codes for "Instance Level Facial Attributes Transfer with Geometry-Aware Flow".
Picture: Our model can transfer facial attributes with realistic details under high resolution.
- Pytorch 0.4
download these datasets and put them under celeba_data:
- celeba
- CelebA-HQ
- download the 68 landmark annotation of CelebA dataset.
You can use different training options in options.py. Here is an example:
#!/usr/bin/env bash
job_name="goatee"
attr_name="Goatee"
python -u train.py --n_blocks 3 --ngf 16 --ndf 64 --batch_size 24 --img_size 256\
--sel_attrs $attr_name --name $job_name --gpu_ids 0 --use_lsgan --display_freq 50 \
--lambda_gan_feat 5 --lambda_cls 2e-1 --print_freq 20 --lambda_flow_reg 1 --lambda_mask 1e-1The testing consists of two phases: creating the image folder to store input images and run models on those input images. You can create your own input folders or using scripts provided below.
-
selecting inputs from celebA:
python -u test.py --exp_folder $location_of_your_model --dataset_size 30\ --which_epoch $num_epoch --which_iter $num_iter --attr_folder $your_input_folder \ --result_folder $your_output_folder \ --create_attr_folder --test_img_size 256
-
testing on selected inputs from celebA:
python -u test.py --exp_folder $location_of_your_model --dataset_size 30\ --which_epoch $num_epoch --which_iter $num_iter --attr_folder $your_input_folder \ --result_folder $your_output_folder --test_img_size 256
-
selecting inputs from celebA-HQ:
python -u test.py --exp_folder $location_of_your_model --dataset_size 30\ --which_epoch $num_epoch --which_iter $num_iter --attr_folder $your_input_folder \ --result_folder $your_output_folder \ --create_attr_folder --is_hd --test_img_size 1024
-
testing on selected inputs from celebA:
python -u test.py --exp_folder $location_of_your_model --dataset_size 30\ --which_epoch $num_epoch --which_iter $num_iter --attr_folder $your_input_folder \ --result_folder $your_output_folder --test_img_size 1024 --is_hd
If you use the codes, please cite the following publications:
@article{yin2019geogan,
title={Instance Level Facial Attributes Transfer with Geometry-Aware Flow},
author={Weidong Yin, Ziwei Liu and Chen Change Loy},
booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
month = {February},
year = {2019}
}