📝 This repository contains the official PyTorch implementation of the following paper:
StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation
Wonjong Jang, Gwangjin Ju, Yucheol Jung, Jiaolong Yang, Xin Tong, Seungyong Lee, SIGGRAPH 2021
🚀 >> Project page
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Explanation
✔️ PyTorch 1.3.1
✔️ torchvision 0.4.2
✔️ CUDA 10.1/10.2
✔️ dlib 19.22.0
✔️ requests 2.23.0
✔️ tqdm 4.46.2
First download pre-trained model weights:
bash ./download.sh
python -m torch.distributed.launch --nproc_per_node=N_GPU train.py --name EXPERIMENT_NAME --freeze_D
Test on user's input images:
python test.py --ckpt CHECKPOINT_PATH --input_dir INPUT_IMAGE_PATH --output_dir OUTPUT_CARICATURE_PATH --invert_images
We provide some sample images. Test on sample images:
python test.py --ckpt CHECKPOINT_PATH --input_dir examples/samples --output_dir examples/results --invert_images
It inverts latent codes from input photos and generates caricatures from latent codes.
| Input image | Output caricature |
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If you find this code useful, please consider citing:
@article{Jang2021StyleCari,
author = {Wonjong Jang and Gwangjin Ju and Yucheol Jung and Jiaolong Yang and Xin Tong and Seungyong Lee},
title = {StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation},
booktitle = {ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH)},
publisher = {ACM},
volume = {40},
number = {4},
year = {2021}
}
🏷️ StyleCariGAN
🏷️ Photo-StyleGAN (generator_ffhq.pt)
🏷️ Caricature-StyleGAN (generator_cari.pt)
🏷️ Photo-Attribute-Classifier (photo_resnet.pth)
🏷️ Cari-Attribute-Classifier (cari_resnet.pth)
📫 You can have contact with wonjong@postech.ac.kr or ycjung@postech.ac.kr
This software is being made available under the terms in the LICENSE file.
Any exemptions to these terms require a license from the Pohang University of Science and Technology.
❤️ Our code is based on the official StyleGAN2 implementation and rosinality's StyleGAN2-pytorch code
❤️ Specially thanks to CJWBW who ported our project to Replicate.
