();
     setupTriggerWordsEditMode();
     setupModelNameEditing();
+    setupBaseModelEditing();
+    setupFileNameEditing();
     
     // If we have a model ID but no description, fetch it
     if (lora.civitai?.modelId && !lora.modelDescription) {
@@ -202,61 +211,165 @@ function renderShowcaseContent(images) {
                         nsfwText = "R-rated Content";
                     }
                     
-                    if (img.type === 'video') {
-                        return `
-                            <div class="media-wrapper ${shouldBlur ? 'nsfw-media-wrapper' : ''}" style="padding-bottom: ${heightPercent}%">
-                                ${shouldBlur ? `
-                                    <button class="toggle-blur-btn showcase-toggle-btn" title="Toggle blur">
-                                        <i class="fas fa-eye"></i>
-                                    </button>
-                                ` : ''}
-                                <video controls autoplay muted loop crossorigin="anonymous" 
-                                       referrerpolicy="no-referrer" data-src="${img.url}"
-                                       class="lazy ${shouldBlur ? 'blurred' : ''}">
-                                    <source data-src="${img.url}" type="video/mp4">
-                                    Your browser does not support video playback
-                                </video>
-                                ${shouldBlur ? `
-                                    <div class="nsfw-overlay">
-                                        <div class="nsfw-warning">
-                                            <p>${nsfwText}</p>
-                                            <button class="show-content-btn">Show</button>
-                                        </div>
-                                    </div>
-                                ` : ''}
-                            </div>
-                        `;
-                    }
-                    return `
-                        <div class="media-wrapper ${shouldBlur ? 'nsfw-media-wrapper' : ''}" style="padding-bottom: ${heightPercent}%">
-                            ${shouldBlur ? `
-                                <button class="toggle-blur-btn showcase-toggle-btn" title="Toggle blur">
-                                    <i class="fas fa-eye"></i>
-                                </button>
-                            ` : ''}
-                            <img data-src="${img.url}" 
-                                alt="Preview" 
-                                crossorigin="anonymous" 
-                                referrerpolicy="no-referrer"
-                                width="${img.width}"
-                                height="${img.height}"
-                                class="lazy ${shouldBlur ? 'blurred' : ''}"> 
-                            ${shouldBlur ? `
-                                <div class="nsfw-overlay">
-                                    <div class="nsfw-warning">
-                                        <p>${nsfwText}</p>
-                                        <button class="show-content-btn">Show</button>
+                    // Extract metadata from the image
+                    const meta = img.meta || {};
+                    const prompt = meta.prompt || '';
+                    const negativePrompt = meta.negative_prompt || meta.negativePrompt || '';
+                    const size = meta.Size || `${img.width}x${img.height}`;
+                    const seed = meta.seed || '';
+                    const model = meta.Model || '';
+                    const steps = meta.steps || '';
+                    const sampler = meta.sampler || '';
+                    const cfgScale = meta.cfgScale || '';
+                    const clipSkip = meta.clipSkip || '';
+                    
+                    // Check if we have any meaningful generation parameters
+                    const hasParams = seed || model || steps || sampler || cfgScale || clipSkip;
+                    const hasPrompts = prompt || negativePrompt;
+                    
+                    // If no metadata available, show a message
+                    if (!hasParams && !hasPrompts) {
+                        const metadataPanel = `
+                            <div class="image-metadata-panel">
+                                <div class="metadata-content">
+                                    <div class="no-metadata-message">
+                                        <i class="fas fa-info-circle"></i>
+                                        <span>No generation parameters available</span>
                                     </div>
                                 </div>
-                            ` : ''}
+                            </div>
+                        `;
+                        
+                        if (img.type === 'video') {
+                            return generateVideoWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel);
+                        }
+                        return generateImageWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel);
+                    }
+                    
+                    // Create a data attribute with the prompt for copying instead of trying to handle it in the onclick
+                    // This avoids issues with quotes and special characters
+                    const promptIndex = Math.random().toString(36).substring(2, 15);
+                    const negPromptIndex = Math.random().toString(36).substring(2, 15);
+                    
+                    // Create parameter tags HTML
+                    const paramTags = `
+                        <div class="params-tags">
+                            ${size ? `<div class="param-tag"><span class="param-name">Size:</span><span class="param-value">${size}</span></div>` : ''}
+                            ${seed ? `<div class="param-tag"><span class="param-name">Seed:</span><span class="param-value">${seed}</span></div>` : ''}
+                            ${model ? `<div class="param-tag"><span class="param-name">Model:</span><span class="param-value">${model}</span></div>` : ''}
+                            ${steps ? `<div class="param-tag"><span class="param-name">Steps:</span><span class="param-value">${steps}</span></div>` : ''}
+                            ${sampler ? `<div class="param-tag"><span class="param-name">Sampler:</span><span class="param-value">${sampler}</span></div>` : ''}
+                            ${cfgScale ? `<div class="param-tag"><span class="param-name">CFG:</span><span class="param-value">${cfgScale}</span></div>` : ''}
+                            ${clipSkip ? `<div class="param-tag"><span class="param-name">Clip Skip:</span><span class="param-value">${clipSkip}</span></div>` : ''}
                         </div>
                     `;
+                    
+                    // Metadata panel HTML
+                    const metadataPanel = `
+                        <div class="image-metadata-panel">
+                            <div class="metadata-content">
+                                ${hasParams ? paramTags : ''}
+                                ${!hasParams && !hasPrompts ? `
+                                <div class="no-metadata-message">
+                                    <i class="fas fa-info-circle"></i>
+                                    <span>No generation parameters available</span>
+                                </div>
+                                ` : ''}
+                                ${prompt ? `
+                                <div class="metadata-row prompt-row">
+                                    <span class="metadata-label">Prompt:</span>
+                                    <div class="metadata-prompt-wrapper">
+                                        <div class="metadata-prompt">${prompt}</div>
+                                        <button class="copy-prompt-btn" data-prompt-index="${promptIndex}">
+                                            <i class="fas fa-copy"></i>
+                                        </button>
+                                    </div>
+                                </div>
+                                <div class="hidden-prompt" id="prompt-${promptIndex}" style="display:none;">${prompt}</div>
+                                ` : ''}
+                                ${negativePrompt ? `
+                                <div class="metadata-row prompt-row">
+                                    <span class="metadata-label">Negative Prompt:</span>
+                                    <div class="metadata-prompt-wrapper">
+                                        <div class="metadata-prompt">${negativePrompt}</div>
+                                        <button class="copy-prompt-btn" data-prompt-index="${negPromptIndex}">
+                                            <i class="fas fa-copy"></i>
+                                        </button>
+                                    </div>
+                                </div>
+                                <div class="hidden-prompt" id="prompt-${negPromptIndex}" style="display:none;">${negativePrompt}</div>
+                                ` : ''}
+                            </div>
+                        </div>
+                    `;
+                    
+                    if (img.type === 'video') {
+                        return generateVideoWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel);
+                    }
+                    return generateImageWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel);
                 }).join('')}
             </div>
         </div>
     `;
 }
 
+// Helper function to generate video wrapper HTML
+function generateVideoWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel) {
+    return `
+        <div class="media-wrapper ${shouldBlur ? 'nsfw-media-wrapper' : ''}" style="padding-bottom: ${heightPercent}%">
+            ${shouldBlur ? `
+                <button class="toggle-blur-btn showcase-toggle-btn" title="Toggle blur">
+                    <i class="fas fa-eye"></i>
+                </button>
+            ` : ''}
+            <video controls autoplay muted loop crossorigin="anonymous" 
+                referrerpolicy="no-referrer" data-src="${img.url}"
+                class="lazy ${shouldBlur ? 'blurred' : ''}">
+                <source data-src="${img.url}" type="video/mp4">
+                Your browser does not support video playback
+            </video>
+            ${shouldBlur ? `
+                <div class="nsfw-overlay">
+                    <div class="nsfw-warning">
+                        <p>${nsfwText}</p>
+                        <button class="show-content-btn">Show</button>
+                    </div>
+                </div>
+            ` : ''}
+            ${metadataPanel}
+        </div>
+    `;
+}
+
+// Helper function to generate image wrapper HTML
+function generateImageWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel) {
+    return `
+        <div class="media-wrapper ${shouldBlur ? 'nsfw-media-wrapper' : ''}" style="padding-bottom: ${heightPercent}%">
+            ${shouldBlur ? `
+                <button class="toggle-blur-btn showcase-toggle-btn" title="Toggle blur">
+                    <i class="fas fa-eye"></i>
+                </button>
+            ` : ''}
+            <img data-src="${img.url}" 
+                alt="Preview" 
+                crossorigin="anonymous" 
+                referrerpolicy="no-referrer"
+                width="${img.width}"
+                height="${img.height}"
+                class="lazy ${shouldBlur ? 'blurred' : ''}"> 
+            ${shouldBlur ? `
+                <div class="nsfw-overlay">
+                    <div class="nsfw-warning">
+                        <p>${nsfwText}</p>
+                        <button class="show-content-btn">Show</button>
+                    </div>
+                </div>
+            ` : ''}
+            ${metadataPanel}
+        </div>
+    `;
+}
+
 // New function to handle tab switching
 function setupTabSwitching() {
     const tabButtons = document.querySelectorAll('.showcase-tabs .tab-btn');
@@ -620,13 +733,74 @@ export function toggleShowcase(element) {
         
         // Initialize NSFW content blur toggle handlers
         initNsfwBlurHandlers(carousel);
+        
+        // Initialize metadata panel interaction handlers
+        initMetadataPanelHandlers(carousel);
     } else {
         const count = carousel.querySelectorAll('.media-wrapper').length;
         indicator.textContent = `Scroll or click to show ${count} examples`;
         icon.classList.replace('fa-chevron-up', 'fa-chevron-down');
+        
+        // Make sure any open metadata panels get closed
+        const carouselContainer = carousel.querySelector('.carousel-container');
+        if (carouselContainer) {
+            carouselContainer.style.height = '0';
+            setTimeout(() => {
+                carouselContainer.style.height = '';
+            }, 300);
+        }
     }
 }
 
