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

Yuheng Li · Haotian Liu · Qingyang Wu · Fangzhou Mu · Jianwei Yang · Jianfeng Gao · Chunyuan Li · Yong Jae Lee

West Building Exhibit Halls ABC 182


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: Open-Set Grounded Text-to-Image Generation, a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs. To preserve the vast concept knowledge of the pre-trained model, we freeze all of its weights and inject the grounding information into new trainable layers via a gated mechanism. Our model achieves open-world grounded text2img generation with caption and bounding box condition inputs, and the grounding ability generalizes well to novel spatial configurations and concepts. GLIGEN’s zero-shot performance on COCO and LVIS outperforms existing supervised layout-to-image baselines by a large margin.

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