  Iterative generative models, such as noise conditional score networks and
denoising diffusion probabilistic models, produce high quality samples by
gradually denoising an initial noise vector. However, their denoising process
has many steps, making them 2-3 orders of magnitude slower than other
generative models such as GANs and VAEs. In this paper, we establish a novel
connection between knowledge distillation and image generation with a technique
that distills a multi-step denoising process into a single step, resulting in a
sampling speed similar to other single-step generative models. Our Denoising
Student generates high quality samples comparable to GANs on the CIFAR-10 and
CelebA datasets, without adversarial training. We demonstrate that our method
scales to higher resolutions through experiments on 256 x 256 LSUN. Code and
checkpoints are available at https://github.com/tcl9876/Denoising_Student
