The Principles of Diffusion Models by Lai et al.: thoughts on the monograph [D]

Reddit r/MachineLearning Papers

Summary

A reviewer praises 'The Principles of Diffusion Models' by Lai et al. as an exceptional monograph that balances mathematical rigor with intuition, aimed at researchers, grad students, and practitioners, with the full text freely available on the official website.

I recently finished The Principles of Diffusion Models, and honestly I think it’s exceptional. The authors strike a really good balance between mathematical rigor and intuition, with dedicated appendices for anyone who wants to go deeper into the math. It’s aimed at researchers, graduate students, and practitioners with basic deep learning knowledge, so you don’t need to already specialize in diffusion models (in my case, a strong background in Information and Probability Theory and a solid understanding of DDPMs helped me get more out of it). Just wanted to share it in case anyone missed it. The full text is freely available on the official website. Has anyone else read it? Would love to hear your thoughts.
Original Article

Similar Articles

A Mathematical Introduction to Diffusion Models

arXiv cs.LG

This paper provides a proof-oriented introduction to diffusion models, covering Langevin dynamics, score-based models, discretization, discrete diffusion, and inference-time control, intended for graduate students.

@volokuleshov: New blog post: How to Build a Diffusion Language Model. Diffusion LLMs went from open problem to reality in 2 years (Me…

X AI KOLs Timeline

A comprehensive blog post by Volodymyr Kuleshov's Cornell group explains how to build diffusion language models, covering core techniques like masked diffusion, iterative refinement, variable-length generation, controllable generation, fast samplers, and RL post-training, using open-source models such as Mercury, Gemma Diffusion, and Nemotron Diffusion as examples.

Diffusion Language Models: An Experimental Analysis

arXiv cs.AI

A systematic experimental analysis evaluating eight state-of-the-art Diffusion Language Models across multiple benchmarks, analyzing trade-offs between generation quality and computational efficiency.

How To Build A Modern Diffusion Language Model

arXiv cs.AI

A Cornell University article/survey explaining how to build modern discrete diffusion language models, covering iterative parallel refinement, fast sampling at thousands of tokens per second, and their potential to improve scaling laws over autoregressive LLMs.