During March 2025, this developer focused on stabilizing NPU hardware support in the luanfujun/diffusers repository. Addressing a compatibility issue in the get_1d_rotary_pos_embed function, they resolved an aclnnRepeatInterleaveIntWithDim error by ensuring frequency tensors were converted to float type on NPU devices. This targeted hotfix, implemented in Python and leveraging deep learning and model embedding expertise, improved cross-hardware deployment reliability without introducing new features. The work demonstrated careful attention to maintainability and minimal code intrusion, resulting in enhanced stability and reduced runtime errors for NPU-accelerated environments. The contribution reflects depth in hardware-aware deep learning engineering.