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
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