Generative Image Editing with Prompt-Based Model Selection
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Solution Overview
Problem
Existing generative AI systems for image creation and editing are fragmented, requiring users to switch between different applications or tools, lack integration for intelligent model selection, and are computationally expensive due to the use of large, general-purpose models for all tasks, leading to suboptimal and inconsistent results.
Innovation Solution
A system that selects a machine-learning model from a set of specialized models based on the text prompt, optimizing computational resources by using smaller, specialized models for specific tasks, and integrating seamlessly with other applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large general-purpose machine learning models are used for all image generation tasks, then versatility and coverage of tasks are improved, but power consumption and computational cost increase
Solution Approach 1:
The patent segments the machine learning model selection process by creating multiple specialized models for different image generation tasks (e.g., photorealistic images, cartoons, stickers, avatars). Instead of using one large general-purpose model for all tasks, the system divides the workload across multiple smaller specialized models, reducing overall power consumption while maintaining task coverage.
Solution Approach 2:
The system changes the parameter of model selection by introducing task-type classification. Based on the analyzed prompt category (photorealistic, cartoon, sticker, avatar), the system dynamically selects which specialized model to use, optimizing the balance between task coverage and computational efficiency for each specific request.
2Adaptability or versatility
If large general-purpose machine learning models are used for all image generation tasks, then model coverage is improved, but latency increases
Solution Approach 1:
The patent segments the machine learning model selection process by creating multiple specialized models for different image generation tasks (e.g., photorealistic images, cartoons, stickers, avatars). Instead of using one large general-purpose model for all tasks, the system divides the workload across multiple smaller specialized models, reducing overall power consumption while maintaining task coverage.
Solution Approach 2:
The system changes the parameter of model selection by introducing task-type classification. Based on the analyzed prompt category (photorealistic, cartoon, sticker, avatar), the system dynamically selects which specialized model to use, optimizing the balance between task coverage and computational efficiency for each specific request.
3Use of energy by moving object
If multiple specialized machine learning models are used for different tasks, then power consumption and latency are reduced, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary component - the prompt analysis module - that acts as a mediator between the user's text prompt and the multiple specialized machine learning models. This intermediary analyzes the prompt, determines the appropriate task type, and selects the corresponding specialized model, thereby managing system complexity through modular architecture and intelligent routing.
4Productivity
If multiple specialized machine learning models are used for different tasks, then computational efficiency is improved, but integration complexity increases
Solution Approach 1:
The patent introduces an intermediary component - the prompt analysis module - that acts as a mediator between the user's text prompt and the multiple specialized machine learning models. This intermediary analyzes the prompt, determines the appropriate task type, and selects the corresponding specialized model, thereby managing system complexity through modular architecture and intelligent routing.
Data Source
AI summary
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.