Personalized Image Generation With Balanced Identity-Text Embeddings
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Solution Overview
Problem
Existing personalized image generation technologies produce images that are not accurate enough due to imbalanced feature contributions between identity and text embeddings, leading to simplified and inconsistent results.
Innovation Solution
An image processing method that employs a multi-dimensional feature extraction approach using a text-to-image generation model with cross attention layers and an interlaced condition mechanism to balance identity and text embeddings, enhancing local texture information through a local enhancement mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If text feature is directly used to generate personalized image, then generation process is simple, but image accuracy is not enough
Solution Approach 1:
The patent segments the feature extraction process into multiple dimensions: identity features (global and local) and text features are extracted separately through different encoding pathways. This segmentation allows comprehensive feature capture while maintaining organized processing, resolving the contradiction between simple generation and high accuracy.
Solution Approach 2:
The patent combines multiple feature types (identity embeddings from global and local encoders, text embeddings) into a composite feature representation. This composite approach integrates diverse information sources to achieve high image accuracy while using a unified generation model.
2Manufacturing precision
If multi-dimensional feature extraction is performed, then image accuracy is improved, but processing complexity increases
Solution Approach 1:
The text-to-image generation model serves multiple functions: it processes identity embeddings from global encoder, local encoder, and text embeddings simultaneously through cross-attention mechanisms. This multi-functionality handles complex multi-dimensional features within a single unified model, improving accuracy without proportionally increasing overall system complexity.
Solution Approach 2:
The patent introduces embedding layers and cross-attention mechanisms as intermediaries that efficiently bridge different feature dimensions. These intermediary components organize and integrate complex features systematically, managing processing complexity while achieving high image accuracy.
3Productivity
If feature contributions are not balanced, then generation process is fast, but image consistency deteriorates
Solution Approach 1:
The cross-attention mechanisms provide feedback loops between identity and text features during generation. Each feature type can modulate the other through attention weighting, ensuring balanced contributions and consistent image output while maintaining efficient generation through parallel processing.
Data Source
AI summary
An image processing method includes: obtaining an input image comprising a preset object, and obtaining text data; encoding the text data to obtain a text embedding feature; performing image feature extraction on the input image according to a plurality of predefined data dimensions, to obtain identity embedding features of the preset object in the data dimensions; and fusing and recognizing the identity embedding features of the preset object in the data dimensions and the text embedding feature by using an interlaced condition mechanism, to generate an output image that includes the preset object and that includes a feature described by the text data.