  Diffusion models have shown an impressive ability to model complex data
distributions, with several key advantages over GANs, such as stable training,
better coverage of the training distribution's modes, and the ability to solve
inverse problems without extra training. However, most diffusion models learn
the distribution of fixed-resolution images. We propose to learn the
distribution of continuous images by training diffusion models on image neural
fields, which can be rendered at any resolution, and show its advantages over
fixed-resolution models. To achieve this, a key challenge is to obtain a latent
space that represents photorealistic image neural fields. We propose a simple
and effective method, inspired by several recent techniques but with key
changes to make the image neural fields photorealistic. Our method can be used
to convert existing latent diffusion autoencoders into image neural field
autoencoders. We show that image neural field diffusion models can be trained
using mixed-resolution image datasets, outperform fixed-resolution diffusion
models followed by super-resolution models, and can solve inverse problems with
conditions applied at different scales efficiently.
