ComfyUI compacts widgets_values inside configure, before onConfigure, and drops slots aligned with the serialize:false buttons. Snapshot the rows first and store them on node.properties.h3Rows so the enabled list survives a reload.
Eliminates the module-global loraListPromise and makes fetchLoraList always query /object_info/LoraLoaderModelOnly. This avoids caching a promise (which could be serialized into saved workflows or return stale folder listings) and simplifies error handling to log and return an empty list. Comments updated to reflect per-open fetching.
Prevent duplicated stack rows by changing how widget values are serialized and restored. Adds a ROW_FORMAT marker and LEGACY_BUTTON_SLOTS constant, stamps the node.properties with the new format, and introduces isRowValue for robust row detection. Marks the Add/Balance/Restore buttons as non-serializing and tags the add button with h3Role so rows insert correctly. onConfigure now recognizes legacy saved arrays (trims trailing button slots) and migrates them to the new format when loading. This fixes the bug where saved workflows would repeatedly grow extra rows on load/clone.
Introduce auto-balance measurement and per-modality adaLN scaling for MiniMax H3.
- Add gain.py to measure LoRA perturbation (rel, factor) and cache results; expose measure_file/measure_state_dict.
- Add modality.py to scale adaLN rows by {video,text,audio} and describe/apply those changes.
- Add server route /h3_power_lora_stack/balance (h3lora/server.py) and register it in __init__.py to support the UI button.
- Integrate measurement and modality scaling into apply_stack and report LoRA rel/factor suggestions.
- Add H3AdalnModality node and adaln_modality input in nodes.py.
- Update web UI (web/h3_power_lora_stack.js) with auto-balance/restore buttons, server call, badges, and client-side behavior.
- Document features and rationale in README.md.
These changes keep measurements cached and never boost measured LoRAs (factor clamped ≤1).
Add a ComfyUI plugin implementing a stacked multi-LoRA loader for MiniMax H3. Introduces H3PowerLoraStack and H3LoraInspector node classes, plus modules: adaln (adaLN dense↔curve basis conversion), branch (runtime low-rank branches for quantized weights), apply (stack orchestration), detect (safetensors header introspection), keymap (key normalization), nodes, and a web UI (h3_power_lora_stack.js). Includes pyproject metadata and README. Preserves quantized kernels via branching, rebases adaLN pairs, handles many naming conventions, and provides a browser picker and per-LoRA reporting.
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