---
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
- preview
---

# FastVideo-FastH3-4-step-Preview-v1-VSA-Synthetic-Step1900

FastH3 4-Step Preview v1 checkpoint from FastVideo. This variant is the
`dmd2_sp2_fsdp64_v11_dataonly_mixed_vsa64` synthetic-data DMD2 MiniMax-H3
student at training step 1900. It generates synchronized video and audio with
four transformer forwards and uses VSA-H3 at 90% sparsity.

See the complete [FastH3 4-Step Preview v1
collection](https://huggingface.co/collections/FastVideo/fastvideo-fasth3).

## Checkpoint identity

- Training run: `20260825T003627Z-v11-dataonly-sp2-fastgen-fix`
- Lineage parent: `20260823T191211Z-v11-dataonly-sp2-initial`
- W&B: [arg07fnt](https://wandb.ai/wlsaidhi/h3-dmd2-vsa/runs/arg07fnt)
- Step: 1900
- Transformer content SHA-256: `932493fce3c32de6bb49e0d568ec493bec365a62b0e5a92262ab73a1619f0dda`
- Checkpoint metadata SHA-256: `d93f3974fa6a916a0989327b8861c0fb9f5c028b33f95e65c45925ea49dcdbfe`
- FastVideo execution commit: `6ac300b5a7c472e3a139f1cb3ff7a03b58f3e26e`
- Data commit: `b92be606a439c67006220459df236d18c48fd38c3e11f4771d21fe0d078a3371`

`checkpoint_content.json` records every indexed transformer shard and digest.
`checkpoint_metadata.json` preserves the source training configuration.

## Contents

This is a self-contained Diffusers modular-pipeline package. The distilled
bf16 student is under `transformer/`; the text encoder, tokenizer, processor,
video VAE, audio VAE, and schedulers are unmodified MiniMax-H3 components.

The `transformer_ref` component is not bundled because this student targets
T2AV. Its modular-index entry remains on `MiniMaxAI/MiniMax-H3`.

## Exact FastVideo inference contract

The student was trained on the explicit ladder `[999, 749, 500, 250]`. These
are four transformer forwards; do not replace the ladder with an arbitrary
four-point scheduler grid.

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

python examples/inference/basic/basic_fasth3.py \
  --model-path FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-Synthetic-Step1900 \
  --prompt '<MiniMax-H3 T2AV prompt>' \
  --steps 5 \
  --num-gpus 4 \
  --vsa-sparsity 0.9 \
  --vsa-tile-size 64 \
  --vsa-kernel sm100a
```

The five scheduler grid points execute the four trained jumps. Use guidance
scale 1.0 and VSA-H3 at 90% sparsity with 64-token tiles. Compilation is an
optional runtime optimization; first validate output parity on the pinned
FastVideo commit.

## Training summary

- Method: data-only DMD2 on synthetic Base-H3 video latents. The student is
  trained from forward-noised target latents; no data-free carried rollout is
  mixed into this run.
- Student grid: `[999, 749, 500, 250]`.
- Student attention: VSA-H3 at 90% sparsity with 64-token tiles.
- Global batch size: 64.
- Student and critic learning rate: `2e-06` and
  `2e-06`.
- Precision: fp32 training state and bf16 inference export.
- Training topology: 64 B200 GPUs, SP=2,
  HSDP replicate=1 / shard=64.
- Continuous score-clock shift: `2.4`; warp max: `0.999`.

## Limitations

- Intermediate checkpoint at step 1900 of a planned 4000-step run.
- T2AV only; the reference-conditioned transformer was not distilled here.
- Quality remains below base MiniMax-H3 on difficult motion, fine structure,
  and some audio details.
- Sampling outside the trained ladder or attention geometry is
  off-distribution.
- Inherits the MiniMax H3 Community License and base-model limitations.

Indexed student: 688 bf16 tensors / 70,099,502,592 logical bytes.