  Large text-to-image models have revolutionized the ability to generate
imagery using natural language. However, particularly unique or personal visual
concepts, such as your pet, an object in your house, etc., will not be captured
by the original model. This has led to interest in how to inject new visual
concepts, bound to a new text token, using as few as 4-6 examples. Despite
significant progress, this task remains a formidable challenge, particularly in
preserving the subject's identity. While most researchers attempt to to address
this issue by modifying model architectures, our approach takes a data-centric
perspective, advocating the modification of data rather than the model itself.
We introduce a novel regularization dataset generation strategy on both the
text and image level; demonstrating the importance of a rich and structured
regularization dataset (automatically generated) to prevent losing text
coherence and better identity preservation. The better quality is enabled by
allowing up to 5x more fine-tuning iterations without overfitting and
degeneration. The generated renditions of the desired subject preserve even
fine details such as text and logos; all while maintaining the ability to
generate diverse samples that follow the input text prompt. Since our method
focuses on data augmentation, rather than adjusting the model architecture, it
is complementary and can be combined with prior work. We show on established
benchmarks that our data-centric approach forms the new state of the art in
terms of image quality, with the best trade-off between identity preservation,
diversity, and text alignment.

Computer Vision and Pattern Recognition