The DreamBooth dataset is a dataset for training diffusion models to recognize and generate images of specific individuals. It allows a model to be trained with a small number of images (e.g., a few photos of a specific object or person) to generate images of that specific individual in a variety of different contexts while maintaining its key visual features.

The dataset contains 30 subjects of different categories, including 9 living subjects (such as dogs and cats) and 21 objects, with 4 to 6 images per subject. These images are usually taken under different conditions, environments, and angles to ensure that the model can learn the appearance of the subject in different contexts.

• The dataset also includes a file prompts_and_classes.txt, which contains all the prompts used for live topics and objects in the paper, as well as the category names used for the topics.
• These images were either taken by the authors of the paper or are from www.unsplash.com.
• Should references_and_licenses.txt The file contains a list of reference links to all the images on www.unsplash.com, as well as attribution to the photographer and the license of the images.

This dataset is from Google's paperDreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation" is part of the official repository of the paper, and the paper results have been published in CVPR 2023.