Diffusion Explainer: Visual Explanation for Text-to-image Stable
  Diffusion
Authors: name: Seongmin Lee, name: Benjamin Hoover, name: Hendrik Strobelt, name: Zijie J. Wang, name: ShengYun Peng, name: Austin Wright, name: Kevin Li, name: Haekyu Park, name: Haoyang Yang, name: Duen Horng Chau

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/.