Diffusion models are a class of generative models that have gained popularity in the AI field, particularly for image generation. This article provides a simplified explanation of how diffusion models work, particularly in contrast to other generative AI techniques such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). The comments reflect a recognition of the need for clearer distinctions between these technologies, emphasizing that many users may not understand the fundamental differences despite the growing presence of LLMs (Large Language Models) and image generation models. The article is commended for its approachability and clarity, making it accessible to a wider audience.