Jianhan Mei developed a scalable Synthetic Data Iterator for the AI-Hypercomputer/maxdiffusion repository, enabling both testing and training workflows without reliance on real datasets. Using Python and JAX, Jianhan designed a modular, configuration-driven data generation module that supports customizable synthetic data dimensions for WAN and FLUX models. This approach reduced data provisioning requirements and accelerated experimentation cycles, while improving test reproducibility and safety. The implementation included comprehensive documentation and usage examples, reflecting attention to maintainability and usability. Jianhan’s work demonstrated depth in configuration management and cross-model integration, delivering a robust solution for scalable, efficient, and safer machine learning validation pipelines.