Abstract: In implementations of systems for stylizing digital content, a computing device receives digital content having a plurality of content entities. Classified content entities are generated by classifying the plurality of content entities using one or more machine-learning models. A determination is then made regarding correspondence of the classified content entities with visual styles of a digital template. Based on the determined correspondence, the plurality of content entities of the digital content are displayed as having the visual styles, respectively, of the digital template.

Abstract: In implementations of systems for stylizing digital content, a computing device implements a style system to receive input data describing digital content to be stylized based on visual styles of example content included in a digital template. The style system generates embeddings for content entities included in the digital content using a machine learning model. Classified content entities are determined based on the embeddings using the machine learning model. The style system generates an output digital template that includes portions of the digital content having the visual styles of example content included in the digital template based on the classified content entities.

Abstract: In implementations of systems for stylizing digital content, a computing device implements a style system to receive input data describing digital content to be stylized based on visual styles of example content included in a digital template. The style system generates embeddings for content entities included in the digital content using a machine learning model. Classified content entities are determined based on the embeddings using the machine learning model. The style system generates an output digital template that includes portions of the digital content having the visual styles of example content included in the digital template based on the classified content entities.