mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-21 21:22:11 -03:00
1106 lines
51 KiB
Python
1106 lines
51 KiB
Python
import json
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import logging
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import os
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import re
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from typing import Dict, List, Any, Optional, Tuple
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from abc import ABC, abstractmethod
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from ..config import config
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logger = logging.getLogger(__name__)
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# Constants for generation parameters
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GEN_PARAM_KEYS = [
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'prompt',
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'negative_prompt',
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'steps',
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'sampler',
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'cfg_scale',
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'seed',
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'size',
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'clip_skip',
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]
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class RecipeMetadataParser(ABC):
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"""Interface for parsing recipe metadata from image user comments"""
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METADATA_MARKER = None
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@abstractmethod
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def is_metadata_matching(self, user_comment: str) -> bool:
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"""Check if the user comment matches the metadata format"""
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pass
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@abstractmethod
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async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
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"""
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Parse metadata from user comment and return structured recipe data
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Args:
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user_comment: The EXIF UserComment string from the image
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recipe_scanner: Optional recipe scanner instance for local LoRA lookup
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civitai_client: Optional Civitai client for fetching model information
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Returns:
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Dict containing parsed recipe data with standardized format
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"""
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pass
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async def populate_lora_from_civitai(self, lora_entry: Dict[str, Any], civitai_info_tuple: Tuple[Dict[str, Any], Optional[str]],
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recipe_scanner=None, base_model_counts=None, hash_value=None) -> Dict[str, Any]:
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"""
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Populate a lora entry with information from Civitai API response
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Args:
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lora_entry: The lora entry to populate
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civitai_info_tuple: The response tuple from Civitai API (data, error_msg)
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recipe_scanner: Optional recipe scanner for local file lookup
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base_model_counts: Optional dict to track base model counts
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hash_value: Optional hash value to use if not available in civitai_info
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Returns:
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The populated lora_entry dict
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"""
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try:
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# Unpack the tuple to get the actual data
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civitai_info, error_msg = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
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if civitai_info and civitai_info.get("error") != "Model not found":
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# Check if this is an early access lora
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if civitai_info.get('earlyAccessEndsAt'):
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# Convert earlyAccessEndsAt to a human-readable date
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early_access_date = civitai_info.get('earlyAccessEndsAt', '')
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lora_entry['isEarlyAccess'] = True
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lora_entry['earlyAccessEndsAt'] = early_access_date
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# Update model name if available
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if 'model' in civitai_info and 'name' in civitai_info['model']:
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lora_entry['name'] = civitai_info['model']['name']
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# Update version if available
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if 'name' in civitai_info:
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lora_entry['version'] = civitai_info.get('name', '')
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# Get thumbnail URL from first image
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if 'images' in civitai_info and civitai_info['images']:
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lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
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# Get base model
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current_base_model = civitai_info.get('baseModel', '')
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lora_entry['baseModel'] = current_base_model
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# Update base model counts if tracking them
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if base_model_counts is not None and current_base_model:
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base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
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# Get download URL
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lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
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# Process file information if available
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if 'files' in civitai_info:
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# Find the primary model file (type="Model" and primary=true) in the files list
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model_file = next((file for file in civitai_info.get('files', [])
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if file.get('type') == 'Model' and file.get('primary') == True), None)
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if model_file:
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# Get size
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lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
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# Get SHA256 hash
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sha256 = model_file.get('hashes', {}).get('SHA256', hash_value)
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if sha256:
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lora_entry['hash'] = sha256.lower()
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# Check if exists locally
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if recipe_scanner and lora_entry['hash']:
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lora_scanner = recipe_scanner._lora_scanner
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exists_locally = lora_scanner.has_lora_hash(lora_entry['hash'])
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if exists_locally:
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try:
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local_path = lora_scanner.get_lora_path_by_hash(lora_entry['hash'])
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lora_entry['existsLocally'] = True
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lora_entry['localPath'] = local_path
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lora_entry['file_name'] = os.path.splitext(os.path.basename(local_path))[0]
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# Get thumbnail from local preview if available
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lora_cache = await lora_scanner.get_cached_data()
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lora_item = next((item for item in lora_cache.raw_data
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if item['sha256'].lower() == lora_entry['hash'].lower()), None)
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if lora_item and 'preview_url' in lora_item:
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lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
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except Exception as e:
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logger.error(f"Error getting local lora path: {e}")
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else:
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# For missing LoRAs, get file_name from model_file.name
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file_name = model_file.get('name', '')
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lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else ''
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else:
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# Model not found or deleted
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lora_entry['isDeleted'] = True
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lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
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except Exception as e:
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logger.error(f"Error populating lora from Civitai info: {e}")
