DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation

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. We also provide a new dataset and evaluation protocol for this new task of subject-driven generation.

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• 1 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.
• 2 In this work, we present a new approach for “personalization” of text-to-image diffusion models.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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