Guided diffusion sampling relies on approximating often intractable likelihood scores, which introduces significant noise into the sampling dynamics. We propose using adaptive moment estimation to stabilize these noisy likelihood scores during sampling. Despite its simplicity, our approach achieves state-of-the-art results on image restoration and class-conditional generation tasks, outperforming more complicated methods, which are often computationally more expensive. We provide empirical analysis of our method on both synthetic and real data, demonstrating that mitigating gradient noise through adaptive moments offers an effective way to improve alignment.

翻译：引导扩散采样依赖于近似通常难以处理的似然分数，这给采样动力学引入了显著噪声。我们提出在采样过程中使用自适应矩估计来稳定这些含噪的似然分数。尽管方法简单，我们的方法在图像修复和类条件生成任务上取得了最先进的结果，超越了通常计算成本更高的复杂方法。我们在合成数据和真实数据上提供了该方法的实证分析，证明通过自适应矩估计缓解梯度噪声为提高对齐性提供了一种有效途径。