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return lora_entry
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async def populate_checkpoint_from_civitai(self, checkpoint: Dict[str, Any], civitai_info: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Populate checkpoint information from Civitai API response
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Args:
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checkpoint: The checkpoint entry to populate
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civitai_info: The response from Civitai API
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Returns:
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The populated checkpoint dict
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"""
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try:
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if civitai_info and civitai_info.get("error") != "Model not found":
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# Update model name if available
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if 'model' in civitai_info and 'name' in civitai_info['model']:
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checkpoint['name'] = civitai_info['model']['name']
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# Update version if available
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if 'name' in civitai_info:
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checkpoint['version'] = civitai_info.get('name', '')
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# Get thumbnail URL from first image
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if 'images' in civitai_info and civitai_info['images']:
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checkpoint['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
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# Get base model
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checkpoint['baseModel'] = civitai_info.get('baseModel', '')
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# Get download URL
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checkpoint['downloadUrl'] = civitai_info.get('downloadUrl', '')
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else:
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# Model not found or deleted
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checkpoint['isDeleted'] = True
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except Exception as e:
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logger.error(f"Error populating checkpoint from Civitai info: {e}")
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return checkpoint
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class RecipeFormatParser(RecipeMetadataParser):
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"""Parser for images with dedicated recipe metadata format"""
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# Regular expression pattern for extracting recipe metadata
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METADATA_MARKER = r'Recipe metadata: (\{.*\})'
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def is_metadata_matching(self, user_comment: str) -> bool:
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"""Check if the user comment matches the metadata format"""
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return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
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async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
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"""Parse metadata from images with dedicated recipe metadata format"""
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try:
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# Extract recipe metadata from user comment
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try:
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# Look for recipe metadata section
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recipe_match = re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL)
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if not recipe_match:
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recipe_metadata = None
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else:
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recipe_json = recipe_match.group(1)
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recipe_metadata = json.loads(recipe_json)
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except Exception as e:
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logger.error(f"Error extracting recipe metadata: {e}")
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recipe_metadata = None
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if not recipe_metadata:
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return {"error": "No recipe metadata found", "loras": []}
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# Process the recipe metadata
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loras = []
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for lora in recipe_metadata.get('loras', []):
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# Convert recipe lora format to frontend format
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lora_entry = {
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'id': lora.get('modelVersionId', ''),
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'name': lora.get('modelName', ''),
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'version': lora.get('modelVersionName', ''),
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'type': 'lora',
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'weight': lora.get('strength', 1.0),
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'file_name': lora.get('file_name', ''),
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'hash': lora.get('hash', '')
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}
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# Check if this LoRA exists locally by SHA256 hash
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if lora.get('hash') and recipe_scanner:
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lora_scanner = recipe_scanner._lora_scanner
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exists_locally = lora_scanner.has_lora_hash(lora['hash'])
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if exists_locally:
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lora_cache = await lora_scanner.get_cached_data()
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lora_item = next((item for item in lora_cache.raw_data if item['sha256'].lower() == lora['hash'].lower()), None)
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if lora_item:
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lora_entry['existsLocally'] = True
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lora_entry['localPath'] = lora_item['file_path']
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lora_entry['file_name'] = lora_item['file_name']
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lora_entry['size'] = lora_item['size']
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lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
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else:
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lora_entry['existsLocally'] = False
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lora_entry['localPath'] = None
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# Try to get additional info from Civitai if we have a model version ID
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if lora.get('modelVersionId') and civitai_client:
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try:
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civitai_info_tuple = await civitai_client.get_model_version_info(lora['modelVersionId'])
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# Populate lora entry with Civitai info
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lora_entry = await self.populate_lora_from_civitai(
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lora_entry,
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civitai_info_tuple,
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recipe_scanner,
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None, # No need to track base model counts
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lora['hash']
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)
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except Exception as e:
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logger.error(f"Error fetching Civitai info for LoRA: {e}")
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lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
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loras.append(lora_entry)
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logger.info(f"Found {len(loras)} loras in recipe metadata")
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# Filter gen_params to only include recognized keys
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filtered_gen_params = {}
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if 'gen_params' in recipe_metadata:
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for key, value in recipe_metadata['gen_params'].items():
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if key in GEN_PARAM_KEYS:
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filtered_gen_params[key] = value
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return {
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'base_model': recipe_metadata.get('base_model', ''),
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'loras': loras,
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'gen_params': filtered_gen_params,
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'tags': recipe_metadata.get('tags', []),
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'title': recipe_metadata.get('title', ''),
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'from_recipe_metadata': True
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}
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except Exception as e:
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logger.error(f"Error parsing recipe format metadata: {e}", exc_info=True)
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return {"error": str(e), "loras": []}
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class StandardMetadataParser(RecipeMetadataParser):
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"""Parser for images with standard civitai metadata format (prompt, negative prompt, etc.)"""
