mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 13:42:12 -03:00
Refactor recipe metadata processing in RecipeRoutes
- Introduced a new RecipeParserFactory to streamline the parsing of recipe metadata from user comments, supporting multiple formats. - Removed legacy metadata extraction logic from RecipeRoutes, delegating responsibilities to the new parser classes. - Enhanced error handling for cases where no valid parser is found, ensuring graceful responses. - Updated the RecipeScanner to improve the handling of LoRA metadata and reduce logging verbosity for better performance.
This commit is contained in:
Will Miao
@@ -8,6 +8,7 @@ import json
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import aiohttp
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import asyncio
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from ..utils.exif_utils import ExifUtils
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from ..utils.recipe_parsers import RecipeParserFactory
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from ..services.civitai_client import CivitaiClient
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from ..services.recipe_scanner import RecipeScanner
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@@ -220,197 +221,27 @@ class RecipeRoutes:
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"loras": [] # Return empty loras array to prevent client-side errors
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}, status=200) # Return 200 instead of 400 to handle gracefully
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# First, check if this image has recipe metadata from a previous share
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recipe_metadata = ExifUtils.extract_recipe_metadata(user_comment)
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if recipe_metadata:
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logger.info("Found existing recipe metadata in image")
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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'):
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exists_locally = self.recipe_scanner._lora_scanner.has_lora_hash(lora['hash'])
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if exists_locally:
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lora_entry['existsLocally'] = True
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lora_cache = await self.recipe_scanner._lora_scanner.get_cached_data()
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lora_item = next((item for item in lora_cache.raw_data if item['sha256'] == lora['hash']), None)
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if lora_item:
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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'):
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try:
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civitai_info = await self.civitai_client.get_model_version_info(lora['modelVersionId'])
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if civitai_info and civitai_info.get("error") != "Model not found":
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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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lora_entry['baseModel'] = civitai_info.get('baseModel', '')
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# Get download URL
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lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
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# Get size from files if available
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if 'files' in civitai_info:
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model_file = next((file for file in civitai_info.get('files', [])
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if file.get('type') == 'Model'), None)
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if model_file:
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lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
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else:
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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 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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# Use the parser factory to get the appropriate parser
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parser = RecipeParserFactory.create_parser(user_comment)
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logger.info(f"Found {len(loras)} loras in recipe metadata")
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if parser is None:
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return web.json_response({
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'base_model': recipe_metadata.get('base_model', ''),
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'loras': loras,
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'gen_params': recipe_metadata.get('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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"error": "No parser found for this image",
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"loras": [] # Return empty loras array to prevent client-side errors
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}, status=200) # Return 200 instead of 400 to handle gracefully
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# If no recipe metadata, parse the standard metadata
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metadata = ExifUtils.parse_recipe_metadata(user_comment)
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# Parse the metadata
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result = await parser.parse_metadata(
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user_comment,
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recipe_scanner=self.recipe_scanner,
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civitai_client=self.civitai_client
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)
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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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# Check for errors
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if "error" in result and not result.get("loras"):
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return web.json_response(result, status=200)
