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[ECCV'22] Language-Driven Artistic Style Transfer
A PyTorch implementation of LDAST
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<img src='_imgs/ldast.png' width='75%' />Overview
LDAST is an implementation of <br> "Language-Driven Artistic Style Transfer" <br> Tsu-Jui Fu, Xin Eric Wang, and William Yang Wang <br> in European Conference on Computer Vision (<b>ECCV</b>) 2022 <br>
<img src='_imgs/clva.png' width='60%' />Language visual artist (LVA) extracts content structures from <I>C</I> and visual patterns from <I>X</I> to perform LDAST. LVA adopts the patch-wise style discriminator <I>D</I> to connect extracted visual semantics to patches of paired style image (<I>P<sub>S</sub></I>). Contrastive reasoning (CR) allows comparing contrastive pairs <I>C<sub>1</sub>-X<sub>1</sub></I>, <I>C<sub>2</sub>-X<sub>1</sub></I>, and <I>C<sub>2</sub>-X<sub>2</sub></I> of content image and style instruction.
Requirements
This code is implemented under Python 3.8, PyTorch 1.7, and Torchvision 0.8. <br>
Usage
Dataset
The dataset includes content images and visual attribute instructions (DTD). <br> Please visit WikiArt and here for emotional effect instructions (ArtEmis).
Train
Put sanet.pt in ./_ckpt and dtd.pkl in ./_data.
python train_lva.py
python train_ctr.py
Inference & GUI
Put clva_dtd.pt in ./_ckpt.
python inference.py
python gui.py
Citation
@inproceedings{fu2022ldast,
author = {Tsu-Jui Fu and Xin Eric Wang and William Yang Wang},
title = {{Language-Driven Artistic Style Transfer}},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2022}
}
Acknowledgement
This code is based on SANet