Fast Sampling of Diffusion Models via Operator Learning

Understand

Diffusion models have found widespread adoption in various areas.

• However, their sampling process is slow because it requires hundreds to thousands of network evaluations to emulate a continuous process defined by differential equations.
• In this work, we use neural operators, an efficient method to solve the probability flow differential equations, to accelerate the sampling process of diffusion models.
• Compared to other fast sampling methods that have a sequential nature, we are the first to propose a parallel decoding method that generates images with only one model forward pass.