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.

翻译：扩散模型在图像、音频和视频生成领域取得了显著进展，但其依赖迭代采样过程导致生成速度缓慢。为克服这一限制，我们提出一致性模型——一种通过直接将噪声映射到数据来生成高质量样本的新型模型族。该模型在设计上支持快速单步生成，同时仍允许通过多步采样在计算量与样本质量之间进行权衡。此外，它还能在无需针对特定任务进行显式训练的情况下，支持零样本数据编辑，如图像修复、着色与超分辨率。一致性模型可通过蒸馏预训练扩散模型进行训练，也可作为独立生成模型进行端到端训练。通过大量实验证明，该模型在一步与少步采样中显著优于现有扩散模型蒸馏技术：在CIFAR-10上实现FID=3.55、在ImageNet 64x64上实现FID=6.20，均达到当前一步生成的最优水平。当作为独立模型训练时，一致性模型成为一类新型生成模型，在CIFAR-10、ImageNet 64x64及LSUN 256x256等标准基准测试中，其性能超越现有一步式非对抗生成模型。