Diffusion Forcing

Diffusion forcing is a training technique in which context frames fed to a generative model are corrupted with varying amounts of noise, forcing the model to practice reconstructing coherent output from imperfect or degraded inputs. This mirrors the errors a model actually makes during autoregressive rollout, where earlier mistakes compound, making the resulting system more robust to its own accumulating prediction errors over long sequences.