  Large text-to-image models achieved a remarkable leap in the evolution of AI,
enabling high-quality and diverse synthesis of images from a given text prompt.
However, these models lack the ability to mimic the appearance of subjects in a
given reference set and synthesize novel renditions of them in different
contexts. In this work, we present a new approach for "personalization" of
text-to-image diffusion models. Given as input just a few images of a subject,
we fine-tune a pretrained text-to-image model such that it learns to bind a
unique identifier with that specific subject. Once the subject is embedded in
the output domain of the model, the unique identifier can be used to synthesize
novel photorealistic images of the subject contextualized in different scenes.
By leveraging the semantic prior embedded in the model with a new autogenous
class-specific prior preservation loss, our technique enables synthesizing the
subject in diverse scenes, poses, views and lighting conditions that do not
appear in the reference images. We apply our technique to several
previously-unassailable tasks, including subject recontextualization,
text-guided view synthesis, and artistic rendering, all while preserving the
subject's key features. We also provide a new dataset and evaluation protocol
for this new task of subject-driven generation. Project page:
https://dreambooth.github.io/
