TensorFlow implementation of StackGAN++, described in the paper by Zhang, Xu et al.
Dependencies. Python 3, TensorFlow 1.4 (, TensorBoard)
Data. You can find used datasets on FloydHub:
Training.
-
Clone the repo, including the TensorFlow models submodule:
git clone --recurse-submodules https://github.com/jppgks/stackgan-pp.git
-
Run the training script
python ./train.py
optionally with arguments. All possible arguments, with their doc strings, are listed when running:
python ./train.py --help
-
Follow progress in TensorBoard:
tensorboard --logdir=<TRAIN_LOG_DIR location>
The project aims to reproduce StackGAN++ paper results by introducing as little modifications as possible to the existing TFGAN framework.
TFGAN → TFSTACKGAN.
tfstackgan
mimics the folder structure of TFGAN.
tfstackgan/python/train.py
contains
modified TFGAN train.py
functions.
The color loss for the generator is defined in tfstackgan/python/losses/python/losses_impl.py
.
The ./train.py
and ./networks.py
scripts are modeled after the TFGAN CIFAR example.
Upsampling. This implementation does not make use of GLUs and/or residual blocks at the moment. Upsampling in all generator stages happens through fractionally strided convolutions.