Generative Powers of Ten
Authors: name: Xiaojuan Wang, name: Janne Kontkanen, name: Brian Curless, name: Steve Seitz, name: Ira Kemelmacher, name: Ben Mildenhall, name: Pratul Srinivasan, name: Dor Verbin, name: Aleksander Holynski

We present a method that uses a text-to-image model to generate consistent
content across multiple image scales, enabling extreme semantic zooms into a
scene, e.g., ranging from a wide-angle landscape view of a forest to a macro
shot of an insect sitting on one of the tree branches. We achieve this through
a joint multi-scale diffusion sampling approach that encourages consistency
across different scales while preserving the integrity of each individual
sampling process. Since each generated scale is guided by a different text
prompt, our method enables deeper levels of zoom than traditional
super-resolution methods that may struggle to create new contextual structure
at vastly different scales. We compare our method qualitatively with
alternative techniques in image super-resolution and outpainting, and show that
our method is most effective at generating consistent multi-scale content.