Text-to-Image Generation With Feature-Preserving Multi-Stage Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing text to image generating systems struggle to effectively generate high-quality images with detailed features that match text descriptions, often losing calculated features and requiring excessive computational resources.
Innovation Solution
A text to image generating system comprising a text editor, first and second stage generators, and feature preserving and enhancing modules, including channel excitation, fast feature extraction, and squeeze-and-excitation networks, to enhance and preserve image features, ensuring the generated images closely match text descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multi-stage generation methods are used to increase image resolution, then image quality improves, but calculated image feature data is lost and more calculating resources are required
Solution Approach 1:
The patent implements a multi-stage generation architecture where each stage nests within the previous one. The first stage generator creates initial image features, which are then refined by the second stage generator. Crucially, the feature preserving module is nested within this pipeline to continuously preserve and transfer feature data from earlier stages to later stages, preventing information loss while maintaining the benefits of progressive resolution enhancement.
Solution Approach 2:
The feature preserving module acts as an intermediary component between the multi-stage generators. It receives image feature data from the first stage generator, preserves critical features through channel excitation and squeezing operations, and passes the preserved features to the second stage generator. This intermediary mechanism ensures that calculated features are not lost during the multi-stage processing while still enabling resolution enhancement.
2Manufacturing precision
If multi-stage generation methods are used to increase image resolution, then image quality improves, but more calculating resources are required
Solution Approach 1:
The feature preserving module extracts and preserves only the most critical image features from each generation stage using channel excitation and channel squeezing operations. Instead of processing and storing all intermediate feature data through multiple stages, the system extracts essential features and preserves them for later use. This extraction approach reduces the computational burden of multi-stage generation while maintaining image quality.
Solution Approach 2:
The system discards redundant intermediate feature data that is not essential for final image quality, and recovers critical features through the feature preserving module. By selectively preserving important features and discarding less important ones, the multi-stage generation process requires fewer calculating resources while still achieving high-quality output images.
3Ease of operation
If standard text to image generation is used, then processing is simpler, but generated images do not match text descriptions accurately
Solution Approach 1:
The system performs preliminary text analysis using a text editor to extract word features and generate sentence vectors before the image generation process begins. This preliminary processing of text information creates a detailed semantic representation that guides the multi-stage image generation, ensuring that the generated images accurately reflect the text description while maintaining a relatively simple overall process.
Data Source
AI summary
A text to image generating system includes a text editor, a first stage generator, a weight processing unit, a second stage generator, a feature preserving module, a feature enhancing module and a final image forming module. The text editor is for analyzing a sentence to obtain a sentence vector, and an initial image generation condition is obtained. The first stage generator generates a plurality of first stage image feature vectors. The weight processing unit calculates a plurality of first stage updated image feature vectors. The second stage generator generates a plurality of second stage image feature vectors. The feature preserving module conducts an expanding process, a convolution process, and a channel squeezing process for the second stage image feature vectors. The feature enhancing module conducts a squeezing process and a feature enhancing process for the second stage image feature vectors. The final image forming module generates a final image.