Ming-Flash-Omni: A Sparse, Unified Architecture for Multimodal
  Perception and Generation
We propose Ming-Flash-Omni, an upgraded version of Ming-Omni, built upon a
sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100 billion
total parameters, of which only 6.1 billion are active per token. This
architecture enables highly efficient scaling (dramatically improving
computational efficiency while significantly expanding model capacity) and
empowers stronger unified multimodal intelligence across vision, speech, and
language, representing a key step toward Artificial General Intelligence (AGI).
Compared to its predecessor, the upgraded version exhibits substantial
improvements across multimodal understanding and generation. We significantly
advance speech recognition capabilities, achieving state-of-the-art performance
in contextual ASR and highly competitive results in dialect-aware ASR. In image
generation, Ming-Flash-Omni introduces high-fidelity text rendering and
demonstrates marked gains in scene consistency and identity preservation during
image editing. Furthermore, Ming-Flash-Omni introduces generative segmentation,
a capability that not only achieves strong standalone segmentation performance
but also enhances spatial control in image generation and improves editing
consistency. Notably, Ming-Flash-Omni achieves state-of-the-art results in
text-to-image generation and generative segmentation, and sets new records on
all 12 contextual ASR benchmarks, all within a single unified architecture.
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Cheng Zou
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Zizheng Yang
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Zhengyu He
Ming-Flash-Omni: A Sparse, Unified Architecture for Multimodal
  Perception and Generation
We propose Ming-Flash-Omni, an upgraded version of Ming-Omni, built upon a
sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100 billion
total parameters, of which only 6.1 billion are active per token. This
architecture enables highly efficient scaling (dramatically improving
computational efficiency while significantly expanding model capacity) and
empowers stronger unified multimodal intelligence across vision, speech, and
language, representing a key step toward Artificial General Intelligence (AGI).
Compared to its predecessor, the upgraded version exhibits substantial
improvements across multimodal understanding and generation. We significantly
advance speech recognition capabilities, achieving state-of-the-art performance
in contextual ASR and highly competitive results in dialect-aware ASR. In image
generation, Ming-Flash-Omni introduces high-fidelity text rendering and
demonstrates marked gains in scene consistency and identity preservation during
image editing. Furthermore, Ming-Flash-Omni introduces generative segmentation,
a capability that not only achieves strong standalone segmentation performance
but also enhances spatial control in image generation and improves editing
consistency. Notably, Ming-Flash-Omni achieves state-of-the-art results in
text-to-image generation and generative segmentation, and sets new records on
all 12 contextual ASR benchmarks, all within a single unified architecture.
Ming-Flash-Omni, an advanced Mixture-of-Experts model, enhances computational efficiency and multimodal intelligence, achieving state-of-the-art performance in speech recognition, image generation, and text-to-image generation.
Mixture-of-Experts (MoE) Ling-Flash-2.0 unified multimodal intelligence Artificial General Intelligence (AGI) contextual ASR dialect-aware ASR high-fidelity text rendering scene consistency identity preservation image editing generative segmentation spatial control editing consistency text-to-image generation