mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-08-20 12:31:27 -03:00
Fix recipe parsing for metadata-free local LoRAs (#1065)
* fix(recipes): resolve metadata-free local LoRAs * fix(recipes): prioritize LoRA hashes over names
This commit is contained in:
Aaalice
+95
-68
@@ -31,79 +31,15 @@ class ComfyMetadataParser(RecipeMetadataParser):
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metadata_provider = await get_default_metadata_provider()
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data = json.loads(user_comment)
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loras = []
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# Find all LoraLoader nodes
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lora_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'LoraLoader'}
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# Process each LoraLoader node
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for node_id, node in lora_nodes.items():
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if 'inputs' not in node or 'lora_name' not in node['inputs']:
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continue
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lora_name = node['inputs'].get('lora_name', '')
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# Parse the URN to extract model ID and version ID
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# Format: "urn:air:sdxl:lora:civitai:1107767@1253442"
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lora_id_match = re.search(r'civitai:(\d+)@(\d+)', lora_name)
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if not lora_id_match:
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continue
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model_id = lora_id_match.group(1)
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model_version_id = lora_id_match.group(2)
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# Get strength from node inputs
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weight = node['inputs'].get('strength_model', 1.0)
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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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'modelId': model_id,
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'name': f"Lora {model_id}", # Default name
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'version': '',
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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': '',
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'hash': '',
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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 additional info from Civitai if metadata provider is available
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if metadata_provider:
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try:
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civitai_info_tuple = await metadata_provider.get_model_version_info(model_version_id)
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# Populate lora entry with Civitai info
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populated_entry = await self.populate_lora_from_civitai(
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lora_entry,
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civitai_info_tuple,
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recipe_scanner
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)
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if populated_entry is None:
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continue # Skip invalid LoRA types
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lora_entry = populated_entry
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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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loras.append(lora_entry)
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# Find checkpoint info
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checkpoint_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'CheckpointLoaderSimple'}
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checkpoint = None
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checkpoint_id = None
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checkpoint_version_id = None
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if checkpoint_nodes:
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# Get the first checkpoint node
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checkpoint_node = next(iter(checkpoint_nodes.values()))
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if 'inputs' in checkpoint_node and 'ckpt_name' in checkpoint_node['inputs']:
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checkpoint_name = checkpoint_node['inputs']['ckpt_name']
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# Parse checkpoint URN
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checkpoint_match = re.search(r'civitai:(\d+)@(\d+)', checkpoint_name)
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if checkpoint_match:
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checkpoint_id = checkpoint_match.group(1)
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@@ -115,16 +51,107 @@ class ComfyMetadataParser(RecipeMetadataParser):
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'version': '',
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'type': 'checkpoint'
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}
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# Get additional checkpoint info from Civitai
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if metadata_provider:
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try:
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civitai_info_tuple = await metadata_provider.get_model_version_info(checkpoint_version_id)
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civitai_info, _ = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
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# Populate checkpoint with Civitai info
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checkpoint = await self.populate_checkpoint_from_civitai(checkpoint, civitai_info)
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except Exception as e:
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logger.error(f"Error fetching Civitai info for checkpoint: {e}")
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recipe_base_model = checkpoint.get('baseModel') if checkpoint else None
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loras = []
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lora_candidates = []
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for node in data.values():
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if not isinstance(node, dict):
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continue
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inputs = node.get('inputs')
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if not isinstance(inputs, dict):
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continue
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if node.get('class_type') == 'LoraLoader':
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lora_name = inputs.get('lora_name', '')
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if isinstance(lora_name, str) and lora_name:
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lora_candidates.append((lora_name, inputs.get('strength_model', 1.0)))
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continue
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if node.get('class_type') != 'LoraLoaderLM':
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continue
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loras_data = inputs.get('loras', [])
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if isinstance(loras_data, dict):
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loras_data = loras_data.get('__value__', [])
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if isinstance(loras_data, list) and len(loras_data) == 1 and isinstance(loras_data[0], list):
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loras_data = loras_data[0]
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if not isinstance(loras_data, list):
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continue
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for lora in loras_data:
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if not isinstance(lora, dict) or not lora.get('active', False) or lora.get('_isDummy', False):
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continue
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lora_name = lora.get('name', '')
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if isinstance(lora_name, str) and lora_name:
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lora_candidates.append((lora_name, lora.get('strength', 1.0)))
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for lora_name, weight in lora_candidates:
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if isinstance(weight, str):
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try:
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weight = float(weight)
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except ValueError:
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weight = 1.0
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lora_id_match = re.search(r'civitai:(\d+)@(\d+)', lora_name)
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if lora_id_match:
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model_id = lora_id_match.group(1)
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model_version_id = lora_id_match.group(2)
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entry_name = f"Lora {model_id}"
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else:
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model_id = 0
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model_version_id = 0
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entry_name = re.split(r'[\\/]', lora_name)[-1]
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entry_name = re.sub(r'\.[^.]+$', '', entry_name)
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lora_entry = {
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'id': model_version_id,
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'modelId': model_id,
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'name': entry_name,
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'version': '',
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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': entry_name,
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'hash': '',
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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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if lora_id_match:
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if metadata_provider:
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try:
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civitai_info_tuple = await metadata_provider.get_model_version_info(model_version_id)
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populated_entry = await self.populate_lora_from_civitai(
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lora_entry,
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civitai_info_tuple,
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recipe_scanner
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)
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if populated_entry is None:
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continue
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lora_entry = populated_entry
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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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else:
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if not recipe_scanner:
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continue
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local_lora = await recipe_scanner.get_local_lora(lora_name, recipe_base_model)
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if not local_lora:
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continue
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lora_entry = self.populate_lora_from_local(lora_entry, local_lora)
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loras.append(lora_entry)
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# Extract generation parameters
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gen_params = {}
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