Language Model Image Generation Through Unified Text-Image Tokens
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Solution Overview
Problem
Existing image generation technologies primarily rely on visual models like GANs and Diffusion series, while language models are underutilized for this task despite their potential in generating natural language texts.
Innovation Solution
A method and apparatus that utilize a trained language model to process an input text sequence, convert it into image encodings, and use a trained image decoder to generate a target image, integrating language and visual dictionaries to facilitate cross-modal image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If visual models (GANs, Diffusion) are used for image generation, then image generation capability is achieved, but language model utilization is insufficient
Solution Approach 1:
The language model is extended to perform multiple functions: it processes both text tokens and image tokens through a unified vocabulary, enabling it to generate images directly while maintaining its text processing capabilities. This multi-functionality allows the language model to serve both natural language generation and image generation tasks.
Solution Approach 2:
Image tokens are introduced as an intermediary representation that bridges text and images. The language model processes text input and generates image tokens, which are then decoded into actual images by a separate decoder. This intermediary approach allows the language model to leverage its existing architecture while achieving image generation capability.
2Loss of information
If language models are used for image generation, then cross-modal information exchange is improved, but image generation quality may deteriorate
Solution Approach 1:
Image tokens serve as an intermediary that preserves information during cross-modal transformation. The language model generates these tokens based on text input, maintaining the semantic information, while the image decoder reconstructs the visual details from these tokens, ensuring both information exchange and generation quality.
Solution Approach 2:
The image generation process is segmented into two specialized components: the language model handles semantic understanding and token generation, while the image decoder handles visual reconstruction. This segmentation allows each component to optimize for its specific function, preserving information accuracy and generation quality.
3Adaptability or versatility
If a unified vocabulary including image encodings is used, then device complexity increases, but adaptability improves
Solution Approach 1:
A single unified vocabulary is created that serves multiple purposes: it contains both text tokens for natural language processing and image tokens for image representation. This universal vocabulary eliminates the need for separate processing pipelines for text and images, reducing overall system complexity despite the increased vocabulary size.
Solution Approach 2:
The text vocabulary and image vocabulary are merged into a single unified vocabulary structure. This combination allows the language model to process both text and image tokens through the same architecture and parameters, simplifying the system by eliminating redundant components while maintaining adaptability for both modalities.
Data Source
AI summary
A method for image generation includes: processing an input text sequence by using a trained language model to obtain an output sequence output by the language model, the output sequence including a plurality of indices in a language dictionary associated with the language model, the language model being trained on the language dictionary, the language dictionary including at least an index set corresponding to text encodings in a natural language and an index set corresponding to image encodings; constructing image encodings corresponding to the plurality of indices in the output sequence into a target feature map; and determining, by using a trained image decoder, a target image matching the text sequence from the target feature map, the image decoder being trained on a visual dictionary including the index set corresponding to the image encodings.