AI BriefingConsistency Models
Key point
To overcome the slow generation speed of diffusion models, we propose **Consistency Models**, which map noise directly to data.
Details
Diffusion models have advanced image, audio, and video generation, but they have the limitation of slow generation speed due to their iterative sampling process. To address this, we propose Consistency Models, a new family of models that generate high-quality samples by mapping noise directly to data.
Consistency Models have the following characteristics:
- They support one-step generation by design, while also allowing multi-step sampling to trade off compute for quality.
- They support zero-shot data editing such as image inpainting, colorization, and super-resolution without additional training.
They can be trained either by distilling a pretrained diffusion model or as a fully standalone generative model.
Experimental results show that they outperform existing distillation techniques in one-step and few-step sampling. In particular, they achieve a new SOTA (State-of-the-art), recording an FID of 3.55 on CIFAR-10 and an FID of 6.20 on ImageNet 64x64.
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