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METADATA_MARKER = r'Civitai resources: '
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def is_metadata_matching(self, user_comment: str) -> bool:
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"""Check if the user comment matches the metadata format"""
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return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
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async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
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"""Parse metadata from images with standard metadata format"""
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try:
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# Parse the standard metadata
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metadata = self._parse_recipe_metadata(user_comment)
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# Look for Civitai resources in the metadata
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civitai_resources = metadata.get('loras', [])
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checkpoint = metadata.get('checkpoint')
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if not civitai_resources and not checkpoint:
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return {
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"error": "No LoRA information found in this image",
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"loras": []
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}
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# Process LoRAs and collect base models
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base_model_counts = {}
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loras = []
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# Process LoRAs
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for resource in civitai_resources:
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# Get model version ID
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model_version_id = resource.get('modelVersionId')
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if not model_version_id:
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continue
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# Initialize lora entry with default values
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lora_entry = {
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'id': model_version_id,
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'name': resource.get('modelName', ''),
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'version': resource.get('modelVersionName', ''),
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'type': resource.get('type', 'lora'),
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'weight': resource.get('weight', 1.0),
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'existsLocally': False,
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'localPath': None,
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'file_name': '',
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'hash': '',
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'thumbnailUrl': '',
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'baseModel': '',
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'size': 0,
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'downloadUrl': '',
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'isDeleted': False
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}
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# Get additional info from Civitai if client is available
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if civitai_client:
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try:
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civitai_info_tuple = await civitai_client.get_model_version_info(model_version_id)
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# Populate lora entry with Civitai info
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lora_entry = await self.populate_lora_from_civitai(
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lora_entry,
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civitai_info_tuple,
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recipe_scanner,
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base_model_counts
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)
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except Exception as e:
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logger.error(f"Error fetching Civitai info for LoRA: {e}")
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loras.append(lora_entry)
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# Set base_model to the most common one from civitai_info
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base_model = None
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if base_model_counts:
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base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
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# Extract generation parameters for recipe metadata
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gen_params = {}
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for key in GEN_PARAM_KEYS:
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if key in metadata:
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gen_params[key] = metadata.get(key, '')
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return {
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'base_model': base_model,
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'loras': loras,
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'gen_params': gen_params,
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'raw_metadata': metadata
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}
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except Exception as e:
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logger.error(f"Error parsing standard metadata: {e}", exc_info=True)
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return {"error": str(e), "loras": []}
|
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def _parse_recipe_metadata(self, user_comment: str) -> Dict[str, Any]:
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"""Parse recipe metadata from UserComment"""
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try:
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# Split by 'Negative prompt:' to get the prompt
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parts = user_comment.split('Negative prompt:', 1)
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prompt = parts[0].strip()
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# Initialize metadata with prompt
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metadata = {"prompt": prompt, "loras": [], "checkpoint": None}
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|
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# Extract additional fields if available
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if len(parts) > 1:
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negative_and_params = parts[1]
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|
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# Extract negative prompt
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if "Steps:" in negative_and_params:
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neg_prompt = negative_and_params.split("Steps:", 1)[0].strip()
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metadata["negative_prompt"] = neg_prompt
|
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|
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# Extract key-value parameters (Steps, Sampler, CFG scale, etc.)
