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--- |
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license: mit |
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pipeline_tag: image-to-image |
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library_name: pytorch |
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--- |
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# Color Encoder for Color Transfer with Modulated Flows |
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These are color encoders with EfficientNet B0 and B6 architectures for the AAAI 2025 paper "Color Transfer with Modulated Flows". The paper was also presented at ["Workshop SPIGM @ ICML 2024"](https://openreview.net/forum?id=Lztt4WVusu). |
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arXiv: https://arxiv.org/abs/2503.19062 |
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Please find the demo notebook at Github: [ModFlows_demo.ipynb](https://github.com/maria-larchenko/modflows/blob/main/ModFlows_demo.ipynb) and [ModFlows_demo_batched.ipynb](https://github.com/maria-larchenko/modflows/blob/main/ModFlows_demo_batched.ipynb) to use the pretrained model for color transfer on your own images. |
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<p align="center"> |
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<img src="results_unsplash.png" style="width: 1000px"/> |
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</p> |
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How to clone and download pre-trained weights: |
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```bash |
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git clone https://github.com/maria-larchenko/modflows.git |
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cd modflows |
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git clone https://huggingface.co/MariaLarchenko/modflows_color_encoder |
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``` |
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Call `python3 run_inference.py --help` to see a full list of arguments for inference. |
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`Ctrl+C` cancels the execution. |
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<p align="center"> |
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<img src="SPIGM_visual_abstract.png" style="width: 500px"/> |
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</p> |
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## Citation |
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If you use this code in your research, please cite our work: |
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``` |
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@inproceedings{larchenko2024color, |
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title={Color Style Transfer with Modulated Flows}, |
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author={Larchenko, Maria and Lobashev, Alexander and Guskov, Dmitry and Palyulin, Vladimir Vladimirovich}, |
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booktitle={ICML 2024 Workshop on Structured Probabilistic Inference $\\{$$\\backslash$\\&$\\}$ Generative Modeling} |
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} |
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``` |
