Add _normalize_checkpoint_entry method to handle legacy and malformed checkpoint data by:
- Converting string entries to structured dict format
- Handling single-element lists/tuples recursively
- Dropping invalid entries with appropriate warnings
- Maintaining backward compatibility while improving data consistency
Add test case to verify string checkpoint conversion works correctly.
- Add token parsing to support include/exclude search terms using "-" prefix
- Implement token-based matching logic for relative path searches
- Improve search result sorting by prioritizing prefix matches and match position
- Add frontend test for multi-token highlighting with exclusion support
- Add checkpoint hash parameter parsing to backend routes
- Implement checkpoint hash filtering in frontend API client
- Add click navigation from recipe modal to checkpoints page
- Update checkpoint items to use pointer cursor for better UX
Checkpoint items in recipe modal are now clickable and will navigate to the checkpoints page with appropriate hash filtering applied. This improves user workflow when wanting to view checkpoint details from recipes.
- Add CheckpointScanner dependency to RecipeScanner singleton
- Implement checkpoint enrichment in recipe data processing
- Add _enrich_checkpoint_entry method to enhance checkpoint metadata
- Update recipe formatting to include checkpoint information
- Extend get_instance, __new__, and __init__ methods to support checkpoint scanner
- Add _get_checkpoint_from_version_index method for cache lookup
This enables recipe scanner to handle checkpoint models alongside existing LoRA support, providing complete model metadata for recipes.
- Extract checkpoint entry from multiple metadata locations using helper method
- Sanitize checkpoint metadata by removing transient/local-only fields
- Remove checkpoint duplication from generation parameters to store only at top level
- Update frontend to properly populate checkpoint metadata during import
- Add tests for new checkpoint handling functionality
This ensures consistent checkpoint metadata structure and prevents data duplication across different storage locations.
Add _normalize_preview_url method to ensure preview URLs are properly formatted for browser access. The method handles absolute paths by converting them to static URLs via config.get_preview_static_url, while preserving API paths and other valid URLs. This ensures consistent preview image display across different URL formats.
Update _enrich_lora_entry to apply URL normalization to preview URLs obtained from both hash-based lookups and version entries. Add comprehensive test coverage for absolute path normalization scenarios.
Add metadata service integration to automatically resolve base model information from checkpoint metadata during recipe import. This replaces the previous approach of relying solely on request parameters and provides more accurate base model information.
- Add _resolve_base_model_from_checkpoint method to fetch base model from metadata provider
- Update recipe import logic to use resolved base model when available
- Add comprehensive tests for base model resolution with fallback behavior
- Remove debug print statement from import parameters
Add _get_lora_from_version_index method to fetch cached LoRA entries by modelVersionId when hash is unavailable. This improves LoRA enrichment by using version index as fallback when hash is missing, ensuring proper library status, file paths, and preview URLs are set even without hash values.
Update test suite to include version_index in stub cache and add test coverage for version-based lookup functionality.
Add support for importing recipes from remote sources by:
- Adding import_remote_recipe endpoint to RecipeHandlerSet
- Injecting downloader_factory and civitai_client_getter dependencies
- Implementing image download and resource parsing logic
- Supporting Civitai resource payloads with checkpoints and LoRAs
- Adding required imports for regex and temporary file handling
This enables users to import recipes directly from external sources like Civitai without manual file downloads.
- Add auto_organize_exclusions to settings handler proxy keys
- Refactor model file service to handle exclusions relative to model roots
- Improve auto-organize progress reporting for empty operations
- Fix exclusion pattern matching to consider relative paths within model roots
- Ensure proper validation when no model roots are configured
- Add comprehensive cleanup reporting for empty auto-organize operations
- 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
- Track when Civitai API returns "Model not found" for default provider
- Use dedicated flag instead of error string comparison for deletion detection
- Ensure archive-sourced models don't get marked as deleted
- Add test coverage for archive source deletion flag behavior
- Fix deletion flag logic to properly handle provider fallback scenarios
- Add optional main_extension parameter to delete_model_artifacts function
- Extract file extension from model filename to handle different file types
- Update model scanner to pass file extension when deleting models
- Add test case for GGUF file deletion to ensure proper cleanup
- Maintain backward compatibility with existing safetensors models
This change allows the model lifecycle service to properly delete GGUF model files along with their associated metadata and preview files, expanding support beyond just safetensors format.
Add ModelUpdateService dependency to ModelLifecycleService to enable synchronization during model deletion. The service is now passed through BaseModelRoutes initialization and used in delete_model to trigger updates when a model is removed. This ensures external systems stay in sync with local model state changes.
Key changes:
- Inject update_service into ModelLifecycleService constructor
- Extract model ID from metadata during deletion
- Call update service sync method after successful deletion
- Add proper type hints and TYPE_CHECKING imports
- Add model_types parameter to ModelListingHandler to support filtering by model type
- Implement get_model_types endpoint in ModelQueryHandler to retrieve available model types
- Register new /api/lm/{prefix}/model-types route for model type queries
- Extend BaseModelService to handle model type filtering in queries
- Support both model_type and civitai_model_type query parameters for backward compatibility
This enables users to filter models by specific types, improving model discovery and organization capabilities.
Add new "updateFlags" section to settings navigation and implement update flag strategy configuration. The strategy allows users to choose when update badges appear:
- Match updates by base model (only show when new release shares same base model)
- Flag any available update (show whenever newer version exists)
Includes translations for English, German, Spanish, and French locales.
