Accelerating Parallel Sampling of Diffusion Models

Understand

Diffusion models have emerged as state-of-the-art generative models for image generation.

• However, sampling from diffusion models is usually time-consuming due to the inherent autoregressive nature of their sampling process.
• In this work, we propose a novel approach that accelerates the sampling of diffusion models by parallelizing the autoregressive process.
• Specifically, we reformulate the sampling process as solving a system of triangular nonlinear equations through fixed-point iteration.