Diffusion Transformers (DiT) Explained: Replacing U-Nets with Transformers

Transformers revolutionized NLP and computer vision — but can they replace U-Nets in diffusion models? In this video, we break down the DiT (Diffusion Transformer) paper by William Peebles and Saining Xie, covering: How diffusion models work Why latent diffusion matters Patchifying latent representations Conditioning methods: In-context tokens Cross-attention adaLN / adaLN-Zero Why adaLN-Zero works so well Scaling laws in diffusion transformers Why GFlops matter more than parameter count State-of-the-art ImageNet results We also compare DiT against traditional U-Net diffusion architectures and explain why Transformers scale so effectively for image generation. Slides based on: “Scalable Diffusion Models with Transformers”