Super-resolution is a critical area in image processing, dealing with the resolution enhancement of an image. The rather new technique using generative adversarial networks has made a huge leap into realistic high-resolution results. A problem that exists with ESRGAN is that it is not ideal for handling unpredictable and complex real-world degradations. Real-ESRGAN addresses this issue by extending ESRGAN with a high-order degradation model trained on synthetic data, which better simulates the real world. It also uses spectral normalization for the discriminator UNet so that a structure with details can be held and artifacts reduced in real-world applications. Despite the improvements elaborated in previous works, generalization to all kinds of real-world data remains a big challenge. In this work, we present different techniques to significantly improve Real-ESRGAN’s performance and usability. Tile padding and pre-padding techniques are presented in this article, which are notable contributions since they guarantee smooth, high resolution outputs, while minimizing tiling artifacts. Alongside these, the processing efficiency is significantly increased by optimizing data handling with the integration of a PrefetchReader. Additionally, to guarantee smoother operation and robustness in a variety of super-resolution tasks, better file management techniques and error handling mechanisms have been incorporated. All of these improvements increase Real-ESRGAN’s practicality and dependability in various real-world scenarios.