Local Intrinsic Dimension Unveils Hallucinations in Diffusion Models
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917464503222272 |
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| author | Sobieski, Bartlomiej Tivnan, Matthew Płudowski, Dawid Włodarczyk, Michał Jan Jin, Pengfei Biecek, Przemyslaw Li, Quanzheng |
| author_facet | Sobieski, Bartlomiej Tivnan, Matthew Płudowski, Dawid Włodarczyk, Michał Jan Jin, Pengfei Biecek, Przemyslaw Li, Quanzheng |
| contents | Diffusion models are prone to generating structural hallucinations - samples that match the statistical properties of the training data yet defy underlying structural rules, resulting in anomalies like hands with more than five fingers. Recent research studied this failure mode from several viewpoints, offering partial explanations to their occurrence, such as mode interpolation. In this work, we propose a complementary perspective that treats hallucinations as instabilities on the model-induced manifold. We begin by showing that a hallucination filter based on such instabilities matches or exceeds the performance of the recently proposed temporal one. By tracing the source of these instabilities, we identify local intrinsic dimension (LID) as their primary driver and propose Intrinsic Quenching (IQ), a direct corrective mechanism that deflates it to alleviate hallucinations. IQ consistently outperforms standard hallucination reduction baselines across a wide array of benchmarks and offers a highly promising solution for enforcing anatomical consistency in downstream medical imaging tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05026 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Local Intrinsic Dimension Unveils Hallucinations in Diffusion Models Sobieski, Bartlomiej Tivnan, Matthew Płudowski, Dawid Włodarczyk, Michał Jan Jin, Pengfei Biecek, Przemyslaw Li, Quanzheng Computer Vision and Pattern Recognition Artificial Intelligence Diffusion models are prone to generating structural hallucinations - samples that match the statistical properties of the training data yet defy underlying structural rules, resulting in anomalies like hands with more than five fingers. Recent research studied this failure mode from several viewpoints, offering partial explanations to their occurrence, such as mode interpolation. In this work, we propose a complementary perspective that treats hallucinations as instabilities on the model-induced manifold. We begin by showing that a hallucination filter based on such instabilities matches or exceeds the performance of the recently proposed temporal one. By tracing the source of these instabilities, we identify local intrinsic dimension (LID) as their primary driver and propose Intrinsic Quenching (IQ), a direct corrective mechanism that deflates it to alleviate hallucinations. IQ consistently outperforms standard hallucination reduction baselines across a wide array of benchmarks and offers a highly promising solution for enforcing anatomical consistency in downstream medical imaging tasks. |
| title | Local Intrinsic Dimension Unveils Hallucinations in Diffusion Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2605.05026 |