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Add model card for DMVAE

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This PR adds a comprehensive model card for the Distribution Matching Variational AutoEncoder (DMVAE) model.

It includes:
- A link to the paper: [Distribution Matching Variational AutoEncoder](https://huggingface.co/papers/2512.07778)
- The `pipeline_tag: unconditional-image-generation`
- A link to the official GitHub repository: [https://github.com/sen-ye/dmvae](https://github.com/sen-ye/dmvae)
- A brief model description.
- The framework image from the GitHub README.
- The BibTeX citation.

Please review and merge this PR if everything looks good.

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+ ---
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+ pipeline_tag: unconditional-image-generation
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+ ---
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+ # Distribution Matching Variational AutoEncoder (DMVAE)
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+ This repository contains the official implementation of the paper [**"Distribution Matching Variational AutoEncoder"**](https://huggingface.co/papers/2512.07778).
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+ ![DMVAE Framework](https://github.com/sen-ye/dmvae/raw/main/figs/dmvae.png)
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+ 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.
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+ For more details on the installation, training, and evaluation, please refer to the official [GitHub repository](https://github.com/sen-ye/dmvae).
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+ ## Citation
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+ If you find this repository useful in your research or applications, please consider citing:
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+ ```text
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+ @misc{dmvae,
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+ title={Distribution Matching Variational AutoEncoder},
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+ author={Sen Ye and Jianning Pei and Mengde Xu and Shuyang Gu and Chunyu Wang and Liwei Wang and Han Hu},
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+ year={2025},
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+ eprint={2512.07778},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV},
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+ url={https://arxiv.org/abs/2512.07778},
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+ }
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+ ```