Diffusion models have become increasingly popular for synthesizing high-quality samples based on training datasets. However, given the oftentimes enormous sizes of the training datasets, it is difficult to assess how training data impact the samples produced by a trained diffusion model. The difficulty of relating diffusion model inputs and outputs poses significant challenges to model explainability and training data attribution. Here we propose a novel solution that reveals how training data influence the output of diffusion models through the use of ensembles. In our approach individual models in an encoded ensemble are trained on carefully engineered splits of the overall training data to permit the identification of influential training examples. The resulting model ensembles enable efficient ablation of training data influence, allowing us to assess the impact of training data on model outputs. We demonstrate the viability of these ensembles as generative models and the validity of our approach to assessing influence.

翻译：扩散模型因能够基于训练数据集合成高质量样本而日益流行。然而，由于训练数据集往往规模巨大，难以评估训练数据如何影响训练后扩散模型生成的样本。这种将扩散模型输入与输出相关联的困难性，给模型可解释性及训练数据归因带来了重大挑战。本文提出一种创新解决方案，通过集成方法揭示训练数据如何影响扩散模型的输出。在该方法中，编码集成中的单个模型基于对整体训练数据精心划分的子集进行训练，从而能够识别具有影响力的训练样本。由此产生的模型集成能够高效消除训练数据的影响，使我们能够评估训练数据对模型输出的作用。我们证明了这些集成作为生成模型的可行性，以及所提影响评估方法的有效性。