Introduce a new private method `_normalize_trigger_words` to handle consistent splitting and cleaning of trigger word strings. This method splits input by both single and double commas, strips whitespace, and filters out empty strings, returning a set of normalized words. It is now used in `process_trigger_words` to compare trigger word overrides, ensuring accurate detection of changes by comparing normalized sets instead of raw strings.
- 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
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.
- 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
Remove direct imports of IO type constants from comfy.comfy_types and replace them with string literals "STRING" in input type definitions and return types. This improves code portability and reduces dependency on external type definitions.
Changes made across multiple files:
- Remove `from comfy.comfy_types import IO` imports
- Replace `IO.STRING` with "STRING" in INPUT_TYPES and RETURN_TYPES
- Move CLIPTextEncode import to function scope in prompt.py for better dependency management
This refactor maintains the same functionality while making the code more self-contained and reducing external dependencies.
- Introduced logging to track unexpected formats in LoRA and trigger word data.
- Refactored LoRA processing to support both old and new kwargs formats in LoraLoader and LoraStacker.
- Enhanced trigger word processing to handle different data formats in TriggerWordToggle.
- Improved code readability and maintainability by extracting common logic into helper methods.
