Image Generator Using Selective Training for Specific Visual Characteristics
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Solution Overview
Problem
Current computer image generation techniques struggle to reliably produce images with specific or unique visual characteristics, as they inherit biases and limitations from the training data, and lack diversity and creative guidance.
Innovation Solution
An image generator that trains generative systems to produce images with specific visual characteristics by exposing them to user-defined visual characteristics in a training process, using natural language expressions to represent these characteristics, and refining images based on quality criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional generative systems are used to produce images, then images can be generated from training data, but the images inherit biases and lack specific visual characteristics
Solution Approach 1:
The patent segments the image generation process into distinct components: a base generative model for overall image synthesis and a specialized training module for injecting specific visual characteristics. This segmentation allows each component to focus on specific tasks, improving both precision and reliability of generating images with targeted visual features.
Solution Approach 2:
The system performs preliminary training of the generative model with curated training images that embody desired visual characteristics before actual image generation. This preliminary action embeds the visual characteristics into the model's parameters, ensuring reliable and precise generation of images with specific features during subsequent operations.
2Adaptability or versatility
If generative systems are trained on diverse training data, then image diversity increases, but the ability to produce images with specific unique concepts decreases
Solution Approach 1:
The patent applies local quality by using diverse training data for general image synthesis while introducing specialized training images with specific visual characteristics for targeted concepts. The system selectively applies different training regimes to different aspects of image generation, maintaining both diversity and specificity where needed.
Solution Approach 2:
The system changes training parameters and data composition based on the desired outcome. For general diversity, it uses broad training datasets; for specific concepts, it adjusts parameters and introduces curated training images with targeted visual features, allowing the model to adapt its behavior to different requirements.
3Ease of operation
If current methods are used to interact with generative systems, then basic image generation is possible, but creative guidance and modification of objects in new scenes is limited
Solution Approach 1:
The patent introduces an intermediary training layer between the base generative model and the user interface. This intermediary module is trained on diverse images and can interpret user requests to generate images with specific visual characteristics, thereby enhancing creative guidance while maintaining ease of operation through natural language or simple interface interactions.
Data Source
AI summary
The subject technology includes an image generator that generates images having specific visual concepts. The image generator uses a selective training process to fine-tune a text to a image generative system. The constrained text to image generative system may be trained to understand multiple custom tokens that embody visual characteristics of images included in fine-tuning datasets. Image generation prompts including one or more custom tokens may be used to condition the image creation process of the constrained text to image system to produce synthetic images having improved specificity, more creativity, and higher performance. Images created by the constrained text to image system may be ranked based on one or more criteria to further refine the created images for one or more specific use cases.