Design Dreamer: ControlNet Based Generative AI Application for Interior Designing

Param Dhingana¹ , Vinamra Khandelwal² , Sudeep Ganguly³ , Dipti Chauhan⁴

1. Dept. of Artificial Intelligence and Data Science, Prestige Institute of Engineering Management & Research, Indore, India.
2. Dept. of Artificial Intelligence and Data Science, Prestige Institute of Engineering Management & Research, Indore, India.
3. Dept. of Artificial Intelligence and Data Science, Prestige Institute of Engineering Management & Research, Indore, India.
4. Dept. of Artificial Intelligence and Data Science, Prestige Institute of Engineering Management & Research, Indore, India.

Section:Research Paper, Product Type: Journal-Paper
Vol.12 , Issue.3 , pp.1-7, Jun-2024

Online published on Jun 30, 2024

Copyright © Param Dhingana, Vinamra Khandelwal, Sudeep Ganguly, Dipti Chauhan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The emergence of state-of-the-art technologies, which powers the process of Generative AI, is now altering the interior design landscape. This study starts with Design Dreamer, the latest application among all, which gives users an opportunity to dream about and customize their living space in a remarkable way. Design Dreamer makes it possible to implement the most advanced systems in the ControlNet family that provide best-in-class immersive and interactive experience for the end users. Users share their room photos, give a purpose of their preferred design styles and room type then they get digital replays, which are AI-generated, and they are capable of visually reflecting what a person wants to see in their room. Our approach involves a multi-step procedure, uploading images, choosing style preferences, using the ControlNet Hough Model by calling through Replicate API, and efficiently retrieving the generated image when users pose a design. Key findings of our research demonstrate that Design Dreamer significantly enhances image quality and provides a unique output each time. The significance of this research lies in its potential to leverage Diffusion and ControlNet based models for their applications in Interior Design industry.

Key-Words / Index Term :
Generative AI, Interior Design, State-of-The-Art Technology, Image-Processing, Text-to-Image Generation, Image-to-Image Generation, Image-to-Image Transformation, Stable-Diffusion, ControlNet, AI-Powered Design Tools, Diffusion Models

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