<ul data-eligibleForWebStory="true"><li>Ming-Omni is a unified multimodal model capable of processing images, text, audio, and video efficiently.</li><li>It demonstrates proficiency in speech and image generation using dedicated encoders and an MoE architecture named Ling.</li><li>The model uses modality-specific routers to process tokens from different modalities within a unified framework.</li><li>Ming-Omni can handle diverse tasks without needing separate models, task-specific fine-tuning, or structural redesign.</li><li>It supports audio and image generation, featuring an advanced audio decoder for natural speech generation and Ming-Lite-Uni for high-quality image generation.</li><li>The model can engage in tasks like context-aware chatting, text-to-speech conversion, and versatile image editing.</li><li>Experimental results demonstrate that Ming-Omni offers a powerful solution for unified perception and generation across all modalities.</li><li>Ming-Omni is the first open-source model known to match GPT-4o in modality support.</li><li>All code and model weights of Ming-Omni have been released to encourage further research and development in the community.</li></ul>

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

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