cheind / Py Style Transfer

Neural style transfer

Style transfer applied to a picture of the Golden Gate bridge using on Vincent van Gogh's 'The Starry Night' artistic style. A result using Picasso's work can be found here.

py-style-transfer implements image style transfer as proposed by [1,4,5] using PyTorch. Given an artistic image and a content image, the method iteratively generates an image that is similar to the content but drawn in the desired artistic style. While the method is not real-time capable, it is the most flexible approach, not requiring any style pre-training expect for a readily available pre-trained convolutional architecture such as VGG. While this implementation is based on [1,4,5] we also incorporate ideas from [2,3].

We also extend the approach to two more use-cases

• Seamless mode generates tiles that can be stacked vertically/horizontally without visual seams.
• Tiled mode allows generation of very large images that would otherwise not fit into memory. Like this 8192x8192 10Mb/JPEG pure Picasso artistic style image.

See the interactive StyleTransfer.ipynb notebook for usage and examples.

Features

• Various style losses such as gram-based, patch-based, semantic-based.
• Capability to process on multiple scales.
• Support for generating huge image sizes through tiling.
• Support for generating images that stitch seamlessly.
• Easily add new losses or modify the optimization through plugins.

References

[1] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015). [2] Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." European Conference on Computer Vision. Springer, Cham, 2016. [3] Gatys, Leon A., et al. "Controlling perceptual factors in neural style transfer." IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. [4] Li, Chuan, and Michael Wand. "Combining markov random fields and convolutional neural networks for image synthesis." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. [5] Champandard, Alex J. "Semantic style transfer and turning two-bit doodles into fine artworks." arXiv preprint arXiv:1603.01768 (2016).

License

Copyright 2018 Christoph Heindl

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