# Diffusion Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/diffusion
> Markdown URL: https://aitinkerers.org/technologies/diffusion.md
> Technology record last updated: 2026-03-26T15:53:03Z
> Generated: 2026-10-23T19:05:57Z

Diffusion models are generative AI: they synthesize high-fidelity images and data by iteratively denoising a random starting point over hundreds of steps.

Diffusion is a class of generative AI models (like Stable Diffusion XL and DALL-E 2) that synthesize high-fidelity content, primarily images, from pure noise. The core mechanism is a two-phase process: 'forward diffusion' gradually adds noise to training data; 'reverse diffusion' then iteratively learns to remove that noise, reconstructing the image. This iterative denoising, often executed by a UNet architecture, allows for fine-grained, text-prompt control (text-to-image) over the final output, consistently outperforming previous generative adversarial networks (GANs) in both quality and sample diversity.

- Official technology site: https://stability.ai
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Your Webcam Knows the Geometry - Real-Time Relighting & Single-Shot Novel Views](https://lausanne.aitinkerers.org/talks/rsvp_cMMuinG2WV4)

- Event context: AI Tinkerers Lausanne June 2026 Meetup — 2026-06-25 — Lausanne
- Public talk page: https://lausanne.aitinkerers.org/talks/rsvp_cMMuinG2WV4

### [Curie - Gen3D for Commerce](https://ee.dream.page/talks/rsvp_8wRsH-2EoC4)

- Event context: Bellevue+Eastside Entrepreneurs - August 3, 2023 Event — 2023-08-03 — Eastside Entrepreneurs
- Public talk page: https://ee.dream.page/talks/rsvp_8wRsH-2EoC4

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