Hallucinations in diffusion models are driven by local intrinsic dimension instabilities on the manifold, which Intrinsic Quenching corrects by deflating it.
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DiffRGD is a plug-and-play inference-time guidance method that casts each diffusion sampling step as constrained optimization on a spherical manifold and solves it with Riemannian gradient descent to preserve the Gaussian latent structure.
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Local Intrinsic Dimension Unveils Hallucinations in Diffusion Models
Hallucinations in diffusion models are driven by local intrinsic dimension instabilities on the manifold, which Intrinsic Quenching corrects by deflating it.
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DiffRGD: An Inference-Time Diffusion Guidance Through Riemannian Gradient Descent
DiffRGD is a plug-and-play inference-time guidance method that casts each diffusion sampling step as constrained optimization on a spherical manifold and solves it with Riemannian gradient descent to preserve the Gaussian latent structure.