arXiv:2510.21890v2 Announce Type: replace Abstract: This book presents the core principles that have guided the development of diffusion models, tracing their origins and showing how diverse formulations arise from shared mathematical ideas. Diffusion modeling starts by defining a forward process that gradually corrupts data into noise, linking the data distribution to a simple prior through a continuum of intermediate distributions. The goal is to learn a reverse process that transforms noise back into data while recovering the same intermediates. We describe three complementary views. The va
The publication of a comprehensive principles-based book on diffusion models signifies their maturation as a foundational AI technology and a critical moment for widespread understanding and application.
A deeper foundational understanding of diffusion models will accelerate their deployment across diverse applications, from generative AI to scientific discovery, driving innovation and new market opportunities.
The accessibility of core principles will broaden the base of researchers and developers capable of building upon and optimizing diffusion models, leading to more robust and diverse applications.
- · AI researchers and developers
- · Generative AI companies
- · Creative industries relying on AI tooling
- · Hardware manufacturers for AI compute
- · Companies relying on older generative techniques
- · Individuals/companies resistant to AI adoption
Increased innovation and development of diffusion model applications across various domains.
New business models emerging from the capabilities unlocked by advanced generative AI.
Ethical and societal debates intensifying around the implications of highly realistic synthetic content creation.
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Read at arXiv cs.LG