AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset
This repository is the official PyTorch implementation 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.
π₯π₯π₯ News
- May 26, 2025: We release the inference code and model weights of AccVideo based on WanXT2V-14B.
- Mar 31, 2025: ComfyUI-Kijai (FP8 Inference): ComfyUI-Integration by Kijai
- Mar 26, 2025: We release the inference code and model weights of AccVideo based on HunyuanT2V.
π₯ Demo (Based on HunyuanT2V)
https://github.com/user-attachments/assets/59f3c5db-d585-4773-8d92-366c1eb040f0
π₯ Demo (Based on WanXT2V-14B)
π Open-source Plan
- Inference
- Checkpoints
- Multi-GPU Inference
- Synthetic Video Dataset, SynVid
- Training
π§ 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]"
π€ Checkpoints
To download the checkpoints (based on HunyuanT2V), use the following command:
# Download the model weight
huggingface-cli download aejion/AccVideo --local-dir ./ckpts
To download the checkpoints (based on WanX-T2V-14B), use the following command:
# Download the model weight
huggingface-cli download aejion/AccVideo-WanX-T2V-14B --local-dir ./wanx_t2v_ckpts
π Inference
We recommend using a GPU with 80GB of memory. We use AccVideo to distill Hunyuan and WanX.
Inference for HunyuanT2V
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 5 \
--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:
Model | Setting(height/width/frame) | Inference Time(s) |
---|---|---|
HunyuanVideo | 720px1280px129f | 3234 |
Ours | 720px1280px129f | 380(8.5x faster) |
HunyuanVideo | 544px960px93f | 704 |
Ours | 544px960px93f | 91(7.7x faster) |
Inference for WanXT2V
To run the inference, use the following command:
python sample_wanx_t2v.py \
--task t2v-14B \
--size 832*480 \
--ckpt_dir ./wanx_t2v_ckpts \
--sample_solver 'unipc' \
--save_dir ./results/accvideo_wanx_14B \
--sample_steps 10
The following table shows the comparisons on inference time using a single A100 GPU:
Model | Setting(height/width/frame) | Inference Time(s) |
---|---|---|
Wanx | 480px832px81f | 932 |
Ours | 480px832px81f | 97(9.6x faster) |
π 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.
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