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if not civitai_resources and not checkpoint:
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return web.json_response({
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"error": "No LoRA information found in this image",
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"loras": [] # Return empty loras array
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}, status=200) # Return 200 instead of 400
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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 # New flag to indicate if the LoRA is deleted from Civitai
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}
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# Get additional info from Civitai
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civitai_info = await self.civitai_client.get_model_version_info(model_version_id)
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# Check if this LoRA exists locally by SHA256 hash
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if civitai_info and civitai_info.get("error") != "Model not found":
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# LoRA exists on Civitai, process its information
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if 'files' in civitai_info:
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# Find the model file (type="Model") 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'), None)
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if model_file:
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sha256 = model_file.get('hashes', {}).get('SHA256', '')
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if sha256:
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exists_locally = self.recipe_scanner._lora_scanner.has_lora_hash(sha256)
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if exists_locally:
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local_path = self.recipe_scanner._lora_scanner.get_lora_path_by_hash(sha256)
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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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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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lora_entry['hash'] = sha256
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lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
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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 and update counts
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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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if 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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else:
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# LoRA is deleted from Civitai or not found
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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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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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'prompt': metadata.get('prompt', ''),
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'negative_prompt': metadata.get('negative_prompt', ''),
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'checkpoint': checkpoint,
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'steps': metadata.get('steps', ''),
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'sampler': metadata.get('sampler', ''),
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'cfg_scale': metadata.get('cfg_scale', ''),
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'seed': metadata.get('seed', ''),
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'size': metadata.get('size', ''),
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'clip_skip': metadata.get('clip_skip', '')
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}
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return web.json_response({
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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 # Include the raw metadata for saving
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})
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return web.json_response(result)
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except Exception as e:
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logger.error(f"Error analyzing recipe image: {e}", exc_info=True)
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@@ -48,7 +48,6 @@ class RecipeScanner:
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# config.loras_roots already sorted case-insensitively, use the first one
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recipes_dir = os.path.join(config.loras_roots[0], "recipes")
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os.makedirs(recipes_dir, exist_ok=True)
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logger.info(f"Using recipes directory: {recipes_dir}")
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return recipes_dir
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@@ -60,7 +59,6 @@ class RecipeScanner:
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# If another initialization is already in progress, wait for it to complete
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if self._is_initializing and not force_refresh:
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logger.info("Initialization already in progress, returning current cache state")
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return self._cache or RecipeCache(raw_data=[], sorted_by_name=[], sorted_by_date=[])
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# Try to acquire the lock with a timeout to prevent deadlocks
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@@ -79,19 +77,16 @@ class RecipeScanner:
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# First ensure the lora scanner is initialized
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if self._lora_scanner:
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try:
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logger.info("Recipe Manager: Waiting for lora scanner initialization")
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lora_cache = await asyncio.wait_for(
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self._lora_scanner.get_cached_data(),
