Latent Code Blending for Adjustable Image Stylization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image generation methods struggle to balance realism and aesthetics while providing user control, often resulting in images that are either overly literal or highly stylized without coherence, lacking flexibility in adjusting the level of stylization.
Innovation Solution
A method that generates synthetic images by combining content and style latent codes based on a visual intensity parameter, allowing users to control the balance between realism and aesthetics, using a machine learning model to create images ranging from highly realistic to heavily stylized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image generation models prioritize realism, then the images are more faithful to the input content, but they lack aesthetic stylization and visual appeal
Solution Approach 1:
The patent segments the image generation process into two distinct latent code components: content latent code (preserving realism and fidelity to input) and style latent code (enabling aesthetic stylization). By separating these functions into independent code representations, the system can control each aspect independently and combine them according to user preferences, resolving the contradiction between maintaining realism and applying aesthetic style.
2Ease of manufacture
If image generation models apply heavy stylization, then the images are more aesthetically pleasing, but they lose coherence and become overly artistic without maintaining content integrity
Solution Approach 1:
The patent segments the image generation process into two distinct latent code components: content latent code (preserving realism and fidelity to input) and style latent code (enabling aesthetic stylization). By separating these functions into independent code representations, the system can control each aspect independently and combine them according to user preferences, resolving the contradiction between maintaining realism and applying aesthetic style.
Solution Approach 2:
The patent introduces a controllable parameter (aesthetic score or visual intensity parameter) that dynamically adjusts the balance between content latent code and style latent code. By changing this parameter, users can control the degree of stylization applied while maintaining content coherence, allowing the system to adapt between highly realistic and heavily stylized outputs without losing compositional stability.
3Device complexity
If image generation models provide fixed output style, then the generation process is simpler, but users lack control over the balance between realism and aesthetics
Solution Approach 1:
The patent transforms the fixed output style into a dynamic system where users can adjust the aesthetic score or visual intensity parameter to control the balance between realism and aesthetics. The system adapts its behavior based on user input, allowing continuous adjustment from highly realistic to heavily stylized outputs. This dynamic control mechanism maintains relative simplicity while providing versatile user control over the generation process.
Data Source
AI summary
An image generation method comprises obtaining a content prompt, a style prompt, and a visual intensity parameter, where the content prompt indicates an object, the style prompt indicates a style, and the visual intensity parameter indicates a level of the style. A content latent code and a style latent code are generated based on the content prompt and the style prompt, respectively, and the content latent code and the style latent code are combined based on the visual intensity parameter to obtain a combined latent code. An image generation model generates a synthetic image based on the combined latent code, where the synthetic image includes the object from the content prompt and the style from the style prompt at the level indicated by the visual intensity parameter.