Zhizeng Ye — accepted AI-conference papers

2026

Diffusion Probe: Generated Image Result Prediction Using CNN Probes

Bukun Huang, Benlei Cui, Zhizeng Ye, Xuemei Dong, Tuo Chen, Hui Xue +4

CVPR 2026

Text-to-image (T2I) diffusion models currently lack an efficient mechanism for early quality assessment, forcing costly random trial-and-error in scenarios requiring multiple generations (e.g., iterating on prompts, agent-based image generation, flow-grpo). To address this, we first reveal a strong

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2026

TC-Pade: Trajectory-Consistent Pade Approximation for Diffusion Acceleration

Shaoxuan He, Benlei Cui, Bukun Huang, Zhizeng Ye, Yunyun Sun, Longtao Huang +5

CVPR 2026

Despite achieving state-of-the-art generation quality, diffusion models are hindered by the substantial computational burden of their iterative sampling process. While feature caching techniques achieve effective acceleration at higher step counts (e.g., 50 steps), they exhibit critical limitations

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