字体设计团队和AI生成研究者可以关注这个方案——它解决了少样本字体生成中全局与局部细节难以兼顾的痛点，直接提升字形质量和风格一致性。
少样本字体生成需要同时保证全局结构完整性和局部风格细节。现有方法要么依赖全局内容-风格建模（鲁棒但解耦不完美），要么强调局部组件建模（捕捉细节但依赖局部先验和参考覆盖）。SmartFont 提出通过多级条件分配来组织互补但有偏的全局和局部条件，结合扩散模型与弱监督局部校正专家，实现全局-局部平衡。实验表明，该方法在字形质量和局部细节保真度上优于现有方法。
SmartFont: Dynamic Condition Allocation for Few-Shot Font Generation
Few-shot font generation simultaneously requires global structural completeness and fine-grained local style fidelity. Existing methods usually either rely on global content-style modeling, which is robust but imperfectly disentangled, or emphasize component/local modeling, which captures fine details but relies heavily on local priors and reference coverage. We argue that the key challenge is not merely to learn purer conditions, but to organize complementary yet biased global and local conditions through multi-level allocation during generation. To this end, we propose SmartFont, a diffusion-based few-shot font generation framework that combines global content-style generation with weakly supervised local corrective experts. The local branch performs semantic-spatial allocation by learning expert-wise local concepts and semantically meaningful spatial maps under weak component supervision, enabling fine-grained correction without requiring explicit component-conditioned inference. On top of this, a denoising-state condition allocation module adaptively weights global content, global style, and local corrective feature across timesteps and injection blocks. Extensive experiments show that SmartFont achieves better global-local balance, improves glyph quality and local detail fidelity.