---
title: FastVideo-FastH3-8-Step-V2
canonical_url: "https://www.modelscope.cn/models/FastVideo/FastVideo-FastH3-8-Step-V2"
md_url: "https://www.modelscope.cn/models/FastVideo/FastVideo-FastH3-8-Step-V2.md"
repository: FastVideo/FastVideo-FastH3-8-Step-V2
last_updated: 2026-09-15
license: other
pipeline_tag: text-to-video-synthesis
tasks:
  - text-to-video-synthesis
base_model:
  - MiniMaxAI/MiniMax-H3
base_model_relation: finetune
parameters: 71.2B
tensor_type:
  - BF16
  - F32
library_name:
  - safetensors
  - diffusers
  - pytorch
frameworks:
  - pytorch
downloads: 101
stars: 1
tags:
  - text-to-video
  - video
  - audio
  - text-to-audio-video
  - distillation
  - dmd2
  - few-step
  - minimax-h3
  - fastvideo
  - fasth3
---

# FastVideo-FastH3-8-Step-V2

> FastVideo-FastH3-8-Step-V2 - FastVideo 在 ModelScope 开源的模型。FastVideo-FastH3-8-Step-V2

FastVideo/FastVideo-FastH3-8-Step-V2 是 ModelScope 魔搭社区上的 71.2B 参数text-to-video-synthesis模型，采用 other 许可，基于 MiniMaxAI/MiniMax-H3 构建。

- **Repository**: FastVideo/FastVideo-FastH3-8-Step-V2
- **License**: other
- **Tasks**: text-to-video-synthesis
- **Parameters**: 71.2B
- **Base model**: MiniMaxAI/MiniMax-H3
- **Tags**: text-to-video, video, audio, text-to-audio-video, distillation, dmd2, few-step, minimax-h3, fastvideo, fasth3
- **Downloads**: 101
- **Stars**: 1
- **Last updated**: 2026-09-15

Source: https://www.modelscope.cn/models/FastVideo/FastVideo-FastH3-8-Step-V2

---

<p align="center">
  <a href="https://github.com/hao-ai-lab/FastVideo"><img src="https://raw.githubusercontent.com/hao-ai-lab/FastVideo/main/assets/logos/logo.svg" width="320" alt="FastVideo"></a>
</p>

# FastVideo-FastH3-8-Step-V2

The FastH3 8-Step V2 checkpoint from
[FastVideo](https://github.com/hao-ai-lab/FastVideo). It generates synchronized
video and audio from text with eight transformer forwards. This step-1300 model
was trained with data-free DMD2 and VSA-H3 at 80% sparsity.

[Blog](https://haoailab.com/blogs/fasth3-preview/) ·
[FastH3 collection](https://huggingface.co/collections/FastVideo/fastvideo-fasth3)

> This checkpoint requires FastVideo's VSA-H3 attention backend. Its video
> scheduler shift is 10, not the base model's 12; use the example below, which
> reads the trained schedule from the checkpoint.

## Run with FastVideo

Install [uv](https://docs.astral.sh/uv/getting-started/installation/), then use
the CUDA 13 / Blackwell path below. It selects FastVideo's published CUDA
kernel wheel instead of compiling the kernel locally. See the
[installation guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/)
for other platforms.

```bash
git clone https://github.com/hao-ai-lab/FastVideo.git
cd FastVideo
uv venv --python 3.12 --seed
source .venv/bin/activate
UV_TORCH_BACKEND=cu130 uv pip install \
  --no-sources-package fastvideo-kernel \
  -e ".[fasth3]"
```

```bash
python examples/inference/basic/basic_fasth3_8step.py \
  --prompt "your prompt" \
  --no-warmup \
  --repeats 1
```

The tested defaults use four B200 GPUs and the trained eight-forward schedule.
On other multi-GPU CUDA systems, follow the installation guide and add
`--no-replicated-dit --vsa-kernel triton --no-fa4`. The GPU count must divide
H3's 56 attention heads.

## Scope

This checkpoint supports text-to-audio-video generation. FL2VA and Ref2VA were
not distilled. Difficult motion, fine detail, and some audio may remain below
the base MiniMax H3 model. This checkpoint inherits the
[MiniMax H3 Community License](LICENSE).

## Acknowledgements

We thank [Nuva Lab](https://nuvalab.ai/) for bringing production grounding to FastH3 through its experience with real-world creative video-agent workloads. Its production-aligned post-training insights help bridge open-source research to practical data-assisted distillation for commercial video workflows, with Omni Ref as the next focus.

We thank the [NVIDIA FastGen](https://github.com/NVlabs/FastGen) team for the [DMD2](https://arxiv.org/abs/2405.14867) framework and H3 reference experiment that helped us align the score clock, modality shifts, and backward simulation.

We also thank [MiniMax](https://huggingface.co/MiniMaxAI/MiniMax-H3) for releasing H3-Base, and the [vLLM project](https://vllm.ai/), [NVIDIA](https://www.nvidia.com/en-us/), and [MBZUAI](https://mbzuai.ac.ae/) for their continued sponsorship and support of [FastVideo](https://github.com/hao-ai-lab/FastVideo).