Simple WAN T2V Workflow for Self ForcingSelf 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: <a target="_blank" rel="ugc" href="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">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</a><hr />Download self_forcing_dmd.pt from <a target="_blank" rel="ugc" href="https://huggingface.co/gdhe17/Self-Forcing/tree/main/checkpoints">https://huggingface.co/gdhe17/Self-Forcing/tree/main/checkpoints</a> and use it as the t2v checkpoint. Project website: <a target="_blank" rel="ugc" href="https://self-forcing.github.io/">https://self-forcing.github.io/</a>

Self Forcing Simple WAN I2V, V2V & T2V Workflow

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