arXiv:2505.13273v2 Announce Type: replace-cross Abstract: Large text-to-image diffusion models rarely expose reliable signals of when a prompt is likely to produce a poorly aligned generation, especially when training data is undisclosed. We study whether expert disagreement inside pre-trained mixture-of-experts (MoE) diffusion models can serve as a reliable estimate for epistemic uncertainty. We introduce EMoE, a training-free method that separates expert-specific computation paths at an early MoE layer, uses the same initial noise across paths, and measures variance among their latent repres
The rapid advancement of text-to-image diffusion models necessitates improved methods for understanding and mitigating generation uncertainty, especially with increasingly complex prompts.
This development allows for better quality control and reliability in AI-generated imagery, directly addressing a critical limitation in current diffusion models.
The ability to estimate epistemic uncertainty in pre-trained MoE diffusion models without additional training makes these systems more robust and interpretable.
- · AI developers
- · Creative industries using AI
- · Users of text-to-image models
- · Companies relying on opaque AI models
- · Manual quality assurance processes
Improved reliability and explainability of AI-generated content in various applications.
Accelerated adoption of diffusion models in sensitive or high-stakes domains due to increased trustworthiness.
New tooling and standards emerging for uncertainty quantification across different AI generative tasks.
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Read at arXiv cs.LG