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timeout=10.0
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)
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logger.info(f"Recipe Manager: Lora scanner initialized with {len(lora_cache.raw_data)} loras")
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except asyncio.TimeoutError:
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logger.error("Timeout waiting for lora scanner initialization")
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except Exception as e:
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logger.error(f"Error waiting for lora scanner: {e}")
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# Scan for recipe data
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logger.info("Recipe Manager: Starting recipe scan")
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raw_data = await self.scan_all_recipes()
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# Update cache
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@@ -104,7 +99,6 @@ class RecipeScanner:
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# Resort cache
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await self._cache.resort()
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logger.info(f"Recipe Manager: Cache initialization completed with {len(raw_data)} recipes")
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return self._cache
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except Exception as e:
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@@ -139,11 +133,9 @@ class RecipeScanner:
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# Get all recipe JSON files in the recipes directory
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recipe_files = []
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logger.info(f"Scanning for recipe JSON files in {recipes_dir}")
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for root, _, files in os.walk(recipes_dir):
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recipe_count = sum(1 for f in files if f.lower().endswith('.recipe.json'))
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if recipe_count > 0:
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logger.info(f"Found {recipe_count} recipe files in {root}")
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for file in files:
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if file.lower().endswith('.recipe.json'):
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recipe_files.append(os.path.join(root, file))
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@@ -153,17 +145,12 @@ class RecipeScanner:
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recipe_data = await self._load_recipe_file(recipe_path)
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if recipe_data:
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recipes.append(recipe_data)
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logger.info(f"Processed recipe: {recipe_data.get('title')}")
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logger.info(f"Successfully processed {len(recipes)} recipes")
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return recipes
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async def _load_recipe_file(self, recipe_path: str) -> Optional[Dict]:
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"""Load recipe data from a JSON file"""
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try:
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logger.info(f"Loading recipe file: {recipe_path}")
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with open(recipe_path, 'r', encoding='utf-8') as f:
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recipe_data = json.load(f)
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@@ -188,7 +175,6 @@ class RecipeScanner:
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image_filename = os.path.basename(image_path)
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alternative_path = os.path.join(recipe_dir, image_filename)
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if os.path.exists(alternative_path):
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logger.info(f"Found alternative image path: {alternative_path}")
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recipe_data['file_path'] = alternative_path
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else:
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logger.warning(f"Could not find alternative image path for {image_path}")
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@@ -223,30 +209,23 @@ class RecipeScanner:
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metadata_updated = False
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for lora in recipe_data['loras']:
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logger.info(f"Processing LoRA: {lora.get('modelName', 'Unknown')}, ID: {lora.get('modelVersionId', 'No ID')}")
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# Skip if already has complete information
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if 'hash' in lora and 'file_name' in lora and lora['file_name']:
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logger.info(f"LoRA already has complete information")
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continue
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# If has modelVersionId but no hash, look in lora cache first, then fetch from Civitai
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if 'modelVersionId' in lora and not lora.get('hash'):
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model_version_id = lora['modelVersionId']
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logger.info(f"Looking up hash for modelVersionId: {model_version_id}")
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# Try to find in lora cache first
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hash_from_cache = await self._find_hash_in_lora_cache(model_version_id)
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if hash_from_cache:
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logger.info(f"Found hash in lora cache: {hash_from_cache}")
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lora['hash'] = hash_from_cache
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metadata_updated = True
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else:
