Abstract: Some implementations are directed to editing a source image, where the source image is one generated based on processing a source natural language (NL) prompt using a Large-scale language-image (LLI) model. Those implementations edit the source image based on user interface input that indicates an edit to the source NL prompt, and optionally independent of any user interface input that specifies a mask in the source image and/or independent of any other user interface input. Some implementations of the present disclosure are additionally or alternatively directed to applying prompt-to-prompt editing techniques to editing a source image that is one generated based on a real image, and that approximates the real image.

Abstract: A user device receives a request to generate a composite image. The media application a first image that includes one or more first subjects. The media application determines a previous pose of the user device associated with capture of the first image. The media application segments the one or more first subjects from the first image. The media application generates one or more overlays that correspond to the one or more first subjects based on segmenting the one or more first subjects. The media application displays the one or more overlays on a viewfinder of the user device to provide guidance for a user to capture a second image based on a comparison of a current pose of the user device to the previous pose of the user device. The media application generates the composite image.

Abstract: Some implementations are directed to editing a source image, where the source image is one generated based on processing a source natural language (NL) prompt using a Large-scale language-image (LLI) model. Those implementations edit the source image based on user interface input that indicates an edit to the source NL prompt, and optionally independent of any user interface input that specifies a mask in the source image and/or independent of any other user interface input. Some implementations of the present disclosure are additionally or alternatively directed to applying prompt-to-prompt editing techniques to editing a source image that is one generated based on a real image, and that approximates the real image.

Abstract: A media application receives a set of images that include a source image and a target image, the source image and the target image including at least a subject. The media application determines whether to use one or more editors selected from a group of a head editor, a face editor, or combinations thereof. Responsive to determining to use the head editor, the media application generates a compositive image by replacing at least a portion of head pixels associated with a target head of the subject in the target image with head pixels from a source head of the subject in the source image and replacing neck pixels associated with a target neck and shoulder pixels associated with target shoulders that include an area between the target head and a target torso with an interpolated region that is generated from an interpolation of the source image and the target image.

Abstract: Some implementations are directed to editing a source image, where the source image is one generated based on processing a source natural language (NL) prompt using a Large-scale language-image (LLI) model. Those implementations edit the source image based on user interface input that indicates an edit to the source NL prompt, and optionally independent of any user interface input that specifies a mask in the source image and/or independent of any other user interface input. Some implementations of the present disclosure are additionally or alternatively directed to applying prompt-to-prompt editing techniques to editing a source image that is one generated based on a real image, and that approximates the real image.