Update version to 0.8.13; enhance recipe management and source tracking features in release notes

Dev

README.md

@@ -20,6 +20,13 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 
 ## Release Notes
 
+### v0.8.13
+* **Enhanced Recipe Management** - Added "Find duplicates" feature to identify and batch delete duplicate recipes with duplicate detection notifications during imports
+* **Improved Source Tracking** - Source URLs are now saved with recipes imported via URL, allowing users to view original content with one click or manually edit links
+* **Advanced LoRA Control** - Double-click LoRAs in Loader/Stacker nodes to access expanded CLIP strength controls for more precise adjustments of model and CLIP strength separately
+* **Lycoris Model Support** - Added compatibility with Lycoris models for expanded creative options
+* **Bug Fixes & UX Improvements** - Resolved various issues and enhanced overall user experience with numerous optimizations
+
 ### v0.8.12
 * **Enhanced Model Discovery** - Added alphabetical navigation bar to LoRAs page for faster browsing through large collections
 * **Optimized Example Images** - Improved download logic to automatically refresh stale metadata before fetching example images

py/nodes/lora_loader.py

@@ -1,10 +1,7 @@
 import logging
 from nodes import LoraLoader
 from comfy.comfy_types import IO # type: ignore
-from ..services.lora_scanner import LoraScanner
-from ..config import config
 import asyncio
-import os
 from .utils import FlexibleOptionalInputType, any_type, get_lora_info, extract_lora_name, get_loras_list
 
 logger = logging.getLogger(__name__)
@@ -51,7 +48,11 @@ class LoraManagerLoader:
                 _, trigger_words = asyncio.run(get_lora_info(lora_name))
                 
                 all_trigger_words.extend(trigger_words)
-                loaded_loras.append(f"{lora_name}: {model_strength}")
+                # Add clip strength to output if different from model strength
+                if abs(model_strength - clip_strength) > 0.001:
+                    loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
+                else:
+                    loaded_loras.append(f"{lora_name}: {model_strength}")
         
         # Then process loras from kwargs with support for both old and new formats
         loras_list = get_loras_list(kwargs)
@@ -60,14 +61,21 @@ class LoraManagerLoader:
                 continue
                 
             lora_name = lora['name']
-            strength = float(lora['strength'])
+            model_strength = float(lora['strength'])
+            # Get clip strength - use model strength as default if not specified
+            clip_strength = float(lora.get('clipStrength', model_strength))
             
             # Get lora path and trigger words
             lora_path, trigger_words = asyncio.run(get_lora_info(lora_name))
             
-            # Apply the LoRA using the resolved path
-            model, clip = LoraLoader().load_lora(model, clip, lora_path, strength, strength)
-            loaded_loras.append(f"{lora_name}: {strength}")
+            # Apply the LoRA using the resolved path with separate strengths
+            model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+            
+            # Include clip strength in output if different from model strength
+            if abs(model_strength - clip_strength) > 0.001:
+                loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
+            else:
+                loaded_loras.append(f"{lora_name}: {model_strength}")
             
             # Add trigger words to collection
             all_trigger_words.extend(trigger_words)
@@ -75,8 +83,23 @@ class LoraManagerLoader:
         # use ',, ' to separate trigger words for group mode
         trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
         
-        # Format loaded_loras as <lora:lora_name:strength> separated by spaces
-        formatted_loras = " ".join([f"<lora:{name.split(':')[0].strip()}:{str(strength).strip()}>" 
-                                  for name, strength in [item.split(':') for item in loaded_loras]])
+        # Format loaded_loras with support for both formats
+        formatted_loras = []
+        for item in loaded_loras:
+            parts = item.split(":")
+            lora_name = parts[0].strip()
+            strength_parts = parts[1].strip().split(",")
+            
+            if len(strength_parts) > 1:
+                # Different model and clip strengths
+                model_str = strength_parts[0].strip()
+                clip_str = strength_parts[1].strip()
+                formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
+            else:
+                # Same strength for both
+                model_str = strength_parts[0].strip()
+                formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
+                
+        formatted_loras_text = " ".join(formatted_loras)
 
-        return (model, clip, trigger_words_text, formatted_loras)
+        return (model, clip, trigger_words_text, formatted_loras_text)

py/nodes/lora_stacker.py

@@ -38,7 +38,7 @@ class LoraStacker:
         
         # Process existing lora_stack if available
         lora_stack = kwargs.get('lora_stack', None)
-        if lora_stack:
+        if (lora_stack):
             stack.extend(lora_stack)
             # Get trigger words from existing stack entries
             for lora_path, _, _ in lora_stack:
@@ -54,7 +54,8 @@ class LoraStacker:
                 
             lora_name = lora['name']
             model_strength = float(lora['strength'])
-            clip_strength = model_strength  # Using same strength for both as in the original loader
+            # Get clip strength - use model strength as default if not specified
+            clip_strength = float(lora.get('clipStrength', model_strength))
             
             # Get lora path and trigger words
             lora_path, trigger_words = asyncio.run(get_lora_info(lora_name))
@@ -62,15 +63,24 @@ 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))
+            active_loras.append((lora_name, model_strength, clip_strength))
             
             # Add trigger words to collection
             all_trigger_words.extend(trigger_words)
         
         # use ',, ' to separate trigger words for group mode
         trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
-        # 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])
+        
+        # Format active_loras with support for both formats
+        formatted_loras = []
+        for name, model_strength, clip_strength in active_loras:
+            if abs(model_strength - clip_strength) > 0.001:
+                # Different model and clip strengths
+                formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}:{str(clip_strength).strip()}>")
+            else:
+                # Same strength for both
+                formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}>")
+                
+        active_loras_text = " ".join(formatted_loras)
 
         return (stack, trigger_words_text, active_loras_text)

py/recipes/__init__.py

@@ -0,0 +1,22 @@
+"""Recipe metadata parser package for ComfyUI-Lora-Manager."""
+
+from .base import RecipeMetadataParser
+from .factory import RecipeParserFactory
+from .constants import GEN_PARAM_KEYS, VALID_LORA_TYPES
+from .parsers import (
+    RecipeFormatParser,
+    ComfyMetadataParser,
+    MetaFormatParser,
+    AutomaticMetadataParser
+)
+
+__all__ = [
+    'RecipeMetadataParser',
+    'RecipeParserFactory',
+    'GEN_PARAM_KEYS',
+    'VALID_LORA_TYPES',
+    'RecipeFormatParser',
+    'ComfyMetadataParser',
+    'MetaFormatParser',
+    'AutomaticMetadataParser'
+]

py/recipes/base.py

@@ -0,0 +1,181 @@
+"""Base classes for recipe parsers."""
+
+import json
+import logging
+import os
+import re
+from typing import Dict, List, Any, Optional, Tuple
+from abc import ABC, abstractmethod
+from ..config import config
+from .constants import VALID_LORA_TYPES
+
+logger = logging.getLogger(__name__)
+
+class RecipeMetadataParser(ABC):
+    """Interface for parsing recipe metadata from image user comments"""
+
+    METADATA_MARKER = None
+
+    @abstractmethod
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the metadata format"""
+        pass
+    
+    @abstractmethod
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """
+        Parse metadata from user comment and return structured recipe data
+        
+        Args:
+            user_comment: The EXIF UserComment string from the image
+            recipe_scanner: Optional recipe scanner instance for local LoRA lookup
+            civitai_client: Optional Civitai client for fetching model information
+            
+        Returns:
+            Dict containing parsed recipe data with standardized format
+        """
+        pass
+    
+    async def populate_lora_from_civitai(self, lora_entry: Dict[str, Any], civitai_info_tuple: Tuple[Dict[str, Any], Optional[str]], 
+                                         recipe_scanner=None, base_model_counts=None, hash_value=None) -> Optional[Dict[str, Any]]:
+        """
+        Populate a lora entry with information from Civitai API response
+        
+        Args:
+            lora_entry: The lora entry to populate
+            civitai_info_tuple: The response tuple from Civitai API (data, error_msg)
+            recipe_scanner: Optional recipe scanner for local file lookup
+            base_model_counts: Optional dict to track base model counts
+            hash_value: Optional hash value to use if not available in civitai_info
+            
+        Returns:
+            The populated lora_entry dict if type is valid, None otherwise
+        """
+        try:
+            # Unpack the tuple to get the actual data
+            civitai_info, error_msg = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
+            
+            if not civitai_info or civitai_info.get("error") == "Model not found":
+                # Model not found or deleted
+                lora_entry['isDeleted'] = True
+                lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                return lora_entry
+                
+            # Get model type and validate
+            model_type = civitai_info.get('model', {}).get('type', '').lower()
+            lora_entry['type'] = model_type
+            if model_type not in VALID_LORA_TYPES:
+                logger.debug(f"Skipping non-LoRA model type: {model_type}")
+                return None
+
+            # 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:
+                # Find the primary model file (type="Model" and primary=true) in the files list
+                model_file = next((file for file in civitai_info.get('files', []) 
+                                    if file.get('type') == 'Model' and file.get('primary') == True), 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 ''
+                
+        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

py/recipes/constants.py

@@ -0,0 +1,16 @@
+"""Constants used across recipe parsers."""
+
+# Constants for generation parameters
+GEN_PARAM_KEYS = [
+    'prompt',
+    'negative_prompt', 
+    'steps',
+    'sampler',
+    'cfg_scale',
+    'seed',
+    'size',
+    'clip_skip',
+]
+
+# Valid Lora types
+VALID_LORA_TYPES = ['lora', 'locon']

py/recipes/factory.py

@@ -0,0 +1,43 @@
+"""Factory for creating recipe metadata parsers."""
+
+import logging
+from .parsers import (
+    RecipeFormatParser,
+    ComfyMetadataParser,
+    MetaFormatParser,
+    AutomaticMetadataParser
+)
+from .base import RecipeMetadataParser
+
+logger = logging.getLogger(__name__)
+
+class RecipeParserFactory:
+    """Factory for creating recipe metadata parsers"""
+    
+    @staticmethod
+    def create_parser(user_comment: str) -> RecipeMetadataParser:
+        """
+        Create appropriate parser based on the user comment content
+        
+        Args:
+            user_comment: The EXIF UserComment string from the image
+            
+        Returns:
+            Appropriate RecipeMetadataParser implementation
+        """
+        # 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 AutomaticMetadataParser().is_metadata_matching(user_comment):
+            return AutomaticMetadataParser()
+        elif MetaFormatParser().is_metadata_matching(user_comment):
+            return MetaFormatParser()
+        else:
+            return None

py/recipes/parsers/__init__.py

@@ -0,0 +1,13 @@
+"""Recipe parsers package."""
+
+from .recipe_format import RecipeFormatParser
+from .comfy import ComfyMetadataParser
+from .meta_format import MetaFormatParser
+from .automatic import AutomaticMetadataParser
+
+__all__ = [
+    'RecipeFormatParser',
+    'ComfyMetadataParser',
+    'MetaFormatParser',
+    'AutomaticMetadataParser',
+]

py/recipes/parsers/automatic.py

@@ -0,0 +1,304 @@
+"""Parser for Automatic1111 metadata format."""
+
+import re
+import json
+import logging
+from typing import Dict, Any
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+class AutomaticMetadataParser(RecipeMetadataParser):
+    """Parser for Automatic1111 metadata format"""
+    
+    METADATA_MARKER = r"Steps: \d+"
+    
+    # Regular expressions for extracting specific metadata
+    HASHES_REGEX = r', Hashes:\s*({[^}]+})'
+    LORA_HASHES_REGEX = r', Lora hashes:\s*"([^"]+)"'
+    CIVITAI_RESOURCES_REGEX = r', Civitai resources:\s*(\[\{.*?\}\])'
+    CIVITAI_METADATA_REGEX = r', Civitai metadata:\s*(\{.*?\})'
+    EXTRANETS_REGEX = r'<(lora|hypernet):([a-zA-Z0-9_\.\-]+):([0-9.]+)>'
+    MODEL_HASH_PATTERN = r'Model hash: ([a-zA-Z0-9]+)'
+    VAE_HASH_PATTERN = r'VAE hash: ([a-zA-Z0-9]+)'
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the Automatic1111 format"""
+        return re.search(self.METADATA_MARKER, user_comment) is not None
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from Automatic1111 format"""
+        try:
+            # Split on Negative prompt if it exists
+            if "Negative prompt:" in user_comment:
+                parts = user_comment.split('Negative prompt:', 1)
+                prompt = parts[0].strip()
+                negative_and_params = parts[1] if len(parts) > 1 else ""
+            else:
+                # No negative prompt section
+                param_start = re.search(self.METADATA_MARKER, user_comment)
+                if param_start:
+                    prompt = user_comment[:param_start.start()].strip()
+                    negative_and_params = user_comment[param_start.start():]
+                else:
+                    prompt = user_comment.strip()
+                    negative_and_params = ""
+            
+            # Initialize metadata
+            metadata = {
+                "prompt": prompt,
+                "loras": []
+            }
+            
+            # Extract negative prompt and parameters
+            if negative_and_params:
+                # If we split on "Negative prompt:", check for params section
+                if "Negative prompt:" in user_comment:
+                    param_start = re.search(r'Steps: ', 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:
+                        metadata["negative_prompt"] = negative_and_params.strip()
+                        params_section = ""
+                else:
+                    # No negative prompt, entire section is params
+                    params_section = negative_and_params
+                
+                # Extract generation parameters
+                if params_section:
+                    # Extract Civitai resources
+                    civitai_resources_match = re.search(self.CIVITAI_RESOURCES_REGEX, params_section)
+                    if civitai_resources_match:
+                        try:
+                            civitai_resources = json.loads(civitai_resources_match.group(1))
+                            metadata["civitai_resources"] = civitai_resources
+                            params_section = params_section.replace(civitai_resources_match.group(0), '')
+                        except json.JSONDecodeError:
+                            logger.error("Error parsing Civitai resources JSON")
+                    
+                    # Extract Hashes
+                    hashes_match = re.search(self.HASHES_REGEX, params_section)
+                    if hashes_match:
+                        try:
+                            hashes = json.loads(hashes_match.group(1))
+                            # Process hash keys
+                            processed_hashes = {}
+                            for key, value in hashes.items():
+                                # Convert Model: or LORA: prefix to lowercase if present
+                                if ':' in key:
+                                    prefix, name = key.split(':', 1)
+                                    prefix = prefix.lower()
+                                else:
+                                    prefix = ''
+                                    name = key
+
+                                # Clean up the name part
+                                if '/' in name:
+                                    name = name.split('/')[-1]  # Get last part after /
+                                if '.safetensors' in name:
+                                    name = name.split('.safetensors')[0]  # Remove .safetensors
+                                
+                                # Reconstruct the key
+                                new_key = f"{prefix}:{name}" if prefix else name
+                                processed_hashes[new_key] = value
+
+                            metadata["hashes"] = processed_hashes
+                            # Remove hashes from params section to not interfere with other parsing
+                            params_section = params_section.replace(hashes_match.group(0), '')
+                        except json.JSONDecodeError:
+                            logger.error("Error parsing hashes JSON")
+                    
+                    # Extract Lora hashes in alternative format
+                    lora_hashes_match = re.search(self.LORA_HASHES_REGEX, params_section)
+                    if not hashes_match and lora_hashes_match:
+                        try:
+                            lora_hashes_str = lora_hashes_match.group(1)
+                            lora_hash_entries = lora_hashes_str.split(', ')
+                            
+                            # Initialize hashes dict if it doesn't exist
+                            if "hashes" not in metadata:
+                                metadata["hashes"] = {}
+                                
+                            # Parse each lora hash entry (format: "name: hash")
+                            for entry in lora_hash_entries:
+                                if ': ' in entry:
+                                    lora_name, lora_hash = entry.split(': ', 1)
+                                    # Add as lora type in the same format as regular hashes
+                                    metadata["hashes"][f"lora:{lora_name}"] = lora_hash.strip()
+                            
+                            # Remove lora hashes from params section
+                            params_section = params_section.replace(lora_hashes_match.group(0), '')
+                        except Exception as e:
+                            logger.error(f"Error parsing Lora hashes: {e}")
+                    
+                    # Extract basic parameters
+                    param_pattern = r'([A-Za-z\s]+): ([^,]+)'
+                    params = re.findall(param_pattern, params_section)
+                    gen_params = {}
+                    
+                    for key, value in params:
+                        clean_key = key.strip().lower().replace(' ', '_')
+                        
+                        # Skip if not in recognized gen param keys
+                        if clean_key not in GEN_PARAM_KEYS:
+                            continue
+                            
+                        # Convert numeric values
+                        if clean_key in ['steps', 'seed']:
+                            try:
+                                gen_params[clean_key] = int(value.strip())
+                            except ValueError:
+                                gen_params[clean_key] = value.strip()
+                        elif clean_key in ['cfg_scale']:
+                            try:
+                                gen_params[clean_key] = float(value.strip())
+                            except ValueError:
+                                gen_params[clean_key] = value.strip()
+                        else:
+                            gen_params[clean_key] = value.strip()
+                    
+                    # Extract size if available and add to gen_params if a recognized key
+                    size_match = re.search(r'Size: (\d+)x(\d+)', params_section)
+                    if size_match and 'size' in GEN_PARAM_KEYS:
+                        width, height = size_match.groups()
+                        gen_params['size'] = f"{width}x{height}"
+                    
+                    # Add prompt and negative_prompt to gen_params if they're in GEN_PARAM_KEYS
+                    if 'prompt' in GEN_PARAM_KEYS and 'prompt' in metadata:
+                        gen_params['prompt'] = metadata['prompt']
+                    if 'negative_prompt' in GEN_PARAM_KEYS and 'negative_prompt' in metadata:
+                        gen_params['negative_prompt'] = metadata['negative_prompt']
+                    
+                    metadata["gen_params"] = gen_params
+            
+            # Extract LoRA information 
+            loras = []
+            base_model_counts = {}
+            
+            # First use Civitai resources if available (more reliable source)
+            if metadata.get("civitai_resources"):
+                for resource in metadata.get("civitai_resources", []):
+                    if resource.get("type") in ["lora", "lycoris", "hypernet"] and resource.get("modelVersionId"):
+                        # Initialize lora entry
+                        lora_entry = {
+                            'id': str(resource.get("modelVersionId")),
+                            'modelId': str(resource.get("modelId")) if resource.get("modelId") else None,
+                            'name': resource.get("modelName", "Unknown LoRA"),
+                            'version': resource.get("modelVersionName", ""),
+                            'type': resource.get("type", "lora"),
+                            'weight': round(float(resource.get("weight", 1.0)), 2),
+                            'existsLocally': False,
+                            'thumbnailUrl': '/loras_static/images/no-preview.png',
+                            'baseModel': '',
+                            'size': 0,
+                            'downloadUrl': '',
+                            'isDeleted': False
+                        }
+                        
+                        # Get additional info from Civitai
+                        if civitai_client:
+                            try:
+                                civitai_info = await civitai_client.get_model_version_info(resource.get("modelVersionId"))
+                                populated_entry = await self.populate_lora_from_civitai(
+                                    lora_entry,
+                                    civitai_info,
+                                    recipe_scanner,
+                                    base_model_counts
+                                )
+                                if populated_entry is None:
+                                    continue  # Skip invalid LoRA types
+                                lora_entry = populated_entry
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for LoRA {lora_entry['name']}: {e}")
+                        
+                        loras.append(lora_entry)
+            
+            # If no LoRAs from Civitai resources or to supplement, extract from metadata["hashes"]
+            if not loras or len(loras) == 0:
+                # Extract lora weights from extranet tags in prompt (for later use)
+                lora_weights = {}
+                lora_matches = re.findall(self.EXTRANETS_REGEX, prompt)
+                for lora_type, lora_name, lora_weight in lora_matches:
+                    key = f"{lora_type}:{lora_name}"
+                    lora_weights[key] = round(float(lora_weight), 2)
+                
+                # Use hashes from metadata as the primary source
+                if metadata.get("hashes"):
+                    for hash_key, lora_hash in metadata.get("hashes", {}).items():
+                        # Only process lora or hypernet types
+                        if not hash_key.startswith(("lora:", "hypernet:")):
+                            continue
+                            
+                        lora_type, lora_name = hash_key.split(':', 1)
+                        
+                        # Get weight from extranet tags if available, else default to 1.0
+                        weight = lora_weights.get(hash_key, 1.0)
+                        
+                        # Initialize lora entry
+                        lora_entry = {
+                            'name': lora_name,
+                            'type': lora_type,  # 'lora' or 'hypernet'
+                            'weight': weight,
+                            'hash': lora_hash,
+                            'existsLocally': False,
+                            'localPath': None,
+                            'file_name': lora_name,
+                            'thumbnailUrl': '/loras_static/images/no-preview.png',
+                            'baseModel': '',
+                            'size': 0,
+                            'downloadUrl': '',
+                            'isDeleted': False
+                        }
+                        
+                        # Try to get info from Civitai
+                        if civitai_client:
+                            try:
+                                if lora_hash:
+                                    # If we have hash, use it for lookup
+                                    civitai_info = await civitai_client.get_model_by_hash(lora_hash)
+                                else:
+                                    civitai_info = None
+                                
+                                populated_entry = await self.populate_lora_from_civitai(
+                                    lora_entry, 
+                                    civitai_info, 
+                                    recipe_scanner,
+                                    base_model_counts,
+                                    lora_hash
+                                )
+                                if populated_entry is None:
+                                    continue  # Skip invalid LoRA types
+                                lora_entry = populated_entry
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for LoRA {lora_name}: {e}")
+                        
+                        loras.append(lora_entry)
+                
+            # Try to get base model from resources or make educated guess
+            base_model = None
+            if base_model_counts:
+                # Use the most common base model from the loras
+                base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
+            
+            # Prepare final result structure
+            # Make sure gen_params only contains recognized keys
+            filtered_gen_params = {}
+            for key in GEN_PARAM_KEYS:
+                if key in metadata.get("gen_params", {}):
+                    filtered_gen_params[key] = metadata["gen_params"][key]
+            
+            result = {
+                'base_model': base_model,
+                'loras': loras,
+                'gen_params': filtered_gen_params,
+                'from_automatic_metadata': True
+            }
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"Error parsing Automatic1111 metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}

