Mengying Yang — accepted AI-conference papers

2026

Elucidating the Design Space of Arbitrary-Noise-Based Diffusion Models

Xingyu Qiu, Mengying Yang, Xinghua Ma, Dong Liang, Fanding Li, Gongning Luo +3

CVPR 2026

Although EDM aims to unify the design space of diffusion models, its reliance on fixed Gaussian noise prevents it from explaining emerging flow-based methods that diffuse arbitrary noise. Moreover, our study reveals that EDM's forcible injection of Gaussian noise has adverse effects on image restora

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2025

Finding Local Diffusion Schrodinger Bridge using Kolmogorov-Arnold Network

Xingyu Qiu, Mengying Yang, Xinghua Ma, Fanding Li, Dong Liang, Gongning Luo +3

CVPR 2025poster

In image generation, Schrodinger Bridge (SB)-based methods theoretically enhance the efficiency and quality compared to the diffusion models by finding the least costly path between two distributions. However, they are computationally expensive and time-consuming when applied to complex image data.…

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