  Consistency models (CMs) are an emerging class of generative models that
offer faster sampling than traditional diffusion models. CMs enforce that all
points along a sampling trajectory are mapped to the same initial point. But
this target leads to resource-intensive training: for example, as of 2024,
training a SoTA CM on CIFAR-10 takes one week on 8 GPUs. In this work, we
propose an alternative scheme for training CMs, vastly improving the efficiency
of building such models. Specifically, by expressing CM trajectories via a
particular differential equation, we argue that diffusion models can be viewed
as a special case of CMs with a specific discretization. We can thus fine-tune
a consistency model starting from a pre-trained diffusion model and
progressively approximate the full consistency condition to stronger degrees
over the training process. Our resulting method, which we term Easy Consistency
Tuning (ECT), achieves vastly improved training times while indeed improving
upon the quality of previous methods: for example, ECT achieves a 2-step FID of
2.73 on CIFAR10 within 1 hour on a single A100 GPU, matching Consistency
Distillation trained of hundreds of GPU hours. Owing to this computational
efficiency, we investigate the scaling law of CMs under ECT, showing that they
seem to obey classic power law scaling, hinting at their ability to improve
efficiency and performance at larger scales. Code
(https://github.com/locuslab/ect) is available.
