The FL2VA profile reads keyframe-anchor rows (build_packed_sequence anchors, noised
like inference); the coverage builder's --mode fl2va makes them from the v4 rows'
exact first/last frames re-encoded at the canvas size with the FL2VA Qwen3-VL
presentation; the keyframe-fidelity guard covers the FL2VA tasks.
Add MiniMaxH3FlexRef2VAModel / MiniMaxH3FlexFL2VAModel for OmniRefPDDQADMethod:
dense attention, Pika's shifted 1000..40 ladder, re-noise steps whose noise is a
function of (row seed, forward, modality), NVFP4 linears only. Add an offline
fp32 adapter merge script and QAD configs for both adapters. Let model profiles
pick their attention backend and receive the prepared row in schedulers().
Resolve the FP4 attention kernel and backend conflicts with main's #1980 versions
(re-adding install_fp4_vsa_attention), keep main's FP32 FSDP grouping, and keep both
save_inference and save_tagged in the checkpointer.
- V2 config now matches the canonical 16-GPU stage 1: warmup 10,
min LR ratio 0.3, HSDP 16.
- State that the published V2.1 checkpoint is linears-only (BF16 VSA
attention); the FP4 adaptation is a separate, non-public stage.
- Name the released checkpoints and starting models.
- Vendor and pin both calibrated amax tables.
- Add the V2 prompt/embedding and OmniRef eval data-prep scripts.
Add the OmniRef (Ref2VA, VSA tile 128) and V2/V2.1 Consumer (T2VA,
VSA tile 64) profiles of one quantization-aware distillation recipe:
NVFP4 W4A4 q/k/v/out and FFN linears in all 50 blocks, the FP4
sparse-attention emulator, frozen VSA gates and norms, output plus
layer-reconstruction regression against a frozen bf16 teacher, and
best-by-score checkpoints with audio eligibility guards.
Training code: the PDD-QAD method, the two H3 model plugins, the
NVFP4 emulated linear, and the tile-64/128 Triton emulator with its
PyTorch numeric reference. The VSA attention impl gains a fine
attention override hook and lets tile 128 run grad-capable forwards
through it. The trainer gains opt-in tagged checkpoints and early
stop hooks for the QAD guards.
Also add two runnable configs, the training guide, and CPU/GPU tests.
Adds opt-in regional torch.compile to the YAML-driven modular Wan trainer (training.model.enable_torch_compile / torch_compile_kwargs) with FA4 and Wan modulation forward/backward as opaque custom ops, compiled repeated DiT blocks after FSDP setup, and activation checkpointing applied before FSDP for trainable Wan transformers.
Merged with main's #1716 (WanModel resolves the checkpointing type in __init__; the PR's pre-FSDP wrap uses it and the post-load wrap is gone) and #1757 (pre_fsdp_model_transform runs before this PR's pre_fsdp_transform). Maintainer review (Swarm order #776): AdaLN skip names are canonicalized through checkpoint-wrapper prefixes, test_inference_regional_compile.py joins the unit lane, the Wan modulation custom-op test moved to the transformer lane, regional_compile is classified in the schema inventory, and a 1/2-GPU training smoke covers fully_shard(CheckpointWrapper) in eager and regional-compile modes. Validated on GB200: the FA4 CuTe forward/backward parity and fullgraph-backward tests and all four smoke cases pass.
Also exempts MMAudio recipes, whose student builds its pipeline config at init, from the fine-tuning recipe construction check that #1648 left failing on main.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Aryan Kumar