  We show that cascaded diffusion models are capable of generating high
fidelity images on the class-conditional ImageNet generation benchmark, without
any assistance from auxiliary image classifiers to boost sample quality. A
cascaded diffusion model comprises a pipeline of multiple diffusion models that
generate images of increasing resolution, beginning with a standard diffusion
model at the lowest resolution, followed by one or more super-resolution
diffusion models that successively upsample the image and add higher resolution
details. We find that the sample quality of a cascading pipeline relies
crucially on conditioning augmentation, our proposed method of data
augmentation of the lower resolution conditioning inputs to the
super-resolution models. Our experiments show that conditioning augmentation
prevents compounding error during sampling in a cascaded model, helping us to
train cascading pipelines achieving FID scores of 1.48 at 64x64, 3.52 at
128x128 and 4.88 at 256x256 resolutions, outperforming BigGAN-deep, and
classification accuracy scores of 63.02% (top-1) and 84.06% (top-5) at 256x256,
outperforming VQ-VAE-2.