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# If not in cache, fetch from Civitai
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logger.info(f"Fetching hash from Civitai for {model_version_id}")
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hash_from_civitai = await self._get_hash_from_civitai(model_version_id)
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if hash_from_civitai:
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logger.info(f"Got hash from Civitai: {hash_from_civitai}")
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lora['hash'] = hash_from_civitai
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metadata_updated = True
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else:
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@@ -255,18 +234,15 @@ class RecipeScanner:
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# If has hash but no file_name, look up in lora library
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if 'hash' in lora and (not lora.get('file_name') or not lora['file_name']):
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hash_value = lora['hash']
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logger.info(f"Looking up file_name for hash: {hash_value}")
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if self._lora_scanner.has_lora_hash(hash_value):
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lora_path = self._lora_scanner.get_lora_path_by_hash(hash_value)
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if lora_path:
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file_name = os.path.splitext(os.path.basename(lora_path))[0]
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logger.info(f"Found lora in library: {file_name}")
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lora['file_name'] = file_name
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metadata_updated = True
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else:
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# Lora not in library
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logger.info(f"LoRA with hash {hash_value} not found in library")
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lora['file_name'] = ''
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metadata_updated = True
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@@ -300,7 +276,6 @@ class RecipeScanner:
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if not self._civitai_client:
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return None
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logger.info(f"Fetching model version info from Civitai for ID: {model_version_id}")
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version_info = await self._civitai_client.get_model_version_info(model_version_id)
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if not version_info or not version_info.get('files'):
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@@ -324,7 +299,6 @@ class RecipeScanner:
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if not self._civitai_client:
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return None
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logger.info(f"Fetching model version info from Civitai for ID: {model_version_id}")
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version_info = await self._civitai_client.get_model_version_info(model_version_id)
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if version_info and 'name' in version_info:
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@@ -58,77 +58,6 @@ class ExifUtils:
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except Exception as e:
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logger.error(f"Error updating EXIF data in {image_path}: {e}")
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return image_path
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@staticmethod
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def parse_recipe_metadata(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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# 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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# 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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# 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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# 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]
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if '],' in resources_part:
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resources_json = resources_part.split('],', 1)[0] + ']'
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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}
|
||||
|
||||
@staticmethod
|
||||
def extract_recipe_metadata(user_comment: str) -> Optional[Dict]:
|
||||
"""Extract recipe metadata section from UserComment if it exists"""
|
||||
try:
|
||||
# Look for recipe metadata section
|
||||
recipe_match = re.search(r'Recipe metadata: (\{.*\})', user_comment, re.IGNORECASE | re.DOTALL)
|
||||
if not recipe_match:
|
||||
return None
|
||||
|
||||
recipe_json = recipe_match.group(1)
|
||||
return json.loads(recipe_json)
|
||||
except Exception as e:
|
||||
logger.error(f"Error extracting recipe metadata: {e}")
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def append_recipe_metadata(image_path, recipe_data) -> str:
|
||||
|
||||
355
py/utils/recipe_parsers.py
Normal file
355
py/utils/recipe_parsers.py
Normal file
@@ -0,0 +1,355 @@
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
from typing import Dict, List, Any, Optional
|
||||
from abc import ABC, abstractmethod
|
||||
from ..config import config
|
||||
|
||||
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
|
||||
|
||||
|
||||
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": []}
|
||||
|
||||
logger.info("Found existing recipe metadata in image")
|
||||
|
||||
# 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'] == lora['hash']), 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 = await civitai_client.get_model_version_info(lora['modelVersionId'])
|
||||
if civitai_info and civitai_info.get("error") != "Model not found":
|
||||
# Get thumbnail URL from first image
|
||||
if 'images' in civitai_info and civitai_info['images']:
|
||||
lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
|
||||
|
||||
# Get base model
|
||||
lora_entry['baseModel'] = civitai_info.get('baseModel', '')
