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
+201
-61
@@ -362,68 +362,208 @@ class AutomaticMetadataParser(RecipeMetadataParser):
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checkpoint = checkpoint_entry
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# If no LoRAs from Civitai resources or to supplement, extract from metadata["hashes"]
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if not loras or len(loras) == 0:
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# Extract lora weights from extranet tags in prompt (for later use)
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lora_weights = {}
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lora_matches = re.findall(self.EXTRANETS_REGEX, prompt)
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for lora_type, lora_name, lora_weight in lora_matches:
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key = f"{lora_type}:{lora_name}"
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lora_weights[key] = round(float(lora_weight), 2)
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# Use hashes from metadata as the primary source
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if metadata.get("hashes"):
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for hash_key, lora_hash in metadata.get("hashes", {}).items():
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# Only process lora or hypernet types
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if not hash_key.startswith(("lora:", "hypernet:")):
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def normalize_lora_name(name, basename=False):
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normalized = str(name or '').replace('\\', '/')
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if normalized.casefold().endswith('.safetensors'):
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normalized = normalized[:-12]
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if basename:
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normalized = normalized.rsplit('/', 1)[-1]
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return normalized.casefold()
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def get_version_id(lora):
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version_id = lora.get('id')
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if version_id in (None, '', 0, '0'):
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version_id = lora.get('modelVersionId')
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if version_id in (None, '', 0, '0'):
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return None
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return str(version_id)
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prompt_loras = {}
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for match in re.findall(self.EXTRANETS_REGEX, prompt):
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lora_type, lora_name, _ = match
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prompt_loras[(lora_type, normalize_lora_name(lora_name))] = match
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prompt_by_basename = {}
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for lora_type, lora_name, lora_weight in prompt_loras.values():
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key = (lora_type, normalize_lora_name(lora_name, True))
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prompt_by_basename.setdefault(key, []).append((lora_name, round(float(lora_weight), 2)))
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hash_basenames = {
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(hash_key.split(':', 1)[0], normalize_lora_name(hash_key.split(':', 1)[1], True))
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for hash_key, hash_value in metadata.get("hashes", {}).items()
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if hash_value and hash_key.startswith(("lora:", "hypernet:"))
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}
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recipe_base_model = checkpoint.get("baseModel") if checkpoint else None
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if not recipe_base_model and len(base_model_counts) == 1:
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recipe_base_model = next(iter(base_model_counts))
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resource_lora_count = len(loras)
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def make_lora_entry(lora_type, lora_name, weight, lora_hash=''):
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return {
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'name': lora_name,
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'type': lora_type,
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'weight': weight,
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'hash': lora_hash,
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'existsLocally': False,
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'localPath': None,
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'file_name': lora_name,
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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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def merge_or_append_civitai(civitai_entry, preserve_existing_weight=False):
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civitai_id = get_version_id(civitai_entry)
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civitai_hash = (civitai_entry.get('hash') or '').lower()
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for index, existing in enumerate(loras):
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existing_id = get_version_id(existing)
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existing_hash = (existing.get('hash') or '').lower()
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if not (
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(civitai_id and existing_id == civitai_id)
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or (civitai_hash and existing_hash == civitai_hash)
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):
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continue
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if preserve_existing_weight:
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civitai_entry['weight'] = existing.get('weight', civitai_entry['weight'])
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existing_base = existing.get('baseModel')
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if not civitai_entry.get('baseModel'):
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civitai_entry['baseModel'] = existing_base or ''
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elif existing_base:
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remaining = base_model_counts.get(existing_base, 0) - 1
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if remaining > 0:
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base_model_counts[existing_base] = remaining
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else:
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base_model_counts.pop(existing_base, None)
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loras[index] = civitai_entry
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return
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loras.append(civitai_entry)
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def merge_or_append_local(local_entry):
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local_id = get_version_id(local_entry)
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local_hash = (local_entry.get('hash') or '').lower()
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for existing in loras:
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existing_id = get_version_id(existing)
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existing_hash = (existing.get('hash') or '').lower()
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if not (
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(local_id and existing_id == local_id)
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or (local_hash and existing_hash == local_hash)
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):
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continue
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existing['weight'] = local_entry['weight']
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existing['hash'] = local_entry['hash']
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existing['file_name'] = local_entry['file_name']
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existing['existsLocally'] = True
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existing['localPath'] = local_entry['localPath']
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existing['size'] = local_entry['size']
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existing['isDeleted'] = False
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if not existing.get('modelId') and local_entry.get('modelId'):
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existing['modelId'] = local_entry['modelId']
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if not existing.get('baseModel') and local_entry.get('baseModel'):
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existing['baseModel'] = local_entry['baseModel']
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base_model_counts[local_entry['baseModel']] = base_model_counts.get(local_entry['baseModel'], 0) + 1
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thumbnail_url = local_entry.get('thumbnailUrl')
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if thumbnail_url and not thumbnail_url.endswith('/images/no-preview.png'):
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existing['thumbnailUrl'] = thumbnail_url
