Multistep Consistency Models
Authors: name: Jonathan Heek, name: Emiel Hoogeboom, name: Tim Salimans

Diffusion models are relatively easy to train but require many steps to
generate samples. Consistency models are far more difficult to train, but
generate samples in a single step.
  In this paper we propose Multistep Consistency Models: A unification between
Consistency Models (Song et al., 2023) and TRACT (Berthelot et al., 2023) that
can interpolate between a consistency model and a diffusion model: a trade-off
between sampling speed and sampling quality. Specifically, a 1-step consistency
model is a conventional consistency model whereas we show that a infty-step
consistency model is a diffusion model.
  Multistep Consistency Models work really well in practice. By increasing the
sample budget from a single step to 2-8 steps, we can train models more easily
that generate higher quality samples, while retaining much of the sampling
speed benefits. Notable results are 1.4 FID on Imagenet 64 in 8 step and 2.1
FID on Imagenet128 in 8 steps with consistency distillation. We also show that
our method scales to a text-to-image diffusion model, generating samples that
are very close to the quality of the original model.