Manual generation of Unified Modeling Language (UML) diagrams creates bottlenecks in agile development due to inconsistency and labor intensity. While Large Language Models (LLMs) offer generative capabilities, existing solutions suffer from data scarcity and inadequate evaluation tools. To address this, we present a dual-LLM pipeline integrating lightweight specification generation with reasoning-oriented code synthesis. Uniquely, this framework employs a weighted multimodal validation module utilizing diverse Vision-Language Models (VLMs) to assess diagrammatic fidelity. We further address the data shortage by releasing benchmark datasets comprising 5,000 Class and 3,000 Use Case diagrams. Empirical results demonstrate a 95.8\% rendering success rate for Class Diagrams and strong semantic alignment for Use Case models. By mitigating structural and behavioral reasoning conflicts, this research provides a replicable architecture and rigorous assessment methodology, establishing a robust foundation for scalable, AI-driven automation in software engineering.