Score Accuracy Along the Forward Diffusion Does Not Certify Numerical Stability in Diffusion Sampling

Researchers demonstrate that high accuracy in score-based diffusion models during forward diffusion does not guarantee numerical stability during the reverse sampling process, revealing a critical gap between training metrics and inference reliability. This finding has direct implications for enterprises deploying diffusion models in production, particularly in regulated sectors where model stability and predictability are essential for compliance and safety.

Researchers demonstrate that high accuracy in score-based diffusion models during forward diffusion does not guarantee numerical stability during the reverse sampling process, revealing a critical gap between training metrics and inference reliability. This finding has direct implications for enterprises deploying diffusion models in production, particularly in regulated sectors where model stability and predictability are essential for compliance and safety.

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