title: Diffusion Adversarial Post-Training for One-Step Video Generation
summary: The diffusion models are widely used for image and video generation, but
  their iterative generation process is slow and expansive. While existing
  distillation approaches have demonstrated the potential for one-step generation
  in the image domain, they still suffer from significant quality degradation. In
  this work, we propose Adversarial Post-Training (APT) against real data
  following diffusion pre-training for one-step video generation. To improve the
  training stability and quality, we introduce several improvements to the model
  architecture and training procedures, along with an approximated R1
  regularization objective. Empirically, our experiments show that our
  adversarial post-trained model, Seaweed-APT, can generate 2-second, 1280x720,
  24fps videos in real time using a single forward evaluation step. Additionally,
  our model is capable of generating 1024px images in a single step, achieving
  quality comparable to state-of-the-art methods.
paper_authors._id: 678725b38c1e7b6c4a69f88a
paper_authors.name: Shanchuan Lin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-15T08:48:34.462Z
paper_authors._id: 645863f7dc18eb1a9b5d29df
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/645863f7dc18eb1a9b5d29df/t49Nnyl4tbkUn7CmQqKZh.jpeg
paper_authors.fullname: Peter Lin
paper_authors.type: user
paper_authors.user: PeterL1n
paper_authors._id: 678725b38c1e7b6c4a69f88b
paper_authors.name: Xin Xia
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-17T10:26:06.599Z
paper_authors._id: 63089a78ff78e2aead8d10e7
paper_authors.avatarUrl: /avatars/f326fd08abee7e31599a78923be30003.svg
paper_authors.fullname: XinXia
paper_authors.type: user
paper_authors.user: XiaXin-Aloys
paper_authors._id: 678725b38c1e7b6c4a69f88c
paper_authors.name: Yuxi Ren
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-15T16:46:42.832Z
paper_authors._id: 6618d5e83b412cdc85334ca8
paper_authors.avatarUrl: /avatars/5fe356d58c4c822a60370dbee8d78a69.svg
paper_authors.fullname: renyuxi
paper_authors.type: user
paper_authors.user: renyuxi
paper_authors._id: 678725b38c1e7b6c4a69f88d
paper_authors.name: Ceyuan Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-15T16:46:55.730Z
paper_authors._id: 64accf3fc1da7c4dbc69392d
paper_authors.avatarUrl: /avatars/ec8ad2498f8a49a7a00c77a7f70d34bb.svg
paper_authors.fullname: Ceyuan
paper_authors.type: user
paper_authors.user: Ceyuan
paper_authors._id: 678725b38c1e7b6c4a69f88e
paper_authors.name: Xuefeng Xiao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-15T16:47:01.595Z
paper_authors._id: 646b7f71df2609a541c1ab9f
paper_authors.avatarUrl: /avatars/48b82e5fd9b06f41ff825507c36816cd.svg
paper_authors.fullname: Xuefeng Xiao
paper_authors.type: user
paper_authors.user: xiaoxuefeng
paper_authors._id: 678725b38c1e7b6c4a69f88f
paper_authors.name: Lu Jiang
paper_title: Diffusion Adversarial Post-Training for One-Step Video Generation
paper_summary: The diffusion models are widely used for image and video generation, but
  their iterative generation process is slow and expansive. While existing
  distillation approaches have demonstrated the potential for one-step generation
  in the image domain, they still suffer from significant quality degradation. In
  this work, we propose Adversarial Post-Training (APT) against real data
  following diffusion pre-training for one-step video generation. To improve the
  training stability and quality, we introduce several improvements to the model
  architecture and training procedures, along with an approximated R1
  regularization objective. Empirically, our experiments show that our
  adversarial post-trained model, Seaweed-APT, can generate 2-second, 1280x720,
  24fps videos in real time using a single forward evaluation step. Additionally,
  our model is capable of generating 1024px images in a single step, achieving
  quality comparable to state-of-the-art methods.
paper_ai_summary: Adversarial Post-Training improves the quality and speed of one-step video and image generation using diffusion models.
paper_ai_keywords: diffusion models one-step generation adversarial post-training R1 regularization Seaweed-APT real-time video generation