LAG: Latent Adversarial Generator

Code for the paper: "Creating High Resolution Images with a Latent Adversarial Generator" by David Berthelot, Peyman Milanfar, and Ian Goodfellow.

This is not an officially supported Google product.

Setup

Important: ML_DATA is a shell environment variable that should point to the location where the datasets are installed. See the Install datasets section for more details.

Install dependencies

sudo apt install python3-dev python3-virtualenv python3-tk imagemagick virtualenv
virtualenv -p python3 --system-site-packages ~/env3
~/env3/bin/pip install -r requirements.txt

To use virtualenv, when starting a new shell, always run the following lines first:

. ~/env3/bin/activate
export PYTHONPATH=$PYTHONPATH:.

Install datasets

Directory for storing training data

# You can define ML_DATA to whatever path you like.
export ML_DATA="path to where you want the datasets saved"
mkdir $ML_DATA $ML_DATA/Models

Download vgg19.npy from https://github.com/machrisaa/tensorflow-vgg to $ML_DATA/Models.

Download datasets

# Important ML_DATA and PYTONPATH must be defined (see previous sections).
scripts/create_datasets.py

Alternatively you can download just some selected datasets to save space and time, for example:

scripts/create_datasets.py celeba svhn

The supported datasets are (%s represents the image size, when present it can be replaced by 32, 64, 128 or 256):

celeba%s, cifar10, mnist, svhn,

lsun_bedroom%s, lsun_bridge%s, lsun_church_outdoor%s, lsun_classroom%s,
lsun_conference_room%s, lsun_dining_room%s, lsun_kitchen%s, lsun_living_room%s,
lsun_restaurant%s, lsun_tower%s

Running

Setup

All commands must be ran from the project root. The following environment variables must be defined:

export ML_DATA="path to where you want the datasets saved"
export PYTHONPATH=$PYTHONPATH:.

Example

For example, training lag for a 32x zoom using CelebA128:

CUDA_VISIBLE_DEVICES=0 python lag.py --dataset=celeba128 --scale=32

Multi-GPU training

Just pass more GPUs and the code automatically scales to them, here we assign GPUs 4-7 to the program:

CUDA_VISIBLE_DEVICES=4,5,6,7 python lag.py --dataset=celeba128 --scale=32

Flags

python lag.py --help
# The following option might be too slow to be really practical.
# python lag.py --helpfull
# So instead I use this hack to find the flags:
fgrep -R flags.DEFINE libml lag.py

Monitoring training progress

You can point tensorboard to the training folder (by default it is --train_dir=./experiments) to monitor the training process:

tensorboard --logdir experiments
# And point your browser the link printed when tensorboard starts.

Generating samples

Following the previous example, for a LAG model trained on CelebA128, the ckpt is the folder where the model was trained:

python scripts/lag_generate_candidates.py\
 --dataset=celeba128\
 --samples 0,3,4,6,8,9,12,13,15\
 --ckpt experiments/celeba128/average32X/LAG_batch16_blocks8_filters256_filters_min64_lod_min1_lr0.001_mse_weight10.0_noise_dim64_training_kimg2048_transition_kimg2048_ttur4_wass_target1.0_weight_avg0.999/

Citing this work

@article{berthelot2020creating,
  title={Creating High Resolution Images with a Latent Adversarial Generator},
  author={Berthelot, David and Milanfar, Peyman and Goodfellow, Ian},
  journal={arXiv preprint arXiv:2003.02365},
  year={2020}
}