Generative Powers of Ten

• URL: http://arxiv.org/abs/2312.02149v2
• Date: Wed, 22 May 2024 00:23:00 GMT
• Title: Generative Powers of Ten
• Authors: Xiaojuan Wang, Janne Kontkanen, Brian Curless, Steve Seitz, Ira Kemelmacher, Ben Mildenhall, Pratul Srinivasan, Dor Verbin, Aleksander Holynski,
• Abstract summary: We present a method that uses a text-to-image model to generate consistent content across multiple image scales. We achieve this through a joint multi-scale diffusion sampling approach. Our method enables deeper levels of zoom than traditional super-resolution methods.
• Score: 60.6740997942711
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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