Alignment of Diffusion Models: Fundamentals, Challenges, and Future

Understand

Diffusion models have emerged as the leading paradigm in generative modeling, excelling in various applications.

• Despite their success, these models often misalign with human intentions and generate results with undesired properties or even harmful content.
• Inspired by the success and popularity of alignment in tuning large language models, recent studies have investigated aligning diffusion models with human expectations and preferences.
• This work mainly reviews alignment of diffusion models, covering advancements in fundamentals of alignment, alignment techniques of diffusion models, preference benchmarks, and evaluation for diffusion models.