HouseDiffusion++: Constraint-Consistent Vector Floorplan Generation

In real architectural design scenarios, floorplans must satisfy a variety of requirements imposed by regulations, design briefs and project-specific constraints. Often combining geometric, functional, and semantic requirements, these conditions make practical floorplan generation a highly constrained design problem rather than an unconstrained layout synthesis problem. Current floorplan generation methods usually address only a small subset of these conditions. In this work, we introduce HouseDiffusion++, a constraint-consistent diffusion framework for controllable vector floorplan generation. Building on HouseDiffusion, our approach extends vector denoising with several conditioning mechanisms to enable joint control over room adjacency graphs, outer boundaries, room sizes, windows and entrance doors. We also address a significant mismatch between training and inference in the original formulation, whereby the number of room corners is known during training but unavailable at test time. To overcome this issue, we introduce a polygon augmentation strategy that enhances robustness to redundant points. Experiments on the RPLAN dataset demonstrate that HouseDiffusion++ outperforms baselines in terms of quantitative metrics and visual quality, while offering greater controllability under different constraints.