{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/universal-guidance-for-diffusion-models","title":"Universal Guidance for Diffusion Models","arxiv_id":"2302.07121","date":"2023-02-14","proceeding":null,"authors":["Arpit Bansal","Hong-Min Chu","Avi Schwarzschild","Soumyadip Sengupta","Micah Goldblum","Jonas Geiping","Tom Goldstein"],"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. 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