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.

通过在图像神经场上训练扩散模型，我们可以学习连续图像的分布，并显示其在分辨率上的优势。我们提出了一种简单而有效的方法，可以将现有的潜在扩散自动编码器转换为图像神经场自动编码器。通过使用混合分辨率图像数据集对其进行训练，我们证明了图像神经场扩散模型优于固定分辨率扩散模型后跟超分辨率模型，并能高效地解决应用于不同尺度的逆问题。