mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-23 22:22:11 -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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