Universal Guidance for Diffusion Models

Understand

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, and classifier signals.
• Code is available at https://github.com/arpitbansal297/Universal-Guided-Diffusion.