Simple WAN T2V Workflow for Self Forcing

Self Forcing trains autoregressive video diffusion models by simulating the inference process during training, performing autoregressive rollout with KV caching. It resolves the train-test distribution mismatch and enables real-time, streaming video generation on a single RTX 4090 while matching the quality of state-of-the-art diffusion models.

Update (i2v):

To use Vace, you will need to use a different checkpoint: https://huggingface.co/lym00/Wan2.1-T2V-1.3B-Self-Forcing-VACE/blob/main/Wan2.1-T2V-1.3B-Self-Forcing-DMD-VACE-FP16.safetensors

Download self_forcing_dmd.pt from https://huggingface.co/gdhe17/Self-Forcing/tree/main/checkpoints and use it as the t2v checkpoint.

Project website: https://self-forcing.github.io/