Move the 'empty/no LoRA' cycling functionality from the LoRA Pool node
to the Lora Cycler widget for cleaner architecture:
Frontend changes:
- Add include_no_lora field to CyclerConfig interface
- Add includeNoLora state and logic to useLoraCyclerState composable
- Add toggle UI in LoraCyclerSettingsView with special styling
- Show 'No LoRA' entry in LoraListModal when enabled
- Update LoraCyclerWidget to integrate new logic
Backend changes:
- lora_cycler.py reads include_no_lora from config
- Calculate effective_total_count (actual count + 1 when enabled)
- Return empty lora_stack when on No LoRA position
- Return actual LoRA count in total_count (not effective count)
Reverted files to pre-PR state:
- lora_loader.py, lora_pool.py, lora_randomizer.py, lora_stacker.py
- lora_routes.py, lora_service.py
- LoraPoolWidget.vue and related files
Related to PR #861
Co-authored-by: dogatech <dogatech@dogatech.home>
Add file_path as a tie-breaker for all sort modes in ModelCache, BaseModelService, LoraService, and RecipeCache to ensure deterministic ordering when primary keys are identical. Resolves issue #859.
- Remove backward compatibility code for `model_type` in `ModelScanner._build_cache_entry()`
- Update `CheckpointScanner` to only handle `sub_type` in `adjust_metadata()` and `adjust_cached_entry()`
- Delete deprecated aliases `resolve_civitai_model_type` and `normalize_civitai_model_type` from `model_query.py`
- Update frontend components (`RecipeModal.js`, `ModelCard.js`, etc.) to use `sub_type` instead of `model_type`
- Update API response format to return only `sub_type`, removing `model_type` from service responses
- Revise technical documentation to mark Phase 5 as completed and remove outdated TODO items
All cleanup tasks for the model type refactoring are now complete, ensuring consistent use of `sub_type` across the codebase.
This commit resolves the semantic confusion around the model_type field by
clearly distinguishing between:
- scanner_type: architecture-level (lora/checkpoint/embedding)
- sub_type: business-level subtype (lora/locon/dora/checkpoint/diffusion_model/embedding)
Backend Changes:
- Rename model_type to sub_type in CheckpointMetadata and EmbeddingMetadata
- Add resolve_sub_type() and normalize_sub_type() in model_query.py
- Update checkpoint_scanner to use _resolve_sub_type()
- Update service format_response to include both sub_type and model_type
- Add VALID_*_SUB_TYPES constants with backward compatible aliases
Frontend Changes:
- Add MODEL_SUBTYPE_DISPLAY_NAMES constants
- Keep MODEL_TYPE_DISPLAY_NAMES as backward compatible alias
Testing:
- Add 43 new tests covering sub_type resolution and API response
Documentation:
- Add refactoring todo document to docs/technical/
BREAKING CHANGE: None - full backward compatibility maintained
Add Lora Cycler node that cycles through LoRAs sequentially from a filtered pool. Supports configurable sort order, strength settings, and persists cycle progress across workflow save/load.
Backend:
- New LoraCyclerNode with cycle() method
- New /api/lm/loras/cycler-list endpoint
- LoraService.get_cycler_list() for filtered/sorted list
Frontend:
- LoraCyclerWidget with Vue.js component
- useLoraCyclerState composable
- LoraCyclerSettingsView for UI display
- Add execution_seed and next_seed parameters to support deterministic randomization across batch executions
- Separate UI display generation from execution stack generation to maintain consistency in batch queues
- Update LoraService to accept optional seed parameter for reproducible randomization
- Ensure each execution with a different seed produces unique results without affecting global random state
Add support for respecting recommended strength values from LoRA usage_tips
when randomizing LoRA selection.
Features:
- New toggle setting to enable/disable recommended strength respect (default off)
- Scale range slider (0-2, default 0.5-1.0) to adjust recommended values
- Uses recommended strength × random(scale) when feature enabled
- Fallbacks to original Model/Clip Strength range when no recommendation exists
- Clip strength recommendations only apply when using Custom Range mode
Backend changes:
- Parse usage_tips JSON string to extract strength/clipStrength
- Apply scale factor to recommended values during randomization
- Pass new parameters through API route and node
Frontend changes:
- Update RandomizerConfig type with new properties
- Add new UI section with toggle and dual-range slider
- Wire up state management and event handlers
- No layout shift (removed description text)
Tests:
- Add tests for enabled/disabled recommended strength in API routes
- Add test verifying config passed to service
- All existing tests pass
Build: Include compiled Vue widgets
- Add `_preprocess_loras_input` method to handle different widget input formats
- Move core randomization logic to `LoraService` for better separation of concerns
- Update `_select_loras` method to use new service-based approach
- Add comprehensive test fixtures for license filtering scenarios
- Include debug print statement for pool config inspection during development
This refactor improves code organization by centralizing business logic in the service layer while maintaining backward compatibility with existing widget inputs.
- Document dual UI systems: standalone web UI and ComfyUI custom node widgets
- Add ComfyUI widget development guidelines including styling and constraints
- Update terminology in LoraRandomizerNode from 'frontend/backend' to 'fixed/always' for clarity
- Include UI constraints for ComfyUI widgets: minimize vertical space, avoid dynamic height changes, keep UI simple
- Implement LoRA locking to prevent specific LoRAs from being changed during randomization
- Add visual styling for locked state with amber accents and distinct backgrounds
- Introduce `roll_mode` configuration with 'backend' (execute current selection while generating new) and 'frontend' (execute newly generated selection) behaviors
- Move LoraPoolNode to 'Lora Manager/randomizer' category and remove standalone class mappings
- Standardize RETURN_NAMES in LoraRandomizerNode for consistency
- Import and register two new nodes: LoraDemoNode and LoraRandomizerNode
- Update import exception handling for better readability with multi-line formatting
- Add comprehensive documentation file `docs/custom-node-ui-output.md` for UI output usage in custom nodes
- Ensure proper node registration in NODE_CLASS_MAPPINGS for ComfyUI integration
- Maintain backward compatibility with existing node structure and import fallbacks
Add update_available field to checkpoint, embedding, and LoRA service response formatting. The flag indicates whether a model update is available and defaults to false when not specified.
Include comprehensive tests to verify the update flag is properly included in formatted responses and defaults to false when not present in the payload.
- Pass ModelUpdateService to CheckpointService, EmbeddingService, and LoraService constructors
- Add has_update query parameter filter to model listing handler
- Update BaseModelService to accept optional update_service parameter
These changes enable model update functionality across different model types and provide filtering capability for models with available updates.
- Added AutoComplete class to handle input suggestions based on user input.
- Integrated TextAreaCaretHelper for accurate positioning of the dropdown.
- Enhanced dropdown styling with a new color scheme and custom scrollbar.
- Implemented dynamic loading of preview tooltips for selected items.
- Added keyboard navigation support for dropdown items.
- Included functionality to insert selected items into the input field with usage tips.
- Created a separate TextAreaCaretHelper module for managing caret position calculations.
- Added BaseModelRoutes class to handle common routes and logic for model types.
- Created CheckpointRoutes class inheriting from BaseModelRoutes for checkpoint-specific routes.
- Implemented CheckpointService class for handling checkpoint-related data and operations.
- Developed LoraService class for managing LoRA-specific functionalities.
- Introduced ModelServiceFactory to manage service and route registrations for different model types.
- Established methods for fetching, filtering, and formatting model data across services.
- Integrated CivitAI metadata handling within model routes and services.
- Added pagination and filtering capabilities for model data retrieval.
