Improving Sample Quality of Diffusion Models Using Self-Attention Guidance

Understand

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.