AI Image Editing for Contextual Digital Components
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Solution Overview
Problem
Existing technologies face challenges in generating customized digital components that accurately reflect the context of user queries, requiring substantial storage and bandwidth to manage various contextual environments.
Innovation Solution
The use of artificial intelligence (AI) systems that receive user queries, select digital components, and interact with machine learning models to edit images based on digital component data and query data, generating customized digital components that align with user intent and contextual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional methods are used to manage various contextual environments for digital components, then storage capacity and bandwidth are required to manage pre-generated images for different contexts, but this results in substantial storage and bandwidth consumption
Solution Approach 1:
The system performs preliminary actions by pre-processing the image to identify the main subject and generating a masked image where the background is removed. This prepared image can then be efficiently combined with different contextual backgrounds through AI generation, avoiding the need to store multiple pre-computed contextualized images while maintaining the ability to adapt to various contexts
Solution Approach 2:
Instead of storing multiple original images for different contexts, the system creates a reusable masked image copy that can be efficiently combined with different AI-generated backgrounds. This single masked image copy serves multiple contextual purposes, dramatically reducing storage requirements while maintaining versatility
2Adaptability or versatility
If multiple pre-generated images are stored for different contextual environments, then storage capacity increases, but the system complexity and management overhead increase
Solution Approach 1:
The system segments the image into distinct components: the main subject (preserved from the original image) and the background (removed via masking). This segmentation allows independent handling of each component, where the subject remains constant and only the background needs to be adapted to different contexts through AI generation, simplifying system management
Solution Approach 2:
The system implements a dynamic approach where the background is not fixed but can be dynamically generated and changed based on the specific context required. This allows the same masked image to adapt to different contexts on-demand, reducing the need for complex static image management systems
3Quantity of substance
If AI models are used to generate customized digital components in real-time, then storage requirements are reduced, but computational processing time and model coordination complexity increase
Solution Approach 1:
The system performs preliminary processing by pre-identifying the main subject and generating the masked image before AI background generation. This preparation work is done once and reused across different contextual generations, reducing the computational burden and time required for each real-time customization request
Solution Approach 2:
By segmenting the image processing into distinct stages (subject identification, masking, AI background generation, composition), the system can optimize each stage independently and parallelize operations where possible, improving overall processing efficiency while maintaining real-time capability
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for enabling artificial intelligence to generate new images based on contextual data and to generate digital components based on the images. In one aspect, a method includes receiving one or more queries from a client device of a user. A digital component is selected based on the one or more queries. A customized digital component is generated by obtaining an image of an object corresponding to the selected digital component and generating, using a language model, an image editing prompt for editing the image based on digital component data related to the digital component and query data including the one or more queries and contextual data. The image and the image editing prompt are provided to an image editing model. An edited image is received and used to generate the customized digital component.