  Classifier guidance is a recently introduced method to trade off mode
coverage and sample fidelity in conditional diffusion models post training, in
the same spirit as low temperature sampling or truncation in other types of
generative models. Classifier guidance combines the score estimate of a
diffusion model with the gradient of an image classifier and thereby requires
training an image classifier separate from the diffusion model. It also raises
the question of whether guidance can be performed without a classifier. We show
that guidance can be indeed performed by a pure generative model without such a
classifier: in what we call classifier-free guidance, we jointly train a
conditional and an unconditional diffusion model, and we combine the resulting
conditional and unconditional score estimates to attain a trade-off between
sample quality and diversity similar to that obtained using classifier
guidance.
