专为室内设计打造的扩散模型，生成效果更准更美
RoomDiffusion: A Specialized Diffusion Model in the Interior Design Industry
- 从零构建数据流水线，结合多阶段微调与模型融合提升精度
- 在20+专业设计师评估中，美学、准确率和效率均超越Stable Diffusion等开源模型
- 针对家具重复、风格不准等设计痛点优化，适合家装设计场景
文本到图像的扩散模型虽在视觉内容生成上取得显著进展，但在室内设计等专业领域的应用仍不充分。本文提出RoomDiffusion，一种专为室内设计行业量身定制的开创性扩散模型。我们从零构建完整数据流水线，用于数据更新与迭代优化；采用多视角训练、多阶段微调及模型融合技术，提升生成结果的视觉表现力与准确性；最后通过潜在一致性蒸馏方法，实现模型压缩与推理加速。相较通用模型，RoomDiffusion有效解决时尚感缺失、家具重复率高、风格偏差等问题。基于20余位专业设计师的综合评估，其在美学、准确性和效率方面均达到行业领先水平，全面超越Stable Diffusion、SDXL等现有开源模型。
原文摘要 · Abstract (English)
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.
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