RoomDiffusion: A Specialized Diffusion Model in the Interior Design Industry
This work provides a solution for the interior design industry by improving image generation, though it is incremental as it adapts existing diffusion techniques to a specific domain.
The authors tackled the problem of generating interior design images by developing RoomDiffusion, a specialized diffusion model that addresses issues like lack of fashion and high furniture duplication, achieving industry-leading performance in aesthetics, accuracy, and efficiency as validated by over 20 professional evaluators.
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.