Towards Consistent and Efficient Dataset Distillation via Diffusion-Driven Selection

Dataset distillation provides an effective approach to reducememory and computational costs by optimizing a compact dataset thatachieves performance comparable to the full original. However, for large-scale datasets and complex deep networks (e.g., ImageNet-1K with ResNet-101), the vast optimization space hinders distillation effectiveness, limit-ing practical applications. Recent methods leverage pre-trained diffusionmodels to directly generate informative images, thereby bypassing pixel-level optimization and achieving promising results. Nonetheless, theseapproaches often suffer from distribution shifts between the pre-traineddiffusion prior and target datasets, as well as the need for multiple dis-tillation steps under varying settings. To overcome these challenges, wepropose a novel framework that is orthogonal to existing diffusion-baseddistillation techniques by utilizing the diffusion prior for patch selectionrather than generation. Our method predicts noise from the diffusionmodel conditioned on input images and optional text prompts (with orwithout label information), and computes the associated loss for eachimage-patch pair. Based on the loss differences, we identify distinctiveregions within the original images. Furthermore, we apply intra-classclustering and ranking on the selected patches to enforce diversity con-straints. This streamlined pipeline enables a one-step distillation process.Extensive experiments demonstrate that our approach consistently out-performs state-of-the-art methods across various metrics and settings.