Z Tech｜对话CV泰斗何恺明新作研究团队，三位05后MIT本科生，Diffusion真的需要噪声条件吗？

Core Viewpoint - The recent research led by renowned scholar He Kaiming and three MIT freshmen challenges the traditional understanding of noise conditioning in denoising models, suggesting that it may not be essential for model performance [1][3]. Group 1: Research Findings - The study demonstrates that removing noise conditioning from many mainstream denoising models results in only a modest degradation in performance [4]. - The newly designed unconditional model, uEDM, achieves a near-state-of-the-art FID score of 2.23 in the CIFAR-10 benchmark, only slightly behind the top noise-conditioned model, EDM, which has an FID score of 1.97 [2][6]. - The research provides a theoretical framework and experimental results that validate the stability of mainstream denoising models when noise conditioning is removed, indicating the non-necessity of traditional noise conditioning techniques in practical applications [3][5]. Group 2: Implications and Future Directions - The findings open avenues for reducing model computational complexity and inspire new model designs that do not rely on noise conditioning [3]. - The upcoming live lecture will feature discussions on generative models and potential development directions, including a Q&A session with the authors [2].