Ming-Omni: A Unified Multimodal Model for Perception and Generation (2506.09344v1)

Abstract: We propose Ming-Omni, a unified multimodal model capable of processing images, text, audio, and video, while demonstrating strong proficiency in both speech and image generation. Ming-Omni employs dedicated encoders to extract tokens from different modalities, which are then processed by Ling, an MoE architecture equipped with newly proposed modality-specific routers. This design enables a single model to efficiently process and fuse multimodal inputs within a unified framework, thereby facilitating diverse tasks without requiring separate models, task-specific fine-tuning, or structural redesign. Importantly, Ming-Omni extends beyond conventional multimodal models by supporting audio and image generation. This is achieved through the integration of an advanced audio decoder for natural-sounding speech and Ming-Lite-Uni for high-quality image generation, which also allow the model to engage in context-aware chatting, perform text-to-speech conversion, and conduct versatile image editing. Our experimental results showcase Ming-Omni offers a powerful solution for unified perception and generation across all modalities. Notably, our proposed Ming-Omni is the first open-source model we are aware of to match GPT-4o in modality support, and we release all code and model weights to encourage further research and development in the community.

• The paper introduces Ming-Omni, a unified multimodal model that integrates text, image, audio, and video processing using modality-specific encoders and a novel Mixture-of-Experts architecture.
• It achieves competitive benchmarks with a GenEval score of 0.64 and a Fréchet Inception Distance of 4.85, outperforming traditional systems.
• The open-sourced model democratizes access to sophisticated AI, paving the way for scalable, unified research in multimodal perception and generation.

Analysis of Ming-Omni: A Unified Multimodal Model for Perception and Generation

The paper presents the development of Ming-Omni, an advanced multimodal model designed to process and generate content across text, image, audio, and video modalities. It introduces an innovative approach to multimodal learning through a unified framework that streamlines the integration of diverse input types, enabling complex outputs without necessitating separate models or bespoke tuning for specific tasks.

Core Contributions

Model Architecture

Ming-Omni is centered around the use of modality-specific encoders and a Mixture-of-Experts (MoE) architecture, known as Ling. This architecture effectively isolates the processing of each modality through dedicated routers, enhancing the model's ability to manage input diversity with precision. The MoE's distinct routing mechanisms for different modality tokens facilitate efficient and coherent multimodal comprehension and generation, marking a significant evolution from current multimodal models that typically handle either perception or generation rather than both.

Performance and Capabilities

Strikingly, Ming-Omni challenges the traditional paradigm by supporting both audio and image generation tasks within a single framework. It leverages a high-performance audio decoder and an image generator termed Ming-Lite-Uni, boosting its proficiency in various applications, from real-time speech to image editing and styling.

The model demonstrates competitive results across various benchmarks, notably achieving a GenEval score of 0.64 and setting a new state-of-the-art with a Fréchet Inception Distance (FID) score of 4.85. Such metrics underscore its superior quality in generating coherent visual content. Furthermore, Ming-Omni offers an impressive expansion of capabilities typically seen in proprietary models like GPT-4o by being open-sourced, encouraging broad experimentation and further research.

Implications and Future Directions

The introduction of Ming-Omni has far-reaching implications in both practical and theoretical domains. Practically, it negates the requirement for domain-specific models, thereby democratizing access to sophisticated AI capabilities for diverse applications. Theoretically, it prompts a reconsideration of the architectures used in AI models for handling complex cross-modal tasks—highlighting the benefits and feasibility of unified processing systems.

Future Trajectories, as hinted in the paper, see further exploration in the scalable training of multimodal models and enhancing cross-modal interactions. There is also a roadmap for evolving the MoE architecture further to support more intricate integrations, particularly beyond the traditional vision coverage to embrace more nuanced tasks involving natural language and visual complexities.

In summary, the research encapsulated in Ming-Omni paves the way for significant advancements in the AI landscape, particularly in the field of coherent and integrated multimodal processing. It provides a robust foundation for building future models that seek to imitate human-like intelligence by seamlessly blending perception with generation across varied modalities.