From SRA to Self-Flow: Data Augmentation or Self-Supervision?

A research team studied the mechanisms behind self-alignment methods in diffusion transformers. They found that the performance improvements come primarily from data augmentation along the noise dimension, not from token interactions between noise levels. This discovery impacts the understanding and development of diffusion transformer training methods.

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