mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-21 21:22:11 -03:00
feat: Enhance Civitai image metadata parser to prevent duplicate LoRAs
This commit is contained in:
Will Miao
@@ -50,6 +50,9 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
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'from_civitai_image': True
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}
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# Track already added LoRAs to prevent duplicates
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added_loras = {} # key: model_version_id or hash, value: index in result["loras"]
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# Extract prompt and negative prompt
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if "prompt" in metadata:
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result["gen_params"]["prompt"] = metadata["prompt"]
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@@ -96,11 +99,17 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
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for resource in metadata["resources"]:
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# Modified to process resources without a type field as potential LoRAs
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if resource.get("type", "lora") == "lora":
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lora_hash = resource.get("hash", "")
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# Skip if we've already added this LoRA by hash
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if lora_hash and lora_hash in added_loras:
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continue
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lora_entry = {
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'name': resource.get("name", "Unknown LoRA"),
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'type': "lora",
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'weight': float(resource.get("weight", 1.0)),
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'hash': resource.get("hash", ""),
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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': resource.get("name", "Unknown"),
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@@ -114,7 +123,6 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
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# Try to get info from Civitai if hash is available
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if lora_entry['hash'] and civitai_client:
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try:
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lora_hash = lora_entry['hash']
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civitai_info = await civitai_client.get_model_by_hash(lora_hash)
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populated_entry = await self.populate_lora_from_civitai(
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@@ -129,43 +137,124 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
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continue # Skip invalid LoRA types
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lora_entry = populated_entry
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# If we have a version ID from Civitai, track it for deduplication
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if 'id' in lora_entry and lora_entry['id']:
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added_loras[str(lora_entry['id'])] = len(result["loras"])
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except Exception as e:
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logger.error(f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}")
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# Track by hash if we have it
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if lora_hash:
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added_loras[lora_hash] = len(result["loras"])
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result["loras"].append(lora_entry)
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# Process civitaiResources array
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if "civitaiResources" in metadata and isinstance(metadata["civitaiResources"], list):
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for resource in metadata["civitaiResources"]:
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# Modified to process resources without a type field as potential LoRAs
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if resource.get("type") in ["lora", "lycoris"] or "type" not in resource:
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# Initialize lora entry with the same structure as in automatic.py
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lora_entry = {
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'id': resource.get("modelVersionId", 0),
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'modelId': resource.get("modelId", 0),
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'name': resource.get("modelName", "Unknown LoRA"),
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'version': resource.get("modelVersionName", ""),
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'type': resource.get("type", "lora"),
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'weight': round(float(resource.get("weight", 1.0)), 2),
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'existsLocally': False,
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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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# Skip resources that aren't LoRAs or LyCORIS
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if resource.get("type") not in ["lora", "lycoris"] and "type" not in resource:
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continue
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# Try to get info from Civitai if modelVersionId is available
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if resource.get('modelVersionId') and civitai_client:
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try:
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version_id = str(resource.get('modelVersionId'))
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# Use get_model_version_info instead of get_model_version
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civitai_info, error = await civitai_client.get_model_version_info(version_id)
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if error:
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logger.warning(f"Error getting model version info: {error}")
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continue
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# Get unique identifier for deduplication
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version_id = str(resource.get("modelVersionId", ""))
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# Skip if we've already added this LoRA
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if version_id and version_id in added_loras:
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continue
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# Initialize lora entry
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lora_entry = {
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'id': resource.get("modelVersionId", 0),
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'modelId': resource.get("modelId", 0),
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'name': resource.get("modelName", "Unknown LoRA"),
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'version': resource.get("modelVersionName", ""),
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'type': resource.get("type", "lora"),
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'weight': round(float(resource.get("weight", 1.0)), 2),
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'existsLocally': False,
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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 if modelVersionId is available
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if version_id and civitai_client:
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try:
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# Use get_model_version_info instead of get_model_version
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civitai_info, error = await civitai_client.get_model_version_info(version_id)
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if error:
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logger.warning(f"Error getting model version info: {error}")
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continue
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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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)
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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 model version {version_id}: {e}")
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# Track this LoRA in our deduplication dict
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if version_id:
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added_loras[version_id] = len(result["loras"])
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result["loras"].append(lora_entry)
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# Process additionalResources array
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if "additionalResources" in metadata and isinstance(metadata["additionalResources"], list):
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for resource in metadata["additionalResources"]:
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# Skip resources that aren't LoRAs or LyCORIS
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if resource.get("type") not in ["lora", "lycoris"] and "type" not in resource:
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continue
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lora_type = resource.get("type", "lora")
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name = resource.get("name", "")
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# Extract ID from URN format if available
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version_id = None
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if name and "civitai:" in name:
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parts = name.split("@")
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if len(parts) > 1:
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version_id = parts[1]
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# Skip if we've already added this LoRA
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if version_id in added_loras:
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continue
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lora_entry = {
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'name': name,
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'type': lora_type,
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'weight': float(resource.get("strength", 1.0)),
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'hash': "",
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'existsLocally': False,
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'localPath': None,
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'file_name': 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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# If we have a version ID and civitai client, try to get more info
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if version_id and civitai_client:
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try:
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# Use get_model_version_info with the version ID
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civitai_info, error = await civitai_client.get_model_version_info(version_id)
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if error:
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logger.warning(f"Error getting model version info: {error}")
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else:
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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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@@ -177,65 +266,14 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
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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 model version {resource.get('modelVersionId')}: {e}")
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result["loras"].append(lora_entry)
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# Process additionalResources array
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if "additionalResources" in metadata and isinstance(metadata["additionalResources"], list):
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for resource in metadata["additionalResources"]:
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# Modified to process resources without a type field as potential LoRAs
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if resource.get("type") in ["lora", "lycoris"] or "type" not in resource:
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lora_type = resource.get("type", "lora")
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name = resource.get("name", "")
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# Extract ID from URN format if available
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model_id = None
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if name and "civitai:" in name:
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parts = name.split("@")
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if len(parts) > 1:
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model_id = parts[1]
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lora_entry = {
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'name': name,
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'type': lora_type,
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'weight': float(resource.get("strength", 1.0)),
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'hash': "",
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'existsLocally': False,
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'localPath': None,
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'file_name': 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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# If we have a model ID and civitai client, try to get more info
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if model_id and civitai_client:
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try:
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# Use get_model_version_info with the model ID
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civitai_info, error = await civitai_client.get_model_version_info(model_id)
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if error:
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logger.warning(f"Error getting model version info: {error}")
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else:
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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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)
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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 model ID {model_id}: {e}")
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result["loras"].append(lora_entry)
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# Track this LoRA for deduplication
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if version_id:
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added_loras[version_id] = len(result["loras"])
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except Exception as e:
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logger.error(f"Error fetching Civitai info for model ID {version_id}: {e}")
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result["loras"].append(lora_entry)
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# If base model wasn't found earlier, use the most common one from LoRAs
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if not result["base_model"] and base_model_counts:
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