Classifier-Free Diffusion Guidance
This provides a method for improving sample quality in generative models without external classifiers, though it is incremental as it builds on existing guidance techniques.
The paper tackles the problem of trading off mode coverage and sample fidelity in conditional diffusion models without requiring a separate classifier, achieving a trade-off similar to classifier guidance by jointly training conditional and unconditional diffusion models.
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.