Predicting future states in uncertain environments, such as wildfire spread, medical diagnosis, or autonomous driving, requires models that can consider multiple plausible outcomes. While diffusion models can effectively learn such multi-modal distributions, naively sampling from these models is computationally inefficient, potentially requiring hundreds of samples to find low-probability modes that may still be operationally relevant. In this work, we address the challenge of sample-efficient ambiguous segmentation by evaluating several training-free sampling methods that encourage diverse predictions. We adapt two techniques, particle guidance and SPELL, originally designed for the generation of diverse natural images, to discrete segmentation tasks, and additionally propose a simple clustering-based technique. We validate these approaches on the LIDC medical dataset, a modified version of the Cityscapes dataset, and MMFire, a new simulation-based wildfire spread dataset introduced in this paper. Compared to naive sampling, these approaches increase the HM IoU* metric by up to 7.5% on MMFire and 16.4% on Cityscapes, demonstrating that training-free methods can be used to efficiently increase the sample diversity of segmentation diffusion models with little cost to image quality and runtime. Code and dataset: https://github.com/SebastianGer/wildfire-spread-scenarios

翻译：在野火蔓延、医学诊断或自动驾驶等不确定环境中预测未来状态，需要能考虑多种合理结果的模型。尽管扩散模型能有效学习这种多模态分布，但直接对这些模型采样计算效率低下，可能需要数百个样本才能找到即使与操作相关但概率较低的模态。本文通过评估几种无需训练的采样方法，解决样本高效模糊分割的挑战，这些方法能促进预测多样性。我们将两种原本为生成多样化自然图像而设计的技术——粒子引导（particle guidance）和SPELL——适配到离散分割任务中，并提出一种简单的基于聚类的技术。我们在LIDC医学数据集、Cityscapes数据集的修改版本以及MMFire（本文提出的基于模拟的新野火蔓延数据集）上验证了这些方法。与直接采样相比，这些方法在MMFire和Cityscapes上分别将HM IoU*指标提升了7.5%和16.4%，证明免训练方法能以极小的图像质量和运行时间成本，高效地提升分割扩散模型的样本多样性。代码和数据集：https://github.com/SebastianGer/wildfire-spread-scenarios