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📄 Abstract
Abstract: Diffusion models have achieved state-of-the-art performance in generative
modeling, yet their sampling procedures remain vulnerable to
hallucinations-often stemming from inaccuracies in score approximation. In this
work, we reinterpret diffusion sampling through the lens of optimization and
introduce RODS (Robust Optimization-inspired Diffusion Sampler), a novel method
that detects and corrects high-risk sampling steps using geometric cues from
the loss landscape. RODS enforces smoother sampling trajectories and adaptively
adjusts perturbations, reducing hallucinations without retraining and at
minimal additional inference cost. Experiments on AFHQv2, FFHQ, and 11k-hands
demonstrate that RODS maintains comparable image quality and preserves
generation diversity. More importantly, it improves both sampling fidelity and
robustness, detecting over 70% of hallucinated samples and correcting more than
25%, all while avoiding the introduction of new artifacts. We release our code
at https://github.com/Yiqi-Verna-Tian/RODS.
Authors (6)
Yiqi Tian
Pengfei Jin
Mingze Yuan
Na Li
Bo Zeng
Quanzheng Li
Key Contributions
Introduces RODS, a novel diffusion sampling method inspired by robust optimization, which detects and corrects high-risk sampling steps using geometric cues from the loss landscape. This method reduces hallucinations without retraining and at minimal inference cost, improving sampling fidelity and robustness.
Business Value
Enhances the reliability and quality of generated images, which is crucial for applications in creative industries, synthetic data generation, and content creation.