  Diffusion models have recently gained unprecedented attention in the field of
image synthesis due to their remarkable generative capabilities.
Notwithstanding their prowess, these models often incur substantial
computational costs, primarily attributed to the sequential denoising process
and cumbersome model size. Traditional methods for compressing diffusion models
typically involve extensive retraining, presenting cost and feasibility
challenges. In this paper, we introduce DeepCache, a novel training-free
paradigm that accelerates diffusion models from the perspective of model
architecture. DeepCache capitalizes on the inherent temporal redundancy
observed in the sequential denoising steps of diffusion models, which caches
and retrieves features across adjacent denoising stages, thereby curtailing
redundant computations. Utilizing the property of the U-Net, we reuse the
high-level features while updating the low-level features in a very cheap way.
This innovative strategy, in turn, enables a speedup factor of 2.3$\times$ for
Stable Diffusion v1.5 with only a 0.05 decline in CLIP Score, and 4.1$\times$
for LDM-4-G with a slight decrease of 0.22 in FID on ImageNet. Our experiments
also demonstrate DeepCache's superiority over existing pruning and distillation
methods that necessitate retraining and its compatibility with current sampling
techniques. Furthermore, we find that under the same throughput, DeepCache
effectively achieves comparable or even marginally improved results with DDIM
or PLMS. The code is available at https://github.com/horseee/DeepCache
