Multilingual Text-to-Image Generation via Diffusion Prior
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Solution Overview
Problem
Current image generation models are limited to processing natural language text in a single language, restricting their accessibility to non-English speakers and requiring separate models for each language, which is inefficient and costly.
Innovation Solution
A multilingual image processing apparatus that uses a multilingual encoder, diffusion prior model, and diffusion model to generate images from text prompts in multiple languages, trained on parallel image captions in different languages to produce consistent images across languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single-language image generation model is used, then the model can be simpler and cheaper to train, but it restricts accessibility to non-English speakers and requires separate models for each language
Solution Approach 1:
The patent implements a universal image generation model that can process text prompts in multiple languages through a multilingual encoder. The diffusion prior model is trained on parallel image captions from multiple languages, enabling a single model to serve multiple language functions without requiring separate models for each language, thus achieving multi-functionality while maintaining reasonable complexity
Solution Approach 2:
The patent introduces a multilingual encoder as an intermediary component that translates text prompts from different languages into a unified embedding space. This mediator allows the diffusion model to process multilingual inputs without directly handling language-specific complexities, resolving the contradiction between versatility and complexity
2Measurement precision
If separate image generation models are trained for each language, then language-specific accuracy can be improved, but computational costs and resource requirements increase significantly
Solution Approach 1:
The patent merges multiple language-specific training processes into a single unified training process. By training one diffusion prior model on parallel image captions from multiple languages simultaneously, the system achieves language-specific accuracy for each language while sharing computational resources, thereby reducing overall computational costs compared to training separate models for each language
Solution Approach 2:
The patent changes the training parameters by using parallel image captions from multiple languages as training data. This allows the model to learn language-specific patterns while maintaining a unified architecture, achieving high accuracy across languages without the computational overhead of multiple separate models
3Ease of operation
If a multilingual image generation model is implemented, then accessibility to non-English speakers is improved, but the model requires more complex training data processing
Solution Approach 1:
The patent performs preliminary action by collecting and preparing parallel image captions in multiple languages before training the diffusion prior model. This advance preparation of multilingual training data simplifies the actual training process, as the model receives pre-organized parallel corpora that can be directly used for efficient training, thereby improving user accessibility without excessively complicating the training process
Data Source
AI summary
Systems and methods for image processing are provided. One aspect of the systems and methods includes obtaining a text prompt in a first language. Another aspect of the systems and methods includes encoding the text prompt using a multilingual encoder to obtain a multilingual text embedding. Yet another aspect of the systems and methods includes processing the multilingual text embedding using a diffusion prior model to obtain an image embedding, wherein the diffusion prior model is trained to process multilingual text embeddings from the first language and a second language based on training data from the first language and the second language. Yet another aspect of the systems and methods includes generating an image using a diffusion model based on the image embedding, wherein the image includes an element corresponding to the text prompt.