Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
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While the success of diffusion models has been witnessed in various domains, only a few works have investigated the variation of the generative process. In this paper, we introduce a new generative process that is closer to the reverse process than the original generative process, given the identical score checkpoint. Specifically, we adjust the generative process with the auxiliary discriminator between the real data and the generated data. Consequently, the adjusted generative process with the discriminator generates more realistic samples than the original process. In experiments, we achieve new SOTA FIDs of 1.74 on CIFAR-10, 1.33 on CelebA, and 1.88 on FFHQ in the unconditional generation.
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