title: AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset
summary: 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.
paper_authors._id: 67e3641cd8da46951f860d84
paper_authors.name: Haiyu Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:02:45.776Z
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paper_authors.fullname: Haiyu Zhang
paper_authors.type: user
paper_authors.user: aejion
paper_authors._id: 67e3641cd8da46951f860d85
paper_authors.name: Xinyuan Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:02:58.635Z
paper_authors._id: 643e943a70c6a27621eb1c89
paper_authors.avatarUrl: /avatars/73ec521ab5ba84cc7908c52c0acef6ef.svg
paper_authors.fullname: Xinyuan Chen
paper_authors.type: user
paper_authors.user: AriaChen
paper_authors._id: 67e3641cd8da46951f860d86
paper_authors.name: Yaohui Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:03:18.769Z
paper_authors._id: 63201256c6b20f03c829c4b8
paper_authors.avatarUrl: /avatars/a42092119777d65e60b12eb5ba0e45f1.svg
paper_authors.fullname: Yaohui Wang
paper_authors.type: user
paper_authors.user: YaohuiW
paper_authors._id: 67e3641cd8da46951f860d87
paper_authors.name: Xihui Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:03:26.468Z
paper_authors._id: 65d5ec74cd05bc1eaa125040
paper_authors.avatarUrl: /avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
paper_authors.fullname: Xihui Liu
paper_authors.type: user
paper_authors.user: XihuiLiu
paper_authors._id: 67e3641cd8da46951f860d88
paper_authors.name: Yunhong Wang
paper_authors._id: 67e3641cd8da46951f860d89
paper_authors.name: Yu Qiao
paper_title: AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset
paper_summary: 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.
paper_ai_summary: AccVideo accelerates video diffusion models by using a synthetic dataset and trajectory-based guidance, significantly reducing inference steps while maintaining or improving video quality and resolution.
paper_ai_keywords: diffusion models denoising inference steps diffusion distillation synthetic dataset pretrained video diffusion model denoising trajectories noise-to-video mapping adversarial training output distribution