Two-Stage Score Estimation for Group-Specific Diffusion Models

The paper introduces a statistical framework for diffusion transfer in generative modeling across related distributions. It analyzes a shared pooled score and a target‑specific offset, deriving convergence rates that separate the learning costs of these components. The authors prove that under certain regularity assumptions the offset remains bounded and jointly regular as diffusion time approaches zero, enabling polynomially vanishing lower time cutoffs and total‑variation guarantees for diffusion models as density estimators, with synthetic simulations and image benchmarks illustrating the approach.

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• diffusion
• benchmarks