Iterative denoising-based generation, also known as denoising diffusion
models, has recently been shown to be comparable in quality to other classes of
generative models, and even surpass them. Including, in particular, Generative
Adversarial Networks, which are currently the state of the art in many
sub-tasks of image generation. However, a major drawback of this method is that
it requires hundreds of iterations to produce a competitive result. Recent
works have proposed solutions that allow for faster generation with fewer
iterations, but the image quality gradually deteriorates with increasingly
fewer iterations being applied during generation. In this paper, we reveal some
of the causes that affect the generation quality of diffusion models,
especially when sampling with few iterations, and come up with a simple, yet
effective, solution to mitigate them. We consider two opposite equations for
the iterative denoising, the first predicts the applied noise, and the second
predicts the image directly. Our solution takes the two options and learns to
dynamically alternate between them through the denoising process. Our proposed
solution is general and can be applied to any existing diffusion model. As we
show, when applied to various SOTA architectures, our solution immediately
improves their generation quality, with negligible added complexity and
parameters. We experiment on multiple datasets and configurations and run an
extensive ablation study to support these findings.

在这篇文章中，我们发现了扩散模型生成质量受到迭代次数限制的根本原因，并提出了一个简单而有效的解决方案来缓解这些影响。我们的解决方案可以应用于任何现有的扩散模型，并且在各种 SOTA 体系结构上运行多个数据集和配置进行实验和详尽的消融研究，证明能够立即提高它们的生成质量。