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arXiv · 2302.07121

Universal Guidance for Diffusion Models

Arpit Bansal·Hong-Min Chu·Avi Schwarzschild·Soumyadip Sengupta·Micah Goldblum·Jonas Geiping·Tom Goldstein
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Abstract

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.

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Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein. 2023-02-14. Universal Guidance for Diffusion Models. https://arxiv.org/abs/2302.07121

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