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Large text-to-image models have revolutionized the ability to generateimagery using natural language. However, particularly unique or personal visualconcepts, such as your pet, an object in your house, etc., will not be capturedby the original model. This has led to interest in how to inject new visualconcepts, bound to a new text token, using as few as 4-6 examples. Despitesignificant progress, this task remains a formidable challenge, particularly inpreserving the subject's identity. While most researchers attempt to to addressthis issue by modifying model architectures, our approach takes a data-centricperspective, advocating the modification of data rather than the model itself.We introduce a novel regularization dataset generation strategy on both thetext and image level; demonstrating the importance of a rich and structuredregularization dataset (automatically generated) to prevent losing textcoherence and better identity preservation. The better quality is enabled byallowing up to 5x more fine-tuning iterations without overfitting anddegeneration. The generated renditions of the desired subject preserve evenfine details such as text and logos; all while maintaining the ability togenerate diverse samples that follow the input text prompt. Since our methodfocuses on data augmentation, rather than adjusting the model architecture, itis complementary and can be combined with prior work. We show on establishedbenchmarks that our data-centric approach forms the new state of the art interms of image quality, with the best trade-off between identity preservation,diversity, and text alignment.

A Data Perspective on Enhanced Identity Preservation for Diffusion Personalization