  Diffusion models have demonstrated their capability to synthesize
high-quality and diverse images from textual prompts. However, simultaneous
control over both global contexts (e.g., object layouts and interactions) and
local details (e.g., colors and emotions) still remains a significant
challenge. The models often fail to understand complex descriptions involving
multiple objects and reflect specified visual attributes to wrong targets or
ignore them. This paper presents Global-Local Diffusion (\textit{GLoD}), a
novel framework which allows simultaneous control over the global contexts and
the local details in text-to-image generation without requiring training or
fine-tuning. It assigns multiple global and local prompts to corresponding
layers and composes their noises to guide a denoising process using pre-trained
diffusion models. Our framework enables complex global-local compositions,
conditioning objects in the global prompt with the local prompts while
preserving other unspecified identities. Our quantitative and qualitative
evaluations demonstrate that GLoD effectively generates complex images that
adhere to both user-provided object interactions and object details.
