Text-conditioned style transfer enables users to communicate their desired artistic styles through text descriptions, offering a new and expressive means of achieving stylization. In this work, we evaluate the text-conditioned image editing and style transfer techniques on their fine-grained understanding of user prompts for precise "local" style transfer. We find that current methods fail to accomplish localized style transfers effectively, either failing to localize style transfer to certain regions in the image, or distorting the content and structure of the input image. To this end, we develop an end-to-end pipeline for "local" style transfer tailored to align with users' intent. Further, we substantiate the effectiveness of our approach through quantitative and qualitative analysis. The project code is available at: https://github.com/silky1708/local-style-transfer.
@article{arxiv.2405.16330,
title = {LEAST: "Local" text-conditioned image style transfer},
author = {Silky Singh and Surgan Jandial and Simra Shahid and Abhinav Java},
journal= {arXiv preprint arXiv:2405.16330},
year = {2025}
}
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Accepted to AI for Content Creation (AI4CC) Workshop at CVPR 2024