title: VideoLCM: Video Latent Consistency Model
summary: Consistency models have demonstrated powerful capability in efficient image
  generation and allowed synthesis within a few sampling steps, alleviating the
  high computational cost in diffusion models. However, the consistency model in
  the more challenging and resource-consuming video generation is still less
  explored. In this report, we present the VideoLCM framework to fill this gap,
  which leverages the concept of consistency models from image generation to
  efficiently synthesize videos with minimal steps while maintaining high
  quality. VideoLCM builds upon existing latent video diffusion models and
  incorporates consistency distillation techniques for training the latent
  consistency model. Experimental results reveal the effectiveness of our
  VideoLCM in terms of computational efficiency, fidelity and temporal
  consistency. Notably, VideoLCM achieves high-fidelity and smooth video
  synthesis with only four sampling steps, showcasing the potential for real-time
  synthesis. We hope that VideoLCM can serve as a simple yet effective baseline
  for subsequent research. The source code and models will be publicly available.
paper_authors._id: 657bd3317e11375c6f28d62d
paper_authors.name: Xiang Wang
paper_authors._id: 657bd3317e11375c6f28d62e
paper_authors.name: Shiwei Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-15T10:10:30.162Z
paper_authors._id: 63a1200045edac9f7508bae9
paper_authors.avatarUrl: /avatars/e84f0b045f32c5b8b4da43458650b925.svg
paper_authors.fullname: Shiwei Zhang
paper_authors.type: user
paper_authors.user: StevenZhang
paper_authors._id: 657bd3317e11375c6f28d62f
paper_authors.name: Han Zhang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-12-15T07:04:32.498Z
paper_authors._id: 63c3b12da361002ba0ae5381
paper_authors.avatarUrl: /avatars/f505a612c7698b5975a1daa3cca6a1c3.svg
paper_authors.fullname: Han Zhang
paper_authors.type: user
paper_authors.user: bibona
paper_authors._id: 657bd3317e11375c6f28d630
paper_authors.name: Yu Liu
paper_authors._id: 657bd3317e11375c6f28d631
paper_authors.name: Yingya Zhang
paper_authors._id: 657bd3317e11375c6f28d632
paper_authors.name: Changxin Gao
paper_authors._id: 657bd3317e11375c6f28d633
paper_authors.name: Nong Sang
paper_title: VideoLCM: Video Latent Consistency Model
paper_summary: Consistency models have demonstrated powerful capability in efficient image
  generation and allowed synthesis within a few sampling steps, alleviating the
  high computational cost in diffusion models. However, the consistency model in
  the more challenging and resource-consuming video generation is still less
  explored. In this report, we present the VideoLCM framework to fill this gap,
  which leverages the concept of consistency models from image generation to
  efficiently synthesize videos with minimal steps while maintaining high
  quality. VideoLCM builds upon existing latent video diffusion models and
  incorporates consistency distillation techniques for training the latent
  consistency model. Experimental results reveal the effectiveness of our
  VideoLCM in terms of computational efficiency, fidelity and temporal
  consistency. Notably, VideoLCM achieves high-fidelity and smooth video
  synthesis with only four sampling steps, showcasing the potential for real-time
  synthesis. We hope that VideoLCM can serve as a simple yet effective baseline
  for subsequent research. The source code and models will be publicly available.
paper_ai_keywords: consistency models image generation video generation sampling steps computational cost Prompt Models VideoLCM noise constraint models latent video diffusion models consistency distillation techniques latent consistency model computational efficiency fidelity temporal consistency real-time synthesis