  We present SR3, an approach to image Super-Resolution via Repeated
Refinement. SR3 adapts denoising diffusion probabilistic models to conditional
image generation and performs super-resolution through a stochastic denoising
process. Inference starts with pure Gaussian noise and iteratively refines the
noisy output using a U-Net model trained on denoising at various noise levels.
SR3 exhibits strong performance on super-resolution tasks at different
magnification factors, on faces and natural images. We conduct human evaluation
on a standard 8X face super-resolution task on CelebA-HQ, comparing with SOTA
GAN methods. SR3 achieves a fool rate close to 50%, suggesting photo-realistic
outputs, while GANs do not exceed a fool rate of 34%. We further show the
effectiveness of SR3 in cascaded image generation, where generative models are
chained with super-resolution models, yielding a competitive FID score of 11.3
on ImageNet.
