Story-Adapter is an iterative framework designed specifically for visualizing long-form stories without the need for training. It optimizes the image generation process through an iterative paradigm and a global reference cross-attention module, maintaining semantic coherence in the story while reducing computational costs. The significance of this technology lies in its ability to generate high-quality, detail-rich images within long narratives, addressing the challenges faced by traditional text-to-image models in visualizing extended stories, such as semantic consistency and computational feasibility.