+// Function to initialize metadata panel interactions
+function initMetadataPanelHandlers(container) {
+    // Find all media wrappers
+    const mediaWrappers = container.querySelectorAll('.media-wrapper');
+    
+    mediaWrappers.forEach(wrapper => {
+        // Get the metadata panel
+        const metadataPanel = wrapper.querySelector('.image-metadata-panel');
+        if (!metadataPanel) return;
+        
+        // Prevent events from the metadata panel from bubbling
+        metadataPanel.addEventListener('click', (e) => {
+            e.stopPropagation();
+        });
+        
+        // Handle copy prompt button clicks
+        const copyBtns = metadataPanel.querySelectorAll('.copy-prompt-btn');
+        copyBtns.forEach(copyBtn => {
+            const promptIndex = copyBtn.dataset.promptIndex;
+            const promptElement = wrapper.querySelector(`#prompt-${promptIndex}`);
+            
+            copyBtn.addEventListener('click', async (e) => {
+                e.stopPropagation(); // Prevent bubbling
+                
+                if (!promptElement) return;
+                
+                try {
+                    await navigator.clipboard.writeText(promptElement.textContent);
+                    showToast('Prompt copied to clipboard', 'success');
+                } catch (err) {
+                    console.error('Copy failed:', err);
+                    showToast('Copy failed', 'error');
+                }
+            });
+        });
+        
+        // Prevent scrolling in the metadata panel from scrolling the whole modal
+        metadataPanel.addEventListener('wheel', (e) => {
+            const isAtTop = metadataPanel.scrollTop === 0;
+            const isAtBottom = metadataPanel.scrollHeight - metadataPanel.scrollTop === metadataPanel.clientHeight;
+            
+            // Only prevent default if scrolling would cause the panel to scroll
+            if ((e.deltaY < 0 && !isAtTop) || (e.deltaY > 0 && !isAtBottom)) {
+                e.stopPropagation();
+            }
+        }, { passive: true });
+    });
+}
+
 // New function to initialize blur toggle handlers for showcase images/videos
 function initNsfwBlurHandlers(container) {
     // Handle toggle blur buttons
@@ -1225,3 +1399,335 @@ function setupModelNameEditing() {
         }
     });
 }
+
+// Add save model base model function
+window.saveBaseModel = async function(filePath, originalValue) {
+    const baseModelElement = document.querySelector('.base-model-content');
+    const newBaseModel = baseModelElement.textContent.trim();
+    
+    // Only save if the value has actually changed
+    if (newBaseModel === originalValue) {
+        return; // No change, no need to save
+    }
+    
+    try {
+        await saveModelMetadata(filePath, { base_model: newBaseModel });
+        
+        // Update the corresponding lora card's dataset
+        const loraCard = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
+        if (loraCard) {
+            loraCard.dataset.base_model = newBaseModel;
+        }
+        
+        showToast('Base model updated successfully', 'success');
+    } catch (error) {
+        showToast('Failed to update base model', 'error');
+    }
+};
+
+// New function to handle base model editing
+function setupBaseModelEditing() {
+    const baseModelContent = document.querySelector('.base-model-content');
+    const editBtn = document.querySelector('.edit-base-model-btn');
+    
+    if (!baseModelContent || !editBtn) return;
+    
+    // Show edit button on hover
+    const baseModelDisplay = document.querySelector('.base-model-display');
+    baseModelDisplay.addEventListener('mouseenter', () => {
+        editBtn.classList.add('visible');
+    });
+    
+    baseModelDisplay.addEventListener('mouseleave', () => {
+        if (!baseModelDisplay.classList.contains('editing')) {
+            editBtn.classList.remove('visible');
+        }
+    });
+    
+    // Handle edit button click
+    editBtn.addEventListener('click', () => {
+        baseModelDisplay.classList.add('editing');
+        
+        // Store the original value to check for changes later
+        const originalValue = baseModelContent.textContent.trim();
+        
+        // Create dropdown selector to replace the base model content
+        const currentValue = originalValue;
+        const dropdown = document.createElement('select');
+        dropdown.className = 'base-model-selector';
+        
+        // Flag to track if a change was made
+        let valueChanged = false;
+        
+        // Add options from BASE_MODELS constants
+        const baseModelCategories = {
+            'Stable Diffusion 1.x': [BASE_MODELS.SD_1_4, BASE_MODELS.SD_1_5, BASE_MODELS.SD_1_5_LCM, BASE_MODELS.SD_1_5_HYPER],
+            'Stable Diffusion 2.x': [BASE_MODELS.SD_2_0, BASE_MODELS.SD_2_1],
+            'Stable Diffusion 3.x': [BASE_MODELS.SD_3, BASE_MODELS.SD_3_5, BASE_MODELS.SD_3_5_MEDIUM, BASE_MODELS.SD_3_5_LARGE, BASE_MODELS.SD_3_5_LARGE_TURBO],
+            'SDXL': [BASE_MODELS.SDXL, BASE_MODELS.SDXL_LIGHTNING, BASE_MODELS.SDXL_HYPER],
+            'Video Models': [BASE_MODELS.SVD, BASE_MODELS.WAN_VIDEO, BASE_MODELS.HUNYUAN_VIDEO],
+            'Other Models': [
+                BASE_MODELS.FLUX_1_D, BASE_MODELS.FLUX_1_S, BASE_MODELS.AURAFLOW,
+                BASE_MODELS.PIXART_A, BASE_MODELS.PIXART_E, BASE_MODELS.HUNYUAN_1,
+                BASE_MODELS.LUMINA, BASE_MODELS.KOLORS, BASE_MODELS.NOOBAI,
+                BASE_MODELS.ILLUSTRIOUS, BASE_MODELS.PONY, BASE_MODELS.UNKNOWN
+            ]
+        };
+        
+        // Create option groups for better organization
+        Object.entries(baseModelCategories).forEach(([category, models]) => {
+            const group = document.createElement('optgroup');
+            group.label = category;
+            
+            models.forEach(model => {
+                const option = document.createElement('option');
+                option.value = model;
+                option.textContent = model;
+                option.selected = model === currentValue;
+                group.appendChild(option);
+            });
+            
+            dropdown.appendChild(group);
+        });
+        
+        // Replace content with dropdown
+        baseModelContent.style.display = 'none';
+        baseModelDisplay.insertBefore(dropdown, editBtn);
+        
+        // Hide edit button during editing
+        editBtn.style.display = 'none';
+        
+        // Focus the dropdown
+        dropdown.focus();
+        
+        // Handle dropdown change
+        dropdown.addEventListener('change', function() {
+            const selectedModel = this.value;
+            baseModelContent.textContent = selectedModel;
+            
+            // Mark that a change was made if the value differs from original
+            if (selectedModel !== originalValue) {
+                valueChanged = true;
+            } else {
+                valueChanged = false;
+            }
+        });
+        
+        // Function to save changes and exit edit mode
+        const saveAndExit = function() {
+            // Check if dropdown still exists and remove it
+            if (dropdown && dropdown.parentNode === baseModelDisplay) {
+                baseModelDisplay.removeChild(dropdown);
+            }
+            
+            // Show the content and edit button
+            baseModelContent.style.display = '';
+            editBtn.style.display = '';
+            
+            // Remove editing class
+            baseModelDisplay.classList.remove('editing');
+            
+            // Only save if the value has actually changed
+            if (valueChanged || baseModelContent.textContent.trim() !== originalValue) {
+                // Get file path for saving
+                const filePath = document.querySelector('#loraModal .modal-content')
+                    .querySelector('.file-path').textContent + 
+                    document.querySelector('#loraModal .modal-content')
+                    .querySelector('#file-name').textContent + '.safetensors';
+                
+                // Save the changes, passing the original value for comparison
+                saveBaseModel(filePath, originalValue);
+            }
+            
+            // Remove this event listener
+            document.removeEventListener('click', outsideClickHandler);
+        };
+        
+        // Handle outside clicks to save and exit
+        const outsideClickHandler = function(e) {
+            // If click is outside the dropdown and base model display
+            if (!baseModelDisplay.contains(e.target)) {
+                saveAndExit();
+            }
+        };
+        
+        // Add delayed event listener for outside clicks
+        setTimeout(() => {
+            document.addEventListener('click', outsideClickHandler);
+        }, 0);
+        
+        // Also handle dropdown blur event
+        dropdown.addEventListener('blur', function(e) {
+            // Only save if the related target is not the edit button or inside the baseModelDisplay
+            if (!baseModelDisplay.contains(e.relatedTarget)) {
+                saveAndExit();
+            }
+        });
+    });
+}
+
+// New function to handle file name editing
+function setupFileNameEditing() {
+    const fileNameContent = document.querySelector('.file-name-content');
+    const editBtn = document.querySelector('.edit-file-name-btn');
+    
+    if (!fileNameContent || !editBtn) return;
+    
+    // Show edit button on hover
+    const fileNameWrapper = document.querySelector('.file-name-wrapper');
+    fileNameWrapper.addEventListener('mouseenter', () => {
+        editBtn.classList.add('visible');
+    });
+    
+    fileNameWrapper.addEventListener('mouseleave', () => {
+        if (!fileNameWrapper.classList.contains('editing')) {
+            editBtn.classList.remove('visible');
+        }
+    });
+    
+    // Handle edit button click
+    editBtn.addEventListener('click', () => {
+        fileNameWrapper.classList.add('editing');
+        fileNameContent.setAttribute('contenteditable', 'true');
+        fileNameContent.focus();
+        
+        // Store original value for comparison later
+        fileNameContent.dataset.originalValue = fileNameContent.textContent.trim();
+        
+        // Place cursor at the end
+        const range = document.createRange();
+        const sel = window.getSelection();
+        range.selectNodeContents(fileNameContent);
+        range.collapse(false);
+        sel.removeAllRanges();
+        sel.addRange(range);
+        
+        editBtn.classList.add('visible');
+    });
+    
+    // Handle keyboard events in edit mode
+    fileNameContent.addEventListener('keydown', function(e) {
+        if (!this.getAttribute('contenteditable')) return;
+        
+        if (e.key === 'Enter') {
+            e.preventDefault();
+            this.blur(); // Trigger save on Enter
+        } else if (e.key === 'Escape') {
+            e.preventDefault();
+            // Restore original value
+            this.textContent = this.dataset.originalValue;
+            exitEditMode();
+        }
+    });
+    
+    // Handle input validation
+    fileNameContent.addEventListener('input', function() {
+        if (!this.getAttribute('contenteditable')) return;
+        
+        // Replace invalid characters for filenames
+        const invalidChars = /[\\/:*?"<>|]/g;
+        if (invalidChars.test(this.textContent)) {
+            const cursorPos = window.getSelection().getRangeAt(0).startOffset;
+            this.textContent = this.textContent.replace(invalidChars, '');
+            
+            // Restore cursor position
+            const range = document.createRange();
+            const sel = window.getSelection();
+            const newPos = Math.min(cursorPos, this.textContent.length);
+            
+            if (this.firstChild) {
+                range.setStart(this.firstChild, newPos);
+                range.collapse(true);
+                sel.removeAllRanges();
+                sel.addRange(range);
+            }
+            
+            showToast('Invalid characters removed from filename', 'warning');
+        }
+    });
+    
+    // Handle focus out - save changes
+    fileNameContent.addEventListener('blur', async function() {
+        if (!this.getAttribute('contenteditable')) return;
+        
+        const newFileName = this.textContent.trim();
+        const originalValue = this.dataset.originalValue;
+        
+        // Basic validation
+        if (!newFileName) {
+            // Restore original value if empty
+            this.textContent = originalValue;
+            showToast('File name cannot be empty', 'error');
+            exitEditMode();
+            return;
+        }
+        
+        if (newFileName === originalValue) {
+            // No changes, just exit edit mode
+            exitEditMode();
+            return;
+        }
+        
+        try {
+            // Get the full file path
+            const filePath = document.querySelector('#loraModal .modal-content')
+                .querySelector('.file-path').textContent + originalValue + '.safetensors';
+                
+            // Call API to rename the file
+            const response = await fetch('/api/rename_lora', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({
+                    file_path: filePath,
+                    new_file_name: newFileName
+                })
+            });
+            
+            const result = await response.json();
+            
+            if (result.success) {
+                showToast('File name updated successfully', 'success');
+                
+                // Update card in the gallery
+                const loraCard = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
+                if (loraCard) {
+                    // Update the card's filepath attribute to the new path
+                    loraCard.dataset.filepath = result.new_file_path;
+                    loraCard.dataset.file_name = newFileName;
+                    
+                    // Update the filename display in the card
+                    const cardFileName = loraCard.querySelector('.card-filename');
+                    if (cardFileName) {
+                        cardFileName.textContent = newFileName;
+                    }
+                }
+                
+                // Handle the case where we need to reload the page
+                if (result.reload_required) {
+                    showToast('Reloading page to apply changes...', 'info');
+                    setTimeout(() => {
+                        window.location.reload();
+                    }, 1500);
+                }
+            } else {
+                // Show error and restore original filename
+                showToast(result.error || 'Failed to update file name', 'error');
+                this.textContent = originalValue;
+            }
+        } catch (error) {
+            console.error('Error saving filename:', error);
+            showToast('Failed to update file name', 'error');
+            this.textContent = originalValue;
+        } finally {
+            exitEditMode();
+        }
+    });
+    
+    function exitEditMode() {
+        fileNameContent.removeAttribute('contenteditable');
+        fileNameWrapper.classList.remove('editing');
+        editBtn.classList.remove('visible');
+    }
+}

static/js/components/RecipeCard.js

@@ -25,7 +25,7 @@ class RecipeCard {
         const lorasCount = loras.length;
         
         // Check if all LoRAs are available in the library
-        const missingLorasCount = loras.filter(lora => !lora.inLibrary).length;
+        const missingLorasCount = loras.filter(lora => !lora.inLibrary && !lora.isDeleted).length;
         const allLorasAvailable = missingLorasCount === 0 && lorasCount > 0;
         
