GroundingBooth: Grounding Text-to-Image Customization

Zhexiao Xiong, Wei Xiong, Jing Shi, He Zhang, Yizhi Song, Nathan Jacobs

Keywords: Subject-Driven Generation; Text-to-image Customization; Diffusion Models, Vision-Language, Grounding

TL;DR: About gounding text-to-image customization

Abstract: Recent studies in text-to-image customization show great success in generating personalized object variants given several images of a subject. While existing methods focus more on preserving the identity of the subject, they often fall short of controlling the spatial relationship between objects. In this work, we introduce GroundingBooth, a framework that achieves zero-shot instance-level spatial grounding on both foreground subjects and background objects in the text-to-image customization task. Our proposed text-image grounding module and masked cross-attention layer allow us to generate personalized images with both accurate layout alignment and identity preservation while maintaining text-image coherence. With such layout control, our model inherently enables the customization of multiple subjects at once. Our model is evaluated on both layout-guided image synthesis and reference-based customization tasks, showing strong results compared to existing methods. Our work achieves a joint grounding on both subject-driven foreground generation and text-driven background generation. Our code will be publicly available.

Primary Area: generative models

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Submission Number: 2211