title: TPDiff: Temporal Pyramid Video Diffusion Model
summary: The development of video diffusion models unveils a significant challenge:
  the substantial computational demands. To mitigate this challenge, we note that
  the reverse process of diffusion exhibits an inherent entropy-reducing nature.
  Given the inter-frame redundancy in video modality, maintaining full frame
  rates in high-entropy stages is unnecessary. Based on this insight, we propose
  TPDiff, a unified framework to enhance training and inference efficiency. By
  dividing diffusion into several stages, our framework progressively increases
  frame rate along the diffusion process with only the last stage operating on
  full frame rate, thereby optimizing computational efficiency. To train the
  multi-stage diffusion model, we introduce a dedicated training framework:
  stage-wise diffusion. By solving the partitioned probability flow ordinary
  differential equations (ODE) of diffusion under aligned data and noise, our
  training strategy is applicable to various diffusion forms and further enhances
  training efficiency. Comprehensive experimental evaluations validate the
  generality of our method, demonstrating 50% reduction in training cost and 1.5x
  improvement in inference efficiency.
paper_authors._id: 67d274c467e782a7eeb4ab70
paper_authors.name: Lingmin Ran
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T11:19:50.355Z
paper_authors._id: 63fb063d1b4b1bd4e7fb3c86
paper_authors.avatarUrl: /avatars/241e3b1340bf6450893a821ae73f2f56.svg
paper_authors.fullname: Andy
paper_authors.type: user
paper_authors.user: Lingmin-Ran
paper_authors._id: 67d274c467e782a7eeb4ab71
paper_authors.name: Mike Zheng Shou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T11:19:35.941Z
paper_authors._id: 661ab3da2b14565c7acccf5c
paper_authors.avatarUrl: /avatars/fa4fc03664803e02aede4d4c3d50b393.svg
paper_authors.fullname: Mike Zheng Shou
paper_authors.type: user
paper_authors.user: AnalMom
paper_title: TPDiff: Temporal Pyramid Video Diffusion Model
paper_summary: The development of video diffusion models unveils a significant challenge:
  the substantial computational demands. To mitigate this challenge, we note that
  the reverse process of diffusion exhibits an inherent entropy-reducing nature.
  Given the inter-frame redundancy in video modality, maintaining full frame
  rates in high-entropy stages is unnecessary. Based on this insight, we propose
  TPDiff, a unified framework to enhance training and inference efficiency. By
  dividing diffusion into several stages, our framework progressively increases
  frame rate along the diffusion process with only the last stage operating on
  full frame rate, thereby optimizing computational efficiency. To train the
  multi-stage diffusion model, we introduce a dedicated training framework:
  stage-wise diffusion. By solving the partitioned probability flow ordinary
  differential equations (ODE) of diffusion under aligned data and noise, our
  training strategy is applicable to various diffusion forms and further enhances
  training efficiency. Comprehensive experimental evaluations validate the
  generality of our method, demonstrating 50% reduction in training cost and 1.5x
  improvement in inference efficiency.
paper_ai_summary: A multi-stage diffusion framework, TPDiff, enhances video diffusion model efficiency by reducing full frame rate during high-entropy stages, leading to diminished training costs and improved inference efficiency.
paper_ai_keywords: video diffusion models entropy-reducing inter-frame redundancy frame rate stage-wise diffusion probability flow ordinary differential equations diffusion forms training efficiency inference efficiency