  This work aims to improve the efficiency of text-to-image diffusion models.
While diffusion models use computationally expensive UNet-based denoising
operations in every generation step, we identify that not all operations are
equally relevant for the final output quality. In particular, we observe that
UNet layers operating on high-res feature maps are relatively sensitive to
small perturbations. In contrast, low-res feature maps influence the semantic
layout of the final image and can often be perturbed with no noticeable change
in the output. Based on this observation, we propose Clockwork Diffusion, a
method that periodically reuses computation from preceding denoising steps to
approximate low-res feature maps at one or more subsequent steps. For multiple
baselines, and for both text-to-image generation and image editing, we
demonstrate that Clockwork leads to comparable or improved perceptual scores
with drastically reduced computational complexity. As an example, for Stable
Diffusion v1.5 with 8 DPM++ steps we save 32% of FLOPs with negligible FID and
CLIP change.