py/recipes/parsers/comfy.py

@@ -0,0 +1,216 @@
+"""Parser for ComfyUI metadata format."""
+
+import re
+import json
+import logging
+from typing import Dict, Any
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+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_tuple = await civitai_client.get_model_version_info(model_version_id)
+                        # Populate lora entry with Civitai info
+                        populated_entry = await self.populate_lora_from_civitai(
+                            lora_entry, 
+                            civitai_info_tuple, 
+                            recipe_scanner
+                        )
+                        if populated_entry is None:
+                            continue  # Skip invalid LoRA types
+                        lora_entry = populated_entry
+                    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_tuple = await civitai_client.get_model_version_info(checkpoint_version_id)
+                                civitai_info, _ = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
+                                # Populate checkpoint with Civitai info
+                                checkpoint = await self.populate_checkpoint_from_civitai(checkpoint, civitai_info)
+                            except Exception as e:
+                                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": []}

py/recipes/parsers/meta_format.py

@@ -0,0 +1,174 @@
+"""Parser for meta format (Lora_N Model hash) metadata."""
+
+import re
+import logging
+from typing import Dict, Any
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+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
+                        populated_entry = await self.populate_lora_from_civitai(
+                            lora_entry, 
+                            civitai_info, 
+                            recipe_scanner,
+                            base_model_counts,
+                            hash_value
+                        )
+                        if populated_entry is None:
+                            continue  # Skip invalid LoRA types
+                        lora_entry = populated_entry
+                    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": []}

py/recipes/parsers/recipe_format.py

@@ -0,0 +1,114 @@
+"""Parser for dedicated recipe metadata format."""
+
+import re
+import json
+import logging
+from typing import Dict, Any
+from ...config import config
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+class RecipeFormatParser(RecipeMetadataParser):
+    """Parser for images with dedicated recipe metadata format"""
+    
+    # Regular expression pattern for extracting recipe metadata
+    METADATA_MARKER = r'Recipe metadata: (\{.*\})'
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the metadata format"""
+        return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from images with dedicated recipe metadata format"""
+        try:
+            # Extract recipe metadata from user comment
+            try:
+                # Look for recipe metadata section
+                recipe_match = re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL)
+                if not recipe_match:
+                    recipe_metadata = None
+                else:
+                    recipe_json = recipe_match.group(1)
+                    recipe_metadata = json.loads(recipe_json)
+            except Exception as e:
+                logger.error(f"Error extracting recipe metadata: {e}")
+                recipe_metadata = None
+            if not recipe_metadata:
+                return {"error": "No recipe metadata found", "loras": []}
+                
+            # Process the recipe metadata
+            loras = []
+            for lora in recipe_metadata.get('loras', []):
+                # Convert recipe lora format to frontend format
+                lora_entry = {
+                    'id': lora.get('modelVersionId', ''),
+                    'name': lora.get('modelName', ''),
+                    'version': lora.get('modelVersionName', ''),
+                    'type': 'lora',
+                    'weight': lora.get('strength', 1.0),
+                    'file_name': lora.get('file_name', ''),
+                    'hash': lora.get('hash', '')
+                }
+                
+                # Check if this LoRA exists locally by SHA256 hash
+                if lora.get('hash') and recipe_scanner:
+                    lora_scanner = recipe_scanner._lora_scanner
+                    exists_locally = lora_scanner.has_lora_hash(lora['hash'])
+                    if exists_locally:
+                        lora_cache = await lora_scanner.get_cached_data()
+                        lora_item = next((item for item in lora_cache.raw_data if item['sha256'].lower() == lora['hash'].lower()), None)
+                        if lora_item:
+                            lora_entry['existsLocally'] = True
+                            lora_entry['localPath'] = lora_item['file_path']
+                            lora_entry['file_name'] = lora_item['file_name']
+                            lora_entry['size'] = lora_item['size']
+                            lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
+                            
+                    else:
+                        lora_entry['existsLocally'] = False
+                        lora_entry['localPath'] = None
+                        
+                        # Try to get additional info from Civitai if we have a model version ID
+                        if lora.get('modelVersionId') and civitai_client:
+                            try:
+                                civitai_info_tuple = await civitai_client.get_model_version_info(lora['modelVersionId'])
+                                # Populate lora entry with Civitai info
+                                populated_entry = await self.populate_lora_from_civitai(
+                                    lora_entry, 
+                                    civitai_info_tuple, 
+                                    recipe_scanner,
+                                    None,  # No need to track base model counts
+                                    lora['hash']
+                                )
+                                if populated_entry is None:
+                                    continue  # Skip invalid LoRA types
+                                lora_entry = populated_entry
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for LoRA: {e}")
+                                lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                
+                loras.append(lora_entry)
+
+            logger.info(f"Found {len(loras)} loras in recipe metadata")
+            
+            # Filter gen_params to only include recognized keys
+            filtered_gen_params = {}
+            if 'gen_params' in recipe_metadata:
+                for key, value in recipe_metadata['gen_params'].items():
+                    if key in GEN_PARAM_KEYS:
+                        filtered_gen_params[key] = value
+            
+            return {
+                'base_model': recipe_metadata.get('base_model', ''),
+                'loras': loras,
+                'gen_params': filtered_gen_params,
+                'tags': recipe_metadata.get('tags', []),
+                'title': recipe_metadata.get('title', ''),
+                'from_recipe_metadata': True
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing recipe format metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}

py/routes/api_routes.py

@@ -386,10 +386,10 @@ class ApiRoutes:
             versions = response.get('modelVersions', [])
             model_type = response.get('type', '')
             
-            # Check model type - should be LORA
-            if model_type.lower() != 'lora':
+            # Check model type - should be LORA or LoCon
+            if model_type.lower() not in ['lora', 'locon']:
                 return web.json_response({
-                    'error': f"Model type mismatch. Expected LORA, got {model_type}"
+                    'error': f"Model type mismatch. Expected LORA or LoCon, got {model_type}"
                 }, status=400)
             
             # Check local availability for each version

py/routes/recipe_routes.py

@@ -1,5 +1,6 @@
 import os
 import time
+import base64
 import numpy as np
 from PIL import Image
 import torch
@@ -12,7 +13,7 @@ import json
 import asyncio
 import sys
 from ..utils.exif_utils import ExifUtils
-from ..utils.recipe_parsers import RecipeParserFactory
+from ..recipes import RecipeParserFactory
 from ..utils.constants import CARD_PREVIEW_WIDTH
 
 from ..config import config
@@ -56,6 +57,7 @@ class RecipeRoutes:
         app.router.add_get('/api/recipes', routes.get_recipes)
         app.router.add_get('/api/recipe/{recipe_id}', routes.get_recipe_detail)
         app.router.add_post('/api/recipes/analyze-image', routes.analyze_recipe_image)
+        app.router.add_post('/api/recipes/analyze-local-image', routes.analyze_local_image)
         app.router.add_post('/api/recipes/save', routes.save_recipe)
         app.router.add_delete('/api/recipe/{recipe_id}', routes.delete_recipe)
         
@@ -70,12 +72,18 @@ class RecipeRoutes:
         # 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)
+        # Add new endpoint for updating recipe metadata (name, tags and source_path)
         app.router.add_put('/api/recipe/{recipe_id}/update', routes.update_recipe)
         
         # Add new endpoint for reconnecting deleted LoRAs
         app.router.add_post('/api/recipe/lora/reconnect', routes.reconnect_lora)
         
+        # Add new endpoint for finding duplicate recipes
+        app.router.add_get('/api/recipes/find-duplicates', routes.find_duplicates)
+        
+        # Add new endpoint for bulk deletion of recipes
+        app.router.add_post('/api/recipes/bulk-delete', routes.bulk_delete)
+        
         # Start cache initialization
         app.on_startup.append(routes._init_cache)
         
@@ -300,7 +308,6 @@ class RecipeRoutes:
                 
                 # For URL mode, include the image data as base64
                 if is_url_mode and temp_path:
-                    import base64
                     with open(temp_path, "rb") as image_file:
                         result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
                     
@@ -317,7 +324,6 @@ class RecipeRoutes:
                 
                 # For URL mode, include the image data as base64
                 if is_url_mode and temp_path:
-                    import base64
                     with open(temp_path, "rb") as image_file:
                         result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
                     
@@ -332,7 +338,6 @@ class RecipeRoutes:
             
             # For URL mode, include the image data as base64
             if is_url_mode and temp_path:
-                import base64
                 with open(temp_path, "rb") as image_file:
                     result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
             
@@ -340,6 +345,21 @@ class RecipeRoutes:
             if "error" in result and not result.get("loras"):
                 return web.json_response(result, status=200)
             
+            # Calculate fingerprint from parsed loras
+            from ..utils.utils import calculate_recipe_fingerprint
+            fingerprint = calculate_recipe_fingerprint(result.get("loras", []))
+            
+            # Add fingerprint to result
+            result["fingerprint"] = fingerprint
+            
+            # Find matching recipes with the same fingerprint
+            matching_recipes = []
+            if fingerprint:
+                matching_recipes = await self.recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+                
+            # Add matching recipes to result
+            result["matching_recipes"] = matching_recipes
+            
             return web.json_response(result)
             
         except Exception as e:
@@ -355,7 +375,100 @@ class RecipeRoutes:
                     os.unlink(temp_path)
                 except Exception as e:
                     logger.error(f"Error deleting temporary file: {e}")
+    
+    async def analyze_local_image(self, request: web.Request) -> web.Response:
+        """Analyze a local image file for recipe metadata"""
+        try:
+            # Ensure services are initialized 
+            await self.init_services()
+            
+            # Get JSON data from request
+            data = await request.json()
+            file_path = data.get('path')
+            
+            if not file_path:
+                return web.json_response({
+                    'error': 'No file path provided',
+                    'loras': []
+                }, status=400)
+                
+            # Normalize file path for cross-platform compatibility
+            file_path = os.path.normpath(file_path.strip('"').strip("'"))
+            
+            # Validate that the file exists
+            if not os.path.isfile(file_path):
+                return web.json_response({
+                    'error': 'File not found',
+                    'loras': []
+                }, status=404)
+                
+            # Extract metadata from the image using ExifUtils
+            metadata = ExifUtils.extract_image_metadata(file_path)
+            
+            # If no metadata found, return error
+            if not metadata:
+                # Get base64 image data
+                with open(file_path, "rb") as image_file:
+                    image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
+                    
+                return web.json_response({
+                    "error": "No metadata found in this image",
+                    "loras": [],  # Return empty loras array to prevent client-side errors
+                    "image_base64": image_base64
+                }, status=200)
+            
+            # Use the parser factory to get the appropriate parser
+            parser = RecipeParserFactory.create_parser(metadata)
 
+            if parser is None:
+                # Get base64 image data
+                with open(file_path, "rb") as image_file:
+                    image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
+                    
+                return web.json_response({
+                    "error": "No parser found for this image",
+                    "loras": [],  # Return empty loras array to prevent client-side errors
+                    "image_base64": image_base64
+                }, status=200)
+            
+            # Parse the metadata
+            result = await parser.parse_metadata(
+                metadata, 
+                recipe_scanner=self.recipe_scanner, 
+                civitai_client=self.civitai_client
+            )
+            
+            # Add base64 image data to result
+            with open(file_path, "rb") as image_file:
+                result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
+            
+            # Check for errors
+            if "error" in result and not result.get("loras"):
+                return web.json_response(result, status=200)
+            
+            # Calculate fingerprint from parsed loras
+            from ..utils.utils import calculate_recipe_fingerprint
+            fingerprint = calculate_recipe_fingerprint(result.get("loras", []))
+            
+            # Add fingerprint to result
+            result["fingerprint"] = fingerprint
+            
+            # Find matching recipes with the same fingerprint
+            matching_recipes = []
+            if fingerprint:
+                matching_recipes = await self.recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+                
+            # Add matching recipes to result
+            result["matching_recipes"] = matching_recipes
+            
+            return web.json_response(result)
+            
+        except Exception as e:
+            logger.error(f"Error analyzing local image: {e}", exc_info=True)
+            return web.json_response({
+                'error': str(e),
+                'loras': []  # Return empty loras array to prevent client-side errors
+            }, status=500)
 
     async def save_recipe(self, request: web.Request) -> web.Response:
         """Save a recipe to the recipes folder"""
@@ -425,7 +538,6 @@ class RecipeRoutes:
             if not image:
                 if image_base64:
                     # Convert base64 to binary
-                    import base64
                     try:
                         # Remove potential data URL prefix
                         if ',' in image_base64:
@@ -474,7 +586,7 @@ class RecipeRoutes:
             with open(image_path, 'wb') as f:
                 f.write(optimized_image)
             
-            # Create the recipe JSON
+            # Create the recipe data structure
             current_time = time.time()
             
             # Format loras data according to the recipe.json format
@@ -514,6 +626,10 @@ class RecipeRoutes:
                     "clip_skip": raw_metadata.get("clip_skip", "")
                 }
             
+            # Calculate recipe fingerprint 
+            from ..utils.utils import calculate_recipe_fingerprint
+            fingerprint = calculate_recipe_fingerprint(loras_data)
+            
             # Create the recipe data structure
             recipe_data = {
                 "id": recipe_id,
@@ -523,13 +639,18 @@ class RecipeRoutes:
                 "created_date": current_time,
                 "base_model": metadata.get("base_model", ""),
                 "loras": loras_data,
-                "gen_params": gen_params
+                "gen_params": gen_params,
+                "fingerprint": fingerprint
             }
             
             # Add tags if provided
             if tags:
                 recipe_data["tags"] = tags
             
+            # Add source_path if provided in metadata
+            if metadata.get("source_path"):
+                recipe_data["source_path"] = metadata.get("source_path")
+            
             # Save the recipe JSON
             json_filename = f"{recipe_id}.recipe.json"
             json_path = os.path.join(recipes_dir, json_filename)
@@ -539,6 +660,14 @@ class RecipeRoutes:
             # Add recipe metadata to the image
             ExifUtils.append_recipe_metadata(image_path, recipe_data)
             
+            # Check for duplicates
+            matching_recipes = []
+            if fingerprint:
+                matching_recipes = await self.recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+                # Remove current recipe from matches
+                if recipe_id in matching_recipes:
+                    matching_recipes.remove(recipe_id)
+            
             # Simplified cache update approach
             # Instead of trying to update the cache directly, just set it to None
             # to force a refresh on the next get_cached_data call
@@ -554,7 +683,8 @@ class RecipeRoutes:
                 'success': True,
                 'recipe_id': recipe_id,
                 'image_path': image_path,
-                'json_path': json_path
+                'json_path': json_path,
+                'matching_recipes': matching_recipes
             })
             
         except Exception as e:
@@ -1089,9 +1219,9 @@ class RecipeRoutes:
             data = await request.json()
             
             # Validate required fields
-            if 'title' not in data and 'tags' not in data:
+            if 'title' not in data and 'tags' not in data and 'source_path' not in data:
                 return web.json_response({
-                    "error": "At least one field to update must be provided (title or tags)"
+                    "error": "At least one field to update must be provided (title or tags or source_path)"
                 }, status=400)
             
             # Use the recipe scanner's update method
@@ -1186,6 +1316,10 @@ class RecipeRoutes:
                     
             if not found:
                 return web.json_response({"error": "Could not find matching deleted LoRA in recipe"}, status=404)
+            
+            # Recalculate recipe fingerprint after updating LoRA
+            from ..utils.utils import calculate_recipe_fingerprint
+            recipe_data['fingerprint'] = calculate_recipe_fingerprint(recipe_data.get('loras', []))
                 
             # Save updated recipe
             with open(recipe_path, 'w', encoding='utf-8') as f:
@@ -1201,6 +1335,8 @@ class RecipeRoutes:
                     if cache_item.get('id') == recipe_id:
                         # Replace loras array with updated version
                         cache_item['loras'] = recipe_data['loras']
+                        # Update fingerprint in cache
+                        cache_item['fingerprint'] = recipe_data['fingerprint']
                         
                         # Resort the cache
                         asyncio.create_task(scanner._cache.resort())
@@ -1211,11 +1347,20 @@ class RecipeRoutes:
             if image_path and os.path.exists(image_path):
                 from ..utils.exif_utils import ExifUtils
                 ExifUtils.append_recipe_metadata(image_path, recipe_data)
+            
+            # Find other recipes with the same fingerprint
+            matching_recipes = []
+            if 'fingerprint' in recipe_data:
+                matching_recipes = await scanner.find_recipes_by_fingerprint(recipe_data['fingerprint'])
+                # Remove current recipe from matches
+                if recipe_id in matching_recipes:
+                    matching_recipes.remove(recipe_id)
                 
             return web.json_response({
                 "success": True,
                 "recipe_id": recipe_id,
-                "updated_lora": updated_lora
+                "updated_lora": updated_lora,
+                "matching_recipes": matching_recipes
             })
             
         except Exception as e:
@@ -1291,3 +1436,150 @@ class RecipeRoutes:
                 'success': False,
                 'error': str(e)
             }, status=500)
+
+    async def find_duplicates(self, request: web.Request) -> web.Response:
+        """Find all duplicate recipes based on fingerprints"""
+        try:
+            # Ensure services are initialized
+            await self.init_services()
+            
+            # Get all duplicate recipes
+            duplicate_groups = await self.recipe_scanner.find_all_duplicate_recipes()
+            
+            # Create response data with additional recipe information
+            response_data = []
+            
+            for fingerprint, recipe_ids in duplicate_groups.items():
+                # Skip groups with only one recipe (not duplicates)
+                if len(recipe_ids) <= 1:
+                    continue
+                    
+                # Get recipe details for each recipe in the group
+                recipes = []
+                for recipe_id in recipe_ids:
+                    recipe = await self.recipe_scanner.get_recipe_by_id(recipe_id)
+                    if recipe:
+                        # Add only needed fields to keep response size manageable
+                        recipes.append({
+                            'id': recipe.get('id'),
+                            'title': recipe.get('title'),
+                            'file_url': recipe.get('file_url') or self._format_recipe_file_url(recipe.get('file_path', '')),
+                            'modified': recipe.get('modified'),
+                            'created_date': recipe.get('created_date'),
+                            'lora_count': len(recipe.get('loras', [])),
+                        })
+                        
+                # Only include groups with at least 2 valid recipes
+                if len(recipes) >= 2:
+                    # Sort recipes by modified date (newest first)
+                    recipes.sort(key=lambda x: x.get('modified', 0), reverse=True)
+                    
+                    response_data.append({
+                        'fingerprint': fingerprint,
+                        'count': len(recipes),
+                        'recipes': recipes
+                    })
+            
+            # Sort groups by count (highest first)
+            response_data.sort(key=lambda x: x['count'], reverse=True)
+            
+            return web.json_response({
+                'success': True,
+                'duplicate_groups': response_data
+            })
+            
+        except Exception as e:
+            logger.error(f"Error finding duplicate recipes: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def bulk_delete(self, request: web.Request) -> web.Response:
+        """Delete multiple recipes by ID"""
+        try:
+            # Ensure services are initialized
+            await self.init_services()
+            
+            # Parse request data
+            data = await request.json()
+            recipe_ids = data.get('recipe_ids', [])
+            
+            if not recipe_ids:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No recipe IDs provided'
+                }, status=400)
+                
+            # Get recipes directory
+            recipes_dir = self.recipe_scanner.recipes_dir
+            if not recipes_dir or not os.path.exists(recipes_dir):
+                return web.json_response({
+                    'success': False,
+                    'error': 'Recipes directory not found'
+                }, status=404)
+            
+            # Track deleted and failed recipes
+            deleted_recipes = []
+            failed_recipes = []
+            
+            # Process each recipe ID
+            for recipe_id in recipe_ids:
+                # Find recipe JSON file
+                recipe_json_path = os.path.join(recipes_dir, f"{recipe_id}.recipe.json")
+                
+                if not os.path.exists(recipe_json_path):
+                    failed_recipes.append({
+                        'id': recipe_id,
+                        'reason': 'Recipe not found'
+                    })
+                    continue
+                
+                try:
+                    # Load recipe data to get image path
+                    with open(recipe_json_path, 'r', encoding='utf-8') as f:
+                        recipe_data = json.load(f)
+                    
+                    # Get image path
+                    image_path = recipe_data.get('file_path')
+                    
+                    # Delete recipe JSON file
+                    os.remove(recipe_json_path)
+                    
+                    # Delete recipe image if it exists
+                    if image_path and os.path.exists(image_path):
+                        os.remove(image_path)
+                        
+                    deleted_recipes.append(recipe_id)
+                    
+                except Exception as e:
+                    failed_recipes.append({
+                        'id': recipe_id,
+                        'reason': str(e)
+                    })
+            
+            # Update cache if any recipes were deleted
+            if deleted_recipes and self.recipe_scanner._cache is not None:
+                # Remove deleted recipes from raw_data
+                self.recipe_scanner._cache.raw_data = [
+                    r for r in self.recipe_scanner._cache.raw_data 
+                    if r.get('id') not in deleted_recipes
+                ]
+                # Resort the cache
+                asyncio.create_task(self.recipe_scanner._cache.resort())
+                logger.info(f"Removed {len(deleted_recipes)} recipes from cache")
+            
+            return web.json_response({
+                'success': True,
+                'deleted': deleted_recipes,
+                'failed': failed_recipes,
+                'total_deleted': len(deleted_recipes),
+                'total_failed': len(failed_recipes)
+            })
+            
+        except Exception as e:
+            logger.error(f"Error performing bulk delete: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/server_routes.py

