Arbitrary-scale image super-resolution aims to upsample images to any desired resolution, offering greater flexibility than traditional fixed-scale super-resolution. Recent approaches based on regression-based or generative models have shown promising results but often suffer from scale inconsistency due to their single-stage formulation, which must handle a wide range of scaling factors simultaneously. To address this, we propose CasArbi, a self-cascaded diffusion framework for arbitrary-scale image super-resolution. CasArbi decomposes varying scaling factors into smaller sequential steps, progressively enhancing the image resolution at each step with seamless transitions for arbitrary scales. CasArbi leverages a coordinate-conditioned diffusion model for learning continuous image representations and adopts self-consistency guidance to generate scale-consistent details at inference time. Extensive experiments show that CasArbi outperforms existing methods in both perceptual and distortion metrics and demonstrates superior scale consistency across diverse arbitrary-scale super-resolution benchmarks. Our code is available at https://github.com/junseo88/CasArbi.
@article{arxiv.2506.07813,
title = {Self-Cascaded Diffusion Models for Arbitrary-Scale Image Super-Resolution},
author = {Junseo Bang and Joonhee Lee and Kyeonghyun Lee and Haechang Lee and Dong Un Kang and Se Young Chun},
journal= {arXiv preprint arXiv:2506.07813},
year = {2026}
}