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LaViDa 模型通过更快、更可控的扩散技术推进多模态人工智能 · 跟踪 3 个来源

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了 LaViDa，这是一系列基于离散扩散的多模态模型，与自回归模型相比，它提供了更快的推理速度和更好的可控性。LaViDa 在 MMMU 等基准测试中取得了有竞争力的性能，并在需要双向推理的任务中表现出色。在此基础上，Lavida-O 被引入为一个统一的理解和生成框架，能够处理从对象定位到高分辨率文本到图像合成的各种任务，并取得了最先进的结果。进一步的优化产生了 Sparse-LaViDa，它通过动态截断不必要的 token 来加速扩散模型的采样，同时保持生成质量。 AI

影响 这些基于扩散的多模态模型为更快的推理和更可控的生成提供了潜力，影响了图像理解、编辑和合成等领域的应用。

排序理由 该集群包含三篇 arXiv 论文，介绍了新颖的多模态人工智能模型和技术。

在 arXiv cs.CV 阅读 →

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LaViDa 模型通过更快、更可控的扩散技术推进多模态人工智能 · 跟踪 3 个来源

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该集群包含三篇 arXiv 论文，介绍了新颖的多模态人工智能模型和技术。
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报道来源 [3]

  1. arXiv cs.CV TIER_1 English(EN) · Shufan Li, Konstantinos Kallidromitis, Hritik Bansal, Akash Gokul, Yusuke Kato, Kazuki Kozuka, Jason Kuen, Zhe Lin, Kai-Wei Chang, Aditya Grover ·

    LaViDa：用于多模态理解的大型扩散语言模型

    arXiv:2505.16839v4 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) can solve a wide range of tasks requiring visual reasoning. In real-world scenarios, desirable properties for VLMs include fast inference and controllable generation (e.g., constraining outpu…

  2. arXiv cs.CV TIER_1 English(EN) · Shufan Li, Jiuxiang Gu, Kangning Liu, Zhe Lin, Zijun Wei, Aditya Grover, Jason Kuen ·

    Lavida-O: 用于统一多模态理解与生成的弹性大型掩码扩散模型

    arXiv:2509.19244v3 Announce Type: replace 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-…

  3. arXiv cs.CV TIER_1 Italiano(IT) · Shufan Li, Jiuxiang Gu, Kangning Liu, Zhe Lin, Zijun Wei, Aditya Grover, Jason Kuen ·

    Sparse-LaViDa: 稀疏多模态离散扩散语言模型

    arXiv:2512.14008v2 Announce Type: replace Abstract: Masked Discrete Diffusion Models (MDMs) have achieved strong performance across a wide range of multimodal tasks, including image understanding, generation, and editing. However, their inference speed remains suboptimal due to t…

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