arXiv Open Access 2023

Universal Guidance for Diffusion Models

Arpit Bansal Hong-Min Chu Avi Schwarzschild Soumyadip Sengupta Micah Goldblum +2 lainnya

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Abstrak

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.

Topik & Kata Kunci

cs.CV cs.LG

Penulis (7)

Arpit Bansal

Hong-Min Chu

Avi Schwarzschild

Soumyadip Sengupta

Micah Goldblum

Jonas Geiping

Tom Goldstein

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APA MLA BibTeX

Bansal, A., Chu, H., Schwarzschild, A., Sengupta, S., Goldblum, M., Geiping, J. et al. (2023). Universal Guidance for Diffusion Models. https://arxiv.org/abs/2302.07121

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