         // Ensure file_url exists, fallback to file_path if needed
@@ -96,22 +96,24 @@ class RecipeCard {
     
     copyRecipeSyntax() {
         try {
-            // Generate recipe syntax in the format <lora:file_name:strength> separated by spaces
-            const loras = this.recipe.loras || [];
-            if (loras.length === 0) {
-                showToast('No LoRAs in this recipe to copy', 'warning');
+            // Get recipe ID
+            const recipeId = this.recipe.id;
+            if (!recipeId) {
+                showToast('Cannot copy recipe syntax: Missing recipe ID', 'error');
                 return;
             }
+
             
-            const syntax = loras.map(lora => {
-                // Use file_name if available, otherwise use empty placeholder
-                const fileName = lora.file_name || '[missing-lora]';
-                const strength = lora.strength || 1.0;
-                return `<lora:${fileName}:${strength}>`;
-            }).join(' ');
-            
-            // Copy to clipboard
-            navigator.clipboard.writeText(syntax)
+            // Fallback if button not found
+            fetch(`/api/recipe/${recipeId}/syntax`)
+                .then(response => response.json())
+                .then(data => {
+                    if (data.success && data.syntax) {
+                        return navigator.clipboard.writeText(data.syntax);
+                    } else {
+                        throw new Error(data.error || 'No syntax returned');
+                    }
+                })
                 .then(() => {
                     showToast('Recipe syntax copied to clipboard', 'success');
                 })

static/js/components/RecipeModal.js

@@ -1,5 +1,6 @@
 // Recipe Modal Component
 import { showToast } from '../utils/uiHelpers.js';
+import { state } from '../state/index.js';
 
 class RecipeModal {
     constructor() {
@@ -12,6 +13,25 @@ class RecipeModal {
         document.addEventListener('DOMContentLoaded', () => {
             this.setupTooltipPositioning();
         });
+        
+        // Set up document click handler to close edit fields
+        document.addEventListener('click', (event) => {
+            // Handle title edit
+            const titleEditor = document.getElementById('recipeTitleEditor');
+            if (titleEditor && titleEditor.classList.contains('active') && 
+                !titleEditor.contains(event.target) && 
+                !event.target.closest('.edit-icon')) {
+                this.saveTitleEdit();
+            }
+            
+            // Handle tags edit
+            const tagsEditor = document.getElementById('recipeTagsEditor');
+            if (tagsEditor && tagsEditor.classList.contains('active') && 
+                !tagsEditor.contains(event.target) && 
+                !event.target.closest('.edit-icon')) {
+                this.saveTagsEdit();
+            }
+        });
     }
     
     // Add tooltip positioning handler to ensure correct positioning of fixed tooltips
@@ -31,73 +51,153 @@ class RecipeModal {
                     tooltip.style.left = (badgeRect.right - tooltip.offsetWidth) + 'px';
                 }
             }
+            
+            // Add tooltip positioning for missing badge
+            if (event.target.closest('.recipe-status.missing')) {
+                const badge = event.target.closest('.recipe-status.missing');
+                const tooltip = badge.querySelector('.missing-tooltip');
+                
+                if (tooltip) {
+                    // Get badge position
+                    const badgeRect = badge.getBoundingClientRect();
+                    
+                    // Position the tooltip
+                    tooltip.style.top = (badgeRect.bottom + 4) + 'px';
+                    tooltip.style.left = (badgeRect.left) + 'px';
+                }
+            }
         }, true);
     }
     
     showRecipeDetails(recipe) {
-        // Set modal title
+        // Store the full recipe for editing
+        this.currentRecipe = JSON.parse(JSON.stringify(recipe)); // 深拷贝以避免对原始对象的修改
+        
+        // Set modal title with edit icon
         const modalTitle = document.getElementById('recipeModalTitle');
         if (modalTitle) {
-            modalTitle.textContent = recipe.title || 'Recipe Details';
+            modalTitle.innerHTML = `
+                <div class="editable-content">
+                    <span class="content-text">${recipe.title || 'Recipe Details'}</span>
+                    <button class="edit-icon" title="Edit recipe name"><i class="fas fa-pencil-alt"></i></button>
+                </div>
+                <div id="recipeTitleEditor" class="content-editor">
+                    <input type="text" class="title-input" value="${recipe.title || ''}">
+                </div>
+            `;
+            
+            // Add event listener for title editing
+            const editIcon = modalTitle.querySelector('.edit-icon');
+            editIcon.addEventListener('click', () => this.showTitleEditor());
+            
+            // Add key event listener for Enter key
+            const titleInput = modalTitle.querySelector('.title-input');
+            titleInput.addEventListener('keydown', (e) => {
+                if (e.key === 'Enter') {
+                    e.preventDefault();
+                    this.saveTitleEdit();
+                } else if (e.key === 'Escape') {
+                    e.preventDefault();
+                    this.cancelTitleEdit();
+                }
+            });
         }
         
+        // Store the recipe ID for copy syntax API call
+        this.recipeId = recipe.id;
+        
         // Set recipe tags if they exist
         const tagsCompactElement = document.getElementById('recipeTagsCompact');
         const tagsTooltipContent = document.getElementById('recipeTagsTooltipContent');
         
-        if (tagsCompactElement && tagsTooltipContent && recipe.tags && recipe.tags.length > 0) {
-            // Clear previous tags
-            tagsCompactElement.innerHTML = '';
-            tagsTooltipContent.innerHTML = '';
+        if (tagsCompactElement) {
+            // Add tags container with edit functionality
+            tagsCompactElement.innerHTML = `
+                <div class="editable-content tags-content">
+                    <div class="tags-display"></div>
+                    <button class="edit-icon" title="Edit tags"><i class="fas fa-pencil-alt"></i></button>
+                </div>
+                <div id="recipeTagsEditor" class="content-editor tags-editor">
+                    <input type="text" class="tags-input" placeholder="Enter tags separated by commas">
+                </div>
+            `;
             
-            // Limit displayed tags to 5, show a "+X more" button if needed
-            const maxVisibleTags = 5;
-            const visibleTags = recipe.tags.slice(0, maxVisibleTags);
-            const remainingTags = recipe.tags.length > maxVisibleTags ? recipe.tags.slice(maxVisibleTags) : [];
+            const tagsDisplay = tagsCompactElement.querySelector('.tags-display');
             
-            // Add visible tags
-            visibleTags.forEach(tag => {
-                const tagElement = document.createElement('div');
-                tagElement.className = 'recipe-tag-compact';
-                tagElement.textContent = tag;
-                tagsCompactElement.appendChild(tagElement);
-            });
-            
-            // Add "more" button if needed
-            if (remainingTags.length > 0) {
-                const moreButton = document.createElement('div');
-                moreButton.className = 'recipe-tag-more';
-                moreButton.textContent = `+${remainingTags.length} more`;
-                tagsCompactElement.appendChild(moreButton);
+            if (recipe.tags && recipe.tags.length > 0) {
+                // Limit displayed tags to 5, show a "+X more" button if needed
+                const maxVisibleTags = 5;
+                const visibleTags = recipe.tags.slice(0, maxVisibleTags);
+                const remainingTags = recipe.tags.length > maxVisibleTags ? recipe.tags.slice(maxVisibleTags) : [];
                 
-                // Add tooltip functionality
-                moreButton.addEventListener('mouseenter', () => {
-                    document.getElementById('recipeTagsTooltip').classList.add('visible');
+                // Add visible tags
+                visibleTags.forEach(tag => {
+                    const tagElement = document.createElement('div');
+                    tagElement.className = 'recipe-tag-compact';
+                    tagElement.textContent = tag;
+                    tagsDisplay.appendChild(tagElement);
                 });
                 
-                moreButton.addEventListener('mouseleave', () => {
-                    setTimeout(() => {
-                        if (!document.getElementById('recipeTagsTooltip').matches(':hover')) {
-                            document.getElementById('recipeTagsTooltip').classList.remove('visible');
-                        }
-                    }, 300);
-                });
-                
-                document.getElementById('recipeTagsTooltip').addEventListener('mouseleave', () => {
-                    document.getElementById('recipeTagsTooltip').classList.remove('visible');
-                });
-                
-                // Add all tags to tooltip
-                recipe.tags.forEach(tag => {
-                    const tooltipTag = document.createElement('div');
-                    tooltipTag.className = 'tooltip-tag';
-                    tooltipTag.textContent = tag;
-                    tagsTooltipContent.appendChild(tooltipTag);
-                });
+                // Add "more" button if needed
+                if (remainingTags.length > 0) {
+                    const moreButton = document.createElement('div');
+                    moreButton.className = 'recipe-tag-more';
+                    moreButton.textContent = `+${remainingTags.length} more`;
+                    tagsDisplay.appendChild(moreButton);
+                    
+                    // Add tooltip functionality
+                    moreButton.addEventListener('mouseenter', () => {
+                        document.getElementById('recipeTagsTooltip').classList.add('visible');
+                    });
+                    
+                    moreButton.addEventListener('mouseleave', () => {
+                        setTimeout(() => {
+                            if (!document.getElementById('recipeTagsTooltip').matches(':hover')) {
+                                document.getElementById('recipeTagsTooltip').classList.remove('visible');
+                            }
+                        }, 300);
+                    });
+                    
+                    document.getElementById('recipeTagsTooltip').addEventListener('mouseleave', () => {
+                        document.getElementById('recipeTagsTooltip').classList.remove('visible');
+                    });
+                    
+                    // Add all tags to tooltip
+                    if (tagsTooltipContent) {
+                        tagsTooltipContent.innerHTML = '';
+                        recipe.tags.forEach(tag => {
+                            const tooltipTag = document.createElement('div');
+                            tooltipTag.className = 'tooltip-tag';
+                            tooltipTag.textContent = tag;
+                            tagsTooltipContent.appendChild(tooltipTag);
+                        });
+                    }
+                }
+            } else {
+                tagsDisplay.innerHTML = '<div class="no-tags">No tags</div>';
             }
-        } else if (tagsCompactElement) {
-            // No tags to display
-            tagsCompactElement.innerHTML = '';
+            
+            // Add event listeners for tags editing
+            const editTagsIcon = tagsCompactElement.querySelector('.edit-icon');
+            const tagsInput = tagsCompactElement.querySelector('.tags-input');
+            
+            // Set current tags in the input
+            if (recipe.tags && recipe.tags.length > 0) {
+                tagsInput.value = recipe.tags.join(', ');
+            }
+            
+            editTagsIcon.addEventListener('click', () => this.showTagsEditor());
+            
+            // Add key event listener for Enter key
+            tagsInput.addEventListener('keydown', (e) => {
+                if (e.key === 'Enter') {
+                    e.preventDefault();
+                    this.saveTagsEdit();
+                } else if (e.key === 'Escape') {
+                    e.preventDefault();
+                    this.cancelTagsEdit();
+                }
+            });
         }
         
         // Set recipe image
@@ -107,8 +207,33 @@ class RecipeModal {
             const imageUrl = recipe.file_url || 
                             (recipe.file_path ? `/loras_static/root1/preview/${recipe.file_path.split('/').pop()}` : 
                             '/loras_static/images/no-preview.png');
-            modalImage.src = imageUrl;
-            modalImage.alt = recipe.title || 'Recipe Preview';
+            
+            // Check if the file is a video (mp4)
+            const isVideo = imageUrl.toLowerCase().endsWith('.mp4');
+            
+            // Replace the image element with appropriate media element
+            const mediaContainer = modalImage.parentElement;
+            mediaContainer.innerHTML = '';
+            
+            if (isVideo) {
+                const videoElement = document.createElement('video');
+                videoElement.id = 'recipeModalVideo';
+                videoElement.src = imageUrl;
+                videoElement.controls = true;
+                videoElement.autoplay = false;
+                videoElement.loop = true;
+                videoElement.muted = true;
+                videoElement.className = 'recipe-preview-media';
+                videoElement.alt = recipe.title || 'Recipe Preview';
+                mediaContainer.appendChild(videoElement);
+            } else {
+                const imgElement = document.createElement('img');
+                imgElement.id = 'recipeModalImage';
+                imgElement.src = imageUrl;
+                imgElement.className = 'recipe-preview-media';
+                imgElement.alt = recipe.title || 'Recipe Preview';
+                mediaContainer.appendChild(imgElement);
+            }
         }
         