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

py/services/download_manager.py

@@ -136,15 +136,9 @@ class DownloadManager:
             # 3. Prepare download
             file_name = file_info['name']
             save_path = os.path.join(save_dir, file_name)
-            file_size = file_info.get('sizeKB', 0) * 1024
 
             # 4. Notify file monitor - use normalized path and file size
-            file_monitor = await self._get_lora_monitor() if model_type == "lora" else await self._get_checkpoint_monitor()
-            if file_monitor and file_monitor.handler:
-                file_monitor.handler.add_ignore_path(
-                    save_path.replace(os.sep, '/'),
-                    file_size
-                )
+            # file monitor is despreted, so we don't need to use it
 
             # 5. Prepare metadata based on model type
             if model_type == "checkpoint":

py/services/lora_cache.py

@@ -2,6 +2,7 @@ import asyncio
 from typing import List, Dict
 from dataclasses import dataclass
 from operator import itemgetter
+from natsort import natsorted
 
 @dataclass
 class LoraCache:
@@ -17,7 +18,7 @@ class LoraCache:
     async def resort(self, name_only: bool = False):
         """Resort all cached data views"""
         async with self._lock:
-            self.sorted_by_name = sorted(
+            self.sorted_by_name = natsorted(
                 self.raw_data, 
                 key=lambda x: x['model_name'].lower()  # Case-insensitive sort
             )

py/services/model_cache.py

@@ -2,6 +2,7 @@ import asyncio
 from typing import List, Dict
 from dataclasses import dataclass
 from operator import itemgetter
+from natsort import natsorted
 
 @dataclass
 class ModelCache:
@@ -17,7 +18,7 @@ class ModelCache:
     async def resort(self, name_only: bool = False):
         """Resort all cached data views"""
         async with self._lock:
-            self.sorted_by_name = sorted(
+            self.sorted_by_name = natsorted(
                 self.raw_data, 
                 key=lambda x: x['model_name'].lower()  # Case-insensitive sort
             )

py/services/recipe_cache.py

@@ -2,6 +2,7 @@ import asyncio
 from typing import List, Dict
 from dataclasses import dataclass
 from operator import itemgetter
+from natsort import natsorted
 
 @dataclass
 class RecipeCache:
@@ -16,7 +17,7 @@ class RecipeCache:
     async def resort(self, name_only: bool = False):
         """Resort all cached data views"""
         async with self._lock:
-            self.sorted_by_name = sorted(
+            self.sorted_by_name = natsorted(
                 self.raw_data, 
                 key=lambda x: x.get('title', '').lower()  # Case-insensitive sort
             )

py/services/recipe_scanner.py

@@ -9,6 +9,7 @@ from .recipe_cache import RecipeCache
 from .service_registry import ServiceRegistry
 from .lora_scanner import LoraScanner
 from ..utils.utils import fuzzy_match
+from natsort import natsorted
 import sys
 
 logger = logging.getLogger(__name__)
@@ -164,7 +165,7 @@ class RecipeScanner:
                 if hasattr(self._cache, "resort"):
                     try:
                         # Sort by name
-                        self._cache.sorted_by_name = sorted(
+                        self._cache.sorted_by_name = natsorted(
                             self._cache.raw_data,
                             key=lambda x: x.get('title', '').lower()
                         )
@@ -321,6 +322,20 @@ class RecipeScanner:
             
             # Update lora information with local paths and availability
             await self._update_lora_information(recipe_data)
+
+            # Calculate and update fingerprint if missing
+            if 'loras' in recipe_data and 'fingerprint' not in recipe_data:
+                from ..utils.utils import calculate_recipe_fingerprint
+                fingerprint = calculate_recipe_fingerprint(recipe_data['loras'])
+                recipe_data['fingerprint'] = fingerprint
+                
+                # Write updated recipe data back to file
+                try:
+                    with open(recipe_path, 'w', encoding='utf-8') as f:
+                        json.dump(recipe_data, f, indent=4, ensure_ascii=False)
+                    logger.info(f"Added fingerprint to recipe: {recipe_path}")
+                except Exception as e:
+                    logger.error(f"Error writing updated recipe with fingerprint: {e}")
             
             return recipe_data
         except Exception as e:
@@ -801,3 +816,60 @@ class RecipeScanner:
             logger.info(f"Resorted recipe cache after updating {cache_updated_count} items")
             
         return file_updated_count, cache_updated_count
+
+    async def find_recipes_by_fingerprint(self, fingerprint: str) -> list:
+        """Find recipes with a matching fingerprint
+        
+        Args:
+            fingerprint: The recipe fingerprint to search for
+            
+        Returns:
+            List of recipe details that match the fingerprint
+        """
+        if not fingerprint:
+            return []
+            
+        # Get all recipes from cache
+        cache = await self.get_cached_data()
+        
+        # Find recipes with matching fingerprint
+        matching_recipes = []
+        for recipe in cache.raw_data:
+            if recipe.get('fingerprint') == fingerprint:
+                recipe_details = {
+                    'id': recipe.get('id'),
+                    'title': recipe.get('title'),
+                    'file_url': self._format_file_url(recipe.get('file_path')),
+                    'modified': recipe.get('modified'),
+                    'created_date': recipe.get('created_date'),
+                    'lora_count': len(recipe.get('loras', []))
+                }
+                matching_recipes.append(recipe_details)
+        
+        return matching_recipes
+        
+    async def find_all_duplicate_recipes(self) -> dict:
+        """Find all recipe duplicates based on fingerprints
+        
+        Returns:
+            Dictionary where keys are fingerprints and values are lists of recipe IDs
+        """
+        # Get all recipes from cache
+        cache = await self.get_cached_data()
+        
+        # Group recipes by fingerprint
+        fingerprint_groups = {}
+        for recipe in cache.raw_data:
+            fingerprint = recipe.get('fingerprint')
+            if not fingerprint:
+                continue
+                
+            if fingerprint not in fingerprint_groups:
+                fingerprint_groups[fingerprint] = []
+                
+            fingerprint_groups[fingerprint].append(recipe.get('id'))
+        
+        # Filter to only include groups with more than one recipe
+        duplicate_groups = {k: v for k, v in fingerprint_groups.items() if len(v) > 1}
+        
+        return duplicate_groups

py/utils/file_utils.py

@@ -233,6 +233,17 @@ async def load_metadata(file_path: str, model_class: Type[BaseModelMetadata] = L
                     data['usage_tips'] = "{}"
                     needs_update = True
                 
+                # Update preview_nsfw_level if needed
+                civitai_data = data.get('civitai', {})
+                civitai_images = civitai_data.get('images', []) if civitai_data else []
+                if (data.get('preview_url') and 
+                    data.get('preview_nsfw_level', 0) == 0 and 
+                    civitai_images and 
+                    civitai_images[0].get('nsfwLevel', 0) != 0):
+                    data['preview_nsfw_level'] = civitai_images[0]['nsfwLevel']
+                    # TODO: write to metadata file
+                    # needs_update = True
+
                 if needs_update:
                     with open(metadata_path, 'w', encoding='utf-8') as f:
                         json.dump(data, f, indent=2, ensure_ascii=False)

py/utils/lora_metadata.py

@@ -2,6 +2,9 @@ from safetensors import safe_open
 from typing import Dict
 from .model_utils import determine_base_model
 import os
+import logging
+
+logger = logging.getLogger(__name__)
 
 async def extract_lora_metadata(file_path: str) -> Dict:
     """Extract essential metadata from safetensors file"""

py/utils/utils.py

@@ -114,3 +114,49 @@ def fuzzy_match(text: str, pattern: str, threshold: float = 0.7) -> bool:
         
         # All words found either as substrings or fuzzy matches
         return True
+
+def calculate_recipe_fingerprint(loras):
+    """
+    Calculate a unique fingerprint for a recipe based on its LoRAs.
+    
+    The fingerprint is created by sorting LoRA hashes, filtering invalid entries,
+    normalizing strength values to 2 decimal places, and joining in format:
+    hash1:strength1|hash2:strength2|...
+    
+    Args:
+        loras (list): List of LoRA dictionaries with hash and strength values
+        
+    Returns:
+        str: The calculated fingerprint
+    """
+    if not loras:
+        return ""
+    
+    # Filter valid entries and extract hash and strength
+    valid_loras = []
+    for lora in loras:
+        # Skip excluded loras
+        if lora.get("exclude", False):
+            continue
+            
+        # Get the hash - use modelVersionId as fallback if hash is empty
+        hash_value = lora.get("hash", "").lower()
+        if not hash_value and lora.get("isDeleted", False) and lora.get("modelVersionId"):
+            hash_value = lora.get("modelVersionId")
+            
+        # Skip entries without a valid hash
+        if not hash_value:
+            continue
+            
+        # Normalize strength to 2 decimal places (check both strength and weight fields)
+        strength = round(float(lora.get("strength", lora.get("weight", 1.0))), 2)
+        
+        valid_loras.append((hash_value, strength))
+    
+    # Sort by hash
+    valid_loras.sort()
+    
+    # Join in format hash1:strength1|hash2:strength2|...
+    fingerprint = "|".join([f"{hash_value}:{strength}" for hash_value, strength in valid_loras])
+    
+    return fingerprint

py/workflow/__init__.py

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

py/workflow/cli.py

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

py/workflow/ext/__init__.py

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

py/workflow/ext/comfyui_core.py

@@ -1,285 +0,0 @@
-"""
-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/kjnodes.py

@@ -1,74 +0,0 @@
-"""
-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/main.py

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

py/workflow/mappers.py

@@ -1,282 +0,0 @@
-"""
-Node mappers for ComfyUI workflow parsing
-"""
-import logging
-import os
-import importlib.util
-import inspect
-from typing import Dict, List, Any, Optional, Union, Type, Callable, Tuple
-
-logger = logging.getLogger(__name__)
-
-# Global mapper registry
-_MAPPER_REGISTRY: Dict[str, Dict] = {}
-
-# =============================================================================
-# Mapper Definition Functions
-# =============================================================================
-
-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']}")
-
-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, Dict]:
-    """Get all registered mappers"""
-    return _MAPPER_REGISTRY.copy()
-
-# =============================================================================
-# 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)
-    
-    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 and isinstance(inputs["clip"], dict):
-        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
-# =============================================================================
-
-def load_extensions(ext_dir: str = None) -> None:
-    """
-    Load mapper extensions from the specified directory
-    
-    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:
-        # Get the directory of this file
-        current_dir = os.path.dirname(os.path.abspath(__file__))
-        ext_dir = os.path.join(current_dir, 'ext')
-    
-    # Ensure the extension directory exists
-    if not os.path.exists(ext_dir):
-        os.makedirs(ext_dir, exist_ok=True)
-        logger.info(f"Created extension directory: {ext_dir}")
-        return
-    
-    # Load each Python file in the extension directory
-    for filename in os.listdir(ext_dir):
-        if filename.endswith('.py') and not filename.startswith('_'):
-            module_path = os.path.join(ext_dir, filename)
-            module_name = f"workflow.ext.{filename[:-3]}"  # Remove .py
-            
-            try:
-                # Load the module
-                spec = importlib.util.spec_from_file_location(module_name, module_path)
-                if spec and spec.loader:
-                    module = importlib.util.module_from_spec(spec)
-                    spec.loader.exec_module(module)
-                    
-                    # 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() 

py/workflow/parser.py

@@ -1,181 +0,0 @@
-"""
-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, process_node
-from .utils import (
-    load_workflow, save_output, find_node_by_type,
-    trace_model_path
-)
-
-logger = logging.getLogger(__name__)
-
-class WorkflowParser:
-    """Parser for ComfyUI workflows"""
-    
-    def __init__(self):
-        """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
-        load_extensions()
-    
-    def process_node(self, node_id: str, workflow: Dict) -> Any:
-        """Process a single node and extract relevant information"""
-        # Return cached result if available
-        if node_id in self.node_results_cache:
-            return self.node_results_cache[node_id]
-            
-        # Check if we're in a cycle
-        if node_id in self.processed_nodes:
-            return None
-        
-        # Mark this node as being processed (to detect cycles)
-        self.processed_nodes.add(node_id)
-        
-        if node_id not in workflow:
-            self.processed_nodes.remove(node_id)
-            return None
-        
-        node_data = workflow[node_id]
-        node_type = node_data.get("class_type")
-        
-        result = None
-        if get_mapper(node_type):
-            try:
-                result = process_node(node_id, node_data, workflow, self)
-                # Cache the result
-                self.node_results_cache[node_id] = result
-            except Exception as e:
-                logger.error(f"Error processing node {node_id} of type {node_type}: {e}", exc_info=True)
-                # Return a partial result or None depending on how we want to handle errors
-                result = {}
-        
-        # Remove node from processed set to allow it to be processed again in a different context
-        self.processed_nodes.remove(node_id)
-        return result
-    
-    def find_primary_sampler_node(self, workflow: Dict) -> Optional[str]:
-        """
-        Find the primary sampler node in the workflow.
-        
-        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
-        
-        Args:
-            workflow: The workflow data as a dictionary
-            
-        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:
-        """
-        Parse the workflow and extract generation parameters
-        
-        Args:
-            workflow_data: The workflow data as a dictionary or a file path
-            output_path: Optional path to save the output JSON
-            
-        Returns:
-            Dictionary containing extracted parameters
-        """
-        # Load workflow from file if needed
-        if isinstance(workflow_data, str):
-            workflow = load_workflow(workflow_data)
-        else:
-            workflow = workflow_data
-            
-        # Reset the processed nodes tracker and cache
-        self.processed_nodes = set()
-        self.node_results_cache = {}
-        
-        # Find the 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 {}
-        
-        # Process sampler node to extract parameters
-        sampler_result = self.process_node(sampler_node_id, workflow)
-        if not sampler_result:
-            return {}
-        
-        # 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 sampler_result:
-            sampler_result["cfg_scale"] = sampler_result.pop("cfg")
-            
-        # 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 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(sampler_result, output_path)
-            
-        return sampler_result
-
-
-def parse_workflow(workflow_path: str, output_path: Optional[str] = None) -> Dict:
-    """
-    Parse a ComfyUI workflow file and extract generation parameters
-    
-    Args:
-        workflow_path: Path to the workflow JSON file
-        output_path: Optional path to save the output JSON
-        
-    Returns:
-        Dictionary containing extracted parameters
-    """
-    parser = WorkflowParser()
-    return parser.parse_workflow(workflow_path, output_path)

py/workflow/test.py

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

py/workflow/utils.py

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

pyproject.toml

@@ -1,7 +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 = "0.8.12"
+version = "0.8.13"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",
@@ -13,7 +13,8 @@ dependencies = [
     "Pillow",
     "olefile", # for getting rid of warning message
     "requests",
-    "toml"
+    "toml",
+    "natsort"
 ]
 
 [project.urls]

requirements.txt

@@ -9,4 +9,5 @@ olefile
 requests
 toml
 numpy
-torch
+torch
+natsort

static/css/base.css

@@ -38,7 +38,7 @@ html, body {
     --lora-border: oklch(90% 0.02 256 / 0.15);
     --lora-text: oklch(95% 0.02 256);
     --lora-error: oklch(75% 0.32 29);
-    --lora-warning: oklch(75% 0.25 80); /* Add warning color for deleted LoRAs */
+    --lora-warning: oklch(75% 0.25 80); /* Modified to be used with oklch() */
     
     /* Spacing Scale */
     --space-1: calc(8px * 1);
@@ -79,7 +79,7 @@ html[data-theme="light"] {
     --lora-surface: oklch(25% 0.02 256 / 0.98);
     --lora-border: oklch(90% 0.02 256 / 0.15);
     --lora-text: oklch(98% 0.02 256);
-    --lora-warning: oklch(75% 0.25 80); /* Add warning color for dark theme too */
+    --lora-warning: oklch(75% 0.25 80); /* Modified to be used with oklch() */
 }
 
 body {

static/css/components/duplicates.css

@@ -0,0 +1,259 @@
+/* Duplicates Management Styles */
+
+/* Duplicates banner */
+.duplicates-banner {
+    position: sticky;
+    top: 48px; /* Match header height */
+    left: 0;
+    width: 100%;
+    background-color: var(--card-bg);
+    color: var(--text-color);
+    border-bottom: 1px solid var(--border-color);
+    z-index: var(--z-overlay);
+    padding: 12px 16px;
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.15);
+    transition: all 0.3s ease;
+}
+
+.duplicates-banner .banner-content {
+    max-width: 1400px;
+    margin: 0 auto;
+    display: flex;
+    align-items: center;
+    gap: 12px;
+}
+
+.duplicates-banner i.fa-exclamation-triangle {
+    font-size: 18px;
+    color: oklch(var(--lora-warning));
+}
+
+.duplicates-banner .banner-actions {
+    margin-left: auto;
+    display: flex;
+    gap: 8px;
+    align-items: center;
+}
+
+.duplicates-banner button {
+    min-width: 100px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 4px;
+    border-radius: var(--border-radius-xs);
+    padding: 4px 10px;
+    border: 1px solid var(--border-color);
+    background: var(--card-bg);
+    color: var(--text-color);
+    font-size: 0.85em;
+    transition: all 0.2s ease;
+    cursor: pointer;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+}
+
+.duplicates-banner button:hover {
+    border-color: var(--lora-accent);
+    background: var(--bg-color);
+    transform: translateY(-1px);
+    box-shadow: 0 3px 5px rgba(0, 0, 0, 0.08);
+}
+
+.duplicates-banner button.btn-exit {
+    min-width: unset;
+    width: 28px;
+    height: 28px;
+    padding: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    border-radius: 50%;
+}
+
+.duplicates-banner button.disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+/* Duplicate groups */
+.duplicate-group {
+    position: relative;
+    border: 2px solid oklch(var(--lora-warning));
+    border-radius: var(--border-radius-base);
+    padding: 16px;
+    margin-bottom: 24px;
+    background: var(--card-bg);
+}
+
+.duplicate-group-header {
+    background-color: var(--bg-color);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+    padding: 8px 16px;
+    border-radius: var(--border-radius-xs);
+    margin-bottom: 16px;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+}
+
+.duplicate-group-header span:last-child {
+    display: flex;
+    gap: 8px;
+    align-items: center;
+}
+
+.duplicate-group-header button {
+    min-width: 80px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 4px;
+    border-radius: var(--border-radius-xs);
+    padding: 4px 8px;
+    border: 1px solid var(--border-color);
+    background: var(--card-bg);
+    color: var(--text-color);
+    font-size: 0.85em;
+    transition: all 0.2s ease;
+    cursor: pointer;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+    margin-left: 8px;
+}
+
+.duplicate-group-header button:hover {
+    border-color: var(--lora-accent);
+    background: var(--bg-color);
+    transform: translateY(-1px);
+    box-shadow: 0 3px 5px rgba(0, 0, 0, 0.08);
+}
+
+.card-group-container {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 16px;
+    justify-content: flex-start;
+    align-items: flex-start;
+}
+
+/* Make cards in duplicate groups have consistent width */
+.card-group-container .lora-card {
+    flex: 0 0 auto;
+    width: 240px;
+    margin: 0;
+    cursor: pointer; /* Indicate the card is clickable */
+}
+
+/* Ensure the grid layout is only applied to the main recipe grid, not duplicate groups */
+.duplicate-mode .card-grid {
+    display: block;
+}
+
+/* Scrollable container for large duplicate groups */
+.card-group-container.scrollable {
+    max-height: 450px;
+    overflow-y: auto;
+    padding-right: 8px;
+}
+
+/* Add a toggle button to expand/collapse large duplicate groups */
+.group-toggle-btn {
+    position: absolute;
+    right: 16px;
+    bottom: -12px;
+    background: var(--card-bg);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+    border-radius: 50%;
+    width: 24px;
+    height: 24px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    cursor: pointer;
+    z-index: 1;
+    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
+    transition: all 0.2s ease;
+}
+
+.group-toggle-btn:hover {
+    border-color: var(--lora-accent);
+    transform: translateY(-1px);
+    box-shadow: 0 3px 5px rgba(0, 0, 0, 0.08);
+}
+
+/* Duplicate card styling */
+.lora-card.duplicate {
+    position: relative;
+    transition: all 0.2s ease;
+}
+
+.lora-card.duplicate:hover {
+    border-color: var(--lora-accent);
+}
+
+.lora-card.duplicate.latest {
+    border-style: solid;
+    border-color: oklch(var(--lora-warning));
+}
+
+.lora-card.duplicate-selected {
+    border: 2px solid oklch(var(--lora-accent));
+    box-shadow: 0 0 8px rgba(0, 0, 0, 0.2);
+}
+
+.lora-card .selector-checkbox {
+    position: absolute;
+    top: 10px;
+    right: 10px;
+    z-index: 10;
+    width: 20px;
+    height: 20px;
+    cursor: pointer;
+}
+
+/* Latest indicator */
+.lora-card.duplicate.latest::after {
+    content: "Latest";
+    position: absolute;
+    top: 10px;
+    left: 10px;
+    background: oklch(var(--lora-accent));
+    color: white;
+    font-size: 12px;
+    padding: 2px 6px;
+    border-radius: var(--border-radius-xs);
+    z-index: 5;
+}
+
+/* Responsive adjustments */
+@media (max-width: 768px) {
+    .duplicates-banner .banner-content {
+        flex-direction: column;
+        align-items: flex-start;
+        gap: 8px;
+    }
+    
+    .duplicates-banner .banner-actions {
+        width: 100%;
+        margin-left: 0;
+        justify-content: space-between;
+    }
+    
+    .duplicate-group-header {
+        flex-direction: column;
+        gap: 8px;
+        align-items: flex-start;
+    }
+    
+    .duplicate-group-header span:last-child {
+        display: flex;
+        gap: 8px;
+        width: 100%;
+    }
+    
+    .duplicate-group-header button {
+        margin-left: 0;
+        flex: 1;
+    }
+}

static/css/components/import-modal.css

@@ -291,7 +291,7 @@
     gap: 8px;
     padding: var(--space-1);
     border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-sm);
+    border-radius: var (--border-radius-sm);
     background: var(--lora-surface);
 }
 