|
||||
|
||||
# Get download URL
|
||||
lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
|
||||
|
||||
# Get size from files if available
|
||||
if 'files' in civitai_info:
|
||||
model_file = next((file for file in civitai_info.get('files', [])
|
||||
if file.get('type') == 'Model'), None)
|
||||
if model_file:
|
||||
lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
|
||||
else:
|
||||
lora_entry['isDeleted'] = True
|
||||
lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
|
||||
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")
|
||||
|
||||
return {
|
||||
'base_model': recipe_metadata.get('base_model', ''),
|
||||
'loras': loras,
|
||||
'gen_params': recipe_metadata.get('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": []}
|
||||
|
||||
|
||||
class StandardMetadataParser(RecipeMetadataParser):
|
||||
"""Parser for images with standard civitai metadata format (prompt, negative prompt, etc.)"""
|
||||
|
||||
METADATA_MARKER = r'Civitai resources: '
|
||||
|
||||
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 standard metadata format"""
|
||||
try:
|
||||
# Parse the standard metadata
|
||||
metadata = self._parse_recipe_metadata(user_comment)
|
||||
|
||||
# Look for Civitai resources in the metadata
|
||||
civitai_resources = metadata.get('loras', [])
|
||||
checkpoint = metadata.get('checkpoint')
|
||||
|
||||
if not civitai_resources and not checkpoint:
|
||||
return {
|
||||
"error": "No LoRA information found in this image",
|
||||
"loras": []
|
||||
}
|
||||
|
||||
# Process LoRAs and collect base models
|
||||
base_model_counts = {}
|
||||
loras = []
|
||||
|
||||
# Process LoRAs
|
||||
for resource in civitai_resources:
|
||||
# Get model version ID
|
||||
model_version_id = resource.get('modelVersionId')
|
||||
if not model_version_id:
|
||||
continue
|
||||
|
||||
# Initialize lora entry with default values
|
||||
lora_entry = {
|
||||
'id': model_version_id,
|
||||
'name': resource.get('modelName', ''),
|
||||
'version': resource.get('modelVersionName', ''),
|
||||
'type': resource.get('type', 'lora'),
|
||||
'weight': resource.get('weight', 1.0),
|
||||
'existsLocally': False,
|
||||
'localPath': None,
|
||||
'file_name': '',
|
||||
'hash': '',
|
||||
'thumbnailUrl': '',
|
||||
'baseModel': '',
|
||||
'size': 0,
|
||||
'downloadUrl': '',
|
||||
'isDeleted': False
|
||||
}
|
||||
|
||||
# Get additional info from Civitai if client is available
|
||||
if civitai_client:
|
||||
civitai_info = await civitai_client.get_model_version_info(model_version_id)
|
||||
|
||||
# Check if this LoRA exists locally by SHA256 hash
|
||||
if civitai_info and civitai_info.get("error") != "Model not found":
|
||||
# LoRA exists on Civitai, process its information
|
||||
if 'files' in civitai_info:
|
||||
# Find the model file (type="Model") in the files list
|
||||
model_file = next((file for file in civitai_info.get('files', [])
|
||||
if file.get('type') == 'Model'), None)
|
||||
|
||||
if model_file and recipe_scanner:
|
||||
sha256 = model_file.get('hashes', {}).get('SHA256', '')
|
||||
if sha256:
|
||||
lora_scanner = recipe_scanner._lora_scanner
|
||||
exists_locally = lora_scanner.has_lora_hash(sha256)
|
||||
if exists_locally:
|
||||
local_path = lora_scanner.get_lora_path_by_hash(sha256)
|
||||
lora_entry['existsLocally'] = True
|
||||
lora_entry['localPath'] = local_path
|
||||
lora_entry['file_name'] = os.path.splitext(os.path.basename(local_path))[0]
|
||||
else:
|
||||
# For missing LoRAs, get file_name from model_file.name
|
||||
file_name = model_file.get('name', '')
|
||||
lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else ''
|
||||
|
||||
lora_entry['hash'] = sha256
|
||||
lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
|
||||
|
||||
# Get thumbnail URL from first image
|
||||
if 'images' in civitai_info and civitai_info['images']:
|
||||
lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
|
||||
|
||||
# Get base model and update counts
|
||||
current_base_model = civitai_info.get('baseModel', '')
|
||||
lora_entry['baseModel'] = current_base_model
|
||||
if current_base_model:
|
||||
base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
|
||||
|
||||
# Get download URL
|
||||
lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
|
||||
else:
|
||||
# LoRA is deleted from Civitai or not found
|
||||
lora_entry['isDeleted'] = True
|
||||
lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
|
||||
|
||||
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 = {
|
||||
'prompt': metadata.get('prompt', ''),
|
||||
'negative_prompt': metadata.get('negative_prompt', ''),
|
||||
'checkpoint': checkpoint,
|
||||
'steps': metadata.get('steps', ''),
|
||||
'sampler': metadata.get('sampler', ''),
|
||||
'cfg_scale': metadata.get('cfg_scale', ''),
|
||||
'seed': metadata.get('seed', ''),
|
||||
'size': metadata.get('size', ''),
|
||||
'clip_skip': metadata.get('clip_skip', '')
|
||||
}
|
||||
|
||||
return {
|
||||
'base_model': base_model,
|
||||
'loras': loras,
|
||||
'gen_params': gen_params,
|
||||
'raw_metadata': metadata
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error parsing standard metadata: {e}", exc_info=True)
|
||||
return {"error": str(e), "loras": []}
|
||||
|
||||
def _parse_recipe_metadata(self, user_comment: str) -> Dict[str, Any]:
|
||||
"""Parse recipe metadata from UserComment"""
|
||||
try:
|
||||
# Split by 'Negative prompt:' to get the prompt
|
||||
parts = user_comment.split('Negative prompt:', 1)
|
||||
prompt = parts[0].strip()
|
||||
|
||||
# Initialize metadata with prompt
|
||||
metadata = {"prompt": prompt, "loras": [], "checkpoint": None}
|
||||
|
||||
# Extract additional fields if available
|
||||
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 Civitai resources
|
||||
if 'Civitai resources:' in user_comment:
|
||||
resources_part = user_comment.split('Civitai resources:', 1)[1]
|
||||
if '],' in resources_part:
|
||||
resources_json = resources_part.split('],', 1)[0] + ']'
|
||||
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 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
|
||||
"""
|
||||
if RecipeFormatParser().is_metadata_matching(user_comment):
|
||||
print("RecipeFormatParser")
|
||||
return RecipeFormatParser()
|
||||
elif StandardMetadataParser().is_metadata_matching(user_comment):
|
||||
print("StandardMetadataParser")
|
||||
return StandardMetadataParser()
|
||||
else:
|
||||
print("None")
|
||||
return None
|
||||
Reference in New Issue
Block a user