Generation and management of personalized images using machine learning technologies
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.
1 . A system comprising:
at least one processor;
at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
receiving, from a device, a portrait image of an entity;
generating an identity that represents the entity by using the portrait image and a machine learning model, the machine learning model comprising a stable diffusion model;
causing display of a user interface on the device, the user interface comprising an invitation to generate a plurality of personalized images associated with a theme:
detecting an indication of a user selection of the invitation;
identifying a fixed number of templates associated with the theme for image generation based on a user account associated with the device, each of the fixed number of templates for image generation comprising a text description of a themed story scene and an image conditioning; and
using the machine learning model to generate a fixed number of personalized images based on the identity and the fixed number of templates.
2 . The system of claim 1 , wherein the personalized image comprises a personalized narrative image that illustrates the entity in a story scene.
3 . The system of claim 1 , wherein the condition comprises a visual resource that controls a generation of the personalized image, the visual resource comprising one of a sketch, a pose map, a depth map, a normal map, and a canny edge.
4 . The system of claim 1 , wherein the fixed number of templates are identified based on a random selection, and wherein the plurality of templates associated with the theme comprises a preselected template that is not subject to the random selection.
5 . The system of claim 1 , wherein the fixed number of templates is made available for the generating of the fixed number of personalized images without initiating a transaction process, wherein the user interface is a first user interface, wherein the invitation is a first invitation, and wherein the operations further comprise:
identifying a remaining set of templates from the plurality of templates associated with the theme; and
causing display of a second user interface on the device, the second user interface comprising a second invitation to unlock availability of the remaining set of templates.
6 . The system of claim 5 , wherein the operations further comprise:
detecting an indication of a user selection of the second invitation;
initiating the transaction process to unlock the availability of the remaining set of templates in exchange for an element; and
in response to detecting completion of the transaction process, using the machine learning model to generate a remaining set of personalized images based on the remaining set of templates and the identity that represents the entity.
7 . The system of claim 1 , wherein the operations further comprise:
causing display of the personalized image on a user interface of the device, the user interface comprising a user-selectable element that allows transmission of the personalized image to a further device associated with a further user account that is connected to the user account;
detecting an indication of a user selection of the user-selectable element; and
transmitting the personalized image as an ephemeral message to the further device.
8 . The system of claim 1 , wherein the user account is a first user account, and wherein the operations further comprise:
determining the template corresponds to a plurality of entities; and
identifying a plurality of user accounts that are connected to the first user account, each of the plurality of user accounts being associated with an identity that is generated based on one or more portrait images of a corresponding entity.
9 . The system of claim 8 , wherein the operations further comprise:
detecting an indication of a user selection of one or more entities from the plurality of entities; and
using the machine learning model to generate the personalized image based on the template and the one or more entities.
10 . A method comprising:
receiving, from a device, a portrait image of an entity;
generating an identity that represents the entity by using the portrait image and a machine learning model, the machine learning model comprising a stable diffusion model;
causing display of a user interface on the device, the user interface comprising an invitation to generate a plurality of personalized images associated with a theme;
detecting an indication of a user selection of the invitation;
identifying a fixed number of templates associated with the theme for image generation based on a user account associated with the device, each of the fixed number of templates for image generation comprising a text description of a themed story scene and an image conditioning; and
using the machine learning model to generate a fixed number of personalized images based on the identity and the fixed number of templates.
11 . The method of claim 10 , wherein the personalized image comprises a personalized narrative image that illustrates the entity in a story scene.
12 . The method of claim 10 , wherein the condition comprises a visual resource that controls a generation of the personalized image, the visual resource comprising one of a sketch, a pose map, a depth map, a normal map, and a canny edge.
13 . The method of claim 10 , wherein the fixed number of templates are identified based on a random selection, and wherein the plurality of templates associated with the theme comprises a preselected template that is not subject to the random selection.
14 . The method of claim 10 , wherein the fixed number of templates is made available for the generating of the fixed number of personalized images without initiating a transaction process, wherein the user interface is a first user interface, and wherein the invitation is a first invitation, further comprising:
identifying a remaining set of templates from the plurality of templates associated with the theme; and
causing display of a second user interface on the device, the second user interface comprising a second invitation to unlock availability of the remaining set of templates.
15 . The method of claim 14 , further comprising:
detecting an indication of a user selection of the second invitation;
initiating the transaction process to unlock the availability of the remaining set of templates in exchange for an element; and
in response to detecting completion of the transaction process, using the machine learning model to generate a remaining set of personalized images based on the remaining set of templates and the identity that represents the entity.
16 . The method of claim 10 , further comprising:
causing display of the personalized image on a user interface of the device, the user interface comprising a user-selectable element that allows transmission of the personalized image to a further device associated with a further user account that is connected to the user account;
detecting an indication of a user selection of the user-selectable element; and
transmitting the personalized image as an ephemeral message to the further device.
17 . The method of claim 10 , wherein the user account is a first user account, further comprising:
determining the template corresponds to a plurality of entities;
identifying a plurality of user accounts that are connected to the first user account, each of the plurality of user accounts being associated with an identity that is generated based on one or more portrait images of a corresponding entity;
detecting an indication of a user selection of one or more entities from the plurality of entities; and
using the machine learning model to generate the personalized image based on the template and the one or more entities.
18 . A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
receiving, from a device, a portrait image of an entity;
generating an identity that represents the entity by using the portrait image and a machine learning model, the machine learning model comprising a stable diffusion model;
causing display of a user interface on the device, the user interface comprising an invitation to generate a plurality of personalized images associated with a theme;
detecting an indication of a user selection of the invitation;
identifying a fixed number of templates associated with the theme for image generation based on a user account associated with the device, each of the fixed number of templates for image generation comprising a text description of a themed story scene and an image conditioning; and
using the machine learning model to generate a fixed number of personalized images based on the identity and the fixed number of templates.