  Adapting a segmentation model from a labeled source domain to a target
domain, where a single unlabeled datum is available, is one the most
challenging problems in domain adaptation and is otherwise known as one-shot
unsupervised domain adaptation (OSUDA). Most of the prior works have addressed
the problem by relying on style transfer techniques, where the source images
are stylized to have the appearance of the target domain. Departing from the
common notion of transferring only the target ``texture'' information, we
leverage text-to-image diffusion models (e.g., Stable Diffusion) to generate a
synthetic target dataset with photo-realistic images that not only faithfully
depict the style of the target domain, but are also characterized by novel
scenes in diverse contexts. The text interface in our method Data AugmenTation
with diffUsion Models (DATUM) endows us with the possibility of guiding the
generation of images towards desired semantic concepts while respecting the
original spatial context of a single training image, which is not possible in
existing OSUDA methods. Extensive experiments on standard benchmarks show that
our DATUM surpasses the state-of-the-art OSUDA methods by up to +7.1%. The
implementation is available at https://github.com/yasserben/DATUM
