Add model card

README.md

@@ -1,3 +1,42 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: unconditional-image-generation
+---
+
+# MixFlow: Mixed Source Distributions Improve Rectified Flows
+
+This repository contains the weights for the model presented in the paper [MixFlow: Mixed Source Distributions Improve Rectified Flows](https://huggingface.co/papers/2604.09181).
+
+MixFlow is a training strategy for rectified flows that reduces the generative path curvatures and improves sampling efficiency. It trains a flow model on linear mixtures of a fixed unconditional distribution and a distribution conditioned on an arbitrary signal (called $\kappa$-FC), which aligns the source distribution better with the data distribution.
+
+- **Paper:** [https://arxiv.org/abs/2604.09181](https://arxiv.org/abs/2604.09181)
+- **Repository:** [https://github.com/NazirNayal8/MixFlow](https://github.com/NazirNayal8/MixFlow)
+
+## Usage
+
+Inference can be performed using the scripts provided in the official repository. After setting up the environment, you can run inference from a checkpoint as follows:
+
+```bash
+bash scripts/run_inference.sh cifar10 /path/to/model.ckpt outputs/inference/cifar10
+```
+
+For higher-resolution experiments such as FFHQ or AFHQv2 64x64:
+
+```bash
+bash scripts/run_inference.sh ffhq_64x64 /path/to/model.ckpt outputs/inference/ffhq \
+  test.num_samples=10000 \
+  test.num_inference_timesteps=64
+```
+
+## 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}
+}
+```