https://github.com/lucasjinreal/pytorch_cycle_gan
CycleGAN with Productive Generate APIs. Generate Any Image from Your Transfer Model.
https://github.com/lucasjinreal/pytorch_cycle_gan
cyclegan gan pytorch style-transfer
Last synced: 3 months ago
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CycleGAN with Productive Generate APIs. Generate Any Image from Your Transfer Model.
- Host: GitHub
- URL: https://github.com/lucasjinreal/pytorch_cycle_gan
- Owner: lucasjinreal
- Created: 2017-05-25T14:20:16.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-05-26T03:22:18.000Z (over 8 years ago)
- Last Synced: 2025-03-22T19:45:50.380Z (7 months ago)
- Topics: cyclegan, gan, pytorch, style-transfer
- Language: Python
- Size: 134 KB
- Stars: 26
- Watchers: 2
- Forks: 9
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# CycleGAN Production Version
> this repo based on the original implementation of CycleGAN: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.git, in this version I reconstruct some code and made a generate API to simply generate image from your own single image and your trained model.
# CycleGAN - Generate Image Like Magic
I have trained `apple2orange` and `horse2zebra` for now, here is the real result of convert
apple -> orange:
I only trained about 50 epochs, but the result is fair enough for now. Laterly I will finish horse2zebra model, and update some more results.
Here is the horse2zebra result:
# Requirements
* Python3+
* PyTorch
* visdom
* PIL
# Usage
* For Train
About how to train, simply run this:
```
python3 train.py --dataroot ./datasets/apple2orange --name apple2orange --model cycle_gan
```
One things have to mention that, `--name` indicates the model save dir, and `--model` is using `cycle_gan` or `pixel2pixel` , I only tried `cycle_gan`.
* For Generate
Train is very simple, but the original repo have not implement predict API, so I managed to write by myself. Here is the way to use:
```
python3 generate.py --image_path ./apple_test.jpg --name apple2orange --model cycle_gan --gpu_ids -1
```
As you can see, you only need to specific image path where stores your image to generate, and `--name` is the same as previous trained, as well as model type. `--gpu_ids` indicates we are inference using CPU.
OK, that's all.
# Research and Discuss
I really love to connect to people, so if you have any question about this repo, you can find me on wechat `jintianiloveu`, I have some groups which discuss about GANs I will invite you in if you like.
# Copyright
```
(c) 2017 Jin Fagang under LICENSE Apache 2.0
```