LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model

Zebin You, Xiaolu Zhang, Jun Zhou, Chongxuan Li, Ji-Rong Wen

LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model: 22 upvotes on Hugging Face Daily Papers, #12 of 41 papers on 2026-03-03. Day-by-day upvote history.

We present LLaDA-o, an effective and length-adaptive omni diffusion model for multimodal understanding and generation. LLaDA-o is built on a Mixture of Diffusion (MoD) framework that decouples discrete masked diffusion for text understanding and continuous diffusion for visual generation, while coupling them through a shared, simple, and efficient attention backbone that reduces redundant computation for fixed conditions. Building on MoD, we further introduce a data-centric length adaptation strategy that enables flexible-length decoding in multimodal settings without architectural changes. Extensive experiments show that LLaDA-o achieves state-of-the-art performance among omni-diffusion models on multimodal understanding and generation benchmarks, and reaches 87.04 on DPG-Bench for text-to-image generation, supporting the effectiveness of unified omni diffusion modeling. Code is available at https://github.com/ML-GSAI/LLaDA-o.

Paper page on Hugging Face · arXiv

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