Add support for configurable update flag strategy with new "same_base" mode that considers base model versions when determining update availability. The strategy is controlled by the "update_flag_strategy" setting.
When strategy is set to "same_base":
- Uses get_records_bulk instead of has_updates_bulk
- Compares model versions against highest local versions per base model
- Provides more granular update detection based on base model relationships
Fallback to existing bulk or individual update checks when:
- Strategy is not "same_base"
- Bulk operations fail
- Records are unavailable
This enables more precise update flagging for models sharing common bases.
- Add CheckpointLoaderKJ to NODE_EXTRACTORS mapping for KJNodes support
- Enhance model filename generation in SaveImage to handle different data types
- Add proper type checking and fallback for model metadata values
- Improve robustness when processing checkpoint paths for filename generation
Add KJNodesModelLoaderExtractor to handle metadata extraction from KJNodes loaders that expose model_name. This supports GGUFLoaderKJ and DiffusionModelLoaderKJ nodes, ensuring consistent checkpoint metadata collection across different node types.
Change `load_metadata` method to return a tuple containing both the metadata object and a boolean success flag instead of just the metadata object. This provides clearer error handling and allows callers to distinguish between successful loads with null metadata versus actual load failures.
Add `allow_strength_adjustment` parameter to enable mouse wheel adjustment of trigger word strengths. When enabled, strength values are preserved and can be modified interactively. Also improves trigger word parsing by handling whitespace more consistently and adding debug logging for trigger data inspection.
- Add optional license_flags parameter to model preview API endpoint
- Include license flags in response when requested via query parameter
- Add CSS styles for license overlay and icons in tooltip
- Implement license flag parsing and icon mapping logic
- Display license restrictions as icons in preview tooltip overlay
This allows users to see model license restrictions directly in the preview tooltip without needing to navigate to detailed model information pages.
- Update frontend tag filter to cycle through include/exclude/clear states
- Add backend support for tag_include and tag_exclude query parameters
- Maintain backward compatibility with legacy tag parameter
- Store tag states as dictionary with 'include'/'exclude' values
- Update test matrix documentation to reflect new tag behavior
The changes enable more granular tag filtering where users can now explicitly include or exclude specific tags, rather than just adding tags to a simple inclusion list. This provides better control over search results and improves the filtering user experience.
Add support for filtering models by license requirements:
- credit_required: filter models that require credits or allow free use
- allow_selling_generated_content: filter models based on commercial usage rights
These filters use license_flags bitmask to determine model permissions and enable users to find models that match their specific usage requirements and budget constraints.
- Extract and preserve strength values from trigger words in format "(word:strength)"
- Maintain strength formatting when filtering active trigger words in both group and individual modes
- Update active state tracking to handle strength-modified words correctly
- Ensure backward compatibility with existing trigger word formats
Add RateLimitRetryingProvider and _RateLimitRetryHelper classes to handle rate limiting with exponential backoff retries. Update get_metadata_provider function to automatically wrap providers with rate limit handling. This improves reliability when external APIs return rate limit errors by implementing automatic retries with configurable delays and jitter.
Add hard reset and clean operations to ensure a clean working directory
before switching branches or checking out release tags. This prevents
local changes from interfering with the update process and ensures
consistent behavior across both nightly and release update paths.
- Simplify error handling logic by checking for "not found" message directly
- Extract model data fetching into separate _fetch_model_data method
- Extract version enrichment into separate _enrich_version_with_model_data method
- Improve logging consistency using %s formatting
- Rename variables for better clarity (result -> version, e -> exc)
- Update DEFAULT_LICENSE_FLAGS from 57 to 127 to enable all commercial modes by default
- Replace CommercialUseLevel enum with bitwise commercial permission handling
- Simplify commercial value normalization and validation using allowed values set
- Adjust bit shifting in license flag construction to accommodate new commercial bits structure
- Remove CommercialUseLevel from exports and update tests accordingly
- Improve handling of empty commercial use values with proper type checking
The changes streamline commercial permission processing and align with CivitAI's default license configuration while maintaining backward compatibility.
Add license resolution utilities and integrate license information into model metadata processing. The changes include:
- Add `resolve_license_payload` function to extract license data from Civitai model responses
- Integrate license information into model metadata in CivitaiClient and MetadataSyncService
- Add license flags support in model scanning and caching
- Implement CommercialUseLevel enum for standardized license classification
- Update model scanner to handle unknown fields when extracting metadata values
This ensures proper license attribution and compliance when working with Civitai models.
Remove legacy HTML scraping implementation of get_model_by_url method
and associated BeautifulSoup dependency. The functionality has been
replaced by API-based implementation in get_model_version method.
This simplifies the codebase and removes the optional bs4 dependency,
making the client more maintainable and reliable.
Convert all tags to lowercase in tag processing logic to prevent case sensitivity issues on Windows filesystems. This ensures consistent tag matching and prevents duplicate tags with different cases from being created.
Changes include:
- TagUpdateService now converts tags to lowercase before comparison
- Utils function converts model tags to lowercase before priority resolution
- Test cases updated to reflect lowercase tag expectations
Add GGUFLoaderExtractor class to handle metadata extraction for GGUF model loaders. Register extractor for both LoaderGGUF and LoaderGGUFAdvanced node types to capture checkpoint metadata from gguf_name input parameter. This enables proper metadata tracking for GGUF model files used in the system.
Update the plugin cleanup process to preserve the model_cache folder
along with settings.json and civitai. This prevents accidental deletion
of cached model files during plugin updates, improving performance by
avoiding unnecessary model re-downloads.
