FastVideo is a unified framework for accelerated video generation.

FastVideo is an inference and post-training framework for diffusion models. It features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.

| Documentation | Quick Start | 🤗 FastWan2.1 | 🤗 FastWan2.2 | 🟣💬 Slack |

NEWS

• 2025/08/04: Release FastWan models andSparse-Distillation.
• 2025/06/14: Release finetuning and inference code for VSA
• 2025/04/24: FastVideo V1 is released!
• 2025/02/18: Release the inference code for Sliding Tile Attention.

Key Features

FastVideo has the following features:

• State-of-the-art performance optimizations for inference
  • Sliding Tile Attention
  • TeaCache
  • Sage Attention
• E2E post-training support
  • Data preprocessing pipeline for video data.
  • Sparse distillation for Wan2.1 and Wan2.2 using Video Sparse Attention and Distribution Matching Distillation
  • Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs.
  • Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing, with near linear scaling to 64 GPUs.

Getting Started

We recommend using an environment manager such as Conda to create a clean environment:

# Create and activate a new conda environment
conda create -n fastvideo python=3.12
conda activate fastvideo

# Install FastVideo
pip install fastvideo

Please see our docs for more detailed installation instructions.

Sparse Distillation

For our sparse distillation techniques, please see our distillation docs and check out our blog.

See below for recipes and datasets:

Model	Sparse Distillation	Dataset
FastWan2.1-T2V-1.3B	Recipe	FastVideo Synthetic Wan2.1 480P
FastWan2.1-T2V-14B-Preview	Coming soon!	FastVideo Synthetic Wan2.1 720P
FastWan2.2-TI2V-5B	Recipe	FastVideo Synthetic Wan2.2 720P

Inference

Generating Your First Video

Here's a minimal example to generate a video using the default settings. Create a file called example.py with the following code:

from fastvideo import VideoGenerator

def main():
    # Create a video generator with a pre-trained model
    generator = VideoGenerator.from_pretrained(
        "Wan-AI/Wan2.1-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,
        return_frames=True,  # Also return frames from this call (defaults to False)
        output_path="my_videos/",  # Controls where videos are saved
        save_video=True
    )

if __name__ == '__main__':
    main()

Run the script with:

python example.py

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

Other docs:

• Design Overview
• Contribution Guide

Distillation and Finetuning

• Distillation Guide
• Finetuning Guide

📑 Development Plan

• More models support
  • Add StepVideo to V1
• Optimization features
  • Teacache in V1
  • SageAttention in V1
• Code updates
  • V1 Configuration API
  • Support Training in V1

🤝 Contributing

We welcome all contributions. Please check out our guide here

Acknowledgement

We learned and reused code from the following projects:

• Wan-Video
• ThunderKittens
• Triton
• DMD2
• diffusers
• xDiT
• vLLM
• SGLang

We thank MBZUAI and Anyscale for their support throughout this project.

Citation

If you use FastVideo for your research, please cite our work:

@software{fastvideo2024,
  title        = {FastVideo: A Unified Framework for Accelerated Video Generation},
  author       = {The FastVideo Team},
  url          = {https://github.com/hao-ai-lab/FastVideo},
  month        = apr,
  year         = {2024},
}

@misc{zhang2025vsafastervideodiffusion,
      title={VSA: Faster Video Diffusion with Trainable Sparse Attention}, 
      author={Peiyuan Zhang and Haofeng Huang and Yongqi Chen and Will Lin and Zhengzhong Liu and Ion Stoica and Eric Xing and Hao Zhang},
      year={2025},
      eprint={2505.13389},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.13389}, 
}
@misc{zhang2025fastvideogenerationsliding,
      title={Fast Video Generation with Sliding Tile Attention},
      author={Peiyuan Zhang and Yongqi Chen and Runlong Su and Hangliang Ding and Ion Stoica and Zhenghong Liu and Hao Zhang},
      year={2025},
      eprint={2502.04507},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.04507},
}