2K-Characters-10K-Stories: A Quality-Gated Stylized Narrative Dataset with Disentangled Control and Sequence Consistency

Sequential identity consistency under precise transient attribute control remains a long-standing challenge in controllable visual storytelling. Existing datasets lack sufficient fidelity and fail to disentangle stable identities from transient attributes, limiting structured control over pose, expression, and scene composition and thus constraining reliable sequential synthesis. To address this gap, we introduce $\mathbf{\text{2K-Characters-10K-Stories}}$, a multi-modal stylized narrative dataset of $\mathbf{\text{2,000}}$ uniquely stylized characters appearing across $\mathbf{\text{10,000}}$ illustration stories. It is the first dataset that pairs large-scale unique identities with explicit, decoupled control signals for sequential identity consistency. We introduce a $\mathbf{\text{Human-in-the-Loop}}\mathbf{\text{pipeline}}\mathbf{\text{(HiL)}}$ that leverages expert-verified character templates and LLM-guided narrative planning to generate highly-aligned structured data. A $\mathbf{\text{decoupled}}\mathbf{\text{control}}$ scheme separates persistent identity from transient attributes – pose and expression – while a $\mathbf{\text{Quality-Gated}}\mathbf{\text{loop}}$ integrating MMLM evaluation, Auto-Prompt Tuning, and Local Image Editing enforces pixel-level consistency. Extensive experiments demonstrate that models fine-tuned on our dataset achieves performance comparable to closed-source models in generating visual narratives.