AI Image Attribute Caching for Low-Latency Editing Workflows
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Solution Overview
Problem
Conventional image editing systems are inefficient in retaining and sharing derived attributes, leading to repetitive computation of computationally heavy algorithms, resulting in wasted time and resources due to latencies from neural networks.
Innovation Solution
An object attribute cache and management system that stores derived attributes from artificial-intelligence models in a cache, allowing for efficient retrieval and reuse, and manages requests through a central application programming interface to reduce redundant computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems reprocess images using computationally heavy algorithms to generate derived attributes, then attribute accuracy is ensured, but processing time and computational resources are wasted
Solution Approach 1:
The system performs preliminary computation of derived attributes using computationally heavy algorithms and stores them in a cache before they are needed. When subsequent requests for the same attributes are made, the cached results are reused, eliminating the need to re-execute the heavy algorithms and thus reducing processing time while maintaining attribute accuracy.
Solution Approach 2:
The system creates copies of derived attributes generated by computationally heavy algorithms and stores them in a cache. These copied attributes can be retrieved and reused multiple times without re-computation, significantly reducing processing time and computational resource consumption while preserving the original attribute accuracy.
2Reliability
If conventional systems re-run algorithms for each edit workflow, then attribute freshness is maintained, but computational resources are wasted
Solution Approach 1:
The system creates and stores copies of derived attributes in a cache that can be shared across multiple edit workflows. When the same attribute is needed in different workflows, the cached copy is reused instead of re-computing, reducing computational resource consumption while maintaining attribute freshness through selective updates.
Solution Approach 2:
The cache system serves multiple edit workflows universally, allowing a single computed attribute to be reused across different workflows. This multi-functional approach eliminates redundant computations and reduces overall computational resource usage while ensuring that each workflow receives fresh attributes when needed.
3Adaptability or versatility
If neural networks are used for image editing, then editing capabilities are enhanced, but latencies increase causing users to close applications
Solution Approach 1:
The system performs preliminary processing using neural networks to generate derived attributes and caches these results before users need them. This preliminary action moves the computationally intensive neural network operations to occur in the background, allowing the user interface to respond quickly without waiting for heavy processing, thus enhancing editing capabilities while maintaining responsive performance.
Solution Approach 2:
The cache acts as an intermediary between the neural network processing and the user interface. Instead of directly querying the neural network for each editing operation, the system first checks the cache for pre-computed attributes. This intermediary layer absorbs the latency of neural network operations, allowing the user interface to remain responsive while still providing enhanced editing capabilities through AI processing.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for determining whether a derived attribute from a request is stored at a derived attribute cache. In particular, in one or more embodiments, the disclosed systems obtain the derived attribute from artificial-intelligence models if the derived attribute is unavailable at the derived attribute cache. If the derived attribute is available at the derived attribute cache, the disclosed system returns the derived attribute in response to a request without having the artificial-intelligence models rederive the attribute.