Image Generation Using Language Model Metadata
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Solution Overview
Problem
Existing techniques for generating image content are inefficient, particularly when multiple pieces of image content need to be created, as they require users to repeatedly select and combine various parts, leading to increased workload and variability in operational efficiency due to differing prior knowledge among users.
Innovation Solution
An image generation apparatus and method that acquire text information and a prompt to derive metadata using a language model, which is then used to generate images by selecting and combining appropriate image parts based on the metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select and combine multiple parts to generate image content, then image content can be generated with user control, but the workload increases significantly when more pieces of content are generated
Solution Approach 1:
The system enables self-service image generation by automatically selecting and combining image parts based on text prompts. The image generation model autonomously processes the text input and generates the complete image without requiring manual user intervention in the selection and combination steps, thus reducing user workload while maintaining control through the initial text prompt.
Solution Approach 2:
The patent introduces an intermediary image generation model that acts as a mediator between the user's text prompt and the final generated image. This model translates the text description into appropriate image part selections and combinations, eliminating the need for users to manually perform these complex selection and assembly tasks while still achieving the desired image content.
2Productivity
If content generation operations are allocated to multiple users to reduce individual workload, then more content can be generated, but operational efficiency varies depending on user prior knowledge
Solution Approach 1:
The patent implements a universal image generation model that performs multiple functions: text analysis, image part selection, and image combination. This single multi-functional system can be used by any user regardless of their prior knowledge, ensuring consistent operational efficiency across different users while enabling high-volume content generation through automated processing.
Solution Approach 2:
The patent replaces the mechanical manual process of selecting and combining image parts with an automated computational system. The image generation model uses algorithms to automatically analyze text prompts and generate appropriate images, eliminating the variability in human operational efficiency while maintaining high productivity through scalable automated processing.
3Adaptability or versatility
If a large number of image contents are generated by manually selecting parts, then diverse image content can be created, but the workload for selecting parts and generating content increases
Solution Approach 1:
The system performs self-service by automatically selecting diverse image parts and combining them according to the text prompt requirements. The image generation model independently handles the complex task of selecting appropriate parts for each specific image generation request, enabling diverse content creation without increasing user workload.
Solution Approach 2:
The patent applies preliminary action by pre-training the image generation model on diverse image data and part combinations. This preliminary training enables the model to quickly and accurately select appropriate parts for new image generation tasks without requiring users to manually explore or select from large numbers of parts, thus maintaining diversity while reducing time investment.
Data Source
AI summary
An image generation apparatus acquires text information and a prompt, the prompt being an instruction to output metadata that includes a plurality of attribute values composed of attribute values that respectively correspond to a plurality of attributes and semantically match the text information, derives metadata corresponding to the text information by inputting the text information and the prompt to a language model, generates an image based on the derived metadata.