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.
@article{arxiv.2312.02149,
title = {Generative Powers of Ten},
author = {Xiaojuan Wang and Janne Kontkanen and Brian Curless and Steve Seitz and Ira Kemelmacher and Ben Mildenhall and Pratul Srinivasan and Dor Verbin and Aleksander Holynski},
journal= {arXiv preprint arXiv:2312.02149},
year = {2024}
}