@inproceedings{
liao2024unidllora,
title={Uni-DlLo{RA}: Style Fine-Tuning for Fashion Image Translation},
author={Fangjian Liao and Xingxing Zou and Waikeung Wong},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=R6yqrO3tNU}
}
Image-to-image (i2i) translation has achieved notable success, yet remains challenging in scenarios like real-to-illustrative style transfer of fashion. Existing methods focus on enhancing the generative model with diversity while lacking ID-preserved domain translation. This paper introduces a novel model named Uni-DlLoRA to release this constraint. The proposed model combines the original images within a pretrained diffusion-based model using the proposed Uni-adapter extractors, while adopting the proposed Dual-LoRA module to provide distinct style guidance. This approach optimizes generative capabilities and reduces the number of additional parameters required. In addition, a new multimodal dataset featuring higher-quality images with captions built upon an existing real-to-illustration dataset is proposed. Experimentation validates the effectiveness of our proposed method.
Uni-DlLoRA: Style Fine-Tuning for Fashion Image Translation
Fangjian Liao Xingxing Zou Waikeung Wong