Core Viewpoint - The article discusses the introduction of a new method called S²-Guidance, developed by a research team from Tsinghua University, Alibaba AMAP, and the Chinese Academy of Sciences, which enhances the quality and coherence of AI-generated images and videos through a self-correcting mechanism [1][4]. Group 1: Methodology and Mechanism - S²-Guidance utilizes a technique called Stochastic Block-Dropping to dynamically construct "weak" sub-networks, allowing the AI to self-correct during the generation process [3][10]. - The method addresses the limitations of Classifier-Free Guidance (CFG), which often leads to distortion and lacks generalizability due to its linear extrapolation nature [5][8]. - By avoiding the need for external weak models and complex parameter tuning, S²-Guidance offers a universal and automated solution for self-optimization [12][11]. Group 2: Performance Improvements - S²-Guidance significantly enhances visual quality across multiple dimensions, including temporal dynamics, detail rendering, and artifact reduction, compared to previous methods like CFG and Autoguidance [19][21]. - The method demonstrates superior performance in generating coherent and aesthetically pleasing images, effectively avoiding common issues such as unnatural artifacts and distorted objects [22][24]. - In video generation, S²-Guidance resolves key challenges related to physical realism and complex instruction adherence, producing stable and visually rich scenes [25][26]. Group 3: Experimental Validation - The research team validated the effectiveness of S²-Guidance through rigorous experiments, showing that it balances guidance strength with distribution fidelity, outperforming CFG in capturing true data distributions [14][18]. - S²-Guidance achieved leading scores on authoritative benchmarks like HPSv2.1 and T2I-CompBench, surpassing all comparative methods in various quality dimensions [26][27].