Fast Diffusion Probabilistic Model Sampling through the lens of Backward Error Analysis

Understand

Denoising diffusion probabilistic models (DDPMs) are a class of powerful generative models.

• The past few years have witnessed the great success of DDPMs in generating high-fidelity samples.
• A significant limitation of the DDPMs is the slow sampling procedure.
• DDPMs generally need hundreds or thousands of sequential function evaluations (steps) of neural networks to generate a sample.