Prompt Partitioning in Generative AI for Large Visual Prompts
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Solution Overview
Problem
Generative artificial intelligence models struggle to accurately generate visual content for large input prompts due to the uniform processing of tokens, which leads to inaccuracies in handling local and global concepts and spatial relationships.
Innovation Solution
The input prompt is partitioned into sub-prompts based on contextual information using a gating mechanism, allowing different layers of the model to process these sub-prompts independently, considering spatial and temporal relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform processing of tokens is used in generative AI models, then the model structure remains simple, but the accuracy of generating visual content for large input prompts deteriorates
Solution Approach 1:
The input prompt is divided into multiple sub-prompts based on spatial relationships and contextual information. Each sub-prompt is processed independently by different layers of the model, allowing for more accurate handling of local and global concepts while maintaining a manageable model structure.
Solution Approach 2:
Different processing strategies are applied to different parts of the input prompt based on their spatial and contextual characteristics. Local regions are processed with attention to their specific spatial relationships, while global context is maintained through the gating mechanism, enabling differentiated quality processing without uniform complexity.
2Device complexity
If large input prompts are processed uniformly, then the processing approach remains simple, but the handling of local and global concepts and spatial relationships deteriorates
Solution Approach 1:
The processing approach segments the input prompt into sub-prompts based on spatial relationships and contextual relevance. This segmentation allows the model to handle local concepts with appropriate spatial attention while maintaining global context, improving reliability without requiring uniformly complex processing throughout.
Solution Approach 2:
A gating mechanism acts as an intermediary that determines which sub-prompts are processed by which model layers based on spatial and contextual information. This intermediary structure enables sophisticated handling of concepts and relationships without requiring the entire processing system to be uniformly complex.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for generating responses to large input prompts using a generative artificial intelligence model. An example method generally includes receiving an input prompt for processing using a generative artificial intelligence model. The input prompt is partitioned into a plurality of sub-prompts based on contextual information associated with tokens in the input prompt. A response to the input prompt is generated using the generative artificial intelligence model based on the plurality of sub-prompts and the contextual information associated with the tokens in the input prompt. The generated response is output.