Torch implementation of various types of GAN (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN, LSGAN)
Torch implementation of various types of GANs (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN). Note that EBGAN and BEGAN implementation is still not stable yet. I am working on this.
python download.py --datasets <dataset>
(e.g) python run.py --datasets celebA
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The training data folder should look like :
<train_data_root>
|--classA
|--image1A
|--image2B ...
|--classB
|--image1B
|--image2B ...
---------------------------------------
python run.py --type <gan_type>
(e.g) python run.py --type dcgan
step by step instruction:
1. set server-related options(ip, port, etc.) in "script.opts.lua"
2. run server (python server.py --type <gan_type>)
3. open web browser, and connect. (https://<server_ip>:<server_port>)
you will see like this:
training | Final |
---|---|
MinchulShin, @nashory
Will keep updating other types of GANs.
Any insane bug reports or questions are welcome. (min.stellastra[at]gmail.com) :-)