Diffusion models, a powerful and universal generative AI technology, have achieved
tremendous success in computer vision, audio, reinforcement learning, and computational …

This tutorial provides a comprehensive survey of methods for fine-tuning diffusion models to
optimize downstream reward functions. While diffusion models are widely known to provide …

Generating ligand molecules for specific protein targets, known as structure-based drug
design, is a fundamental problem in therapeutics development and biological discovery …

This is an expository article on the score-based diffusion models, with a particular focus on
the formulation via stochastic differential equations (SDE). After a gentle introduction, we …

Diffusion models, a powerful and universal generative artificial intelligence technology, have
achieved tremendous success and opened up new possibilities in diverse applications. In …

Diffusion models are powerful generative models that allow for precise control over the
characteristics of the generated samples. While these diffusion models trained on large …

Diffusion models have emerged as the leading paradigm in generative modeling, excelling
in various applications. Despite their success, these models often misalign with human …

In this work, we focus on the alignment problem of diffusion models with a continuous
reward function, which represents specific objectives for downstream tasks, such as …

Reinforcement Learning from human feedback (RLHF) has been shown a promising
direction for aligning generative models with human intent and has also been explored in …

This tutorial provides an in-depth guide on inference-time guidance and alignment methods
for optimizing downstream reward functions in diffusion models. While diffusion models are …