BigGAN-PyTorch
Pytorch implementation of LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS (BigGAN)
train imagenet
for 128*128*3 resolution
python main.py --batch_size 64 --dataset imagenet --adv_loss hinge --version biggan_imagenet --image_path /data/datasets
python main.py --batch_size 64 --dataset lsun --adv_loss hinge --version biggan_lsun --image_path /data1/datasets/lsun/lsun
python main.py --batch_size 64 --dataset lsun --adv_loss hinge --version biggan_lsun --parallel True --gpus 0,1,2,3 --use_tensorboard True
Different
- not use cross-replica BatchNorm (Ioffe & Szegedy, 2015) in G
Compatability
- CPU
- GPU
Pretrained Models
LSUN Pretrained model Download
Some methods in the paper to avoid model collapse, please see the paper and retrain your model.
Performance
- Infact, as mentioned in the paper, the model will collapse
- I use LSUN datasets to train this model maybe cause bad performance due to the class of classroom is more complex than ImageNet
Results
LSUN DATASETS(two classes): classroom and church_outdoor
- iter 82200 (128x128) batch_size 64
- iter 128200
- iter 365000
- iter 800000
- iter 900000
