CAMS: Color-Aware Multi-Style Transfer

Mahmoud Afifi¹, Abdullah Abuolaim*¹, Mostafa Hussien*², Marcus A. Brubaker¹, Michael S. Brown¹

¹York University
²École de technologie supérieure

* denotes equal contribution

Reference code for the paper CAMS: Color-Aware Multi-Style Transfer. Mahmoud Afifi, Abdullah Abuolaim, Mostafa Hussien, Marcus A. Brubaker, and Michael S. Brown. arXiv preprint, 2021. If you use this code, please cite our paper:

@article{afifi2021coloraware,
  title={CAMS: Color-Aware Multi-Style Transfer},
  author={Afifi, Mahmoud and Abuolaim, Abdullah and Hussien, Mostafa and Brubaker, Marcus A. and Brown, Michael S.},
  journal={arXiv preprint arXiv:2106.13920},
  year={2021}
}

Get Started

Run color_aware_st.py or check the Colab link from here.

Manual Selection

Our method allows the user to manually select the color correspondences between palettes or ignore some colors when optimizing.

To enable this mode, use SELECT_MATCHES = True.

Other useful parameters:

• SMOOTH: smooth generated mask before optimizing.
• SHOW_MASKS: to visualize the generated masks during optimization.
• SIGMA: to control the fall off in the radial basis function when generating the masks. Play with its value to get different results; generally, 0.25 and 0.3 work well in most cases.
• PALETTE_SIZE: number of colors in each palette.
• ADD_BLACK_WHITE: to append black and white colors to the final palette before optimizing.
• STYLE_LOSS_WEIGHT: weight of style loss
• CONTENT_LOSS_WEIGHT: weight of content loss.
• COLOR_DISTANCE: similarity metric when computing the mask. Options include: 'chroma_L2' (L2 on chroma space) or 'L2' (L2 on RGB space).
• STYLE_FEATURE_DISTANCE: similarity metric for style loss. Options include: 'L2' or 'COSINE' (for cosine similarity).
• CONTENT_FEATURE_DISTANCE: = similarity metric for content loss. Options include: 'L2' or 'COSINE' (for cosine similarity).
• OPTIMIZER: optimization algorithm. Options include: 'LBFGS', 'Adam', 'Adagrad'.

MIT License

Related Research Projects

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