Ming-Omni: Unified Multimodal AI Framework

• Ming-Omni is a unified multimodal AI model that processes text, vision, audio, and video through dedicated encoders and a shared language core.
• It uses a Mixture-of-Experts transformer with modality-specific routing to efficiently fuse and specialize diverse data streams without separate fine-tuning.
• The framework supports applications like context-aware chatting, text-to-speech, and high-fidelity image editing, setting a new open-source standard for comprehensive AI research.

Ming-Omni is a unified multimodal artificial intelligence model and open-source framework designed for comprehensive perception and generation across text, vision, audio, and video modalities. It is the first open-source system documented to match GPT-4o’s modality coverage, combining dedicated encoders, a Mixture-of-Experts (MoE) language core with modality-specific routing, and highly capable generation decoders for both speech and images. Ming-Omni integrates these components within a single architecture, enabling seamless context-aware chatting, text-to-speech, image editing, and more—without the need for separate models or task-specific fine-tuning. All models, code, and weights are openly released for research and applied development.

1. Multimodal Unified Architecture

Ming-Omni is architected to support end-to-end multimodal processing. Its primary components are:

• Dedicated Modality Encoders: Specialized for vision (Qwen2.5-vl), audio (Whisper), text, and video. Each encoder transforms its input into dense token representations aligned to the language core's embedding space.
• Ling MoE Transformer: An internal LLM based on the sparse MoE architecture, augmented with modality-specific routers to dynamically dispatch tokens to appropriate experts according to their source modality.
• Bridging and Projection Layers: Project encoder outputs into a shared token space and concatenate them for unified processing by the LLM.
• Audio Decoder: An autoregressive neural module for natural, context-aware speech synthesis, leveraging a BPE-based tokenization to optimize inference length and quality.
• Ming-Lite-Uni Visual Generator: An advanced diffusion-based module for high-fidelity image generation and editing, featuring multi-scale, learnable tokens and tight alignment with LLM representations.

This design allows multimodal input streams to be processed jointly, with modality-specific specialization preserved through dynamic routing, and cross-modal interactions facilitated seamlessly at the sequence and decoding levels.

2. Modality-Specific Routing and Mixture-of-Experts

A technical innovation in Ming-Omni is the use of modality-specific routers within its MoE Transformer core. Each input token is tagged by its source modality; the router computes a distribution over experts $\mathbf{E}$ specific to the modality $m$ of token $z_t$: $\mathbf{r}_t = \mathrm{Router}_m(z_t)$ This enables:

• Specialization: Experts can learn distinct functions for different modalities, preventing gradient conflict and enabling better convergence even with sparse or uneven modality coverage.
• Efficient Fusion: Tokens from disparate modalities are processed within a unified framework, but routed to experts that maximize efficiency and representation quality depending on their type.

During training, loss balancing and adaptive weighting are employed to ensure that learning progresses evenly across all modalities.

3. Speech and Image Generation: Audio Decoder and Ming-Lite-Uni

Audio Generation

Ming-Omni’s speech system consists of:

• Audio Encoder (Whisper backbone): Extracts features from incoming speech for perception tasks (ASR, QA).
• Autoregressive Audio Decoder: Generates natural-sounding speech, conditioned on LLM outputs. It applies Byte Pair Encoding (BPE) to discrete audio tokens, reducing sequence length by 36% (from 50Hz to 32Hz), which increases efficiency.

The audio decoder is trained with context states from the LLM, enabling expressiveness (emotion, prosody) and robust conversational speech generation.

Image Generation (Ming-Lite-Uni Integration)

• Visual Generation: Employs a Diffusion Transformer (DiT) architecture.
• Multi-scale Learnable Tokens: The LLM generates aligned latent tokens at multiple spatial resolutions:

$$Q_{s_k} \in \mathbb{R}^{N_{s_k} \times d},\quad \mathcal{S} = \{s_1, ..., s_K\}$$

• Alignment Loss: Mean squared error is used to enforce semantic consistency between the LLM and image generator features:

$$\mathcal{L}_{align} = \mathrm{MSE}(\mathrm{DiT}_h, \mathrm{LM}_z)$$

• Tasks Supported: Text-to-image, style transfer, high-fidelity editing, all enabled via tight semantic bridging.

4. Unified Perception and Generation Tasks

Ming-Omni supports a range of perception and generation capabilities:

Modality	Perception	Generation
Text	✓	✓ (auto & guided)
Images	✓ (analysis, QA)	✓ (T2I, editing)
Audio (Speech)	✓ (ASR, QA, dialogue)	✓ (TTS, context TTS)
Video	✓ (QA, reasoning)	— (Perception only)

This facilitates applications such as vision-language instruction following, context-aware chatting, multimodal QA, robust ASR, controllable speech synthesis, image analysis, text-to-image, and advanced image editing, within a single model and inference pipeline.

5. Experimental Evaluation

Ming-Omni achieves strong results across a range of multimodal benchmarks:

• Speech (Audio ASR and TTS): SOTA on 6/13 Chinese & English public ASR benchmarks, including dialectal and noisy data.
• Image Generation: FID of 4.85 (surpassing SDXL at 8.76 and Janus at 10.10) and GenEval 0.64 (top among unified open models).
• Vision-Language Perception: On par with Qwen2.5-VL-7B using just 2.8B parameters in the Ming-Lite-Omni module.
• Video QA and Long-Video Understanding: Outperforms Qwen2.5-VL-7B-Instruct and LLaVA-OneVision.
• Instruction Following: Demonstrates high-quality, multi-turn understanding and instruction execution in both vision and audio contexts.

Comparative performance matches or exceeds leading closed-source and open baselines (Qwen2.5-Omni, SDXL, GPT-4o) in modality coverage and accuracy.

6. Open-Source Impact and Development

Ming-Omni is fully open-source, with code and models released for academic and commercial use. By releasing all underlying components—including training, inference, and application code—Ming-Omni:

• Sets a new standard for open multimodal and generative AI research.
• Lowers the barrier for developing AGI agents, creative tools, and universal communication interfaces.
• Provides a robust platform for future research in unified multimodal perception, generation, and cross-modal instruction.

Links for access:

• Code Repository: https://github.com/inclusionAI/Ming/tree/main
• Project Overview: https://lucaria-academy.github.io/Ming-Omni/

7. Technical Summary and Significance

Ming-Omni’s architectural advances—modality-specific MoE routing, phase-wise training for unified perception and generation, and rigorous bridging of language, audio, and visual domains—establish a robust, scalable framework for generalist AI agents. Its open-source release parallels the capability spectrum offered by closed commercial systems, democratizing access to AGI-scale multimodal tools and encouraging broad-based research and application development in comprehensive AI.