AI-Based Approach using Generative Adversarial Network for Interior Design System

Abstract

This research paper presents an AI-based interior design system utilizing Generative adversarial Networks (GANs) for realistic textual content-to-image conversion, incorporating room dimensions and user preferences. The system leverages Black forest Labs' FLUX.1 (schnell) model, a high-pace, open-source variant optimized for rapid and high-constancy image generation. by inputting textual descriptions and spatial constraints, the model generates customized interior layout visualizations, enabling architects, designers, and homeowners to explore diverse layouts, fixtures arrangements, and aesthetics in actual-time. The proposed approach complements the performance of layout workflows by way of providing AI-driven innovative assistance, decreasing guide effort, and presenting photorealistic previews tailor-made to person specifications. This examine evaluates the device’s effectiveness in producing coherent, high-quality interior designs and discusses potential applications in architecture, real estate, and virtual staging.