@@ -733,3 +733,150 @@
     font-size: 0.9em;
     line-height: 1.4;
 }
+
+/* Duplicate Recipes Styles */
+.duplicate-recipes-container {
+    margin-bottom: var(--space-3);
+    border-radius: var(--border-radius-sm);
+    overflow: hidden;
+    animation: fadeIn 0.3s ease-in-out;
+}
+
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(-10px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+
+.duplicate-warning {
+    display: flex;
+    align-items: flex-start;
+    gap: 12px;
+    padding: 12px 16px;
+    background: oklch(var(--lora-warning) / 0.1);
+    border: 1px solid var(--lora-warning);
+    border-radius: var(--border-radius-sm) var(--border-radius-sm) 0 0;
+    color: var(--text-color);
+}
+
+.duplicate-warning .warning-icon {
+    color: var(--lora-warning);
+    font-size: 1.2em;
+    padding-top: 2px;
+}
+
+.duplicate-warning .warning-content {
+    flex: 1;
+}
+
+.duplicate-warning .warning-title {
+    font-weight: 600;
+    margin-bottom: 4px;
+}
+
+.duplicate-warning .warning-text {
+    font-size: 0.9em;
+    line-height: 1.4;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    flex-wrap: wrap;
+    gap: 8px;
+}
+
+.toggle-duplicates-btn {
+    background: none;
+    border: none;
+    color: var(--lora-warning);
+    cursor: pointer;
+    font-size: 0.9em;
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+}
+
+.toggle-duplicates-btn:hover {
+    background: oklch(var(--lora-warning) / 0.1);
+}
+
+.duplicate-recipes-list {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(150px, 1fr));
+    gap: 12px;
+    padding: 16px;
+    border: 1px solid var(--border-color);
+    border-top: none;
+    border-radius: 0 0 var(--border-radius-sm) var(--border-radius-sm);
+    background: var(--bg-color);
+    max-height: 300px;
+    overflow-y: auto;
+    transition: max-height 0.3s ease, padding 0.3s ease;
+}
+
+.duplicate-recipes-list.collapsed {
+    max-height: 0;
+    padding: 0 16px;
+    overflow: hidden;
+}
+
+.duplicate-recipe-card {
+    position: relative;
+    border-radius: var(--border-radius-sm);
+    overflow: hidden;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+    transition: transform 0.2s ease;
+}
+
+.duplicate-recipe-card:hover {
+    transform: translateY(-2px);
+}
+
+.duplicate-recipe-preview {
+    width: 100%;
+    position: relative;
+    aspect-ratio: 2/3;
+    background: var(--bg-color);
+}
+
+.duplicate-recipe-preview img {
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+.duplicate-recipe-title {
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    right: 0;
+    padding: 8px;
+    background: rgba(0, 0, 0, 0.7);
+    color: white;
+    font-size: 0.85em;
+    line-height: 1.3;
+    max-height: 50%;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    display: -webkit-box;
+    -webkit-line-clamp: 2;
+    -webkit-box-orient: vertical;
+}
+
+.duplicate-recipe-details {
+    padding: 8px;
+    background: var(--bg-color);
+    font-size: 0.75em;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    color: var(--text-color);
+    opacity: 0.8;
+}
+
+.duplicate-recipe-date, 
+.duplicate-recipe-lora-count {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+}

static/css/components/progress-panel.css

@@ -12,11 +12,13 @@
     transition: transform 0.3s ease, opacity 0.3s ease;
     opacity: 0;
     transform: translateY(20px);
+    pointer-events: none; /* Ignore mouse events when invisible */
 }
 
 .progress-panel.visible {
     opacity: 1;
     transform: translateY(0);
+    pointer-events: auto; /* Capture mouse events when visible */
 }
 
 .progress-panel.collapsed .progress-panel-content {

static/css/components/recipe-modal.css

@@ -229,8 +229,10 @@
     background: var(--lora-surface);
     border: 1px solid var(--border-color);
     display: flex;
+    flex-direction: column;
     align-items: center;
     justify-content: center;
+    position: relative;
 }
 
 .recipe-preview-container img,
@@ -246,6 +248,133 @@
     object-fit: contain;
 }
 
+/* Source URL container */
+.source-url-container {
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    right: 0;
+    background: rgba(0, 0, 0, 0.5);
+    padding: 8px 12px;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    transition: transform 0.3s ease;
+    transform: translateY(100%);
+}
+
+.recipe-preview-container:hover .source-url-container {
+    transform: translateY(0);
+}
+
+.source-url-container.active {
+    transform: translateY(0);
+}
+
+.source-url-content {
+    display: flex;
+    align-items: center;
+    color: #fff;
+    flex: 1;
+    overflow: hidden;
+    font-size: 0.85em;
+}
+
+.source-url-icon {
+    margin-right: 8px;
+    flex-shrink: 0;
+}
+
+.source-url-text {
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    cursor: pointer;
+    flex: 1;
+}
+
+.source-url-edit-btn {
+    background: none;
+    border: none;
+    color: #fff;
+    cursor: pointer;
+    padding: 4px;
+    margin-left: 8px;
+    border-radius: var(--border-radius-xs);
+    opacity: 0.7;
+    transition: opacity 0.2s ease;
+    flex-shrink: 0;
+}
+
+.source-url-edit-btn:hover {
+    opacity: 1;
+    background: rgba(255, 255, 255, 0.1);
+}
+
+/* Source URL editor */
+.source-url-editor {
+    display: none;
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    right: 0;
+    background: var(--bg-color);
+    border-top: 1px solid var(--border-color);
+    padding: 12px;
+    flex-direction: column;
+    gap: 10px;
+    z-index: 5;
+}
+
+.source-url-editor.active {
+    display: flex;
+}
+
+.source-url-input {
+    width: 100%;
+    padding: 8px 10px;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    color: var(--text-color);
+    font-size: 0.9em;
+}
+
+.source-url-actions {
+    display: flex;
+    justify-content: flex-end;
+    gap: 8px;
+}
+
+.source-url-cancel-btn, 
+.source-url-save-btn {
+    padding: 6px 12px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.85em;
+    cursor: pointer;
+    border: none;
+    transition: all 0.2s;
+}
+
+.source-url-cancel-btn {
+    background: var(--bg-color);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+}
+
+.source-url-save-btn {
+    background: var(--lora-accent);
+    color: white;
+}
+
+.source-url-cancel-btn:hover {
+    background: var(--lora-surface);
+}
+
+.source-url-save-btn:hover {
+    background: color-mix(in oklch, var(--lora-accent), black 10%);
+}
+
 /* Generation Parameters */
 .recipe-gen-params {
     height: 360px;

static/css/style.css

@@ -22,6 +22,7 @@
 @import 'components/initialization.css';
 @import 'components/progress-panel.css';
 @import 'components/alphabet-bar.css'; /* Add alphabet bar component */
+@import 'components/duplicates.css'; /* Add duplicates component */
 
 .initialization-notice {
     display: flex;

static/js/components/DuplicatesManager.js

@@ -0,0 +1,395 @@
+// Duplicates Manager Component
+import { showToast } from '../utils/uiHelpers.js';
+import { RecipeCard } from './RecipeCard.js';
+import { getCurrentPageState } from '../state/index.js';
+import { initializeInfiniteScroll } from '../utils/infiniteScroll.js';
+
+export class DuplicatesManager {
+    constructor(recipeManager) {
+        this.recipeManager = recipeManager;
+        this.duplicateGroups = [];
+        this.inDuplicateMode = false;
+        this.selectedForDeletion = new Set();
+    }
+    
+    async findDuplicates() {
+        try {
+            document.body.classList.add('loading');
+            
+            const response = await fetch('/api/recipes/find-duplicates');
+            if (!response.ok) {
+                throw new Error('Failed to find duplicates');
+            }
+            
+            const data = await response.json();
+            if (!data.success) {
+                throw new Error(data.error || 'Unknown error finding duplicates');
+            }
+            
+            this.duplicateGroups = data.duplicate_groups || [];
+            
+            if (this.duplicateGroups.length === 0) {
+                showToast('No duplicate recipes found', 'info');
+                return false;
+            }
+            
+            this.enterDuplicateMode();
+            return true;
+        } catch (error) {
+            console.error('Error finding duplicates:', error);
+            showToast('Failed to find duplicates: ' + error.message, 'error');
+            return false;
+        } finally {
+            document.body.classList.remove('loading');
+        }
+    }
+    
+    enterDuplicateMode() {
+        this.inDuplicateMode = true;
+        this.selectedForDeletion.clear();
+        
+        // Update state
+        const pageState = getCurrentPageState();
+        pageState.duplicatesMode = true;
+        
+        // Show duplicates banner
+        const banner = document.getElementById('duplicatesBanner');
+        const countSpan = document.getElementById('duplicatesCount');
+        
+        if (banner && countSpan) {
+            countSpan.textContent = `Found ${this.duplicateGroups.length} duplicate group${this.duplicateGroups.length !== 1 ? 's' : ''}`;
+            banner.style.display = 'block';
+        }
+        
+        // Disable infinite scroll
+        if (this.recipeManager.observer) {
+            this.recipeManager.observer.disconnect();
+            this.recipeManager.observer = null;
+        }
+        
+        // Add duplicate-mode class to the body
+        document.body.classList.add('duplicate-mode');
+        
+        // Render duplicate groups
+        this.renderDuplicateGroups();
+        
+        // Update selected count
+        this.updateSelectedCount();
+    }
+    
+    exitDuplicateMode() {
+        this.inDuplicateMode = false;
+        this.selectedForDeletion.clear();
+        
+        // Update state
+        const pageState = getCurrentPageState();
+        pageState.duplicatesMode = false;
+        
+        // Hide duplicates banner
+        const banner = document.getElementById('duplicatesBanner');
+        if (banner) {
+            banner.style.display = 'none';
+        }
+        
+        // Remove duplicate-mode class from the body
+        document.body.classList.remove('duplicate-mode');
+        
+        // Reload normal recipes view
+        this.recipeManager.loadRecipes();
+        
+        // Reinitialize infinite scroll
+        setTimeout(() => {
+            initializeInfiniteScroll('recipes');
+        }, 500);
+    }
+    
+    renderDuplicateGroups() {
+        const recipeGrid = document.getElementById('recipeGrid');
+        if (!recipeGrid) return;
+        
+        // Clear existing content
+        recipeGrid.innerHTML = '';
+        
+        // Render each duplicate group
+        this.duplicateGroups.forEach((group, groupIndex) => {
+            const groupDiv = document.createElement('div');
+            groupDiv.className = 'duplicate-group';
+            groupDiv.dataset.fingerprint = group.fingerprint;
+            
+            // Create group header
+            const header = document.createElement('div');
+            header.className = 'duplicate-group-header';
+            header.innerHTML = `
+                <span>Duplicate Group #${groupIndex + 1} (${group.recipes.length} recipes)</span>
+                <span>
+                    <button class="btn-select-all" onclick="recipeManager.duplicatesManager.toggleSelectAllInGroup('${group.fingerprint}')">
+                        Select All
+                    </button>
+                    <button class="btn-select-latest" onclick="recipeManager.duplicatesManager.selectLatestInGroup('${group.fingerprint}')">
+                        Keep Latest
+                    </button>
+                </span>
+            `;
+            groupDiv.appendChild(header);
+            
+            // Create cards container
+            const cardsDiv = document.createElement('div');
+            cardsDiv.className = 'card-group-container';
+            
+            // Add scrollable class if there are many recipes in the group
+            if (group.recipes.length > 6) {
+                cardsDiv.classList.add('scrollable');
+                
+                // Add expand/collapse toggle button
+                const toggleBtn = document.createElement('button');
+                toggleBtn.className = 'group-toggle-btn';
+                toggleBtn.innerHTML = '<i class="fas fa-chevron-down"></i>';
+                toggleBtn.title = "Expand/Collapse";
+                toggleBtn.onclick = function() {
+                    cardsDiv.classList.toggle('scrollable');
+                    this.innerHTML = cardsDiv.classList.contains('scrollable') ? 
+                        '<i class="fas fa-chevron-down"></i>' : 
+                        '<i class="fas fa-chevron-up"></i>';
+                };
+                groupDiv.appendChild(toggleBtn);
+            }
+            
+            // Sort recipes by date (newest first)
+            const sortedRecipes = [...group.recipes].sort((a, b) => b.modified - a.modified);
+            
+            // Add all recipe cards in this group
+            sortedRecipes.forEach((recipe, index) => {
+                // Create recipe card
+                const recipeCard = new RecipeCard(recipe, (recipe) => {
+                    this.recipeManager.showRecipeDetails(recipe);
+                });
+                const card = recipeCard.element;
+                
+                // Add duplicate class
+                card.classList.add('duplicate');
+                
+                // Mark the latest one
+                if (index === 0) {
+                    card.classList.add('latest');
+                }
+                
+                // Add selection checkbox
+                const checkbox = document.createElement('input');
+                checkbox.type = 'checkbox';
+                checkbox.className = 'selector-checkbox';
+                checkbox.dataset.recipeId = recipe.id;
+                checkbox.dataset.groupFingerprint = group.fingerprint;
+                
+                // Check if already selected
+                if (this.selectedForDeletion.has(recipe.id)) {
+                    checkbox.checked = true;
+                    card.classList.add('duplicate-selected');
+                }
+                
+                // Add change event to checkbox
+                checkbox.addEventListener('change', (e) => {
+                    e.stopPropagation();
+                    this.toggleCardSelection(recipe.id, card, checkbox);
+                });
+                
+                // Make the entire card clickable for selection
+                card.addEventListener('click', (e) => {
+                    // Don't toggle if clicking on the checkbox directly or card actions
+                    if (e.target === checkbox || e.target.closest('.card-actions')) {
+                        return;
+                    }
+                    
+                    // Toggle checkbox state
+                    checkbox.checked = !checkbox.checked;
+                    this.toggleCardSelection(recipe.id, card, checkbox);
+                });
+                
+                card.appendChild(checkbox);
+                cardsDiv.appendChild(card);
+            });
+            
+            groupDiv.appendChild(cardsDiv);
+            recipeGrid.appendChild(groupDiv);
+        });
+    }
+    
+    // Helper method to toggle card selection state
+    toggleCardSelection(recipeId, card, checkbox) {
+        if (checkbox.checked) {
+            this.selectedForDeletion.add(recipeId);
+            card.classList.add('duplicate-selected');
+        } else {
+            this.selectedForDeletion.delete(recipeId);
+            card.classList.remove('duplicate-selected');
+        }
+        
+        this.updateSelectedCount();
+    }
+    
+    updateSelectedCount() {
+        const selectedCountEl = document.getElementById('selectedCount');
+        if (selectedCountEl) {
+            selectedCountEl.textContent = this.selectedForDeletion.size;
+        }
+        
+        // Update delete button state
+        const deleteBtn = document.querySelector('.btn-delete-selected');
+        if (deleteBtn) {
+            deleteBtn.disabled = this.selectedForDeletion.size === 0;
+            deleteBtn.classList.toggle('disabled', this.selectedForDeletion.size === 0);
+        }
+    }
+    
+    toggleSelectAllInGroup(fingerprint) {
+        const checkboxes = document.querySelectorAll(`.selector-checkbox[data-group-fingerprint="${fingerprint}"]`);
+        const allSelected = Array.from(checkboxes).every(checkbox => checkbox.checked);
+        
+        // If all are selected, deselect all; otherwise select all
+        checkboxes.forEach(checkbox => {
+            checkbox.checked = !allSelected;
+            const recipeId = checkbox.dataset.recipeId;
+            const card = checkbox.closest('.lora-card');
+            
+            if (!allSelected) {
+                this.selectedForDeletion.add(recipeId);
+                card.classList.add('duplicate-selected');
+            } else {
+                this.selectedForDeletion.delete(recipeId);
+                card.classList.remove('duplicate-selected');
+            }
+        });
+        
+        // Update the button text
+        const button = document.querySelector(`.duplicate-group[data-fingerprint="${fingerprint}"] .btn-select-all`);
+        if (button) {
+            button.textContent = !allSelected ? "Deselect All" : "Select All";
+        }
+        
+        this.updateSelectedCount();
+    }
+    
+    selectAllInGroup(fingerprint) {
+        const checkboxes = document.querySelectorAll(`.selector-checkbox[data-group-fingerprint="${fingerprint}"]`);
+        checkboxes.forEach(checkbox => {
+            checkbox.checked = true;
+            this.selectedForDeletion.add(checkbox.dataset.recipeId);
+            checkbox.closest('.lora-card').classList.add('duplicate-selected');
+        });
+        
+        // Update the button text
+        const button = document.querySelector(`.duplicate-group[data-fingerprint="${fingerprint}"] .btn-select-all`);
+        if (button) {
+            button.textContent = "Deselect All";
+        }
+        
+        this.updateSelectedCount();
+    }
+    
+    selectLatestInGroup(fingerprint) {
+        // Find all checkboxes in this group
+        const checkboxes = document.querySelectorAll(`.selector-checkbox[data-group-fingerprint="${fingerprint}"]`);
+        
+        // Get all the recipes in this group
+        const group = this.duplicateGroups.find(g => g.fingerprint === fingerprint);
+        if (!group) return;
+        
+        // Sort recipes by date (newest first)
+        const sortedRecipes = [...group.recipes].sort((a, b) => b.modified - a.modified);
+        
+        // Skip the first (latest) one and select the rest for deletion
+        for (let i = 1; i < sortedRecipes.length; i++) {
+            const recipeId = sortedRecipes[i].id;
+            const checkbox = document.querySelector(`.selector-checkbox[data-recipe-id="${recipeId}"]`);
+            
+            if (checkbox) {
+                checkbox.checked = true;
+                this.selectedForDeletion.add(recipeId);
+                checkbox.closest('.lora-card').classList.add('duplicate-selected');
+            }
+        }
+        
+        // Make sure the latest one is not selected
+        const latestId = sortedRecipes[0].id;
+        const latestCheckbox = document.querySelector(`.selector-checkbox[data-recipe-id="${latestId}"]`);
+        
+        if (latestCheckbox) {
+            latestCheckbox.checked = false;
+            this.selectedForDeletion.delete(latestId);
+            latestCheckbox.closest('.lora-card').classList.remove('duplicate-selected');
+        }
+        
+        this.updateSelectedCount();
+    }
+    
+    selectLatestDuplicates() {
+        // For each duplicate group, select all but the latest recipe
+        this.duplicateGroups.forEach(group => {
+            this.selectLatestInGroup(group.fingerprint);
+        });
+    }
+    
+    async deleteSelectedDuplicates() {
+        if (this.selectedForDeletion.size === 0) {
+            showToast('No recipes selected for deletion', 'info');
+            return;
+        }
+        
+        try {
+            // Show the delete confirmation modal instead of a simple confirm
+            const duplicateDeleteCount = document.getElementById('duplicateDeleteCount');
+            if (duplicateDeleteCount) {
+                duplicateDeleteCount.textContent = this.selectedForDeletion.size;
+            }
+            
+            // Use the modal manager to show the confirmation modal
+            modalManager.showModal('duplicateDeleteModal');
+        } catch (error) {
+            console.error('Error preparing delete:', error);
+            showToast('Error: ' + error.message, 'error');
+        }
+    }
+    
+    // Add new method to execute deletion after confirmation
+    async confirmDeleteDuplicates() {
+        try {
+            document.body.classList.add('loading');
+            
+            // Close the modal
+            modalManager.closeModal('duplicateDeleteModal');
+            
+            // Prepare recipe IDs for deletion
+            const recipeIds = Array.from(this.selectedForDeletion);
+            
+            // Call API to bulk delete
+            const response = await fetch('/api/recipes/bulk-delete', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ recipe_ids: recipeIds })
+            });
+            
+            if (!response.ok) {
+                throw new Error('Failed to delete selected recipes');
+            }
+            
+            const data = await response.json();
+            if (!data.success) {
+                throw new Error(data.error || 'Unknown error deleting recipes');
+            }
+            
+            showToast(`Successfully deleted ${data.total_deleted} recipes`, 'success');
+            
+            // Exit duplicate mode if deletions were successful
+            if (data.total_deleted > 0) {
+                this.exitDuplicateMode();
+            }
+            
+        } catch (error) {
+            console.error('Error deleting recipes:', error);
+            showToast('Failed to delete recipes: ' + error.message, 'error');
+        } finally {
+            document.body.classList.remove('loading');
+        }
+    }
+}

static/js/components/RecipeCard.js

@@ -1,12 +1,16 @@
 // Recipe Card Component
 import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
 import { modalManager } from '../managers/ModalManager.js';
+import { getCurrentPageState } from '../state/index.js';
 
 class RecipeCard {
     constructor(recipe, clickHandler) {
         this.recipe = recipe;
         this.clickHandler = clickHandler;
         this.element = this.createCardElement();
+        
+        // Store reference to this instance on the DOM element for updates
+        this.element._recipeCardInstance = this;
     }
     
     createCardElement() {
@@ -33,10 +37,15 @@ class RecipeCard {
                          (this.recipe.file_path ? `/loras_static/root1/preview/${this.recipe.file_path.split('/').pop()}` : 
                          '/loras_static/images/no-preview.png');
 
