Diffusion models have achieved remarkable success in text-to-image generation
tasks; however, the role of initial noise has been rarely explored. In this
study, we identify specific regions within the initial noise image, termed
trigger patches, that play a key role for object generation in the resulting
images. Notably, these patches are ``universal'' and can be generalized across
various positions, seeds, and prompts. To be specific, extracting these patches
from one noise and injecting them into another noise leads to object generation
in targeted areas. We identify these patches by analyzing the dispersion of
object bounding boxes across generated images, leading to the development of a
posterior analysis technique. Furthermore, we create a dataset consisting of
Gaussian noises labeled with bounding boxes corresponding to the objects
appearing in the generated images and train a detector that identifies these
patches from the initial noise. To explain the formation of these patches, we
reveal that they are outliers in Gaussian noise, and follow distinct
distributions through two-sample tests. Finally, we find the misalignment
between prompts and the trigger patch patterns can result in unsuccessful image
generations. The study proposes a reject-sampling strategy to obtain optimal
noise, aiming to improve prompt adherence and positional diversity in image
generation.

通过分析生成图像中物体边界框的离散程度，研究发现初始噪声图像中的特定区域（称为触发补丁）在生成的图像中起到了关键作用；通过在不同的噪声之间提取和注入这些补丁，可以在目标区域生成物体。研究还提出了一种拒绝采样策略来获得最佳噪声，旨在改善提示遵从性和图像生成的位置多样性。