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return
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if local_entry.get('baseModel'):
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base_model = local_entry['baseModel']
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base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
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loras.append(local_entry)
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resolved_prompt_basenames = set()
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queried_local_basenames = set()
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for lora_type, lora_name, lora_weight in prompt_loras.values():
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weight = round(float(lora_weight), 2)
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basename_key = (lora_type, normalize_lora_name(lora_name, True))
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matching_resources = [
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lora
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for lora in loras[:resource_lora_count]
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if lora.get('file_name')
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and normalize_lora_name(lora['file_name'], True) == basename_key[1]
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and (
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(lora_type == 'hypernet' and str(lora.get('type', '')).casefold() in ('hypernet', 'hypernetwork'))
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or (lora_type == 'lora' and str(lora.get('type', '')).casefold() not in ('hypernet', 'hypernetwork'))
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)
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]
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if len(prompt_by_basename[basename_key]) == 1 and len(matching_resources) == 1:
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matching_resources[0]['weight'] = weight
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if basename_key not in hash_basenames:
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resolved_prompt_basenames.add(basename_key)
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continue
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if basename_key in hash_basenames:
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continue
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if not recipe_scanner or lora_type != 'lora':
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continue
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queried_local_basenames.add(basename_key)
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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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local_entry = self.populate_lora_from_local(
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make_lora_entry(lora_type, lora_name, weight),
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local_lora,
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)
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merge_or_append_local(local_entry)
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resolved_prompt_basenames.add(basename_key)
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for hash_key, lora_hash in metadata.get("hashes", {}).items():
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if not hash_key.startswith(("lora:", "hypernet:")):
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continue
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lora_type, lora_name = hash_key.split(':', 1)
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basename_key = (lora_type, normalize_lora_name(lora_name, True))
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if basename_key in resolved_prompt_basenames:
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continue
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prompt_entries = prompt_by_basename.get(basename_key, [])
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weight = prompt_entries[0][1] if len(prompt_entries) == 1 else 1.0
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lora_entry = make_lora_entry(lora_type, lora_name, weight, lora_hash)
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if lora_hash and recipe_scanner and lora_type == 'lora':
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local_lora = await recipe_scanner.get_local_lora_by_hash(lora_hash)
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if local_lora:
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local_entry = self.populate_lora_from_local(lora_entry, local_lora)
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merge_or_append_local(local_entry)
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continue
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hash_resolved = False
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if lora_hash and metadata_provider:
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try:
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civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
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populated_entry = await self.populate_lora_from_civitai(
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lora_entry,
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civitai_info,
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recipe_scanner,
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base_model_counts,
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lora_hash,
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)
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if populated_entry is None:
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continue
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# Skip entries without a hash value — they can't be
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# resolved via CivitAI and would only produce a
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# useless "Deleted" entry in the recipe.
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if not lora_hash:
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continue
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lora_type, lora_name = hash_key.split(':', 1)
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# Get weight from extranet tags if available, else default to 1.0
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weight = lora_weights.get(hash_key, 1.0)
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# Initialize lora entry
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lora_entry = {
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'name': lora_name,
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'type': lora_type, # 'lora' or 'hypernet'
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'weight': weight,
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'hash': lora_hash,
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'existsLocally': False,
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'localPath': None,
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'file_name': lora_name,
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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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# Try to get info from Civitai
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if metadata_provider:
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try:
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civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
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populated_entry = await self.populate_lora_from_civitai(
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lora_entry,
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civitai_info,
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recipe_scanner,
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base_model_counts,
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lora_hash
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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 {lora_name}: {e}")
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loras.append(lora_entry)
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lora_entry = populated_entry
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hash_resolved = not lora_entry.get('isDeleted')
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except Exception as e:
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logger.error(f"Error fetching Civitai info for LoRA {lora_name}: {e}")
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if hash_resolved:
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merge_or_append_civitai(lora_entry, preserve_existing_weight=not prompt_entries)
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continue
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if recipe_scanner and lora_type == 'lora' and basename_key not in queried_local_basenames:
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local_lora = await recipe_scanner.get_local_lora(lora_name, recipe_base_model)
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if local_lora:
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local_entry = self.populate_lora_from_local(lora_entry, local_lora)
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merge_or_append_local(local_entry)
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continue
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if lora_hash and not resource_lora_count:
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loras.append(lora_entry)
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# Try to get base model from resources or make educated guess
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base_model = None
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