A tensorflow implementation of style transfer (neural style) described in the papers:
- A Neural Algorithm of Artistic Style : submitted version
- Image Style Transfer Using Convolutional Neural Networks : published version
by Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
- Download the VGG-19 model weights (see the "VGG-VD models from the Very Deep Convolutional Networks for Large-Scale Visual Recognition project" section). More info about the VGG-19 network can be found here.
- After downloading, copy the weights file
imagenet-vgg-verydeep-19.matto the project directory.
- Copy 1 content image to the default image content directory
./image_input - Copy 1 or more style images to the default style directory
./styles
Note: Supported image formats include: .png, .jpg, .ppm, .pgm
- Run the command with specific arguments:
python neural_style_transfer.py <arguments>
Example:
python neural_style_transfer.py --content_img c1.jpg \
--style_imgs s1.jpg \
--max_size 1024 \
--max_iterations 100 \
--device /cpu:0 \
--verbose;
To use multiple style images, pass a space-separated list of the image names and image weights like this:
--style_imgs s1.jpg s2.jpg --style_imgs_weights 0.5 0.5
--meta_name: Filename of the configuration settings--content_img: Filename of the content image. Example:c1.jpg--content_img_dir: Relative or absolute directory path to the content image. Default:./image_input--style_imgs: Filenames of the style images. To use multiple style images, pass a space-separated list. Example:--style_imgs s1.jpg--style_imgs_weights: The blending weights for each style image. Default:1.0(assumes only 1 style image)--style_imgs_dir: Relative or absolute directory path to the style images. Default:./styles--init_img_type: Image used to initialize the network. Default:content--max_size: Maximum width or height of the input images. Default:1920--content_weight: Weight for the content loss function. Default:5e0--style_weight: Weight for the style loss function. Default:1e4--tv_weight: Weight for the total variational loss function. Default:1e-3--content_layers: Space-separated VGG-19 layer names used for the content image. Default:conv4_2--style_layers: Space-separated VGG-19 layer names used for the style image. Default:relu1_1 relu2_1 relu3_1 relu4_1 relu5_1--content_layer_weights: Space-separated weights of each content layer to the content loss. Default:1.0--style_layer_weights: Space-separated weights of each style layer to loss. Default:0.2 0.2 0.2 0.2 0.2--model_weights: Weights and biases of the VGG-19 network. Download here. Default:imagenet-vgg-verydeep-19.mat--pooling_type: Type of pooling in convolutional neural network. Choices:avg,max. Default:avg--device: GPU or CPU device. GPU mode highly recommended but requires NVIDIA CUDA. Choices:/gpu:0/cpu:0. Default:/gpu:0--img_output_dir: Directory to write output to. Default:./image_output--img_name: Filename of the output image. Default:result--verbose: Boolean flag indicating if statements should be printed to the console.--write_iterations_adam: Boolean flag indicating if output images should be writed in every iteration under the Adam optimizer.
--optimizer: Loss minimization optimizer. L-BFGS gives better results. Adam uses less memory. Choices:lbfgs,adam. Default:lbfgs--learning_rate: Learning-rate parameter for the Adam optimizer. Default:1e0--max_iterations: Max number of iterations for the Adam or L-BFGS optimizer. Default:1000--print_iterations: Number of iterations between optimizer print statements. Default:50
This implementation is based on the project:
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