25 lines
1.4 KiB
Markdown
25 lines
1.4 KiB
Markdown
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# GLEAN
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DLAS contains an attempt at implementing [GLEAN](https://ckkelvinchan.github.io/papers/glean.pdf), which performs image
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super-resolution guided by pretrained StyleGAN networks. Since this paper is currently closed-source, it was
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implemented entirely on what information I could glean from the paper.
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## Training
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GLEAN requires a pre-trained StyleGAN network to operate. DLAS currently only has support for StyleGAN2 models, so
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you will need to use one of those. The pre-eminent StyleGAN 2 model is the one trained on FFHQ faces, so I will use
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that in this training example.
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1. Download the ffhq model from [nVidias Drive](https://drive.google.com/drive/folders/1yanUI9m4b4PWzR0eurKNq6JR1Bbfbh6L).
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This repo currently only supports the "-f.pkl" files without further modifications, so choose one of those.
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1. Download and extract the [FFHQ dataset](https://github.com/NVlabs/ffhq-dataset).
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1. Convert the TF model to a Pytorch one supported by DLAS:
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`python scripts/stylegan2/convert_weights_rosinality.py stylegan2-ffhq-config-f.pkl`
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1. The above conversion script outputs a *.pth file as well as JPG preview of model outputs. Check the JPG to ensure
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the StyleGAN is performing as expected. If so, copy the *.pth file to your experiments/ directory within DLAS.
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1. Edit the provided trainer configuration. Find comments starting with '<--' and make changes as indicated.
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1. Train the model:
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`python train.py -opt train_ffhq_glean.yml`
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