HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image Generation
This addresses layout-to-image generation for applications in computer vision and graphics, representing an incremental improvement over prior methods.
The paper tackles the problem of layout-to-image generation, where existing methods struggle with complex layouts leading to issues like object missing and inconsistent lighting, by proposing HiCo, a hierarchical controllable diffusion model that introduces a new benchmark and achieves improved performance, though specific numbers are not provided.
The task of layout-to-image generation involves synthesizing images based on the captions of objects and their spatial positions. Existing methods still struggle in complex layout generation, where common bad cases include object missing, inconsistent lighting, conflicting view angles, etc. To effectively address these issues, we propose a \textbf{Hi}erarchical \textbf{Co}ntrollable (HiCo) diffusion model for layout-to-image generation, featuring object seperable conditioning branch structure. Our key insight is to achieve spatial disentanglement through hierarchical modeling of layouts. We use a multi branch structure to represent hierarchy and aggregate them in fusion module. To evaluate the performance of multi-objective controllable layout generation in natural scenes, we introduce the HiCo-7K benchmark, derived from the GRIT-20M dataset and manually cleaned. https://github.com/360CVGroup/HiCo_T2I.