We are sharing a new approach, called sCM, which simplifies the theoretical formulation of continuous-time consistency models, allowing us to stabilize and scale their training for large scale datasets. This approach achieves comparable sample quality to leading diffusion models, while using only two sampling steps. We are also sharing our research paper⁠(opens in a new window) to support further progress in this field.

Sampling procedure of consistency models. Sampling time measured on a single A100 GPU with batch size = 1.

Sampling procedure of diffusion models. Sampling time measured on a single A100 GPU with batch size = 1.

Current sampling approaches of diffusion models often require dozens to hundreds of sequential steps to generate a single sample, which limits their efficiency and scalability for real-time applications. Various distillation techniques have been developed to accelerate sampling, but they often come with limitations, such as high computational costs, complex training, and reduced sample quality.