📝 SummarXiv

Abstract

We proposed Lavida-O, a unified multi-modal Masked Diffusion Model (MDM) capable of image understanding and generation tasks. Unlike existing multimodal diffsion language models such as MMaDa and Muddit which only support simple image-level understanding tasks and low-resolution image generation, Lavida-O exhibits many new capabilities such as object grounding, image-editing, and high-resolution (1024px) image synthesis. It is also the first unified MDM that uses its understanding capabilities to improve image generation and editing results through planning and iterative self-reflection. To allow effective and efficient training and sampling, Lavida-O ntroduces many novel techniques such as Elastic Mixture-of-Transformer architecture, universal text conditioning, and stratified sampling. \ours~achieves state-of-the-art performance on a wide range of benchmarks such as RefCOCO object grounding, GenEval text-to-image generation, and ImgEdit image editing, outperforming existing autoregressive and continuous diffusion models such as Qwen2.5-VL and FluxKontext-dev, while offering considerable speedup at inference.