  Data valuation plays a crucial role in machine learning. Existing data
valuation methods have primarily focused on discriminative models, neglecting
generative models that have recently gained considerable attention. A very few
existing attempts of data valuation method designed for deep generative models
either concentrates on specific models or lacks robustness in their outcomes.
Moreover, efficiency still reveals vulnerable shortcomings. To bridge the gaps,
we formulate the data valuation problem in generative models from a
similarity-matching perspective. Specifically, we introduce Generative Model
Valuator (GMValuator), the first training-free and model-agnostic approach to
provide data valuation for generation tasks. It empowers efficient data
valuation through our innovatively similarity matching module, calibrates
biased contribution by incorporating image quality assessment, and attributes
credits to all training samples based on their contributions to the generated
samples. Additionally, we introduce four evaluation criteria for assessing data
valuation methods in generative models, aligning with principles of
plausibility and truthfulness. GMValuator is extensively evaluated on various
datasets and generative architectures to demonstrate its effectiveness.
