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This work derives the uniform-phase initialization, which obviates distributional approximation and fully decouples the layers in networks with sine activations, and is the first work to use the sine function's periodic symmetry.
This work presents a systematic scaling law study for text-to-image diffusion models using Abra, a controlled family of flow-matching transformers trained across three orders of magnitude worth of compute, demonstrating that diffusion models scale just as predictably as language models but require far more data to trai...
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