Rapid advancements have been made in artificial intelligence applications recently, and generative models have prominently emerged as effective tools for domain transfer, image enhancement, and simulation. However, when dealing with large-scale gigapixel images, the use of traditional patch-based image aggregation methods introduces checkerboard or blocking artifacts, which compromises image quality and fidelity. Here we propose a parametric kernel that is specifically designed to target the underlying grid structure to mitigate these artifacts. The proposed parametric kernels are validated using three medical imaging modalities for three different generative model tasks, demonstrating improved visual fidelity and quantitative quality evaluation of the generated patch-aggregated images. The proposed method is versatile and compatible with various generative models, offering a robust framework for artifact reduction that can be seamlessly adjusted by modifying kernel parameters, and they can be directly applied and extended to other imaging modalities that employ large-scale images, such as astronomy and satellite imaging. The findings of this study have significant implications for medical imaging applications: by mitigating aggregation artifacts, our approach enhances the overall quality of medical images synthesized with generative models, which is crucial for accurate clinical assessment and subsequent image analysis. Furthermore, the proposed kernels provide a general formulation that can be extended to unpaired tasks, semantic segmentation, classification networks, and other large field-of-view imaging applications.