arXiv:2510.05356v2 Announce Type: replace-cross Abstract: Hallucinations in diffusion models are samples with structural inconsistencies that can emerge due to the excessive smoothing of the learned score function, which in turn leads to interpolations between modes of the data distribution. Since semantic interpolations are often desirable and contribute to sample diversity, we believe that a nuanced and targeted solution is required to address diffusion model hallucinations. In this work, we introduce Dynamic Guidance, which mitigates hallucinations by selectively sharpening the score functi
The rapid advancement and widespread adoption of diffusion models highlight the increasing criticality of addressing their inherent limitations, such as hallucinations, to ensure practical utility and trustworthiness.
Improved reliability of diffusion models through hallucination mitigation directly enhances their applicability across various industries, from content generation to scientific research, making AI outputs more dependable.
The introduction of Dynamic Guidance provides a targeted engineering solution to a known flaw in diffusion models, potentially accelerating their deployment in sensitive applications by improving output quality and reducing errors.
- · AI content creators
- · Diffusion model developers
- · AI-reliant industries
- · Image and video generation platforms
- · AI models without hallucination mitigation
- · Companies relying on unreliable AI outputs
Diffusion models produce more consistent and structurally sound outputs, reducing the need for extensive human post-processing.
Increased trust in AI-generated content leads to broader adoption of diffusion models in critical applications, such as medical imaging or engineering design.
The reduced risk of 'AI anomolies' could accelerate the integration of generative AI into autonomous systems and decision-making processes, particularly in domains where accuracy is paramount.
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