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https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-21 13:12:12 -03:00
Add MetaFormatParser for Lora_N Model hash format metadata handling
- Introduced MetaFormatParser class to parse metadata from images with Lora_N Model hash format. - Implemented methods to validate metadata structure, extract prompts, negative prompts, and LoRA information. - Enhanced error handling and logging for metadata parsing failures. - Updated RecipeParserFactory to include MetaFormatParser for relevant user comments.
This commit is contained in:
Will Miao
@@ -767,6 +767,213 @@ class ComfyMetadataParser(RecipeMetadataParser):
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return {"error": str(e), "loras": []}
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class MetaFormatParser(RecipeMetadataParser):
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"""Parser for images with meta format metadata (Lora_N Model hash format)"""
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METADATA_MARKER = r'Lora_\d+ Model hash:'
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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 meta format metadata"""
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try:
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# Extract prompt and negative 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
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metadata = {"prompt": prompt, "loras": []}
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# Extract negative prompt and parameters 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 - everything until the first parameter (usually "Steps:")
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param_start = re.search(r'([A-Za-z]+): ', negative_and_params)
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if param_start:
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neg_prompt = negative_and_params[:param_start.start()].strip()
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metadata["negative_prompt"] = neg_prompt
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params_section = negative_and_params[param_start.start():]
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else:
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params_section = negative_and_params
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# Extract key-value parameters (Steps, Sampler, Seed, etc.)
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param_pattern = r'([A-Za-z_0-9 ]+): ([^,]+)'
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params = re.findall(param_pattern, params_section)
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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 LoRA information
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# Pattern to match lora entries: Lora_0 Model name: ArtVador I.safetensors, Lora_0 Model hash: 08f7133a58, etc.
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lora_pattern = r'Lora_(\d+) Model name: ([^,]+), Lora_\1 Model hash: ([^,]+), Lora_\1 Strength model: ([^,]+), Lora_\1 Strength clip: ([^,]+)'
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lora_matches = re.findall(lora_pattern, user_comment)
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# If the regular pattern doesn't match, try a more flexible approach
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if not lora_matches:
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# First find all Lora indices
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lora_indices = set(re.findall(r'Lora_(\d+)', user_comment))
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# For each index, extract the information
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for idx in lora_indices:
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lora_info = {}
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# Extract model name
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name_match = re.search(f'Lora_{idx} Model name: ([^,]+)', user_comment)
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if name_match:
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lora_info['name'] = name_match.group(1).strip()
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# Extract model hash
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hash_match = re.search(f'Lora_{idx} Model hash: ([^,]+)', user_comment)
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if hash_match:
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lora_info['hash'] = hash_match.group(1).strip()
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# Extract strength model
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strength_model_match = re.search(f'Lora_{idx} Strength model: ([^,]+)', user_comment)
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if strength_model_match:
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lora_info['strength_model'] = float(strength_model_match.group(1).strip())
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# Extract strength clip
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strength_clip_match = re.search(f'Lora_{idx} Strength clip: ([^,]+)', user_comment)
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if strength_clip_match:
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lora_info['strength_clip'] = float(strength_clip_match.group(1).strip())
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# Only add if we have at least name and hash
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if 'name' in lora_info and 'hash' in lora_info:
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lora_matches.append((idx, lora_info['name'], lora_info['hash'],
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str(lora_info.get('strength_model', 1.0)),
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str(lora_info.get('strength_clip', 1.0))))
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# Process LoRAs
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base_model_counts = {}
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loras = []
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for match in lora_matches:
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if len(match) == 5: # Regular pattern match
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idx, name, hash_value, strength_model, strength_clip = match
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else: # Flexible approach match
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continue # Should not happen now
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# Clean up the values
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name = name.strip()
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if name.endswith('.safetensors'):
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name = name[:-12] # Remove .safetensors extension
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hash_value = hash_value.strip()
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weight = float(strength_model) # Use model strength as weight
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# Initialize lora entry with default values
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lora_entry = {
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'name': name,
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'type': 'lora',
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'weight': weight,
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'existsLocally': False,
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'localPath': None,
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'file_name': name,
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'hash': hash_value,
