ComfyUI Extension: FastVideo

Authored by hao-ai-lab

Created about a year ago

Updated 4 days ago

2,738 stars

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

Custom Nodes (0)

README

<div align="center"> <img src=assets/logos/logo.svg width="30%"/> </div> **FastVideo is a unified post-training and inference framework for accelerated video generation.**

FastVideo 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.

<p align="center"> | 🕹️ <a href="https://fastwan.fastvideo.org/"<b>Online Demo</b></a> | <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/"><b> Quick Start</b></a> | 🤗 <a href="https://huggingface.co/collections/FastVideo/fastwan-6886a305d9799c8cd1496408" target="_blank"><b>FastWan</b></a> | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> | 🟣💬 <a href="https://ibb.co/TM8JyJCd" target="_blank"> <b> WeChat </b> </a> | </p> <div align="center"> <img src=assets/fastwan.png width="90%"/> </div>

NEWS

2025/11/19: Release CausalWan2.2 I2V A14B Preview models, Blog and Inference Code!
2025/08/04: Release FastWan models and Sparse-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:

End-to-end post-training support:
- Sparse distillation for Wan2.1 and Wan2.2 to achineve >50x denoising speedup
- Data preprocessing pipeline for video data
- 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
State-of-the-art performance optimizations for inference
Diverse hardware and OS support
- Support H100, A100, 4090
- Support Linux, Windows, MacOS

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:

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,
        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

Distillation Guide

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.
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 and reused code from the following projects:

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

Citation

If you find FastVideo useful, please considering citing 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},
}

@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}
}