Accelerating Diffusion Models for Inverse Problems through Shortcut Sampling

Understand

Diffusion models have recently demonstrated an impressive ability to address inverse problems in an unsupervised manner.

• While existing methods primarily focus on modifying the posterior sampling process, the potential of the forward process remains largely unexplored.
• In this work, we propose Shortcut Sampling for Diffusion(SSD), a novel approach for solving inverse problems in a zero-shot manner.
• Instead of initiating from random noise, the core concept of SSD is to find a specific transitional state that bridges the measurement image y and the restored image x.