Diffusion models and Transformers both generate content, but they approach it from opposite directions.
Diffusion works like sculpting: start with a block of noise and iteratively chip away until the image appears. It’s parallel across the whole image—every pixel gets updated simultaneously at each step. This makes diffusion great for images where the whole composition matters at once, but it’s slow (20–50 steps to generate one image).
Transformers work like writing: they generate one token at a time, left-to-right (or bidirectionally). GPT-4 predicts the next word; DALL-E 3 actually uses a Transformer to generate image tokens sequentially. Transformers are autoregressive—each new piece depends on everything that came before. This is fast for text (one pass per token) but was historically tricky for images until researchers figured out how to tokenize visual patches.
The key difference: diffusion denoises everything together across many steps, while Transformers generate sequentially in one pass per token. Diffusion dominates image generation (Stable Diffusion, Midjourney) because it handles spatial coherence well. Transformers dominate language (GPT, Claude) because text is inherently sequential. But the lines blur—recent models like Stable Diffusion 3 and Flux use Transformer architectures inside the diffusion process, combining both approaches.
Pick diffusion when you need spatial reasoning and can wait for quality. Pick pure Transformers when you need speed and sequential structure matters.