Self Forcing: Bridging the Train-Test Gap in Autoregressive Video
  Diffusion
Authors: name: Xun Huang, name: Zhengqi Li, name: Guande He, name: Mingyuan Zhou, name: Eli Shechtman

We introduce Self Forcing, a novel training paradigm for autoregressive video
diffusion models. It addresses the longstanding issue of exposure bias, where
models trained on ground-truth context must generate sequences conditioned on
their own imperfect outputs during inference. Unlike prior methods that denoise
future frames based on ground-truth context frames, Self Forcing conditions
each frame's generation on previously self-generated outputs by performing
autoregressive rollout with key-value (KV) caching during training. This
strategy enables supervision through a holistic loss at the video level that
directly evaluates the quality of the entire generated sequence, rather than
relying solely on traditional frame-wise objectives. To ensure training
efficiency, we employ a few-step diffusion model along with a stochastic
gradient truncation strategy, effectively balancing computational cost and
performance. We further introduce a rolling KV cache mechanism that enables
efficient autoregressive video extrapolation. Extensive experiments demonstrate
that our approach achieves real-time streaming video generation with sub-second
latency on a single GPU, while matching or even surpassing the generation
quality of significantly slower and non-causal diffusion models. Project
website: http://self-forcing.github.io/