# diffusion transformers Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/diffusion-transformers
> Markdown URL: https://aitinkerers.org/technologies/diffusion-transformers.md
> Technology record last updated: 2026-02-23T01:19:44Z
> Generated: 2026-09-22T05:45:51Z

Diffusion Transformers (DiT) replace the conventional U-Net backbone in latent diffusion models with a pure Vision Transformer (ViT) architecture, enabling superior image generation scalability and performance.

Diffusion Transformer (DiT) is a scalable generative model architecture developed by William Peebles and Saining Xie (2022). It fundamentally shifts the diffusion process by replacing the standard convolutional U-Net with a Transformer network operating on latent image patches: This design leverages the Transformer's global self-attention mechanism, which is critical for scaling performance. The largest configuration, DiT-XL/2 (675M parameters), achieved a state-of-the-art FID score of 2.27 on the ImageNet 256x256 benchmark, demonstrating that the Transformer is a highly effective, scalable backbone for high-fidelity image synthesis.

- Official technology site: https://wpeebles.com/DiT
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [nanoDiffusion](https://zurich.aitinkerers.org/talks/rsvp_iPlQoUZ8s9U)

Nano sized image generator than can be trained in a couple of CPU minutes on your personal laptop.

- Event context: AI Tinkerers Zurich - February 6 — 2025-02-06 — Zürich
- Public talk page: https://zurich.aitinkerers.org/talks/rsvp_iPlQoUZ8s9U

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