Abstract: Methods and systems are disclosed for sharing collections of content items in chat sessions. The methods and systems receive a request to share a first content item and present a GUI comprising a first set of options and a second set of options, the first set of options being associated with adding the first content item to a collection of content items that is accessible to a plurality of recipients, the second set of options being associated with sending the first content item to individual recipients. The methods and systems determine a set of target recipients of the first content item and select a content sharing link between a first link to the collection of content items and a second link directly to the first content item. The methods and systems send, to a target recipient, the content sharing link that has been selected.

Abstract: Various examples described herein support or provide generation and management operations of personalized images using machine learning technologies, including receiving portrait images of an entity; using machine learning models to generate an identity that represents the entity; identifying a template that comprises text descriptions of a scene and conditions; and using a machine learning models to generate a personalized image based on the identity and the template.

Abstract: A method and system for identifying and displaying a messaging application user's top groups based on their activity levels are disclosed. The messaging application tracks messaging interactions initiated by a user to multiple user groups over a predetermined time period. The application assigns a score to each group based on attributes of the interactions such as type, frequency, and recency. The groups are ranked based on the scores, and a subset of top scoring groups is selected. These top groups are displayed in the messaging interface in a dedicated region, allowing quick access to recent highly-engaged groups. The top groups may be displayed alongside individual contacts identified as best friends. Users can customize graphical icons associated with each top group. The techniques rely on observed user interaction patterns to identify meaningful top groups specific to each user.

Abstract: Methods and systems are disclosed for enhancing or modifying an image by a diffusion model. The methods and systems receive a first image depicting a real-world scene including a target object and receive input associated with adjusting a zoom level of the first image. The methods and systems, in response to receiving the input, modify the zoom level associated with the first image to generate a second image having a view of the target object that is different from a view of the target object in the first image. The methods and systems analyze the second image using a generative machine learning model to generate an artificial image that modifies portions of the second image to improve the view of the target object relative to the second image.

Abstract: In one embodiment, a method includes receiving an indication to apply a dynamic mask to an input video, wherein the dynamic mask comprises a plurality of graphical features according to a plurality of mask effects, and wherein the dynamic mask is applied based on one or more user selections specifying one or more of the mask effects, identifying a first user in an input image, and generating an output video comprising the graphical features rendered using the mask effects.

Abstract: In one embodiment, a method includes identifying an emotion associated with an identified first object in one or more input images, selecting, based on the emotion, a mask from a set of masks, where the mask specifies one or more mask effects, and for each of the input images, applying the mask to the input image. Applying the mask includes generating graphical features based on the identified first object or a second object in the input images according to instructions specified by the mask effects, and incorporating the graphical features into an output image. The emotion may be identified based on graphical features of the identified first object. The graphical features of the identified object may include facial features. The selected mask may be selected from a lookup table that maps the identified emotion to the selected mask.