  Denoising Diffusion models have demonstrated their proficiency for generative
sampling. However, generating good samples often requires many iterations.
Consequently, techniques such as binary time-distillation (BTD) have been
proposed to reduce the number of network calls for a fixed architecture. In
this paper, we introduce TRAnsitive Closure Time-distillation (TRACT), a new
method that extends BTD. For single step diffusion,TRACT improves FID by up to
2.4x on the same architecture, and achieves new single-step Denoising Diffusion
Implicit Models (DDIM) state-of-the-art FID (7.4 for ImageNet64, 3.8 for
CIFAR10). Finally we tease apart the method through extended ablations. The
PyTorch implementation will be released soon.
