LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model

About this episode

This episode discusses LLaDA2.0-Uni, a unified discrete diffusion large language model that enhances multimodal understanding and generation.

🤗 Upvotes: 213 | cs.CV Authors: Inclusion AI, Tiwei Bie, Haoxing Chen, Tieyuan Chen, Zhenglin Cheng, Long Cui, Kai Gan, Zhicheng Huang, Zhenzhong Lan, Haoquan Li, Jianguo Li, Tao Lin, Qi Qin, Hongjun Wang, Xiaomei Wang, Haoyuan Wu, Yi Xin, Junbo Zhao Title: LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model Arxiv: http://arxiv.org/abs/2604.20796v1 Abstract: 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…

People in this episode

Hosts: Jingwen Liang, Gengyu Wang

Topics covered

• multimodal understanding
• large language model
• image generation
• diffusion model
• artificial intelligence
• machine learning

Keywords

• LLaDA2.0-Uni
• multimodal
• diffusion model
• image generation
• language model
• artificial intelligence
• machine learning

Mentioned in this episode

Organizations: Inclusion AI

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