Downloads failed with "No suitable file found in metadata" for models whose
only file uses newer CivitAI file types (e.g. 'Enhancement LoRA' for
Anima/AIR image-editing LoRAs) because the primary-file allowlist only
covered legacy types.
- unify the weights-type allowlist as MODEL_WEIGHT_FILE_TYPES
(py/utils/constants.py) and apply it across download, recipe and
metadata-refresh lookups
- mirror CivitAI's getPrimaryFile() semantics: prefer weights-type primary,
fall back to weights files, then trust CivitAI's primary flag (excluding
non-downloadable artifacts like Config/Archive/Workflow)
- mirror the allowlist in the frontend via shared isModelWeightFile() helper
- add regression tests for the Enhancement LoRA primary-file download,
primary-flag fallback and weights-over-non-weights-primary preference
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.
- Import rewrite_preview_url utility for optimized image URL handling
- Update thumbnail URL processing for both LoRA and checkpoint entries to use rewritten URLs
- Expand checkpoint metadata with modelId, file size, SHA256 hash, and file name
- Improve error handling and data validation for Civitai API responses
- Maintain backward compatibility with existing data structures
- Implemented the base class `RecipeMetadataParser` for parsing recipe metadata from user comments.
- Created a factory class `RecipeParserFactory` to instantiate appropriate parser based on user comment content.
- Developed multiple parser classes: `ComfyMetadataParser`, `AutomaticMetadataParser`, `MetaFormatParser`, and `RecipeFormatParser` to handle different metadata formats.
- Introduced constants for generation parameters and valid LoRA types.
- Enhanced error handling and logging throughout the parsing process.
- Added functionality to populate LoRA and checkpoint information from Civitai API responses.
- Structured the output of parsed metadata to include prompts, LoRAs, generation parameters, and model information.
Will Miao