UniversalBooth: Model-Agnostic Personalized Text-To-Image Generation

for various local patches to enhance appearance consistency. To improve the performance when deployed on unseen diffusion models, we further devise an optimal transport prior to the model and encourage the attention scores allocated by cross-attention to fulfill the optimal transport constraint. Experiments demonstrate that our personalized generation model can be generalized to unseen text-toimage diffusion models with a wide spectrum of architectures and functionalities without any additional optimization, while other methods cannot. Meanwhile, it achieves comparable zero-shot personalization performance on seen architectures with existing works.