企业级私有云 AI 模型 - 安全部署和数据保护方案

LoRA微调模型，一种高效的参数微调技术。通过低秩适应方法，在不重新训练整个模型的情况下，实现对特定任务的高效适配，大幅减少计算资源需求。

LoRA fine-tuning model, an efficient parameter tuning technique. Through low-rank adaptation methods, it achieves efficient adaptation to specific tasks without retraining the entire model, significantly reducing computational resource requirements.

ControlNet条件控制模型，实现精确控制AI图像生成的模型。通过额外的条件输入，如边缘图、姿态图等，可以精确控制生成图像的结构和布局，提升生成结果的可控性。

ControlNet conditional control model, a model achieving precise control of AI image generation. Through additional conditional inputs, such as edge maps, pose diagrams, etc., it enables precise control of the structure and layout of generated images, enhancing the controllability of generation results.

无显卡运行AI模型的方法，提供CPU优化和云端协作方案。通过CPU优化配置和云端算力结合，即使没有独立显卡也能运行AI模型，实现经济高效的AI体验。

Methods for running AI models without a dedicated GPU, providing CPU optimization and cloud collaboration solutions. Combining CPU optimization configurations with cloud computing power, allowing AI models to run even without a dedicated GPU, achieving cost-effective AI experience.