  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 carefully design an
end-to-end pipeline that guarantees local style transfer according to 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.
