  Diffusion Transformers have recently demonstrated unprecedented generative
capabilities for various tasks. The encouraging results, however, come with the
cost of slow inference, since each denoising step requires inference on a
transformer model with a large scale of parameters. In this study, we make an
interesting and somehow surprising observation: the computation of a large
proportion of layers in the diffusion transformer, through introducing a
caching mechanism, can be readily removed even without updating the model
parameters. In the case of U-ViT-H/2, for example, we may remove up to 93.68%
of the computation in the cache steps (46.84% for all steps), with less than
0.01 drop in FID. To achieve this, we introduce a novel scheme, named
Learning-to-Cache (L2C), that learns to conduct caching in a dynamic manner for
diffusion transformers. Specifically, by leveraging the identical structure of
layers in transformers and the sequential nature of diffusion, we explore
redundant computations between timesteps by treating each layer as the
fundamental unit for caching. To address the challenge of the exponential
search space in deep models for identifying layers to cache and remove, we
propose a novel differentiable optimization objective. An input-invariant yet
timestep-variant router is then optimized, which can finally produce a static
computation graph. Experimental results show that L2C largely outperforms
samplers such as DDIM and DPM-Solver, alongside prior cache-based methods at
the same inference speed.
