一本打通扩散模型理论与代码实现的实用手册
Generative Diffusion Modeling: A Practical Handbook
- 统一符号并对接代码，解决论文到实现的断层
- 涵盖预训练与蒸馏、奖励微调等实用后训练技术
- 适合想快速上手扩散模型的开发者和研究者
本手册从扩散概率模型、基于得分的生成模型、一致性模型、修正流等方法出发，提供统一视角。通过标准化符号并与其代码实现对齐，旨在弥合‘论文到代码’的差距，促进稳健实现与公平比较。内容涵盖扩散模型基础、预训练流程及多种后训练方法，包括模型蒸馏与基于奖励的微调。作为实用指南，强调清晰性与可用性，聚焦生成建模中广泛采用的扩散模型方法。
原文摘要 · Abstract (English)
This handbook offers a unified perspective on diffusion models, encompassing diffusion probabilistic models, score-based generative models, consistency models, rectified flow, and related methods. By standardizing notations and aligning them with code implementations, it aims to bridge the "paper-to-code" gap and facilitate robust implementations and fair comparisons. The content encompasses the fundamentals of diffusion models, the pre-training process, and various post-training methods. Post-training techniques include model distillation and reward-based fine-tuning. Designed as a practical guide, it emphasizes clarity and usability over theoretical depth, focusing on widely adopted approaches in generative modeling with diffusion models.
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