Text-to-Image Customization With Identity-Preserving Guidance
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Solution Overview
Problem
Conventional text-to-image generation models struggle to generate creative images based on ambiguous or uncertain text prompts and fail to preserve the identity of objects in the input image while providing user-friendly explanations.
Innovation Solution
A system comprising an image encoder, language generation model, and image generation model is used to encode input images and text prompts, generating a guidance embedding that includes high-level semantic information, allowing the image generation model to create synthetic images with modifications and a text response explaining the changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional text-to-image generation models are used, then image generation based on text prompts is achieved, but the models fail to handle ambiguous or uncertain text prompts and cannot preserve object identity from input images
Solution Approach 1:
The patent introduces an image encoder as an intermediary component that processes the input image to extract visual features and object identities. This encoder acts as a mediator between the text prompt and the image generation model, providing structured visual information that helps the model understand both the explicit text instructions and the implicit visual context, thereby resolving the contradiction between handling ambiguous prompts and preserving object identity
Solution Approach 2:
The patent transforms the input image into a different dimensional representation through encoding, converting spatial image data into feature vectors or embeddings. This dimensional transformation allows the system to process visual information in a format that can be effectively combined with text embeddings, enabling the model to simultaneously consider both text prompt ambiguity and visual object identity without direct conflict
2Ease of operation
If conventional models generate images based on text prompts, then image generation is achieved, but the models fail to provide user-friendly explanations for the generated images
Solution Approach 1:
The patent designs the image generation model to perform multiple functions simultaneously: generating the synthetic image and producing explanatory text. By making the model universal and multi-functional, it can handle both the creative image generation task and the explanatory communication task within a single system framework, thereby providing user-friendly explanations without proportionally increasing perceived complexity
Solution Approach 2:
The patent incorporates a feedback mechanism where the model generates explanations about its own image generation process. This self-reflection capability allows the system to communicate its reasoning and decisions to users, providing transparency and understanding of how the synthetic image was created from the input image and text prompt, thus improving ease of operation through explanatory feedback
3Measurement precision
If comprehensive training datasets are created for image generation, then model accuracy is improved, but the training dataset creation time increases significantly
Solution Approach 1:
The patent performs preliminary encoding of the input image into feature representations before the main image generation process. By pre-processing and encoding visual information in advance, the system prepares structured data that can be efficiently utilized during generation, reducing the need for extensive trial-and-error training and thereby reducing dataset creation and model training time while maintaining accuracy
Solution Approach 2:
The patent uses the input image as a reference or template to generate the synthetic image. By copying and transforming features from the input image rather than generating entirely new content from scratch, the system maintains consistency and accuracy with fewer training requirements, as the structural information is already present in the source image and can be directly leveraged
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining an input image and a text prompt including an image modification request, generating a text response based on the input image and the text prompt, where the text response describes a modification to the input image corresponding to the image modification request, and generating a synthetic image based on the input image and an output embedding of a language generation model, where the synthetic image depicts the modification to the input image.