Counting Hallucinations in Diffusion Models
Paper • 2510.13080 • Published
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Rendered dataset from Counting Hallucinations in Diffusion Models (arXiv:2510.13080). Part of CountHalluSet, a suite with well-defined counting criteria used to measure counting hallucination — a diffusion model generating the wrong number of instances, even for patterns absent from its training data.
256×256 RGB rendered images of everyday objects, each labelled with the per-class instance count over three object classes. As with ToyShape, a correct sample contains at most one instance per class; extra or missing instances are counting hallucinations.
SimObject/
├── images/ # 00000.png, 00001.png, ...
└── labels.csv # filename, <class_1>, <class_2>, <class_3>
huggingface-cli download ShyFoo/CountHallu-dataset-SimObject \
--repo-type dataset --local-dir $DATASET_ROOT/SimObject
Load with the reference code (counthallu.datasets.SimObject). See the
CountHallu repository for training and the full evaluation protocol.
@article{fu2025counting,
title={Counting Hallucinations in Diffusion Models},
author={Fu, Shuai and Zhou, Jian and Chen, Qi and Jing, Huang and Nguyen, Huy Anh and Liu, Xiaohan and Zeng, Zhixiong and Ma, Lin and Zhang, Quanshi and Wu, Qi},
journal={arXiv preprint arXiv:2510.13080},
year={2025}
}