Denoising Diffusion Probabilistic Models (DDPMs) have garnered popularity for
data generation across various domains. However, a significant bottleneck is
the necessity for whole-network computation during every step of the generative
process, leading to high computational overheads. This paper presents a novel
framework, Denoising Diffusion Step-aware Models (DDSM), to address this
challenge. Unlike conventional approaches, DDSM employs a spectrum of neural
networks whose sizes are adapted according to the importance of each generative
step, as determined through evolutionary search. This step-wise network
variation effectively circumvents redundant computational efforts, particularly
in less critical steps, thereby enhancing the efficiency of the diffusion
model. Furthermore, the step-aware design can be seamlessly integrated with
other efficiency-geared diffusion models such as DDIMs and latent diffusion,
thus broadening the scope of computational savings. Empirical evaluations
demonstrate that DDSM achieves computational savings of 49% for CIFAR-10, 61%
for CelebA-HQ, 59% for LSUN-bedroom, 71% for AFHQ, and 76% for ImageNet, all
without compromising the generation quality. Our code and models will be
publicly available.

该论文介绍了一种名为 Denoising Diffusion Step-aware Models (DDSM) 的新型框架，通过使用一系列根据每个生成步骤重要性进行自适应调整的神经网络，以进化搜索的方式解决了生成过程中存在的整体网络计算的瓶颈问题，有效地提高了扩散模型的效率，并且可以与其他以效率为目标的扩散模型进行无缝集成，从而扩大了计算节约的范围，同时不影响生成质量。