This project provides an interactive browser-based visualization tool for understanding the Stable Diffusion text-to-image generation process. It targets users interested in AI art and machine learning, offering a no-installation, no-GPU way to explore how prompts translate into images.

How It Works

Diffusion Explainer visualizes the diffusion process, a core component of Stable Diffusion models. It breaks down the iterative denoising steps, allowing users to see how noise is gradually refined into an image based on a given text prompt. This approach demystifies the "black box" nature of diffusion models by providing a step-by-step, visual breakdown.

Quick Start & Requirements

• Install: git clone https://github.com/poloclub/diffusion-explainer.git followed by cd diffusion-explainer and python -m http.server 8000.
• Access: Open http://localhost:8000 in a web browser.
• Prerequisites: None beyond a standard web browser and Python for local hosting.
• Resources: Runs entirely client-side in the browser, requiring no GPU.
• Links: Live Demo: https://poloclub.github.io/diffusion-explainer, Demo Video: https://youtu.be/Zg4gxdIWDds, Research Paper: https://arxiv.org/abs/2405.13426

Highlighted Details

• Interactive visualization of the diffusion process.
• No installation or GPU required for browser-based use.
• Explains the transformation from text prompts to images.
• Developed by researchers from Georgia Tech and IBM Research.

Maintenance & Community

• Developed by a team of researchers from Georgia Tech and IBM Research.
• Contact available via GitHub issues or directly with Seongmin Lee.

Licensing & Compatibility

• License: MIT License.
• Compatibility: Permissive for commercial use and integration with closed-source projects.

Limitations & Caveats

The tool visualizes a simplified or representative diffusion process; it does not run the full Stable Diffusion model locally, meaning users cannot input arbitrary prompts or modify model parameters directly within the interactive visualization itself.