+        // Check if in duplicates mode
+        const pageState = getCurrentPageState();
+        const isDuplicatesMode = pageState.duplicatesMode;
+
         card.innerHTML = `
-            <div class="recipe-indicator" title="Recipe">R</div>
+            ${!isDuplicatesMode ? `<div class="recipe-indicator" title="Recipe">R</div>` : ''}
             <div class="card-preview">
                 <img src="${imageUrl}" alt="${this.recipe.title}">
+                ${!isDuplicatesMode ? `
                 <div class="card-header">
                     <div class="base-model-wrapper">
                         ${baseModel ? `<span class="base-model-label" title="${baseModel}">${baseModel}</span>` : ''}
@@ -47,19 +56,22 @@ class RecipeCard {
                         <i class="fas fa-trash" title="Delete Recipe"></i>
                     </div>
                 </div>
+                ` : ''}
                 <div class="card-footer">
                     <div class="model-info">
                         <span class="model-name">${this.recipe.title}</span>
                     </div>
+                    ${!isDuplicatesMode ? `
                     <div class="lora-count ${allLorasAvailable ? 'ready' : (lorasCount > 0 ? 'missing' : '')}" 
                          title="${this.getLoraStatusTitle(lorasCount, missingLorasCount)}">
                         <i class="fas fa-layer-group"></i> ${lorasCount}
                     </div>
+                    ` : ''}
                 </div>
             </div>
         `;
         
-        this.attachEventListeners(card);
+        this.attachEventListeners(card, isDuplicatesMode);
         return card;
     }
     
@@ -69,29 +81,31 @@ class RecipeCard {
         return `${missingCount} of ${totalCount} LoRAs missing`;
     }
     
-    attachEventListeners(card) {
-        // Recipe card click event
-        card.addEventListener('click', () => {
-            this.clickHandler(this.recipe);
-        });
-        
-        // Share button click event - prevent propagation to card
-        card.querySelector('.fa-share-alt')?.addEventListener('click', (e) => {
-            e.stopPropagation();
-            this.shareRecipe();
-        });
-        
-        // Copy button click event - prevent propagation to card
-        card.querySelector('.fa-copy')?.addEventListener('click', (e) => {
-            e.stopPropagation();
-            this.copyRecipeSyntax();
-        });
-        
-        // Delete button click event - prevent propagation to card
-        card.querySelector('.fa-trash')?.addEventListener('click', (e) => {
-            e.stopPropagation();
-            this.showDeleteConfirmation();
-        });
+    attachEventListeners(card, isDuplicatesMode) {
+        // Recipe card click event - only attach if not in duplicates mode
+        if (!isDuplicatesMode) {
+            card.addEventListener('click', () => {
+                this.clickHandler(this.recipe);
+            });
+            
+            // Share button click event - prevent propagation to card
+            card.querySelector('.fa-share-alt')?.addEventListener('click', (e) => {
+                e.stopPropagation();
+                this.shareRecipe();
+            });
+            
+            // Copy button click event - prevent propagation to card
+            card.querySelector('.fa-copy')?.addEventListener('click', (e) => {
+                e.stopPropagation();
+                this.copyRecipeSyntax();
+            });
+            
+            // Delete button click event - prevent propagation to card
+            card.querySelector('.fa-trash')?.addEventListener('click', (e) => {
+                e.stopPropagation();
+                this.showDeleteConfirmation();
+            });
+        }
     }
     
     copyRecipeSyntax() {

static/js/components/RecipeModal.js

@@ -2,6 +2,7 @@
 import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { setSessionItem, removeSessionItem } from '../utils/storageHelpers.js';
+import { updateRecipeCard } from '../utils/cardUpdater.js';
 
 class RecipeModal {
     constructor() {
@@ -82,7 +83,7 @@ class RecipeModal {
     
     showRecipeDetails(recipe) {
         // 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
+        this.currentRecipe = recipe;
         
         // Set modal title with edit icon
         const modalTitle = document.getElementById('recipeModalTitle');
@@ -245,6 +246,45 @@ class RecipeModal {
                 imgElement.alt = recipe.title || 'Recipe Preview';
                 mediaContainer.appendChild(imgElement);
             }
+
+            // Add source URL container if the recipe has a source_path
+            const sourceUrlContainer = document.createElement('div');
+            sourceUrlContainer.className = 'source-url-container';
+            const hasSourceUrl = recipe.source_path && recipe.source_path.trim().length > 0;
+            const sourceUrl = hasSourceUrl ? recipe.source_path : '';
+            const isValidUrl = hasSourceUrl && (sourceUrl.startsWith('http://') || sourceUrl.startsWith('https://'));
+            
+            sourceUrlContainer.innerHTML = `
+                <div class="source-url-content">
+                    <span class="source-url-icon"><i class="fas fa-link"></i></span>
+                    <span class="source-url-text" title="${isValidUrl ? 'Click to open source URL' : 'No valid URL'}">${
+                        hasSourceUrl ? sourceUrl : 'No source URL'
+                    }</span>
+                </div>
+                <button class="source-url-edit-btn" title="Edit source URL">
+                    <i class="fas fa-pencil-alt"></i>
+                </button>
+            `;
+            
+            // Add source URL editor
+            const sourceUrlEditor = document.createElement('div');
+            sourceUrlEditor.className = 'source-url-editor';
+            sourceUrlEditor.innerHTML = `
+                <input type="text" class="source-url-input" placeholder="Enter source URL (e.g., https://civitai.com/...)" value="${sourceUrl}">
+                <div class="source-url-actions">
+                    <button class="source-url-cancel-btn">Cancel</button>
+                    <button class="source-url-save-btn">Save</button>
+                </div>
+            `;
+            
+            // Append both containers to the media container
+            mediaContainer.appendChild(sourceUrlContainer);
+            mediaContainer.appendChild(sourceUrlEditor);
+            
+            // Set up event listeners for source URL functionality
+            setTimeout(() => {
+                this.setupSourceUrlHandlers();
+            }, 50);
         }
         
         // Set generation parameters
@@ -451,8 +491,6 @@ class RecipeModal {
             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');
@@ -648,50 +686,8 @@ class RecipeModal {
                 // 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);
-                }
+                // Update the recipe card in the UI
+                updateRecipeCard(this.recipeId, updates);
             } else {
                 showToast(`Failed to update recipe: ${data.error}`, 'error');
             }
@@ -951,8 +947,8 @@ class RecipeModal {
             let loraSyntaxMatch = inputValue.match(/<lora:([^:>]+)(?::[^>]+)?>/);
             let fileName = loraSyntaxMatch ? loraSyntaxMatch[1] : inputValue.trim();
             
-            // Remove any file extension if present
-            fileName = fileName.replace(/\.\w+$/, '');
+            // Remove .safetensors extension if present
+            fileName = fileName.replace(/\.safetensors$/, '');
             
             // Get the deleted lora data
             const deletedLora = this.currentRecipe.loras[loraIndex];
@@ -1069,6 +1065,56 @@ class RecipeModal {
             });
         });
     }
+
+    // New method to set up source URL handlers
+    setupSourceUrlHandlers() {
+        const sourceUrlContainer = document.querySelector('.source-url-container');
+        const sourceUrlEditor = document.querySelector('.source-url-editor');
+        const sourceUrlText = sourceUrlContainer.querySelector('.source-url-text');
+        const sourceUrlEditBtn = sourceUrlContainer.querySelector('.source-url-edit-btn');
+        const sourceUrlCancelBtn = sourceUrlEditor.querySelector('.source-url-cancel-btn');
+        const sourceUrlSaveBtn = sourceUrlEditor.querySelector('.source-url-save-btn');
+        const sourceUrlInput = sourceUrlEditor.querySelector('.source-url-input');
+        
+        // Show editor on edit button click
+        sourceUrlEditBtn.addEventListener('click', () => {
+            sourceUrlContainer.classList.add('hide');
+            sourceUrlEditor.classList.add('active');
+            sourceUrlInput.focus();
+        });
+        
+        // Cancel editing
+        sourceUrlCancelBtn.addEventListener('click', () => {
+            sourceUrlEditor.classList.remove('active');
+            sourceUrlContainer.classList.remove('hide');
+            sourceUrlInput.value = this.currentRecipe.source_path || '';
+        });
+        
+        // Save new source URL
+        sourceUrlSaveBtn.addEventListener('click', () => {
+            const newSourceUrl = sourceUrlInput.value.trim();
+            if (newSourceUrl && newSourceUrl !== this.currentRecipe.source_path) {
+                // Update source URL in the UI
+                sourceUrlText.textContent = newSourceUrl;
+                sourceUrlText.title = newSourceUrl.startsWith('http://') || newSourceUrl.startsWith('https://') ? 'Click to open source URL' : 'No valid URL';
+                
+                // Update the recipe on the server
+                this.updateRecipeMetadata({ source_path: newSourceUrl });
+            }
+            
+            // Hide editor
+            sourceUrlEditor.classList.remove('active');
+            sourceUrlContainer.classList.remove('hide');
+        });
+        
+        // Open source URL in a new tab if it's valid
+        sourceUrlText.addEventListener('click', () => {
+            const url = sourceUrlText.textContent.trim();
+            if (url.startsWith('http://') || url.startsWith('https://')) {
+                window.open(url, '_blank');
+            }
+        });
+    }
 }
 
 export { RecipeModal };

static/js/managers/ModalManager.js

@@ -158,6 +158,18 @@ export class ModalManager {
             });
         }
 
+        // Add duplicateDeleteModal registration
+        const duplicateDeleteModal = document.getElementById('duplicateDeleteModal');
+        if (duplicateDeleteModal) {
+            this.registerModal('duplicateDeleteModal', {
+                element: duplicateDeleteModal,
+                onClose: () => {
+                    this.getModal('duplicateDeleteModal').element.classList.remove('show');
+                    document.body.classList.remove('modal-open');
+                }
+            });
+        }
+        
         // Set up event listeners for modal toggles
         const supportToggle = document.getElementById('supportToggleBtn');
         if (supportToggle) {
@@ -221,7 +233,7 @@ export class ModalManager {
         // Store current scroll position before showing modal
         this.scrollPosition = window.scrollY;
 
-        if (id === 'deleteModal' || id === 'excludeModal') {
+        if (id === 'deleteModal' || id === 'excludeModal' || id === 'duplicateDeleteModal') {
             modal.element.classList.add('show');
         } else {
             modal.element.style.display = 'block';

static/js/managers/import/DownloadManager.js

@@ -0,0 +1,256 @@
+import { showToast } from '../../utils/uiHelpers.js';
+
+export class DownloadManager {
+    constructor(importManager) {
+        this.importManager = importManager;
+    }
+
+    async saveRecipe() {
+        // Check if we're in download-only mode (for existing recipe)
+        const isDownloadOnly = !!this.importManager.recipeId;
+        
+        if (!isDownloadOnly && !this.importManager.recipeName) {
+            showToast('Please enter a recipe name', 'error');
+            return;
+        }
+        
+        try {
+            // Show progress indicator
+            this.importManager.loadingManager.showSimpleLoading(isDownloadOnly ? 'Downloading LoRAs...' : 'Saving recipe...');
+            
+            // Only send the complete recipe to save if not in download-only mode
+            if (!isDownloadOnly) {
+                // Create FormData object for saving recipe
+                const formData = new FormData();
+                
+                // Add image data - depends on import mode
+                if (this.importManager.recipeImage) {
+                    // Direct upload
+                    formData.append('image', this.importManager.recipeImage);
+                } else if (this.importManager.recipeData && this.importManager.recipeData.image_base64) {
+                    // URL mode with base64 data
+                    formData.append('image_base64', this.importManager.recipeData.image_base64);
+                } else if (this.importManager.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');
+                }
+                
+                formData.append('name', this.importManager.recipeName);
+                formData.append('tags', JSON.stringify(this.importManager.recipeTags));
+                
+                // Prepare complete metadata including generation parameters
+                const completeMetadata = {
+                    base_model: this.importManager.recipeData.base_model || "",
+                    loras: this.importManager.recipeData.loras || [],
+                    gen_params: this.importManager.recipeData.gen_params || {},
+                    raw_metadata: this.importManager.recipeData.raw_metadata || {}
+                };
+                
+                // Add source_path to metadata to track where the recipe was imported from
+                if (this.importManager.importMode === 'url') {
+                    const urlInput = document.getElementById('imageUrlInput');
+                    if (urlInput && urlInput.value) {
+                        completeMetadata.source_path = urlInput.value;
+                    }
+                }
+                
+                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;
+                }
+            }
+
+            // Check if we need to download LoRAs
+            let failedDownloads = 0;
+            if (this.importManager.downloadableLoRAs && this.importManager.downloadableLoRAs.length > 0) {
+                await this.downloadMissingLoras();
+            }
+
+            // Show success message
+            if (isDownloadOnly) {
+                if (failedDownloads === 0) {    
+                    showToast('LoRAs downloaded successfully', 'success');
+                }
+            } else {
+                showToast(`Recipe "${this.importManager.recipeName}" saved successfully`, 'success');
+            }
+            
+            // Close modal
+            modalManager.closeModal('importModal');
+            
+            // Refresh the recipe
+            window.recipeManager.loadRecipes();
+            
+        } catch (error) {
+            console.error('Error:', error);
+            showToast(error.message, 'error');
+        } finally {
+            this.importManager.loadingManager.hide();
+        }
+    }
+
+    async downloadMissingLoras() {
+        // For download, we need to validate the target path
+        const loraRoot = document.getElementById('importLoraRoot')?.value;
+        if (!loraRoot) {
+            throw new Error('Please select a LoRA root directory');
+        }
+        
+        // Build target path
+        let targetPath = loraRoot;
+        if (this.importManager.selectedFolder) {
+            targetPath += '/' + this.importManager.selectedFolder;
+        }
+        
+        const newFolder = document.getElementById('importNewFolder')?.value?.trim();
+        if (newFolder) {
+            targetPath += '/' + newFolder;
+        }
+        
+        // Set up WebSocket for progress updates
+        const wsProtocol = window.location.protocol === 'https:' ? 'wss://' : 'ws://';
+        const ws = new WebSocket(`${wsProtocol}${window.location.host}/ws/fetch-progress`);
+        
+        // Show enhanced loading with progress details for multiple items
+        const updateProgress = this.importManager.loadingManager.showDownloadProgress(
+            this.importManager.downloadableLoRAs.length
+        );
+        
+        let completedDownloads = 0;
+        let failedDownloads = 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;
+                
+                // Get current LoRA name
+                const currentLora = this.importManager.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.importManager.loadingManager.setStatus(
+                        `Preparing download for LoRA ${completedDownloads + failedDownloads + 1}/${this.importManager.downloadableLoRAs.length}`
+                    );
+                } else if (currentLoraProgress === 3) {
+                    this.importManager.loadingManager.setStatus(
+                        `Downloaded preview for LoRA ${completedDownloads + failedDownloads + 1}/${this.importManager.downloadableLoRAs.length}`
+                    );
+                } else if (currentLoraProgress > 3 && currentLoraProgress < 100) {
+                    this.importManager.loadingManager.setStatus(
+                        `Downloading LoRA ${completedDownloads + failedDownloads + 1}/${this.importManager.downloadableLoRAs.length}`
+                    );
+                } else {
+                    this.importManager.loadingManager.setStatus(
+                        `Finalizing LoRA ${completedDownloads + failedDownloads + 1}/${this.importManager.downloadableLoRAs.length}`
+                    );
+                }
+            }
+        };
+        
+        for (let i = 0; i < this.importManager.downloadableLoRAs.length; i++) {
+            const lora = this.importManager.downloadableLoRAs[i];
+            
+            // Reset current LoRA progress for new download
+            currentLoraProgress = 0;
+            
+            // Initial status update for new LoRA
+            this.importManager.loadingManager.setStatus(`Starting download for LoRA ${i+1}/${this.importManager.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.importManager.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.importManager.downloadableLoRAs.length) {
+                        this.importManager.loadingManager.setStatus(
+                            `Completed ${completedDownloads}/${this.importManager.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.importManager.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.importManager.downloadableLoRAs.length} LoRAs`, 'error');
+            }
+        }
+        
+        return failedDownloads;
+    }
+}

