This implementation is adapted from the stylegan2 codebase by Matthias Wright.

Specifically, the features we've added allow for better scaling of StyleGAN2 training on TPUs:

• 🏭 Enable data-parallel training on TPU pods (tested on TPU v2 to v4 generations)
• 💾 Google Cloud Storage (GCS) integration/dataset sharding between workers
• 🏖 Quality-of-life improvements (e.g. improved W&B logging)

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1. Clone the repository:

   git clone https://github.com/nyx-ai/stylegan2-flax-tpu.git

2. Go into the directory:

   cd stylegan2-flax-tpu

3. Install Jax according to your platform.
4. Install requirements:

   pip install -r requirements.txt

We released four 256x256 pretrained models: cookie, cheesecake, sushi and cocktail. Download them from the latest release.

python generate_images.py \
   --checkpoint checkpoints/cookie-256.pkl \
   --seeds 0 42 420 666 \
   --truncation_psi 0.7 \
   --out_path generated_images

Check the Colab notebook for more examples:

Add your images into a folder /path/to/image_dir:

/path/to/image_dir/
    0.jpg
    1.jpg
    2.jpg
    4.jpg
    ...

and create a TFRecord dataset:

python dataset_utils/images_to_tfrecords.py --image_dir /path/to/image_dir/ --data_dir /path/to/tfrecord

For more detailed instructions please refer to this README.

The following command trains with 128 resolution and batch size of 8.

python main.py --data_dir /path/to/tfrecord

Read more about suitable training parameters here.

• This work is based on Matthias Wright's stylegan2 implementation.
• The project received generous support from Google's TPU Research Cloud (TRC).
• The image datasets were built using the LAION5B index
• We are grateful to Weights & Biases for preserving our sanity