This is the PyTorch code for ''Learning from Multi-domain Artistic Images for Arbitrary Style Transfer'' in Expressive 2019.
The pre-trained model on the behance-face dataset can be found here.
To test the pre-trained model, put the downloaded models in folder named ''models'', put content images in ''data/content/test'', style images in ''data/style/test'' and run
python test_autoencoder.py --content-data data/content --style-data data/style --enc-model models/vgg_normalised_conv5_1.t7 --dec-model none --dropout 0.5 --gpuid 0 --train-dec --dec-last tanh --trans-flag adin --diag-flag batch --ae-mix mask --ae-dep E5-E4 --base-mode c4 --st-layer 4w --test-dp --save-image output/face_mask --dise-model models/behance_release.pth
The stylized images can be found in folder ''output''. Here are some test cases used in the paper:
(content/style/output)
To train a model, please run
python train_mask.py --dataset face --content-data data/behance_images/faces_align_content_gender --style-data data/behance_images/faces_behance_lfw_celebA_dtd --enc-model models/vgg_normalised_conv5_1.t7 --dec-model none --epochs 150 --lr-freq 60 --batch-size 56 --test-batch-size 24 --num-workers 8 --print-freq 200 --dropout 0.5 --g-optm adam --lr 0.0002 --optm padam --d-lr 0.00002 --adam-b1 0.5 --weight-decay 0 --ae-mix mask --dise-model none --cla-w 1 --gan-w 1 --per-w 1 --gram-w 200 --cycle-w 0 --save-run debug_gan --gpuid 0,1,2,3 --train-dec --use-proj --dec-last tanh --trans-flag adin --ae-dep E5-E4 --base-mode c4 --st-layer 4w --seed 2017
@article{xu2018beyond,
title={Beyond textures: Learning from multi-domain artistic images for arbitrary style transfer},
author={Xu, Zheng and Wilber, Michael and Fang, Chen and Hertzmann, Aaron and Jin, Hailin},
journal={arXiv preprint arXiv:1805.09987},
year={2018}
}
We thank the released PyTorch code and model of WCT style transfer.