Typical diffusion models are trained to accept a particular form of conditioning, most commonly text, and cannot be conditioned on other modalities without retraining. In this work, we propose a universal guidance algorithm that enables diffusion models to be controlled by arbitrary guidance modalities without the need to retrain any use-specific components. We show that our algorithm successfully generates quality images with guidance functions including segmentation, face recognition, object detection, style guidance and classifier signals.
Universal Guidance for Diffusion Models
Arpit Bansal Hong-Min Chu Avi Schwarzschild Soumyadip Sengupta Micah Goldblum Jonas Geiping Tom Goldstein