Text-to-Image Model Training With Semantic Alignment for Simple Prompts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing text-to-image models require complex and detailed description texts for effective training, making it difficult for non-expert users to generate high-quality images with simple prompts due to limited semantic understanding and knowledge reasoning capabilities.
Innovation Solution
Integrate a neural network module with a pre-trained language model to align semantic representations of simple and complex prompts, enhancing the image generation model's understanding and reasoning capabilities through knowledge distillation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex and detailed description texts are used for training, then the model's image generation quality is improved, but the ease of operation deteriorates as non-expert users cannot easily generate high-quality images with simple prompts
Solution Approach 1:
The patent introduces an intermediary module (semantic alignment module) that translates simple user prompts into complex semantic representations. This intermediary bridges the gap between simple input and complex processing requirements, allowing non-experts to generate high-quality images without manually crafting detailed prompts. The module acts as a mediator that automatically enriches simple prompts with semantic information equivalent to complex expert-level descriptions.
2Manufacturing precision
If the model's semantic understanding and knowledge reasoning capabilities are enhanced, then the image generation quality with simple prompts is improved, but the device complexity increases due to additional neural network modules
Solution Approach 1:
The patent segments the complex semantic understanding task into distinct modular components: a text encoding module, a semantic alignment module, and an image generation module. Each module has a specific function and can be independently trained and optimized. This segmentation reduces overall system complexity by breaking down the monolithic complex model into manageable parts that work together through standardized interfaces.
Solution Approach 2:
The semantic alignment module serves multiple functions: it aligns semantic representations, enhances simple prompts, and bridges different modalities (text and image). This multi-functionality reduces the need for separate specialized modules for each task, thereby reducing overall device complexity while maintaining enhanced capabilities.
3Productivity
If knowledge distillation is used to align semantic representations, then the training efficiency is improved, but the loss of information may occur during the distillation process from complex to simple representations
Solution Approach 1:
The patent implements a feedback mechanism in the knowledge distillation process where the aligned semantic representations are continuously refined based on their performance in image generation tasks. The system uses gradient-based optimization to adjust the alignment parameters, ensuring that semantic information is preserved while achieving efficient training. This feedback loop prevents information loss by iteratively improving the distillation process based on actual generation outcomes.
Data Source
AI summary
A method, apparatus, and computer-readable storage medium for training image generation models using varying text description complexity. The method obtains training samples containing first and second description texts corresponding to original images, where the second text contains more keywords than the first. Shallow and deep representations are extracted from the first description text using a text encoder and neural network, forming a comprehensive text representation. A diffusion model generates predicted images from this representation. The second description text is processed to extract a reference text representation. Model parameters are adjusted based on both comprehensive and reference text representations to obtain a trained image generation model capable of enhanced text-to-image generation performance.