Layer-Wise Style Masking for Coherent Image Generation
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Solution Overview
Problem
Existing image generation models struggle with maintaining high realism and coherence under complex or novel style conditions, often integrating unintended content features and lacking control over style influence.
Innovation Solution
The use of layer-wise masks to selectively influence content and style prompts at each layer of an image generation model, with a user-configurable 'style influence parameter' to adjust the degree of style applied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If style conditioning is applied to generate images in a particular style, then the stylistic quality is improved, but unwanted content features from the style prompt are integrated into the generated image
Solution Approach 1:
The patent segments the style embedding into multiple style tokens that are selectively applied to different layers of the image generation model. By dividing the style influence across multiple discrete tokens and layers, the model can apply style conditioning without uniformly integrating all style features at all levels, thereby reducing unwanted content integration while maintaining stylistic quality.
Solution Approach 2:
The patent applies different style weights to different layers of the image generation model. Earlier layers receive lower style weights to preserve content fidelity, while later layers receive higher style weights to achieve desired stylistic effects. This local differentiation of style application strength allows the model to maintain high stylistic quality in appropriate regions while avoiding unwanted content integration.
2Manufacturing precision
If the style influence from the style prompt is increased, then the stylistic impact on the generated image is improved, but control over style influence becomes limited
Solution Approach 1:
The patent introduces dynamic style weighting mechanisms that allow the style influence to be adjusted across different layers and conditions. The style weights are not fixed but can be dynamically modified based on layer depth, style token selection, and user preferences, enabling fine-grained control over style influence while maintaining strong stylistic impact where needed.
Solution Approach 2:
The patent modifies the style embedding parameters by creating multiple style tokens with different weights and applying them selectively. By changing the parameter values (weights) assigned to different style tokens and layers, the model can precisely control the degree and distribution of style influence, allowing users to achieve desired stylistic impact while maintaining control over the generation process.
3Device complexity
If content and style embeddings are combined uniformly across all layers, then the generation process is simplified, but the transfer of content and style efficiency varies by layer
Solution Approach 1:
The patent segments the uniform embedding combination into layer-specific applications. Instead of applying a single uniform combination strategy across all layers, the model divides the process into multiple stages with different combination weights and strategies for each layer, optimizing content and style transfer efficiency at each stage while managing complexity through structured segmentation.
Solution Approach 2:
The patent applies different combination strategies to different layers based on their specific functions. Earlier layers use combinations optimized for content preservation, while later layers use combinations optimized for style transfer. This local optimization of embedding combination strategies improves overall transfer efficiency without requiring complete redesign of the entire generation process.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for generating style-matched images include obtaining a content prompt and a style prompt. The content prompt includes an object and the style prompt includes a style element. Embodiments then encode the content prompt and the style prompt to obtain a content embedding and a style embedding, respectively. Subsequently, embodiments apply a content mask to the content embedding and a style mask to the style embedding to obtain a weighted content embedding and a weighted style embedding, respectively. Embodiments then generate, using an image generation model, a synthetic image based on the weighted content embedding and the weighted style embedding. The synthetic image depicts the object from the content prompt and the style element from the style prompt.