Transfusion: Autoregressive Text + Diffusion Image in One Transformer

Chameleon and Emu3 bet everything on discrete tokens. They work, but the quantization bottleneck is visible — the image quality plateaus below continuous-space diffusion models. Transfusion (Meta, Zhou et al., August 2024) takes the opposite bet: keep images continuous, drop the VQ-VAE entirely, and train one transformer with two losses. Text tokens get next-token-prediction. Image patches get a flow-matching / diffusion loss. Both objectives optimize the same weights. The architecture underlying Stable Diffusion 3 (MMDiT) is a close cousin. This lesson reads the Transfusion thesis, builds a toy two-loss trainer, and traces the attention mask that lets one transformer do both jobs.