stylegan2-ada-lightning

Simplified pytorch-lightning port of StyleGAN2-ADA.

Configuration provided with hydra config file config/stylegan2.yaml. Once configured, train with:

python trainer/train_stylegan.py wandb_main=True

Configuration can be overriden with command line flags.

Configuration

Key	Description	Default
dataset_path	Directory with training images	data/ffhq
img_list	Path to the .txt with a list of images, useful when you have many files (optional; if provided, used instead of dataset_path)	null
experiment	Experiment name used for logs	fast_dev
wandb_main	If false, results logged to "-dev" wandb project (for dev logs)	False
num_mapping_layers	Number of layers in the mapping network	2
lr_g	Generator learning rate	0.002
lr_d	Discriminator learning rate	0.00235
lambda_gp	Gradient penalty weight	0.0256
lambda_plp	Path length penalty weight	2
lazy_gradient_penalty_interval	Gradient penalty regularizer interval	16
lazy_path_penalty_after	Iteration after which path lenght penalty is active	0
lazy_path_penalty_interval	Path length penalty regularizer interval	4
latent_dim	Latent dim of starting noise and mapping network output	512
image_size	Size of generated images	64
num_eval_images	Number of images on which FID is computed	8096
num_vis_images	Number of image visualized	1024
batch_size	Mini batch size	16
num_workers	Number of dataloader workers	8
seed	RNG seed	null
save_epoch	Epoch interval for checkpoint saves	1
sanity_steps	Validation sanity runs before training start	1
max_epoch	Maximum training epochs	250
val_check_interval	Epoch interval for evaluating metrics and saving generated samples	1
resume	Resume checkpoint	null

The code has been tested with PyTorch 2.0.0+cu118, PyTorch Lightning 2.0.6, CUDA 11.8, Python 3.8.5.

References

Official stylegan2-ada code and paper.

@article{Karras2019stylegan2,
    title   = {Analyzing and Improving the Image Quality of {StyleGAN}},
    author  = {Tero Karras and Samuli Laine and Miika Aittala and Janne Hellsten and Jaakko Lehtinen and Timo Aila},
    journal = {CoRR},
    volume  = {abs/1912.04958},
    year    = {2019},
}

License

Copyright © 2021 nihalsid

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.