Natural Language Image Editing via LLM-Generated Executable Code
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Solution Overview
Problem
Conventional image editing systems are inefficient and inflexible, requiring numerous user interactions and lacking controllability in implementing fine-grained edits, especially when using diffusion neural networks.
Innovation Solution
A text-to-image editing system that generates executable code from natural language input, leveraging a large language model and segmentation model to automate image editing, allowing user interaction and fine-tuning of editing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image editing systems are used, then image editing functionality is provided, but numerous user interactions are required and the system lacks flexibility
Solution Approach 1:
The system enables self-service by automatically generating executable code from natural language instructions. The large language model interprets user intent and directly translates it into code that performs image editing operations without requiring users to manually configure multiple parameters or navigate complex interfaces.
Solution Approach 2:
The patent replaces the mechanical interaction system (manual parameter adjustment, menu navigation, tool selection) with an automated code generation system. Natural language input is converted into executable code that automatically performs editing operations, substituting manual mechanical operations with automated computational processes.
2Extent of automation
If diffusion neural networks are used for image editing, then automated editing is achieved, but controllability and fine-grained editing capability are lost
Solution Approach 1:
The system segments the image editing process into discrete, controllable steps represented by executable code. Each code snippet corresponds to a specific editing operation that can be independently controlled, modified, and executed. This segmentation allows users to have fine-grained control over each editing step while maintaining automation through code execution.
Solution Approach 2:
The system introduces dynamics by allowing the editing process to be adjusted and controlled during execution. The executable code structure enables dynamic modification of editing parameters and operations based on user feedback, combining automated execution with flexible controllability that diffusion models alone cannot provide.
3Adaptability or versatility
If text-based frameworks are used for image editing, then natural language input is supported, but compatibility with existing editing applications is limited
Solution Approach 1:
The system uses executable code as an intermediary between natural language input and existing editing applications. The large language model generates code that serves as a bridge, translating user intent into a format that existing editing applications can execute, thereby maintaining compatibility while enabling natural language control.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that perform text-to-image editing using executable code generated from natural language text input. For instance, in one or more embodiments, the disclosed systems receive, from a client device, a digital image and natural language text input providing instructions for modifying the digital image. The disclosed systems also generate, using a large language model, executable action code for modifying the digital image in accordance with the instructions of the natural language text input, the executable action code being compatible with an editing application. The disclosed systems further modify the digital image by executing the executable action code via the editing application and provide the modified digital image for display via a graphical user interface of the client device.