用300行代码拆解扩散模型运行机制，打通理论与实践的鸿沟。
Understanding Diffusion Models via Code Execution
- 从代码执行视角重构扩散模型核心流程
- 完整实现前向扩散、逆向采样与噪声预测网络
- 适合想快速理解模型落地细节的研究者
扩散模型在生成建模中取得了显著成果，但其理论基础往往复杂，论文中的数学推导与开源实现之间存在难以弥合的差距。现有教程多聚焦于公式推导，对模型在代码中如何实际运行指导有限。为此，我们提出一个约300行的精简实现，从代码执行角度解释扩散模型。该最小化示例保留了前向扩散、反向采样、噪声预测网络及训练循环等核心组件，剔除了不必要的工程细节。本技术报告旨在为研究者提供以实现为导向的清晰理解，揭示代码与理论之间的对应关系。代码与预训练模型已公开：https://github.com/disanda/GM/tree/main/DDPM-DDIM-ClassifierFree。
原文摘要 · Abstract (English)
Diffusion models have achieved remarkable performance in generative modeling, yet their theoretical foundations are often intricate, and the gap between mathematical formulations in papers and practical open-source implementations can be difficult to bridge. Existing tutorials primarily focus on deriving equations, offering limited guidance on how diffusion models actually operate in code. To address this, we present a concise implementation of approximately 300 lines that explains diffusion models from a code-execution perspective. Our minimal example preserves the essential components -- including forward diffusion, reverse sampling, the noise-prediction network, and the training loop -- while removing unnecessary engineering details. This technical report aims to provide researchers with a clear, implementation-first understanding of how diffusion models work in practice and how code and theory correspond. Our code and pre-trained models are available at: https://github.com/disanda/GM/tree/main/DDPM-DDIM-ClassifierFree.
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