---
id: 20260918-T0-08
title: "投机起草树加速扩散模型采样，减少昂贵评估次数"
title_en: "Speculative Draft Trees Speed Up Diffusion Sampling with Fewer Target Evaluations"
url: https://ai.daily.yangsir.net/daily/20260918-T0-08
issue_date: 2026-09-18
publish_date: 2026-09-17T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2609.17691
---

# 投机起草树加速扩散模型采样，减少昂贵评估次数

arXiv论文提出用投机起草树加速扩散模型采样。现有投机采样方法通过起草低成本候选状态再校正来减少昂贵的目标评估次数，但已有扩散采样方案在起草效率和校正精度上仍有提升空间。新方法构建树状起草结构，在保持目标分布精确匹配的前提下进一步减少目标模型调用次数，从而降低扩散模型生成的计算开销。

## English Version

**Speculative Draft Trees Speed Up Diffusion Sampling with Fewer Target Evaluations**

A new arXiv paper proposes using speculative draft trees to accelerate diffusion model sampling. Existing speculative methods reduce expensive target evaluations by drafting cheap candidate states and correcting them under a coupling that preserves the target distribution exactly. The new approach builds a tree-structured draft to further cut target model calls while maintaining exact distribution matching, lowering the computational cost of diffusion generation.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2609.17691)

**详情页**：https://ai.daily.yangsir.net/daily/20260918-T0-08

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