Highlights
- MMGeneration is released.
Main Features
- High-quality Training Performance: We currently support training on Unconditional GANs (
DCGAN,WGAN-GP,PGGAN,StyleGANV1,StyleGANV2,Positional Encoding in GANs), Internal GANs (SinGAN), and Image Translation Models (Pix2Pix,CycleGAN). Support for conditional models will come soon. - Powerful Application Toolkit: A plentiful toolkit containing multiple applications in GANs is provided to users. GAN interpolation, GAN projection, and GAN manipulations are integrated into our framework. It's time to play with your GANs!
- Efficient Distributed Training for Generative Models: For the highly dynamic training in generative models, we adopt a new way to train dynamic models with
MMDDP. - New Modular Design for Flexible Combination: A new design for complex loss modules is proposed for customizing the links between modules, which can achieve flexible combination among different modules.
- Support new methods: LSGAN, GGAN.
- Support mixed-precision training (FP16): official PyTorch Implementation and APEX (#11, #20)
- Add the experiment of MNIST in DCGAN (#24)
- Add support for uploading checkpoints to
Cephsystem (cloud server) (#27) - Add the functionality of saving the best checkpoint in GenerativeEvalHook (#21)
- Fix loss of sample-cfg argument (#13)
- Add
pbarto offline eval and fix bug in grayscale image evaluation/saving (#23) - Fix error when data_root option in val_cfg or test_cfg are set as None (#28)
- Change latex in quick_run.md to svg url and fix number of checkpoints in modelzoo_statistics.md (#34)
- Support conditional GANs: Projection GAN, SNGAN, SAGAN, and BigGAN
- Add support for persistent_workers in PyTorch >= 1.7.0 #71
- Support warm-up for EMA #55
- Fix failing to build docs #64
- Revise the logic of
num_classesin basic conditional gan #69 - Support dynamic eval internal in eval hook #73