A Review on the Consistency of AI ‐Generated Images for Interior Design Rendering
Shuangyang Tan, Shasha Chen · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · 2026
ABSTRACT With the advancement of generative artificial intelligence, AI‐generated image methods have experienced rapid development in interior design rendering. These methods enable the rapid generation of creative interior design renderings but accompany uncertainties in the generated images, which challenges the requirements of design renderings. Researchers have explored various approaches to enhance consistency in AI‐generated images. This review summarizes the methods and roles of generative artificial intelligence in interior design compared with traditional techniques and the relationships between the AI‐generated images and controlled parameters such as the workflow nodes, prompts, and models. Image consistency is a critical factor in the design generation process; their methods to control interior design renderings include prompts, image‐to‐image, ControlNet, IP‐Adapter, LoRA, SAM, and so forth. Much evidence reveals that ControlNet could control the positional relationship, IP‐Adapter could influence different styles, LoRA could excel in customized styles, and SAM could modify local regions. This article is categorized under: Technologies > Artificial Intelligence Commercial, Legal, and Ethical Issues > Fairness in Data Mining