Diffusion models have significantly advanced the fields of image, audio, and
video generation, but they depend on an iterative sampling process that causes
slow generation. To overcome this limitation, we propose consistency models, a
new family of models that generate high quality samples by directly mapping
noise to data. They support fast one-step generation by design, while still
allowing multistep sampling to trade compute for sample quality. They also
support zero-shot data editing, such as image inpainting, colorization, and
super-resolution, without requiring explicit training on these tasks.
Consistency models can be trained either by distilling pre-trained diffusion
models, or as standalone generative models altogether. Through extensive
experiments, we demonstrate that they outperform existing distillation
techniques for diffusion models in one- and few-step sampling, achieving the
new state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 for
one-step generation. When trained in isolation, consistency models become a new
family of generative models that can outperform existing one-step,
non-adversarial generative models on standard benchmarks such as CIFAR-10,
ImageNet 64x64 and LSUN 256x256.