| license: mit | |
| pipeline_tag: image-to-image | |
| library_name: pytorch | |
| # Color Encoder for Color Transfer with Modulated Flows | |
| 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). | |
| arXiv: https://arxiv.org/abs/2503.19062 | |
| 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. | |
| <p align="center"> | |
| <img src="results_unsplash.png" style="width: 1000px"/> | |
| </p> | |
| How to clone and download pre-trained weights: | |
| ```bash | |
| git clone https://github.com/maria-larchenko/modflows.git | |
| cd modflows; git clone https://huggingface.co/MariaLarchenko/modflows_color_encoder | |
| ``` | |
| Call `python3 run_inference.py --help` to see a full list of arguments for inference. | |
| `Ctrl+C` cancels the execution. | |
| <p align="center"> | |
| <img src="./img/SPIGM_visual_abstract.png" style="width: 500px"/> | |
| </p> | |
| ## Citation | |
| If you use this code in your research, please cite our work: | |
| ``` | |
| @inproceedings{larchenko2024color, | |
| title={Color Style Transfer with Modulated Flows}, | |
| author={Larchenko, Maria and Lobashev, Alexander and Guskov, Dmitry and Palyulin, Vladimir Vladimirovich}, | |
| booktitle={ICML 2024 Workshop on Structured Probabilistic Inference $\\{$$\\backslash$\\&$\\}$ Generative Modeling} | |
| } | |
| ``` |