imageX is an AICG (Artificial Intelligence Computer Graphics) application similar to Midjourney and Stable Diffusion, available on both Apple and Android platforms. It is an application that generates images from text, providing users with different styles of image generation effects through AI-driven artistic technology. This article discusses the principle of large-scale operation for imageX, aiming to help readers better understand how this application works when handling a significant number of tasks. imageX leverages artificial intelligence technology to achieve image generation. It undergoes extensive training using deep learning models to understand the input text and generate the corresponding images. In large-scale operations, imageX utilizes distributed computing and parallel processing techniques to improve efficiency and processing capacity. Firstly, imageX employs multiple computers for task distribution and collaborative work in large-scale operations. This distributed computing method divides a large task into smaller ones, with each computer handling a portion of the tasks. The results are then merged, significantly speeding up image generation and enhancing system scalability. Secondly, imageX employs parallel processing technology to handle multiple tasks simultaneously. Image generation is a computationally intensive task, and parallel processing allows multiple tasks to be executed concurrently, thereby increasing system throughput and efficiency. By effectively allocating tasks and managing resources, imageX fully utilizes the computational power of computers, accelerating the speed of image generation. Furthermore, imageX utilizes caching and preloading techniques in large-scale operations. Caching involves storing frequently accessed data and model parameters in computer memory, reducing data retrieval and transmission time and improving the efficiency of image generation. Preloading, on the other hand, involves loading data and model parameters that might be required in advance into memory for quick access and utilization. In addition to these technological approaches, imageX enhances work efficiency and image generation quality through algorithm optimization and model design. To meet the requirements of large-scale operations, imageX employs efficient algorithms and model structures that minimize computational and memory requirements while improving the accuracy and fine detail expression of the generated images. In summary, imageX achieves efficient image generation in large-scale operations through distributed computing, parallel processing, caching, preloading, and optimized algorithms. These technologies enhance the speed and quality of image generation while meeting the demands of large-scale task processing. With further advancements in AI technology, it is expected that imageX will continuously improve its performance in large-scale operations, providing users with even more impressive image generation experiences.