  Personalized text-to-image generation models enable users to create images
that depict their individual possessions in diverse scenes, finding
applications in various domains. To achieve the personalization capability,
existing methods rely on finetuning a text-to-image foundation model on a
user's custom dataset, which can be non-trivial for general users,
resource-intensive, and time-consuming. Despite attempts to develop
finetuning-free methods, their generation quality is much lower compared to
their finetuning counterparts. In this paper, we propose Joint-Image Diffusion
(\jedi), an effective technique for learning a finetuning-free personalization
model. Our key idea is to learn the joint distribution of multiple related
text-image pairs that share a common subject. To facilitate learning, we
propose a scalable synthetic dataset generation technique. Once trained, our
model enables fast and easy personalization at test time by simply using
reference images as input during the sampling process. Our approach does not
require any expensive optimization process or additional modules and can
faithfully preserve the identity represented by any number of reference images.
Experimental results show that our model achieves state-of-the-art generation
quality, both quantitatively and qualitatively, significantly outperforming
both the prior finetuning-based and finetuning-free personalization baselines.
