Bai LiChen — accepted AI-conference papers

2025

IV-mixed Sampler: Leveraging Image Diffusion Models for Enhanced Video Synthesis

Shitong Shao, zikai zhou, Bai LiChen, Haoyi Xiong, Zeke Xie

ICLR 2025poster

Exploring suitable solutions to improve performance by increasing the computational cost of inference in visual diffusion models is a highly promising direction. Sufficient prior studies have demonstrated that correctly scaling up computation in the sampling process can successfully lead to improved…

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2025

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection

Bai LiChen, Shitong Shao, zikai zhou, Zipeng Qi, zhiqiang xu, Haoyi Xiong +1

ICLR 2025poster

Diffusion models, the most popular generative paradigm so far, can inject conditional information into the generation path to guide the latent towards desired directions. However, existing text-to-image diffusion models often fail to maintain high image quality and high prompt-image alignment for th…

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