GenForce Lib for Generative Modeling

An efficient PyTorch library for deep generative modeling. May the Generative Force (GenForce) be with You.

Highlights

• Distributed training framework.
• Fast training speed.
• Modular design for prototyping new models.
• Highly reproducing the training of StyleGAN compared to the official TensorFlow version.
• Model zoo containing a rich set of pretrained GAN models, with Colab live demo to play.

We will also support following functions in the very near future. Please STAY TUNED.

• Training of PGGAN and StyleGAN2 (and likely BigGAN too).
• Benchmark on model training.
• Training of GAN encoder from In-Domain GAN Inversion.
• Other recent work from our GenForce.

Installation

1. Create a virtual environment via conda.

   conda create -n genforce python=3.7
   conda activate genforce

2. Install torch and torchvision.

   conda install pytorch cudatoolkit=10.1 torchvision -c pytorch

3. Install requirements.

   pip install -r requirements.txt

Quick Demo

We provide a quick training demo, scripts/stylegan_training_demo.py, which allows to train StyleGAN on a toy dataset (500 animeface images with 64 x 64 resolution). Try it via

./scripts/stylegan_training_demo.sh

We also provide an inference demo, synthesize.py, which allows to synthesize images with pre-trained models. Generated images can be found at work_dirs/synthesis_results/. Try it via

python synthesize.py stylegan_ffhq1024

You can also play the demo at Colab.

Get Started

Test

Pre-trained models can be found at model zoo.

• On local machine:

  GPUS=8
  CONFIG=configs/stylegan_ffhq256_val.py
  WORK_DIR=work_dirs/stylegan_ffhq256_val
  CHECKPOINT=checkpoints/stylegan_ffhq256.pth
  ./scripts/dist_test.sh ${GPUS} ${CONFIG} ${WORK_DIR} ${CHECKPOINT}

• Using slurm:

  CONFIG=configs/stylegan_ffhq256_val.py
  WORK_DIR=work_dirs/stylegan_ffhq256_val
  CHECKPOINT=checkpoints/stylegan_ffhq256.pth
  GPUS=8 ./scripts/slurm_test.sh ${PARTITION} ${JOB_NAME} \
      ${CONFIG} ${WORK_DIR} ${CHECKPOINT}

Train

All log files in the training process, such as log message, checkpoints, synthesis snapshots, etc, will be saved to the work directory.

• On local machine:

  GPUS=8
  CONFIG=configs/stylegan_ffhq256.py
  WORK_DIR=work_dirs/stylegan_ffhq256_train
  ./scripts/dist_train.sh ${GPUS} ${CONFIG} ${WORK_DIR} \
      [--options additional_arguments]

• Using slurm:

  CONFIG=configs/stylegan_ffhq256.py
  WORK_DIR=work_dirs/stylegan_ffhq256_train
  GPUS=8 ./scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} \
      ${CONFIG} ${WORK_DIR} \
      [--options additional_arguments]

Contributors

Member	Module
Yujun Shen	models and running controllers
Yinghao Xu	runner and loss functions
Ceyuan Yang	data loader
Jiapeng Zhu	evaluation metrics
Bolei Zhou	cheerleader

NOTE: The above form only lists the person in charge for each module. We help each other a lot and develop as a TEAM.

We welcome external contributors to join us for improving this library.

License

The project is under the MIT License.

Acknowledgement

We thank PGGAN, StyleGAN, StyleGAN2 for their work on high-quality image synthesis. We also thank MMCV for the inspiration on the design of controllers.

BibTex

We open source this library to the community to facilitate the research of generative modeling. If you do like our work and use the codebase or models for your research, please cite our work as follows.

@misc{genforce2020,
  title =        {GenForce},
  author =       {Shen, Yujun and Xu, Yinghao and Yang, Ceyuan and Zhu, Jiapeng and Zhou, Bolei},
  howpublished = {\url{https://github.com/genforce/genforce}},
  year =         {2020}
}