S^2-Guidance: Stochastic Self Guidance for Training-Free Enhancement of
  Diffusion Models
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.
68a3ea3db65388761d07450b
Chubin Chen
68a3ea3db65388761d07450c
Jiashu Zhu
claimed_verified
2025-10-17T05:57:16.488Z
6682775501c30ad93ec5e500
/avatars/971ee2028589f6089559306b40a58da0.svg
Jiashu Zhu
user
Jiashuz
68a3ea3db65388761d07450d
Xiaokun Feng
68a3ea3db65388761d07450e
Nisha Huang
68a3ea3db65388761d07450f
Meiqi Wu
68a3ea3db65388761d074510
Fangyuan Mao
68a3ea3db65388761d074511
Jiahong Wu
68a3ea3db65388761d074512
Xiangxiang Chu
68a3ea3db65388761d074513
Xiu Li
S^2-Guidance: Stochastic Self Guidance for Training-Free Enhancement of
  Diffusion Models
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.
S^2-Guidance, a novel method using stochastic block-dropping, improves sample quality and prompt adherence in diffusion models by refining suboptimal predictions, outperforming Classifier-free Guidance and other advanced strategies.
Classifier-free Guidance Gaussian mixture modeling suboptimal predictions sub-networks stochastic block-dropping S^2-Guidance text-to-image text-to-video generation