Post-Training Diffusion Models: Enhancing Capabilities, Control, and Alignment

Pre-trained generative models are built using massive, typically unlabeled corpora, enabling them to capture broad, generic knowledge across diverse domains. However, at inference time, we often aim to adjust and customize these models — to exert control, enhance specific capabilities, and align their behavior with user intent and preferences. Post-training techniques have therefore emerged as both a practical necessity and an accessible means of adapting these powerful, yet static, models. This tutorial surveys the state-of-the-art in post-training methods for diffusion models, analyzing their strengths, limitations, and areas of application. We conclude with a critical discussion on the boundaries of post-training — asking whether fundamental semantic malfunctions can truly be resolved without revisiting the pretraining process.