  Existing models on super-resolution often specialized for one scale,
fundamentally limiting their use in practical scenarios. In this paper, we aim
to develop a general plugin that can be inserted into existing super-resolution
models, conveniently augmenting their ability towards Arbitrary Resolution
Image Scaling, thus termed ARIS. We make the following contributions: (i) we
propose a transformer-based plugin module, which uses spatial coordinates as
query, iteratively attend the low-resolution image feature through
cross-attention, and output visual feature for the queried spatial location,
resembling an implicit representation for images; (ii) we introduce a novel
self-supervised training scheme, that exploits consistency constraints to
effectively augment the model's ability for upsampling images towards unseen
scales, i.e. ground-truth high-resolution images are not available; (iii)
without loss of generality, we inject the proposed ARIS plugin module into
several existing models, namely, IPT, SwinIR, and HAT, showing that the
resulting models can not only maintain their original performance on fixed
scale factor but also extrapolate to unseen scales, substantially outperforming
existing any-scale super-resolution models on standard benchmarks, e.g.
Urban100, DIV2K, etc.
