  While large text-to-image models are able to synthesize "novel" images, these
images are necessarily a reflection of the training data. The problem of data
attribution in such models -- which of the images in the training set are most
responsible for the appearance of a given generated image -- is a difficult yet
important one. As an initial step toward this problem, we evaluate attribution
through "customization" methods, which tune an existing large-scale model
toward a given exemplar object or style. Our key insight is that this allows us
to efficiently create synthetic images that are computationally influenced by
the exemplar by construction. With our new dataset of such exemplar-influenced
images, we are able to evaluate various data attribution algorithms and
different possible feature spaces. Furthermore, by training on our dataset, we
can tune standard models, such as DINO, CLIP, and ViT, toward the attribution
problem. Even though the procedure is tuned towards small exemplar sets, we
show generalization to larger sets. Finally, by taking into account the
inherent uncertainty of the problem, we can assign soft attribution scores over
a set of training images.
