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feat(metadata): add source tracking for SQLite metadata and implement Civitai API metadata validation

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Will Miao
2025-09-10 11:20:58 +08:00
parent 6fd74952b7
commit 68f8871403
2 changed files with 33 additions and 15 deletions
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42
py/utils/routes_common.py
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@@ -37,6 +37,18 @@ class ModelRouteUtils:
local_metadata['from_civitai'] = False
await MetadataManager.save_metadata(metadata_path, local_metadata)
@staticmethod
def is_civitai_api_metadata(meta: dict) -> bool:
"""
Determine if the given civitai metadata is from the civitai API.
Returns True if both 'files' and 'images' exist and are non-empty.
"""
if not isinstance(meta, dict):
return False
files = meta.get('files')
images = meta.get('images')
return bool(files) and bool(images)
@staticmethod
async def update_model_metadata(metadata_path: str, local_metadata: Dict,
civitai_metadata: Dict, metadata_provider=None) -> None:
@@ -44,21 +56,25 @@ class ModelRouteUtils:
# Save existing trainedWords and customImages if they exist
existing_civitai = local_metadata.get('civitai') or {} # Use empty dict if None
# Create a new civitai metadata by updating existing with new
merged_civitai = existing_civitai.copy()
merged_civitai.update(civitai_metadata)
# Check if we should skip the update to avoid overwriting richer data
if civitai_metadata.get('source') == 'archive_db' and ModelRouteUtils.is_civitai_api_metadata(existing_civitai):
logger.info(f"Skip civitai update for {local_metadata.get('model_name', '')}: {existing_civitai.get('name', '')}")
else:
# Create a new civitai metadata by updating existing with new
merged_civitai = existing_civitai.copy()
merged_civitai.update(civitai_metadata)
# Special handling for trainedWords - ensure we don't lose any existing trained words
if 'trainedWords' in existing_civitai:
existing_trained_words = existing_civitai.get('trainedWords', [])
new_trained_words = civitai_metadata.get('trainedWords', [])
# Use a set to combine words without duplicates, then convert back to list
merged_trained_words = list(set(existing_trained_words + new_trained_words))
merged_civitai['trainedWords'] = merged_trained_words
# Special handling for trainedWords - ensure we don't lose any existing trained words
if 'trainedWords' in existing_civitai:
existing_trained_words = existing_civitai.get('trainedWords', [])
new_trained_words = civitai_metadata.get('trainedWords', [])
# Use a set to combine words without duplicates, then convert back to list
merged_trained_words = list(set(existing_trained_words + new_trained_words))
merged_civitai['trainedWords'] = merged_trained_words
# Update local metadata with merged civitai data
local_metadata['civitai'] = merged_civitai
local_metadata['from_civitai'] = True
# Update local metadata with merged civitai data
local_metadata['civitai'] = merged_civitai
local_metadata['from_civitai'] = True
# Update model name if available
if 'model' in civitai_metadata: