title: DREAM: Diffusion Rectification and Estimation-Adaptive Models
summary: We present DREAM, a novel training framework representing Diffusion
  Rectification and Estimation-Adaptive Models, requiring minimal code changes
  (just three lines) yet significantly enhancing the alignment of training with
  sampling in diffusion models. DREAM features two components: diffusion
  rectification, which adjusts training to reflect the sampling process, and
  estimation adaptation, which balances perception against distortion. When
  applied to image super-resolution (SR), DREAM adeptly navigates the tradeoff
  between minimizing distortion and preserving high image quality. Experiments
  demonstrate DREAM's superiority over standard diffusion-based SR methods,
  showing a 2 to 3times  faster training convergence and a 10 to
  20times reduction in necessary sampling steps to achieve comparable or
  superior results. We hope DREAM will inspire a rethinking of diffusion model
  training paradigms.
paper_authors._id: 656d3b848fb1ddf0d553f9fb
paper_authors.name: Jinxin Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-04T10:50:12.159Z
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paper_authors.avatarUrl: /avatars/866d4e65de6da31e1a3f3842db51b798.svg
paper_authors.fullname: Jinxin Zhou
paper_authors.type: user
paper_authors.user: jinxinz
paper_authors._id: 656d3b848fb1ddf0d553f9fc
paper_authors.name: Tianyu Ding
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-04T10:50:18.467Z
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paper_authors.fullname: Tianyu Ding
paper_authors.type: user
paper_authors.user: tding1
paper_authors._id: 656d3b848fb1ddf0d553f9fd
paper_authors.name: Tianyi Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-05T08:40:54.745Z
paper_authors._id: 64ad94f05a4a60156925ec96
paper_authors.avatarUrl: /avatars/643bdb076e703bfcc89cec6fccb756c6.svg
paper_authors.fullname: Tianyi Chen
paper_authors.type: user
paper_authors.user: tianyic
paper_authors._id: 656d3b848fb1ddf0d553f9fe
paper_authors.name: Jiachen Jiang
paper_authors._id: 656d3b848fb1ddf0d553f9ff
paper_authors.name: Ilya Zharkov
paper_authors._id: 656d3b848fb1ddf0d553fa00
paper_authors.name: Zhihui Zhu
paper_authors._id: 656d3b848fb1ddf0d553fa01
paper_authors.name: Luming Liang
paper_title: DREAM: Diffusion Rectification and Estimation-Adaptive Models
paper_summary: We present DREAM, a novel training framework representing Diffusion
  Rectification and Estimation-Adaptive Models, requiring minimal code changes
  (just three lines) yet significantly enhancing the alignment of training with
  sampling in diffusion models. DREAM features two components: diffusion
  rectification, which adjusts training to reflect the sampling process, and
  estimation adaptation, which balances perception against distortion. When
  applied to image super-resolution (SR), DREAM adeptly navigates the tradeoff
  between minimizing distortion and preserving high image quality. Experiments
  demonstrate DREAM's superiority over standard diffusion-based SR methods,
  showing a 2 to 3times  faster training convergence and a 10 to
  20times reduction in necessary sampling steps to achieve comparable or
  superior results. We hope DREAM will inspire a rethinking of diffusion model
  training paradigms.
paper_ai_keywords: diffusion rectification estimation adaptation training convergence sampling steps image super-resolution (SR)