
Conditional diffusion models struggle with out-of-distribution (OOD) features, leading to structural hallucinations in generated images. Our method alleviates this issue by performing separate diffusion processes for in-distribution (IND) and OOD regions, followed by a fusion module to produce coherent outputs.


coremsg

reducing-structural-hallucination-in-conditional-diffusion-models-through-local-image-generation


Reducing Structural Hallucination in Conditional Diffusion Models through Local Image Generation


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StegoGAN leverages steganography to prevent the hallucination of spurious features when translating between image domains with non-bijective class mappings.


leveraging-steganography-to-improve-semantic-consistency-in-non-bijective-image-to-image-translation


Leveraging Steganography to Improve Semantic Consistency in Non-Bijective Image-to-Image Translation