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param_pattern = r'([A-Za-z ]+): ([^,]+)'
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params = re.findall(param_pattern, negative_and_params)
|
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for key, value in params:
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clean_key = key.strip().lower().replace(' ', '_')
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metadata[clean_key] = value.strip()
|
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|
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# Extract Civitai resources
|
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if 'Civitai resources:' in user_comment:
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||
resources_part = user_comment.split('Civitai resources:', 1)[1].strip()
|
||
|
||
# Look for the opening and closing brackets to extract the JSON array
|
||
if resources_part.startswith('['):
|
||
# Find the position of the closing bracket
|
||
bracket_count = 0
|
||
end_pos = -1
|
||
|
||
for i, char in enumerate(resources_part):
|
||
if char == '[':
|
||
bracket_count += 1
|
||
elif char == ']':
|
||
bracket_count -= 1
|
||
if bracket_count == 0:
|
||
end_pos = i
|
||
break
|
||
|
||
if end_pos != -1:
|
||
resources_json = resources_part[:end_pos+1]
|
||
try:
|
||
resources = json.loads(resources_json)
|
||
# Filter loras and checkpoints
|
||
for resource in resources:
|
||
if resource.get('type') == 'lora':
|
||
# 确保 weight 字段被正确保留
|
||
lora_entry = resource.copy()
|
||
# 如果找不到 weight，默认为 1.0
|
||
if 'weight' not in lora_entry:
|
||
lora_entry['weight'] = 1.0
|
||
# Ensure modelVersionName is included
|
||
if 'modelVersionName' not in lora_entry:
|
||
lora_entry['modelVersionName'] = ''
|
||
metadata['loras'].append(lora_entry)
|
||
elif resource.get('type') == 'checkpoint':
|
||
metadata['checkpoint'] = resource
|
||
except json.JSONDecodeError:
|
||
pass
|
||
|
||
return metadata
|
||
except Exception as e:
|
||
logger.error(f"Error parsing recipe metadata: {e}")
|
||
return {"prompt": user_comment, "loras": [], "checkpoint": None}
|
||
|
||
|
||
class A1111MetadataParser(RecipeMetadataParser):
|
||
"""Parser for images with A1111 metadata format (Lora hashes)"""
|
||
|
||
METADATA_MARKER = r'Lora hashes:'
|
||
LORA_PATTERN = r'<lora:([^:]+):([^>]+)>'
|
||
LORA_HASH_PATTERN = r'([^:]+): ([a-f0-9]+)'
|
||
|
||
def is_metadata_matching(self, user_comment: str) -> bool:
|
||
"""Check if the user comment matches the A1111 metadata format"""
|
||
return 'Lora hashes:' in user_comment
|
||
|
||
async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
|
||
"""Parse metadata from images with A1111 metadata format"""
|
||
try:
|
||
# Extract prompt and negative prompt
|
||
parts = user_comment.split('Negative prompt:', 1)
|
||
prompt = parts[0].strip()
|
||
|
||
# Initialize metadata
|
||
metadata = {"prompt": prompt, "loras": []}
|
||
|
||
# Extract negative prompt and parameters
|
||
if len(parts) > 1:
|
||
negative_and_params = parts[1]
|
||
|
||
# Extract negative prompt
|
||
if "Steps:" in negative_and_params:
|
||
neg_prompt = negative_and_params.split("Steps:", 1)[0].strip()
|
||
metadata["negative_prompt"] = neg_prompt
|
||
|
||
# Extract key-value parameters (Steps, Sampler, CFG scale, etc.)
|
||
param_pattern = r'([A-Za-z ]+): ([^,]+)'
|
||
params = re.findall(param_pattern, negative_and_params)
|
||
for key, value in params:
|
||
clean_key = key.strip().lower().replace(' ', '_')
|
||
metadata[clean_key] = value.strip()
|
||
|
||
# Extract LoRA information from prompt
|
||
lora_weights = {}
|
||
lora_matches = re.findall(self.LORA_PATTERN, prompt)
|
||
for lora_name, weights in lora_matches:
|
||
# Take only the first strength value (before the colon)
|
||
weight = weights.split(':')[0]
|
||
lora_weights[lora_name.strip()] = float(weight.strip())
|
||
|
||
# Remove LoRA patterns from prompt
|
