Generative Diffusion Modeling: A Practical Handbook

Zihan Ding; Chi Jin

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arXiv · 2412.17162

Generative Diffusion Modeling: A Practical Handbook

Zihan Ding·Chi Jin

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Abstract

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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Zihan Ding, Chi Jin. 2024-12-22. Generative Diffusion Modeling: A Practical Handbook. https://arxiv.org/abs/2412.17162

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