One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation

• URL: http://arxiv.org/abs/2503.13358v1
• Date: Mon, 17 Mar 2025 16:44:08 GMT
• Title: One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation
• Authors: Daniil Selikhanovych, David Li, Aleksei Leonov, Nikita Gushchin, Sergei Kushneriuk, Alexander Filippov, Evgeny Burnaev, Iaroslav Koshelev, Alexander Korotin,
• Abstract summary: Diffusion models for super-resolution (SR) produce high-quality visual results but require expensive computational costs.<n>We present RSD, a new distillation method for ResShift, one of the top diffusion-based SR models.<n>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.
• Score: 90.84654430620971
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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