FastGen is a training and inference framework for building fast diffusion-based generative models. It implements multiple acceleration techniques including consistency models, distribution matching distillation (DMD2), and self-forcing. The framework supports training models with over 10 billion parameters across text-to-image, image-to-video, and video-to-video generation tasks.