Refine, Control and Distill: A Text-to-Image Framework for Faithful Image Generation.

Abstract

While text-to-image diffusion models exhibit outstanding results, they struggle to faithfully generate key subjects with corresponding attributes in prompts, challenges known as catastrophic neglect and attribute binding. Previous works typically utilize attention adjustments to solve the above problems, whereas we observe that they may still generate unfaithful images. In this paper, we carefully analyze the text-to-image process and pinpoint three pivotal bottlenecks that hinder image faithful generation: (1) unequal responses of neglected subjects in text embedding, (2) competition and entanglement between subjects' attention, and (3) suboptimal quality of intermediate features from U-Net. Based on the aforementioned observations, we propose a Refine, Control, and Distill (RCD) framework built upon the stable diffusion model to alleviate the negative effects raised by the bottlenecks mentioned above, respectively. Specifically, we achieve the above goals through a text embedding refinement module, three region-level attention control losses, and self-distillation of intermediate semantic features in the denoising process. Our approach exhibits promising capability in generating faithful and high-quality images and outperforms state-of-the-art methods through extensive quantitative and qualitative evaluations on recent advanced base diffusion models.