Stanford U & Google Brain’s Classifier-Free Guidance Model Diffusion Technique Reduces Sampling Steps by 256x

In the new paper On Distillation of Guided Diffusion Models, researchers from Google Brain and Stanford University propose a novel approach for distilling classifier-free guided diffusion models with high sampling efficiency. The resulting models achieve performance comparable to the original model but with sampling steps reduced by up to 256 times.

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