AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset

• URL: http://arxiv.org/abs/2503.19462v1
• Date: Tue, 25 Mar 2025 08:52:07 GMT
• Title: AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset
• Authors: Haiyu Zhang, Xinyuan Chen, Yaohui Wang, Xihui Liu, Yunhong Wang, Yu Qiao,
• Abstract summary: We propose AccVideo to reduce the inference steps for accelerating video diffusion models with synthetic dataset.<n>Our model achieves 8.5x improvements in generation speed compared to the teacher model.<n>Compared to previous accelerating methods, our approach is capable of generating videos with higher quality and resolution.
• Score: 55.82208863521353
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion models have achieved remarkable progress in the field of video generation. However, their iterative denoising nature requires a large number of inference steps to generate a video, which is slow and computationally expensive. In this paper, we begin with a detailed analysis of the challenges present in existing diffusion distillation methods and propose a novel efficient method, namely AccVideo, to reduce the inference steps for accelerating video diffusion models with synthetic dataset. We leverage the pretrained video diffusion model to generate multiple valid denoising trajectories as our synthetic dataset, which eliminates the use of useless data points during distillation. Based on the synthetic dataset, we design a trajectory-based few-step guidance that utilizes key data points from the denoising trajectories to learn the noise-to-video mapping, enabling video generation in fewer steps. Furthermore, since the synthetic dataset captures the data distribution at each diffusion timestep, we introduce an adversarial training strategy to align the output distribution of the student model with that of our synthetic dataset, thereby enhancing the video quality. Extensive experiments demonstrate that our model achieves 8.5x improvements in generation speed compared to the teacher model while maintaining comparable performance. Compared to previous accelerating methods, our approach is capable of generating videos with higher quality and resolution, i.e., 5-seconds, 720x1280, 24fps.

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