Progressive Growing GAN-PyTorch

A Pytorch implementation of Progressive Growing GAN based on the paper Progressive Growing of GANs for Improved Quality, Stability, and Variation .

Requirement

• Argparse
• Numpy
• Matplotlib
• Pillow
• Python 3.7
• PyTorch
• TorchVision
• tqdm

Usage

Training

Run the script train.py to train the network with CelebA dataset.

$ python train.py --h    

usage: train.py [-h] [--root ROOT] [--epochs EPOCHS] [--out_res OUT_RES]
                [--resume RESUME] [--cuda]

optional arguments:
  -h, --help         show this help message and exit
  --root ROOT        directory contrains the data and outputs
  --epochs EPOCHS    training epoch number
  --out_res OUT_RES  The resolution of final output image
  --resume RESUME    continues from epoch number
  --cuda             Using GPU to train

Training Process

Testing

Download the weight to generate 128x128 faces.

Run the script generate_sample.py

$ python generate_sample.py -h               

usage: generate_sample.py [-h] [--seed SEED] [--out_dir OUT_DIR]
                          [--num_imgs NUM_IMGS] [--weight WEIGHT]
                          [--out_res OUT_RES] [--cuda]

optional arguments:
  -h, --help           show this help message and exit
  --seed SEED          Seed for generate images
  --out_dir OUT_DIR    Directory for the output images
  --num_imgs NUM_IMGS  Number of images to generate
  --weight WEIGHT      Generator weight
  --out_res OUT_RES    The resolution of final output image
  --cuda               Using GPU to train

Sample Results