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'thumbnailUrl': '/loras_static/images/no-preview.png',
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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 info from Civitai by hash if available
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if civitai_client and hash_value:
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try:
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civitai_info = await civitai_client.get_model_by_hash(hash_value)
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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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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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# Get model version ID
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lora_entry['id'] = civitai_info.get('id', '')
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# Get model name and version
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lora_entry['name'] = civitai_info.get('model', {}).get('name', name)
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lora_entry['version'] = civitai_info.get('name', '')
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# Get thumbnail URL
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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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# Get file name and size from Civitai
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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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file_name = model_file.get('name', '')
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lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else name
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lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
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# Update hash to sha256
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new_hash = model_file.get('hashes', {}).get('SHA256', hash_value).lower()
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lora_entry['hash'] = new_hash
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# Check if exists locally with sha256 hash
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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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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_entry['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['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
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else:
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lora_entry['isDeleted'] = True
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except Exception as e:
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logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}")
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loras.append(lora_entry)
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# Extract model information
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model = None
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if 'model' in metadata:
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model = metadata['model']
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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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# Try to extract size information if available
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if 'width' in metadata and 'height' in metadata:
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gen_params['size'] = f"{metadata['width']}x{metadata['height']}"
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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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'from_meta_format': True
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}
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except Exception as e:
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logger.error(f"Error parsing meta format metadata: {e}", exc_info=True)
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return {"error": str(e), "loras": []}
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class RecipeParserFactory:
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"""Factory for creating recipe metadata parsers"""
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@@ -795,5 +1002,7 @@ class RecipeParserFactory:
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return StandardMetadataParser()
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elif A1111MetadataParser().is_metadata_matching(user_comment):
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return A1111MetadataParser()
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elif MetaFormatParser().is_metadata_matching(user_comment):
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return MetaFormatParser()
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else:
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return None
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@@ -95,7 +95,6 @@
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"onSite": false,
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"remixOfId": null
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}
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// more images here
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],
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"downloadUrl": "https://civitai.com/api/download/models/1387174"
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}
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3
refs/meta_format.txt
Normal file
3
refs/meta_format.txt
Normal file
@@ -0,0 +1,3 @@
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In this ethereal masterpiece, metallic sculptures juxtapose effortlessly against a subtle backdrop of misty neutral hues. Exquisite curvatures and geometric shapes converge harmoniously, creating an illuminating realm of polished metallic surfaces. Shimmering copper, gleaming silver, and lustrous gold hues dance in perfect balance, highlighting the intricate play of light and shadow cast upon these celestial forms. A halo of diffused radiance envelops each piece, enhancing their textured depths and metallic brilliance while allowing delicate details to emerge from obscurity. The composition conveys a serene yet mesmerizing atmosphere, as if suspended in a dreamlike limbo between reality and fantasy. The tantalizing interplay of colors within this transcendent realm creates a profound sense of depth and grandeur that invites the viewer into an enchanting voyage through abstract metallic beauty. This captivating artwork evokes emotions of boundless curiosity and reverence reminiscent of the timeless works by artists such as Giorgio de Chirico or Paul Klee, while asserting a unique, modern artistic sensibility. With every observation, a new nuance unfolds, as if a never-ending story waiting to be discovered through the lens of metallic artistry.
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Negative prompt:
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Steps: 25, Sampler: dpmpp_2m_sgm_uniform, Seed: 471889513588087, Model: Fluxmania V5P.safetensors, Model hash: 8ae0583b06, VAE: ae.sft, VAE hash: afc8e28272, Lora_0 Model name: ArtVador I.safetensors, Lora_0 Model hash: 08f7133a58, Lora_0 Strength model: 0.65, Lora_0 Strength clip: 0.65, Lora_1 Model name: Kaoru Yamada.safetensors, Lora_1 Model hash: d4893f7202, Lora_1 Strength model: 0.75, Lora_1 Strength clip: 0.75, Hashes: {"model": "8ae0583b06", "vae": "afc8e28272", "lora:ArtVador I": "08f7133a58", "lora:Kaoru Yamada": "d4893f7202"}
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