static/js/managers/import/FolderBrowser.js

@@ -0,0 +1,220 @@
+import { showToast } from '../../utils/uiHelpers.js';
+import { getStorageItem } from '../../utils/storageHelpers.js';
+
+export class FolderBrowser {
+    constructor(importManager) {
+        this.importManager = importManager;
+        this.folderClickHandler = null;
+        this.updateTargetPath = this.updateTargetPath.bind(this);
+    }
+
+    async proceedToLocation() {
+        // Show the location step with special handling
+        this.importManager.stepManager.showStep('locationStep');
+        
+        // Double-check after a short delay to ensure the step is visible
+        setTimeout(() => {
+            const locationStep = document.getElementById('locationStep');
+            if (locationStep.style.display !== 'block' || 
+                window.getComputedStyle(locationStep).display !== 'block') {
+                // Force display again
+                locationStep.style.display = 'block';
+                
+                // If still not visible, try with injected style
+                if (window.getComputedStyle(locationStep).display !== 'block') {
+                    this.importManager.stepManager.injectedStyles = document.createElement('style');
+                    this.importManager.stepManager.injectedStyles.innerHTML = `
+                        #locationStep {
+                            display: block !important;
+                            opacity: 1 !important;
+                            visibility: visible !important;
+                        }
+                    `;
+                    document.head.appendChild(this.importManager.stepManager.injectedStyles);
+                }
+            }
+        }, 100);
+        
+        try {
+            // Display missing LoRAs that will be downloaded
+            const missingLorasList = document.getElementById('missingLorasList');
+            if (missingLorasList && this.importManager.downloadableLoRAs.length > 0) {
+                // Calculate total size
+                const totalSize = this.importManager.downloadableLoRAs.reduce((sum, lora) => {
+                    return sum + (lora.size ? parseInt(lora.size) : 0);
+                }, 0);
+                
+                // Update total size display
+                const totalSizeDisplay = document.getElementById('totalDownloadSize');
+                if (totalSizeDisplay) {
+                    totalSizeDisplay.textContent = this.importManager.formatFileSize(totalSize);
+                }
+                
+                // Update header to include count of missing LoRAs
+                const missingLorasHeader = document.querySelector('.summary-header h3');
+                if (missingLorasHeader) {
+                    missingLorasHeader.innerHTML = `Missing LoRAs <span class="lora-count-badge">(${this.importManager.downloadableLoRAs.length})</span> <span id="totalDownloadSize" class="total-size-badge">${this.importManager.formatFileSize(totalSize)}</span>`;
+                }
+                
+                // Generate missing LoRAs list
+                missingLorasList.innerHTML = this.importManager.downloadableLoRAs.map(lora => {
+                    const sizeDisplay = lora.size ? 
+                        this.importManager.formatFileSize(lora.size) : 'Unknown size';
+                    const baseModel = lora.baseModel ? 
+                        `<span class="lora-base-model">${lora.baseModel}</span>` : '';
+                    const isEarlyAccess = lora.isEarlyAccess;
+                    
+                    // Early access badge
+                    let earlyAccessBadge = '';
+                    if (isEarlyAccess) {
+                        earlyAccessBadge = `<span class="early-access-badge">
+                            <i class="fas fa-clock"></i> Early Access
+                        </span>`;
+                    }
+                    
+                    return `
+                        <div class="missing-lora-item ${isEarlyAccess ? 'is-early-access' : ''}">
+                            <div class="missing-lora-info">
+                                <div class="missing-lora-name">${lora.name}</div>
+                                ${baseModel}
+                                ${earlyAccessBadge}
+                            </div>
+                            <div class="missing-lora-size">${sizeDisplay}</div>
+                        </div>
+                    `;
+                }).join('');
+                
+                // Set up toggle for missing LoRAs list
+                const toggleBtn = document.getElementById('toggleMissingLorasList');
+                if (toggleBtn) {
+                    toggleBtn.addEventListener('click', () => {
+                        missingLorasList.classList.toggle('collapsed');
+                        const icon = toggleBtn.querySelector('i');
+                        if (icon) {
+                            icon.classList.toggle('fa-chevron-down');
+                            icon.classList.toggle('fa-chevron-up');
+                        }
+                    });
+                }
+            }
+            
+            // Fetch LoRA roots
+            const rootsResponse = await fetch('/api/lora-roots');
+            if (!rootsResponse.ok) {
+                throw new Error(`Failed to fetch LoRA roots: ${rootsResponse.status}`);
+            }
+            
+            const rootsData = await rootsResponse.json();
+            const loraRoot = document.getElementById('importLoraRoot');
+            if (loraRoot) {
+                loraRoot.innerHTML = rootsData.roots.map(root => 
+                    `<option value="${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
+            const foldersResponse = await fetch('/api/folders');
+            if (!foldersResponse.ok) {
+                throw new Error(`Failed to fetch folders: ${foldersResponse.status}`);
+            }
+            
+            const foldersData = await foldersResponse.json();
+            const folderBrowser = document.getElementById('importFolderBrowser');
+            if (folderBrowser) {
+                folderBrowser.innerHTML = foldersData.folders.map(folder => 
+                    folder ? `<div class="folder-item" data-folder="${folder}">${folder}</div>` : ''
+                ).join('');
+            }
+
+            // Initialize folder browser after loading data
+            this.initializeFolderBrowser();
+        } catch (error) {
+            console.error('Error in API calls:', error);
+            showToast(error.message, 'error');
+        }
+    }
+
+    initializeFolderBrowser() {
+        const folderBrowser = document.getElementById('importFolderBrowser');
+        if (!folderBrowser) return;
+
+        // Cleanup existing handler if any
+        this.cleanup();
+
+        // Create new handler
+        this.folderClickHandler = (event) => {
+            const folderItem = event.target.closest('.folder-item');
+            if (!folderItem) return;
+
+            if (folderItem.classList.contains('selected')) {
+                folderItem.classList.remove('selected');
+                this.importManager.selectedFolder = '';
+            } else {
+                folderBrowser.querySelectorAll('.folder-item').forEach(f => 
+                    f.classList.remove('selected'));
+                folderItem.classList.add('selected');
+                this.importManager.selectedFolder = folderItem.dataset.folder;
+            }
+            
+            // Update path display after folder selection
+            this.updateTargetPath();
+        };
+
+        // Add the new handler
+        folderBrowser.addEventListener('click', this.folderClickHandler);
+        
+        // Add event listeners for path updates
+        const loraRoot = document.getElementById('importLoraRoot');
+        const newFolder = document.getElementById('importNewFolder');
+        
+        if (loraRoot) loraRoot.addEventListener('change', this.updateTargetPath);
+        if (newFolder) newFolder.addEventListener('input', this.updateTargetPath);
+        
+        // Update initial path
+        this.updateTargetPath();
+    }
+
+    cleanup() {
+        if (this.folderClickHandler) {
+            const folderBrowser = document.getElementById('importFolderBrowser');
+            if (folderBrowser) {
+                folderBrowser.removeEventListener('click', this.folderClickHandler);
+                this.folderClickHandler = null;
+            }
+        }
+        
+        // Remove path update listeners
+        const loraRoot = document.getElementById('importLoraRoot');
+        const newFolder = document.getElementById('importNewFolder');
+        
+        if (loraRoot) loraRoot.removeEventListener('change', this.updateTargetPath);
+        if (newFolder) newFolder.removeEventListener('input', this.updateTargetPath);
+    }
+    
+    updateTargetPath() {
+        const pathDisplay = document.getElementById('importTargetPathDisplay');
+        if (!pathDisplay) return;
+        
+        const loraRoot = document.getElementById('importLoraRoot')?.value || '';
+        const newFolder = document.getElementById('importNewFolder')?.value?.trim() || '';
+        
+        let fullPath = loraRoot || 'Select a LoRA root directory'; 
+        
+        if (loraRoot) {
+            if (this.importManager.selectedFolder) {
+                fullPath += '/' + this.importManager.selectedFolder;
+            }
+            if (newFolder) {
+                fullPath += '/' + newFolder;
+            }
+        }
+    
+        pathDisplay.innerHTML = `<span class="path-text">${fullPath}</span>`;
+    }
+}

static/js/managers/import/ImageProcessor.js

@@ -0,0 +1,208 @@
+import { showToast } from '../../utils/uiHelpers.js';
+
+export class ImageProcessor {
+    constructor(importManager) {
+        this.importManager = importManager;
+    }
+
+    handleFileUpload(event) {
+        const file = event.target.files[0];
+        const errorElement = document.getElementById('uploadError');
+        
+        if (!file) return;
+        
+        // Validate file type
+        if (!file.type.match('image.*')) {
+            errorElement.textContent = 'Please select an image file';
+            return;
+        }
+        
+        // Reset error
+        errorElement.textContent = '';
+        this.importManager.recipeImage = file;
+        
+        // Auto-proceed to next step if file is selected
+        this.importManager.uploadAndAnalyzeImage();
+    }
+
+    async handleUrlInput() {
+        const urlInput = document.getElementById('imageUrlInput');
+        const errorElement = document.getElementById('urlError');
+        const input = urlInput.value.trim();
+        
+        // Validate input
+        if (!input) {
+            errorElement.textContent = 'Please enter a URL or file path';
+            return;
+        }
+        
+        // Reset error
+        errorElement.textContent = '';
+        
+        // Show loading indicator
+        this.importManager.loadingManager.showSimpleLoading('Processing input...');
+        
+        try {
+            // Check if it's a URL or a local file path
+            if (input.startsWith('http://') || input.startsWith('https://')) {
+                // Handle as URL
+                await this.analyzeImageFromUrl(input);
+            } else {
+                // Handle as local file path
+                await this.analyzeImageFromLocalPath(input);
+            }
+        } catch (error) {
+            errorElement.textContent = error.message || 'Failed to process input';
+        } finally {
+            this.importManager.loadingManager.hide();
+        }
+    }
+
+    async analyzeImageFromUrl(url) {
+        try {
+            // Call the API with URL data
+            const response = await fetch('/api/recipes/analyze-image', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ url: url })
+            });
+            
+            if (!response.ok) {
+                const errorData = await response.json();
+                throw new Error(errorData.error || 'Failed to analyze image from URL');
+            }
+            
+            // Get recipe data from response
+            this.importManager.recipeData = await response.json();
+            
+            // Check if we have an error message
+            if (this.importManager.recipeData.error) {
+                throw new Error(this.importManager.recipeData.error);
+            }
+            
+            // Check if we have valid recipe data
+            if (!this.importManager.recipeData || 
+                !this.importManager.recipeData.loras || 
+                this.importManager.recipeData.loras.length === 0) {
+                throw new Error('No LoRA information found in this image');
+            }
+            
+            // Find missing LoRAs
+            this.importManager.missingLoras = this.importManager.recipeData.loras.filter(
+                lora => !lora.existsLocally
+            );
+            
+            // Reset import as new flag
+            this.importManager.importAsNew = false;
+            
+            // Proceed to recipe details step
+            this.importManager.showRecipeDetailsStep();
+            
+        } catch (error) {
+            console.error('Error analyzing URL:', error);
+            throw error;
+        }
+    }
+
+    async analyzeImageFromLocalPath(path) {
+        try {
+            // Call the API with local path data
+            const response = await fetch('/api/recipes/analyze-local-image', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ path: path })
+            });
+            
+            if (!response.ok) {
+                const errorData = await response.json();
+                throw new Error(errorData.error || 'Failed to load image from local path');
+            }
+            
+            // Get recipe data from response
+            this.importManager.recipeData = await response.json();
+            
+            // Check if we have an error message
+            if (this.importManager.recipeData.error) {
+                throw new Error(this.importManager.recipeData.error);
+            }
+            
+            // Check if we have valid recipe data
+            if (!this.importManager.recipeData || 
+                !this.importManager.recipeData.loras || 
+                this.importManager.recipeData.loras.length === 0) {
+                throw new Error('No LoRA information found in this image');
+            }
+            
+            // Find missing LoRAs
+            this.importManager.missingLoras = this.importManager.recipeData.loras.filter(
+                lora => !lora.existsLocally
+            );
+            
+            // Reset import as new flag
+            this.importManager.importAsNew = false;
+            
+            // Proceed to recipe details step
+            this.importManager.showRecipeDetailsStep();
+            
+        } catch (error) {
+            console.error('Error analyzing local path:', error);
+            throw error;
+        }
+    }
+
+    async uploadAndAnalyzeImage() {
+        if (!this.importManager.recipeImage) {
+            showToast('Please select an image first', 'error');
+            return;
+        }
+        
+        try {
+            this.importManager.loadingManager.showSimpleLoading('Analyzing image metadata...');
+            
+            // Create form data for upload
+            const formData = new FormData();
+            formData.append('image', this.importManager.recipeImage);
+            
+            // Upload image for analysis
+            const response = await fetch('/api/recipes/analyze-image', {
+                method: 'POST',
+                body: formData
+            });
+             
+            // Get recipe data from response
+            this.importManager.recipeData = await response.json();
+
+            // Check if we have an error message
+            if (this.importManager.recipeData.error) {
+                throw new Error(this.importManager.recipeData.error);
+            }
+            
+            // Check if we have valid recipe data
+            if (!this.importManager.recipeData || 
+                !this.importManager.recipeData.loras || 
+                this.importManager.recipeData.loras.length === 0) {
+                throw new Error('No LoRA information found in this image');
+            }
+            
+            // Find missing LoRAs
+            this.importManager.missingLoras = this.importManager.recipeData.loras.filter(
+                lora => !lora.existsLocally
+            );
+            
+            // Reset import as new flag
+            this.importManager.importAsNew = false;
+            
+            // Proceed to recipe details step
+            this.importManager.showRecipeDetailsStep();
+            
+        } catch (error) {
+            document.getElementById('uploadError').textContent = error.message;
+        } finally {
+            this.importManager.loadingManager.hide();
+        }
+    }
+}

static/js/managers/import/ImportStepManager.js

@@ -0,0 +1,57 @@
+export class ImportStepManager {
+    constructor() {
+        this.injectedStyles = null;
+    }
+
+    removeInjectedStyles() {
+        if (this.injectedStyles && this.injectedStyles.parentNode) {
+            this.injectedStyles.parentNode.removeChild(this.injectedStyles);
+            this.injectedStyles = null;
+        }
+        
+        // Reset inline styles
+        document.querySelectorAll('.import-step').forEach(step => {
+            step.style.cssText = '';
+        });
+    }
+
+    showStep(stepId) {
+        // Remove any injected styles to prevent conflicts
+        this.removeInjectedStyles();
+        
+        // Hide all steps first
+        document.querySelectorAll('.import-step').forEach(step => {
+            step.style.display = 'none';
+        });
+        
+        // Show target step with a monitoring mechanism
+        const targetStep = document.getElementById(stepId);
+        if (targetStep) {
+            // Use direct style setting
+            targetStep.style.display = 'block';
+            
+            // For the locationStep specifically, we need additional measures
+            if (stepId === 'locationStep') {
+                // Create a more persistent style to override any potential conflicts
+                this.injectedStyles = document.createElement('style');
+                this.injectedStyles.innerHTML = `
+                    #locationStep {
+                        display: block !important;
+                        opacity: 1 !important;
+                        visibility: visible !important;
+                    }
+                `;
+                document.head.appendChild(this.injectedStyles);
+                
+                // Force layout recalculation
+                targetStep.offsetHeight;
+            }
+            
+            // Scroll modal content to top
+            const modalContent = document.querySelector('#importModal .modal-content');
+            if (modalContent) {
+                modalContent.scrollTop = 0;
+            }
+        }
+    }
+}

