ComfyUI Extension: ComfyUI-FastVideo

Authored by hao-ai-lab

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A custom node suite for ComfyUI that provides accelerated multi-GPU video generation using a/FastVideo.

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    README

    <div align="center"> <img src=assets/logo.jpg width="30%"/> </div>

    FastVideo is a unified framework for accelerated video generation.

    It features a clean, consistent API that works across popular video models, making it easier for developers to author new models and incorporate system- or kernel-level optimizations. With FastVideo's optimizations, you can achieve more than 3x inference improvement compared to other systems.

    <p align="center"> | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start.html"><b> Quick Start</b></a> | 🤗 <a href="https://huggingface.co/FastVideo/FastHunyuan" target="_blank"><b>FastHunyuan</b></a> | 🤗 <a href="https://huggingface.co/FastVideo/FastMochi-diffusers" target="_blank"><b>FastMochi</b></a> | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ" target="_blank"> <b>Slack</b> </a> | </p> <div align="center"> <img src=assets/perf.png width="90%"/> </div>

    NEWS

    Key Features

    FastVideo has the following features:

    • State-of-the-art performance optimizations for inference
    • Cutting edge models
      • Wan2.1 T2V, I2V
      • HunyuanVideo
      • FastHunyuan: consistency distilled video diffusion models for 8x inference speedup.
      • StepVideo T2V
    • Distillation support
      • Recipes for video DiT, based on PCM.
      • Support distilling/finetuning/inferencing state-of-the-art open video DiTs: 1. Mochi 2. Hunyuan.
    • Scalable training with FSDP, sequence parallelism, and selective activation checkpointing, with near linear scaling to 64 GPUs.
    • Memory efficient finetuning with LoRA, precomputed latent, and precomputed text embeddings.

    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.

    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:

    Distillation and Finetuning

    📑 Development Plan

    <!-- - More distillation methods --> <!-- - [ ] Add Distribution Matching Distillation -->
    • More models support <!-- - [ ] Add CogvideoX model -->
      • [x] Add StepVideo to V1
    • Optimization features
      • [x] Teacache in V1
      • [x] SageAttention in V1
    • Code updates
      • [x] V1 Configuration API
      • [ ] Support Training in V1
      <!-- - [ ] fp8 support --> <!-- - [ ] faster load model and save model support -->

    🤝 Contributing

    We welcome all contributions. Please check out our guide here

    Acknowledgement

    We learned and reused code from the following projects:

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

    Citation

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

    @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},
}
@misc{ding2025efficientvditefficientvideodiffusion,
      title={Efficient-vDiT: Efficient Video Diffusion Transformers With Attention Tile},
      author={Hangliang Ding and Dacheng Li and Runlong Su and Peiyuan Zhang and Zhijie Deng and Ion Stoica and Hao Zhang},
      year={2025},
      eprint={2502.06155},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.06155},
}