Add model card for DMVAE

README.md

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+---
+pipeline_tag: unconditional-image-generation
+---
+
+# Distribution Matching Variational AutoEncoder (DMVAE)
+
+This repository contains the official implementation of the paper [**"Distribution Matching Variational AutoEncoder"**](https://huggingface.co/papers/2512.07778).
+
+![DMVAE Framework](https://github.com/sen-ye/dmvae/raw/main/figs/dmvae.png)
+
+DMVAE introduces a novel approach to visual generative models by explicitly aligning the encoder's latent distribution with an arbitrary reference distribution via a distribution matching constraint. This method generalizes beyond the Gaussian prior of conventional VAEs, enabling alignment with distributions derived from self-supervised features, diffusion noise, or other prior distributions, leading to efficient and high-fidelity image synthesis.
+
+For more details on the installation, training, and evaluation, please refer to the official [GitHub repository](https://github.com/sen-ye/dmvae).
+
+## Citation
+
+If you find this repository useful in your research or applications, please consider citing:
+
+```text
+@misc{dmvae,
+      title={Distribution Matching Variational AutoEncoder}, 
+      author={Sen Ye and Jianning Pei and Mengde Xu and Shuyang Gu and Chunyu Wang and Liwei Wang and Han Hu},
+      year={2025},
+      eprint={2512.07778},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2512.07778}, 
+}
+```