static/js/managers/import/RecipeDataManager.js

@@ -0,0 +1,436 @@
+import { showToast } from '../../utils/uiHelpers.js';
+
+export class RecipeDataManager {
+    constructor(importManager) {
+        this.importManager = importManager;
+    }
+
+    showRecipeDetailsStep() {
+        this.importManager.stepManager.showStep('detailsStep');
+        
+        // Set default recipe name from prompt or image filename
+        const recipeName = document.getElementById('recipeName');
+        
+        // Check if we have recipe metadata from a shared recipe
+        if (this.importManager.recipeData && this.importManager.recipeData.from_recipe_metadata) {
+            // Use title from recipe metadata
+            if (this.importManager.recipeData.title) {
+                recipeName.value = this.importManager.recipeData.title;
+                this.importManager.recipeName = this.importManager.recipeData.title;
+            }
+            
+            // Use tags from recipe metadata
+            if (this.importManager.recipeData.tags && Array.isArray(this.importManager.recipeData.tags)) {
+                this.importManager.recipeTags = [...this.importManager.recipeData.tags];
+                this.updateTagsDisplay();
+            }
+        } else if (this.importManager.recipeData && 
+                  this.importManager.recipeData.gen_params && 
+                  this.importManager.recipeData.gen_params.prompt) {
+            // Use the first 10 words from the prompt as the default recipe name
+            const promptWords = this.importManager.recipeData.gen_params.prompt.split(' ');
+            const truncatedPrompt = promptWords.slice(0, 10).join(' ');
+            recipeName.value = truncatedPrompt;
+            this.importManager.recipeName = truncatedPrompt;
+            
+            // Set up click handler to select all text for easy editing
+            if (!recipeName.hasSelectAllHandler) {
+                recipeName.addEventListener('click', function() {
+                    this.select();
+                });
+                recipeName.hasSelectAllHandler = true;
+            }
+        } else if (this.importManager.recipeImage && !recipeName.value) {
+            // Fallback to image filename if no prompt is available
+            const fileName = this.importManager.recipeImage.name.split('.')[0];
+            recipeName.value = fileName;
+            this.importManager.recipeName = fileName;
+        }
+        
+        // Always set up click handler for easy editing if not already set
+        if (!recipeName.hasSelectAllHandler) {
+            recipeName.addEventListener('click', function() {
+                this.select();
+            });
+            recipeName.hasSelectAllHandler = true;
+        }
+        
+        // Display the uploaded image in the preview
+        const imagePreview = document.getElementById('recipeImagePreview');
+        if (imagePreview) {
+            if (this.importManager.recipeImage) {
+                // For file upload mode
+                const reader = new FileReader();
+                reader.onload = (e) => {
+                    imagePreview.innerHTML = `<img src="${e.target.result}" alt="Recipe preview">`;
+                };
+                reader.readAsDataURL(this.importManager.recipeImage);
+            } else if (this.importManager.recipeData && this.importManager.recipeData.image_base64) {
+                // For URL mode - use the base64 image data returned from the backend
+                imagePreview.innerHTML = `<img src="data:image/jpeg;base64,${this.importManager.recipeData.image_base64}" alt="Recipe preview">`;
+            } else if (this.importManager.importMode === 'url') {
+                // Fallback for URL mode if no base64 data
+                const urlInput = document.getElementById('imageUrlInput');
+                if (urlInput && urlInput.value) {
+                    imagePreview.innerHTML = `<img src="${urlInput.value}" alt="Recipe preview" crossorigin="anonymous">`;
+                }
+            }
+        }
+
+        // Update LoRA count information
+        const totalLoras = this.importManager.recipeData.loras.length;
+        const existingLoras = this.importManager.recipeData.loras.filter(lora => lora.existsLocally).length;
+        const loraCountInfo = document.getElementById('loraCountInfo');
+        if (loraCountInfo) {
+            loraCountInfo.textContent = `(${existingLoras}/${totalLoras} in library)`;
+        }
+        
+        // Display LoRAs list
+        const lorasList = document.getElementById('lorasList');
+        if (lorasList) {
+            lorasList.innerHTML = this.importManager.recipeData.loras.map(lora => {
+                const existsLocally = lora.existsLocally;
+                const isDeleted = lora.isDeleted;
+                const isEarlyAccess = lora.isEarlyAccess;
+                const localPath = lora.localPath || '';
+                
+                // Create status badge based on LoRA status
+                let statusBadge;
+                if (isDeleted) {
+                    statusBadge = `<div class="deleted-badge">
+                        <i class="fas fa-exclamation-circle"></i> Deleted from Civitai
+                    </div>`;
+                } else {
+                    statusBadge = existsLocally ? 
+                        `<div class="local-badge">
+                            <i class="fas fa-check"></i> In Library
+                            <div class="local-path">${localPath}</div>
+                        </div>` :
+                        `<div class="missing-badge">
+                            <i class="fas fa-exclamation-triangle"></i> Not in Library
+                        </div>`;
+                }
+
+                // Early access badge (shown additionally with other badges)
+                let earlyAccessBadge = '';
+                if (isEarlyAccess) {
+                    // Format the early access end date if available
+                    let earlyAccessInfo = 'This LoRA requires early access payment to download.';
+                    if (lora.earlyAccessEndsAt) {
+                        try {
+                            const endDate = new Date(lora.earlyAccessEndsAt);
+                            const formattedDate = endDate.toLocaleDateString();
+                            earlyAccessInfo += ` Early access ends on ${formattedDate}.`;
+                        } catch (e) {
+                            console.warn('Failed to format early access date', e);
+                        }
+                    }
+                    
+                    earlyAccessBadge = `<div class="early-access-badge">
+                        <i class="fas fa-clock"></i> Early Access
+                        <div class="early-access-info">${earlyAccessInfo} Verify that you have purchased early access before downloading.</div>
+                    </div>`;
+                }
+
+                // Format size if available
+                const sizeDisplay = lora.size ? 
+                    `<div class="size-badge">${this.importManager.formatFileSize(lora.size)}</div>` : '';
+
+                return `
+                    <div class="lora-item ${existsLocally ? 'exists-locally' : isDeleted ? 'is-deleted' : 'missing-locally'} ${isEarlyAccess ? 'is-early-access' : ''}">
+                        <div class="lora-thumbnail">
+                            <img src="${lora.thumbnailUrl || '/loras_static/images/no-preview.png'}" alt="LoRA preview">
+                        </div>
+                        <div class="lora-content">
+                            <div class="lora-header">
+                                <h3>${lora.name}</h3>
+                                <div class="badge-container">
+                                    ${statusBadge}
+                                    ${earlyAccessBadge}
+                                </div>
+                            </div>
+                            ${lora.version ? `<div class="lora-version">${lora.version}</div>` : ''}
+                            <div class="lora-info">
+                                ${lora.baseModel ? `<div class="base-model">${lora.baseModel}</div>` : ''}
+                                ${sizeDisplay}
+                                <div class="weight-badge">Weight: ${lora.weight || 1.0}</div>
+                            </div>
+                        </div>
+                    </div>
+                `;
+            }).join('');
+        }
+        
+        // Check for early access loras and show warning if any exist
+        const earlyAccessLoras = this.importManager.recipeData.loras.filter(lora => 
+            lora.isEarlyAccess && !lora.existsLocally && !lora.isDeleted);
+        if (earlyAccessLoras.length > 0) {
+            // Show a warning about early access loras
+            const warningMessage = `
+                <div class="early-access-warning">
+                    <div class="warning-icon"><i class="fas fa-clock"></i></div>
+                    <div class="warning-content">
+                        <div class="warning-title">${earlyAccessLoras.length} LoRA(s) require Early Access</div>
+                        <div class="warning-text">
+                            These LoRAs require a payment to access. Download will fail if you haven't purchased access.
+                            You may need to log in to your Civitai account in browser settings.
+                        </div>
+                    </div>
+                </div>
+            `;
+            
+            // Show the warning message
+            const buttonsContainer = document.querySelector('#detailsStep .modal-actions');
+            if (buttonsContainer) {
+                // Remove existing warning if any
+                const existingWarning = document.getElementById('earlyAccessWarning');
+                if (existingWarning) {
+                    existingWarning.remove();
+                }
+                
+                // Add new warning
+                const warningContainer = document.createElement('div');
+                warningContainer.id = 'earlyAccessWarning';
+                warningContainer.innerHTML = warningMessage;
+                buttonsContainer.parentNode.insertBefore(warningContainer, buttonsContainer);
+            }
+        }
+        
+        // Check for duplicate recipes and display warning if found
+        this.checkAndDisplayDuplicates();
+        
+        // Update Next button state based on missing LoRAs and duplicates
+        this.updateNextButtonState();
+    }
+    
+    checkAndDisplayDuplicates() {
+        // Check if we have duplicate recipes
+        if (this.importManager.recipeData && 
+            this.importManager.recipeData.matching_recipes && 
+            this.importManager.recipeData.matching_recipes.length > 0) {
+            
+            // Store duplicates in the importManager for later use
+            this.importManager.duplicateRecipes = this.importManager.recipeData.matching_recipes;
+            
+            // Create duplicate warning container
+            const duplicateContainer = document.getElementById('duplicateRecipesContainer') || 
+                this.createDuplicateContainer();
+                
+            // Format date helper function
+            const formatDate = (timestamp) => {
+                try {
+                    const date = new Date(timestamp * 1000);
+                    return date.toLocaleDateString() + ' ' + date.toLocaleTimeString();
+                } catch (e) {
+                    return 'Unknown date';
+                }
+            };
+            
+            // Generate the HTML for duplicate recipes warning
+            duplicateContainer.innerHTML = `
+                <div class="duplicate-warning">
+                    <div class="warning-icon"><i class="fas fa-clone"></i></div>
+                    <div class="warning-content">
+                        <div class="warning-title">
+                            ${this.importManager.duplicateRecipes.length} identical ${this.importManager.duplicateRecipes.length === 1 ? 'recipe' : 'recipes'} found in your library
+                        </div>
+                        <div class="warning-text">
+                            These recipes contain the same LoRAs with identical weights.
+                            <button id="toggleDuplicatesList" class="toggle-duplicates-btn">
+                                Show duplicates <i class="fas fa-chevron-down"></i>
+                            </button>
+                        </div>
+                    </div>
+                </div>
+                <div class="duplicate-recipes-list collapsed">
+                    ${this.importManager.duplicateRecipes.map((recipe) => `
+                        <div class="duplicate-recipe-card">
+                            <div class="duplicate-recipe-preview">
+                                <img src="${recipe.file_url}" alt="Recipe preview">
+                                <div class="duplicate-recipe-title">${recipe.title}</div>
+                            </div>
+                            <div class="duplicate-recipe-details">
+                                <div class="duplicate-recipe-date">
+                                    <i class="fas fa-calendar-alt"></i> ${formatDate(recipe.modified)}
+                                </div>
+                                <div class="duplicate-recipe-lora-count">
+                                    <i class="fas fa-layer-group"></i> ${recipe.lora_count} LoRAs
+                                </div>
+                            </div>
+                        </div>
+                    `).join('')}
+                </div>
+            `;
+            
+            // Show the duplicate container
+            duplicateContainer.style.display = 'block';
+            
+            // Add click event for the toggle button
+            const toggleButton = document.getElementById('toggleDuplicatesList');
+            if (toggleButton) {
+                toggleButton.addEventListener('click', () => {
+                    const list = duplicateContainer.querySelector('.duplicate-recipes-list');
+                    if (list) {
+                        list.classList.toggle('collapsed');
+                        const icon = toggleButton.querySelector('i');
+                        if (icon) {
+                            if (list.classList.contains('collapsed')) {
+                                toggleButton.innerHTML = `Show duplicates <i class="fas fa-chevron-down"></i>`;
+                            } else {
+                                toggleButton.innerHTML = `Hide duplicates <i class="fas fa-chevron-up"></i>`;
+                            }
+                        }
+                    }
+                });
+            }
+        } else {
+            // No duplicates, hide the container if it exists
+            const duplicateContainer = document.getElementById('duplicateRecipesContainer');
+            if (duplicateContainer) {
+                duplicateContainer.style.display = 'none';
+            }
+            
+            // Reset duplicate tracking
+            this.importManager.duplicateRecipes = [];
+        }
+    }
+    
+    createDuplicateContainer() {
+        // Find where to insert the duplicate container
+        const lorasListContainer = document.querySelector('.input-group:has(#lorasList)');
+        
+        if (!lorasListContainer) return null;
+        
+        // Create container
+        const duplicateContainer = document.createElement('div');
+        duplicateContainer.id = 'duplicateRecipesContainer';
+        duplicateContainer.className = 'duplicate-recipes-container';
+        
+        // Insert before the LoRA list
+        lorasListContainer.parentNode.insertBefore(duplicateContainer, lorasListContainer);
+        
+        return duplicateContainer;
+    }
+    
+    updateNextButtonState() {
+        const nextButton = document.querySelector('#detailsStep .primary-btn');
+        const actionsContainer = document.querySelector('#detailsStep .modal-actions');
+        if (!nextButton || !actionsContainer) return;
+        
+        // Always clean up previous warnings and buttons first
+        const existingWarning = document.getElementById('deletedLorasWarning');
+        if (existingWarning) {
+            existingWarning.remove();
+        }
+        
+        // Remove any existing "import anyway" button
+        const importAnywayBtn = document.getElementById('importAnywayBtn');
+        if (importAnywayBtn) {
+            importAnywayBtn.remove();
+        }
+        
+        // Count deleted LoRAs
+        const deletedLoras = this.importManager.recipeData.loras.filter(lora => lora.isDeleted).length;
+        
+        // If we have deleted LoRAs, show a warning
+        if (deletedLoras > 0) {
+            // Create a new warning container above the buttons
+            const buttonsContainer = document.querySelector('#detailsStep .modal-actions') || nextButton.parentNode;
+            const warningContainer = document.createElement('div');
+            warningContainer.id = 'deletedLorasWarning';
+            warningContainer.className = 'deleted-loras-warning';
+            
+            // Create warning message
+            warningContainer.innerHTML = `
+                <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 remain in the recipe but won't be included when used.</div>
+                </div>
+            `;
+            
+            // Insert before the buttons container
+            buttonsContainer.parentNode.insertBefore(warningContainer, buttonsContainer);
+        }
+        
+        // Check for duplicates but don't change button actions
+        const missingNotDeleted = this.importManager.recipeData.loras.filter(
+            lora => !lora.existsLocally && !lora.isDeleted
+        ).length;
+        
+        // Standard button behavior regardless of duplicates
+        nextButton.classList.remove('warning-btn');
+        
+        if (missingNotDeleted > 0) {
+            nextButton.textContent = 'Download Missing LoRAs';
+        } else {
+            nextButton.textContent = 'Save Recipe';
+        }
+    }
+
+    addTag() {
+        const tagInput = document.getElementById('tagInput');
+        const tag = tagInput.value.trim();
+        
+        if (!tag) return;
+        
+        if (!this.importManager.recipeTags.includes(tag)) {
+            this.importManager.recipeTags.push(tag);
+            this.updateTagsDisplay();
+        }
+        
+        tagInput.value = '';
+    }
+    
+    removeTag(tag) {
+        this.importManager.recipeTags = this.importManager.recipeTags.filter(t => t !== tag);
+        this.updateTagsDisplay();
+    }
+    
+    updateTagsDisplay() {
+        const tagsContainer = document.getElementById('tagsContainer');
+        
+        if (this.importManager.recipeTags.length === 0) {
+            tagsContainer.innerHTML = '<div class="empty-tags">No tags added</div>';
+            return;
+        }
+        
+        tagsContainer.innerHTML = this.importManager.recipeTags.map(tag => `
+            <div class="recipe-tag">
+                ${tag}
+                <i class="fas fa-times" onclick="importManager.removeTag('${tag}')"></i>
+            </div>
+        `).join('');
+    }
+
+    proceedFromDetails() {
+        // Validate recipe name
+        if (!this.importManager.recipeName) {
+            showToast('Please enter a recipe name', 'error');
+            return;
+        }
+        
+        // Automatically mark all deleted LoRAs as excluded
+        if (this.importManager.recipeData && this.importManager.recipeData.loras) {
+            this.importManager.recipeData.loras.forEach(lora => {
+                if (lora.isDeleted) {
+                    lora.exclude = true;
+                }
+            });
+        }
+        
+        // Update missing LoRAs list to exclude deleted LoRAs
+        this.importManager.missingLoras = this.importManager.recipeData.loras.filter(lora => 
+            !lora.existsLocally && !lora.isDeleted);
+        
+        // If we have downloadable missing LoRAs, go to location step
+        if (this.importManager.missingLoras.length > 0) {
+            // Store only downloadable LoRAs for the download step
+            this.importManager.downloadableLoRAs = this.importManager.missingLoras;
+            this.importManager.proceedToLocation();
+        } else {
+            // Otherwise, save the recipe directly
+            this.importManager.saveRecipe();
+        }
+    }
+}

static/js/recipes.js

@@ -6,6 +6,8 @@ import { RecipeModal } from './components/RecipeModal.js';
 import { getCurrentPageState } from './state/index.js';
 import { getSessionItem, removeSessionItem } from './utils/storageHelpers.js';
 import { RecipeContextMenu } from './components/ContextMenu/index.js';
+import { DuplicatesManager } from './components/DuplicatesManager.js';
+import { initializeInfiniteScroll } from './utils/infiniteScroll.js';
 
 class RecipeManager {
     constructor() {
@@ -18,6 +20,9 @@ class RecipeManager {
         // Initialize RecipeModal
         this.recipeModal = new RecipeModal();
         
+        // Initialize DuplicatesManager
+        this.duplicatesManager = new DuplicatesManager(this);
+        
         // Add state tracking for infinite scroll
         this.pageState.isLoading = false;
         this.pageState.hasMore = true;
@@ -179,6 +184,12 @@ class RecipeManager {
     
     async loadRecipes(resetPage = true) {
         try {
+            // Skip loading if in duplicates mode
+            const pageState = getCurrentPageState();
+            if (pageState.duplicatesMode) {
+                return;
+            }
+            
             // Show loading indicator
             document.body.classList.add('loading');
             this.pageState.isLoading = true;
@@ -366,6 +377,28 @@ class RecipeManager {
     showRecipeDetails(recipe) {
         this.recipeModal.showRecipeDetails(recipe);
     }
+    
+    // Duplicate detection and management methods
+    async findDuplicateRecipes() {
+        return await this.duplicatesManager.findDuplicates();
+    }
+    
+    selectLatestDuplicates() {
+        this.duplicatesManager.selectLatestDuplicates();
+    }
+    
+    deleteSelectedDuplicates() {
+        this.duplicatesManager.deleteSelectedDuplicates();
+    }
+
+    confirmDeleteDuplicates() {
+        this.duplicatesManager.confirmDeleteDuplicates();
+    }
+    
+    exitDuplicateMode() {
+        this.duplicatesManager.exitDuplicateMode();
+        initializeInfiniteScroll();
+    }
 }
 
 // Initialize components

static/js/state/index.js

@@ -65,6 +65,7 @@ export const state = {
             },
             pageSize: 20,
             showFavoritesOnly: false,
+            duplicatesMode: false, // Add flag for duplicates mode
         },
         
         checkpoints: {

static/js/utils/cardUpdater.js

@@ -125,4 +125,65 @@ export function updateLoraCard(filePath, updates, newFilePath) {
     });
     
     return loraCard; // Return the updated card element for chaining
+}
+
+/**
+ * Update the recipe card after metadata edits in the modal
+ * @param {string} recipeId - ID of the recipe to update
+ * @param {Object} updates - Object containing the updates (title, tags, source_path)
+ */
+export function updateRecipeCard(recipeId, updates) {
+    // Find the card with matching recipe ID
+    const recipeCard = document.querySelector(`.lora-card[data-id="${recipeId}"]`);
+    if (!recipeCard) return;
+
+    // Get the recipe card component instance
+    const recipeCardInstance = recipeCard._recipeCardInstance;
+    
+    // Update card dataset and visual elements based on the updates object
+    Object.entries(updates).forEach(([key, value]) => {
+        // Update dataset
+        recipeCard.dataset[key] = value;
+
+        // Update visual elements based on the property
+        switch(key) {
+            case 'title':
+                // Update the title in the recipe object
+                if (recipeCardInstance && recipeCardInstance.recipe) {
+                    recipeCardInstance.recipe.title = value;
+                }
+                
+                // Update the title shown in the card
+                const modelNameElement = recipeCard.querySelector('.model-name');
+                if (modelNameElement) modelNameElement.textContent = value;
+                break;
+                
+            case 'tags':
+                // Update tags in the recipe object (not displayed on card UI)
+                if (recipeCardInstance && recipeCardInstance.recipe) {
+                    recipeCardInstance.recipe.tags = value;
+                }
+                
+                // Store in dataset as JSON string
+                try {
+                    if (typeof value === 'string') {
+                        recipeCard.dataset.tags = value;
+                    } else {
+                        recipeCard.dataset.tags = JSON.stringify(value);
+                    }
+                } catch (e) {
+                    console.error('Failed to update recipe tags:', e);
+                }
+                break;
+                
+            case 'source_path':
+                // Update source_path in the recipe object (not displayed on card UI)
+                if (recipeCardInstance && recipeCardInstance.recipe) {
+                    recipeCardInstance.recipe.source_path = value;
+                }
+                break;
+        }
+    });
+    
+    return recipeCard; // Return the updated card element for chaining
 }

static/js/utils/formatters.js

@@ -0,0 +1,12 @@
+/**
+ * Format a file size in bytes to a human-readable string
+ * @param {number} bytes - The size in bytes
+ * @returns {string} Formatted size string (e.g., "1.5 MB")
+ */
+export function formatFileSize(bytes) {
+    if (!bytes || isNaN(bytes)) return '';
+    
+    const sizes = ['B', 'KB', 'MB', 'GB'];
+    const i = Math.floor(Math.log(bytes) / Math.log(1024));
+    return (bytes / Math.pow(1024, i)).toFixed(2) + ' ' + sizes[i];
+}

static/js/utils/infiniteScroll.js

@@ -14,6 +14,11 @@ export function initializeInfiniteScroll(pageType = 'loras') {
     
     // Get the current page state
     const pageState = getCurrentPageState();
+    
+    // Skip initializing if in duplicates mode (for recipes page)
+    if (pageType === 'recipes' && pageState.duplicatesMode) {
+        return;
+    }
 
     // Determine the load more function and grid ID based on page type
     let loadMoreFunction;

templates/components/import_modal.html

@@ -6,12 +6,27 @@
         <!-- Step 1: Upload Image or Input URL -->
         <div class="import-step" id="uploadStep">
             <div class="import-mode-toggle">
-                <button class="toggle-btn active" data-mode="upload" onclick="importManager.toggleImportMode('upload')">
+                <button class="toggle-btn active" data-mode="url" onclick="importManager.toggleImportMode('url')">
+                    <i class="fas fa-link"></i> URL / Local Path
+                </button>
+                <button class="toggle-btn" data-mode="upload" onclick="importManager.toggleImportMode('upload')">
                     <i class="fas fa-upload"></i> Upload Image
                 </button>
-                <button class="toggle-btn" data-mode="url" onclick="importManager.toggleImportMode('url')">
-                    <i class="fas fa-link"></i> Input URL
-                </button>
+            </div>
+            
+            <!-- Input URL/Path Section -->
+            <div class="import-section" id="urlSection">
+                <p>Input a Civitai image URL or local file path to import as a recipe.</p>
+                <div class="input-group">
+                    <label for="imageUrlInput">Image URL or File Path:</label>
+                    <div class="input-with-button">
+                        <input type="text" id="imageUrlInput" placeholder="https://civitai.com/images/... or C:/path/to/image.png">
+                        <button class="primary-btn" onclick="importManager.handleUrlInput()">
+                            <i class="fas fa-download"></i> Fetch Image
+                        </button>
+                    </div>
+                    <div class="error-message" id="urlError"></div>
+                </div>
             </div>
             
             <!-- Upload Image Section -->
@@ -29,21 +44,6 @@
                 </div>
             </div>
             
-            <!-- Input URL Section -->
-            <div class="import-section" id="urlSection" style="display: none;">
-                <p>Input a Civitai image URL to import as a recipe.</p>
-                <div class="input-group">
-                    <label for="imageUrlInput">Image URL:</label>
-                    <div class="input-with-button">
-                        <input type="text" id="imageUrlInput" placeholder="https://civitai.com/images/...">
-                        <button class="primary-btn" onclick="importManager.handleUrlInput()">
-                            <i class="fas fa-download"></i> Fetch Image
-                        </button>
-                    </div>
-                    <div class="error-message" id="urlError"></div>
-                </div>
-            </div>
-            
             <div class="modal-actions">
                 <button class="secondary-btn" onclick="modalManager.closeModal('importModal')">Cancel</button>
             </div>
@@ -84,6 +84,11 @@
                     <!-- LoRAs will be populated here -->
                 </div>
             </div>
+
+            <!-- Container for duplicate recipes warning -->
+            <div id="duplicateRecipesContainer" class="duplicate-recipes-container" style="display: none;">
+                <!-- Duplicate recipes will be populated here -->
+            </div>
             
             <div class="modal-actions">
                 <button class="secondary-btn" onclick="importManager.backToUpload()">Back</button>

templates/components/modals.html

@@ -24,6 +24,21 @@
     </div>
 </div>
 
+<!-- Duplicate Delete Confirmation Modal -->
+<div id="duplicateDeleteModal" class="modal delete-modal">
+    <div class="modal-content delete-modal-content">
+        <h2>Delete Duplicate Recipes</h2>
+        <p class="delete-message">Are you sure you want to delete the selected duplicate recipes?</p>
+        <div class="delete-model-info">
+            <p><span id="duplicateDeleteCount">0</span> recipes will be permanently deleted.</p>
+        </div>
+        <div class="modal-actions">
+            <button class="cancel-btn" onclick="modalManager.closeModal('duplicateDeleteModal')">Cancel</button>
+            <button class="delete-btn" onclick="recipeManager.confirmDeleteDuplicates()">Delete</button>
+        </div>
+    </div>
+</div>
+
 <!-- Settings Modal -->
 <div id="settingsModal" class="modal">
     <div class="modal-content settings-modal">

templates/components/recipe_modal.html

@@ -18,6 +18,7 @@
             <div class="recipe-top-section">
                 <div class="recipe-preview-container" id="recipePreviewContainer">
                     <img id="recipeModalImage" src="" alt="Recipe Preview" class="recipe-preview-media">
+                    <!-- Source URL elements are now added dynamically in RecipeModal.js -->
                 </div>
                 
                 <div class="info-section recipe-gen-params">

templates/recipes.html

@@ -42,6 +42,10 @@
             <div title="Import recipes" class="control-group">
                 <button onclick="importManager.showImportModal()"><i class="fas fa-file-import"></i> Import</button>
             </div>
+            <!-- Add duplicate detection button -->
+            <div title="Find duplicate recipes" class="control-group">
+                <button onclick="recipeManager.findDuplicateRecipes()"><i class="fas fa-clone"></i> Find Duplicates</button>
+            </div>
             <!-- Custom filter indicator button (hidden by default) -->
             <div id="customFilterIndicator" class="control-group hidden">
                 <div class="filter-active">
@@ -51,6 +55,25 @@
             </div>
         </div>
     </div>
+
+    <!-- Duplicates banner (hidden by default) -->
+    <div id="duplicatesBanner" class="duplicates-banner" style="display: none;">
+        <div class="banner-content">
+          <i class="fas fa-exclamation-triangle"></i>
+          <span id="duplicatesCount">Found 0 duplicate groups</span>
+          <div class="banner-actions">
+            <button class="btn-select-latest" onclick="recipeManager.selectLatestDuplicates()">
+              Keep Latest Versions
+            </button>
+            <button class="btn-delete-selected disabled" onclick="recipeManager.deleteSelectedDuplicates()">
+              Delete Selected (<span id="selectedCount">0</span>)
+            </button>
+            <button class="btn-exit" onclick="recipeManager.exitDuplicateMode()">
+              <i class="fas fa-times"></i>
+            </button>
+          </div>
+        </div>
+    </div>
     
     <!-- Recipe grid -->
     <div class="card-grid" id="recipeGrid">

web/comfyui/lora_loader.js

@@ -1,8 +1,8 @@
 import { app } from "../../scripts/app.js";
 import { dynamicImportByVersion } from "./utils.js";
 
-// Extract pattern into a constant for consistent use
-const LORA_PATTERN = /<lora:([^:]+):([-\d\.]+)>/g;
+// Update pattern to match both formats: <lora:name:model_strength> or <lora:name:model_strength:clip_strength>
+const LORA_PATTERN = /<lora:([^:]+):([-\d\.]+)(?::([-\d\.]+))?>/g;
 
 // Function to get the appropriate loras widget based on ComfyUI version
 async function getLorasWidgetModule() {
@@ -61,10 +61,15 @@ function mergeLoras(lorasText, lorasArr) {
     const result = [];
     let match;
 
