aejion/AccVideo

Model

52

stars

7

commits

3

linked in READMEs

Mar 28, 2025

updated

diffusers
safetensors
text-to-video

README

AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset

This repository contains the pre-trained weights of AccVideo. AccVideo is a novel efficient distillation method to accelerate video diffusion models with synthetic datset. Our method is 8.5x faster than HunyuanVideo.

arXivProject Page

πŸ”₯πŸ”₯πŸ”₯ News

  • Mar, 2025: We release the inference code and model weights of AccVideo.

πŸ“‘ Open-source Plan

  • Inference
  • Checkpoints
  • Multi-GPU Inference
  • Synthetic Video Dataset, SynVid
  • Training

πŸ”§ Installation

The code is tested on Python 3.10.0, CUDA 11.8 and A100.

conda create -n accvideo python==3.10.0
conda activate accvideo

pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
pip install flash-attn==2.7.3 --no-build-isolation
pip install "huggingface_hub[cli]"

πŸ€— Checkpoints

To download the checkpoints, use the following command:

# Download the model weight
huggingface-cli download aejion/AccVideo --local-dir ./ckpts

πŸš€ Inference

We recommend using a GPU with 80GB of memory. To run the inference, use the following command:

export MODEL_BASE=./ckpts
python sample_t2v.py \
    --height 544 \
    --width 960 \
    --num_frames 93 \
    --num_inference_steps 50 \
    --guidance_scale 1 \
    --embedded_cfg_scale 6 \
    --flow_shift 7 \
    --flow-reverse \
    --prompt_file ./assets/prompt.txt \
    --seed 1024 \
    --output_path ./results/accvideo-544p \
    --model_path ./ckpts \
    --dit-weight ./ckpts/accvideo-t2v-5-steps/diffusion_pytorch_model.pt

The following table shows the comparisons on inference time using a single A100 GPU:

ModelSetting(height/width/frame)Inference Time(s)
HunyuanVideo720px1280px129f3234
Ours720px1280px129f380(8.5x faster)
HunyuanVideo544px960px93f704
Ours544px960px93f91(7.7x faster)

πŸ”— BibTeX

If you find AccVideo useful for your research and applications, please cite using this BibTeX:

@article{zhang2025accvideo,
    title={AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset},
    author={Zhang, Haiyu and Chen, Xinyuan and Wang, Yaohui and Liu, Xihui and Wang, Yunhong and Qiao, Yu},
    journal={arXiv preprint arXiv:2503.19462},
    year={2025}
}

Acknowledgements

The code is built upon FastVideo and HunyuanVideo, we thank all the contributors for open-sourcing.

Contributors

aejion

6 commits

nielsr

1 commits

aejion/AccVideo

Model

52

stars

7

commits

3

linked in READMEs

Mar 28, 2025

updated

diffusers
safetensors
text-to-video

README

AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset

This repository contains the pre-trained weights of AccVideo. AccVideo is a novel efficient distillation method to accelerate video diffusion models with synthetic datset. Our method is 8.5x faster than HunyuanVideo.

arXivProject Page

πŸ”₯πŸ”₯πŸ”₯ News

  • Mar, 2025: We release the inference code and model weights of AccVideo.

πŸ“‘ Open-source Plan

  • Inference
  • Checkpoints
  • Multi-GPU Inference
  • Synthetic Video Dataset, SynVid
  • Training

πŸ”§ Installation

The code is tested on Python 3.10.0, CUDA 11.8 and A100.

conda create -n accvideo python==3.10.0
conda activate accvideo

pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
pip install flash-attn==2.7.3 --no-build-isolation
pip install "huggingface_hub[cli]"

πŸ€— Checkpoints

To download the checkpoints, use the following command:

# Download the model weight
huggingface-cli download aejion/AccVideo --local-dir ./ckpts

πŸš€ Inference

We recommend using a GPU with 80GB of memory. To run the inference, use the following command:

export MODEL_BASE=./ckpts
python sample_t2v.py \
    --height 544 \
    --width 960 \
    --num_frames 93 \
    --num_inference_steps 50 \
    --guidance_scale 1 \
    --embedded_cfg_scale 6 \
    --flow_shift 7 \
    --flow-reverse \
    --prompt_file ./assets/prompt.txt \
    --seed 1024 \
    --output_path ./results/accvideo-544p \
    --model_path ./ckpts \
    --dit-weight ./ckpts/accvideo-t2v-5-steps/diffusion_pytorch_model.pt

The following table shows the comparisons on inference time using a single A100 GPU:

ModelSetting(height/width/frame)Inference Time(s)
HunyuanVideo720px1280px129f3234
Ours720px1280px129f380(8.5x faster)
HunyuanVideo544px960px93f704
Ours544px960px93f91(7.7x faster)

πŸ”— BibTeX

If you find AccVideo useful for your research and applications, please cite using this BibTeX:

@article{zhang2025accvideo,
    title={AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset},
    author={Zhang, Haiyu and Chen, Xinyuan and Wang, Yaohui and Liu, Xihui and Wang, Yunhong and Qiao, Yu},
    journal={arXiv preprint arXiv:2503.19462},
    year={2025}
}

Acknowledgements

The code is built upon FastVideo and HunyuanVideo, we thank all the contributors for open-sourcing.

Contributors

aejion

6 commits

nielsr

1 commits