Adding consistency-to-pretrained and multiplicative-noise consistency losses to fine-tuning improves subject identity and background diversity over DreamBooth on a 30-subject benchmark.
Disenbooth: Identity- preserving disentangled tuning for subject-driven text-to- image generation
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Noise Consistency Regularization for Improved Subject-Driven Image Synthesis
Adding consistency-to-pretrained and multiplicative-noise consistency losses to fine-tuning improves subject identity and background diversity over DreamBooth on a 30-subject benchmark.