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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 "<project>-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

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.