Unified Multimodal Model for Simultaneous Text Image Audio Generation
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Solution Overview
Problem
Current multimodal large models lack the capability for simultaneous generation of multiple modalities such as text, images, and audio, requiring separate models for each modality, which limits their task processing versatility.
Innovation Solution
A unified multimodal model integrating autoregressive generation for discrete data and diffusion generation for continuous data, enabling the simultaneous understanding and generation of multiple modalities like natural language text, images, and audio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate models are used for each modality (text, images, audio), then model specialization and accuracy for each specific modality is improved, but device complexity and loss of time increase due to requiring multiple separate models
Solution Approach 1:
The patent combines multiple separate modality-specific models into a single unified multimodal model that can process and generate multiple modalities (text, images, audio) simultaneously. This merging approach maintains generation accuracy while reducing the complexity of managing multiple separate models and the time required to switch between them.
Solution Approach 2:
The unified multimodal model achieves multi-functionality by incorporating capabilities to understand and generate multiple different modalities within a single model architecture. This allows the model to serve multiple purposes (text processing, image generation, audio processing) without requiring separate specialized models for each function.
2Measurement precision
If separate models are used for each modality, then specialized processing for each modality is improved, but productivity decreases due to requiring multiple separate models for simultaneous generation
Solution Approach 1:
By merging multiple modality processing capabilities into a single unified model, the system can process multiple modalities simultaneously within one model framework, improving productivity while maintaining the specialized processing capabilities needed for each modality type.
Solution Approach 2:
The unified multimodal model employs dynamic processing that can adaptively handle different modalities and task types within a single architecture, allowing the model to efficiently switch between and process multiple modalities without the overhead of managing multiple separate static models.
3Adaptability or versatility
If a unified multimodal model is created to generate multiple modalities simultaneously, then adaptability and versatility are improved, but device complexity increases due to integrating multiple generation capabilities
Solution Approach 1:
The unified multimodal model achieves universality by designing a single model architecture that can handle multiple modalities (text, images, audio) and various task types. This approach improves adaptability and versatility while managing complexity through a cohesive unified framework rather than multiple separate systems.
Data Source
AI summary
A multimodal data generation method is provided. The method includes: inputting a query data sequence into a multimodal model, to obtain a plurality of tokens in a response data sequence, where a current token is generated through the following operations: inputting the query data sequence and a current response data sequence into the multimodal model, so that the multimodal model generates the current token based on the query data sequence and the current response data sequence, in response to determining that the current token belongs to a first data modality; or inputting the query data sequence and a current response data sequence into the multimodal model, so that the multimodal model denoises an initial token sequence based on the query data sequence and the current response data sequence, to generate a result token sequence, in response to determining that the current token belongs to a second data modality.