  The real world exhibits rich structure and detail across many scales of
observation. It is difficult, however, to capture and represent a broad
spectrum of scales using ordinary images. We devise a novel paradigm for
learning a representation that captures an orders-of-magnitude variety of
scales from an unstructured collection of ordinary images. We treat this
collection as a distribution of scale-space slices to be learned using
adversarial training, and additionally enforce coherency across slices. Our
approach relies on a multiscale generator with carefully injected procedural
frequency content, which allows to interactively explore the emerging
continuous scale space. Training across vastly different scales poses
challenges regarding stability, which we tackle using a supervision scheme that
involves careful sampling of scales. We show that our generator can be used as
a multiscale generative model, and for reconstructions of scale spaces from
unstructured patches. Significantly outperforming the state of the art, we
demonstrate zoom-in factors of up to 256x at high quality and scale
consistency.
