Chainer implementation for Neural Style Transfer & Fast Neural Style Transfer
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README.md

chainer neural-style & fast-neural-style

Chainer implementation of A Neural Algorithm of Artistic Style and Perceptual Losses for Real-Time Style Transfer and Super-Resolution.

neural style

Download VGG-19

Download the original VGG-19 model and then convert it to Chainer model:

python caffe_model_2_pickle.py

Or Download the converted VGG-19 Chainer model here.

Now you're an artist!!

 python neural_style.py --content_image [Content Image] --style_images [Style Image;Style Image;...] 

Note: python neural_style -h for more details.

Your Gallery

  • Content Image

  • Style Image

  • Result

    • --original_color False --style_color False

    • --original_color True

    • --style_color True

  • Content Image

  • Style Images

  • Result Image

fast neural style

Download Dataset

Download COCO training set and validation set. unzip training set and validation set under the same root folder.

Download VGG-19

See above.

Training using Batch Normalization

python fast_neural_style.py --data_root [COCO ROOT FOLDER] --instance_normalization False
  • Training Curve

Training using Instance Normalization

python fast_neural_style.py --data_root [COCO ROOT FOLDER] --instance_normalization True
  • Training Curve

Gallery

  • Batch Normalization

  • Instance Normalization