Self-Attention Reference Features for Preserving Object Detail
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Solution Overview
Problem
Conventional image generation models fail to accurately generate synthetic images that preserve the fine details and spatial relationships of an object from a reference image, leading to loss of intricate visual information during the image embedding process.
Innovation Solution
An image generation model is trained to generate image features based on a reference image using an object mask, which identifies the object's location and generates features that are input into the U-Net architecture's attention layers, combining these features with text prompts to create synthetic images that maintain the object's visual details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image generation models are used, then image generation speed is maintained, but fine details and spatial relationships of objects are lost during the embedding process
Solution Approach 1:
The patent segments the image processing by separating object extraction (using object masks) from the overall image generation process. Image features are extracted specifically for identified objects and fed into attention layers, while the rest of the image is processed conventionally. This segmentation preserves fine details of specific objects without requiring the entire image to undergo lossy embedding.
Solution Approach 2:
The patent applies local quality by providing high-fidelity image features specifically to attention layers where they are most needed for object representation, rather than uniformly processing the entire image. The object mask ensures that fine details are preserved locally for identified objects while maintaining overall image generation efficiency.
2Measurement precision
If image features are extracted from reference images using object masks, then object detail accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent introduces object masks as an intermediary component that selectively extracts image features from reference images. These masks act as a mediator between the reference image and the image generation model, providing only the necessary object-specific features to the attention layers without requiring complex modifications to the entire model architecture.
Solution Approach 2:
The patent performs preliminary object identification and feature extraction using object masks before the main image generation process. By pre-processing the reference image to extract only relevant object features and feeding them into attention layers, the system reduces the computational burden during the actual generation phase while maintaining high object detail accuracy.
3Manufacturing precision
If image features from reference images are integrated into the generation process, then synthetic image quality is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by extracting and processing image features only for identified objects using object masks, rather than processing the entire reference image. This selective approach integrates sufficient detail information into the attention layers to improve synthetic image quality while avoiding the computational overhead of processing all image regions, thus reducing overall processing time.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining a reference image an input prompt describing an image element, identifying an object from the reference image; generating, using an image generation model, image features representing the object based on the reference image, and generating, using the image generation model, a synthetic image depicting the image element and the object based on the input prompt and the image features from the reference image.