  Denoising diffusion models (DDMs) have attracted attention for their
exceptional generation quality and diversity. This success is largely
attributed to the use of class- or text-conditional diffusion guidance methods,
such as classifier and classifier-free guidance. In this paper, we present a
more comprehensive perspective that goes beyond the traditional guidance
methods. From this generalized perspective, we introduce novel condition- and
training-free strategies to enhance the quality of generated images. As a
simple solution, blur guidance improves the suitability of intermediate samples
for their fine-scale information and structures, enabling diffusion models to
generate higher quality samples with a moderate guidance scale. Improving upon
this, Self-Attention Guidance (SAG) uses the intermediate self-attention maps
of diffusion models to enhance their stability and efficacy. Specifically, SAG
adversarially blurs only the regions that diffusion models attend to at each
iteration and guides them accordingly. Our experimental results show that our
SAG improves the performance of various diffusion models, including ADM, IDDPM,
Stable Diffusion, and DiT. Moreover, combining SAG with conventional guidance
methods leads to further improvement.
