Add pipeline tag, library name, link to paper, code snippet and citation

This PR improves the model card by:
- adding the `image-to-image` pipeline tag to improve discoverability.
- adding the `library_name` to the metadata.
- adding the link to the paper.
- adding the code snippet on how to clone and download pre-trained weights
- adding the citation information.

README.md

@@ -1,13 +1,40 @@
 ---
 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 arcitectures for the AAAI 2025 paper "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 \
-https://github.com/maria-larchenko/modflows/blob/main/ModFlows_demo.ipynb
+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>
+</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}
+}
+```