Add new move_recipes_bulk endpoint to handle moving multiple recipes simultaneously. This improves efficiency when reorganizing recipe collections by allowing batch operations instead of individual moves.
- Add move_recipes_bulk handler method with proper error handling
- Register new POST /api/lm/recipes/move-bulk route
- Implement bulk move logic in persistence service
- Validate required parameters (recipe_ids and target_path)
- Handle common error cases including validation, not found, and server errors
- Add GET /api/lm/recipes/roots endpoint to retrieve recipe root directories
- Add POST /api/lm/recipe/move endpoint to move recipes between directories
- Register new endpoints in route definitions
- Implement error handling for both new endpoints with proper status codes
- Enable recipe management operations for better file organization
- Add new API endpoints for folder operations: get_folders, get_folder_tree, and get_unified_folder_tree
- Extend recipe listing handler to support folder and recursive filtering parameters
- Register new folder-related routes in route definitions
- Enable users to organize and browse recipes using folder structures
- Add pre-processing step to populate missing parameters for candidate samplers, especially for SamplerCustomAdvanced requiring tracing
- Change sampler selection from most recent (closest to downstream) to first in execution order to prioritize base samplers over refine samplers
- Improve parameter handling by updating sampler parameters with traced values before ranking
- Maintain backward compatibility with fallback to first sampler if no criteria match
- Add support for `basic_pipe` nodes in metadata processor to handle pipeline nodes like FromBasicPipe
- Optimize `find_primary_checkpoint` by accepting optional `primary_sampler_id` to avoid redundant calculations
- Update `get_workflow_trace` to pass known primary sampler ID for improved efficiency
Removed the forced normalization of path separators to forward slashes in BaseModelService to maintain platform-specific separators. Updated test cases to use os.sep for constructing expected paths, ensuring tests work correctly across different operating systems while preserving native path representations.
Add `_seed_root_symlink_mappings` method to ensure symlinked root folders are recorded before deep scanning, preventing them from being missed during directory traversal. This ensures that root symlinks are properly captured in the path mappings.
Additionally, normalize separators in relative paths for cross-platform consistency in `BaseModelService`, and update tests to verify root symlinks are preserved in the cache.
The SaveImage class has been renamed to SaveImageLM to better reflect its purpose within the Lora Manager module. This change ensures consistent naming across import statements, class mappings, and the actual class definition, improving code readability and maintainability.
- Added threading import and optional `_rescan_thread` for background operations
- Simplified `_load_symlink_cache` to only validate path mappings, removing fingerprint checks
- Updated `_initialize_symlink_mappings` to rebuild preview roots and schedule rescan when cache is loaded
- Added `_schedule_symlink_rescan` method to perform background validation of symlinks
- Cleared `_path_mappings` at start of `_scan_symbolic_links` to prevent stale entries
- Background rescan improves performance by deferring symlink validation after cache load
- Add `time` import for performance measurement
- Change debug logs to info level for better visibility of cache operations
- Add detailed logging for cache validation failures and successes
- Include timing metrics for symlink initialization and scanning
- Log cache save/load operations with mapping counts
Update symlink traversal logic to always record path mappings before checking for visited directories. This prevents valid link->target pairs from being dropped when the target directory has already been visited via another path. Also correct path mapping lookup to properly replace link paths with their actual target paths.
Add new POST endpoint `/api/lm/example-images/set-nsfw-level` to allow updating NSFW classification for individual example images. The endpoint supports both regular and custom images, validates required parameters, and updates the corresponding model metadata. This enables users to manually adjust NSFW ratings for better content filtering.
- Simplify and consolidate the logic for processing trigger words and groups
- Remove redundant code paths and improve maintainability
- Ensure consistent behavior between list and string trigger data inputs
- Preserve existing functionality for strength adjustment and group mode
Introduce `relax_csp_for_remote_media` middleware that modifies Content Security Policy headers to permit loading media from trusted external domains (Civitai and Genur). This is necessary for LoRA Manager UI previews when ComfyUI runs with `--disable-api-nodes`, which otherwise blocks remote images and videos. The middleware is inserted after ComfyUI's `block_external_middleware` to properly extend the restrictive CSP header.
- Add `_get_supported_extensions_for_type` method to return allowed extensions per model type
- Rename `_extract_safetensors_from_archive` to `_extract_model_files_from_archive` and extend to filter by allowed extensions
- Update error message to list supported extensions when archive contains no valid files
- Add test for extracting .pt embedding files from zip archives
Add support for parsing comma-separated and JSON-style commercial use permission values in both Python backend and JavaScript frontend. Implement helper functions to split aggregated values into individual permissions while preserving original values when no aggregation is detected.
Added comprehensive test coverage for the new parsing functionality to ensure correct handling of various input formats including strings, arrays, and iterable objects with aggregated commercial use values.
Add support for storing checkpoint information in image EXIF metadata. The checkpoint data is simplified and includes fields like model ID, version, name, hash, and base model. This allows for better tracking of AI model checkpoints used in image generation workflows.
Add comprehensive local file matching for LoRA entries in recipe metadata:
- Add modelVersionId-based lookup via new _get_lora_from_version_index method
- Extend LoRA entry with additional fields: existsLocally, inLibrary, localPath, thumbnailUrl, size
- Improve local file detection by checking both SHA256 hash and modelVersionId
- Set default thumbnail URL and size values for missing LoRA files
- Add proper typing with Optional imports for better code clarity
This provides more accurate local file status and metadata for LoRA entries in recipes.
- Add MODEL_NAME_PATTERN regex to extract model names from parameters
- Extract model hash from parsed hashes when available in metadata
- Add checkpoint model hash and name extraction from parameters section
- Implement checkpoint resource processing from Civitai metadata
- Improve model information completeness for better recipe tracking
Update metadata registry to remove cache entries when node metadata becomes empty instead of keeping stale data. This prevents accumulation of unused cache entries and ensures cache only contains valid metadata. Added test case to verify cache behavior when LoRA configurations are removed.
Add additional CivitAI image metadata fields to detection logic including generation parameters (prompt, steps, sampler, etc.) and model information. Also improve LoRA hash detection by checking both main metadata and nested meta objects. This ensures more comprehensive identification of CivitAI image metadata across different response formats.
- Refactor metadata detection to handle nested "meta" objects
- Add support for lowercase "lora:" hash keys
- Extract metadata from nested "meta" field when present
- Update tests to verify nested metadata parsing
- Handle case-insensitive LORA hash detection
The changes ensure proper parsing of Civitai image metadata that may be wrapped in nested structures, improving compatibility with different API response formats.
Add _normalize_checkpoint_entry method to handle legacy checkpoint data formats (strings, tuples) by converting them to dictionaries. This prevents errors during enrichment when checkpoint data is not in the expected dictionary format. Invalid checkpoint entries are now removed instead of causing processing failures.
- Update get_paginated_data and get_recipe_by_id methods to use normalization
- Add test cases for legacy string and tuple checkpoint formats
- Ensure backward compatibility with existing checkpoint handling
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
