Add model card and pipeline tag

This PR adds a model card for MixFlow, which includes:
- A link to the paper [MixFlow: Mixed Source Distributions Improve Rectified Flows](https://huggingface.co/papers/2604.09181).
- A link to the official [GitHub repository](https://github.com/NazirNayal8/MixFlow).
- The `unconditional-image-generation` pipeline tag to improve discoverability on the Hub.

README.md

@@ -1,3 +1,28 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: unconditional-image-generation
+---
+
+# MixFlow: Mixed Source Distributions Improve Rectified Flows
+
+This repository contains model checkpoints for **MixFlow**, introduced in the paper [MixFlow: Mixed Source Distributions Improve Rectified Flows](https://huggingface.co/papers/2604.09181).
+
+MixFlow is a simple but effective training strategy for rectified flows that mixes unconditional and conditional source distributions to reduce generative path curvature and improve sampling efficiency. It achieves better generation quality with fewer sampling steps and accelerates training convergence across benchmarks such as CIFAR-10, FFHQ, and AFHQv2.
+
+## Resources
+
+- **Paper:** [MixFlow: Mixed Source Distributions Improve Rectified Flows](https://huggingface.co/papers/2604.09181)
+- **GitHub Repository:** [NazirNayal8/MixFlow](https://github.com/NazirNayal8/MixFlow)
+
+## Citation
+
+```bibtex
+@inproceedings{
+nayal2026mixflow,
+title={MixFlow: Mixed Source Distributions Improve Rectified Flows},
+author={Nazir Nayal and Christopher Wewer and Jan Eric Lenssen},
+booktitle={ICLR 2026 2nd Workshop on Deep Generative Model in Machine Learning: Theory, Principle and Efficacy},
+year={2026},
+url={https://openreview.net/forum?id=uWktyU3OIJ}
+}
+```