13 13 20

JunhaoZhuang

https://zhuang2002.github.io/

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reacted to their post with 🚀 20 days ago

✨ **FlashVSR v1.1 Released** We have released **FlashVSR v1.1**, an improved version of our framework **towards real-time diffusion-based video super-resolution**. This update focuses on enhancing **temporal stability**, **fidelity**, and **robustness** in long-video and streaming scenarios. Compared with v1, the new version provides smoother temporal consistency, better detail preservation, and more stable performance across different resolutions. **FlashVSR v1.1** also includes updated model weights and streamlined inference scripts ⚙️, making it easier to integrate and reproduce. You can explore the update here: * [🚀 FlashVSR v1.1 on Hugging Face](https://huggingface.co/JunhaoZhuang/FlashVSR-v1.1) * [📄 Paper (arXiv:2510.12747)](https://arxiv.org/abs/2510.12747) * [🌐 Github](https://github.com/OpenImagingLab/FlashVSR)

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JunhaoZhuang/FlashVSR-v1.1

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2208

✨ **FlashVSR v1.1 Released**

We have released **FlashVSR v1.1**, an improved version of our framework **towards real-time diffusion-based video super-resolution**.
This update focuses on enhancing **temporal stability**, **fidelity**, and **robustness** in long-video and streaming scenarios.
Compared with v1, the new version provides smoother temporal consistency, better detail preservation, and more stable performance across different resolutions.

**FlashVSR v1.1** also includes updated model weights and streamlined inference scripts ⚙️, making it easier to integrate and reproduce.

You can explore the update here:

* [🚀 FlashVSR v1.1 on Hugging Face]( JunhaoZhuang/FlashVSR-v1.1)
* [📄 Paper (arXiv:2510.12747)](https://arxiv.org/abs/2510.12747)
* [🌐 Github](https://github.com/OpenImagingLab/FlashVSR)

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3893

We are excited to announce the release of our paper, "Cobra: Efficient Line Art COlorization with BRoAder References," along with the official code! Cobra is a novel efficient long-context fine-grained ID preservation framework for line art colorization, achieving high precision, efficiency, and flexible usability for comic colorization. By effectively integrating extensive contextual references, it transforms black-and-white line art into vibrant illustrations.

We invite you to explore Cobra and share your feedback! You can access the paper and code via the following links: [PDF](https://arxiv.org/abs/2504.12240) and [Project page](https://zhuang2002.github.io/Cobra/). We eagerly anticipate your engagement and support!

Thank you for your interest!

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