  As diffusion models are deployed in real-world settings, data attribution is
needed to ensure fair acknowledgment for contributors of high-quality training
data and to identify sources of harmful content. Previous work focuses on
identifying individual training samples important for the generation of a given
image. However, instead of focusing on a given generated image, some use cases
require understanding global properties of the distribution learned by a
diffusion model (e.g., demographic diversity). Furthermore, training data for
diffusion models are often contributed in groups rather than separately (e.g.,
multiple artworks from the same artist). Hence, here we tackle the problem of
attributing global properties of diffusion models to groups of training data.
Specifically, we develop a method to efficiently estimate Shapley values by
leveraging model pruning and fine-tuning. We empirically demonstrate the
utility of our method with three use cases: (i) global image quality for a DDPM
trained on a CIFAR dataset, (ii) demographic diversity for an LDM trained on
CelebA-HQ, and (iii) overall aesthetic quality for a Stable Diffusion model
LoRA-finetuned on Post-Impressionist artworks.
