  Recent text-to-image generation models like DreamBooth have made remarkable
progress in generating highly customized images of a target subject, by
fine-tuning an ``expert model'' for a given subject from a few examples.
However, this process is expensive, since a new expert model must be learned
for each subject. In this paper, we present SuTI, a Subject-driven
Text-to-Image generator that replaces subject-specific fine tuning with
in-context learning. Given a few demonstrations of a new subject, SuTI can
instantly generate novel renditions of the subject in different scenes, without
any subject-specific optimization. SuTI is powered by apprenticeship learning,
where a single apprentice model is learned from data generated by a massive
number of subject-specific expert models. Specifically, we mine millions of
image clusters from the Internet, each centered around a specific visual
subject. We adopt these clusters to train a massive number of expert models,
each specializing in a different subject. The apprentice model SuTI then learns
to imitate the behavior of these fine-tuned experts. SuTI can generate
high-quality and customized subject-specific images 20x faster than
optimization-based SoTA methods. On the challenging DreamBench and
DreamBench-v2, our human evaluation shows that SuTI significantly outperforms
existing models like InstructPix2Pix, Textual Inversion, Imagic, Prompt2Prompt,
Re-Imagen and DreamBooth, especially on the subject and text alignment aspects.
