Base composition confounds classifier-free guidance in DNA diffusion models

Designing short DNA sequences that act as enhancers selectively in a target cell type is a central goal in generative genomics. Diffusion models have recently shown promise for this task, typically using classifier-free guidance to steer generation toward the target cell type. We find that stronger classifier-free guidance can raise predicted chromatin accessibility, a proxy for regulatory activity, while shifting base composition away from that of real sequences and distorting motif frequencies. We introduce composition-neutral guidance (CNG), which subtracts the mean guidance logit update across positions for each base. This removes sequence-wide base preferences from the update without retraining. Applied to BaseDiff, our 21.6M-parameter convolutional discrete diffusion model, CNG reduces composition shifts and improves agreement with real motif frequencies and co-occurrence patterns with little change in predicted accessibility. We compare generators across guidance scales using their accessibility–quality trade-off curves. Across two datasets, BaseDiff sampled with CNG achieves larger trade-off areas than DNA-Diffusion and D3, despite using fewer than one quarter of their parameters.