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https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-21 21:22:11 -03:00
feat: enhance A1111MetadataParser to improve metadata extraction and parsing logic. https://github.com/willmiao/ComfyUI-Lora-Manager/issues/148
This commit is contained in:
Will Miao
@@ -452,7 +452,7 @@ class A1111MetadataParser(RecipeMetadataParser):
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METADATA_MARKER = r'Lora hashes:'
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LORA_PATTERN = r'<lora:([^:]+):([^>]+)>'
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LORA_HASH_PATTERN = r'([^:]+): ([a-f0-9]+)'
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LORA_HASH_PATTERN = r'([^:]+):\s*([a-fA-F0-9]+)'
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def is_metadata_matching(self, user_comment: str) -> bool:
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"""Check if the user comment matches the A1111 metadata format"""
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@@ -461,51 +461,103 @@ class A1111MetadataParser(RecipeMetadataParser):
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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 A1111 metadata format"""
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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 with default empty values
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metadata = {"prompt": "", "loras": []}
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# Initialize metadata
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metadata = {"prompt": prompt, "loras": []}
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# Extract negative prompt and parameters
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if len(parts) > 1:
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negative_and_params = parts[1]
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# Check if the user_comment contains prompt and negative prompt
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if 'Negative prompt:' in user_comment:
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# Extract prompt and negative prompt
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parts = user_comment.split('Negative prompt:', 1)
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metadata["prompt"] = parts[0].strip()
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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 negative prompt and parameters
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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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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 parameters from this section
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self._extract_parameters(params_section, metadata)
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else:
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# No prompt/negative prompt - extract parameters directly
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self._extract_parameters(user_comment, metadata)
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# Extract LoRA information from prompt
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# Extract LoRA information from prompt if available
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lora_weights = {}
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lora_matches = re.findall(self.LORA_PATTERN, prompt)
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for lora_name, weights in lora_matches:
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# Take only the first strength value (before the colon)
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weight = weights.split(':')[0]
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lora_weights[lora_name.strip()] = float(weight.strip())
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# Remove LoRA patterns from prompt
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metadata["prompt"] = re.sub(self.LORA_PATTERN, '', prompt).strip()
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if metadata["prompt"]:
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lora_matches = re.findall(self.LORA_PATTERN, metadata["prompt"])
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for lora_name, weights in lora_matches:
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# Take only the first strength value (before the colon)
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weight = weights.split(':')[0]
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lora_weights[lora_name.strip()] = float(weight.strip())
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# Remove LoRA patterns from prompt
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metadata["prompt"] = re.sub(self.LORA_PATTERN, '', metadata["prompt"]).strip()
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# Extract LoRA hashes
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lora_hashes = {}
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if 'Lora hashes:' in user_comment:
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# Get the LoRA hashes section
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lora_hash_section = user_comment.split('Lora hashes:', 1)[1].strip()
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# Handle various format possibilities
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if lora_hash_section.startswith('"'):
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lora_hash_section = lora_hash_section[1:].split('"', 1)[0]
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hash_matches = re.findall(self.LORA_HASH_PATTERN, lora_hash_section)
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for lora_name, hash_value in hash_matches:
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# Remove any leading comma and space from lora name
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clean_name = lora_name.strip().lstrip(',').strip()
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lora_hashes[clean_name] = hash_value.strip()
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# Extract content within quotes
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quote_match = re.match(r'"([^"]+)"', lora_hash_section)
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if quote_match:
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lora_hash_section = quote_match.group(1)
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# Split by commas and parse each LoRA entry
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lora_entries = []
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current_entry = ""
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for part in lora_hash_section.split(','):
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# Check if this part contains a colon (indicating a complete entry)
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if ':' in part:
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if current_entry:
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lora_entries.append(current_entry.strip())
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current_entry = part.strip()
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else:
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# This is probably a continuation of the previous entry
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current_entry += ',' + part
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# Add the last entry if it exists
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if current_entry:
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lora_entries.append(current_entry.strip())
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# Process each entry
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for entry in lora_entries:
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# Split at the colon to get name and hash
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if ':' in entry:
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lora_name, hash_value = entry.split(':', 1)
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# Clean the values
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lora_name = lora_name.strip()
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hash_value = hash_value.strip()
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# Store in our dictionary
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lora_hashes[lora_name] = hash_value
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# Alternative backup method using regex if the above parsing fails
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if not lora_hashes:
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if 'Lora hashes:' in user_comment:
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lora_hash_section = user_comment.split('Lora hashes:', 1)[1].strip()
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if lora_hash_section.startswith('"'):
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# Extract content within quotes if present
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quote_match = re.match(r'"([^"]+)"', lora_hash_section)
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if quote_match:
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lora_hash_section = quote_match.group(1)
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# Use regex to find all name:hash pairs
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hash_matches = re.findall(self.LORA_HASH_PATTERN, lora_hash_section)
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for lora_name, hash_value in hash_matches:
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# Clean up name by removing any leading comma and spaces
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clean_name = lora_name.strip().lstrip(',').strip()
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lora_hashes[clean_name] = hash_value.strip()
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# Process LoRAs and collect base models
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base_model_counts = {}
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@@ -523,7 +575,7 @@ class A1111MetadataParser(RecipeMetadataParser):
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'existsLocally': False,
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'localPath': None,
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'file_name': lora_name,
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'hash': hash_value,
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'hash': hash_value.lower(), # Ensure hash is lowercase
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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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@@ -573,6 +625,15 @@ class A1111MetadataParser(RecipeMetadataParser):
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except Exception as e:
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logger.error(f"Error parsing A1111 metadata: {e}", exc_info=True)
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return {"error": str(e), "loras": []}
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def _extract_parameters(self, text: str, metadata: Dict[str, Any]) -> None:
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"""Extract parameters from text section and populate metadata dict"""
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# Extract key-value parameters (Steps, Sampler, CFG scale, etc.)
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param_pattern = r'([A-Za-z][A-Za-z0-9 _]+): ([^,]+)(?:,|$)'
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params = re.findall(param_pattern, text)
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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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class ComfyMetadataParser(RecipeMetadataParser):
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