title: One-Step Residual Shifting Diffusion for Image Super-Resolution via
    Distillation
summary: Diffusion models for super-resolution (SR) produce high-quality visual
  results but require expensive computational costs. Despite the development of
  several methods to accelerate diffusion-based SR models, some (e.g., SinSR)
  fail to produce realistic perceptual details, while others (e.g., OSEDiff) may
  hallucinate non-existent structures. To overcome these issues, we present RSD,
  a new distillation method for ResShift, one of the top diffusion-based SR
  models. Our method is based on training the student network to produce such
  images that a new fake ResShift model trained on them will coincide with the
  teacher model. RSD achieves single-step restoration and outperforms the teacher
  by a large margin. We show that our distillation method can surpass the other
  distillation-based method for ResShift - SinSR - making it on par with
  state-of-the-art diffusion-based SR distillation methods. Compared to SR
  methods based on pre-trained text-to-image models, RSD produces competitive
  perceptual quality, provides images with better alignment to degraded input
  images, and requires fewer parameters and GPU memory. We provide experimental
  results on various real-world and synthetic datasets, including RealSR,
  RealSet65, DRealSR, ImageNet, and DIV2K.
paper_authors._id: 67dd2ed0d2550735426e7b6f
paper_authors.name: Daniil Selikhanovych
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paper_authors.fullname: Daniil Selikhanovych
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paper_authors.name: David Li
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paper_authors.fullname: David Li
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paper_authors.user: kekchpek
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paper_authors.name: Aleksei Leonov
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paper_authors.name: Nikita Gushchin
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paper_authors.fullname: Nikita Gushchin
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paper_authors.name: Sergei Kushneriuk
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paper_authors.fullname: Sergey Kushneryuk
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paper_authors.user: skushneryuk
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paper_authors.name: Alexander Filippov
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paper_authors.fullname: Alexander Filippov
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paper_authors.user: agoxandr
paper_authors._id: 67dd2ed0d2550735426e7b75
paper_authors.name: Evgeny Burnaev
paper_authors._id: 67dd2ed0d2550735426e7b76
paper_authors.name: Iaroslav Koshelev
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T11:58:41.132Z
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paper_authors.avatarUrl: /avatars/88a66ffb334a4fc9d4c150b6aae0f537.svg
paper_authors.fullname: Iaroslav Koshelev
paper_authors.type: user
paper_authors.user: ys-koshelev
paper_authors._id: 67dd2ed0d2550735426e7b77
paper_authors.name: Alexander Korotin
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/ca5fd356e3656e1beacb5a28ecaad5be.svg
paper_authors.fullname: Alexander Korotin
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paper_authors.user: akorotin
paper_title: One-Step Residual Shifting Diffusion for Image Super-Resolution via
    Distillation
paper_summary: Diffusion models for super-resolution (SR) produce high-quality visual
  results but require expensive computational costs. Despite the development of
  several methods to accelerate diffusion-based SR models, some (e.g., SinSR)
  fail to produce realistic perceptual details, while others (e.g., OSEDiff) may
  hallucinate non-existent structures. To overcome these issues, we present RSD,
  a new distillation method for ResShift, one of the top diffusion-based SR
  models. Our method is based on training the student network to produce such
  images that a new fake ResShift model trained on them will coincide with the
  teacher model. RSD achieves single-step restoration and outperforms the teacher
  by a large margin. We show that our distillation method can surpass the other
  distillation-based method for ResShift - SinSR - making it on par with
  state-of-the-art diffusion-based SR distillation methods. Compared to SR
  methods based on pre-trained text-to-image models, RSD produces competitive
  perceptual quality, provides images with better alignment to degraded input
  images, and requires fewer parameters and GPU memory. We provide experimental
  results on various real-world and synthetic datasets, including RealSR,
  RealSet65, DRealSR, ImageNet, and DIV2K.
paper_ai_summary: RSD is a distillation method for ResShift, a top diffusion-based super-resolution model, that achieves high-quality results with fewer parameters and GPU memory compared to other methods.
paper_ai_keywords: diffusion models super-resolution SinSR OSEDiff ResShift distillation method image restoration perceptual quality degraded input images real-world datasets synthetic datasets RealSR RealSet65 DRealSR ImageNet DIV2K