Diffusion-based generative models' impressive ability to create convincing images has captured global attention. However, their complex internal structures and operations often make them difficult for non-experts to understand. We present Diffusion Explainer, the first interactive visualization tool that explains how Stable Diffusion transforms text prompts into images. Diffusion Explainer tightly integrates a visual overview of Stable Diffusion's complex components with detailed explanations of their underlying operations, enabling users to fluidly transition between multiple levels of abstraction through animations and interactive elements. By comparing the evolutions of image representations guided by two related text prompts over refinement timesteps, users can discover the impact of prompts on image generation. Diffusion Explainer runs locally in users' web browsers without the need for installation or specialized hardware, broadening the public's education access to modern AI techniques. Our open-sourced tool is available at: https://poloclub.github.io/diffusion-explainer/.

翻译：基于扩散的生成模型在生成逼真图像方面的卓越能力已引起全球关注。然而，其复杂的内部结构和操作往往使非专业人士难以理解。我们提出了Diffusion Explainer，这是首个交互式可视化工具，用于解释稳定扩散模型如何将文本提示转化为图像。Diffusion Explainer将稳定扩散复杂组件的视觉概览与其底层操作的详细解释紧密集成，使用户能够通过动画和交互元素在不同抽象层次之间流畅切换。通过比较两个相关文本提示引导的图像表示在细化时间步长上的演变，用户可以发现提示对图像生成的影响。Diffusion Explainer在用户的网络浏览器中本地运行，无需安装或专用硬件，从而拓宽了公众对现代人工智能技术的教育可及性。我们的开源工具可访问：https://poloclub.github.io/diffusion-explainer/。