Positional encodings have enabled recent works to train a single adversarial network that can generate images of different scales. However, these approaches are either limited to a set of discrete scales or struggle to maintain good perceptual quality at the scales for which the model is not trained explicitly. We propose the design of scale-consistent positional encodings invariant to our generator's layers transformations. This enables the generation of arbitrary-scale images even at scales unseen during training. Moreover, we incorporate novel inter-scale augmentations into our pipeline and partial generation training to facilitate the synthesis of consistent images at arbitrary scales. Lastly, we show competitive results for a continuum of scales on various commonly used datasets for image synthesis.
@article{arxiv.2204.02273,
title = {Arbitrary-Scale Image Synthesis},
author = {Evangelos Ntavelis and Mohamad Shahbazi and Iason Kastanis and Radu Timofte and Martin Danelljan and Luc Van Gool},
journal= {arXiv preprint arXiv:2204.02273},
year = {2022}
}