Boosting Diffusion Models with an Adaptive Momentum Sampler

Understand

Diffusion probabilistic models (DPMs) have been shown to generate high-quality images without the need for delicate adversarial training.

• However, the current sampling process in DPMs is prone to violent shaking.
• In this paper, we present a novel reverse sampler for DPMs inspired by the widely-used Adam optimizer.
• Our proposed sampler can be readily applied to a pre-trained diffusion model, utilizing momentum mechanisms and adaptive updating to smooth the reverse sampling process and ensure stable generation, resulting in outputs of enhanced quality.