  The proposed method, Discriminator Guidance, aims to improve sample
generation of pre-trained diffusion models. The approach introduces a
discriminator that gives explicit supervision to a denoising sample path
whether it is realistic or not. Unlike GANs, our approach does not require
joint training of score and discriminator networks. Instead, we train the
discriminator after score training, making discriminator training stable and
fast to converge. In sample generation, we add an auxiliary term to the
pre-trained score to deceive the discriminator. This term corrects the model
score to the data score at the optimal discriminator, which implies that the
discriminator helps better score estimation in a complementary way. Using our
algorithm, we achive state-of-the-art results on ImageNet 256x256 with FID 1.83
and recall 0.64, similar to the validation data's FID (1.68) and recall (0.66).
We release the code at https://github.com/alsdudrla10/DG.
