title: Arbitrary-steps Image Super-resolution via Diffusion Inversion
summary: This study presents a new image super-resolution (SR) technique based on
  diffusion inversion, aiming at harnessing the rich image priors encapsulated in
  large pre-trained diffusion models to improve SR performance. We design a
  Partial noise Prediction strategy to construct an intermediate state of the
  diffusion model, which serves as the starting sampling point. Central to our
  approach is a deep noise predictor to estimate the optimal noise maps for the
  forward diffusion process. Once trained, this noise predictor can be used to
  initialize the sampling process partially along the diffusion trajectory,
  generating the desirable high-resolution result. Compared to existing
  approaches, our method offers a flexible and efficient sampling mechanism that
  supports an arbitrary number of sampling steps, ranging from one to five. Even
  with a single sampling step, our method demonstrates superior or comparable
  performance to recent state-of-the-art approaches. The code and model are
  publicly available at https://github.com/zsyOAOA/InvSR.
paper_authors._id: 675bc39259bc9663aeea8ab0
paper_authors.name: Zongsheng Yue
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T11:05:19.850Z
paper_authors._id: 630ad0dd2ff113e0fb31c6b0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1671174653229-630ad0dd2ff113e0fb31c6b0.jpeg
paper_authors.fullname: Zongsheng Yue
paper_authors.type: user
paper_authors.user: OAOA
paper_authors._id: 675bc39259bc9663aeea8ab1
paper_authors.name: Kang Liao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-14T09:47:53.390Z
paper_authors._id: 65bc98383b879593a5a2f5e5
paper_authors.avatarUrl: /avatars/70f6fec5bf29c89eda7b909ec1472ace.svg
paper_authors.fullname: Kang Liao
paper_authors.type: user
paper_authors.user: KangLiao
paper_authors._id: 675bc39259bc9663aeea8ab2
paper_authors.name: Chen Change Loy
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T11:06:11.897Z
paper_authors._id: 67459d997a49660f7f62452f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/X25yTdxC_hcLVxpx4voZo.png
paper_authors.fullname: Chen Change Loy
paper_authors.type: user
paper_authors.user: cavanloy
paper_title: Arbitrary-steps Image Super-resolution via Diffusion Inversion
paper_summary: This study presents a new image super-resolution (SR) technique based on
  diffusion inversion, aiming at harnessing the rich image priors encapsulated in
  large pre-trained diffusion models to improve SR performance. We design a
  Partial noise Prediction strategy to construct an intermediate state of the
  diffusion model, which serves as the starting sampling point. Central to our
  approach is a deep noise predictor to estimate the optimal noise maps for the
  forward diffusion process. Once trained, this noise predictor can be used to
  initialize the sampling process partially along the diffusion trajectory,
  generating the desirable high-resolution result. Compared to existing
  approaches, our method offers a flexible and efficient sampling mechanism that
  supports an arbitrary number of sampling steps, ranging from one to five. Even
  with a single sampling step, our method demonstrates superior or comparable
  performance to recent state-of-the-art approaches. The code and model are
  publicly available at https://github.com/zsyOAOA/InvSR.
paper_ai_summary: A novel image super-resolution technique uses diffusion inversion with a noise predictor to enhance performance.
paper_ai_keywords: diffusion inversion image priors diffusion models Partial noise Prediction deep noise predictor sampling process diffusion trajectory image super-resolution