alphard/ComfyUI-Lora-Manager
1
0
Fork 0
mirror of https://github.com/willmiao/ComfyUI-Lora-Manager.git synced 2026-06-10 04:49:24 -03:00
Code Issues Packages Projects Releases Wiki Activity

fix(recipe): use resources type field to identify checkpoint instead of modelVersionIds[0]

Browse Source 
When importing a CivitAI image as a recipe, modelVersionIds[0] was blindly used as the checkpoint version ID. This array mixes checkpoints and LoRAs without ordering guarantees, causing LoRAs to be saved as the recipe checkpoint.

Fix by:
1. Removing the modelVersionIds[0] fallback in _download_remote_media
2. Parsing resources entries with type:"model" as the checkpoint
3. Adding model type validation in populate_checkpoint_from_civitai

Also add 2 tests for the new behavior and fix 3 tests whose mocks lacked the required model.type field.
This commit is contained in:
Will Miao
2026-05-28 15:46:33 +08:00
parent 3f6824eef6
commit 34791c2ad7
6 changed files with 203 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

61
py/recipes/parsers/civitai_image.py
View File

@@ -185,8 +185,67 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
# Process standard resources array
if "resources" in metadata and isinstance(metadata["resources"], list):
for resource in metadata["resources"]:
resource_type = resource.get("type", "lora")
# Track resources with type "model" — these are checkpoint models.
# The resources array is the most reliable source for checkpoint
# identification because it has an explicit type field and hash,
# unlike modelVersionIds which is a flat list with no type info.
if resource_type == "model":
checkpoint_entry = {
"id": 0,
"modelId": 0,
"name": resource.get("name", "Unknown Model"),
"version": "",
"type": resource.get("type", "model"),
"existsLocally": False,
"localPath": None,
"file_name": resource.get("name", ""),
"hash": resource.get("hash", "") or "",
"thumbnailUrl": "/loras_static/images/no-preview.png",
"baseModel": "",
"size": 0,
"downloadUrl": "",
"isDeleted": False,
}
# Try to look up base model from the checkpoint hash
if checkpoint_entry["hash"] and metadata_provider:
try:
civitai_info = (
await metadata_provider.get_model_by_hash(
checkpoint_entry["hash"]
)
)
civitai_data, error_msg = (
(civitai_info, None)
if not isinstance(civitai_info, tuple)
else civitai_info
)
if civitai_data and error_msg != "Model not found":
if 'model' in civitai_data and 'name' in civitai_data['model']:
checkpoint_entry['name'] = civitai_data['model']['name']
checkpoint_entry['id'] = civitai_data.get('id', 0)
checkpoint_entry['modelId'] = civitai_data.get('modelId', 0)
if 'name' in civitai_data:
checkpoint_entry['version'] = civitai_data['name']
base_model = civitai_data.get('baseModel', '')
if base_model:
checkpoint_entry['baseModel'] = base_model
if not result['base_model']:
result['base_model'] = base_model
except Exception as e:
logger.error(
f"Error fetching checkpoint info for hash "
f"{checkpoint_entry['hash']}: {e}"
)
if result["model"] is None:
result["model"] = checkpoint_entry
continue
# Modified to process resources without a type field as potential LoRAs
if resource.get("type", "lora") == "lora":
if resource_type == "lora":
lora_hash = resource.get("hash", "")
# Try to get hash from the hashes field if not present in resource