  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-tuned
in specific domains based on the open-source SD model, such as in anime,
character portraits, etc. However, there are few specialized models in certain
domains, such as interior design, which is attributed to the complex textual
descriptions and detailed visual elements inherent in design, alongside the
necessity for adaptable resolution. Therefore, text-to-image models for
interior design are required to have outstanding prompt-following capabilities,
as well as iterative collaboration with design professionals to achieve the
desired outcome. In this paper, we collect and optimize text-image data in the
design field and continue training in both English and Chinese on the basis of
the open-source CLIP model. We also proposed a fine-tuning strategy with
curriculum learning and reinforcement learning from CLIP feedback to enhance
the prompt-following capabilities of our approach so as to improve the quality
of image generation. The experimental results on the collected dataset
demonstrate the effectiveness of the proposed approach, which achieves
impressive results and outperforms strong baselines.

Computer Vision and Pattern Recognition