Torch implementation of "PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION"
[NOTE] This project was not goint well, so I made PyTorch implementation here. :fire: [pggan-pytorch]
Torch implementation of PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION
YOUR CONTRIBUTION IS INVALUABLE FOR THIS PROJECT :)
[ ] (1) Implementing Pixel-wise normalization layer
[ ] (2) Implementing pre-layer normalization (for equalized learning rate)
(I have tried both, but failed to converge. Anyone can help implementing those two custom layers?)
[step 1.] Prepare dataset
CelebA-HQ dataset is not available yet, so I used 100,000 generated PNGs of CelebA-HQ released by the author.
The quality of the generated image was good enough for training and verifying the preformance of the code.
If the CelebA-HQ dataset is releasted in then near future, I will update the experimental result.
[download]
---------------------------------------------
The training data folder should look like :
<train_data_root>
|--classA
|--image1A
|--image2A ...
|--classB
|--image1B
|--image2B ...
---------------------------------------------
[step 2.] Run training
$ python run.py
[step 3.] Visualization
$ th server.lua
$ <server_ip>:<port> at your browser
(example)
[E:0][T:91][ 91872/202599] errD(real): 0.2820 | errD(fake): 0.1557 | errG: 0.3838 [Res: 4][Trn(G):0.0%][Trn(D):0.0%][Elp(hr):0.2008]
pggan.lua
)MinchulShin, @nashory