Foveated Diffusion: Efficient Spatially Adaptive Image and Video Generation

TL;DR

This paper introduces a foveated diffusion model that leverages human visual acuity patterns to generate high-resolution images and videos efficiently by allocating computational resources non-uniformly based on gaze location.

Contribution

It proposes a novel mixed-resolution token approach for diffusion models, enabling perceptually indistinguishable high-quality generation with reduced computational complexity.

Findings

Significant reduction in token count and generation time.

Perceptually indistinguishable results compared to full-resolution models.

Validated effectiveness through user studies and analysis.

Abstract

Diffusion and flow matching models have unlocked unprecedented capabilities for creative content creation, such as interactive image and streaming video generation. The growing demand for higher resolutions, frame rates, and context lengths, however, makes efficient generation increasingly challenging, as computational complexity grows quadratically with the number of generated tokens. Our work seeks to optimize the efficiency of the generation process in settings where the user's gaze location is known or can be estimated, for example, by using eye tracking. In these settings, we leverage the eccentricity-dependent acuity of human vision: while a user perceives very high-resolution visual information in a small region around their gaze location (the foveal region), the ability to resolve detail quickly degrades in the periphery of the visual field. Our approach starts with a mask modeling the foveated resolution to allocate tokens non-uniformly, assigning higher token density to foveal regions and lower density to peripheral regions. An image or video is generated in a mixed-resolution token setting, yielding results perceptually indistinguishable from full-resolution generation, while drastically reducing the token count and generation time. To this end, we develop a principled mechanism for constructing mixed-resolution tokens directly from high-resolution data, allowing a foveated diffusion model to be post-trained from an existing base model while maintaining content consistency across resolutions. We validate our approach through extensive analysis and a carefully designed user study, demonstrating the efficacy of foveation as a practical and scalable axis for efficient generation.