Personalized Interiors at Scale: Leveraging AI for Efficient and Customizable Design Solutions
Kaiwen Zhou, Tianyu Wang

TL;DR
This paper presents a novel AI-driven approach combining Stable Diffusion and Dreambooth models to enable efficient, personalized interior design generation, reducing traditional labor and democratizing creative processes.
Contribution
It introduces a new framework integrating generative models for rapid, customizable interior design, advancing AI's role in creative and practical design applications.
Findings
High-quality interior images generated effectively
Rapid customization with minimal training data achieved
Demonstrated practical application through case studies
Abstract
In this paper, we introduce an innovative application of artificial intelligence in the realm of interior design through the integration of Stable Diffusion and Dreambooth models. This paper explores the potential of these advanced generative models to streamline and democratize the process of room interior generation, offering a significant departure from conventional, labor-intensive techniques. Our approach leverages the capabilities of Stable Diffusion for generating high-quality images and Dreambooth for rapid customization with minimal training data, addressing the need for efficiency and personalization in the design industry. We detail a comprehensive methodology that combines these models, providing a robust framework for the creation of tailored room interiors that reflect individual tastes and functional requirements. We presents an extensive evaluation of our method,…
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Taxonomy
TopicsArchitecture and Computational Design · Building Energy and Comfort Optimization · Architecture, Design, and Social History
