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CustomShift achieves unprecedented balance between semantic fidelity and subject consistency in image generation, outperforming existing methods by leveraging a novel attention distribution shift.
Batch normalization's power comes from reshaping the geometry of neural network decision boundaries on a per-batch basis, not just from optimization benefits.
