This study investigates human-computer interface generation based on diffusion models to overcome the limitations of traditional template-based design and fixed rule-driven methods. It first analyzes the key challenges of interface generation, including the diversity of interface elements, the complexity of layout logic, and the personalization of user needs. A generative framework centered on the diffusion-reverse diffusion process is then proposed, with conditional control introduced in the reverse diffusion stage to integrate user intent, contextual states, and task constraints, enabling unified modeling of visual presentation and interaction logic. In addition, regularization constraints and optimization objectives are combined to ensure the rationality and stability of the generated interfaces. Experiments are conducted on a public interface dataset with systematic evaluations, including comparative experiments, hyperparameter sensitivity tests, environmental sensitivity tests, and data sensitivity tests. Results show that the proposed method outperforms representative models in mean squared error, structural similarity, peak signal-to-noise ratio, and mean absolute error, while maintaining strong robustness under different parameter settings and environmental conditions. Overall, the diffusion model framework effectively improves the diversity, rationality, and intelligence of interface generation, providing a feasible solution for automated interface generation in complex interaction scenarios.

翻译：本研究针对传统基于模板的设计与固定规则驱动方法在界面生成中的局限性，探索了基于扩散模型的人机界面生成方法。首先分析了界面生成的关键挑战，包括界面元素的多样性、布局逻辑的复杂性以及用户需求的个性化特征。随后提出了以扩散-逆扩散过程为核心的生成框架，通过在逆扩散阶段引入条件控制，融合用户意图、上下文状态与任务约束，实现了视觉呈现与交互逻辑的统一建模。此外，结合正则化约束与优化目标，确保生成界面的合理性与稳定性。在公开界面数据集上进行了系统性实验评估，包括对比实验、超参数敏感性测试、环境敏感性测试与数据敏感性测试。结果表明，所提方法在均方误差、结构相似性、峰值信噪比与平均绝对误差等指标上均优于代表性模型，且在不同参数设置与环境条件下均保持较强的鲁棒性。总体而言，扩散模型框架有效提升了界面生成的多样性、合理性与智能化水平，为复杂交互场景下的自动化界面生成提供了可行解决方案。