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Normalized Low-Rank Adaptation (NoRA) is introduced, a simple yet effective method that normalizes the down-projection matrices during training, improving standard LoRA without requiring repeated normalization throughout training.
High-fidelity image generation faces a trade-off between speed and quality. Diffusion models produce strong visuals but require costly iterative sampling. Existing efficient methods mainly distill pretrained models into few-step samplers, a challenging process that depends heavily on teacher-model quality. In this pape...
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