Recently, visual generative models have made significant progress in single-image synthesis while facing the dual challenges of multi-subject fine-grained consistency and cross-frame style coordination in serialized storyline generation. To address this, we propose an innovative framework named Absolute Story, which enhances subject consistency and style coherence through fine-grained feature alignment and context-aware generation. The framework consists of three core components: (1) We propose Related Subject Selection that utilizes a vision language model to map textual descriptions with reference images, constructing subject-focused masks; (2) We design Storyline ReferenceNet, which integrates a Plot Fusion Module, to encode fine-grained visual features from reference storyline, ensuring spatiotemporal consistency of subject and style; (3) We develop a Story Consistency Attention Block to achieve context-consistency generation by leveraging fine-grained and subject-focused features. Experiments verified that our framework outperformed existing advanced methods in key metrics (FID \(\downarrow \) 7.48%, CLIP-T \(\uparrow \) 3.04 % at most). The visualization results indicate that our approach leads in terms of subject consistency and style coherence.