Libo Zhu — accepted AI-conference papers

2026

QuantVSR: Low-Bit Post-Training Quantization for Real-World Video Super-Resolution

Bowen Chai, Zheng Chen, Libo Zhu, Wenbo Li, Yong Guo, Yulun Zhang

AAAI 2026technical

Diffusion models have shown superior performance in real-world video super-resolution (VSR). However, the slow processing speeds and heavy resource consumption of diffusion models hinder their practical application and deployment. Quantization offers a potential solution for compressing the VSR mode

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2025

PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution

Libo Zhu, Jianze Li, Haotong Qin, Wenbo Li, Yulun Zhang, Yong Guo +1

CVPR 2025poster

Diffusion-based image super-resolution (SR) models have shown superior performance at the cost of multiple denoising steps. However, even though the denoising step has been reduced to one, they require high computational costs and storage requirements, making it difficult for deployment on hardware…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