Dialog-Based Image Editing via Object-Attribute Segmentation
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Solution Overview
Problem
Conventional image editing techniques, such as stable diffusion, are unstable, often distort images, and lack refined editing capabilities, making them inadequate for complex and repetitive image editing tasks.
Innovation Solution
The proposed dialog-based image editing system uses an optimized framework that converts images into detailed lists of objects with attributes, enabling fine-grained, controllable, and flexible image editing through natural language input. This system includes an image understanding layer, verification and filter layer, planning layer, scheduling layer, and execution layer to accurately and efficiently perform image editing tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If stable diffusion is used for natural language image editing, then users can input editing tasks in sentence form, but the output results are unstable and often distorted
Solution Approach 1:
The patent segments the image editing process into distinct stages: text understanding, mask generation, and image editing execution. By dividing the task into smaller sub-tasks with specialized models for each stage, the system achieves more stable and controllable results while maintaining natural language input capability. The segmentation allows each component to optimize for its specific function rather than attempting to handle the entire editing process in one step.
Solution Approach 2:
The patent introduces an intermediary mask generation stage that translates natural language instructions into precise spatial masks before applying edits. This intermediary step acts as a bridge between the text input and the image editing operation, ensuring that edits are applied to the correct regions with accurate boundaries, thereby improving output stability and reducing distortions.
2Adaptability or versatility
If stable diffusion is used for image editing, then users can perform single editing tasks, but the system lacks refined editing capabilities for complex and repetitive tasks
Solution Approach 1:
The patent creates a universal image editing framework that can handle multiple types of editing tasks (inpainting, outpainting, object manipulation, etc.) through a unified architecture. The system uses a common text-to-mask model and editing pipeline that adapts to different task requirements, enabling refined editing capabilities across diverse scenarios while maintaining a consistent user interface.
Solution Approach 2:
The patent implements dynamic task routing that automatically selects the appropriate editing strategy based on the input text and image context. The system can dynamically adjust between different editing modes, mask generation approaches, and model configurations to optimize performance for each specific task, enabling both simple and complex editing operations through the same interface.
3Productivity
If conventional image editing techniques are used, then basic editing operations can be performed, but the techniques distort images and fail to maintain image quality
Solution Approach 1:
The patent replaces traditional mechanical image editing operations with AI-based semantic understanding and generation. Instead of using manual manipulation tools that physically alter pixels and cause distortion, the system uses text-to-image models to generate edits that naturally integrate with the original image, preserving quality while maintaining editing efficiency.
Solution Approach 2:
The patent changes the fundamental parameters of the editing process by using diffusion models with controlled noise schedules and guidance mechanisms. By adjusting parameters such as guidance scale, diffusion steps, and mask precision, the system achieves high-quality edits that preserve image characteristics while allowing substantial modifications, overcoming the quality-loss problem of conventional techniques.
Data Source
AI summary
The present disclosure describes techniques for implementing dialog-based image editing. Text indicating a task of editing an image is received. A list of objects and attributes associated with each of the objects is generated based on the text and the image. The objects are comprised in the image. Operations to be performed on each of the objects are determined. An order of performing the operations on an object-by-object basis are determined. A plan of implementing the task is generated based on the text and the order of performing the operations. The plan comprises information indicating a set of algorithm tools selected for the task. Executable code is generated based at least in part on the plan. The code is executed to generate an edited image.