We present a comprehensive experimental study on image-level conditioning for
diffusion models using cluster assignments. We elucidate how individual
components regarding image clustering impact image synthesis across three
datasets. By combining recent advancements from image clustering and diffusion
models, we show that, given the optimal cluster granularity with respect to
image synthesis (visual groups), cluster-conditioning can achieve
state-of-the-art FID (i.e. 1.67, 2.17 on CIFAR10 and CIFAR100 respectively),
while attaining a strong training sample efficiency. Finally, we propose a
novel method to derive an upper cluster bound that reduces the search space of
the visual groups using solely feature-based clustering. Unlike existing
approaches, we find no significant connection between clustering and
cluster-conditional image generation. The code and cluster assignments will be
released.

通过使用集群分配，我们对图像级别调制的扩散模型进行了全面的实验研究，并阐明了图像集群的个别组件对三个数据集上图像合成的影响。结合图像集群和扩散模型的最新进展，我们表明，给定图像合成的最优集群粒度（可视化组），集群调制可以实现最先进的 FID（即在 CIFAR10 和 CIFAR100 上分别为 1.67 和 2.17），同时具有强大的训练样本效率。最后，我们提出了一种通过仅基于特征的聚类来导出上部集群界限的新方法，从而减少视觉组的搜索空间。与现有方法不同，我们发现聚类与集群条件图像生成之间没有显著的联系。代码和集群分配将会发布。