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.

使用文本到图像模型生成一致的多尺度内容，实现从广角景观到细节的极端语义缩放，通过联合多尺度扩散采样方法，保持不同尺度之间的一致性，并与传统的超分辨率方法进行对比，显示出在生成一致的多尺度内容方面，该方法最为有效。

十的生成力