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aejion/
AccVideo-WanX-I2V-480P-14B

Name: AccVideo-WanX-I2V-480P-14B
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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.

arXiv

🔥🔥🔥 News

Jun 3, 2025: We release the inference code and model weights of AccVideo based on WanXI2V-480P-14B.
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.

🎥 Demo (Based on HunyuanT2V)

https://github.com/user-attachments/assets/59f3c5db-d585-4773-8d92-366c1eb040f0

🎥 Demo (Based on WanXT2V-14B)

https://github.com/user-attachments/assets/ff9724da-b76c-478d-a9bf-0ee7240494b2

🎥 Demo (Based on WanXI2V-480P-14B)

📑 Open-source Plan

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

🔧 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]"

🤗 Checkpoints

To download the checkpoints (based on HunyuanT2V), use the following command:

bash

# 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:

bash

# Download the model weight
huggingface-cli download aejion/AccVideo-WanX-T2V-14B --local-dir ./wanx_t2v_ckpts

To download the checkpoints (based on WanX-I2V-480P-14B), use the following command:

bash

# Download the model weight
huggingface-cli download aejion/AccVideo-WanX-I2V-480P-14B --local-dir ./wanx_i2v_ckpts

🚀 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:

bash

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:

bash

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)

Inference for WanXI2V-480P

To run the inference, use the following command:

bash

python sample_wanx_i2v.py \
       --task i2v-14B \
       --size 832*480 \
       --ckpt_dir ./wanx_i2v_ckpts \
       --sample_solver 'unipc' \
       --save_dir ./results/accvideo_wanx_i2v_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-I2V	480px832px81f	768
Ours	480px832px81f	112(6.8x faster)

🏆 VBench Results

We report VBench evaluation results for our distilled models. We utilized the respective augmented prompts provided by the VBench team to generate videos. (HunyuanVideo augmented prompts for AccVideo-HunyuanT2V and WanX augmented prompts for AccVideo-WanXT2V)

Model	Setting(height/width/frame)	Total Score	Quality Score	Semantic Score	Subject Consistency	Background Consistency	Temporal Flickering	Motion Smoothness	Dynamic Degree	Aesthetic Quality	Image Quality	Object Class	Multiple Objects	Human Action	Color	Spatial Relationship	Scene	Appearance Style	Temporal Style	Overall Consistency
AccVideo-HunyuanT2V	544px960px93f	83.26%	84.58%	77.96%	94.46%	97.45%	99.18%	98.79%	75.00%	62.08%	65.64%	92.99%	67.33%	95.60%	94.11%	75.70%	54.72%	19.87%	23.71%	27.21%
AccVideo-WanXT2V	480px832px81f	85.95%	86.62%	83.25%	95.02%	97.75%	99.54%	97.95%	93.33%	64.21%	68.42%	98.38%	86.58%	97.40%	92.04%	75.68%	59.82%	23.88%	24.62%	27.34%

🔗 BibTeX

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

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.