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Diffusion Transformer (DiT) is a class of generative backbone networks that introduces the Transformer architecture into diffusion models, cutting noisy images into patches and linearly embedding them into token sequences, and using multi-layer Transformer blocks to predict noise or velocity, replacing the U-Net in traditional diffusion models.
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10/15/2026
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