+    // Reset pattern index before using
+    LORA_PATTERN.lastIndex = 0;
+    
     // Parse text input and create initial entries
     while ((match = LORA_PATTERN.exec(lorasText)) !== null) {
         const name = match[1];
-        const inputStrength = Number(match[2]);
+        const modelStrength = Number(match[2]);
+        // Extract clip strength if provided, otherwise use model strength
+        const clipStrength = match[3] ? Number(match[3]) : modelStrength;
         
         // Find if this lora exists in the array data
         const existingLora = lorasArr.find(l => l.name === name);
@@ -72,8 +77,9 @@ function mergeLoras(lorasText, lorasArr) {
         result.push({
             name: name,
             // Use existing strength if available, otherwise use input strength
-            strength: existingLora ? existingLora.strength : inputStrength,
-            active: existingLora ? existingLora.active : true
+            strength: existingLora ? existingLora.strength : modelStrength,
+            active: existingLora ? existingLora.active : true,
+            clipStrength: existingLora ? existingLora.clipStrength : clipStrength,
         });
     }
 
@@ -102,18 +108,7 @@ app.registerExtension({
                 let existingLoras = [];
                 if (node.widgets_values && node.widgets_values.length > 0) {
                     const savedValue = node.widgets_values[1];
-                    // TODO: clean up this code
-                    try {
-                        // Check if the value is already an array/object
-                        if (typeof savedValue === 'object' && savedValue !== null) {
-                            existingLoras = savedValue;
-                        } else if (typeof savedValue === 'string') {
-                            existingLoras = JSON.parse(savedValue);
-                        }
-                    } catch (e) {
-                        console.warn("Failed to parse loras data:", e);
-                        existingLoras = [];
-                    }
+                    existingLoras = savedValue || [];
                 }
                 // Merge the loras data
                 const mergedLoras = mergeLoras(node.widgets[0].value, existingLoras);
@@ -139,7 +134,7 @@ app.registerExtension({
                         const currentLoras = value.map(l => l.name);
                         
                         // Use the constant pattern here as well
-                        let newText = inputWidget.value.replace(LORA_PATTERN, (match, name, strength) => {
+                        let newText = inputWidget.value.replace(LORA_PATTERN, (match, name, strength, clipStrength) => {
                             return currentLoras.includes(name) ? match : '';
                         });
                         

web/comfyui/lora_stacker.js

@@ -1,8 +1,8 @@
 import { app } from "../../scripts/app.js";
 import { dynamicImportByVersion } from "./utils.js";
 
-// Extract pattern into a constant for consistent use
-const LORA_PATTERN = /<lora:([^:]+):([-\d\.]+)>/g;
+// Update pattern to match both formats: <lora:name:model_strength> or <lora:name:model_strength:clip_strength>
+const LORA_PATTERN = /<lora:([^:]+):([-\d\.]+)(?::([-\d\.]+))?>/g;
 
 // Function to get the appropriate loras widget based on ComfyUI version
 async function getLorasWidgetModule() {
@@ -57,10 +57,15 @@ function mergeLoras(lorasText, lorasArr) {
     const result = [];
     let match;
 
+    // Reset pattern index before using
+    LORA_PATTERN.lastIndex = 0;
+    
     // Parse text input and create initial entries
     while ((match = LORA_PATTERN.exec(lorasText)) !== null) {
         const name = match[1];
-        const inputStrength = Number(match[2]);
+        const modelStrength = Number(match[2]);
+        // Extract clip strength if provided, otherwise use model strength
+        const clipStrength = match[3] ? Number(match[3]) : modelStrength;
         
         // Find if this lora exists in the array data
         const existingLora = lorasArr.find(l => l.name === name);
@@ -68,8 +73,9 @@ function mergeLoras(lorasText, lorasArr) {
         result.push({
             name: name,
             // Use existing strength if available, otherwise use input strength
-            strength: existingLora ? existingLora.strength : inputStrength,
-            active: existingLora ? existingLora.active : true
+            strength: existingLora ? existingLora.strength : modelStrength,
+            active: existingLora ? existingLora.active : true,
+            clipStrength: existingLora ? existingLora.clipStrength : clipStrength,
         });
     }
 

web/comfyui/loras_widget.js

@@ -29,10 +29,13 @@ export function addLorasWidget(node, name, opts, callback) {
 
   // Fixed sizes for component calculations
   const LORA_ENTRY_HEIGHT = 40; // Height of a single lora entry
+  const CLIP_ENTRY_HEIGHT = 40; // Height of a clip entry
   const HEADER_HEIGHT = 40; // Height of the header section
   const CONTAINER_PADDING = 12; // Top and bottom padding
   const EMPTY_CONTAINER_HEIGHT = 100; // Height when no loras are present
 
+  // Remove expandedClipEntries Set since we'll determine expansion based on strength values
+
   // Parse LoRA entries from value
   const parseLoraValue = (value) => {
     if (!value) return [];
@@ -367,7 +370,7 @@ export function addLorasWidget(node, name, opts, callback) {
   };
 
   // Function to handle strength adjustment via dragging
-  const handleStrengthDrag = (name, initialStrength, initialX, event, widget) => {
+  const handleStrengthDrag = (name, initialStrength, initialX, event, widget, isClipStrength = false) => {
     // Calculate drag sensitivity (how much the strength changes per pixel)
     // Using 0.01 per 10 pixels of movement
     const sensitivity = 0.001;
@@ -391,7 +394,12 @@ export function addLorasWidget(node, name, opts, callback) {
     const loraIndex = lorasData.findIndex(l => l.name === name);
     
     if (loraIndex >= 0) {
-      lorasData[loraIndex].strength = newStrength;
+      // Update the appropriate strength property based on isClipStrength flag
+      if (isClipStrength) {
+        lorasData[loraIndex].clipStrength = newStrength;
+      } else {
+        lorasData[loraIndex].strength = newStrength;
+      }
       
       // Update the widget value
       widget.value = formatLoraValue(lorasData);
@@ -402,7 +410,7 @@ export function addLorasWidget(node, name, opts, callback) {
   };
   
   // Function to initialize drag operation
-  const initDrag = (loraEl, nameEl, name, widget) => {
+  const initDrag = (dragEl, name, widget, isClipStrength = false) => {
     let isDragging = false;
     let initialX = 0;
     let initialStrength = 0;
@@ -420,9 +428,8 @@ export function addLorasWidget(node, name, opts, callback) {
       document.head.appendChild(styleEl);
     }
     
-    // Create a drag handler that's applied to the entire lora entry
-    // except toggle and strength controls
-    loraEl.addEventListener('mousedown', (e) => {
+    // Create a drag handler
+    dragEl.addEventListener('mousedown', (e) => {
       // Skip if clicking on toggle or strength control areas
       if (e.target.closest('.comfy-lora-toggle') || 
           e.target.closest('input') || 
@@ -437,7 +444,7 @@ export function addLorasWidget(node, name, opts, callback) {
       if (!loraData) return;
       
       initialX = e.clientX;
-      initialStrength = loraData.strength;
+      initialStrength = isClipStrength ? loraData.clipStrength : loraData.strength;
       isDragging = true;
       
       // Add class to body to enforce cursor style globally
@@ -453,7 +460,7 @@ export function addLorasWidget(node, name, opts, callback) {
       if (!isDragging) return;
       
       // Call the strength adjustment function
-      handleStrengthDrag(name, initialStrength, initialX, e, widget);
+      handleStrengthDrag(name, initialStrength, initialX, e, widget, isClipStrength);
       
       // Prevent showing the preview tooltip during drag
       previewTooltip.hide();
@@ -691,10 +698,17 @@ export function addLorasWidget(node, name, opts, callback) {
     header.appendChild(strengthLabel);
     container.appendChild(header);
 
+    // Track the total visible entries for height calculation
+    let totalVisibleEntries = lorasData.length;
+
     // Render each lora entry
     lorasData.forEach((loraData) => {
-      const { name, strength, active } = loraData;
+      const { name, strength, clipStrength, active } = loraData;
       
+      // Determine expansion state using our helper function
+      const isExpanded = shouldShowClipEntry(loraData);
+      
+      // Create the main LoRA entry
       const loraEl = document.createElement("div");
       loraEl.className = "comfy-lora-entry";
       Object.assign(loraEl.style, {
@@ -708,6 +722,41 @@ export function addLorasWidget(node, name, opts, callback) {
         marginBottom: "4px",
       });
 
+      // Add double-click handler to toggle clip entry
+      loraEl.addEventListener('dblclick', (e) => {
+        // Skip if clicking on toggle or strength control areas
+        if (e.target.closest('.comfy-lora-toggle') || 
+            e.target.closest('input') || 
+            e.target.closest('.comfy-lora-arrow')) {
+          return;
+        }
+        
+        // Prevent default behavior
+        e.preventDefault();
+        e.stopPropagation();
+        
+        // Toggle the clip entry expanded state
+        const lorasData = parseLoraValue(widget.value);
+        const loraIndex = lorasData.findIndex(l => l.name === name);
+        
+        if (loraIndex >= 0) {
+          // Explicitly toggle the expansion state
+          const currentExpanded = shouldShowClipEntry(lorasData[loraIndex]);
+          lorasData[loraIndex].expanded = !currentExpanded;
+          
+          // If collapsing, set clipStrength = strength
+          if (!lorasData[loraIndex].expanded) {
+            lorasData[loraIndex].clipStrength = lorasData[loraIndex].strength;
+          } 
+          
+          // Update the widget value
+          widget.value = formatLoraValue(lorasData);
+          
+          // Re-render to show/hide clip entry
+          renderLoras(widget.value, widget);
+        }
+      });
+
       // Create toggle for this lora
       const toggle = createToggle(active, (newActive) => {
         // Update this lora's active state
@@ -740,6 +789,14 @@ export function addLorasWidget(node, name, opts, callback) {
         msUserSelect: "none",
       });
 
+      // Add expand indicator to name element
+      const expandIndicator = document.createElement("span");
+      expandIndicator.textContent = isExpanded ? " ▼" : " ▶";
+      expandIndicator.style.opacity = "0.7";
+      expandIndicator.style.fontSize = "9px";
+      expandIndicator.style.marginLeft = "4px";
+      nameEl.appendChild(expandIndicator);
+
       // Move preview tooltip events to nameEl instead of loraEl
       nameEl.addEventListener('mouseenter', async (e) => {
         e.stopPropagation();
@@ -753,7 +810,7 @@ export function addLorasWidget(node, name, opts, callback) {
       });
       
       // Initialize drag functionality for strength adjustment
-      initDrag(loraEl, nameEl, name, widget);
+      initDrag(loraEl, name, widget, false);
 
       // Remove the preview tooltip events from loraEl
       loraEl.onmouseenter = () => {
@@ -897,10 +954,185 @@ export function addLorasWidget(node, name, opts, callback) {
       loraEl.appendChild(strengthControl);
 
       container.appendChild(loraEl);
+
+      // If expanded, show the clip entry
+      if (isExpanded) {
+        totalVisibleEntries++;
+        const clipEl = document.createElement("div");
+        clipEl.className = "comfy-lora-clip-entry";
+        Object.assign(clipEl.style, {
+          display: "flex",
+          justifyContent: "space-between",
+          alignItems: "center",
+          padding: "6px",
+          paddingLeft: "20px", // Indent to align with parent name
+          borderRadius: "6px",
+          backgroundColor: active ? "rgba(65, 70, 90, 0.6)" : "rgba(50, 55, 65, 0.5)",
+          borderLeft: "2px solid rgba(72, 118, 255, 0.6)",
+          transition: "all 0.2s ease",
+          marginBottom: "4px",
+          marginLeft: "10px",
+          marginTop: "-2px"
+        });
+
+        // Create clip name display
+        const clipNameEl = document.createElement("div");
+        clipNameEl.textContent = "[clip] " + name;
+        Object.assign(clipNameEl.style, {
+          flex: "1",
+          overflow: "hidden",
+          textOverflow: "ellipsis",
+          whiteSpace: "nowrap",
+          color: active ? "rgba(200, 215, 240, 0.9)" : "rgba(200, 215, 240, 0.6)",
+          fontSize: "13px",
+          userSelect: "none",
+          WebkitUserSelect: "none",
+          MozUserSelect: "none",
+          msUserSelect: "none",
+        });
+
+        // Create clip strength control
+        const clipStrengthControl = document.createElement("div");
+        Object.assign(clipStrengthControl.style, {
+          display: "flex",
+          alignItems: "center",
+          gap: "8px",
+        });
+
+        // Left arrow for clip
+        const clipLeftArrow = createArrowButton("left", () => {
+          // Decrease clip strength
+          const lorasData = parseLoraValue(widget.value);
+          const loraIndex = lorasData.findIndex(l => l.name === name);
+          
+          if (loraIndex >= 0) {
+            lorasData[loraIndex].clipStrength = (parseFloat(lorasData[loraIndex].clipStrength) - 0.05).toFixed(2);
+            
+            const newValue = formatLoraValue(lorasData);
+            widget.value = newValue;
+          }
+        });
+
+        // Clip strength display
+        const clipStrengthEl = document.createElement("input");
+        clipStrengthEl.type = "text";
+        clipStrengthEl.value = typeof clipStrength === 'number' ? clipStrength.toFixed(2) : Number(clipStrength).toFixed(2);
+        Object.assign(clipStrengthEl.style, {
+          minWidth: "50px",
+          width: "50px",
+          textAlign: "center",
+          color: active ? "rgba(200, 215, 240, 0.9)" : "rgba(200, 215, 240, 0.6)",
+          fontSize: "13px",
+          background: "none",
+          border: "1px solid transparent",
+          padding: "2px 4px",
+          borderRadius: "3px",
+          outline: "none",
+        });
+
+        // Add hover effect
+        clipStrengthEl.addEventListener('mouseenter', () => {
+          clipStrengthEl.style.border = "1px solid rgba(226, 232, 240, 0.2)";
+        });
+
+        clipStrengthEl.addEventListener('mouseleave', () => {
+          if (document.activeElement !== clipStrengthEl) {
+            clipStrengthEl.style.border = "1px solid transparent";
+          }
+        });
+
+        // Handle focus
+        clipStrengthEl.addEventListener('focus', () => {
+          clipStrengthEl.style.border = "1px solid rgba(72, 118, 255, 0.6)";
+          clipStrengthEl.style.background = "rgba(0, 0, 0, 0.2)";
+          // Auto-select all content
+          clipStrengthEl.select();
+        });
+
+        clipStrengthEl.addEventListener('blur', () => {
+          clipStrengthEl.style.border = "1px solid transparent";
+          clipStrengthEl.style.background = "none";
+        });
+
+        // Handle input changes
+        clipStrengthEl.addEventListener('change', () => {
+          let newValue = parseFloat(clipStrengthEl.value);
+          
+          // Validate input
+          if (isNaN(newValue)) {
+            newValue = 1.0;
+          }
+          
+          // Update value
+          const lorasData = parseLoraValue(widget.value);
+          const loraIndex = lorasData.findIndex(l => l.name === name);
+          
+          if (loraIndex >= 0) {
+            lorasData[loraIndex].clipStrength = newValue.toFixed(2);
+            
+            // Update value and trigger callback
+            const newLorasValue = formatLoraValue(lorasData);
+            widget.value = newLorasValue;
+          }
+        });
+
+        // Handle key events
+        clipStrengthEl.addEventListener('keydown', (e) => {
+          if (e.key === 'Enter') {
+            clipStrengthEl.blur();
+          }
+        });
+
+        // Right arrow for clip
+        const clipRightArrow = createArrowButton("right", () => {
+          // Increase clip strength
+          const lorasData = parseLoraValue(widget.value);
+          const loraIndex = lorasData.findIndex(l => l.name === name);
+          
+          if (loraIndex >= 0) {
+            lorasData[loraIndex].clipStrength = (parseFloat(lorasData[loraIndex].clipStrength) + 0.05).toFixed(2);
+            
+            const newValue = formatLoraValue(lorasData);
+            widget.value = newValue;
+          }
+        });
+
+        clipStrengthControl.appendChild(clipLeftArrow);
+        clipStrengthControl.appendChild(clipStrengthEl);
+        clipStrengthControl.appendChild(clipRightArrow);
+
+        // Assemble clip entry
+        const clipLeftSection = document.createElement("div");
+        Object.assign(clipLeftSection.style, {
+          display: "flex",
+          alignItems: "center",
+          flex: "1",
+          minWidth: "0", // Allow shrinking
+        });
+        
+        clipLeftSection.appendChild(clipNameEl);
+        
+        clipEl.appendChild(clipLeftSection);
+        clipEl.appendChild(clipStrengthControl);
+
+        // Hover effects for clip entry
+        clipEl.onmouseenter = () => {
+          clipEl.style.backgroundColor = active ? "rgba(70, 75, 95, 0.7)" : "rgba(55, 60, 70, 0.6)";
+        };
+        
+        clipEl.onmouseleave = () => {
+          clipEl.style.backgroundColor = active ? "rgba(65, 70, 90, 0.6)" : "rgba(50, 55, 65, 0.5)";
+        };
+
+        // Add drag functionality to clip entry
+        initDrag(clipEl, name, widget, true);
+
+        container.appendChild(clipEl);
+      }
     });
     
     // Calculate height based on number of loras and fixed sizes
-    const calculatedHeight = CONTAINER_PADDING + HEADER_HEIGHT + (Math.min(lorasData.length, 5) * LORA_ENTRY_HEIGHT);
+    const calculatedHeight = CONTAINER_PADDING + HEADER_HEIGHT + (Math.min(totalVisibleEntries, 8) * LORA_ENTRY_HEIGHT);
     updateWidgetHeight(calculatedHeight);
   };
 
@@ -921,7 +1153,37 @@ export function addLorasWidget(node, name, opts, callback) {
         return [...filtered, lora];
       }, []);
 
-      widgetValue = uniqueValue;
+      // Preserve clip strengths and expanded state when updating the value
+      const oldLoras = parseLoraValue(widgetValue);
+      
+      // Apply existing clip strength values and transfer them to the new value
+      const updatedValue = uniqueValue.map(lora => {
+        const existingLora = oldLoras.find(oldLora => oldLora.name === lora.name);
+        
+        // If there's an existing lora with the same name, preserve its clip strength and expanded state
+        if (existingLora) {
+          return {
+            ...lora,
+            clipStrength: existingLora.clipStrength || lora.strength,
+            expanded: existingLora.hasOwnProperty('expanded') ? 
+                      existingLora.expanded : 
+                      Number(existingLora.clipStrength || lora.strength) !== Number(lora.strength)
+          };
+        }
+        
+        // For new loras, default clip strength to model strength and expanded to false
+        // unless clipStrength is already different from strength
+        const clipStrength = lora.clipStrength || lora.strength;
+        return {
+          ...lora,
+          clipStrength: clipStrength,
+          expanded: lora.hasOwnProperty('expanded') ? 
+                    lora.expanded : 
+                    Number(clipStrength) !== Number(lora.strength)
+        };
+      });
+
+      widgetValue = updatedValue;
       renderLoras(widgetValue, widget);
     },
     getMinHeight: function() {
@@ -948,11 +1210,7 @@ export function addLorasWidget(node, name, opts, callback) {
   widget.callback = callback;
 
   widget.serializeValue = () => {
-    // Add dummy items to avoid the 2-element serialization issue, a bug in comfyui
-    return [...widgetValue, 
-        { name: "__dummy_item1__", strength: 0, active: false, _isDummy: true },
-        { name: "__dummy_item2__", strength: 0, active: false, _isDummy: true }
-      ];
+    return widgetValue;
   }
 
   widget.onRemove = () => {
@@ -966,6 +1224,8 @@ export function addLorasWidget(node, name, opts, callback) {
 // Function to directly save the recipe without dialog
 async function saveRecipeDirectly(widget) {
   try {
+    const prompt = await app.graphToPrompt();
+    console.log(prompt);
     // Show loading toast
     if (app && app.extensionManager && app.extensionManager.toast) {
       app.extensionManager.toast.add({
@@ -1014,4 +1274,14 @@ async function saveRecipeDirectly(widget) {
       });
     }
   }
+}
+
+// Determine if clip entry should be shown - now based on expanded property or initial diff values
+const shouldShowClipEntry = (loraData) => {
+  // If expanded property exists, use that
+  if (loraData.hasOwnProperty('expanded')) {
+    return loraData.expanded;
+  }
+  // Otherwise use the legacy logic - if values differ, it should be expanded
+  return Number(loraData.strength) !== Number(loraData.clipStrength);
 }

web/comfyui/tags_widget.js

@@ -220,13 +220,8 @@ export function addTagsWidget(node, name, opts, callback) {
   // Set callback
   widget.callback = callback;
 
-  // Add serialization method to avoid ComfyUI serialization issues
   widget.serializeValue = () => {
-    // Add dummy items to avoid the 2-element serialization issue
-    return [...widgetValue, 
-        { text: "__dummy_item__", active: false, _isDummy: true },
-        { text: "__dummy_item__", active: false, _isDummy: true }
-      ];
+    return widgetValue
   };
 
   return { minWidth: 300, minHeight: defaultHeight, widget };