Lung cancer histopathological diagnosis faces considerable challenges due to equipment-induced variations and staining inconsistencies across institutions. Although Weakly Supervised Object Localization (WSOL) and Source-Free Domain Adaptation (SFDA) methodologies have been investigated to address these issues, existing approaches lack sufficient optimization for Whole Slide Imaging (WSI), resulting in suboptimal performance. Faced with these limitations, we propose a novel SFDA framework tailored for WSOL in histopathology. Specifically, we make the following contributions: (1) We systematically compare SFDA approaches on multiple public histopathology datasets under WSOL settings, and demonstrate that generative-based black-box methods achieve the best performance in cross-domain localization and classification tasks. (2) We design a diffusion-driven training framework that integrates Denoising Diffusion Probabilistic Models with hybrid Vision Transformer architectures, effectively preserving semantic fidelity while adapting to domain shifts. (3) Our model establishes state-of-the-art performance across six benchmark datasets, highlighting its superior generalizability and robustness in real-world multi-center clinical scenarios. The experimental evaluation demonstrates that our method achieves highest precision in the optimal target domain configuration, representing a substantial 30% improvement over previous SFDA approaches and providing a more robust AI-assisted tool for advanced histopathological examination.