||
metadata["prompt"] = re.sub(self.LORA_PATTERN, '', prompt).strip()
|
||
|
||
# Extract LoRA hashes
|
||
lora_hashes = {}
|
||
if 'Lora hashes:' in user_comment:
|
||
lora_hash_section = user_comment.split('Lora hashes:', 1)[1].strip()
|
||
if lora_hash_section.startswith('"'):
|
||
lora_hash_section = lora_hash_section[1:].split('"', 1)[0]
|
||
hash_matches = re.findall(self.LORA_HASH_PATTERN, lora_hash_section)
|
||
for lora_name, hash_value in hash_matches:
|
||
# Remove any leading comma and space from lora name
|
||
clean_name = lora_name.strip().lstrip(',').strip()
|
||
lora_hashes[clean_name] = hash_value.strip()
|
||
|
||
# Process LoRAs and collect base models
|
||
base_model_counts = {}
|
||
loras = []
|
||
|
||
# Process each LoRA with hash and weight
|
||
for lora_name, hash_value in lora_hashes.items():
|
||
weight = lora_weights.get(lora_name, 1.0)
|
||
|
||
# Initialize lora entry with default values
|
||
lora_entry = {
|
||
'name': lora_name,
|
||
'type': 'lora',
|
||
'weight': weight,
|
||
'existsLocally': False,
|
||
'localPath': None,
|
||
'file_name': lora_name,
|
||
'hash': hash_value,
|
||
'thumbnailUrl': '/loras_static/images/no-preview.png',
|
||
'baseModel': '',
|
||
'size': 0,
|
||
'downloadUrl': '',
|
||
'isDeleted': False
|
||
}
|
||
|
||
# Get info from Civitai by hash if available
|
||
if civitai_client and hash_value:
|
||
try:
|
||
civitai_info = await civitai_client.get_model_by_hash(hash_value)
|
||
# Populate lora entry with Civitai info
|
||
lora_entry = await self.populate_lora_from_civitai(
|
||
lora_entry,
|
||
civitai_info,
|
||
recipe_scanner,
|
||
base_model_counts,
|
||
hash_value
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}")
|
||
|
||
loras.append(lora_entry)
|
||
|
||
# Set base_model to the most common one from civitai_info
|
||
base_model = None
|
||
if base_model_counts:
|
||
base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
|
||
|
||
# Extract generation parameters for recipe metadata
|
||
gen_params = {}
|
||
for key in GEN_PARAM_KEYS:
|
||
if key in metadata:
|
||
gen_params[key] = metadata.get(key, '')
|
||
|
||
# Add model information if available
|
||
if 'model' in metadata:
|
||
gen_params['checkpoint'] = metadata['model']
|
||
|
||
return {
|
||
'base_model': base_model,
|
||
'loras': loras,
|
||
'gen_params': gen_params,
|
||
'raw_metadata': metadata
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error parsing A1111 metadata: {e}", exc_info=True)
|
||
return {"error": str(e), "loras": []}
|
||
|
||
|
||
class ComfyMetadataParser(RecipeMetadataParser):
|
||
"""Parser for Civitai ComfyUI metadata JSON format"""
|
||
|
||
METADATA_MARKER = r"class_type"
|
||
|
||
def is_metadata_matching(self, user_comment: str) -> bool:
|
||
"""Check if the user comment matches the ComfyUI metadata format"""
|
||
try:
|
||
data = json.loads(user_comment)
|
||
# Check if it contains class_type nodes typical of ComfyUI workflow
|
||
return isinstance(data, dict) and any(isinstance(v, dict) and 'class_type' in v for v in data.values())
|
||
except (json.JSONDecodeError, TypeError):
|
||
return False
|
||
|
||
async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
|
||
"""Parse metadata from Civitai ComfyUI metadata format"""
|
||
try:
|
||
data = json.loads(user_comment)
|
||
loras = []
|
||
|
||
# Find all LoraLoader nodes
|
||
lora_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'LoraLoader'}
|
||
|
||
if not lora_nodes:
|
||
return {"error": "No LoRA information found in this ComfyUI workflow", "loras": []}
|
||
|
||
# Process each LoraLoader node
|
||
for node_id, node in lora_nodes.items():
|
||
if 'inputs' not in node or 'lora_name' not in node['inputs']:
|
||
continue
|
||
|
||
lora_name = node['inputs'].get('lora_name', '')
|
||
|
||
# Parse the URN to extract model ID and version ID
|
||
# Format: "urn:air:sdxl:lora:civitai:1107767@1253442"
|
||
