LayGraph: Graph Guided Diffusion with Consistent Inference for Remote Sensing Layout-to-Image Generation

Layout-to-image generation can synthesize remote sensing images from oriented layouts and expand training data for detectors. Existing diffusion models improve box-level placement, but encode objects largely independently and leave terrain to a global caption or unconstrained synthesis. Consequently, prescribed boxes need not yield compatible object–background pairings or plausible local arrangements. To address these failures, LayGraph first adopts a novel graph-structured condition that constrains the complex relations in layout-to-image generation, and then constructs a geometry features bank both to bind instance descriptors to this graph and to enable cluster retrieval at inference. Specifically, the graph represents each oriented layout with object nodes linked to spatial neighbors and to a background super-node, so relative geometry, category co-occurrence, and object–terrain couplings condition the denoiser rather than independent box tokens. To bind these nodes to instance descriptors and to keep the same slots when only a layout is given, the bank stores training-crop features on the graph; source-matched rows are read during training, and geometry-aware cluster retrieval fills those slots at layout-only inference. We evaluate this design on DIOR-R and DOTA against recent layout-conditioned generators. Relative to the best competing score on each metric,LayGraph reduces FID by 6.0% and raises YOLOScore by 27.3%; the same synthetic images further improve Oriented R-CNN mAP50 by 8.9% when the detector is trained without real photographs. Controlled ablations further show that the relation graph and the geometry features bank contribute complementary gains.