---
license: other
license_name: minimax-h3-community
license_link: LICENSE
base_model: MiniMaxAI/MiniMax-H3
library_name: diffusers
pipeline_tag: text-to-video
tags:
- text-to-video
- video
- audio
- text-to-audio-video
- distillation
- dmd2
- few-step
- minimax-h3
- fastvideo
- fasth3
---

# FastVideo-Minimax-FastH3-Preview-v0.2

**A few-step (4-step) distillation preview of [MiniMax-H3](https://huggingface.co/MiniMaxAI/MiniMax-H3)**, the
33B dual-modality (video + audio) diffusion transformer — distilled with data-free
DMD2 by the [FastVideo](https://github.com/hao-ai-lab/FastVideo) team.

The base model samples with 50 denoising steps; this student walks a 4-step grid
on the release's shift-12 rectified-flow schedule (12.5× fewer transformer
evaluations), generating synchronized video and audio in one pipeline call.

> **Preview status (v0.2):** step 2900 of a 4000-step run — the same run that
> produced v0.1 (step 1400), carried 1500 steps further. Sample quality is still
> maturing, most visibly on high-motion detail.

## What's new since v0.1

- **1500 more distillation steps** (1400 → 2900) on the same data-free DMD2 run:
  sharper still detail and steadier audio/video sync.
- **Corrected sampling contract in the card.** v0.1 documented
  `num_inference_steps=4`, which makes the scheduler build its own 4-point sigma
  grid — 3 forwards on native spacing, not the 4 trained jump points. Sample with
  the explicit trained ladder instead (see Usage). The same off-operating-point
  mismatch affected this run's in-training validation renders, so judge the
  student by fresh samples on the ladder below, not by earlier validation clips.
- Fixed the repo id in the usage snippet (v0.1's card had it doubled).

## What's in the repo

Diffusers-format (modular pipeline) layout. Only the `transformer/` weights differ
from the base release — the distilled student, in bf16. All other components
(Qwen3-VL text encoder, video/audio VAEs, tokenizer, processor, schedulers) are
unmodified copies of the base release, included so the repo is self-contained.

The student was trained with block-sparse video attention (VSA, 64-token tiles,
90% sparsity) and carries its trained sparse-gate parameters
(`attn.to_gate_compress`); it can be run dense (default) or with VSA for
additional inference speedup.

## Usage (FastVideo)

Sample on the trained ladder — `[999, 749, 500, 250]` on the shared 1000-step
grid, one forward per entry, each scheduler applying its own shift:

```python
from fastvideo import VideoGenerator

gen = VideoGenerator.from_pretrained(
    "FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2",
    num_gpus=1,
    dmd_denoising_steps=[999, 749, 500, 250],
)
video = gen.generate_video(
    prompt="<your H3-format multimodal prompt>",
    guidance_scale=1.0,   # the base model is guidance-distilled
)
```

The ladder can also be set without touching code:

```bash
export FASTVIDEO_DMD_DENOISING_STEPS=999,749,500,250
```

To run the student under the sparse attention it was trained with, select the
VSA-H3 backend and match both knobs of the training contract — sparsity alone,
at the default 256-token tile, is a different operating point:

```bash
export FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN_H3
# generator args: VSA_sparsity=0.9, VSA_tile_size=64
```

Prompts follow the MiniMax-H3 multimodal prompt format
(`integrated_multimodal_description: ... overall_soundscape: ...`); see the base
model card for the prompting guide.

## Training summary

- **Method:** data-free DMD2 (distribution matching distillation) — student /
  frozen teacher / trained fake-score critic, backward-simulation rollout
  (the student walks its own 4-step sampling grid during training), x0-space
  critic regression, shifted score-time sampling matched to the dual video/audio
  noise clocks (shifts 12 / 3).
- **Student grid:** `[999, 749, 500, 250]` — 4 steps on the release sampler's
  shift-12 schedule.
- **Attention:** student trained with VSA block-sparse attention (64-token tiles,
  90% video-tile sparsity); teacher and critic dense.
- **Data:** text prompts only (data-free) — ~258k prompts (VidProM-H3 +
  synthetic t2va prompt set); no video data used.
- **Resolution:** 768×1344, 124 frames (5s) with synchronized audio.
- **Optimization:** global batch 64, lr 1e-6 (student and critic), fp32 master
  weights, bf16 compute, 2900 of 4000 steps.
- **Hardware:** 32× NVIDIA GB200.

## Limitations

- Preview checkpoint — quality below the base model's 50-step sampling,
  especially on fine motion and audio detail; improves with training.
- Inherits all content limitations and usage restrictions of the base model.
- The 4-step ladder is what the student was trained for; other step counts and
  other timestep grids are off-distribution.

## License

Distributed under the MiniMax H3 Community License (see [LICENSE](LICENSE)),
inherited from the base model. Review the license (including its territory and
acceptable-use terms) before use or redistribution.

## Notes

- The `transformer_ref` component (reference-conditioning variant) is **not**
  packaged here; its entry in `modular_model_index.json` points at the base
  `MiniMaxAI/MiniMax-H3` repo and is fetched from there if used. This preview
  distills the text-to-video+audio path only.