What the contrast shows

Put the two side by side, because the differences say something about both.

A standard autoregressive language-model pass commits. Each output token joins the context for the next. An application can still run a later revision pass.

A diffusion model refines. Every pass revisits the entire image. Nothing is final until the last step, and a shape roughed out early can be reworked repeatedly.

Many language models generate in sequence. Causal text generators usually produce one token after another. Other language-model designs and editing systems use different orders.

A diffusion model works everywhere at once. There is no first corner of an image, no reading direction. The whole canvas gets slightly clearer together.

Text tokens are discrete choices. A tokenizer uses a fixed vocabulary, whose size depends on the model.

Image diffusion usually refines continuous-valued arrays. Small numerical changes can gradually alter colour and shape. Diffusion methods can also be adapted to other representations, including discrete ones, so material alone does not force one architecture.

The useful contrast is between two generation procedures: commit to a next piece, or repeatedly revise a whole noisy representation. Designers choose an approach that fits the data, task, and available computation.

Which is worth carrying past this journey. When you meet a new AI system, the useful question is not whether it is powerful. It is what the material looks like, and what that shape permitted.