  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.
