Prompt Reuse for Generative AI Image Editing
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Solution Overview
Problem
In layout data editing, especially when using generative AI technologies, it is cumbersome for users to issue prompt instructions for editing multiple image contents, such as changing seasonal clothing in images, as each content requires separate input.
Innovation Solution
A method where a first prompt received as input for processing a first image is stored, and a second prompt is generated by reusing at least a part of the stored first prompt to process a second image, thereby simplifying the input process for users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user issues prompt instructions for each image content separately to generative AI, then the editing precision for each image can be controlled, but the user operation complexity increases and time consumption increases
Solution Approach 1:
The prompt instruction is segmented into two parts: a common prompt applied to multiple images and image-specific parameters. This allows the user to define the common editing intent once and apply it to multiple images without repeating the entire prompt for each image, thereby reducing operational complexity while maintaining editing precision.
Solution Approach 2:
The user provides the common prompt instruction in advance before processing multiple images. This preliminary action allows the system to pre-process and store the common prompt, which is then automatically applied to each target image, eliminating the need for repeated manual input and reducing time consumption.
2Measurement precision
If a user issues prompt instructions for each image content separately to generative AI, then the editing can be customized for each image, but the time consumption increases
Solution Approach 1:
The common prompt instruction is provided and stored in advance before processing multiple images. This preliminary action enables the system to automatically retrieve and apply the same prompt to each target image, significantly reducing the time required for repeated manual input while maintaining customized editing capability through image-specific parameters.
Solution Approach 2:
The system copies the common prompt instruction from the user's initial input and applies it to multiple target images. This copying mechanism eliminates the need for manual re-typing or re-inputting the same prompt for each image, thereby reducing time consumption while preserving editing precision through parameter adjustments.
3Ease of operation
If common-context-based editing is performed for multiple image contents, then the user operation is simplified, but it becomes difficult to process and edit each image according to specific user intentions
Solution Approach 1:
The prompt system is segmented into a common prompt part that applies to all images and image-specific parameter parts that can be individually adjusted. This segmentation allows users to simplify operations by defining the common context once, while maintaining adaptability by modifying specific parameters for each image as needed.
Solution Approach 2:
The prompt application mechanism is made dynamic, allowing the common prompt to be automatically applied to multiple images while enabling flexible adjustment of image-specific parameters. This dynamic approach balances operational simplicity with editing adaptability, allowing users to switch between batch processing and individual customization as required.
Data Source
AI summary
The information processing apparatus according to the present disclosure stores a first prompt including an instruction for processing of a first image, the first prompt having been received as an input that is provided to a first trained model which performs processing on and outputs an image. The information processing apparatus further generates a second prompt including at least an instruction for processing of a specified second image, by reusing at least a part of the stored first prompt.