         // Set generation parameters
@@ -167,55 +292,104 @@ class RecipeModal {
         const lorasListElement = document.getElementById('recipeLorasList');
         const lorasCountElement = document.getElementById('recipeLorasCount');
         
-        // 检查所有 LoRAs 是否都在库中
+        // Check all LoRAs status
         let allLorasAvailable = true;
         let missingLorasCount = 0;
+        let deletedLorasCount = 0;
         
         if (recipe.loras && recipe.loras.length > 0) {
             recipe.loras.forEach(lora => {
-                if (!lora.inLibrary) {
+                if (lora.isDeleted) {
+                    deletedLorasCount++;
+                } else if (!lora.inLibrary) {
                     allLorasAvailable = false;
                     missingLorasCount++;
                 }
             });
         }
         
-        // 设置 LoRAs 计数和状态
+        // Set LoRAs count and status
         if (lorasCountElement && recipe.loras) {
             const totalCount = recipe.loras.length;
             
-            // 创建状态指示器
+            // Create status indicator based on LoRA states
             let statusHTML = '';
             if (totalCount > 0) {
-                if (allLorasAvailable) {
+                if (allLorasAvailable && deletedLorasCount === 0) {
+                    // All LoRAs are available
                     statusHTML = `<div class="recipe-status ready"><i class="fas fa-check-circle"></i> Ready to use</div>`;
-                } else {
-                    statusHTML = `<div class="recipe-status missing"><i class="fas fa-exclamation-triangle"></i> ${missingLorasCount} missing</div>`;
+                } else if (missingLorasCount > 0) {
+                    // Some LoRAs are missing (prioritize showing missing over deleted)
+                    statusHTML = `<div class="recipe-status missing">
+                        <i class="fas fa-exclamation-triangle"></i> ${missingLorasCount} missing
+                        <div class="missing-tooltip">Click to download missing LoRAs</div>
+                    </div>`;
+                } else if (deletedLorasCount > 0 && missingLorasCount === 0) {
+                    // Some LoRAs are deleted but none are missing
+                    statusHTML = `<div class="recipe-status partial"><i class="fas fa-info-circle"></i> ${deletedLorasCount} deleted</div>`;
                 }
             }
             
             lorasCountElement.innerHTML = `<i class="fas fa-layer-group"></i> ${totalCount} LoRAs ${statusHTML}`;
+            
+            // Add click handler for missing LoRAs status
+            setTimeout(() => {
+                const missingStatus = document.querySelector('.recipe-status.missing');
+                if (missingStatus && missingLorasCount > 0) {
+                    missingStatus.classList.add('clickable');
+                    missingStatus.addEventListener('click', () => this.showDownloadMissingLorasModal());
+                }
+            }, 100);
         }
         
         if (lorasListElement && recipe.loras && recipe.loras.length > 0) {
             lorasListElement.innerHTML = recipe.loras.map(lora => {
                 const existsLocally = lora.inLibrary;
+                const isDeleted = lora.isDeleted;
                 const localPath = lora.localPath || '';
                 
-                // Create local status badge with a more stable structure
-                const localStatus = existsLocally ? 
-                    `<div class="local-badge">
-                        <i class="fas fa-check"></i> In Library
-                        <div class="local-path">${localPath}</div>
-                     </div>` : 
-                    `<div class="missing-badge">
-                        <i class="fas fa-exclamation-triangle"></i> Not in Library
-                     </div>`;
+                // Create status badge based on LoRA state
+                let localStatus;
+                if (existsLocally) {
+                    localStatus = `
+                        <div class="local-badge">
+                            <i class="fas fa-check"></i> In Library
+                            <div class="local-path">${localPath}</div>
+                        </div>`;
+                } else if (isDeleted) {
+                    localStatus = `
+                        <div class="deleted-badge">
+                            <i class="fas fa-trash-alt"></i> Deleted
+                        </div>`;
+                } else {
+                    localStatus = `
+                        <div class="missing-badge">
+                            <i class="fas fa-exclamation-triangle"></i> Not in Library
+                        </div>`;
+                }
+
+                // Check if preview is a video
+                const isPreviewVideo = lora.preview_url && lora.preview_url.toLowerCase().endsWith('.mp4');
+                const previewMedia = isPreviewVideo ?
+                    `<video class="thumbnail-video" autoplay loop muted playsinline>
+                        <source src="${lora.preview_url}" type="video/mp4">
+                     </video>` :
+                    `<img src="${lora.preview_url || '/loras_static/images/no-preview.png'}" alt="LoRA preview">`;
+
+                // Determine CSS class based on LoRA state
+                let loraItemClass = 'recipe-lora-item';
+                if (existsLocally) {
+                    loraItemClass += ' exists-locally';
+                } else if (isDeleted) {
+                    loraItemClass += ' is-deleted';
+                } else {
+                    loraItemClass += ' missing-locally';
+                }
 
                 return `
-                    <div class="recipe-lora-item ${existsLocally ? 'exists-locally' : 'missing-locally'}">
+                    <div class="${loraItemClass}">
                         <div class="recipe-lora-thumbnail">
-                            <img src="${lora.preview_url || '/loras_static/images/no-preview.png'}" alt="LoRA preview">
+                            ${previewMedia}
                         </div>
                         <div class="recipe-lora-content">
                             <div class="recipe-lora-header">
@@ -232,20 +406,263 @@ class RecipeModal {
                 `;
             }).join('');
             
-            // Generate recipe syntax for copy button
-            this.recipeLorasSyntax = recipe.loras.map(lora => 
-                `<lora:${lora.file_name}:${lora.strength || 1.0}>`
-            ).join(' ');
+            // Generate recipe syntax for copy button (this is now a placeholder, actual syntax will be fetched from the API)
+            this.recipeLorasSyntax = '';
             
         } else if (lorasListElement) {
             lorasListElement.innerHTML = '<div class="no-loras">No LoRAs associated with this recipe</div>';
             this.recipeLorasSyntax = '';
         }
+
+        console.log(this.currentRecipe.loras);
         
         // Show the modal
         modalManager.showModal('recipeModal');
     }
     
+    // Title editing methods
+    showTitleEditor() {
+        const titleContainer = document.getElementById('recipeModalTitle');
+        if (titleContainer) {
+            titleContainer.querySelector('.editable-content').classList.add('hide');
+            const editor = titleContainer.querySelector('#recipeTitleEditor');
+            editor.classList.add('active');
+            const input = editor.querySelector('input');
+            input.focus();
+            input.select();
+        }
+    }
+    
+    saveTitleEdit() {
+        const titleContainer = document.getElementById('recipeModalTitle');
+        if (titleContainer) {
+            const editor = titleContainer.querySelector('#recipeTitleEditor');
+            const input = editor.querySelector('input');
+            const newTitle = input.value.trim();
+            
+            // Check if title changed
+            if (newTitle && newTitle !== this.currentRecipe.title) {
+                // Update title in the UI
+                titleContainer.querySelector('.content-text').textContent = newTitle;
+                
+                // Update the recipe on the server
+                this.updateRecipeMetadata({ title: newTitle });
+            }
+            
+            // Hide editor
+            editor.classList.remove('active');
+            titleContainer.querySelector('.editable-content').classList.remove('hide');
+        }
+    }
+    
+    cancelTitleEdit() {
+        const titleContainer = document.getElementById('recipeModalTitle');
+        if (titleContainer) {
+            // Reset input value
+            const editor = titleContainer.querySelector('#recipeTitleEditor');
+            const input = editor.querySelector('input');
+            input.value = this.currentRecipe.title || '';
+            
+            // Hide editor
+            editor.classList.remove('active');
+            titleContainer.querySelector('.editable-content').classList.remove('hide');
+        }
+    }
+    
+    // Tags editing methods
+    showTagsEditor() {
+        const tagsContainer = document.getElementById('recipeTagsCompact');
+        if (tagsContainer) {
+            tagsContainer.querySelector('.editable-content').classList.add('hide');
+            const editor = tagsContainer.querySelector('#recipeTagsEditor');
+            editor.classList.add('active');
+            const input = editor.querySelector('input');
+            input.focus();
+        }
+    }
+    
+    saveTagsEdit() {
+        const tagsContainer = document.getElementById('recipeTagsCompact');
+        if (tagsContainer) {
+            const editor = tagsContainer.querySelector('#recipeTagsEditor');
+            const input = editor.querySelector('input');
+            const tagsText = input.value.trim();
+            
+            // Parse tags
+            let newTags = [];
+            if (tagsText) {
+                newTags = tagsText.split(',')
+                    .map(tag => tag.trim())
+                    .filter(tag => tag.length > 0);
+            }
+            
+            // Check if tags changed
+            const oldTags = this.currentRecipe.tags || [];
+            const tagsChanged = 
+                newTags.length !== oldTags.length || 
+                newTags.some((tag, index) => tag !== oldTags[index]);
+            
+            if (tagsChanged) {
+                // Update the recipe on the server
+                this.updateRecipeMetadata({ tags: newTags });
+                
+                // Update tags in the UI
+                const tagsDisplay = tagsContainer.querySelector('.tags-display');
+                tagsDisplay.innerHTML = '';
+                
+                if (newTags.length > 0) {
+                    // Limit displayed tags to 5, show a "+X more" button if needed
+                    const maxVisibleTags = 5;
+                    const visibleTags = newTags.slice(0, maxVisibleTags);
+                    const remainingTags = newTags.length > maxVisibleTags ? newTags.slice(maxVisibleTags) : [];
+                    
+                    // Add visible tags
+                    visibleTags.forEach(tag => {
+                        const tagElement = document.createElement('div');
+                        tagElement.className = 'recipe-tag-compact';
+                        tagElement.textContent = tag;
+                        tagsDisplay.appendChild(tagElement);
+                    });
+                    
+                    // Add "more" button if needed
+                    if (remainingTags.length > 0) {
+                        const moreButton = document.createElement('div');
+                        moreButton.className = 'recipe-tag-more';
+                        moreButton.textContent = `+${remainingTags.length} more`;
+                        tagsDisplay.appendChild(moreButton);
+                        
+                        // Update tooltip content
+                        const tooltipContent = document.getElementById('recipeTagsTooltipContent');
+                        if (tooltipContent) {
+                            tooltipContent.innerHTML = '';
+                            newTags.forEach(tag => {
+                                const tooltipTag = document.createElement('div');
+                                tooltipTag.className = 'tooltip-tag';
+                                tooltipTag.textContent = tag;
+                                tooltipContent.appendChild(tooltipTag);
+                            });
+                        }
+                        
+                        // Re-add tooltip functionality
+                        moreButton.addEventListener('mouseenter', () => {
+                            document.getElementById('recipeTagsTooltip').classList.add('visible');
+                        });
+                        
+                        moreButton.addEventListener('mouseleave', () => {
+                            setTimeout(() => {
+                                if (!document.getElementById('recipeTagsTooltip').matches(':hover')) {
+                                    document.getElementById('recipeTagsTooltip').classList.remove('visible');
+                                }
+                            }, 300);
+                        });
+                    }
+                } else {
+                    tagsDisplay.innerHTML = '<div class="no-tags">No tags</div>';
+                }
+                
+                // Update the current recipe object
+                this.currentRecipe.tags = newTags;
+            }
+            
+            // Hide editor
+            editor.classList.remove('active');
+            tagsContainer.querySelector('.editable-content').classList.remove('hide');
+        }
+    }
+    
+    cancelTagsEdit() {
+        const tagsContainer = document.getElementById('recipeTagsCompact');
+        if (tagsContainer) {
+            // Reset input value
+            const editor = tagsContainer.querySelector('#recipeTagsEditor');
+            const input = editor.querySelector('input');
+            input.value = this.currentRecipe.tags ? this.currentRecipe.tags.join(', ') : '';
+            
+            // Hide editor
+            editor.classList.remove('active');
+            tagsContainer.querySelector('.editable-content').classList.remove('hide');
+        }
+    }
+    
+    // Update recipe metadata on the server
+    async updateRecipeMetadata(updates) {
+        try {
+            const response = await fetch(`/api/recipe/${this.recipeId}/update`, {
+                method: 'PUT',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify(updates)
+            });
+
+            const data = await response.json();
+
+            if (data.success) {
+                // 显示保存成功的提示
+                if (updates.title) {
+                    showToast('Recipe name updated successfully', 'success');
+                } else if (updates.tags) {
+                    showToast('Recipe tags updated successfully', 'success');
+                } else {
+                    showToast('Recipe updated successfully', 'success');
+                }
+                
+                // 更新当前recipe对象的属性
+                Object.assign(this.currentRecipe, updates);
+                
+                // 确保这个更新也传播到卡片视图
+                // 尝试找到可能显示这个recipe的卡片并更新它
+                try {
+                    const recipeCards = document.querySelectorAll('.recipe-card');
+                    recipeCards.forEach(card => {
+                        if (card.dataset.recipeId === this.recipeId) {
+                            // 更新卡片标题
+                            if (updates.title) {
+                                const titleElement = card.querySelector('.recipe-title');
+                                if (titleElement) {
+                                    titleElement.textContent = updates.title;
+                                }
+                            }
+                            
+                            // 更新卡片标签
+                            if (updates.tags) {
+                                const tagsElement = card.querySelector('.recipe-tags');
+                                if (tagsElement) {
+                                    if (updates.tags.length > 0) {
+                                        tagsElement.innerHTML = updates.tags.map(
+                                            tag => `<div class="recipe-tag">${tag}</div>`
+                                        ).join('');
+                                    } else {
+                                        tagsElement.innerHTML = '';
+                                    }
+                                }
+                            }
+                        }
+                    });
+                } catch (err) {
+                    console.log("Non-critical error updating recipe cards:", err);
+                }
+                
+                // 重要：强制刷新recipes列表，确保从服务器获取最新数据
+                try {
+                    if (window.recipeManager && typeof window.recipeManager.loadRecipes === 'function') {
+                        // 异步刷新recipes列表，不阻塞用户界面
+                        setTimeout(() => {
+                            window.recipeManager.loadRecipes(true);
+                        }, 500);
+                    }
+                } catch (err) {
+                    console.log("Error refreshing recipes list:", err);
+                }
+            } else {
+                showToast(`Failed to update recipe: ${data.error}`, 'error');
+            }
+        } catch (error) {
+            console.error('Error updating recipe:', error);
+            showToast(`Error updating recipe: ${error.message}`, 'error');
+        }
+    }
+    
     // Setup copy buttons for prompts and recipe syntax
     setupCopyButtons() {
         const copyPromptBtn = document.getElementById('copyPromptBtn');
@@ -268,11 +685,42 @@ class RecipeModal {
         
