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

**Authors:** Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, Kfir Aberman  
**arXiv:** [2208.12242](https://arxiv.org/abs/2208.12242)  
**Date:** 2022-08-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2208.12242v1/

## Key points

- Large text-to-image models have advanced AI by synthesizing high-quality and diverse images based on text prompts.
- These models lack the ability to mimic the appearance of subjects from a reference set in different contexts.
- The proposed approach allows users to personalize text-to-image diffusion models according to their specific needs.
- The technique involves training a pretrained model with a few images of a subject and associating a unique identifier with it.
- This enables the synthesis of fully-novel photorealistic images of the subject in various scenes, poses, views, and lighting conditions.
- The technique leverages semantic prior and introduces an autogenous class-specific prior preservation loss to generate diverse instances while preserving key features.
- The super-resolution component of the model is fine-tuned using low-resolution and high-resolution image pairs for fidelity to important details.
- Applications include subject recontextualization, text-guided view synthesis, appearance modification, and artistic rendering while preserving key features.
- Users can imagine their own dog traveling or their favorite bag displayed in an exclusive showroom in Paris, among other scenarios.
- The project addresses the challenge of generating novel renditions of subjects in different contexts using just a few casual images while maintaining key features.

## Summary

Large text-to-image models have made significant advancements in AI by enabling the synthesis of high-quality and diverse images based on a given text prompt. However, these models lack the ability to mimic the appearance of subjects from a reference set and generate novel renditions of them in different contexts. In this work, we propose a new approach for "personalization" of text-to-image diffusion models, allowing users to fine-tune these models according to their specific needs. Our technique involves training a pretrained text-to-image model, such as Imagen, with just a few images of a subject. By associating a unique identifier with that specific subject, we embed it into the output domain of the model. This unique identifier can then be used to synthesize fully-novel photorealistic images of the subject in various scenes, poses, views, and lighting conditions that may not appear in the reference images. To achieve this personalization, we leverage the semantic prior embedded in the model and introduce an autogenous class-specific prior preservation loss. This loss encourages the model to generate diverse instances of the same class as our subject while preserving its key features. We also fine-tune the super-resolution component of the model using pairs of low-resolution and high-resolution versions of the input images to maintain fidelity to small but important details. Our technique has several applications including subject recontextualization, text-guided view synthesis, appearance modification, and artistic rendering while preserving key features. For example, users can imagine their own dog traveling around the world or their favorite bag displayed in an exclusive showroom in Paris. They can even envision their parrot being the main character of an illustrated storybook. This project represents a significant contribution as it addresses a challenging problem setting where users can capture just a few casual images of a subject and generate novel renditions of them in different contexts while maintaining their key features.