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. This representation allows us to render continuously zooming videos, or explore different scales of the scene interactively. 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.

Our method uses a pre-trained diffusion model to jointly denoise multiple images of the scene at various scales. Noisy images from each zoom level, along with the respective prompts are simultaneously fed into the same pretrained diffusion model, returning estimates of the corresponding clean images. These images may have inconsistent estimates for the overlapping regions that they all observe. We employ multi-resolution blending to fuse these regions into a consistent zoom stack and re-render the different zoom levels from the consistent representation. These re-rendered images are then used as the clean image estimates in the DDPM sampling step.