lora_id_match = re.search(r'civitai:(\d+)@(\d+)', lora_name)
|
||
if not lora_id_match:
|
||
continue
|
||
|
||
model_id = lora_id_match.group(1)
|
||
model_version_id = lora_id_match.group(2)
|
||
|
||
# Get strength from node inputs
|
||
weight = node['inputs'].get('strength_model', 1.0)
|
||
|
||
# Initialize lora entry with default values
|
||
lora_entry = {
|
||
'id': model_version_id,
|
||
'modelId': model_id,
|
||
'name': f"Lora {model_id}", # Default name
|
||
'version': '',
|
||
'type': 'lora',
|
||
'weight': weight,
|
||
'existsLocally': False,
|
||
'localPath': None,
|
||
'file_name': '',
|
||
'hash': '',
|
||
'thumbnailUrl': '/loras_static/images/no-preview.png',
|
||
'baseModel': '',
|
||
'size': 0,
|
||
'downloadUrl': '',
|
||
'isDeleted': False
|
||
}
|
||
|
||
# Get additional info from Civitai if client is available
|
||
if civitai_client:
|
||
try:
|
||
civitai_info_tuple = await civitai_client.get_model_version_info(model_version_id)
|
||
# Populate lora entry with Civitai info
|
||
lora_entry = await self.populate_lora_from_civitai(
|
||
lora_entry,
|
||
civitai_info_tuple,
|
||
recipe_scanner
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"Error fetching Civitai info for LoRA: {e}")
|
||
|
||
loras.append(lora_entry)
|
||
|
||
# Find checkpoint info
|
||
checkpoint_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'CheckpointLoaderSimple'}
|
||
checkpoint = None
|
||
checkpoint_id = None
|
||
checkpoint_version_id = None
|
||
|
||
if checkpoint_nodes:
|
||
# Get the first checkpoint node
|
||
checkpoint_node = next(iter(checkpoint_nodes.values()))
|
||
if 'inputs' in checkpoint_node and 'ckpt_name' in checkpoint_node['inputs']:
|
||
checkpoint_name = checkpoint_node['inputs']['ckpt_name']
|
||
# Parse checkpoint URN
|
||
checkpoint_match = re.search(r'civitai:(\d+)@(\d+)', checkpoint_name)
|
||
if checkpoint_match:
|
||
checkpoint_id = checkpoint_match.group(1)
|
||
checkpoint_version_id = checkpoint_match.group(2)
|
||
checkpoint = {
|
||
'id': checkpoint_version_id,
|
||
'modelId': checkpoint_id,
|
||
'name': f"Checkpoint {checkpoint_id}",
|
||
'version': '',
|
||
'type': 'checkpoint'
|
||
}
|
||
|
||
# Get additional checkpoint info from Civitai
|
||
if civitai_client:
|
||
try:
|
||
civitai_info_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": []}
|
||
|
||
|
||
class MetaFormatParser(RecipeMetadataParser):
|
||
"""Parser for images with meta format metadata (Lora_N Model hash format)"""
|
||
|
||
METADATA_MARKER = r'Lora_\d+ Model hash:'
|
||
|
||
def is_metadata_matching(self, user_comment: str) -> bool:
|
||
"""Check if the user comment matches the metadata format"""
|
||
return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
|
||
|
||
async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
|
||
"""Parse metadata from images with meta format metadata"""
|
||
try:
|
||
# Extract prompt and negative prompt
|
||
parts = user_comment.split('Negative prompt:', 1)
|
||
prompt = parts[0].strip()
|
||
|
||
# Initialize metadata
|
||
metadata = {"prompt": prompt, "loras": []}
|
||
|
||
# Extract negative prompt and parameters if available
|
||
if len(parts) > 1:
|
||
negative_and_params = parts[1]
|
||
|
||
# Extract negative prompt - everything until the first parameter (usually "Steps:")
|
||
param_start = re.search(r'([A-Za-z]+): ', negative_and_params)
|
||
if param_start:
|
||
neg_prompt = negative_and_params[:param_start.start()].strip()
|
||
metadata["negative_prompt"] = neg_prompt
|
||
params_section = negative_and_params[param_start.start():]
|
||
else:
|
||
params_section = negative_and_params
|
||
|
||
# Extract key-value parameters (Steps, Sampler, Seed, etc.)
|
||
param_pattern = r'([A-Za-z_0-9 ]+): ([^,]+)'
|
||
params = re.findall(param_pattern, params_section)
|
||
for key, value in params:
|
||
clean_key = key.strip().lower().replace(' ', '_')
|
||
metadata[clean_key] = value.strip()
