Unverified50% confidenceFactExact time
Applying LoRA to all linear layers means adapters are injected into the Q/K/V/O projections of the attention layers and the up and down projections of the feedforward layers, giving broad coverage and strong expressiveness, but also making it easier to learn the stylistic features in the training data as a whole.
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10/13/2026
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UnverifiedIn diffusion models, LoRA is typically injected into the attention layers (Q, K, V projection matrices) of U-Net or Transformer architectures.73% similarUnverifiedPerceptual Loss uses a pre-trained VGG network to extract high-level feature representations of images, computing the gap between generated images and target images in feature space rather than pixel space.72% similarUnverifiedCommon implementations of the cross-modal alignment module include a linear projection layer (used by LLaVA), Q-Former (used by BLIP-2), and cross-attention mechanisms.72% similarUnverified模型中注意力层的QKV投影权重和输出投影通常比MLP层的中间权重更敏感，模型首尾层比中间层更脆弱71% similarUnverifiedLoRA的秩r通常取8至64之间，target_modules通常注入注意力层的Q、K、V、O矩阵70% similar
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