LLM Image Editing With Automatic Masking for Region Control
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Solution Overview
Problem
Current large language models (LLMs) lack the capability for precise image editing, often generating completely different images due to slight changes in textual prompts and fail to retain specific semantic regions of a source image, requiring manual masking and lacking control over image content.
Innovation Solution
An LLM-based image editing system that includes an LLM augmented with image understanding capabilities, automatically generates image masks to preserve or edit specific image regions, determining whether to edit or generate a new image based on user input correlation with the source image, and uses machine learning models to apply image editing instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current LLMs generate images based on text prompts, then image generation capability is achieved, but control over specific semantic regions and retention of source image content is lost
Solution Approach 1:
The patent segments the image editing task into multiple components: text prompt processing, mask generation, and region-specific image modification. The system divides the source image into editable regions based on generated masks, allowing precise control over which semantic regions to modify while preserving others. This segmentation enables the system to maintain both generative capability and regional control precision.
Solution Approach 2:
The patent introduces an intermediary mask generation component that bridges the text prompt and the image generation model. The mask acts as a mediator that specifies which regions of the source image should be edited based on the text prompt, while preserving other regions. This intermediary layer enables precise control over semantic regions without losing the overall image generation capability.
2Manufacturing precision
If manual masking is used to preserve specific image regions, then control over edited content is improved, but user effort and time consumption increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate masks based on text prompts without requiring manual user input. The mask generation component autonomously identifies and creates masks for regions that need to be edited, preserving other regions automatically. This eliminates the need for users to manually create masks, significantly reducing user effort and time consumption while maintaining precise control over edited content.
3Measurement precision
If text prompts are slightly changed to edit specific objects, then editing precision is improved, but image stability and consistency deteriorate
Solution Approach 1:
The patent applies local quality by making different parts of the image have different properties: edited regions are modified according to the text prompt with high precision, while non-edited regions retain their original appearance and consistency. The mask-based approach ensures that only specific local regions are affected by prompt changes, while the rest of the image maintains its stability and compositional integrity. This resolves the contradiction by allowing precise editing without compromising overall image consistency.
Data Source
AI summary
Some implementations are directed to editing a source image based on a user request to edit the source image. The source image and the user request to edit the source image can be processed, using an image-editing system, to generate one or more image editing instructions. The one or more image editing instructions can indicate an image mask that edit (or preserves) one or more portions of the source image and/or can indicate a target object to be present in the edited image to replace a source object in the source image. Based on the one or more image editing instructions and source image, an edited image that shares the one or more portions with the source image and that differs from the source image by replacing the source object in the source image with the target object can be generated.