BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network
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| This image is from BeautyGAN |
This is a modification of Offical Pytorch code for BeautyGAN. The main differences are:
- Dataloaders
- Identities Loss (VGG)
- Add Tensorboard
🌿 If you have trouble running this code, feel free to send me an issue. I'll try my best to help you 👍.
NOTE:: I got serveral checkpoint in Google Drive. The results are not good as stated in the main paper, but check it if you like.
For the best results, I recommend original tensorflow pre-trained model
First, clone this repo:
git clone https://github.com/thaoshibe/BeautyGAN-pytorch-reimplementation.git
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cd BeautyGAN-pytorch-reimplementation
Install all required python packages:
- Via pip:
pip install -r requirements.txt - Via conda:
conda env create -f environment.yml(thenconda activate beautygan)
Please download the Makeup Transfer Dataset.
python train.py --data_path /path/to/dataset
For example: I downloaded Makeup Transfer Dataset to /home/ubuntu/makeup_dataset/. So the command will be python train.py --data_path /home/ubuntu/makeup_dataset/
- For Tensorboard:
tensorboard --logdir runs, then openhttp://localhost:6006/
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| Results at epoch 80. From left to right: Original Image, Makeup Reference, Result |
Tensorboard captured at epoch 0. The results are unpleasant at this time. Don't worry 🔥. To get the final model, I trained the model until epoch 200 (about ONE WEEEK in RTX 1080Ti)
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| Tensorboard Loss |
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| Tensorboard Image. First rows are fake images (results). Second are original images (inputs). Third are recycle images (reconstructed images) |
This code is heavily based on BeautyGAN - Offical Pytorch Implementation. Thanks wtjiang98 so much to make his work available 🙏🙏🙏