想给医疗或表格类基准造数据的朋友可以看看，它把多张结构不同的表统一起来生成，不用再受单表限制了。
arXiv 论文提出 Cross-Tabular Data Generation（CTDG）两阶段框架，解决现有合成表格数据方法只能处理单一输入表的问题。第一阶段把多个异构原始表转换为列结构一致的统计表，保留边际分布和两两相关性。第二阶段用扩散 Transformer 学习统计表的结构模式并生成新统计表，再通过多元高斯采样和逆概率积分变换还原为原始表格。实验显示生成数据的保真度与多样性平衡良好，能支持不限数量的合成异构表生成。
Generating Benchmark Health Data Using a Tabular Diffusion Transformer
Cross-Tabular Data Generation (CTDG) seeks to learn a generative model from multiple heterogeneous tables and produce new synthetic tabular datasets. However, existing synthetic tabular data generation methods are largely restricted to single-input-table scenarios and struggle to effectively handle multiple heterogeneous tables with diverse feature sets. To address this limitation, we propose a two-stage framework for cross-tabular data generation. In the first stage, each heterogeneous raw table is transformed into a standardized statistical table with the same set of columns across all tables. Each statistical table captures the marginal distributions of the original columns and the pairwise correlations among them. In the second stage, a diffusion transformer model is trained to capture structural patterns across these homogeneous statistical tables and to generate synthetic statistical tables. Synthetic raw tables are subsequently reconstructed from the generated statistical tables via multivariate Gaussian sampling followed by an inverse probability integral transform. This two-stage CTDG framework enables the learning of a unified generative model from multiple heterogeneous tables and supports the generation of an unlimited number of realistic synthetic heterogeneous tables. Experimental results demonstrate high fidelity in the learned statistical representations and a favorable fidelity-diversity trade-off in the generated synthetic data, validating the effectiveness of the proposed approach.