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# stylegan2-tf-2.x
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* This is tensorflow 2.0 based keras subclassing reimplementation of official [StyleGAN2 Repo](https://github.com/NVlabs/stylegan2)
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## Inference from official weights
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* Please check [how_to_extract_official_weights.md](./how_to_extract_official_weights.md)
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| Official repo result | Official weight copied result from this repo |
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| :---: | :---: |
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| ![official_result] | ![restored_result] |
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## Training on 256x256
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* batch size 32
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* around 300k steps
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| d_loss | g_loss |
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| :---: | :---: |
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| ![256x256_d_loss]| ![256x256_g_loss]|
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* result samples
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| | | |
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| :---: | :---: | :---: |
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| ![256x256_result_0]| ![256x256_result_1]| ![256x256_result_2]|
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## Using custom data
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* Create dataset(tf.data.Dataset) loader which returns
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* shape: `[None, 3, res, res]` where `res` is desired image resolution
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* image value range: `[-1, +1]`
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## Etc
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### Using with pycharm
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* If .bashrc file's paths don't work in pycharm environment (e.g. `nvcc -h`), open pycharm with following
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```bash
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# move to pycharm installed location (location may vary)
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moono@moono-ubuntu:~$ cd .local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/201.7846.77/bin/
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# launch pycharm with .bashrc
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moono@moono-ubuntu:~/.local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/201.7846.77/bin$ bash pycharm.sh
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```
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[loss_tensorboard]: assets/tf-keras-stylegan2-loss.PNG
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[generation_tensorboard]: assets/tf-keras-stylegan2-fake-images.PNG
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[official_result]: assets/seed6600-official.png
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[restored_result]: assets/seed6600-restored.png
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[256x256_d_loss]: assets/d_loss_256x256.png
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[256x256_g_loss]: assets/g_loss_256x256.png
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[256x256_result_0]: assets/out_256x256_0.png
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[256x256_result_1]: assets/out_256x256_1.png
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[256x256_result_2]: assets/out_256x256_2.png
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