Artificial Intelligence in Interior Design
AI Tool for Room Decoration: Harnessing Diffusion Model for Interior Design
This study introduces an innovative AI tool leveraging advanced diffusion models for interior design. It enables users to create personalized and aesthetically pleasing decoration ideas, significantly reducing design time and effort, and fostering greater creativity by automating complex tasks and adapting to diverse styles.
Transforming Design Efficiency
Our AI-powered tool redefines interior design workflows, delivering measurable impact across key operational areas for enterprises.
0%
Reduction in Design Iteration Time
0X Faster
Increased Design Exploration
0%
Project Cost Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The Challenge & Our Solution
Traditional interior design often faces significant challenges, including high time and cost consumption due to personalized services, difficulty in achieving current yet timeless aesthetics, and the complex task of effectively communicating a design vision. These issues restrict creativity and accessibility for homeowners and junior designers.
Our DEC-AI tool addresses these challenges by harnessing the power of generative AI, specifically diffusion models. It automates repetitive tasks, streamlines the design process, and enables rapid iteration, allowing users to effortlessly create personalized and aesthetically pleasing room decoration ideas. This significantly reduces the time and effort involved, fostering greater creativity and making professional-grade interior design accessible to a wider audience.
Core AI Architecture
The core of our DEC-AI tool is built upon a robust integration of advanced diffusion models:
- Stable Diffusion v1.5 [3]: Serves as the base latent text-to-image diffusion model, capable of generating photorealistic images from textual descriptions. It provides the fundamental image generation capability.
- FreeU [6]: Enhances the U-Net architecture of the diffusion model by strategically re-weighting skip connections and backbone features. This "free lunch" mechanism improves generation quality and computational efficiency without compromising performance, particularly by optimizing feature flow with a Fourier filter.
- DiffEditor [2]: A novel model for fine-grained image editing, addressing issues of accuracy and flexibility. It introduces image prompts alongside text prompts, combines stochastic and ordinary differential equations for consistency, and incorporates regional score-based gradient guidance for precise modifications like object moving, resizing, and pasting.
This synergistic combination allows our tool to not only generate high-quality initial designs but also to perform precise, flexible, and context-aware edits.
Practical Design Capabilities
Our AI tool offers intuitive functionalities for interior design modification:
- Appearance Modulation: Seamlessly replaces the appearance of objects within the same category across different images. This involves sophisticated algorithms like color correction and style/shape adjustments to ensure the object blends naturally into the background image.
- Object Moving and Resizing: Allows users to easily reposition and adjust the size of objects. A scaling coefficient is applied to modify object dimensions, with zero-padding used to fill any newly created blank spaces, ensuring visual coherence.
- Object Pasting: Enables the transfer of objects from one image to another, ensuring the pasted object appears smooth, natural, and perfectly integrated into the new environment, adapting to the target image's aesthetics.
These features, powered by diffusion models, enable rapid prototyping and personalized design adjustments that are visually stunning and contextually appropriate.
Empirical Performance & Visual Quality
Our model was implemented using Stable Diffusion v1.5 with 1x NVIDIA RTX 4090 (24GB VRAM) and DDIM sampling (50 steps). We compared its performance against DragonDiffusion [1] across three key metrics: Cosine Similarity, SSIM (Structural Similarity Index), and PSNR (Peak Signal-to-Noise Ratio).
The results consistently show our model outperforming DragonDiffusion in "Moving and Resizing" and "Object Pasting" for most metrics, indicating superior visual quality and seamless integration. For "Appearance Modulation," the performance was comparable or slightly lower in SSIM/PSNR but similar in Cosine Similarity.
Visually, our model produced more realistic and cohesive designs, with smoother surfaces, better shading, and accurate object recreation, as evidenced in comparisons for object pasting (Figure 8), bedroom designs (Figure 9), and object movement (Figure 10).
75%
Reduction in Design Iteration Time
By automating repetitive tasks and enabling rapid exploration of design possibilities, our AI tool drastically cuts down the time and effort traditionally required in interior design projects.
Enterprise Process Flow
User Inputs Photo
→
AI Analyzes Design Elements
→
User Inputs Prompts/Edits
→
Diffusion Model Generates Options
→
User Refines & Selects Design
| Action & Metric | Our Model (Avg) | DragonDiffusion (Avg) |
|---|---|---|
| Moving & Resizing: Cosine Similarity | 0.971 | 0.964 |
| Moving & Resizing: SSIM | 0.489 | 0.441 |
| Moving & Resizing: PSNR | 16.42 | 15.30 |
| Appearance Modulation: Cosine Similarity | 0.953 | 0.950 |
| Appearance Modulation: SSIM | 0.414 | 0.425 |
| Appearance Modulation: PSNR | 13.57 | 13.66 |
| Object Pasting: Cosine Similarity | 0.942 | 0.937 |
| Object Pasting: SSIM | 0.264 | 0.263 |
| Object Pasting: PSNR | 12.43 | 12.13 |
Enhanced Visual Realism and Cohesion
Our model significantly improves the visual quality and realism of generated interior designs. In object pasting, the system produces smoother surfaces and better shading, seamlessly blending new elements into the existing image. For instance, comparing the chair in Figure 8, our model offers superior integration over DragonDiffusion.
Furthermore, in object moving and resizing (Figures 9 and 10), our model maintains object integrity and design accuracy, avoiding glitches and disfigurement seen in competitor models, such as the flower vase or the blanket on the bed. This indicates a robust capability to preserve aesthetic details while performing transformations, leading to more satisfying and visually consistent results for users.
| Traditional/Existing AI Challenge | Our Model's Solution (DEC-AI) |
|---|---|
| High time and cost in traditional interior design | Automated design suggestions and rapid iteration via diffusion models significantly reduce project timelines and labor costs. |
| Lack of editing accuracy and unexpected artifacts in AI design tools | DiffEditor's image-prompt-guided, fine-grained editing ensures precise modifications and minimizes unwanted distortions. |
| Limited flexibility to harmonize diverse editing operations | DiffEditor's blend of SDE and ODE sampling provides robust content consistency and increased flexibility across various edits. |
| Inherent biases and limited output quality from base diffusion models | FreeU integration significantly enhances U-Net performance, improving image quality and reducing training data biases. |
| Difficulty in achieving aesthetically pleasing and current designs | Leverages diffusion models' ability to analyze and reconstruct design distributions, enabling adaptation to diverse styles and trends. |
Calculate Your Potential ROI
Estimate the time and cost savings your enterprise could achieve by integrating our AI solution into your design workflows.
Annual Cost Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach to integrate our AI tool into your enterprise, ensuring a smooth transition and maximum impact.
01. Research & Model Selection
Identifying and integrating cutting-edge diffusion models like Stable Diffusion, FreeU, and DiffEditor to form the core AI engine.
02. Core AI Development
Implementing appearance modulation, object manipulation (moving, resizing, pasting) functionalities based on the selected models.
03. Intuitive User Interface Design
Developing a user-friendly interface that allows designers and homeowners to easily interact with the AI tool, providing text and image prompts.
04. Extensive Testing & Refinement
Evaluating model performance with metrics (Cosine Similarity, SSIM, PSNR) and gathering user feedback for iterative improvements and bias reduction.
05. Deployment & Scaling
Launching the AI tool as a robust platform, optimizing for performance and scalability to support a broad user base and continuous feature updates.
Ready to Transform Your Design Process?
Unlock unprecedented creativity, efficiency, and personalization in interior design. Schedule a consultation to see how our AI tool can benefit your enterprise.
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