EMMA: Efficient Multimodal Understanding, Generation, and Editing with a Unified Architecture
We propose EMMA, an efficient and unified architecture for multimodal understanding, generation and editing. Specifically, EMMA primarily consists of 1) An efficient autoencoder with a 32x compression ratio, which significantly reduces the number of tokens required for generation. This also ensures the training balance between understanding and generation tasks by applying the same compression ratio to images. 2) Channel-wise concatenation instead of token-wise concatenation among visual understanding and generation tokens, which further reduces the visual tokens in unified architectures. 3) A shared-and-decoupled network that enables mutual improvements across tasks while meeting the task-specific modeling requirements. 4) A mixture-of-experts mechanism adopted for visual understanding encoder, which substantially improves perceptual capabilities with a few parameters increase. Extensive experiments have shown that EMMA-4B can significantly outperform state-of-the-art unified multimodal approaches (e.g., BAGEL-7B) in both efficiency and performance, while also achieving competitive results compared to recent multimodal understanding and generation experts (e.g., Qwen3-VL and Qwen-Image). We believe that EMMA lays a solid foundation for the future development of unified multimodal architectures.
6932753c6d1060ca587a27b3
Xin He
6932753c6d1060ca587a27b4
Longhui Wei
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2025-12-09T20:10:04.711Z
66bb8186150fad8b4e3b1f8c
/avatars/1e95838ed1418e9c52338931b9f9f92e.svg
Longhui Wei
Joohnzxcv
user
Joohnzxcv
6932753c6d1060ca587a27b5
Jianbo Ouyang
6932753c6d1060ca587a27b6
Lingxi Xie
6932753c6d1060ca587a27b7
Qi Tian
EMMA: Efficient Multimodal Understanding, Generation, and Editing with a Unified Architecture
We propose EMMA, an efficient and unified architecture for multimodal understanding, generation and editing. Specifically, EMMA primarily consists of 1) An efficient autoencoder with a 32x compression ratio, which significantly reduces the number of tokens required for generation. This also ensures the training balance between understanding and generation tasks by applying the same compression ratio to images. 2) Channel-wise concatenation instead of token-wise concatenation among visual understanding and generation tokens, which further reduces the visual tokens in unified architectures. 3) A shared-and-decoupled network that enables mutual improvements across tasks while meeting the task-specific modeling requirements. 4) A mixture-of-experts mechanism adopted for visual understanding encoder, which substantially improves perceptual capabilities with a few parameters increase. Extensive experiments have shown that EMMA-4B can significantly outperform state-of-the-art unified multimodal approaches (e.g., BAGEL-7B) in both efficiency and performance, while also achieving competitive results compared to recent multimodal understanding and generation experts (e.g., Qwen3-VL and Qwen-Image). We believe that EMMA lays a solid foundation for the future development of unified multimodal architectures.
EMMA is an efficient unified architecture for multimodal tasks that uses autoencoders, channel-wise concatenation, shared-and-decoupled networks, and mixture-of-experts to achieve superior performance and efficiency.
autoencoder channel-wise concatenation shared-and-decoupled network mixture-of-experts multimodal understanding multimodal generation perceptual capabilities