Vision Transformer, Diffusion Transformer, and Multimodal Diffusion Transformer Explained
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Julia Turc
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Julia TurcTop Viber
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Terms in this piece · Glossary
transformer — The neural network architecture behind modern AI models, built on attention — letting every word directly consider every other word in parallel.
multimodal — A model that works with more than text — reading images, audio, or video, and sometimes generating them too.
attention — The mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.
Why it matters
Traces how the transformerThe neural network architecture behind modern AI models, built on attention — letting every word directly consider every other word in parallel.Full definition → displaced CNNs in vision and became the backbone of modern image generation, explaining the conditioning mechanisms that most practitioners use without understanding.
Key quotes
“When every pixel attends to all the other pixels in the image, the computational complexity is quadratic.”
Julia Turc
“As the VIT authors put it, an image is worth 16 by 16 words.”
Julia Turc
“Operating on patches instead of pixels brings self attention down from a few billion computations to about 65,000. Much more tractable.”
Julia Turc
“The text tokens are static. They're not influenced by the image.”
Julia Turc
“This dual conditioning makes sure that neither modality is processed in isolation which removes ambiguity.”
