Diffusion Transformers (DiT): How Patchification and adaLN-Zero Replaced U-Nets in Generative AI
Generative visual models relied for years on convolutional U-Net architectures to execute iterative denoising. From Denoising Diffusion Probabilistic Models (DDPM) and Ablated Diffusion Models (ADM) to Latent Diffusion Models (LDMs) behind Stable Diffusion, convolutional backbones served as the default engine for image synthesis. While convolutional inductive biases provided translation equivariance and local spatial hierarchies, they imposed architectural rigidities that resisted compute scalin
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