This repository provides the official PyTorch implementation of Diffusion Forcing, a method that bridges next-token prediction (autoregressive) and full-sequence diffusion models. The technique trains a model to denoise subsets of tokens at varying noise levels simultaneously, enabling flexible generation across different timesteps. The codebase includes temporal attention mechanisms and supports applications in video generation, robotics, and planning domains.