Generative Model Fine-Tuning for Image Quality and Ad Performance
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Solution Overview
Problem
Existing generative models prioritize visual realism over performance metrics, leading to inefficient image generation that does not effectively meet commercial objectives such as click-through rates and conversion rates in digital content applications.
Innovation Solution
Fine-tuning generative models to incorporate generation, performance, and quality losses, adjusting the training process to prioritize realistic and high-performing image generation by using a combination of generation, performance, and quality loss functions, with performance and quality losses being incorporated later in the training process to enhance stability and reduce artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If generative models prioritize visual realism, then image quality is improved, but performance metrics such as click-through rate and conversion rate deteriorate
Solution Approach 1:
The patent combines multiple loss functions (generation loss, performance loss, and quality loss) into a unified training objective. This merging allows the model to simultaneously optimize for visual quality and performance metrics, resolving the contradiction by making both objectives part of the same optimization process rather than conflicting separate goals.
Solution Approach 2:
The patent introduces performance-related parameters (click-through rate, conversion rate) as additional loss components that can be adjusted during training. By changing the parameter space to include both quality metrics and performance metrics, the model can balance both objectives through weighted combination of loss functions.
2Manufacturing precision
If high resolution images are generated, then visual quality is improved, but processing cost and memory usage increase
Solution Approach 1:
The patent changes the optimization parameters to include performance metrics that account for processing efficiency. By incorporating loss functions that evaluate both visual quality and performance characteristics, the model learns to generate images that achieve acceptable quality at optimized resolution levels, reducing unnecessary processing costs.
Solution Approach 2:
The patent applies different quality requirements to different regions or aspects of image generation. By using multiple loss functions with different weights, the model can prioritize quality in critical regions while accepting lower quality in less important areas, thereby reducing overall processing requirements while maintaining essential visual appeal.
3Manufacturing precision
If high resolution images are generated, then visual quality is improved, but memory usage increases
Solution Approach 1:
The patent modifies the training parameters to include performance loss that accounts for resource efficiency. This allows the model to learn optimal balance between image resolution and memory requirements, generating images that meet quality thresholds without excessive memory consumption.
4Productivity
If existing generative models are used without fine-tuning, then generation speed is maintained, but performance for commercial objectives deteriorates
Solution Approach 1:
The patent applies preliminary fine-tuning action to the generative model before deployment. By pre-training the model with performance-oriented loss functions, the model is prepared in advance to generate images that meet commercial objectives, rather than requiring post-generation optimization or retraining.
Solution Approach 2:
The patent implements feedback mechanisms through performance loss functions that evaluate generated images against commercial metrics. This feedback loop during training guides the model to adjust its generation process to improve performance while maintaining generation speed, as the model learns efficient patterns that satisfy both speed and performance requirements.
Data Source
AI summary
Aspects of the disclosure are directed to text to image generative models fine-tuned to generate images that account for performance in addition to quality. For example, in a digital content domain, the generated images can be not only visually appealing but perform well as advertising assets, e.g., result in improved click through rate and/or conversion rate. Accounting for performance and quality can reduce processing cost and memory usage when generating images from text prompts, as the resolution of the image can be balanced with its function, allowing for reduced quality images that can still perform well.