Forked from taotaonice fantastic job on FaceShifter. Made the following modifications:
- Moved from Visdom to Tensorboard to monitor the various loss contributors
- Moved to Pytorch native AMP implementation (Pytorch>=1.6): less memory consumption, increased batch size,
- Various corrections: generator hinge loss, dataset logic,...
- Added DiffAugment algorithm to increase the discriminator perceived diversity,
- Tested gradient accumulation, replacing Batch norm with GroupNorm: unsuccessful due to poor attributes transfer,
- Reduced the adversarial loss and the Generator learning rate to achieve better source id transfer.
TODO Rework the dataset generation to keep FFHQ native images and to crop the others images using FFHQ algorithm (using landmarks) to preserve the whole chin and achieve better alignment.