GLIGEN: Open-Set Grounded Text-to-Image Generation

Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propose GLIGEN, Grounded-Language-to-Image Generation, a novel approach that builds upon and extends the functionality of exis...

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Veröffentlicht in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 22511 - 22521
Hauptverfasser:	Li, Yuheng, Liu, Haotian, Wu, Qingyang, Mu, Fangzhou, Yang, Jianwei, Gao, Jianfeng, Li, Chunyuan, Lee, Yong Jae
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2023
Schlagworte:	Computational modeling Computer vision Graphics Grounding Image and video synthesis and generation Image edge detection Logic gates Training data
ISSN:	1063-6919
Online-Zugang:	Volltext
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