|
||
|
||
# Extract LoRA information
|
||
# Pattern to match lora entries: Lora_0 Model name: ArtVador I.safetensors, Lora_0 Model hash: 08f7133a58, etc.
|
||
lora_pattern = r'Lora_(\d+) Model name: ([^,]+), Lora_\1 Model hash: ([^,]+), Lora_\1 Strength model: ([^,]+), Lora_\1 Strength clip: ([^,]+)'
|
||
lora_matches = re.findall(lora_pattern, user_comment)
|
||
|
||
# If the regular pattern doesn't match, try a more flexible approach
|
||
if not lora_matches:
|
||
# First find all Lora indices
|
||
lora_indices = set(re.findall(r'Lora_(\d+)', user_comment))
|
||
|
||
# For each index, extract the information
|
||
for idx in lora_indices:
|
||
lora_info = {}
|
||
|
||
# Extract model name
|
||
name_match = re.search(f'Lora_{idx} Model name: ([^,]+)', user_comment)
|
||
if name_match:
|
||
lora_info['name'] = name_match.group(1).strip()
|
||
|
||
# Extract model hash
|
||
hash_match = re.search(f'Lora_{idx} Model hash: ([^,]+)', user_comment)
|
||
if hash_match:
|
||
lora_info['hash'] = hash_match.group(1).strip()
|
||
|
||
# Extract strength model
|
||
strength_model_match = re.search(f'Lora_{idx} Strength model: ([^,]+)', user_comment)
|
||
if strength_model_match:
|
||
lora_info['strength_model'] = float(strength_model_match.group(1).strip())
|
||
|
||
# Extract strength clip
|
||
strength_clip_match = re.search(f'Lora_{idx} Strength clip: ([^,]+)', user_comment)
|
||
if strength_clip_match:
|
||
lora_info['strength_clip'] = float(strength_clip_match.group(1).strip())
|
||
|
||
# Only add if we have at least name and hash
|
||
if 'name' in lora_info and 'hash' in lora_info:
|
||
lora_matches.append((idx, lora_info['name'], lora_info['hash'],
|
||
str(lora_info.get('strength_model', 1.0)),
|
||
str(lora_info.get('strength_clip', 1.0))))
|
||
|
||
# Process LoRAs
|
||
base_model_counts = {}
|
||
loras = []
|
||
|
||
for match in lora_matches:
|
||
if len(match) == 5: # Regular pattern match
|
||
idx, name, hash_value, strength_model, strength_clip = match
|
||
else: # Flexible approach match
|
||
continue # Should not happen now
|
||
|
||
# Clean up the values
|
||
name = name.strip()
|
||
if name.endswith('.safetensors'):
|
||
name = name[:-12] # Remove .safetensors extension
|
||
|
||
hash_value = hash_value.strip()
|
||
weight = float(strength_model) # Use model strength as weight
|
||
|
||
# Initialize lora entry with default values
|
||
lora_entry = {
|
||
'name': name,
|
||
'type': 'lora',
|
||
'weight': weight,
|
||
'existsLocally': False,
|
||
'localPath': None,
|
||
'file_name': name,
|
||
'hash': hash_value,
|
||
'thumbnailUrl': '/loras_static/images/no-preview.png',
|
||
'baseModel': '',
|
||
'size': 0,
|
||
'downloadUrl': '',
|
||
'isDeleted': False
|
||
}
|
||
|
||
# Get info from Civitai by hash if available
|
||
if civitai_client and hash_value:
|
||
try:
|
||
civitai_info = await civitai_client.get_model_by_hash(hash_value)
|
||
# Populate lora entry with Civitai info
|
||
lora_entry = await self.populate_lora_from_civitai(
|
||
lora_entry,
|
||
civitai_info,
|
||
recipe_scanner,
|
||
base_model_counts,
|
||
hash_value
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}")
|
||
|
||
loras.append(lora_entry)
|
||
|
||
# Extract model information
|
||
model = None
|
||
if 'model' in metadata:
|
||
model = metadata['model']
|
||
|
||
# Set base_model to the most common one from civitai_info
|
||
base_model = None
|
||
if base_model_counts:
|
||
base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
|
||
|
||
# Extract generation parameters for recipe metadata
|
||
gen_params = {}
|
||
for key in GEN_PARAM_KEYS:
|
||
if key in metadata:
|
||
gen_params[key] = metadata.get(key, '')
|
||
|
||
# Try to extract size information if available
|
||
if 'width' in metadata and 'height' in metadata:
|
||
gen_params['size'] = f"{metadata['width']}x{metadata['height']}"
|
||
|
||
return {
|
||
'base_model': base_model,
|
||
'loras': loras,
|
||
'gen_params': gen_params,
|
||
'raw_metadata': metadata,
|
||
'from_meta_format': True
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error parsing meta format metadata: {e}", exc_info=True)
|
||
return {"error": str(e), "loras": []}
|
||
|
||
|
||
class ImageSaverMetadataParser(RecipeMetadataParser):
|
||
"""Parser for ComfyUI Image Saver plugin metadata format"""
|
||
|
||
METADATA_MARKER = r'Hashes: \{"LORA:'
|
||
LORA_PATTERN = r'<lora:([^:]+):([^>]+)>'
|
||
HASH_PATTERN = r'Hashes: (\{.*?\})'
|
||
|
||
def is_metadata_matching(self, user_comment: str) -> bool:
|
||
"""Check if the user comment matches the Image Saver metadata format"""
|
||
return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
|
||
|
||
async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
|
||
"""Parse metadata from Image Saver plugin format"""
|
||
try:
|
||
# Extract prompt and negative prompt
|
||
parts = user_comment.split('Negative prompt:', 1)
|
||
prompt = parts[0].strip()
|
||
|
||
# Initialize metadata
|
||
metadata = {"prompt": prompt, "loras": []}
|
||
|
||
# Extract negative prompt and parameters
|
||
if len(parts) > 1:
|
||
negative_and_params = parts[1]
|
||
|
||
# Extract negative prompt
|
||
if "Steps:" in negative_and_params:
|
||
neg_prompt = negative_and_params.split("Steps:", 1)[0].strip()
