Description
Classifier guided sampling from diffusion models involves the computation of gradients of classifier probabilities, and this is computationally expensive as it involves the use of autograd operators. To mitigate this issue, Classifier-Free Guidance (CFG), was proposed to use an unconditional sample as a reference to increase contrast from. We extend these concepts to devise a novel formulation that utilizes a classifier, without computation of gradients, to generate an conditional sample as the reference. In what follows, we describe this methodology and refer to our method as “gradient-free classifier guidance” (GFCG). Our method is also adaptive in that it computes the guidance scale on the-fly depending on how confused the diffusion model is during the denoising process.