Rhythm: Synchronised Corruptions for Continuous Tabular Diffusion

Mainstream tabular diffusion methods use mixed-noise formulations that separately handle discrete and continuous features. While effective at capturing overall distribution trends, these methods have limited control over constrained data generation. We propose Rhythm, a new tabular diffusion paradigm built entirely on a continuous-noise formulation, enabling theory-grounded conditional generation via training-free guidance. Rhythm incorporates a key property of mixed-noise models: when noise is added, different features deviate from their ground-truths at synchronised rates, making all features equally informative. We theoretically characterise this property as corruption alignment and incorporate it into Rhythm by computing feature-wise noise schedules, via an offline, training-free algorithm. Results on diverse datasets show that Rhythm matches existing models on unconditional generation and significantly outperforms mixed-noise methods under conditional generation, highlighting the benefits of continuous diffusion for high-quality and controllable generation. Code: https://anonymous.4open.science/r/BestDiffusion-3B12/README.md.