S^2-Guidance: Stochastic Self Guidance for Training-Free Enhancement of
  Diffusion Models
Authors: name: Chubin Chen, name: Jiashu Zhu, name: Xiaokun Feng, name: Nisha Huang, name: Meiqi Wu, name: Fangyuan Mao, name: Jiahong Wu, name: Xiangxiang Chu, name: Xiu Li

Classifier-free Guidance (CFG) is a widely used technique in modern diffusion
models for enhancing sample quality and prompt adherence. However, through an
empirical analysis on Gaussian mixture modeling with a closed-form solution, we
observe a discrepancy between the suboptimal results produced by CFG and the
ground truth. The model's excessive reliance on these suboptimal predictions
often leads to semantic incoherence and low-quality outputs. To address this
issue, we first empirically demonstrate that the model's suboptimal predictions
can be effectively refined using sub-networks of the model itself. Building on
this insight, we propose S^2-Guidance, a novel method that leverages stochastic
block-dropping during the forward process to construct stochastic sub-networks,
effectively guiding the model away from potential low-quality predictions and
toward high-quality outputs. Extensive qualitative and quantitative experiments
on text-to-image and text-to-video generation tasks demonstrate that
S^2-Guidance delivers superior performance, consistently surpassing CFG and
other advanced guidance strategies. Our code will be released.