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Distilling Diffusion Score Discrepancy for Efficient Training Data Attribution
Training data attribution for diffusion models aims to identify the training samples that influence a generated instance, but existing methods either require costly per-sample gradient computation or query-specific model optimization. Moreover, most methods attribute changes in a proxy loss rather t
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