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English(EN) AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation

AnyFlow 实现灵活的视频扩散模型生成

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 AnyFlow，一个新颖的视频扩散模型框架，允许在生成过程中使用任意数量的采样步数。与之前步数越多效果越差的方法不同，AnyFlow 通过学习任意时间间隔上的流图转换来优化整个 ODE 采样轨迹。该方法在高达 140 亿参数的模型上进行了演示，其效果与现有的少步蒸馏方法相当或更优，同时提供了更好的可扩展性。 AI

影响 使扩散模型能够进行更灵活、可扩展的视频生成，可能提高质量和控制力。

排序理由 发布关于新 AI 模型架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AnyFlow 实现灵活的视频扩散模型生成

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发布关于新 AI 模型架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mike Zheng Shou ·

    AnyFlow：具有 On-Policy Flow Map Distillation 的 Any-Step 视频扩散模型

    Few-step video generation has been significantly advanced by consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time, limiting their effectiveness for any-step video diffusion. This limita…

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