StacyYang / Mxnet Gluon Style Transfer
Licence: mit
Neural Style and MSG-Net
Stars: ✭ 105
Programming Languages
python
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MXNet-Gluon-Style-Transfer
This repo has been included in official MXNet repo, which provides the implementations of MSG-Net and Neural Style Transfer. We also provide PyTorch and Torch implementations.
Tabe of content
Neural Style
A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge.
python main.py optim --content-image images/content/venice-boat.jpg --style-image images/styles/candy.jpg
-
--content-image
: path to content image. -
--style-image
: path to style image. -
--output-image
: path for saving the output image. -
--content-size
: the content image size to test on. -
--style-size
: the style image size to test on. -
--cuda
: set it to 1 for running on GPU, 0 for CPU.
Real-time Style Transfer
Multi-style Generative Network for Real-time Transfer [arXiv] [project] Hang Zhang, Kristin Dana @article{zhang2017multistyle, title={Multi-style Generative Network for Real-time Transfer}, author={Zhang, Hang and Dana, Kristin}, journal={arXiv preprint arXiv:1703.06953}, year={2017} } |
Stylize Images Using Pre-trained MSG-Net
- Download the pre-trained model
python models/download_model.py
- Test the model
python main.py eval --content-image images/content/venice-boat.jpg --style-image images/styles/candy.jpg --model models/21styles.params --content-size 1024
-
If you don't have a GPU, simply set
--cuda=0
. For a different style, set--style-image path/to/style
. If you would to stylize your own photo, change the--content-image path/to/your/photo
. More options:-
--content-image
: path to content image you want to stylize. -
--style-image
: path to style image (typically covered during the training). -
--model
: path to the pre-trained model to be used for stylizing the image. -
--output-image
: path for saving the output image. -
--content-size
: the content image size to test on. -
--cuda
: set it to 1 for running on GPU, 0 for CPU.
-
Train Your Own MSG-Net Model
- Download the COCO dataset
bash dataset/download_dataset.sh
- Train the model
python main.py train --epochs 4
- If you would like to customize styles, set
--style-folder path/to/your/styles
. More options:-
--style-folder
: path to the folder style images. -
--vgg-model-dir
: path to folder where the vgg model will be downloaded. -
--save-model-dir
: path to folder where trained model will be saved. -
--cuda
: set it to 1 for running on GPU, 0 for CPU.
-
The code is mainly modified from PyTorch-Style-Transfer.
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