|
||
metadata["negative_prompt"] = neg_prompt
|
||
|
||
# Extract key-value parameters (Steps, Sampler, CFG scale, etc.)
|
||
param_pattern = r'([A-Za-z ]+): ([^,]+)'
|
||
params = re.findall(param_pattern, negative_and_params)
|
||
for key, value in params:
|
||
clean_key = key.strip().lower().replace(' ', '_')
|
||
metadata[clean_key] = value.strip()
|
||
|
||
# Extract LoRA information from prompt
|
||
lora_weights = {}
|
||
lora_matches = re.findall(self.LORA_PATTERN, prompt)
|
||
for lora_name, weight in lora_matches:
|
||
lora_weights[lora_name.strip()] = float(weight.split(':')[0].strip())
|
||
|
||
# Remove LoRA patterns from prompt
|
||
metadata["prompt"] = re.sub(self.LORA_PATTERN, '', prompt).strip()
|
||
|
||
# Extract LoRA hashes from Hashes section
|
||
lora_hashes = {}
|
||
hash_match = re.search(self.HASH_PATTERN, user_comment)
|
||
if hash_match:
|
||
try:
|
||
hashes = json.loads(hash_match.group(1))
|
||
for key, hash_value in hashes.items():
|
||
if key.startswith('LORA:'):
|
||
lora_name = key[5:] # Remove 'LORA:' prefix
|
||
lora_hashes[lora_name] = hash_value.strip()
|
||
except json.JSONDecodeError:
|
||
pass
|
||
|
||
# Process LoRAs and collect base models
|
||
base_model_counts = {}
|
||
loras = []
|
||
|
||
# Process each LoRA with hash and weight
|
||
for lora_name, hash_value in lora_hashes.items():
|
||
weight = lora_weights.get(lora_name, 1.0)
|
||
|
||
# Initialize lora entry with default values
|
||
lora_entry = {
|
||
'name': lora_name,
|
||
'type': 'lora',
|
||
'weight': weight,
|
||
'existsLocally': False,
|
||
'localPath': None,
|
||
'file_name': lora_name,
|
||
'hash': hash_value,
|
||
'thumbnailUrl': '/loras_static/images/no-preview.png',
|
||
'baseModel': '',
|
||
'size': 0,
|
||
'downloadUrl': '',
|
||
'isDeleted': False
|
||
}
|
||
|
||
# Get info from Civitai by hash if available
|
||
if civitai_client and hash_value:
|
||
try:
|
||
civitai_info = await civitai_client.get_model_by_hash(hash_value)
|
||
# Populate lora entry with Civitai info
|
||
lora_entry = await self.populate_lora_from_civitai(
|
||
lora_entry,
|
||
civitai_info,
|
||
recipe_scanner,
|
||
base_model_counts,
|
||
hash_value
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}")
|
||
|
||
loras.append(lora_entry)
|
||
|
||
# Set base_model to the most common one from civitai_info
|
||
base_model = None
|
||
if base_model_counts:
|
||
base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
|
||
|
||
# Extract generation parameters for recipe metadata
|
||
gen_params = {}
|
||
for key in GEN_PARAM_KEYS:
|
||
if key in metadata:
|
||
gen_params[key] = metadata.get(key, '')
|
||
|
||
# Add model information if available
|
||
if 'model' in metadata:
|
||
gen_params['checkpoint'] = metadata['model']
|
||
|
||
return {
|
||
'base_model': base_model,
|
||
'loras': loras,
|
||
'gen_params': gen_params,
|
||
'raw_metadata': metadata
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error parsing Image Saver metadata: {e}", exc_info=True)
|
||
return {"error": str(e), "loras": []}
|
||
|
||
|
||
class RecipeParserFactory:
|
||
"""Factory for creating recipe metadata parsers"""
|
||
|
||
@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 StandardMetadataParser().is_metadata_matching(user_comment):
|
||
return StandardMetadataParser()
|
||
elif A1111MetadataParser().is_metadata_matching(user_comment):
|
||
return A1111MetadataParser()
|
||
elif MetaFormatParser().is_metadata_matching(user_comment):
|
||
return MetaFormatParser()
|
||
elif ImageSaverMetadataParser().is_metadata_matching(user_comment):
|
||
return ImageSaverMetadataParser()
|
||
else:
|
||
return None
|