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.

该论文首次全面研究了负面提示的机制和效果，揭示了负面提示的两个主要行为：延迟效果和通过中和效应的删除效果，为负面提示的潜在实际应用提供了重要的见解。

负面指示的影响：何时何种情况下会产生效果？

Understanding the Impact of Negative Prompts: When and How Do They Take  Effect?

While conditional generation models can now generate natural language well
enough to create fluent text, it is still difficult to control the generation
process, leading to irrelevant, repetitive, and hallucinated content. Recent
work shows that planning can be a useful intermediate step to render
conditional generation less opaque and more grounded. We present a web
browser-based demonstration for query-focused summarization that uses a
sequence of question-answer pairs, as a blueprint plan for guiding text
generation (i.e., what to say and in what order). We illustrate how users may
interact with the generated text and associated plan visualizations, e.g., by
editing and modifying the blueprint in order to improve or control the
generated output.
A short video demonstrating our system is available at
this https URL

通过问题 - 答案对的序列作为引导文本生成的蓝图计划，将计划作为中介步骤，可以更好地控制条件生成模型的生成过程。我们提出了一种基于 Web 浏览器的演示，用于查询聚焦摘要，演示用户如何与生成的文本交互并使用基于蓝图的计划可视化来控制生成的输出。