title: FasterCache: Training-Free Video Diffusion Model Acceleration with High
    Quality
summary: In this paper, we present \textit{FasterCache}, a novel
  training-free strategy designed to accelerate the inference of video diffusion
  models with high-quality generation. By analyzing existing cache-based methods,
  we observe that directly reusing adjacent-step features degrades video
  quality due to the loss of subtle variations. We further perform a pioneering
  investigation of the acceleration potential of classifier-free guidance (CFG)
  and reveal significant redundancy between conditional and unconditional
  features within the same timestep. Capitalizing on these observations, we
  introduce FasterCache to substantially accelerate diffusion-based video
  generation. Our key contributions include a dynamic feature reuse strategy that
  preserves both feature distinction and temporal continuity, and CFG-Cache which
  optimizes the reuse of conditional and unconditional outputs to further enhance
  inference speed without compromising video quality. We empirically evaluate
  FasterCache on recent video diffusion models. Experimental results show that
  FasterCache can significantly accelerate video generation (\eg 1.67times
  speedup on Vchitect-2.0) while keeping video quality comparable to the
  baseline, and consistently outperform existing methods in both inference speed
  and video quality.
paper_authors._id: 671f085f943be9e8dfcd3e6b
paper_authors.name: Zhengyao Lv
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-28T15:22:46.709Z
paper_authors._id: 63243004dedcbcf92830e76b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63243004dedcbcf92830e76b/Ftv8UFhHQFMi3utTCoDVF.png
paper_authors.fullname: Zhengyao Lu
paper_authors.type: user
paper_authors.user: zlu
paper_authors._id: 671f085f943be9e8dfcd3e6c
paper_authors.name: Chenyang Si
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-28T15:24:46.068Z
paper_authors._id: 635f8ed47c05eb9f59963d3a
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paper_authors.fullname: ChenyangSi
paper_authors.type: user
paper_authors.user: ChenyangSi
paper_authors._id: 671f085f943be9e8dfcd3e6d
paper_authors.name: Junhao Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-28T15:24:38.601Z
paper_authors._id: 653a88011f981e43044ccd7d
paper_authors.avatarUrl: /avatars/d7aaa588ab1b2491169523666e77d8a9.svg
paper_authors.fullname: Junhao
paper_authors.type: user
paper_authors.user: JunhaoSong
paper_authors._id: 671f085f943be9e8dfcd3e6e
paper_authors.name: Zhenyu Yang
paper_authors._id: 671f085f943be9e8dfcd3e6f
paper_authors.name: Yu Qiao
paper_authors._id: 671f085f943be9e8dfcd3e70
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-28T15:23:53.295Z
paper_authors._id: 62ab1ac1d48b4d8b048a3473
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
paper_authors.fullname: Ziwei Liu
paper_authors.type: user
paper_authors.user: liuziwei7
paper_authors._id: 671f085f943be9e8dfcd3e71
paper_authors.name: Kwan-Yee K. Wong
paper_title: FasterCache: Training-Free Video Diffusion Model Acceleration with High
    Quality
paper_summary: In this paper, we present \textit{FasterCache}, a novel
  training-free strategy designed to accelerate the inference of video diffusion
  models with high-quality generation. By analyzing existing cache-based methods,
  we observe that directly reusing adjacent-step features degrades video
  quality due to the loss of subtle variations. We further perform a pioneering
  investigation of the acceleration potential of classifier-free guidance (CFG)
  and reveal significant redundancy between conditional and unconditional
  features within the same timestep. Capitalizing on these observations, we
  introduce FasterCache to substantially accelerate diffusion-based video
  generation. Our key contributions include a dynamic feature reuse strategy that
  preserves both feature distinction and temporal continuity, and CFG-Cache which
  optimizes the reuse of conditional and unconditional outputs to further enhance
  inference speed without compromising video quality. We empirically evaluate
  FasterCache on recent video diffusion models. Experimental results show that
  FasterCache can significantly accelerate video generation (\eg 1.67times
  speedup on Vchitect-2.0) while keeping video quality comparable to the
  baseline, and consistently outperform existing methods in both inference speed
  and video quality.
paper_ai_summary: FasterCache, a training-free strategy, accelerates video diffusion model inference by dynamically reusing features while maintaining video quality.
paper_ai_keywords: video diffusion models feature reuse classifier-free guidance (CFG) dynamic feature reuse strategy temporal continuity Vchitect-2.0