Text-Guided Style Transfer Using Relational Loss and Style Templates
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Solution Overview
Problem
Conventional image generation systems fail to account for the subjective nature of target style text descriptions and nuances of style-specific vocabulary, limiting the range of stylistic expression in generated images.
Innovation Solution
Incorporating a relational loss function into text-guided image generation models to enforce a relationship between stylized images and a proxy style set similar to the relationship between target style text and the proxy style set, using a natural language style vocabulary to improve the alignment and realism of generated images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image generation systems use basic text guidance, then the generation process is simple and fast, but the style understanding and alignment between target style text and generated images is poor
Solution Approach 1:
The patent introduces style templates as an intermediary between the target style text and the image generation process. The relational loss function compares relationships between style templates and candidate images against relationships between style templates and target style text, using the style templates as a mediating reference frame to achieve better style alignment without directly optimizing the complex text-image relationship
Solution Approach 2:
The patent replaces conventional direct optimization mechanisms with a relational comparison mechanism. Instead of directly optimizing the alignment between target style text and generated images through standard loss functions, the system substitutes this with a relational loss that compares relational structures, thereby achieving more nuanced style understanding through structural similarity rather than direct mechanical optimization
2Reliability
If the model uses a relational loss function with style templates, then style understanding and realism are improved, but the computational complexity and training time increase
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing style templates before the actual image generation process. These style templates serve as pre-prepared reference structures that can be efficiently compared during training and inference, reducing the computational burden during the main generation process while maintaining high style preservation reliability
Solution Approach 2:
The patent segments the style transfer problem into distinct components: style templates representing different style categories, target style text encoding the desired style, and generated images to be evaluated. The relational loss function operates on these segmented components by comparing their relational structures, allowing for more efficient computation than treating the entire problem as a single optimization task
Data Source
AI summary
An image generation system accessing an input image displayed on a user interface. The image generation system receives, via the user interface, a target style text defining a target style for a stylized image to be generated based on the input image and a request to generate the stylized image. The image generation system generates the stylized image. Generating the stylized image includes applying a text guided image generation model to the input image and the target style text, wherein the text guided image generation model minimizes a loss between a first relationship between the generated stylized image and a set of style templates and a second relationship between the target style text and the set of style templates. The image generation system displays, via the user interface responsive to receiving the request, the generated stylized image.