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新的扩散 Transformer 提升了图像生成的多尺度和组合能力 · 已追踪 2 个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新的研究论文介绍了用于图像生成的新型扩散 Transformer 架构。第一篇 MS-pDiT 将空间尺度视为一种模态，同时处理多分辨率图像以提高保真度并实现灵活的标记化。第二篇全景场景程序扩散 Transformer (PSP-DiT) 将全景场景程序视为一等潜在变量，增强了模型处理涉及实例身份、属性和空间关系组合提示的能力。 AI

影响 这些扩散 Transformer 架构的进步可能带来更复杂、更可控的图像生成模型。

排序理由 两篇在 arXiv 上发表的学术论文，介绍了用于图像生成的新型扩散 Transformer 架构。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的扩散 Transformer 提升了图像生成的多尺度和组合能力 · 已追踪 2 个来源

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两篇在 arXiv 上发表的学术论文，介绍了用于图像生成的新型扩散 Transformer 架构。
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2 independent sources
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ruibo Chen, Kaishen Wang, Tianyi Xiong, Chenxi Liu, Yanshuo Chen, Heng Huang, Jian Du ·

    Scale as a Modality: Multi-Scale Pixel-Space Diffusion Transformers

    arXiv:2611.08802v1 Announce Type: new Abstract: Latent diffusion models rely on pretrained autoencoders that introduce lossy compression and constrain spatial granularity. Pixel-space diffusion avoids this bottleneck and enables flexible tokenization at multiple spatial scales, y…

  2. arXiv cs.CV TIER_1 English(EN) · Chika Maduabuchi ·

    全景场景程序扩散Transformer

    arXiv:2609.31780v2 Announce Type: replace Abstract: Modern text-to-image models produce high-fidelity images but still struggle with compositional prompts that require instance identity, attribute ownership, counting, spatial ordering, and role-sensitive relations. We introduce P…

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