Boosting Diffusion Models with an Adaptive Momentum Sampler
Xiyu Wang Anh-Dung Dinh Daochang Liu Chang Xu
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. By implicitly reusing update
directions from early steps, our proposed sampler achieves a better balance
between high-level semantics and low-level details. Additionally, this sampler
is flexible and can be easily integrated into pre-trained DPMs regardless of
the sampler used during training. Our experimental results on multiple
benchmarks demonstrate that our proposed reverse sampler yields remarkable
improvements over different baselines. We will make the source code available.