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.

翻译：随着定制化与预训练生成模型的日益普及，用户已难以全面了解现存的所有模型。针对这一需求，我们提出基于内容的模型搜索任务：给定查询条件与大量生成模型集合，寻找与查询最匹配的模型。由于每个生成模型会产生一个图像分布，我们将搜索任务形式化为优化问题，以筛选出生成与查询内容相似概率最高的模型。我们提出一种方法，可针对不同模态（如图像、草图、文本）的查询近似计算该概率。此外，我们进一步提出面向模型检索的对比学习框架，通过学习适配不同查询模态的特征。实验表明，在我们为模型检索任务创建的新基准数据集Generative Model Zoo上，所提方法性能优于多个基线模型。