Image Generation Model Using Ranked Prompt-Image Pairs
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Solution Overview
Problem
Conventional image generation models struggle to generate synthetic images with high aesthetics and semantic relevance when provided with short text prompts, often resulting in images with poor composition, color, and detail.
Innovation Solution
The proposed solution involves training an image generation model using ranked prompt-image pairs, where the prompt-image pairs are divided into synthetic queries based on noun chunking and ranked by semantic similarity and aesthetic scores, allowing the model to learn relationships between short text prompts and high-quality synthetic images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image generation models are used with short text prompts, then the model can process simple inputs quickly, but the generated images have poor aesthetics and low semantic relevance
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate images from the short text prompt, then ranks them using aesthetic evaluation models and semantic similarity scoring before selection. This preliminary ranking process ensures that the final selected image meets both aesthetic and semantic quality standards, resolving the contradiction between quick processing and quality output.
Solution Approach 2:
The system implements feedback mechanisms by using aesthetic evaluation models to score generated images and semantic similarity metrics to measure relevance to the original prompt. These feedback signals guide the selection process, ensuring that only images meeting both aesthetic and semantic criteria are chosen, thereby resolving the quality-relevance contradiction.
2Reliability
If the model generates images with high aesthetic quality, then visual appeal is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by generating a limited set of candidate images (e.g., top-k candidates) rather than exhaustively searching for perfect images. The aesthetic evaluation model then ranks these candidates, and the highest-ranked image is selected. This approach achieves high aesthetic quality while limiting computational effort to a manageable subset, resolving the quality-speed contradiction.
3Loss of information
If the system uses detailed text prompts, then semantic relevance improves, but user input complexity and processing time increase
Solution Approach 1:
The system introduces an intermediary component - the aesthetic evaluation model and semantic similarity scorer - that bridges the gap between simple user prompts and high-quality image generation. This intermediary process automatically enriches the semantic content by ranking multiple generated candidates, allowing users to input simple prompts while still receiving semantically relevant results without manual prompt engineering.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining a text prompt. The method, apparatus, non-transitory computer readable medium, and system further include selecting an image generation model based on a length of the text prompt. In one aspect, the image generation model is trained to generate images using training data including text prompts below a threshold length. An aspect further includes generating, using the selected image generation model, a synthetic image based on the text prompt. In one aspect, the synthetic image includes an element described by the text prompt.