git clone https://github.com/facebookresearch/FaderNetworks.git
cd FaderNetworks

cd data
./preprocess.py

cd models
./download.sh

python classifier.py

# Main parameters
--img_sz 256                  # image size
--img_fm 3                    # number of feature maps
--attr "*"                    # attributes list. "*" for all attributes

# Network architecture
--init_fm 32                  # number of feature maps in the first layer
--max_fm 512                  # maximum number of feature maps
--hid_dim 512                 # hidden layer size

# Training parameters
--v_flip False                # randomly flip images vertically (data augmentation)
--h_flip True                 # randomly flip images horizontally (data augmentation)
--batch_size 32               # batch size
--optimizer "adam,lr=0.0002"  # optimizer
--clip_grad_norm 5            # clip gradient L2 norm
--n_epochs 1000               # number of epochs
--epoch_size 50000            # number of images per epoch

# Reload
--reload ""                   # reload a trained classifier
--debug False                 # debug mode (if True, load a small subset of the dataset)

# Main parameters
--img_sz 256                      # image size
--img_fm 3                        # number of feature maps
--attr "Male"                     # attributes list. "*" for all attributes

# Networks architecture
--instance_norm False             # use instance normalization instead of batch normalization
--init_fm 32                      # number of feature maps in the first layer
--max_fm 512                      # maximum number of feature maps
--n_layers 6                      # number of layers in the encoder / decoder
--n_skip 0                        # number of skip connections
--deconv_method "convtranspose"   # deconvolution method
--hid_dim 512                     # hidden layer size
--dec_dropout 0                   # dropout in the decoder
--lat_dis_dropout 0.3             # dropout in the latent discriminator

# Training parameters
--n_lat_dis 1                     # number of latent discriminator training steps
--n_ptc_dis 0                     # number of PatchGAN discriminator training steps
--n_clf_dis 0                     # number of classifier training steps
--smooth_label 0.2                # smooth discriminator labels
--lambda_ae 1                     # autoencoder loss coefficient
--lambda_lat_dis 0.0001           # latent discriminator loss coefficient
--lambda_ptc_dis 0                # PatchGAN discriminator loss coefficient
--lambda_clf_dis 0                # classifier loss coefficient
--lambda_schedule 500000          # lambda scheduling (0 to disable)
--v_flip False                    # randomly flip images vertically (data augmentation)
--h_flip True                     # randomly flip images horizontally (data augmentation)
--batch_size 32                   # batch size
--ae_optimizer "adam,lr=0.0002"   # autoencoder optimizer
--dis_optimizer "adam,lr=0.0002"  # discriminator optimizer
--clip_grad_norm 5                # clip gradient L2 norm
--n_epochs 1000                   # number of epochs
--epoch_size 50000                # number of images per epoch

# Reload
--ae_reload ""                    # reload pretrained autoencoder
--lat_dis_reload ""               # reload pretrained latent discriminator
--ptc_dis_reload ""               # reload pretrained PatchGAN discriminator
--clf_dis_reload ""               # reload pretrained classifier
--eval_clf ""                     # evaluation classifier (trained with classifier.py)
--debug False                     # debug mode (if True, load a small subset of the dataset)

# Narrow Eyes
python interpolate.py --model_path models/narrow_eyes.pth --n_images 10 --n_interpolations 10 --alpha_min 10.0 --alpha_max 10.0 --output_path narrow_eyes.png

# Eyeglasses
python interpolate.py --model_path models/eyeglasses.pth --n_images 10 --n_interpolations 10 --alpha_min 2.0 --alpha_max 2.0 --output_path eyeglasses.png

# Age
python interpolate.py --model_path models/young.pth --n_images 10 --n_interpolations 10 --alpha_min 10.0 --alpha_max 10.0 --output_path young.png

# Gender
python interpolate.py --model_path models/male.pth --n_images 10 --n_interpolations 10 --alpha_min 2.0 --alpha_max 2.0 --output_path male.png

# Pointy nose
python interpolate.py --model_path models/pointy_nose.pth --n_images 10 --n_interpolations 10 --alpha_min 10.0 --alpha_max 10.0 --output_path pointy_nose.png

@inproceedings{lample2017fader,
  title={Fader Networks: Manipulating Images by Sliding Attributes},
  author={Lample, Guillaume and Zeghidour, Neil and Usunier, Nicolas and Bordes, Antoine and DENOYER, Ludovic and others},
  booktitle={Advances in Neural Information Processing Systems},
  pages={5963--5972},
  year={2017}
}