Stable Diffusion Explained with Examples — Visualizing Text-to-Image Generation

Diffusion Explainer is 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 fluid 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. 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 designed Diffusion Explainer to help people better understand how Stable Diffusion works.

0:08 How does Stable Diffusion work? Transforming text prompt into image
0:18 Summary of Stable Diffusion's image generation process
0:29 Rewind or fast forward generation
0:48 How text prompt processed by text representation generator
0:55 CLIP's text encoder connects text with image
1:02 Image representation refined over timesteps
1:14 Guidance scale controls image's adherence to text prompt
1:22 Try different guidance scales and random seeds
1:36 Compares how small wording changes lead to a different image
1:50 UMAP visualizes incremental refinement of image representations
1:56 Compare Stable Diffusion generation trajectories

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