Abstract: A media application performs object recognition on an initial image to identify a set of objects in the initial image. The media application determines whether the initial image is an outdoor scene. Responsive to the initial image being an outdoor scene, the media application determining a sky segment from the initial image. The media application determines whether the initial image includes a subject that is human or animal. Responsive to the initial image including the subject, the media application determines a subject segment from the initial image. The media application receives at a user interface that includes the initial image, user input corresponding to selection of a selected object from the set of objects. The media application updates the user interface to include an indication that the selected object was selected.

Abstract: A media application provides, as input to a diffusion model, an initial image and a request to change a lighting in the initial image, wherein the initial image includes a subject and a sky. The media application outputs, with the diffusion model, an output image that satisfies the request. The media application determines, from the initial image, a sky segment and a subject segment. The media application generates a sky mask that corresponds to the sky segment and a subject mask that corresponds to the subject segment. The media application modifies a coloring of the initial image to match a coloring of the output image. The media application blends the modified initial image with the output image to form a blended image while using the subject mask to prevent modification to the subject from the modified initial image and the sky mask to prevent modification to the sky from the output image during the blending.

Abstract: A computer-implemented method includes receiving an initial image and an original prompt from a user, wherein the original prompt includes a request to modify the initial image. The method further includes selecting, based on the original prompt, a machine-learning model from a set of machine-learning models. The method further includes providing the original prompt and the initial image as input to a large language model (LLM). The method further includes receiving, from the LLM and based on the original prompt and the initial image, a rewritten prompt. The method further includes selecting, based on the rewritten prompt, a machine-learning model from a set of machine-learning models. The method further includes generating, by the selected machine-learning model, an output image that satisfies the rewritten prompt.

Abstract: A computer-implemented method includes receiving a request for a type of output image and a prompt from a user that describes an output image. The method further includes selecting, based on the type of output image and the prompt, a machine-learning model from a set of machine-learning models. The method further includes providing the request and the prompt as input to the selected machine-learning model. The method further includes generating, by the selected machine-learning model, the output image that satisfies the request and the prompt.

Abstract: A media application receives an initial image, user input that selects one or more objects in the initial image, and a textual request to generate an output image that modifies the one or more selected objects in the initial image. The media application generates a user-selected mask that includes object pixels corresponding to the one or more selected objects. A diffusion model receives the textual request to generate the output image, a depth map, and the user-selected mask, where the diffusion model is trained to generate output pixels that are not associated with a human subject. The diffusion model outputs the output image that satisfies the textual request.

Abstract: A media application receives user input that indicates one or more objects to be erased from a media item. The media application translates the user input to a bounding box. The media application provides a crop of the media item based on the bounding box to a segmentation machine-learning model. The segmentation machine-learning model outputs a segmentation mask for one or more segmented objects in the crop of the media item and a corresponding segmentation score that indicates a quality of the segmentation mask.

Abstract: A media application receives an input image that includes a subject. The media application segments the subject from the input image. The media application generates, based on segmenting the subject, a subject mask that includes subject pixels associated with the subject. The media application determines, based on the subject mask, whether a portion of the subject is cut off by one or more borders of the input image. Responsive to the portion of the subject not being cut off, the media application provides the input image and the subject mask as input to an inpainter machine-learning model. The media application generates, with the inpainter machine-learning model, an output image that extends one or more borders of the input image by adding inpainted pixels to the input image.

Abstract: A media application receives user input that indicates one or more objects to be erased from a media item. The media application translates the user input to a bounding box. The media application provides a crop of the media item based on the bounding box to a segmentation machine-learning model. The segmentation machine-learning model outputs a segmentation mask for one or more segmented objects in the crop of the media item and a corresponding segmentation score that indicates a quality of the segmentation mask.

Abstract: A media application receives user input that indicates one or more objects to be erased from a media item. The media application translates the user input to a bounding box. The media application provides a crop of the media item based on the bounding box to a segmentation machine-learning model. The segmentation machine-learning model outputs a segmentation mask for one or more segmented objects in the crop of the media item and a corresponding segmentation score that indicates a quality of the segmentation mask.

Abstract: A method and system is disclosed for simulating different types of camera lens on a device by guiding a user through a set of images to be captured in connection with one or more desired lens effects. In one aspect, a wide-angle lens may be simulated by taking a plurality of images that have been taken at a particular location over a set of camera orientations that are determined based on the selection of the wide-angle lens. The mobile device may provide prompts to the user indicating the camera orientations for which images should be captured in order to generate the simulated camera lens effect.

Abstract: A method and system is disclosed for simulating different types of camera lens on a device by guiding a user through a set of images to be captured in connection with one or more desired lens effects. In one aspect, a wide-angle lens may be simulated by taking a plurality of images that have been taken at a particular location over a set of camera orientations that are determined based on the selection of the wide-angle lens. The mobile device may provide prompts to the user indicating the camera orientations for which images should be captured in order to generate the simulated camera lens effect.

Abstract: A method and system is disclosed for simulating different types of camera lens on a device by guiding a user through a set of images to be captured in connection with one or more desired lens effects. In one aspect, a wide-angle lens may be simulated by taking a plurality of images that have been taken at a particular location over a set of camera orientations that are determined based on the selection of the wide-angle lens. The mobile device may provide prompts to the user indicating the camera orientations for which images should be captured in order to generate the simulated camera lens effect.