Improve model card: Add pipeline tag, links, and usage reference

README.md

@@ -1,3 +1,39 @@
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-license: apache-2.0
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+---
+license: apache-2.0
+pipeline_tag: image-to-image
+---
+
+# Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization
+
+This repository contains **FlowMo**, a transformer-based diffusion autoencoder that achieves state-of-the-art performance for image tokenization at multiple compression rates. It is introduced in the paper [Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization](https://huggingface.co/papers/2503.11056).
+
+FlowMo operates without using convolutions, adversarial losses, spatially-aligned two-dimensional latent codes, or distilling from other tokenizers. Its key insight is that training should be broken into a mode-matching pre-training stage and a mode-seeking post-training stage.
+
+<p align="center">
+  <img src="https://github.com/kylesargent/FlowMo/raw/main/demo.gif" alt="FlowMo demo GIF" />
+</p>
+
+## Links
+
+*   **Project Page:** [https://kylesargent.github.io/flowmo](https://kylesargent.github.io/flowmo)
+*   **Code Repository:** [https://github.com/kylesargent/FlowMo](https://github.com/kylesargent/FlowMo)
+
+## Usage
+
+The official GitHub repository provides comprehensive instructions for installation, data preparation, training, and evaluation. A Jupyter notebook, `example.ipynb`, is available to demonstrate how to use the FlowMo tokenizer for image reconstruction.
+
+## Citation
+
+If you find FlowMo useful, please cite our paper:
+
+```bibtex
+@misc{sargent2025flowmodemodeseekingdiffusion,
+      title={Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization},
+      author={Kyle Sargent and Kyle Hsu and Justin Johnson and Li Fei-Fei and Jiajun Wu},
+      year={2025},
+      eprint={2503.11056},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2503.11056},
+}
+```