Advancing Interior Design with AI: Controllable Stable Diffusion for Panoramic Image Generation

AI-driven technologies have significantly advanced panoramic image generation in interior design; however, existing methods often lack controllability and consistency in rendering high-quality, coherent panoramas. To address these limitations, the study proposes CSD-Pano, a controllable and stable diffusion framework tailored for panoramic interior design generation. The study also introduces PSD-4, a curated dataset of panoramic scenes covering diverse interior decoration styles to support training and evaluation. CSD-Pano enables fine-grained control over aesthetic attributes, layout coherence, and stylistic consistency. Furthermore, the study designs a panoramic loss function that enhances spatial coherence, geometric alignment, and perceptual fidelity. Extensive qualitative and quantitative experiments demonstrate that CSD-Pano achieves superior performance compared to existing baselines, with significant improvements in SSIM and LPIPS metrics. These results validate the effectiveness of our approach in advancing automated panoramic interior design.