LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model
Authors: name: Inclusion AI, name: Tiwei Bie, name: Haoxing Chen, name: Tieyuan Chen, name: Zhenglin Cheng, name: Long Cui, name: Kai Gan, name: Zhicheng Huang, name: Zhenzhong Lan, name: Haoquan Li, name: Jianguo Li, name: Tao Lin, name: Qi Qin, name: Hongjun Wang, name: Xiaomei Wang, name: Haoyuan Wu, name: Yi Xin, name: Junbo Zhao
Organization: inclusionAI

We present LLaDA2.0-Uni, a unified discrete diffusion large language model (dLLM) that supports multimodal understanding and generation within a natively integrated framework. Its architecture combines a fully semantic discrete tokenizer, a MoE-based dLLM backbone, and a diffusion decoder. By discretizing continuous visual inputs via SigLIP-VQ, the model enables block-level masked diffusion for both text and vision inputs within the backbone, while the decoder reconstructs visual tokens into high-fidelity images. Inference efficiency is enhanced beyond parallel decoding through prefix-aware optimizations in the backbone and few-step distillation in the decoder. Supported by carefully curated large-scale data and a tailored multi-stage training pipeline, LLaDA2.0-Uni matches specialized VLMs in multimodal understanding while delivering strong performance in image generation and editing. Its native support for interleaved generation and reasoning establishes a promising and scalable paradigm for next-generation unified foundation models. Codes and models are available at https://github.com/inclusionAI/LLaDA2.0-Uni.