Core treats later bank rows as offsets from the first head. set_weight files already use that layout; proj_out banks are converted before the same blend.
Add h3lora/sidecars.py to peel adaln_basis, adaln_mean, and silu_t_emb_grid from checkpoint state dicts and stash them on model/patcher objects so ComfyUI doesn't drop them. Install a load_model_weights wrapper (ensure_load_hook) in __init__.py and call it from h3lora.apply; use sidecar collect when creating AdalnContext. Include unit tests (tests/test_sidecars.py) for peel/attach/collect and the wrapper. Also update README with no attribution by proxy. Thanks pl0x
Introduce PDD/Acc head handling and a robust adaLN grid/basis pipeline.
- Add h3lora/pdd.py: peel Acc/PDD output-head banks from LoRAs, keep banks aside, and blend rows at sample time (PddFinalLayer + attach wrapper) to avoid crashes.
- Refactor h3lora/adaln.py: generate/load silu grids from time_embedder, safetensors, or live embedder; add hashing, basis cache, sidecar support, residual acceptance threshold, and scanning of diffusion_model safetensors.
- Integrate PDD into apply.py, detect.py, nodes.py and README; add .gitignore entries.
Introduce runtime scheduling of per-row LoRA strengths. Adds h3lora/schedule.py (Schedule, ScheduleState, parsing, progress_from), new H3LoraSchedule node in nodes.py, and wiring in apply.py to resolve per-row schedules and preserve bias deltas. branch.py extended with FusedBranch, schedule-aware fusion, LoraBank/LoraBranch changes, and ScheduleController wrapper (attach now returns controller). README updated and unit tests added (tests/test_schedule.py, tests/test_branch_schedule.py) to validate curves, domains, thread-local state and controller nesting.
Add a full technical AUDIT_REPORT.md (2026-08-08) documenting priority findings and recommended fixes. Add Apache-2.0 LICENSE file and update README to reference the license. Update pyproject.toml to declare license as Apache-2.0 (replacing MIT). These changes add project licensing and a comprehensive audit summary for maintainers and users.
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.
cicalooo