1. The Paradigm Shift from Convolutional U-Nets to Diffusion Transformers
The field of generative visual computing has witnessed an architectural transformation with the transition from legacy convolutional U-Nets toward Diffusion Transformers (DiTs). For years, inductive convolutional biases were considered essential for capturing spatial hierarchies and local pixel correlations. However, as...
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