Pytorch Implementation of “Progressive growing GAN (PGGAN)”

PyTorch implementation of PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION
YOUR CONTRIBUTION IS INVALUABLE FOR THIS PROJECT :)

What’s different from official paper?

original: trans(G)—>trans(D)—>stab / my code: trans(G)—>stab—>transition(D)—>stab
no use of NIN layer. The unnecessary layers (like low-resolution blocks) are automatically flushed out and grow.
used torch.utils.weight_norm for to_rgb_layer of generator.
No need to implement the the Celeb A data, Just come with your own dataset :)

How to use?

[step 1.] Prepare dataset
The author of progressive GAN released CelebA-HQ dataset, and which Nash is working on over on the branch that i forked this from. For my version just make sure that all images are the children of that folder that you declare in Config.py. Also i warn you that if you use multiple classes, they should be similar as to not end up with attrocities.

---------------------------------------------
The training data folder should look like : 
<train_data_root>
                |--Your Folder
                        |--image 1
                        |--image 2
                        |--image 3 ...
---------------------------------------------

[step 2.] Prepare environment using virtualenv

you can easily set PyTorch (v0.3) and TensorFlow environment using virtualenv.
CAUTION: if you have trouble installing PyTorch, install it mansually using pip. [PyTorch Install]

For install please take your time and install all dependencies of PyTorch and also install tensorflow

$ virtualenv --python=python2.7 venv
$ . venv/bin/activate
$ pip install -r requirements.txt
$ conda install pytorch torchvision -c pytorch

[step 3.] Run training

edit config.py to change parameters. (don’t forget to change path to training images)
specify which gpu devices to be used, and change “n_gpu” option in config.py to support Multi-GPU training.
run and enjoy!

  (example)
  If using Single-GPU (device_id = 0):
  $ vim config.py   -->   change "n_gpu=1"
  $ CUDA_VISIBLE_DEVICES=0 python trainer.py
  If using Multi-GPUs (device id = 1,3,7):
  $ vim config.py   -->   change "n_gpu=3"
  $ CUDA_VISIBLE_DEVICES=1,3,7 python trainer.py

[step 4.] Display on tensorboard (At the moment skip this part)

you can check the results on tensorboard.

  $ tensorboard --logdir repo/tensorboard --port 8888
  $ <host_ip>:8888 at your browser.

[step 5.] Generate fake images using linear interpolation

CUDA_VISIBLE_DEVICES=0 python generate_interpolated.py

Experimental results

The result of higher resolution(larger than 256x256) will be updated soon.

Generated Images

Loss Curve

To-Do List (will be implemented soon)

Support WGAN-GP loss
training resuming functionality.
loading CelebA-HQ dataset (for 512x512 and 1024x0124 training)

Compatability

cuda v8.0 (if you dont have it dont worry)
Tesla P40 (you may need more than 12GB Memory. If not, please adjust the batch_table in dataloader.py)

Acknowledgement

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Author

MinchulShin, @nashory

Contributors

DeMarcus Edwards, @Djmcflush
MakeDirtyCode, @MakeDirtyCode
Yuan Zhao, @yuanzhaoYZ
zhanpengpan, @szupzp