Class-agnostic object counting aims to count objects of arbitrary classes with limited information (e.g., a few exemplars or the class names) provided. It requires the model to effectively acquire the characteristics of the target objects and accurately perform counting, which can be challenging. In this work, inspired by that text-to-image diffusion models hold rich knowledge and comprehensive understanding of real-world objects, we propose to leverage the pre-trained text-to-image diffusion model to facilitate class-agnostic object counting. Specifically, we propose a novel framework named CountDiff with careful designs, leveraging the pre-trained diffusion model's comprehensive understanding of image contents to perform class-agnostic object counting. The experiments show the effectiveness of CountDiff on both few-shot setting with exemplars provided and zero-shot setting with class names provided.