Text-to-image diffusion models have an unprecedented ability to generate
diverse and high-quality images. However, they often struggle to faithfully
capture the intended semantics of complex input prompts that include multiple
subjects. Recently, numerous layout-to-image extensions have been introduced to
improve user control, aiming to localize subjects represented by specific
tokens. Yet, these methods often produce semantically inaccurate images,
especially when dealing with multiple semantically or visually similar
subjects. In this work, we study and analyze the causes of these limitations.
Our exploration reveals that the primary issue stems from inadvertent semantic
leakage between subjects in the denoising process. This leakage is attributed
to the diffusion model's attention layers, which tend to blend the visual
features of different subjects. To address these issues, we introduce Bounded
Attention, a training-free method for bounding the information flow in the
sampling process. Bounded Attention prevents detrimental leakage among subjects
and enables guiding the generation to promote each subject's individuality,
even with complex multi-subject conditioning. Through extensive
experimentation, we demonstrate that our method empowers the generation of
multiple subjects that better align with given prompts and layouts.

我们研究了文本到图像扩散模型中的语义泄漏问题，并提出了一种名为有界注意力的方法，通过限定信息流的路径来解决泄漏问题，从而提高多主题生成的质量。