  The growing proliferation of customized and pretrained generative models has
made it infeasible for a user to be fully cognizant of every model in
existence. To address this need, we introduce the task of content-based model
search: given a query and a large set of generative models, finding the models
that best match the query. As each generative model produces a distribution of
images, we formulate the search task as an optimization problem to select the
model with the highest probability of generating similar content as the query.
We introduce a formulation to approximate this probability given the query from
different modalities, e.g., image, sketch, and text. Furthermore, we propose a
contrastive learning framework for model retrieval, which learns to adapt
features for various query modalities. We demonstrate that our method
outperforms several baselines on Generative Model Zoo, a new benchmark we
create for the model retrieval task.
