Lijun Liu
To address the problems in interface icon generation, such as a lack of structural expression, difficulty in maintaining style consistency, and limited capability for multi-condition generation, this paper proposes a structure-aware intelligent icon generation method named IconDiff, which is based on a diffusion model. Based on the classical diffusion framework, this method introduces a structure-guided branching mechanism and a multimodal condition fusion mechanism to achieve collaborative modeling of text semantics, style features, and attribute information. It also enhances boundary clarity and semantic identifiability by designing an icon-specific loss function. At the same time, a multidimensional annotation data set containing 268000 icon samples is constructed, and a special evaluation index system for icon tasks is designed. Under a unified experimental setup, compared with various mainstream generation methods, the proposed method reduces the FID by approximately 25.2%, improves structural clarity by about 6.0%, enhances identifiability by about 6.8%, and increases style consistency by about 7.8%. In addition, ablation experiments verify the effectiveness of the key modules. Generalization and robustness analysis show that the model maintains stable performance even in the absence of semantic and style conditions. The research results show that the method in this paper has significantly improved the generation quality and controllability, and provides an effective solution for the automatic design of interface icons.