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M [68] dataset. We adopt the Adam with Weight Decay (AdamW) [52] optimizer with a weight decay of 0.010.01. The initial learning rates are set to, M Original 26.7 20.0 53.3 40.0 20.0 66.7 40.0 20.0 53.3 37.8 DIVA 13.3 26.7 60.0 46.7 13.3 73.3 53.3 26.7 53.3 40.7 GenHancer 20.0 20.0 66.7 60.0 20.0 86.7 40.0 13.0 53.3 42.2 un
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Naive combinations of diffusion and contrastive learning suffer from gradient conflict, but injecting contrastive signals from reconstructed images can jointly optimize discriminative and perceptual abilities in CLIP visual encoders.
