One-Step Residual Shifting Diffusion for Image Super-Resolution via
  Distillation
Authors: name: Daniil Selikhanovych, name: David Li, name: Aleksei Leonov, name: Nikita Gushchin, name: Sergei Kushneriuk, name: Alexander Filippov, name: Evgeny Burnaev, name: Iaroslav Koshelev, name: Alexander Korotin

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.