Interactive Visual Learning for Stable Diffusion
On the map: Diffusion models → Image generation, text-to-image and editing
Abstract (arXiv)
Diffusion-based generative models' impressive ability to create convincing images has garnered global attention. However, their complex internal structures and operations often pose challenges for non-experts to grasp. We introduce Diffusion Explainer, the first interactive visualization tool designed to elucidate how Stable Diffusion transforms text prompts into images. It tightly integrates a visual overview of Stable Diffusion's complex components with detailed explanations of their underlying operations. This integration enables users to fluidly transition between multiple levels of abstraction through animations and interactive elements. Offering real-time hands-on experience, Diffusion Explainer allows users to adjust Stable Diffusion's hyperparameters and prompts without the need for installation or specialized hardware. Accessible via users' web browsers, Diffusion Explainer is making significant strides in democratizing AI education, fostering broader public access. More than 7,200 users spanning 113 countries have used our open-sourced tool at https://poloclub.github.io/diffusion-explainer/. A video demo is available at https://youtu.be/MbkIADZjPnA.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 5 of its 23 references that are in the map. First posted on arXiv 22 Apr 2024.
Builds on
5 of 23 references.
References · 5
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
- 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 3,045
- 2022Acceleration AI Ethics, the Debate between Innovation and Safety, and Stability AI's Diffusion versus OpenAI's Dall-Ecited by 5
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- 2023Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion
- 2024Explaining generative diffusion models via visual analysis for interpretable decision-making process
- 2025Diffusion Explorer: Interactive Exploration of Diffusion Models
- 2023State of the Art on Diffusion Models for Visual Computing
- 2023The Hidden Language of Diffusion Models
- 2024DiffusionPID: Interpreting Diffusion via Partial Information Decomposition
- 2025Emergence and Evolution of Interpretable Concepts in Diffusion Models
- 2025SliderSpace: Decomposing the Visual Capabilities of Diffusion Models