| --- |
| license: apache-2.0 |
| pipeline_tag: text-to-video |
| tags: |
| - video-generation |
| - dpo |
| --- |
| |
| # Mind the Generative Details: Direct Localized Detail Preference Optimization for Video Diffusion Models |
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| This repository contains the weights for **LocalDPO**, a novel post-training framework that constructs localized preference pairs from real videos and optimizes alignment at the spatio-temporal region level for video diffusion models. |
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| LocalDPO addresses the efficiency and ambiguity limitations of existing DPO methods. It treats high-quality real videos as positive samples and generates corresponding negatives by locally corrupting them with random spatio-temporal masks. Experiments on Wan2.1 and CogVideoX demonstrate that LocalDPO consistently improves video fidelity and temporal coherence. |
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| - **Paper:** [Mind the Generative Details: Direct Localized Detail Preference Optimization for Video Diffusion Models](https://huggingface.co/papers/2601.04068) |
| - **Project Page:** [https://1170300714.github.io/LocalDPO/](https://1170300714.github.io/LocalDPO/) |
| - **Code:** [https://github.com/1170300714/Local-DPO](https://github.com/1170300714/Local-DPO) |
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| ## Citation |
|
|
| ```bibtex |
| @article{huang2026mind, |
| title={Mind the Generative Details: Direct Localized Detail Preference Optimization for Video Diffusion Models}, |
| author={Huang, Zitong and Zhang, Kaidong and Ding, Yukang and Gao, Chao and Ding, Rui and Chen, Ying and Zuo, Wangmeng}, |
| journal={arXiv preprint arXiv:2601.04068}, |
| year={2026} |
| } |
| ``` |
