We introduce $\infty$-Diff, a generative diffusion model defined in an infinite-dimensional Hilbert space that allows infinite resolution data to be modelled. By randomly sampling subsets of coordinates during training and learning to denoise the content at those coordinates, a continuous function is learned that allows sampling at arbitrary resolutions. Prior infinite-dimensional generative models use point-wise functions that require latent compression for global context. In contrast, we propose using non-local integral operators to map between Hilbert spaces, allowing spatial information aggregation; to facilitate this, we design a powerful and efficient multi-scale architecture that operates directly on raw sparse coordinates. Training on high-resolution datasets we demonstrate that high-quality diffusion models can be learned with even $8\times$ subsampling rates, enabling substantial improvements in run-time and memory requirements, achieving significantly higher sample quality as evidenced by lower FID scores, while also being able to effectively scale to higher resolutions than the training data while retaining detail.
$\infty$-Diff: Infinite Resolution Diffusion with Subsampled Mollified States
Sam Bond-Taylor Chris G. Willcocks