  Recent studies have demonstrated that diffusion models are capable of
generating high-quality samples, but their quality heavily depends on sampling
guidance techniques, such as classifier guidance (CG) and classifier-free
guidance (CFG). These techniques are often not applicable in unconditional
generation or in various downstream tasks such as image restoration. In this
paper, we propose a novel sampling guidance, called Perturbed-Attention
Guidance (PAG), which improves diffusion sample quality across both
unconditional and conditional settings, achieving this without requiring
additional training or the integration of external modules. PAG is designed to
progressively enhance the structure of samples throughout the denoising
process. It involves generating intermediate samples with degraded structure by
substituting selected self-attention maps in diffusion U-Net with an identity
matrix, by considering the self-attention mechanisms' ability to capture
structural information, and guiding the denoising process away from these
degraded samples. In both ADM and Stable Diffusion, PAG surprisingly improves
sample quality in conditional and even unconditional scenarios. Moreover, PAG
significantly improves the baseline performance in various downstream tasks
where existing guidances such as CG or CFG cannot be fully utilized, including
ControlNet with empty prompts and image restoration such as inpainting and
deblurring.
