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RoomDiffusion: A Specialized Diffusion Model in the Interior Design Industry

Zhaowei Wang, Ying Hao, Hao Wei, Qing Xiao, Lulu Chen, Yulong Li, Yue Yang, Tianyi Li

TL;DR

RoomDiffusion is presented, a pioneering diffusion model meticulously tailored for the interior design industry, surpassing all existing open source models such as stable diffusion and SDXL in terms of aesthetics, accuracy, and efficiency.

Abstract

Recent advancements in text-to-image diffusion models have significantly transformed visual content generation, yet their application in specialized fields such as interior design remains underexplored. In this paper, we present RoomDiffusion, a pioneering diffusion model meticulously tailored for the interior design industry. To begin with, we build from scratch a whole data pipeline to update and evaluate data for iterative model optimization. Subsequently, techniques such as multiaspect training, multi-stage fine-tune and model fusion are applied to enhance both the visual appeal and precision of the generated results. Lastly, leveraging the latent consistency Distillation method, we distill and expedite the model for optimal efficiency. Unlike existing models optimized for general scenarios, RoomDiffusion addresses specific challenges in interior design, such as lack of fashion, high furniture duplication rate, and inaccurate style. Through our holistic human evaluation protocol with more than 20 professional human evaluators, RoomDiffusion demonstrates industry-leading performance in terms of aesthetics, accuracy, and efficiency, surpassing all existing open source models such as stable diffusion and SDXL.

RoomDiffusion: A Specialized Diffusion Model in the Interior Design Industry

TL;DR

RoomDiffusion is presented, a pioneering diffusion model meticulously tailored for the interior design industry, surpassing all existing open source models such as stable diffusion and SDXL in terms of aesthetics, accuracy, and efficiency.

Abstract

Recent advancements in text-to-image diffusion models have significantly transformed visual content generation, yet their application in specialized fields such as interior design remains underexplored. In this paper, we present RoomDiffusion, a pioneering diffusion model meticulously tailored for the interior design industry. To begin with, we build from scratch a whole data pipeline to update and evaluate data for iterative model optimization. Subsequently, techniques such as multiaspect training, multi-stage fine-tune and model fusion are applied to enhance both the visual appeal and precision of the generated results. Lastly, leveraging the latent consistency Distillation method, we distill and expedite the model for optimal efficiency. Unlike existing models optimized for general scenarios, RoomDiffusion addresses specific challenges in interior design, such as lack of fashion, high furniture duplication rate, and inaccurate style. Through our holistic human evaluation protocol with more than 20 professional human evaluators, RoomDiffusion demonstrates industry-leading performance in terms of aesthetics, accuracy, and efficiency, surpassing all existing open source models such as stable diffusion and SDXL.
Paper Structure (20 sections, 8 figures, 1 table)

This paper contains 20 sections, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Method
  3. Data Pipeline
  4. Raw Data Acquisition
  5. Image Quality Assessment
  6. Image Captioning
  7. Data Layering
  8. Model Training
  9. Multi-aspect training
  10. Multi-stage fine-tune
  11. Model Fusion
  12. LCD
  13. Evaluation Protocol
  14. Evaluation Process
  15. Automated Evaluation Metrics
  16. ...and 5 more sections

Figures (8)

  • Figure 1: RoomDiffusion can generate images following long text prompts.
  • Figure 2: RoomDiffusion can generate images in various resolutions.
  • Figure 3: The shortcomings of open source models in interior design
  • Figure 4: The generation results of RoomDiffusion in interior design
  • Figure 5: The architecture diagram of the data pipeline.
  • ...and 3 more figures