  The concept of negative prompts, emerging from conditional generation models
like Stable Diffusion, allows users to specify what to exclude from the
generated images.%, demonstrating significant practical efficacy. Despite the
widespread use of negative prompts, their intrinsic mechanisms remain largely
unexplored. This paper presents the first comprehensive study to uncover how
and when negative prompts take effect. Our extensive empirical analysis
identifies two primary behaviors of negative prompts. Delayed Effect: The
impact of negative prompts is observed after positive prompts render
corresponding content. Deletion Through Neutralization: Negative prompts delete
concepts from the generated image through a mutual cancellation effect in
latent space with positive prompts. These insights reveal significant potential
real-world applications; for example, we demonstrate that negative prompts can
facilitate object inpainting with minimal alterations to the background via a
simple adaptive algorithm. We believe our findings will offer valuable insights
for the community in capitalizing on the potential of negative prompts.
