| # DreamBooth Dataset | |
| ## DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation | |
|  | |
| ### [project page](https://dreambooth.github.io/) | [arxiv](https://arxiv.org/abs/2208.12242) | |
| This is the official repository for the dataset of the Google paper DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. | |
| ## Dataset | |
| The dataset includes 30 subjects of 15 different classes. 9 out of these subjects are live subjects (dogs and cats) and 21 are objects. The dataset contains a variable number of images per subject (4-6). Images of the subjects are usually captured in different conditions, environments and under different angles. | |
| We include a file dataset/prompts\_and\_classes.txt which contains all of the prompts used in the paper for live subjects and objects, as well as the class name used for the subjects. | |
| The images have either been captured by the paper authors, or sourced from www.unsplash.com | |
| The dataset/references\_and\_licenses.txt file contains a list of all the reference links to the images in www.unsplash.com - and attribution to the photographer, along with the license of the image. | |
| ## Academic Citation | |
| If you use this work please cite: | |
| ``` | |
| @inproceedings{ruiz2023dreambooth, | |
| title={Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation}, | |
| author={Ruiz, Nataniel and Li, Yuanzhen and Jampani, Varun and Pritch, Yael and Rubinstein, Michael and Aberman, Kfir}, | |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, | |
| year={2023} | |
| } | |
| ``` | |
| ## Disclaimer | |
| This is not an officially supported Google product. | |