Worked on the HabanaAI/optimum-habana-fork repository to deliver an updated measurement dataset for Stable Diffusion XL, focusing on enhancing performance evaluation on Habana hardware. The approach involved refreshing the .npz measurement data with carefully tuned quantization parameters and updated performance metrics, ensuring accurate benchmarking and alignment with optimization workflows. Leveraged Python for data handling and quantization, emphasizing reproducibility and traceability through Git-based workflows. This work improved the consistency of benchmarking results, enabling more reliable performance comparisons and optimization cycles. The update addressed the need for precise evaluation tools tailored to Habana’s platform, supporting ongoing machine learning and model optimization efforts.