Wan

--- license: apache-2.0 base_model: - Wan-AI/Wan2.1-T2V-1.3B - Wan-AI/Wan2.1-T2V-14B pipeline_tag: text-to-video --- # rCM: Score-Regularized Continuous-Time Consistency Model # Causal-rCM: Teacher-Forcing meets Self-Forcing in Autoregressive Diffusion Distillation for Streaming Video Generation and Interactive World Models [**Paper**](https://arxiv.org/abs/2510.08431) | [**Website**](https://research.nvidia.com/labs/dir/rcm) | [**Code**](https://github.com/NVlabs/rcm) This repo holds converted Wan official checkpoints in (Causal-)rCM/TurboDiffusion style. Specifically, (Causal-)rCM equivalently replaces the `Conv3d` layer in the original Wan with a `Linear` layer for patch embedding, facilitating further optimization. The layer weight is directly reshaped without value change, e.g., from shape [5120, 16, 1, 2, 2] (Conv3d) to shape [5120, 64] (Linear). ## Citation ``` @article{zheng2025rcm, title={Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency}, author={Zheng, Kaiwen and Wang, Yuji and Ma, Qianli and Chen, Huayu and Zhang, Jintao and Balaji, Yogesh and Chen, Jianfei and Liu, Ming-Yu and Zhu, Jun and Zhang, Qinsheng}, journal={arXiv preprint arXiv:2510.08431}, year={2025} } @article{zheng2026causal, title={Causal-rCM: Teacher-Forcing meets Self-Forcing in Autoregressive Diffusion Distillation for Streaming Video Generation and Interactive World Models}, author={Zheng, Kaiwen and He, Guande and Zhao, Min and Zhu, Hongzhou and Zhang, Jintao and Chen, Huayu and Chen, Jianfei and Lin, Chen-Hsuan and Liu, Ming-Yu and Zhu, Jun and Ma, Qianli}, journal={TODO}, year={2026} } ```

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