# SDXL model-based optimization for interior design: Data-driven and deep learning methods

## TL;DR

This paper introduces a new framework to optimize the SDXL model for interior design, improving both the structure and aesthetics of AI-generated designs.

## Contribution

A novel optimization framework for the SDXL model with semantic cleaning and hyperparameter tuning for interior design.

## Key findings

- The optimized framework achieved superior FID, SSIM, and LPIPS scores compared to baseline models.
- Semantic cleaning and structural regularization reduced FID by 51.1% compared to the baseline.
- The framework preserves geometric constraints and improves CLIP Semantic Alignment in interior design.

## Abstract

This study proposes a novel, domain-specific optimization framework for the Stable Diffusion XL (SDXL) model, addressing the critical challenges of structural consistency and aesthetic fidelity in AI-assisted interior design. Unlike generic applications of diffusion models, this research introduces a systematic pipeline integrating automated semantic cleaning with a rigorous hyperparameter optimization strategy. A high-quality, annotated dataset was constructed using a semi-automated YOLO-based filtering process to minimize noise. Furthermore, we established an empirically validated training protocol—combining optimal Dropout rates, L1/L2 regularization, and dynamic learning rates—specifically tuned to preserve the geometric constraints of interior spaces. Experimental results demonstrate that this optimized framework significantly outperforms baseline models, achieving superior Fréchet Inception Distance (FID), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) scores, alongside robust CLIP Semantic Alignment. Furthermore, a systematic ablation study confirms that while domain-specific data provides the foundation, our semantic cleaning pipeline and structural regularization are critical for achieving high geometric fidelity, reducing FID by 51.1% compared to the baseline. The study contributes a technically robust methodology for adapting large-scale diffusion models to the specialized requirements of spatial design.