title: Constrained Diffusion Implicit Models
summary: This paper describes an efficient algorithm for solving noisy linear inverse
  problems using pretrained diffusion models. Extending the paradigm of denoising
  diffusion implicit models (DDIM), we propose constrained diffusion implicit
  models (CDIM) that modify the diffusion updates to enforce a constraint upon
  the final output. For noiseless inverse problems, CDIM exactly satisfies the
  constraints; in the noisy case, we generalize CDIM to satisfy an exact
  constraint on the residual distribution of the noise. Experiments across a
  variety of tasks and metrics show strong performance of CDIM, with analogous
  inference acceleration to unconstrained DDIM: 10 to 50 times faster than
  previous conditional diffusion methods. We demonstrate the versatility of our
  approach on many problems including super-resolution, denoising, inpainting,
  deblurring, and 3D point cloud reconstruction.
paper_authors._id: 6729667d02305fd23643b8ca
paper_authors.name: Vivek Jayaram
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-05T07:58:23.587Z
paper_authors._id: 660e1fef1a20b667fca9adb3
paper_authors.avatarUrl: /avatars/7731122e279df67fbd95cc2b9dc369d3.svg
paper_authors.fullname: Vivek Jayaram
paper_authors.type: user
paper_authors.user: vivjay30
paper_authors._id: 6729667d02305fd23643b8cb
paper_authors.name: Ira Kemelmacher-Shlizerman
paper_authors._id: 6729667d02305fd23643b8cc
paper_authors.name: Steven M. Seitz
paper_authors._id: 6729667d02305fd23643b8cd
paper_authors.name: John Thickstun
paper_title: Constrained Diffusion Implicit Models
paper_summary: This paper describes an efficient algorithm for solving noisy linear inverse
  problems using pretrained diffusion models. Extending the paradigm of denoising
  diffusion implicit models (DDIM), we propose constrained diffusion implicit
  models (CDIM) that modify the diffusion updates to enforce a constraint upon
  the final output. For noiseless inverse problems, CDIM exactly satisfies the
  constraints; in the noisy case, we generalize CDIM to satisfy an exact
  constraint on the residual distribution of the noise. Experiments across a
  variety of tasks and metrics show strong performance of CDIM, with analogous
  inference acceleration to unconstrained DDIM: 10 to 50 times faster than
  previous conditional diffusion methods. We demonstrate the versatility of our
  approach on many problems including super-resolution, denoising, inpainting,
  deblurring, and 3D point cloud reconstruction.
paper_ai_summary: CDIM modifies DDIM to enforce constraints, achieving strong performance and fast inference times across various inverse problems.
paper_ai_keywords: denoising diffusion implicit models DDIM constrained diffusion implicit models CDIM inference acceleration super-resolution denoising inpainting deblurring 3D point cloud reconstruction residual distribution