Image Generation Fine-Tuning Plug-In for Multi-Resolution Quality
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Solution Overview
Problem
Existing machine learning models for image generation struggle to produce high-quality images with resolutions different from their training resolution, leading to issues like repetition, disorder, or increased computational expense.
Innovation Solution
A fine-tuning plug-in is used to adapt a first machine learning model to a second model capable of generating images with specified resolutions by leveraging reference images with different resolutions, preserving the quality of the generated images while reducing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a machine learning model is trained on reference images of a specific resolution, then the model can generate images of that resolution, but the model cannot generate high-quality images with different resolutions
Solution Approach 1:
The patent applies dynamic resolution adaptation by training the machine learning model to handle multiple resolutions dynamically. The model learns to adjust its processing based on the input image resolution, enabling it to generate high-quality images at various resolutions (e.g., 512x512, 1024x1024, 1536x1536) rather than being fixed to a single resolution.
Solution Approach 2:
The patent changes the resolution parameter as a variable in the training process. By incorporating images of different resolutions into the training dataset and adjusting model parameters accordingly, the model learns to maintain image quality across different resolution settings, transforming a static resolution constraint into a flexible parameter.
2Adaptability or versatility
If the model generates images at resolutions different from training resolution, then resolution versatility is achieved, but image quality deteriorates with repetition and disorder
Solution Approach 1:
The patent performs preliminary training on diverse resolution images before actual generation. By pre-training the model on a comprehensive dataset containing images at multiple resolutions and applying data augmentation techniques, the model builds a foundation for maintaining quality during inference at different resolutions.
Solution Approach 2:
The patent implements feedback mechanisms during training where the model evaluates and adjusts its outputs based on resolution variations. This feedback loop enables the model to learn from its performance at different resolutions and continuously improve its ability to maintain image quality while generating at varied resolutions.
3Manufacturing precision
If the model is fine-tuned for specific resolution, then image quality at that resolution improves, but computational expense increases
Solution Approach 1:
The patent trains a universal model that can handle multiple resolutions with a single training process. Instead of creating separate models for different resolutions, the model learns to adapt to various resolutions (512x512, 1024x1024, 1536x1536) using a unified architecture and training methodology, reducing overall computational expense while maintaining quality.
Solution Approach 2:
The patent changes the training approach by incorporating resolution variation as a key parameter. By training the model to handle different resolutions through parameter adjustment and data augmentation rather than separate training processes, the patent reduces computational expense while maintaining image quality across resolutions.
Data Source
AI summary
A method, an apparatus, a device, a medium for generating an image are provided. In a method, a first machine learning model is obtained, the first machine learning model being obtained based on a reference image having a first resolution. The first machine learning model is fine-tuned to a second machine learning model by a fine-tuning plug-in that is obtained based on a reference image having the second resolution. A target image is generated based on a target prompt by a second machine learning model, the target image having a resolution and image content specified by the target prompt. With the example implementations of the disclosure, the fine-tuning plug-in may obtain knowledge related to generating an image(s) with a further resolution(s), so that the second machine learning model may generate images with different resolutions in a more accurate and effective manner.