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Personalized Interiors at Scale: Leveraging AI for Efficient and Customizable Design Solutions

Kaiwen Zhou, Tianyu Wang

TL;DR

The paper addresses scalable, personalized interior design using diffusion-based generative models. It integrates Stable Diffusion with Dreambooth to enable rapid, data-efficient customization of room interiors via text prompts. A comprehensive methodology covers dataset compilation, Dreambooth fine-tuning, text-to-image synthesis, evaluation, ablation, and integration with design tools, validated by experiments and case studies. Findings demonstrate high-quality, customizable interior renderings and highlight practical impact while acknowledging data quality, generalization, IP, and ethical considerations as important avenues for future work.

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, supported by experimental results that demonstrate its effectiveness and a series of case studies that illustrate its practical application in interior design projects. Our study contributes to the ongoing discourse on the role of AI in creative fields, highlighting the benefits of leveraging generative models to enhance creativity and reshape the future of interior design.

Personalized Interiors at Scale: Leveraging AI for Efficient and Customizable Design Solutions

TL;DR

The paper addresses scalable, personalized interior design using diffusion-based generative models. It integrates Stable Diffusion with Dreambooth to enable rapid, data-efficient customization of room interiors via text prompts. A comprehensive methodology covers dataset compilation, Dreambooth fine-tuning, text-to-image synthesis, evaluation, ablation, and integration with design tools, validated by experiments and case studies. Findings demonstrate high-quality, customizable interior renderings and highlight practical impact while acknowledging data quality, generalization, IP, and ethical considerations as important avenues for future work.

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, supported by experimental results that demonstrate its effectiveness and a series of case studies that illustrate its practical application in interior design projects. Our study contributes to the ongoing discourse on the role of AI in creative fields, highlighting the benefits of leveraging generative models to enhance creativity and reshape the future of interior design.
Paper Structure (23 sections, 2 equations, 4 figures)

This paper contains 23 sections, 2 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Stable diffusion
  4. Conceptual Overview
  5. Key Features
  6. Technical Foundation
  7. Applications
  8. Dreambooth
  9. Key Characteristics of Dreambooth
  10. Technical Overview
  11. Mathematical Framework
  12. Applications and Potential
  13. How does it work?
  14. Extensive Dataset Compilation
  15. Data Preprocessing and Augmentation
  16. ...and 8 more sections

Figures (4)

  • Figure 1: We show the pipeline of stable diffusion models.
  • Figure 2: We show the pipeline of Dreambooth.
  • Figure 3: Dreambooth style 1. We show some generation results.
  • Figure 4: Dreambooth style 2. We show some generation results.