One-Step Residual Shifting Diffusion for Image Super-Resolution via
  Distillation
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.
67dd2ed0d2550735426e7b6f
Daniil Selikhanovych
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2025-03-21T09:18:55.946Z
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Daniil Selikhanovych
user
apryc1
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David Li
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2025-03-21T11:37:32.766Z
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David Li
user
kekchpek
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Aleksei Leonov
67dd2ed0d2550735426e7b72
Nikita Gushchin
claimed_verified
2025-03-21T11:37:30.126Z
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Nikita Gushchin
user
ngushchin
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Sergei Kushneriuk
claimed_verified
2025-03-21T11:37:27.722Z
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Sergey Kushneryuk
user
skushneryuk
67dd2ed0d2550735426e7b74
Alexander Filippov
admin_assigned
2025-03-21T11:53:41.165Z
63190c17ca8b18deedc77734
/avatars/5b0e1850df82435b6875f85e03c3702f.svg
Alexander Filippov
user
agoxandr
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Evgeny Burnaev
67dd2ed0d2550735426e7b76
Iaroslav Koshelev
admin_assigned
2025-03-21T11:58:41.132Z
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Iaroslav Koshelev
user
ys-koshelev
67dd2ed0d2550735426e7b77
Alexander Korotin
admin_assigned
2025-03-21T11:58:35.510Z
67a31c9ae5b870d5157657db
/avatars/ca5fd356e3656e1beacb5a28ecaad5be.svg
Alexander Korotin
user
akorotin
One-Step Residual Shifting Diffusion for Image Super-Resolution via
  Distillation
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.
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.
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