Kernel-based distributional discrepancy enables auditing of upstream training data in distilled one-step diffusion models by detecting preserved distributional alignment rather than per-instance memorization.
In this paper, these concepts are used in quantifying the distributional differences in Section 4 and Section
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Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models
Kernel-based distributional discrepancy enables auditing of upstream training data in distilled one-step diffusion models by detecting preserved distributional alignment rather than per-instance memorization.