APDrawingGAN++

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]

Our Proposed Framework

Sample Results

Up: input, Down: output

Citation

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}
}

Prerequisites

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

Getting Started

1.Installation

pip install -r requirements.txt

2.Quick Start (Apply a Pre-trained Model)

• 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.

3.Train

• 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

4.Test

• 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

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

Training/Test Tips

Best practice for training and testing your models.

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

Acknowledgments

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