Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation

• URL: http://arxiv.org/abs/2509.19244v2
• Date: Wed, 24 Sep 2025 09:38:15 GMT
• Title: Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation
• Authors: Shufan Li, Jiuxiang Gu, Kangning Liu, Zhe Lin, Zijun Wei, Aditya Grover, Jason Kuen,
• Abstract summary: We propose Lavida-O, a unified Masked Diffusion Model (MDM) for multimodal understanding and generation.<n>Lavida-O presents a single framework that enables image-level understanding, object grounding, image editing, and high-resolution text-to-image synthesis.<n>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.
• Score: 63.50827603618498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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