This is a Tensorflow implementation of the paper "Image Style Transfer Using Convolutional Neural Networks" by Gatys et al. The authors used the L-BFGS optimizer but since it is not available on Tensorflow, we decided to use ADAM.
There is a requirements.txt you can use. As it is stated, the code was tested on tensorflow-gpu==1.5.
You also have to download VGG pretrained models. For this, you can use the script get_models.sh.
pip install -r requirements.txt
./get_models.shThere is a notebook (notebook.ipynb) you can use to test the implementation.
You can use the recommend values n_steps=1000, learning_rate=5 but you are supposed to tune alpha and beta for better results.
You can fix alpha and only try different values for beta.
In addition, we implemented the total variation denoising.
You can use it by setting a positive value to tv_weight.
It was tested on a NVIDIA GTX 1070 graphics card and it takes approximately 4-5 min to get the output. Note we resize the input image if it is too large to reduce the runtime.
1 L. A. Gatys and A. S. Ecker and M. Bethge, Image Style Transfer Using Convolutional Neural Networks, CVPR, 2016.