# 从 Langevin 视角重新思考扩散模型

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-12 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmnzgvd3p03gbsl0fleo8szfm
- 原文链接：https://arxiv.org/abs/2604.10465

## AI 摘要

从 Langevin 视角重新思考扩散模型，为理解反向过程如何从纯噪声生成数据提供了更简洁直观的解释。该框架统一了基于 ODE 和 SDE 的扩散模型，阐明了扩散模型在理论上优于普通 VAE 的原因，并证明流匹配在最大似然估计下与去噪和分数匹配本质等价。这一视角弥合了现有扩散模型不同解释之间的鸿沟，展示了各类形式化方法如何在统一框架下相互转化，为初学者和资深研究者提供了更清晰的教学价值和理论直觉。

## 正文

Diffusion models are often introduced from multiple perspectives, such as VAEs, score matching, or flow matching, accompanied by dense and technically demanding mathematics that can be difficult for beginners to grasp. One classic question is: how does the reverse process invert the forward process to generate data from pure noise? This article systematically organizes the diffusion model from a fresh Langevin perspective, offering a simpler, clearer, and more intuitive answer. We also address the following questions: how can ODE-based and SDE-based diffusion models be unified under a single framework? Why are diffusion models theoretically superior to ordinary VAEs? Why is flow matching not fundamentally simpler than denoising or score matching, but equivalent under maximum-likelihood? We demonstrate that the Langevin perspective offers clear and straightforward answers to these questions, bridging existing interpretations of diffusion models, showing how different formulations can be converted into one another within a common framework, and offering pedagogical value for both learners and experienced researchers seeking deeper intuition.