Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal
  Understanding and Generation
We propose Lavida-O, a unified Masked Diffusion Model (MDM) for multimodal
understanding and generation. Unlike existing multimodal MDMs such as MMaDa and
Muddit which only support simple image-level understanding tasks and
low-resolution image generation, Lavida-O presents a single framework that
enables image-level understanding, object grounding, image editing, and
high-resolution (1024px) text-to-image synthesis. Lavida-O incorporates a novel
Elastic Mixture-of-Transformers (Elastic-MoT) architecture that couples a
lightweight generation branch with a larger understanding branch, supported by
token compression, universal text conditioning and stratified sampling for
efficient and high-quality generation. Lavida-O further incorporates planning
and iterative self-reflection in image generation and editing tasks, seamlessly
boosting generation quality with its understanding capabilities. Lavida-O
achieves state-of-the-art performance on a wide range of benchmarks including
RefCOCO object grounding, GenEval text-to-image generation, and ImgEdit image
editing, outperforming existing autoregressive models and continuous diffusion
models such as Qwen2.5-VL and FluxKontext-dev, while offering considerable
speedup at inference. These advances establish Lavida-O as a new paradigm for
scalable multimodal reasoning and generation.
68d4b9c136950a9dff1568e2
Shufan Li
admin_assigned
2025-09-25T11:57:08.415Z
6310531914aa81e1044363ed
/avatars/ae7767e591cb7199ea2f62d2db89fc7f.svg
Shufan Li
user
jacklishufan
68d4b9c136950a9dff1568e3
Jiuxiang Gu
admin_assigned
2025-09-25T11:57:15.642Z
642467708d97ce93878e8124
/avatars/f0e464ddb4bd790f470fc0f10275fa26.svg
Jiuxiang Gu
user
JoshuaGu
68d4b9c136950a9dff1568e4
Kangning Liu
admin_assigned
2025-09-25T11:57:24.441Z
6711e867f25bb4f32f880872
/avatars/02ec6a9e59f4c4eec6cc7142939cea79.svg
Kangning Liu
user
kl3141
68d4b9c136950a9dff1568e5
Zhe Lin
68d4b9c136950a9dff1568e6
Zijun Wei
68d4b9c136950a9dff1568e7
Aditya Grover
68d4b9c136950a9dff1568e8
Jason Kuen
admin_assigned
2025-09-25T11:57:41.466Z
65384eaeef7d2bb8c4baf1b2
/avatars/b9217561a59309d6b0ddb417324c6cad.svg
Jason Kuen
user
xternalz
Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal
  Understanding and Generation
We propose Lavida-O, a unified Masked Diffusion Model (MDM) for multimodal
understanding and generation. Unlike existing multimodal MDMs such as MMaDa and
Muddit which only support simple image-level understanding tasks and
low-resolution image generation, Lavida-O presents a single framework that
enables image-level understanding, object grounding, image editing, and
high-resolution (1024px) text-to-image synthesis. Lavida-O incorporates a novel
Elastic Mixture-of-Transformers (Elastic-MoT) architecture that couples a
lightweight generation branch with a larger understanding branch, supported by
token compression, universal text conditioning and stratified sampling for
efficient and high-quality generation. Lavida-O further incorporates planning
and iterative self-reflection in image generation and editing tasks, seamlessly
boosting generation quality with its understanding capabilities. Lavida-O
achieves state-of-the-art performance on a wide range of benchmarks including
RefCOCO object grounding, GenEval text-to-image generation, and ImgEdit image
editing, outperforming existing autoregressive models and continuous diffusion
models such as Qwen2.5-VL and FluxKontext-dev, while offering considerable
speedup at inference. These advances establish Lavida-O as a new paradigm for
scalable multimodal reasoning and generation.
Lavida-O, a unified Masked Diffusion Model, excels in multimodal understanding and generation tasks, including object grounding, image editing, and high-resolution text-to-image synthesis, outperforming existing models with improved efficiency and quality.
Masked Diffusion Model Elastic Mixture-of-Transformers token compression universal text conditioning stratified sampling planning iterative self-reflection RefCOCO GenEval ImgEdit autoregressive models continuous diffusion models