---
id: 20260919-T0-07
title: "冻结扩散模型内部状态做引导，新方法实现更精准的流匹配生成"
title_en: "Probe Guidance Uses Frozen Diffusion Model States to Steer Flow Matching Generation"
url: https://ai.daily.yangsir.net/daily/20260919-T0-07
issue_date: 2026-09-19
publish_date: 2026-09-18T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2609.19356
---

# 冻结扩散模型内部状态做引导，新方法实现更精准的流匹配生成

研究人员提出一种名为"探针引导"（probe guidance）的新方法，用于指导流匹配（flow matching）模型的生成过程。该方法利用已有扩散模型中冻结的内部状态来构建引导信号，工作原理与autoguidance类似。与需要额外训练引导模型或依赖外部条件的方案不同，探针引导直接复用预训练模型内部的表征信息，无需修改原始模型参数。这一思路为流匹配模型的可控生成提供了一条低成本路径，开发者可以在不重新训练模型的情况下，利用现有扩散模型的内部特征来调节生成方向。

## English Version

**Probe Guidance Uses Frozen Diffusion Model States to Steer Flow Matching Generation**

Researchers introduce probe guidance, a new method for steering flow matching models. The approach constructs a guidance signal from the frozen internal states of an existing diffusion model, operating on a principle similar to autoguidance. Unlike methods that require training auxiliary guidance models or rely on external conditioning, probe guidance reuses internal representations from a pretrained model without modifying its parameters. This offers a low-cost path to controllable generation with flow matching models, allowing developers to adjust generation direction using existing diffusion model features without retraining.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2609.19356)

**详情页**：https://ai.daily.yangsir.net/daily/20260919-T0-07

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