Text-to-Image Multi-Concept Fusion With Layered Cross-Attention
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Solution Overview
Problem
Conventional image generation models fail to accurately produce customized images with multiple custom concepts while preserving semantics, often resulting in merged or missing concepts, and do not consistently depict concept-to-concept interactions such as hugging or holding hands.
Innovation Solution
An image generation model utilizing different layers trained for each specific custom concept, combined with cross-attention layers to fuse features from masked regions, and a two-stage pipeline that generates a template image based on an input prompt and incorporates target concepts using spatial guidance from mask regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single image generation model is used to generate images with multiple concepts, then the generation process is simple, but the model fails to accurately preserve multiple custom concepts and their interactions
Solution Approach 1:
The patent divides the image generation model into multiple specialized layers, where each layer is trained to handle a specific custom concept. This segmentation allows each layer to focus on preserving the unique characteristics of its assigned concept, thereby improving overall concept preservation accuracy while maintaining a relatively simple overall model structure.
Solution Approach 2:
Different layers of the model are assigned different levels of specialization for different concepts. Each layer has locally optimized quality for its specific concept, with cross-attention mechanisms allowing selective interaction between layers. This enables high-fidelity preservation of each custom concept while maintaining computational efficiency.
2Device complexity
If conventional image generation models are used, then the model structure is simple, but concept-to-concept interactions such as hugging or holding hands are not consistently depicted
Solution Approach 1:
The patent introduces cross-attention layers as intermediary components between the concept-specific layers. These cross-attention mechanisms act as mediators that facilitate controlled interaction between different custom concepts, enabling consistent depiction of concept-to-concept interactions like hugging or holding hands while maintaining the integrity of each individual concept.
3Manufacturing precision
If multiple layers trained for each custom concept are used, then concept preservation accuracy is improved, but the model complexity increases
Solution Approach 1:
The patent designs the multiple layers with universal cross-attention mechanisms that can handle any combination of custom concepts. Each layer is multi-functional, capable of interacting with any other layer through the cross-attention mechanism, which reduces the need for excessive specialized components and optimizes the balance between accuracy and complexity.
4Manufacturing precision
If a two-stage pipeline with spatial guidance is used, then multi-concept fusion accuracy is improved, but the generation process becomes more complex
Solution Approach 1:
The patent implements a two-stage pipeline where the first stage generates a template image that establishes the spatial layout and relationships between concepts. This preliminary action provides spatial guidance for the second stage, which then refines the image by incorporating the custom concepts into the pre-established spatial framework, improving fusion accuracy while organizing complexity into manageable stages.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image generation includes obtaining an input prompt including a first image element and a second image element. The image generation model generates first image features representing the first image element using a first layer selected based on the first image element and second image features representing the second image element using a second layer selected based on the second image element, wherein the second layer is selected based on the second image element. A synthetic image is generated including the first image element and the second image element based on the first image features and the second image features.