         if (copyRecipeSyntaxBtn) {
             copyRecipeSyntaxBtn.addEventListener('click', () => {
-                this.copyToClipboard(this.recipeLorasSyntax, 'Recipe syntax copied to clipboard');
+                // Use backend API to get recipe syntax
+                this.fetchAndCopyRecipeSyntax();
             });
         }
     }
     
+    // Fetch recipe syntax from backend and copy to clipboard
+    async fetchAndCopyRecipeSyntax() {
+        if (!this.recipeId) {
+            showToast('No recipe ID available', 'error');
+            return;
+        }
+        
+        try {
+            // Fetch recipe syntax from backend
+            const response = await fetch(`/api/recipe/${this.recipeId}/syntax`);
+            
+            if (!response.ok) {
+                throw new Error(`Failed to get recipe syntax: ${response.statusText}`);
+            }
+            
+            const data = await response.json();
+            
+            if (data.success && data.syntax) {
+                // Copy to clipboard
+                await navigator.clipboard.writeText(data.syntax);
+                showToast('Recipe syntax copied to clipboard', 'success');
+            } else {
+                throw new Error(data.error || 'No syntax returned from server');
+            }
+        } catch (error) {
+            console.error('Error fetching recipe syntax:', error);
+            showToast(`Error copying recipe syntax: ${error.message}`, 'error');
+        }
+    }
+    
     // Helper method to copy text to clipboard
     copyToClipboard(text, successMessage) {
         navigator.clipboard.writeText(text).then(() => {
@@ -282,6 +730,105 @@ class RecipeModal {
             showToast('Failed to copy text', 'error');
         });
     }
+
+    // Add new method to handle downloading missing LoRAs
+    async showDownloadMissingLorasModal() {
+        console.log("currentRecipe", this.currentRecipe);
+        // Get missing LoRAs from the current recipe
+        const missingLoras = this.currentRecipe.loras.filter(lora => !lora.inLibrary);
+        console.log("missingLoras", missingLoras);
+        
+        if (missingLoras.length === 0) {
+            showToast('No missing LoRAs to download', 'info');
+            return;
+        }
+
+        try {
+            state.loadingManager.showSimpleLoading('Getting version info for missing LoRAs...');
+
+            // Get version info for each missing LoRA by calling the appropriate API endpoint
+            const missingLorasWithVersionInfoPromises = missingLoras.map(async lora => {
+                let endpoint;
+                
+                // Determine which endpoint to use based on available data
+                if (lora.modelVersionId) {
+                    endpoint = `/api/civitai/model/${lora.modelVersionId}`;
+                } else if (lora.hash) {
+                    endpoint = `/api/civitai/model/${lora.hash}`;
+                } else {
+                    console.error("Missing both hash and modelVersionId for lora:", lora);
+                    return null;
+                }
+                
+                const response = await fetch(endpoint);
+                const versionInfo = await response.json();
+                
+                // Return original lora data combined with version info
+                return {
+                    ...lora,
+                    civitaiInfo: versionInfo
+                };
+            });
+            
+            // Wait for all API calls to complete
+            const lorasWithVersionInfo = await Promise.all(missingLorasWithVersionInfoPromises);
+            console.log("Loras with version info:", lorasWithVersionInfo);
+            
+            // Filter out null values (failed requests)
+            const validLoras = lorasWithVersionInfo.filter(lora => lora !== null);
+            
+            if (validLoras.length === 0) {
+                showToast('Failed to get information for missing LoRAs', 'error');
+                return;
+            }
+            
+            // Close the recipe modal first
+            modalManager.closeModal('recipeModal');
+            
+            // Prepare data for import manager using the retrieved information
+            const recipeData = {
+                loras: validLoras.map(lora => {
+                    const civitaiInfo = lora.civitaiInfo;
+                    const modelFile = civitaiInfo.files ? 
+                        civitaiInfo.files.find(file => file.type === 'Model') : null;
+                    
+                    return {
+                        // Basic lora info
+                        name: civitaiInfo.model?.name || lora.name,
+                        version: civitaiInfo.name || '',
+                        strength: lora.strength || 1.0,
+                        
+                        // Model identifiers
+                        hash: modelFile?.hashes?.SHA256?.toLowerCase() || lora.hash,
+                        modelVersionId: civitaiInfo.id || lora.modelVersionId,
+                        
+                        // Metadata
+                        thumbnailUrl: civitaiInfo.images?.[0]?.url || '',
+                        baseModel: civitaiInfo.baseModel || '',
+                        downloadUrl: civitaiInfo.downloadUrl || '',
+                        size: modelFile ? (modelFile.sizeKB * 1024) : 0,
+                        file_name: modelFile ? modelFile.name.split('.')[0] : '',
+                        
+                        // Status flags
+                        existsLocally: false,
+                        isDeleted: civitaiInfo.error === "Model not found",
+                        isEarlyAccess: !!civitaiInfo.earlyAccessEndsAt,
+                        earlyAccessEndsAt: civitaiInfo.earlyAccessEndsAt || ''
+                    };
+                })
+            };
+            
+            console.log("recipeData for import:", recipeData);
+            
+            // Call ImportManager's download missing LoRAs method
+            window.importManager.downloadMissingLoras(recipeData, this.currentRecipe.id);
+        } catch (error) {
+            console.error("Error downloading missing LoRAs:", error);
+            showToast('Error preparing LoRAs for download', 'error');
+        } finally {
+            state.loadingManager.hide();
+        }
+    }
 }
 
 export { RecipeModal }; 

static/js/loras.js

@@ -17,6 +17,7 @@ import { LoraContextMenu } from './components/ContextMenu.js';
 import { moveManager } from './managers/MoveManager.js';
 import { updateCardsForBulkMode } from './components/LoraCard.js';
 import { bulkManager } from './managers/BulkManager.js';
+import { setStorageItem, getStorageItem } from './utils/storageHelpers.js';
 
 // Initialize the LoRA page
 class LoraPageManager {
@@ -63,7 +64,7 @@ class LoraPageManager {
     
     async initialize() {
         // Initialize page-specific components
-        initializeEventListeners();
+        this.initEventListeners();
         restoreFolderFilter();
         initFolderTagsVisibility();
         new LoraContextMenu();
@@ -77,22 +78,38 @@ class LoraPageManager {
         // Initialize common page features (lazy loading, infinite scroll)
         appCore.initializePageFeatures();
     }
-}
 
-// Initialize event listeners
-function initializeEventListeners() {
-    const sortSelect = document.getElementById('sortSelect');
-    if (sortSelect) {
-        sortSelect.value = state.sortBy;
-        sortSelect.addEventListener('change', async (e) => {
-            state.sortBy = e.target.value;
-            await resetAndReload();
-        });
+    loadSortPreference() {
+        const savedSort = getStorageItem('loras_sort');
+        if (savedSort) {
+            state.sortBy = savedSort;
+            const sortSelect = document.getElementById('sortSelect');
+            if (sortSelect) {
+                sortSelect.value = savedSort;
+            }
+        }
     }
 
-    document.querySelectorAll('.folder-tags .tag').forEach(tag => {
-        tag.addEventListener('click', toggleFolder);
-    });
+    saveSortPreference(sortValue) {
+        setStorageItem('loras_sort', sortValue);
+    }
+
+	initEventListeners() {
+		const sortSelect = document.getElementById('sortSelect');
+		if (sortSelect) {
+			sortSelect.value = state.sortBy;
+			this.loadSortPreference();
+			sortSelect.addEventListener('change', async (e) => {
+				state.sortBy = e.target.value;
+				this.saveSortPreference(e.target.value);
+				await resetAndReload();
+			});
+		}
+
+		document.querySelectorAll('.folder-tags .tag').forEach(tag => {
+			tag.addEventListener('click', toggleFolder);
+		});
+	}
 }
 
 // Initialize everything when DOM is ready

static/js/managers/DownloadManager.js

@@ -3,7 +3,7 @@ import { showToast } from '../utils/uiHelpers.js';
 import { LoadingManager } from './LoadingManager.js';
 import { state } from '../state/index.js';
 import { resetAndReload } from '../api/loraApi.js';
-
+import { getStorageItem } from '../utils/storageHelpers.js';
 export class DownloadManager {
     constructor() {
         this.currentVersion = null;
@@ -246,6 +246,12 @@ export class DownloadManager {
                 `<option value="${root}">${root}</option>`
             ).join('');
 
+            // Set default lora root if available
+            const defaultRoot = getStorageItem('settings', {}).default_loras_root;
+            if (defaultRoot && data.roots.includes(defaultRoot)) {
+                loraRoot.value = defaultRoot;
+            }
+
             // Initialize folder browser after loading roots
             this.initializeFolderBrowser();
         } catch (error) {

static/js/managers/ImportManager.js

@@ -1,6 +1,7 @@
 import { modalManager } from './ModalManager.js';
 import { showToast } from '../utils/uiHelpers.js';
 import { LoadingManager } from './LoadingManager.js';
+import { getStorageItem } from '../utils/storageHelpers.js';
 
 export class ImportManager {
     constructor() {
@@ -26,7 +27,7 @@ export class ImportManager {
         this.importMode = 'upload'; // Default mode: 'upload' or 'url'
     }
 
