hao-ai-lab-FastVideo

2026/10/21: Release FastH3 Omni (Ref2VA): reference-conditioned video-and-audio generation from a prompt plus ordered image, video and audio references, in eight fused forwards. It runs on consumer Blackwell (RTX 5090, RTX PRO 6000, DGX Spark — NVFP4 QAD), on RTX 4090 (FP8) and on Apple Silicon (MLX). See the cookbook and the inference guide.
2026/10/06: FastVideo now supports Kandinsky 6 from Kandinsky Lab: text- and image-to-video with synchronized audio (base and 10-step distilled pi-Flow checkpoints) plus video super-resolution up to 4x. See the Kandinsky 6 recipes.
2026/10/06: FastH3 V2 now runs on a single consumer machine: NVIDIA RTX 5090, RTX 4090 and RTX PRO 6000 GPUs, DGX Spark and Apple Silicon. We also release FastH3 Trim, an experimental pruned model that is 4.2× smaller than base H3 and runs in as little as 8 GB of GPU memory. Get the models and read the Blog.
2026/09/15: Release FastH3 8-Step V2, an eight-forward data-free DMD2 checkpoint distilled from MiniMax-H3 with 80% Video Sparse Attention. Run it with examples/inference/basic/basic_fasth3_8step.py or the FastH3 8-Step V2 recipe.
2026/09/01: FastH3 now runs locally on Apple Silicon through MLX and on NVIDIA DGX Spark through CUDA 13, including two-Spark inference. Follow the FastH3 recipes and read the Blog.
2026/08/27: FastH3 Preview v1 is an open-weight 4-step sparse-distilled MiniMax-H3 model for synchronized video-and-audio generation, developed in collaboration with Nuva Lab and the NVIDIA FastGen team. Download the recommended VSA / Data-Free weights, or see the full FastH3 collection.
2026/08/19: FastVideo now supports MLX on Apple Silicon with FastMetal-QAD, a family of 1.3B, 5B, and 14B models optimized for Mac. Follow the MLX install guide and read the Blog.

More News

All announcements and earlier milestones.

Key Features

FastVideo has the following features:

End-to-end post-training support for bidirectional and autoregressive models:
- Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs
- Data preprocessing pipeline for video, image, and text data
- Distribution Matching Distillation (DMD2) stepwise distillation.
- Sparse attention with Video Sparse Attention
- Sparse distillation to achieve >50x denoising speedup
- Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing.
- Causal distillation through Self-Forcing
- See this page for the supported training workflows, and the support matrix for supported models.
State-of-the-art performance optimizations for inference
- Sequence Parallelism for distributed inference
- Multiple state-of-the-art attention backends
- User-friendly CLI and Python API
- See this page for full list of supported optimizations.
Diverse hardware and OS support
- Support H100, A100, 4090
- Support Linux, Windows, MacOS
- See this page for full list of supported models, hardware assumptions, and optimization compatibility.
Realtime video generation & editing
- Dreamverse: stream and "vibe direct" video in realtime (live demo), deployable on local GPU, a self-hosted B200 server, Docker, or serverless Modal

Getting Started

Install FastVideo with uv:

uv venv --python 3.12 --seed
source .venv/bin/activate

# NVIDIA CUDA 12; use cu130 on CUDA 13
UV_TORCH_BACKEND=cu126 uv pip install fastvideo

Platform guides: NVIDIA GPUs, DGX Spark / GB10, Apple Silicon MLX, AMD / ROCm — or the full installation guide.

To have a coding agent do the install, give it the agent install prompt.

Sparse Distillation

Sparse distillation reaches >50× denoising speedup. See the distillation guide and the blog; the training recipes live under examples/distill/ with the FastVideo Synthetic datasets.

Dreamverse — Realtime Video Generation & Editing

Dreamverse is FastVideo's realtime video generation and editing platform — "vibe directing" a video as it streams. It lives in the monorepo under apps/dreamverse/ and ships its own backend (dreamverse-server) plus a web UI.

Try the live demo, read the blog, or run it yourself. Dreamverse deploys on a local GPU, a self-hosted B200 server over SSH, Docker, or serverless Modal — see the Dreamverse README.

Inference

Generating Your First Video

Here's a minimal example to generate a video using the default settings. Make sure VSA kernels are installed. Create a file called example.py with the following code:

import os
from fastvideo import VideoGenerator

def main():
    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"

    # Create a video generator with a pre-trained model
    generator = VideoGenerator.from_pretrained(
        "FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
        num_gpus=1,  # Adjust based on your hardware
    )

    # Define a prompt for your video
    prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest."

    # Generate the video
    video = generator.generate_video(
        prompt,
        output_path="my_videos/",  # Controls where videos are saved
        save_video=True
    )

if __name__ == '__main__':
    main()

num_gpus=1 runs the worker in-process (weights load once, no extra Python process). On Colab/Kaggle-style machines with ~16GB host RAM, keep num_gpus=1; free-tier system memory does not grow with extra T4s, so num_gpus>1 is likely to OOM.

Run the script with:

python example.py

For a more detailed guide, please see our inference quick start.

More Guides

Awesome work using FastVideo or our research projects

SGLang: SGLang's diffusion inference functionality is based on a fork of FastVideo on Sept. 24, 2025.
DanceGRPO: A unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo.
SRPO: A method to directly align the full diffusion trajectory with fine-grained human preference. Code based on FastVideo.
DCM: Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo.
HY-WorldPlay: An action-conditioned world model model trained using FastVideo framework.
Hunyuan Video 1.5: A leading lightweight video generation model, where they proposed SSTA based on Sliding Tile Attention.
Kandinsky-5.0: A family of diffusion models for video & image generation, where their NABLA attention includes a Sliding Tile Attention branch.
LongCat Video: A foundational video generation model with 13.6B parameters with block-sparse attention similar to Video Sparse Attention.

🤝 Contributing

We welcome all contributions. Please check out our guide here. See details in development roadmap.

Acknowledgement

We learned the design and reused code from the following projects: Wan-Video, ThunderKittens, DMD2, diffusers, xDiT, vLLM, SGLang. We thank MBZUAI, Anyscale, and GMI Cloud for their support throughout this project.

Citation

If you find FastVideo useful, please consider citing our research work:

@article{zhang2025vsa,
  title={Vsa: Faster video diffusion with trainable sparse attention},
  author={Zhang, Peiyuan and Chen, Yongqi and Huang, Haofeng and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
  journal={arXiv preprint arXiv:2505.13389},
  year={2025}
}

@article{zhang2025fast,
  title={Fast video generation with sliding tile attention},
  author={Zhang, Peiyuan and Chen, Yongqi and Su, Runlong and Ding, Hangliang and Stoica, Ion and Liu, Zhengzhong and Zhang, Hao},
  journal={arXiv preprint arXiv:2502.04507},
  year={2025}
}

README.md Unescape Escape

Recent news