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

TL;DR

This paper introduces iDesigner, a specialized high-resolution text-to-image diffusion model tailored for interior design, emphasizing complex prompt understanding and iterative professional collaboration.

Contribution

It presents a novel fine-tuning strategy with curriculum and reinforcement learning to enhance interior design image generation from text prompts.

Findings

Outperforms strong baselines in interior design image generation

Achieves high-resolution, detailed images aligned with complex prompts

Demonstrates effective prompt-following capabilities in design context

Abstract

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.