家庭监控场景下多阶段图像生成算法框架的设计与实现

he role of home cameras is evolving from traditional surveillance tools into multifunctional nodes within smart home ecosystems. This paper proposes a multi-stage image generation system framework for home scenarios, implementing high-quality anime-style transformation through modular design and deep learning technology. The framework comprises two core mechanisms: a parallel computing framework and a multi-model collaborative inference mechanism. The parallel computing framework facilitates efficient resource allocation through priority-based heterogeneous resource scheduling and dynamic batch processing optimization, while the multi-model collaborative inference mechanism integrates three core components - state monitoring, model scheduling, and quality assessment - to ensure seamless model collaboration. In its implementation, the system adopts a six-layer architecture consisting of user interaction, task processing, image preprocessing, core generation, and post-processing layers, connected through standardized interfaces. The preprocessing stage incorporates multiple optimization modules, including super-resolution, low-light enhancement, and edge detection, generating high-quality input features for the generation phase. The generation stage enables customizable style transformation by integrating Stable Diffusion, ControlNet, and LoRA technologies. This approach not only delivers superior image generation results but also provides research insights and technical support for innovating home camera imaging functions, paving the way for future smart home applications.