| Autor: |
Bansal, Arpit, Chu, Hong-Min, Schwarzschild, Avi, Sengupta, Soumyadip, Goldblum, Micah, Geiping, Jonas, Goldstein, Tom |
| Rok vydání: |
2023 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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