  We argue that the theory and practice of diffusion-based generative models
are currently unnecessarily convoluted and seek to remedy the situation by
presenting a design space that clearly separates the concrete design choices.
This lets us identify several changes to both the sampling and training
processes, as well as preconditioning of the score networks. Together, our
improvements yield new state-of-the-art FID of 1.79 for CIFAR-10 in a
class-conditional setting and 1.97 in an unconditional setting, with much
faster sampling (35 network evaluations per image) than prior designs. To
further demonstrate their modular nature, we show that our design changes
dramatically improve both the efficiency and quality obtainable with
pre-trained score networks from previous work, including improving the FID of a
previously trained ImageNet-64 model from 2.07 to near-SOTA 1.55, and after
re-training with our proposed improvements to a new SOTA of 1.36.
