Image Generation Parameter Batching for Multi-Output Task Creation
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Solution Overview
Problem
Existing methods for generating images using neural networks are inefficient, requiring users to repeatedly set parameters and execute tasks multiple times to obtain multiple images, consuming significant time and effort.
Innovation Solution
An information processing method and apparatus that allows users to set multiple parameter values simultaneously for different categories, enabling a generation model to produce a plurality of images based on combined parameter values, thereby improving efficiency and reducing labor and time costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users set parameters and execute tasks multiple times to obtain multiple images, then the number of generated images increases, but the time consumption and operational effort increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-setting multiple parameter values across different categories before task execution. Users configure all parameter combinations in advance, and the system prepares the generation tasks with these pre-configured parameters, enabling batch processing without repeated manual intervention.
Solution Approach 2:
The system implements continuous useful action by automatically executing multiple generation tasks in sequence without requiring user intervention between each task. Once the parameter configurations are set, the system continuously processes all tasks and generates multiple images in an automated workflow, eliminating idle time and repeated operations.
2Quantity of substance
If users set parameters and execute tasks multiple times to obtain multiple images, then the number of generated images increases, but the operational effort increases significantly
Solution Approach 1:
Users perform the setup action once by configuring multiple parameter values across different categories in advance. The system stores these configurations and automatically applies them to multiple task executions, eliminating the need for users to repeatedly set parameters for each generation task.
Solution Approach 2:
The system provides self-service functionality by automatically managing the batch generation process. After initial configuration, the system autonomously executes tasks, manages parameter combinations, and generates multiple images without requiring continuous user intervention, making the operation easier and more efficient.
3Productivity
If the system supports batch generation with multiple parameter combinations, then image generation efficiency improves, but the system complexity increases
Solution Approach 1:
The system segments parameters into different categories, with each category containing multiple possible values. This segmentation allows the system to systematically manage complex parameter combinations by organizing them into structured groups, making the batch generation process more manageable and efficient.
Solution Approach 2:
The system implements a universal parameter configuration mechanism that can handle multiple parameter categories and their combinations through a single integrated interface. This multi-functional approach allows the system to manage diverse parameter types (such as model settings, generation parameters, and output configurations) uniformly, improving efficiency without proportionally increasing complexity.
Data Source
AI summary
Embodiments of the present disclosure relate to an information processing method and apparatus, a device, and a medium. The method includes: displaying a first interface, where the first interface presents a task creation control; displaying a second interface in response to the task creation control being triggered, where the second interface displays a task name setting control and a parameter setting control; obtaining, by the task name setting control, a name of a target task to be performed, and obtaining, by the parameter setting control, a target parameter corresponding to the target task, where there is at least one category of target parameters, and each category of the target parameters corresponds to at least one parameter value; and providing the target parameter to a target generation model causing the target generation model generates a target image corresponding to the target task based on the target parameter.