  Despite significant progress in Text-to-Image (T2I) generative models, even
lengthy and complex text descriptions still struggle to convey detailed
controls. In contrast, Layout-to-Image (L2I) generation, aiming to generate
realistic and complex scene images from user-specified layouts, has risen to
prominence. However, existing methods transform layout information into tokens
or RGB images for conditional control in the generative process, leading to
insufficient spatial and semantic controllability of individual instances. To
address these limitations, we propose a novel Spatial-Semantic Map Guided
(SSMG) diffusion model that adopts the feature map, derived from the layout, as
guidance. Owing to rich spatial and semantic information encapsulated in
well-designed feature maps, SSMG achieves superior generation quality with
sufficient spatial and semantic controllability compared to previous works.
Additionally, we propose the Relation-Sensitive Attention (RSA) and
Location-Sensitive Attention (LSA) mechanisms. The former aims to model the
relationships among multiple objects within scenes while the latter is designed
to heighten the model's sensitivity to the spatial information embedded in the
guidance. Extensive experiments demonstrate that SSMG achieves highly promising
results, setting a new state-of-the-art across a range of metrics encompassing
fidelity, diversity, and controllability.
