This is unofficial pytorch implementation of the paper, "Image Style Transfer Using Convolutional Neural Networks" [Gatys+, CVPR2016].

• Python 3.6+
• PyTorch 1.5.0+ or 1.5.0+cu*
• TorchVision 0.6.0+ or 0.6.0+cu*
• Numpy 1.16.2+
• Pillow 5.1.0+

• --content, -c: The path to the content image. Cannot be omitted.
• --style, -s: The path to the style image. Cannot be omitted
• --output -o: The path to transferred image.
• --epoch, -e: The number of epoch. (Default: 300)
• -content_weight, -c_w: The weight of the content loss. (Default: 1)
• -style_weight, -s_w: The weight of the style loss. (Default: 1000)
• --initialize_noise, -i_n: If you use this option, the transferred image is initialized with white noise. If not, it is initialized with the grayscale content image.
• --cuda: If you have an available GPU, you should use this option.
• --content_layers, -c_l: If you want to name which layers have content-related information, use this. Names of each layers are like conv_i or relu_i
  use like this: python style_transfer.py --content_layers conv_1 conv_2 (Default: conv_1)
• --style_layers, -s_l: If you want to name which layers have style-related information, use this. Names of each layers are like conv_i or relu_i
  use like this: python style_transfer.py --style_layers conv_1 reelu_3 (Default:conv_1 conv_2 conv_3 conv_4 conv_5)
• --model, -m: The net structure. You can choose from vgg11 vgg13 vgg16 vgg19

With CPU:

python style_transfer.py -c contents/golden_gate.jpg -s styles/kandinsky.jpg

With GPU:

python style_transfer.py -c contents/golden_gate.jpg -s styles/kandinsky.jpg --cuda

By default, transferred image will be stored in directory /transferred

git clone https://github.com/zephyr-jebel/pytorch-Neural-Style-Transfer.git

Install PyTorch and dependencies from http:https://pytorch.org.
We have prepared requirement.txt, but it is preferable to use Anaconda as recommended on http:https://pytorch.org.

• Leon A. Gatys, Alexander S. Ecker and Matthias Bethge. "Image Style Transfer Using Convolutional Neural Networks", in CVPR 2016. [Paper]
• Code is inspired by Neural Transfer with PyTorch.
• Code adapted from https://github.com/enomotokenji/pytorch-Neural-Style-Transfer