International Journal For Multidisciplinary Research

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Room Interior Generation Using Stable Diffusion Models

Author(s) Shubh Sahu, Shreya Verma, Om Patil, Aryan Ghogare, Utkarsh Kumar Sharma
Country India
Abstract The present paper puts forward an innovative method for the fine tuning of Stable Diffusion with the use of Dreambooth, which is a very fast technique in the image generation task. In particular, our approach covers the topic of room interior generation. It offers a quick option to instruct the algorithm with the most recent concepts, without the need to retrain it. Using a shortened list of instance and class prompts as examples we introduce a revolutionary training pipeline where class and instance information interacts to order a model's learning process. By conducting elaborate trials where we demonstrate that our methodology out-perform the competitors in the realistic visualization, which is based on the given theme. We use an extensive evaluation process to prove the effectiveness of the method on many datasets, which can ensure its generalization capacity to unseen various layouts of the rooms and their interior design. On one hand, precise ablation studies are carried out in order to evaluate the influence of the distinguished components in the given model. This paper shows the encompassing work of Dreambooth as a tool of choice for the personalized room interior synthesis. The new possibilities to fine-tune the generative models are also becoming a subject for futurological research in the interior design area.
Keywords Interior Modification, Stable Diffusion, DreamBooth
Field Engineering
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-18
Cite This Room Interior Generation Using Stable Diffusion Models - Shubh Sahu, Shreya Verma, Om Patil, Aryan Ghogare, Utkarsh Kumar Sharma - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.18627
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.18627
Short DOI https://doi.org/gtvt4h
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