-    showImportModal() {
+    showImportModal(recipeData = null, recipeId = null) {
         if (!this.initialized) {
             // Check if modal exists
             const modal = document.getElementById('importModal');
@@ -39,6 +40,10 @@ export class ImportManager {
         
         // Always reset the state when opening the modal
         this.resetSteps();
+        if (recipeData) {
+            this.downloadableLoRAs = recipeData.loras;
+            this.recipeId = recipeId;
+        }
         
         // Show the modal
         modalManager.showModal('importModal', null, () => {
@@ -116,6 +121,7 @@ export class ImportManager {
         this.recipeName = '';
         this.recipeTags = [];
         this.missingLoras = [];
+        this.downloadableLoRAs = [];
         
         // Reset import mode to upload
         this.importMode = 'upload';
@@ -398,7 +404,7 @@ export class ImportManager {
                 }
             }
         }
-        
+
         // Update LoRA count information
         const totalLoras = this.recipeData.loras.length;
         const existingLoras = this.recipeData.loras.filter(lora => lora.existsLocally).length;
@@ -548,33 +554,24 @@ export class ImportManager {
                 <div class="warning-icon"><i class="fas fa-exclamation-triangle"></i></div>
                 <div class="warning-content">
                     <div class="warning-title">${deletedLoras} LoRA(s) have been deleted from Civitai</div>
-                    <div class="warning-text">These LoRAs cannot be downloaded. If you continue, they will be removed from the recipe.</div>
+                    <div class="warning-text">These LoRAs cannot be downloaded. If you continue, they will remain in the recipe but won't be included when used.</div>
                 </div>
             `;
             
             // Insert before the buttons container
             buttonsContainer.parentNode.insertBefore(warningContainer, buttonsContainer);
+        }
             
-            // Update next button text to be more clear
-            nextButton.textContent = 'Continue Without Deleted LoRAs';
+        // If we have missing LoRAs (not deleted), show "Download Missing LoRAs"
+        // Otherwise show "Save Recipe"
+        const missingNotDeleted = this.recipeData.loras.filter(
+            lora => !lora.existsLocally && !lora.isDeleted
+        ).length;
+        
+        if (missingNotDeleted > 0) {
+            nextButton.textContent = 'Download Missing LoRAs';
         } else {
-            // Remove warning if no deleted LoRAs
-            const warningMsg = document.getElementById('deletedLorasWarning');
-            if (warningMsg) {
-                warningMsg.remove();
-            }
-            
-            // If we have missing LoRAs (not deleted), show "Download Missing LoRAs"
-            // Otherwise show "Save Recipe"
-            const missingNotDeleted = this.recipeData.loras.filter(
-                lora => !lora.existsLocally && !lora.isDeleted
-            ).length;
-            
-            if (missingNotDeleted > 0) {
-                nextButton.textContent = 'Download Missing LoRAs';
-            } else {
-                nextButton.textContent = 'Save Recipe';
-            }
+            nextButton.textContent = 'Save Recipe';
         }
     }
 
@@ -783,6 +780,12 @@ export class ImportManager {
                 loraRoot.innerHTML = rootsData.roots.map(root => 
                     `<option value="${root}">${root}</option>`
                 ).join('');
+                
+                // Set default lora root if available
+                const defaultRoot = getStorageItem('settings', {}).default_loras_root;
+                if (defaultRoot && rootsData.roots.includes(defaultRoot)) {
+                    loraRoot.value = defaultRoot;
+                }
             }
             
             // Fetch folders
@@ -839,219 +842,233 @@ export class ImportManager {
     }
 
     async saveRecipe() {
-        if (!this.recipeName) {
+        // Check if we're in download-only mode (for existing recipe)
+        const isDownloadOnly = !!this.recipeId;
+
+        console.log("isDownloadOnly", isDownloadOnly);
+        
+        if (!isDownloadOnly && !this.recipeName) {
             showToast('Please enter a recipe name', 'error');
             return;
         }
         
         try {
-            // First save the recipe
-            this.loadingManager.showSimpleLoading('Saving recipe...');
+            this.loadingManager.showSimpleLoading(isDownloadOnly ? 'Preparing download...' : 'Saving recipe...');
             
-            // Create form data for save request
-            const formData = new FormData();
-            
-            // Handle image data - either from file upload or from URL mode
-            if (this.recipeImage) {
-                // File upload mode
-                formData.append('image', this.recipeImage);
-            } else if (this.recipeData && this.recipeData.image_base64) {
-                // URL mode with base64 data
-                formData.append('image_base64', this.recipeData.image_base64);
-            } else if (this.importMode === 'url') {
-                // Fallback for URL mode - tell backend to fetch the image again
-                const urlInput = document.getElementById('imageUrlInput');
-                if (urlInput && urlInput.value) {
-                    formData.append('image_url', urlInput.value);
+            // If we're only downloading LoRAs for an existing recipe, skip the recipe save step
+            if (!isDownloadOnly) {
+                // First save the recipe
+                // Create form data for save request
+                const formData = new FormData();
+                
+                // Handle image data - either from file upload or from URL mode
+                if (this.recipeImage) {
+                    // File upload mode
+                    formData.append('image', this.recipeImage);
+                } else if (this.recipeData && this.recipeData.image_base64) {
+                    // URL mode with base64 data
+                    formData.append('image_base64', this.recipeData.image_base64);
+                } else if (this.importMode === 'url') {
+                    // Fallback for URL mode - tell backend to fetch the image again
+                    const urlInput = document.getElementById('imageUrlInput');
+                    if (urlInput && urlInput.value) {
+                        formData.append('image_url', urlInput.value);
+                    } else {
+                        throw new Error('No image data available');
+                    }
                 } else {
                     throw new Error('No image data available');
                 }
-            } else {
-                throw new Error('No image data available');
+                
+                formData.append('name', this.recipeName);
+                formData.append('tags', JSON.stringify(this.recipeTags));
+                
+                // Prepare complete metadata including generation parameters
+                const completeMetadata = {
+                    base_model: this.recipeData.base_model || "",
+                    loras: this.recipeData.loras || [],
+                    gen_params: this.recipeData.gen_params || {},
+                    raw_metadata: this.recipeData.raw_metadata || {}
+                };
+                
+                formData.append('metadata', JSON.stringify(completeMetadata));
+            
+                // Send save request
+                const response = await fetch('/api/recipes/save', {
+                    method: 'POST',
+                    body: formData
+                });
+                
+                const result = await response.json();
+
+                if (!result.success) {
+                    // Handle save error
+                    console.error("Failed to save recipe:", result.error);
+                    showToast(result.error, 'error');
+                    // Close modal
+                    modalManager.closeModal('importModal');
+                    return;
+                }
             }
-            
-            formData.append('name', this.recipeName);
-            formData.append('tags', JSON.stringify(this.recipeTags));
-            
-            // Prepare complete metadata including generation parameters
-            const completeMetadata = {
-                base_model: this.recipeData.base_model || "",
-                loras: this.recipeData.loras || [],
-                gen_params: this.recipeData.gen_params || {},
-                raw_metadata: this.recipeData.raw_metadata || {}
-            };
-            
-            formData.append('metadata', JSON.stringify(completeMetadata));
-            
-            // Send save request
-            const response = await fetch('/api/recipes/save', {
-                method: 'POST',
-                body: formData
-            });
-            
-            const result = await response.json();
-            if (result.success) {
-                // Handle successful save
+
+            // Check if we need to download LoRAs
+            let failedDownloads = 0;
+            if (this.downloadableLoRAs && this.downloadableLoRAs.length > 0) {
+                // For download, we need to validate the target path
+                const loraRoot = document.getElementById('importLoraRoot')?.value;
+                if (!loraRoot) {
+                    throw new Error('Please select a LoRA root directory');
+                }
                 
+                // Build target path
+                let targetPath = loraRoot;
+                if (this.selectedFolder) {
+                    targetPath += '/' + this.selectedFolder;
+                }
                 
-                // Check if we need to download LoRAs
-                if (this.downloadableLoRAs && this.downloadableLoRAs.length > 0) {
-                    // For download, we need to validate the target path
-                    const loraRoot = document.getElementById('importLoraRoot')?.value;
-                    if (!loraRoot) {
-                        throw new Error('Please select a LoRA root directory');
-                    }
-                    
-                    // Build target path
-                    let targetPath = loraRoot;
-                    if (this.selectedFolder) {
-                        targetPath += '/' + this.selectedFolder;
-                    }
-                    
-                    const newFolder = document.getElementById('importNewFolder')?.value?.trim();
-                    if (newFolder) {
-                        targetPath += '/' + newFolder;
-                    }
-                    
-                    // Set up WebSocket for progress updates
-                    const wsProtocol = window.location.protocol === 'https:' ? 'wss://' : 'ws://';
-                    const ws = new WebSocket(`${wsProtocol}${window.location.host}/ws/fetch-progress`);
-                    
-                    // Show enhanced loading with progress details for multiple items
-                    const updateProgress = this.loadingManager.showDownloadProgress(this.downloadableLoRAs.length);
-                    
-                    let completedDownloads = 0;
-                    let failedDownloads = 0;
-                    let earlyAccessFailures = 0;
-                    let currentLoraProgress = 0;
-                    
-                    // Set up progress tracking for current download
-                    ws.onmessage = (event) => {
-                        const data = JSON.parse(event.data);
-                        if (data.status === 'progress') {
-                            // Update current LoRA progress
-                            currentLoraProgress = data.progress;
-                            
-                            // Get current LoRA name
-                            const currentLora = this.downloadableLoRAs[completedDownloads + failedDownloads];
-                            const loraName = currentLora ? currentLora.name : '';
-                            
-                            // Update progress display
-                            updateProgress(currentLoraProgress, completedDownloads, loraName);
-                            
-                            // Add more detailed status messages based on progress
-                            if (currentLoraProgress < 3) {
-                                this.loadingManager.setStatus(
-                                    `Preparing download for LoRA ${completedDownloads + failedDownloads + 1}/${this.downloadableLoRAs.length}`
-                                );
-                            } else if (currentLoraProgress === 3) {
-                                this.loadingManager.setStatus(
-                                    `Downloaded preview for LoRA ${completedDownloads + failedDownloads + 1}/${this.downloadableLoRAs.length}`
-                                );
-                            } else if (currentLoraProgress > 3 && currentLoraProgress < 100) {
-                                this.loadingManager.setStatus(
-                                    `Downloading LoRA ${completedDownloads + failedDownloads + 1}/${this.downloadableLoRAs.length}`
-                                );
-                            } else {
-                                this.loadingManager.setStatus(
-                                    `Finalizing LoRA ${completedDownloads + failedDownloads + 1}/${this.downloadableLoRAs.length}`
-                                );
-                            }
-                        }
-                    };
-                    
-                    for (let i = 0; i < this.downloadableLoRAs.length; i++) {
-                        const lora = this.downloadableLoRAs[i];
+                const newFolder = document.getElementById('importNewFolder')?.value?.trim();
+                if (newFolder) {
+                    targetPath += '/' + newFolder;
+                }
+                
+                // Set up WebSocket for progress updates
+                const wsProtocol = window.location.protocol === 'https:' ? 'wss://' : 'ws://';
+                const ws = new WebSocket(`${wsProtocol}${window.location.host}/ws/fetch-progress`);
+                
+                // Show enhanced loading with progress details for multiple items
+                const updateProgress = this.loadingManager.showDownloadProgress(this.downloadableLoRAs.length);
+                
+                let completedDownloads = 0;
+                let accessFailures = 0;
+                let currentLoraProgress = 0;
+                
+                // Set up progress tracking for current download
+                ws.onmessage = (event) => {
+                    const data = JSON.parse(event.data);
+                    if (data.status === 'progress') {
+                        // Update current LoRA progress
+                        currentLoraProgress = data.progress;
                         
