A implement of PGGAN for tensorflow version(progressive growing GANs for improved quality, stability and variation)
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pytorch 0.2.0_4
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python 2.7.12
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numpy 1.13.1
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scipy 0.17.0
$ git clone https://github.com/nnUyi/PGGAN.git
$ cd PGGAN
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download dataset and store it in the datasets directory(directory named datasets) and then unzip it. Here I show the example of your dataset storage: /datasets/celebA. In this repo, celebA data is used to train the model, with no attributes and labels used in training time.
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I just use original celebA dataset, and target resolution is setted to 128*128 because I can not download the delta data provided by NVIDIA.
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Anyway, if your want to get 1024*1024 dataset, you can see official codes here, h5tool.py is the script used to create the target training datasets.
$ python main.py --is_training=True
- Sampling process is executed in training time. You can see the sampling results in the directory named sample
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The result shows below, we can clearly obtain such a good experimental result.
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Here I just show you two types rsolution including 64_64 and 128_128, the first four columns in 64_64 images are sampling data while the other four columns are real images. This is the same for 128_128 images.
| sampling image | sampling image |
|---|---|
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| 64*64 resolution | 64*64 resolution |
 |
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| 128*128 resolution | 128*128 resolution |
This repo is finished by referring to github-pengge/PyTorch-progressive_growing_of_gans
Email:computerscienceyyz@163.com, Thank you for contacting if you find something wrong or if you have some problems!!!