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steam-stylegan2-ada-pytorch's Introduction

Steam StyleGAN2-ADA (PyTorch)

The goal of this repository is to capture the distribution of Steam banners with a StyleGAN2-ADA model (PyTorch).

Target AProjection A Target BProjection B Progression videos of the projection of Steam banners (Dog Trainer and My UnReal pet) with a network pre-trained by Nvidia on the LSUN DOG dataset.

Data

The Steam-OneFace dataset, obtained with the retinaface face detection module:

  • consists of 2.5k Steam banners (256x256 resolution) which should all feature exactly one face,
  • can be downloaded (74 MB) with the following commands:
!gdown --id 1-0Nk7H6Cn3Nt60EdHG_NWSA8ohi2oBqr
!tar xf steam-oneface-lr_with_retinaface.tar.gz

Usage

  • Run training.ipynb to train a model from scratch, Open In Colab
  • Run image_sampling.ipynb to generate images with a trained model, Open In Colab
  • To automatically resume training from the latest checkpoint, use my fork of StyleGAN2-ADA (PyTorch).

References

steam-stylegan2-ada-pytorch's People

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steam-stylegan2-ada-pytorch's Issues

What does the 'cfg_map=8' mean?

Hi, thanks for your work.
I'm confused what is the 'cfg_map=8' in your code? and how to use it?
And I also notice that 'cfg=auto_norp' and 'cifar_tune=1' are not included in the original code.
Could you give some annotation on those?

Initial Update

The bot created this issue to inform you that pyup.io has been set up on this repo.
Once you have closed it, the bot will open pull requests for updates as soon as they are available.

RuntimeError: derivative for aten::grid_sampler_2d_backward is not implemented

Hi Woctezuma,

thanks for your repo,

In the cell below, after running it with your custom datasets I get the error posted in the title of my Issue.

Also, do you really need to give it a resume file when you want to train a model for the first time?

!python stylegan2-ada-pytorch/train.py \
 --outdir={output_folder} \
 --snap={snap} \
 --metrics=none \
 --data={custom_dataset} \
 --cfg=auto_norp \
 --cifar_tune=1 \
 --gamma={gamma} \
 --kimg={kimg} \
 --batch={mini_batch} \
 --cfg_map=8 \
 --augpipe=bg \
 --freezed=10 \
 --resume=ffhq256 

Thanks for your help,

L.

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