title: Scaling Properties of Diffusion Models for Perceptual Tasks
summary: In this paper, we argue that iterative computation with diffusion models
  offers a powerful paradigm for not only generation but also visual perception
  tasks. We unify tasks such as depth estimation, optical flow, and segmentation
  under image-to-image translation, and show how diffusion models benefit from
  scaling training and test-time compute for these perception tasks. Through a
  careful analysis of these scaling behaviors, we present various techniques to
  efficiently train diffusion models for visual perception tasks. Our models
  achieve improved or comparable performance to state-of-the-art methods using
  significantly less data and compute. To use our code and models, see
  https://scaling-diffusion-perception.github.io .
paper_authors._id: 67341e4ffce00b60eb491c72
paper_authors.name: Rahul Ravishankar
paper_authors._id: 67341e4ffce00b60eb491c73
paper_authors.name: Zeeshan Patel
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-18T08:08:35.621Z
paper_authors._id: 638ee4df6251c8bd7abd246e
paper_authors.avatarUrl: /avatars/8d8c4dac7d82cba6dcf884460bdc5328.svg
paper_authors.fullname: Zeeshan Patel
paper_authors.type: user
paper_authors.user: zeeshanp
paper_authors._id: 67341e4ffce00b60eb491c74
paper_authors.name: Jathushan Rajasegaran
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-18T08:08:40.499Z
paper_authors._id: 63895b3a43d8b0797a0d0406
paper_authors.avatarUrl: /avatars/04d9952dff70f85d4e481ec80ae818cb.svg
paper_authors.fullname: Jathushan Rajasegaran
paper_authors.type: user
paper_authors.user: brjathu
paper_authors._id: 67341e4ffce00b60eb491c75
paper_authors.name: Jitendra Malik
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-18T08:08:45.996Z
paper_authors._id: 65369a95605a07338de78ab0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/sGFjOjLT2akN-sn5beVWL.jpeg
paper_authors.fullname: Jitendra Malik 
paper_authors.type: user
paper_authors.user: jitendra1995
paper_title: Scaling Properties of Diffusion Models for Perceptual Tasks
paper_summary: In this paper, we argue that iterative computation with diffusion models
  offers a powerful paradigm for not only generation but also visual perception
  tasks. We unify tasks such as depth estimation, optical flow, and segmentation
  under image-to-image translation, and show how diffusion models benefit from
  scaling training and test-time compute for these perception tasks. Through a
  careful analysis of these scaling behaviors, we present various techniques to
  efficiently train diffusion models for visual perception tasks. Our models
  achieve improved or comparable performance to state-of-the-art methods using
  significantly less data and compute. To use our code and models, see
  https://scaling-diffusion-perception.github.io .
paper_ai_keywords: diffusion models iterative computation image-to-image translation depth estimation optical flow segmentation scaling behaviors efficient training