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https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-25 07:05:43 -03:00
Refactor recipe metadata parser package for ComfyUI-Lora-Manager
- Implemented the base class `RecipeMetadataParser` for parsing recipe metadata from user comments. - Created a factory class `RecipeParserFactory` to instantiate appropriate parser based on user comment content. - Developed multiple parser classes: `ComfyMetadataParser`, `AutomaticMetadataParser`, `MetaFormatParser`, and `RecipeFormatParser` to handle different metadata formats. - Introduced constants for generation parameters and valid LoRA types. - Enhanced error handling and logging throughout the parsing process. - Added functionality to populate LoRA and checkpoint information from Civitai API responses. - Structured the output of parsed metadata to include prompts, LoRAs, generation parameters, and model information.
This commit is contained in:
Will Miao
181
py/recipes/base.py
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181
py/recipes/base.py
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"""Base classes for recipe parsers."""
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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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from .constants import VALID_LORA_TYPES
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logger = logging.getLogger(__name__)
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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) -> Optional[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 if type is valid, None otherwise
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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 not civitai_info or civitai_info.get("error") == "Model not found":
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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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return lora_entry
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# Get model type and validate
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model_type = civitai_info.get('model', {}).get('type', '').lower()
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lora_entry['type'] = model_type
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if model_type not in VALID_LORA_TYPES:
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logger.debug(f"Skipping non-LoRA model type: {model_type}")
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return None
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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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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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