@inproceedings{4a182dd91ae745e6ae4535ae75b0e2f9,
title = "Learning Adapters for Text-Guided Portrait Stylization with Pretrained Diffusion Models",
abstract = "This paper presents a framework for text-guided face portrait stylization using a pre-trained large-scale diffusion model. To balance style transformation and content preservation, we introduce an adapter that modifies specific components of the diffusion model. By training the adapter to only modify these components, we reduce the tuning parameter space, resulting in an efficient solution for face portrait stylization. Our approach captures the target style and at the same time, preserves the source portrait content, making it an effective method for personalized image editing. Experimental results show its superiority over state-of-the-art techniques in various stylization tasks.",
keywords = "Diffusion model, Portrait stylization",
author = "Mintu Yang and Xianxu Hou and Hao Li and Linlin Shen and Lixin Fan",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023 ; Conference date: 13-10-2023 Through 15-10-2023",
year = "2024",
doi = "10.1007/978-981-99-8429-9_20",
language = "English",
isbn = "9789819984282",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "247--258",
editor = "Qingshan Liu and Hanzi Wang and Rongrong Ji and Zhanyu Ma and Weishi Zheng and Hongbin Zha and Xilin Chen and Liang Wang",
booktitle = "Pattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings",
}