@inproceedings{
duan2024domaingallery,
title={DomainGallery: Few-shot Domain-driven Image Generation by Attribute-centric Finetuning},
author={Yuxuan Duan and Yan Hong and Bo Zhang and jun lan and Huijia Zhu and Weiqiang Wang and Jianfu Zhang and Li Niu and Liqing Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ZMmJ1z8vee}
}
The recent progress in text-to-image models pretrained on large-scale datasets has enabled us to generate various images as long as we provide a text prompt describing what we want. Nevertheless, the availability of these models is still limited when we expect to generate images that fall into a specific domain either hard to describe or just unseen to the models. In this work, we propose DomainGallery, a few-shot domain-driven image generation method which aims at finetuning pretrained Stable Diffusion on few-shot target datasets in an attribute-centric manner. Specifically, DomainGallery features prior attribute erasure, attribute disentanglement, regularization and enhancement. These techniques are tailored to few-shot domain-driven generation in order to solve key issues that previous works have failed to settle. Extensive experiments are given to validate the superior performance of DomainGallery on a variety of domain-driven generation scenarios.
DomainGallery: Few-shot Domain-driven Image Generation by Attribute-centric Finetuning
Yuxuan Duan Yan Hong Bo Zhang jun lan Huijia Zhu Weiqiang Wang Jianfu Zhang Li Niu Liqing Zhang