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With the open-sourcing of text-to-image models (T2I) such as stable diffusion(SD) and stable diffusion XL (SD-XL), there is an influx of models fine-tunedin specific domains based on the open-source SD model, such as in anime,character portraits, etc. However, there are few specialized models in certaindomains, such as interior design, which is attributed to the complex textualdescriptions and detailed visual elements inherent in design, alongside thenecessity for adaptable resolution. Therefore, text-to-image models forinterior design are required to have outstanding prompt-following capabilities,as well as iterative collaboration with design professionals to achieve thedesired outcome. In this paper, we collect and optimize text-image data in thedesign field and continue training in both English and Chinese on the basis ofthe open-source CLIP model. We also proposed a fine-tuning strategy withcurriculum learning and reinforcement learning from CLIP feedback to enhancethe prompt-following capabilities of our approach so as to improve the qualityof image generation. The experimental results on the collected datasetdemonstrate the effectiveness of the proposed approach, which achievesimpressive results and outperforms strong baselines.

iDesigner: A High-Resolution and Complex-Prompt Following Text-to-Image Diffusion Model for Interior Design