While there are many score-based models with various diffusing strategies as well as many numerical schemes of the denoising process, only a few works have explored the score part of the generative SDE. This paper introduces a new generative SDE with score adjustment using an auxiliary discriminator. The goal is to improve the original generative process of a pre-trained diffusion model by estimating the gap between the pre-trained score estimation and the true data score. This is done by training a discriminator that classifies diffused real data and diffused sample data. The gap estimation is then used to adjust the pre-trained score network. In experiments, the method enables new SOTA FIDs of 1.77/1.64 on unconditional/conditional CIFAR-10, and new SOTA FID/sFID of 3.18/4.53 on ImageNet 256x256.

翻译：尽管已有许多采用不同扩散策略的基于分数的模型以及多种去噪过程的数值方案，但仅有少数研究探索了生成式随机微分方程中的分数部分。本文引入了一种新的生成式随机微分方程，通过辅助判别器进行分数调整。其目标是通过估计预训练分数估计与真实数据分数之间的差距，来改进预训练扩散模型的原始生成过程。具体做法是训练一个判别器，对扩散后的真实数据和扩散后的样本数据进行分类。然后利用所估计的差距对预训练的分数网络进行调整。实验结果表明，该方法在无条件/条件CIFAR-10数据集上实现了新的最优FID值1.77/1.64，在ImageNet 256x256数据集上实现了新的最优FID/sFID值3.18/4.53。