-                        // Reset current LoRA progress for new download
-                        currentLoraProgress = 0;
+                        // Get current LoRA name
+                        const currentLora = this.downloadableLoRAs[completedDownloads + failedDownloads];
+                        const loraName = currentLora ? currentLora.name : '';
                         
-                        // Initial status update for new LoRA
-                        this.loadingManager.setStatus(`Starting download for LoRA ${i+1}/${this.downloadableLoRAs.length}`);
-                        updateProgress(0, completedDownloads, lora.name);
+                        // Update progress display
+                        updateProgress(currentLoraProgress, completedDownloads, loraName);
                         
-                        try {
-                            // Download the LoRA
-                            const response = await fetch('/api/download-lora', {
-                                method: 'POST',
-                                headers: { 'Content-Type': 'application/json' },
-                                body: JSON.stringify({
-                                    download_url: lora.downloadUrl,
-                                    lora_root: loraRoot,
-                                    relative_path: targetPath.replace(loraRoot + '/', '')
-                                })
-                            });
-                            
-                            if (!response.ok) {
-                                const errorText = await response.text();
-                                console.error(`Failed to download LoRA ${lora.name}: ${errorText}`);
-                                
-                                // Check if this is an early access error (status 401 is the key indicator)
-                                if (response.status === 401 || 
-                                   (errorText.toLowerCase().includes('early access') || 
-                                    errorText.toLowerCase().includes('purchase'))) {
-                                    earlyAccessFailures++;
-                                    this.loadingManager.setStatus(
-                                        `Failed to download ${lora.name}: Early Access required`
-                                    );
-                                }
-                                
-                                failedDownloads++;
-                                // Continue with next download
-                            } else {
-                                completedDownloads++;
-                                
-                                // Update progress to show completion of current LoRA
-                                updateProgress(100, completedDownloads, '');
-                                
-                                if (completedDownloads + failedDownloads < this.downloadableLoRAs.length) {
-                                    this.loadingManager.setStatus(
-                                        `Completed ${completedDownloads}/${this.downloadableLoRAs.length} LoRAs. Starting next download...`
-                                    );
-                                }
-                            }
-                        } catch (downloadError) {
-                            console.error(`Error downloading LoRA ${lora.name}:`, downloadError);
-                            failedDownloads++;
-                            // Continue with next download
-                        }
-                    }
-                    
-                    // Close WebSocket
-                    ws.close();
-                    
-                    // Show appropriate completion message based on results
-                    if (failedDownloads === 0) {
-                        showToast(`All ${completedDownloads} LoRAs downloaded successfully`, 'success');
-                    } else {
-                        if (earlyAccessFailures > 0) {
-                            showToast(
-                                `Downloaded ${completedDownloads} of ${this.downloadableLoRAs.length} LoRAs. ${earlyAccessFailures} failed due to Early Access restrictions.`,
-                                'error'
+                        // Add more detailed status messages based on progress
+                        if (currentLoraProgress < 3) {
+                            this.loadingManager.setStatus(
+                                `Preparing download for LoRA ${completedDownloads + failedDownloads + 1}/${this.downloadableLoRAs.length}`
+                            );
+                        } else if (currentLoraProgress === 3) {
+                            this.loadingManager.setStatus(
+                                `Downloaded preview for LoRA ${completedDownloads + failedDownloads + 1}/${this.downloadableLoRAs.length}`
+                            );
+                        } else if (currentLoraProgress > 3 && currentLoraProgress < 100) {
+                            this.loadingManager.setStatus(
+                                `Downloading LoRA ${completedDownloads + failedDownloads + 1}/${this.downloadableLoRAs.length}`
                             );
                         } else {
-                            showToast(`Downloaded ${completedDownloads} of ${this.downloadableLoRAs.length} LoRAs`, 'error');
+                            this.loadingManager.setStatus(
+                                `Finalizing LoRA ${completedDownloads + failedDownloads + 1}/${this.downloadableLoRAs.length}`
+                            );
                         }
                     }
+                };
+                
+                for (let i = 0; i < this.downloadableLoRAs.length; i++) {
+                    const lora = this.downloadableLoRAs[i];
+                    
+                    // Reset current LoRA progress for new download
+                    currentLoraProgress = 0;
+                    
+                    // Initial status update for new LoRA
+                    this.loadingManager.setStatus(`Starting download for LoRA ${i+1}/${this.downloadableLoRAs.length}`);
+                    updateProgress(0, completedDownloads, lora.name);
+                    
+                    try {
+                        // Download the LoRA
+                        const response = await fetch('/api/download-lora', {
+                            method: 'POST',
+                            headers: { 'Content-Type': 'application/json' },
+                            body: JSON.stringify({
+                                download_url: lora.downloadUrl,
+                                model_version_id: lora.modelVersionId,
+                                model_hash: lora.hash,
+                                lora_root: loraRoot,
+                                relative_path: targetPath.replace(loraRoot + '/', '')
+                            })
+                        });
+                        
+                        if (!response.ok) {
+                            const errorText = await response.text();
+                            console.error(`Failed to download LoRA ${lora.name}: ${errorText}`);
+                            
+                            // Check if this is an early access error (status 401 is the key indicator)
+                            if (response.status === 401) {
+                                accessFailures++;
+                                this.loadingManager.setStatus(
+                                    `Failed to download ${lora.name}: Access restricted`
+                                );
+                            }
+                            
+                            failedDownloads++;
+                            // Continue with next download
+                        } else {
+                            completedDownloads++;
+                            
+                            // Update progress to show completion of current LoRA
+                            updateProgress(100, completedDownloads, '');
+                            
+                            if (completedDownloads + failedDownloads < this.downloadableLoRAs.length) {
+                                this.loadingManager.setStatus(
+                                    `Completed ${completedDownloads}/${this.downloadableLoRAs.length} LoRAs. Starting next download...`
+                                );
+                            }
+                        }
+                    } catch (downloadError) {
+                        console.error(`Error downloading LoRA ${lora.name}:`, downloadError);
+                        failedDownloads++;
+                        // Continue with next download
+                    }
                 }
+                
+                // Close WebSocket
+                ws.close();
+                
+                // Show appropriate completion message based on results
+                if (failedDownloads === 0) {
+                    showToast(`All ${completedDownloads} LoRAs downloaded successfully`, 'success');
+                } else {
+                    if (accessFailures > 0) {
+                        showToast(
+                            `Downloaded ${completedDownloads} of ${this.downloadableLoRAs.length} LoRAs. ${accessFailures} failed due to access restrictions. Check your API key in settings or early access status.`,
+                            'error'
+                        );
+                    } else {
+                        showToast(`Downloaded ${completedDownloads} of ${this.downloadableLoRAs.length} LoRAs`, 'error');
+                    }
+                }
+            }
 
-                // Show success message for recipe save
-                showToast(`Recipe "${this.recipeName}" saved successfully`, 'success');
-                
-                // Close modal and reload recipes
-                modalManager.closeModal('importModal');
-                
-                window.recipeManager.loadRecipes(true); // true to reset pagination
-                
+            // Show success message
+            if (isDownloadOnly) {
+                if (failedDownloads === 0) {    
+                    showToast('LoRAs downloaded successfully', 'success');
+                }
             } else {
-                // Handle error
-                console.error(`Failed to save recipe: ${result.error}`);
-                // Show error message to user
-                showToast(result.error, 'error');
+                showToast(`Recipe "${this.recipeName}" saved successfully`, 'success');
             }
             
+            // Close modal
+            modalManager.closeModal('importModal');
+            
+            // Refresh the recipe
+            window.recipeManager.loadRecipes(this.recipeId);
+            
         } catch (error) {
-            console.error('Error saving recipe:', error);
+            console.error('Error:', error);
             showToast(error.message, 'error');
         } finally {
             this.loadingManager.hide();
@@ -1213,4 +1230,33 @@ export class ImportManager {
         
         return true;
     }
-} 
+
+    // Add new method to handle downloading missing LoRAs from a recipe
+    downloadMissingLoras(recipeData, recipeId) {
+        // Store the recipe data and ID
+        this.recipeData = recipeData;
+        this.recipeId = recipeId;
+        
+        // Show the location step directly
+        this.showImportModal(recipeData, recipeId);
+        this.proceedToLocation();
+        
+        // Update the modal title to reflect we're downloading for an existing recipe
+        const modalTitle = document.querySelector('#importModal h2');
+        if (modalTitle) {
+            modalTitle.textContent = 'Download Missing LoRAs';
+        }
+        
+        // Update the save button text
+        const saveButton = document.querySelector('#locationStep .primary-btn');
+        if (saveButton) {
+            saveButton.textContent = 'Download Missing LoRAs';
+        }
+        
+        // Hide the back button since we're skipping steps
+        const backButton = document.querySelector('#locationStep .secondary-btn');
+        if (backButton) {
+            backButton.style.display = 'none';
+        }
+    }
+}

static/js/managers/MoveManager.js

@@ -2,6 +2,7 @@ import { showToast } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { resetAndReload } from '../api/loraApi.js';
 import { modalManager } from './ModalManager.js';
+import { getStorageItem } from '../utils/storageHelpers.js';
 
 class MoveManager {
     constructor() {
@@ -87,6 +88,12 @@ class MoveManager {
                 `<option value="${root}">${root}</option>`
             ).join('');
 
+            // Set default lora root if available
+            const defaultRoot = getStorageItem('settings', {}).default_loras_root;
+            if (defaultRoot && data.roots.includes(defaultRoot)) {
+                this.loraRootSelect.value = defaultRoot;
+            }
+
             this.updatePathPreview();
             modalManager.showModal('moveModal');
             

static/js/managers/SettingsManager.js

@@ -53,7 +53,7 @@ export class SettingsManager {
         this.initialized = true;
     }
 
-    loadSettingsToUI() {
+    async loadSettingsToUI() {
         // Set frontend settings from state
         const blurMatureContentCheckbox = document.getElementById('blurMatureContent');
         if (blurMatureContentCheckbox) {
@@ -65,10 +65,52 @@ export class SettingsManager {
             // Sync with state (backend will set this via template)
             state.global.settings.show_only_sfw = showOnlySFWCheckbox.checked;
         }
+
+        // Load default lora root
+        await this.loadLoraRoots();
         
         // Backend settings are loaded from the template directly
     }
 
+    async loadLoraRoots() {
+        try {
+            const defaultLoraRootSelect = document.getElementById('defaultLoraRoot');
+            if (!defaultLoraRootSelect) return;
+            
+            // Fetch lora roots
+            const response = await fetch('/api/lora-roots');
+            if (!response.ok) {
+                throw new Error('Failed to fetch LoRA roots');
+            }
+            
+            const data = await response.json();
+            if (!data.roots || data.roots.length === 0) {
+                throw new Error('No LoRA roots found');
+            }
+            
+            // Clear existing options except the first one (No Default)
+            const noDefaultOption = defaultLoraRootSelect.querySelector('option[value=""]');
+            defaultLoraRootSelect.innerHTML = '';
+            defaultLoraRootSelect.appendChild(noDefaultOption);
+            
+            // Add options for each root
+            data.roots.forEach(root => {
+                const option = document.createElement('option');
+                option.value = root;
+                option.textContent = root;
+                defaultLoraRootSelect.appendChild(option);
+            });
+            
+            // Set selected value from settings
+            const defaultRoot = state.global.settings.default_loras_root || '';
+            defaultLoraRootSelect.value = defaultRoot;
+            
+        } catch (error) {
+            console.error('Error loading LoRA roots:', error);
+            showToast('Failed to load LoRA roots: ' + error.message, 'error');
+        }
+    }
+
     toggleSettings() {
         if (this.isOpen) {
             modalManager.closeModal('settingsModal');
@@ -81,14 +123,16 @@ export class SettingsManager {
     async saveSettings() {
         // Get frontend settings from UI
         const blurMatureContent = document.getElementById('blurMatureContent').checked;
+        const showOnlySFW = document.getElementById('showOnlySFW').checked;
+        const defaultLoraRoot = document.getElementById('defaultLoraRoot').value;
         
