Graph2Scene: Relation-Aware Text-to-Image Generation with Graph-Guided Diffusion Model

Text-to-image diffusion models still struggle with complex prompts containing many elements and interactions. Existing attention-control methods mainly focus on controlling where elements appear, but often ignore how objects interact with each other. As a result, relation tokens describing interactions between elements may assign high attention to irrelevant regions, leading to missing or incorrect interactions. In this paper, we propose Graph2Scene, a training-free framework that treats scene graphs as a core control signal for diffusion generation. It represents elements as nodes and their relations as edges, and translates these element-relation dependencies into selective attention guidance. It grounds each element to its corresponding region and guides relational interactions among the involved elements. To achieve appropriate relation control without over-constraining the generation process, Graph2Scene further adapts the control duration to scene complexity and progressively relaxes graph guidance during denoising. Experiments on T2I-CompBench and ConceptMix show clear improvements in compositional generation, especially for object relations. On ConceptMix, Graph2Scene improves relationship accuracy from 0.59 to 0.84 over the strongest FLUX.1-based regional-control baseline. Results on LongBench-T2I further show its advantage on long and complex prompts. Code is available at https://anonymous.4open.science/r/GraphExecutor-CF2F.