We provide PyTorch implementations for our TPAMI paper "Line Drawings for Face Portraits from Photos using Global and Local Structure based GANs". It is a journal extension of our previous CVPR 2019 work APDrawingGAN.

This project generates artistic portrait drawings from face photos using a GAN-based model. You may find useful information in preprocessing steps and training/testing tips.

[Jittor implementation]

Up: input, Down: output

If you use this code for your research, please cite our paper.

@inproceedings{YiXLLR20,
  title     = {Line Drawings for Face Portraits from Photos using Global and Local Structure based {GAN}s},
  author    = {Yi, Ran and Xia, Mengfei and Liu, Yong-Jin and Lai, Yu-Kun and Rosin, Paul L},
  booktitle = {{IEEE} Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  doi       = {10.1109/TPAMI.2020.2987931},
  year      = {2020}
}

• Linux or macOS
• Python 2 or 3
• CPU or NVIDIA GPU + CUDA CuDNN

pip install -r requirements.txt

• Download APDrawing dataset from BaiduYun(extract code:0zuv) or YandexDrive, and extract to dataset.

• Download pre-trained models and auxiliary nets from BaiduYun(extract code:imqp) or YandexDrive, and extract to checkpoints.

• Generate artistic portrait drawings for example photos in dataset/test_single using

python test.py --dataroot dataset/test_single --name apdrawinggan++_author --model test --use_resnet --netG resnet_9blocks --which_epoch 150 --how_many 1000 --gpu_ids 0 --gpu_ids_p 0 --imagefolder images-single

The test results will be saved to a html file here: ./results/apdrawinggan++_author/test_150/index-single.html.

• If you want to test on your own data, please first align your pictures and prepare your data's facial landmarks and masks according to tutorial in preprocessing steps, then change the --dataroot flag above to your directory of aligned photos.

• Run python -m visdom.server
• Train a model (with pre-training as initialization): first copy "pre2" models into checkpoints dir of current experiment, e.g. checkpoints/apdrawinggan++_1.

mkdir checkpoints/apdrawinggan++_1/
cp checkpoints/pre2/*.pt checkpoints/apdrawinggan++_1/
python train.py --dataroot dataset/AB_140_aug3_H_hm2 --name apdrawinggan++_1 --model apdrawingpp_style --use_resnet --netG resnet_9blocks --continue_train --continuity_loss --lambda_continuity 40.0 --gpu_ids 0 --gpu_ids_p 1 --display_env apdrawinggan++_1 --niter 200 --niter_decay 0 --lr 0.0001 --batch_size 1 --emphasis_conti_face --auxiliary_root auxiliaryeye2o

• To view training results and loss plots, click the URL http://localhost:8097. To see more intermediate results, check out ./checkpoints/apdrawinggan++_1/web/index.html

• To test the model on test set:

python test.py --dataroot dataset/AB_140_aug3_H_hm2 --name apdrawinggan++_author --model apdrawingpp_style --use_resnet --netG resnet_9blocks --which_epoch 150 --how_many 1000 --gpu_ids 0 --gpu_ids_p 0 --imagefolder images-apd70

The test results will be saved to a html file: ./results/apdrawinggan++_author/test_150/index-apd70.html.

• To test the model on images without paired ground truth, same as 2. Apply a pre-trained model.

You can find these scripts at scripts directory.

Preprocessing steps for your own data (either for testing or training).

Best practice for training and testing your models.

You can contact email [email protected] for any questions.

Our code is inspired by pytorch-CycleGAN-and-pix2pix.