         // Get backend settings
         const apiKey = document.getElementById('civitaiApiKey').value;
-        const showOnlySFW = document.getElementById('showOnlySFW').checked;
         
         // Update frontend state and save to localStorage
         state.global.settings.blurMatureContent = blurMatureContent;
         state.global.settings.show_only_sfw = showOnlySFW;
+        state.global.settings.default_loras_root = defaultLoraRoot;
         
         // Save settings to localStorage
         setStorageItem('settings', state.global.settings);

static/js/managers/UpdateService.js

@@ -178,7 +178,8 @@ export class UpdateService {
                 if (this.updateInfo.changelog && this.updateInfo.changelog.length > 0) {
                     this.updateInfo.changelog.forEach(item => {
                         const listItem = document.createElement('li');
-                        listItem.textContent = item;
+                        // Parse markdown in changelog items
+                        listItem.innerHTML = this.parseMarkdown(item);
                         changelogList.appendChild(listItem);
                     });
                 } else {
@@ -201,6 +202,25 @@ export class UpdateService {
         }
     }
     
+    // Simple markdown parser for changelog items
+    parseMarkdown(text) {
+        if (!text) return '';
+        
+        // Handle bold text (**text**)
+        text = text.replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>');
+        
+        // Handle italic text (*text*)
+        text = text.replace(/\*(.*?)\*/g, '<em>$1</em>');
+        
+        // Handle inline code (`code`)
+        text = text.replace(/`(.*?)`/g, '<code>$1</code>');
+        
+        // Handle links [text](url)
+        text = text.replace(/\[(.*?)\]\((.*?)\)/g, '<a href="$2" target="_blank">$1</a>');
+        
+        return text;
+    }
+    
     toggleUpdateModal() {
         const updateModal = modalManager.getModal('updateModal');
         

static/js/recipes.js

@@ -4,6 +4,7 @@ import { ImportManager } from './managers/ImportManager.js';
 import { RecipeCard } from './components/RecipeCard.js';
 import { RecipeModal } from './components/RecipeModal.js';
 import { getCurrentPageState } from './state/index.js';
+import { toggleApiKeyVisibility } from './managers/SettingsManager.js';
 
 class RecipeManager {
     constructor() {
@@ -54,6 +55,7 @@ class RecipeManager {
         // Only expose what's needed for the page
         window.recipeManager = this;
         window.importManager = this.importManager;
+        window.toggleApiKeyVisibility = toggleApiKeyVisibility;
     }
     
     initEventListeners() {

templates/base.html

@@ -37,6 +37,49 @@
     <link rel="preconnect" href="https://civitai.com">
     <link rel="preconnect" href="https://cdnjs.cloudflare.com">
 
+    <!-- Add styles for initialization notice -->
+    {% if is_initializing %}
+    <style>
+        .initialization-notice {
+            position: fixed;
+            top: 0;
+            left: 0;
+            width: 100%;
+            height: 100%;
+            background-color: rgba(0, 0, 0, 0.85);
+            z-index: 9999;
+            margin-top: 0;
+            border-radius: 0;
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            color: white;
+        }
+        .notice-content {
+            background-color: rgba(30, 30, 30, 0.9);
+            border-radius: 10px;
+            padding: 30px;
+            text-align: center;
+            box-shadow: 0 0 20px rgba(0, 0, 0, 0.5);
+            max-width: 500px;
+            width: 80%;
+        }
+        .loading-spinner {
+            border: 5px solid rgba(255, 255, 255, 0.3);
+            border-radius: 50%;
+            border-top: 5px solid #fff;
+            width: 50px;
+            height: 50px;
+            margin: 0 auto 20px;
+            animation: spin 1s linear infinite;
+        }
+        @keyframes spin {
+            0% { transform: rotate(0deg); }
+            100% { transform: rotate(360deg); }
+        }
+    </style>
+    {% endif %}
+
     <script>
         // 计算滚动条宽度并设置CSS变量
         document.addEventListener('DOMContentLoaded', () => {
@@ -52,6 +95,15 @@
 </head>
 
 <body data-page="{% block page_id %}base{% endblock %}">
+    {% if is_initializing %}
+    <div class="initialization-notice">
+        <div class="notice-content">
+            <div class="loading-spinner"></div>
+            <h2>{% block init_title %}Initializing{% endblock %}</h2>
+            <p>{% block init_message %}Scanning and building cache. This may take a few minutes...{% endblock %}</p>
+        </div>
+    </div>
+    {% else %}
     {% include 'components/header.html' %}
 
     <div class="page-content">
@@ -61,22 +113,12 @@
         {% block additional_components %}{% endblock %}
 
         <div class="container">
-            {% if is_initializing %}
-            <div class="initialization-notice">
-                <div class="notice-content">
-                    <div class="loading-spinner"></div>
-                    <h2>{% block init_title %}Initializing{% endblock %}</h2>
-                    <p>{% block init_message %}Scanning and building cache. This may take a few minutes...{% endblock %}
-                    </p>
-                </div>
-            </div>
-            {% else %}
             {% block content %}{% endblock %}
-            {% endif %}
         </div>
 
         {% block overlay %}{% endblock %}
     </div>
+    {% endif %}
 
     {% block main_script %}{% endblock %}
 
@@ -100,11 +142,11 @@
         }
 
         // 启动状态检查
-        checkInitStatus();
+        setTimeout(checkInitStatus, 1000); // 给页面完全加载的时间
     </script>
     {% endif %}
 
     {% block additional_scripts %}{% endblock %}
 </body>
 
-</html>
+</html>

templates/components/modals.html

@@ -66,6 +66,26 @@
                     </div>
                 </div>
             </div>
+            
+            <!-- Add Folder Settings Section -->
+            <div class="settings-section">
+                <h3>Folder Settings</h3>
+                
+                <div class="setting-item">
+                    <div class="setting-info">
+                        <label for="defaultLoraRoot">Default LoRA Root</label>
+                    </div>
+                    <div class="setting-control select-control">
+                        <select id="defaultLoraRoot">
+                            <option value="">No Default</option>
+                            <!-- Options will be loaded dynamically -->
+                        </select>
+                    </div>
+                    <div class="input-help">
+                        Set the default LoRA root directory for downloads, imports and moves
+                    </div>
+                </div>
+            </div>
         </div>
         <div class="modal-actions">
             <button class="primary-btn" onclick="settingsManager.saveSettings()">Save</button>

templates/components/recipe_modal.html

@@ -16,8 +16,8 @@
         <div class="modal-body">
             <!-- Top Section: Preview and Generation Parameters -->
             <div class="recipe-top-section">
-                <div class="recipe-preview-container">
-                    <img id="recipeModalImage" src="" alt="Recipe Preview">
+                <div class="recipe-preview-container" id="recipePreviewContainer">
+                    <img id="recipeModalImage" src="" alt="Recipe Preview" class="recipe-preview-media">
                 </div>
                 
                 <div class="info-section recipe-gen-params">

templates/loras.html

@@ -4,7 +4,9 @@
 {% block page_id %}loras{% endblock %}
 
 {% block preload %}
+{% if not is_initializing %}
 <link rel="preload" href="/loras_static/js/loras.js" as="script" crossorigin="anonymous">
+{% endif %}
 {% endblock %}
 
 {% block init_title %}Initializing LoRA Manager{% endblock %}
@@ -29,5 +31,7 @@
 {% endblock %}
 
 {% block main_script %}
+{% if not is_initializing %}
 <script type="module" src="/loras_static/js/loras.js"></script>
+{% endif %}
 {% endblock %}

test_parser.py

@@ -1,45 +0,0 @@
-import json
-import sys
-from py.workflow.parser import WorkflowParser
-from py.workflow.utils import trace_model_path
-
-# Load workflow data
-with open('refs/prompt.json', 'r') as f:
-    workflow_data = json.load(f)
-
-# Parse workflow
-parser = WorkflowParser()
-try:
-    # Find KSampler node
-    ksampler_node = None
-    for node_id, node in workflow_data.items():
-        if node.get("class_type") == "KSampler":
-            ksampler_node = node_id
-            break
-    
-    if not ksampler_node:
-        print("KSampler node not found")
-        sys.exit(1)
-    
-    # Trace all Lora nodes
-    print("Finding Lora nodes in the workflow...")
-    lora_nodes = trace_model_path(workflow_data, ksampler_node)
-    print(f"Found Lora nodes: {lora_nodes}")
-    
-    # Print node details
-    for node_id in lora_nodes:
-        node = workflow_data[node_id]
-        print(f"\nNode {node_id}: {node.get('class_type')}")
-        for key, value in node.get("inputs", {}).items():
-            print(f"  - {key}: {value}")
-    
-    # Parse the workflow
-    result = parser.parse_workflow(workflow_data)
-    print("\nParsing successful!")
-    print(json.dumps(result, indent=2))
-    sys.exit(0)
-except Exception as e:
-    print(f"Error parsing workflow: {e}")
-    import traceback
-    traceback.print_exc()
-    sys.exit(1) 

update_logs.md

@@ -2,6 +2,52 @@
 
 ---
 
+### v0.7.37
+* Added NSFW content control settings (blur mature content and SFW-only filter)
+* Implemented intelligent blur effects for previews and showcase media
+* Added manual content rating option through context menu
+* Enhanced user experience with configurable content visibility
+* Fixed various bugs and improved stability
+
+### v0.7.36
+* Enhanced LoRA details view with model descriptions and tags display
+* Added tag filtering system for improved model discovery
+* Implemented editable trigger words functionality
+* Improved TriggerWord Toggle node with new group mode option for granular control
+* Added new Lora Stacker node with cross-compatibility support (works with efficiency nodes, ComfyRoll, easy-use, etc.)
+* Fixed several bugs
+
+### v0.7.35-beta
+* Added base model filtering
+* Implemented bulk operations (copy syntax, move multiple LoRAs)
+* Added ability to edit LoRA model names in details view
+* Added update checker with notification system
+* Added support modal for user feedback and community links
+
+### v0.7.33
+* Enhanced LoRA Loader node with visual strength adjustment widgets
+* Added toggle switches for LoRA enable/disable
+* Implemented image tooltips for LoRA preview
+* Added TriggerWord Toggle node with visual word selection
+* Fixed various bugs and improved stability
+
+### v0.7.3
+* Added "Lora Loader (LoraManager)" custom node for workflows
+* Implemented one-click LoRA integration
+* Added direct copying of LoRA syntax from manager interface
+* Added automatic preset strength value application
+* Added automatic trigger word loading
+
+### v0.7.0
+* Added direct CivitAI integration for downloading LoRAs
+* Implemented version selection for model downloads
+* Added target folder selection for downloads
+* Added context menu with quick actions
+* Added force refresh for CivitAI data
+* Implemented LoRA movement between folders
+* Added personal usage tips and notes for LoRAs
+* Improved performance for details window
+
 ## [Update 0.5.9] Enhanced Search Capabilities
 
 - 🔍 **Advanced Search Features**:

web/comfyui/lora_loader.js

@@ -35,6 +35,10 @@ app.registerExtension({
             // Enable widget serialization
             node.serialize_widgets = true;
 
+            node.addInput('clip', 'CLIP', {
+                "shape": 7
+            });
+
             node.addInput("lora_stack", 'LORA_STACK', {
                 "shape": 7  // 7 is the shape of the optional input
             });

web/comfyui/loras_widget.js

@@ -824,7 +824,7 @@ async function saveRecipeDirectly(widget) {
   try {
     // Get the workflow data from the ComfyUI app
     const prompt = await app.graphToPrompt();
-    console.log('Prompt:', prompt.output);
+    console.log('Prompt:', prompt);
     
     // Show loading toast
     if (app && app.extensionManager && app.extensionManager.toast) {