Pytorch Implementation for paper: IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
Pytorch Implementation for NeuraIPS2018 paper: IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis.
The rep. contains a basic implementation for IntroVAE. However, due to no official implementation released, some hyperparameters can only be guessed and can not reach the performance as stated in paper.
/home/i/dbs/
img_align_celeba # only one folder in this directory
050939.jpg
050940.jpg
050941.jpg
050942.jpg
050943.jpg
050944.jpg
050945.jpg
modify /home/i/dbs
to your specific path, making sure that the /home/i/dbs/
comtains only ONE folder since we use
torchvision.datasets.ImageFolder
API to load dataset.
argparser.add_argument('--root', type=str, default='/home/i/dbs/',
help='root/label/*.jpg')
python main.py --epoch 750000
to train from strach, and use python main.py --resume '' --epoch 1000000
to resume training from latest checkpoint.only tested for CelebA 128x128 exp.
training curves
sampled x