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Rcm Wan

Paper Paper2 | Website | Code

KindOtherBase modelWan-AI/Wan2.1-T2V-1.3BVersionRevision 142d77aCgrade

Model source

Kind
Other
File size
2.64–26.6 GB
Base model
Wan-AI/Wan2.1-T2V-1.3B
Version
vRevision 142d77a
Updated
Oct 20, 2026
Downloads
0
Source
Hugging Face

Source description

rCM: Score-Regularized Continuous-Time Consistency Model

Causal-rCM: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models

Paper Paper2 | Website | Code

This repo holds unofficial rCM and Causal-rCM models for Wan, reproduced by Tsinghua University.

The Wan2.2 rCM checkpoints are obtained by merging Wan2.1 rCM weights to Wan2.2 checkpoints, no extra training included. Should have the same effect as directly using the Wan2.1 rCM LoRAs (https://huggingface.co/Kijai/WanVideo_comfy/tree/main/LoRAs/rCM) and adjusting the strength.

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: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models},
  author={Zheng, Kaiwen and He, Guande and Zhao, Min and Zhang, Jintao and Chen, Huayu and Chen, Jianfei and Lin, Chen-Hsuan and Liu, Ming-Yu and Zhu, Jun and Ma, Qianli},
  journal={arXiv preprint arXiv:2606.25473},
  year={2026}
}

Sources

1 source
Hugging FacePrimaryVersion 142d77a331f1516fea772607c1bf25a2785a2505
huggingface.co/worstcoder/rcm-Wan/tree/142d77a331f1516fea772607c1bf25a2785a2505

Verified Oct 20

Model artifacts

22 artifacts

Causal_rCM_Wan2.1_T2V_1.3B_480p_DF_Diffusion_c1-1.pt

pt · 2.64 GB · SHA-256 4f4629d8854a…3982 · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_DF_Diffusion_c3-3.pt

pt · 2.64 GB · SHA-256 4d972ee09a92…76c5 · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_TF_Diffusion_c1-1.pt

pt · 2.64 GB · SHA-256 412809b9a80b…e579 · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_TF_Diffusion_c3-3.pt

pt · 2.64 GB · SHA-256 2f6e97391e9f…e00f · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_TF-dCM_c1-1.pt

pt · 2.64 GB · SHA-256 eac5ff65265d…ccf9 · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_TF-dCM_c3-3.pt

pt · 2.64 GB · SHA-256 1ace0b656e96…3363 · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_TF-dCM-init_SF-DMD_c1-1_step2_noisy_ctx.pt

pt · 2.64 GB · SHA-256 41b692722a9c…572d · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_TF-dCM-init_SF-DMD_c1-1_step2.pt

pt · 2.64 GB · SHA-256 58c1f495ea09…4585 · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_TF-dCM-init_SF-DMD_c1-1_step4.pt

pt · 2.64 GB · SHA-256 91f3104eee7e…bb93 · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_TF-dCM-init_SF-DMD_c3-3_step2_noisy_ctx.pt

pt · 2.64 GB · SHA-256 f8c39165a6b8…debb · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_TF-dCM-init_SF-DMD_c3-3_step2.pt

pt · 2.64 GB · SHA-256 e7dce84479d4…896f · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_TF-dCM-init_SF-DMD_c3-3_step4.pt

pt · 2.64 GB · SHA-256 cc53e27e4501…f008 · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_TF-sCM_c1-1.pt

pt · 2.64 GB · SHA-256 1f8539b13db5…2c52 · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_TF-sCM_c3-3.pt

pt · 2.64 GB · SHA-256 19146e6d73e8…d861 · Hugging Face

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Causal_rCM_Wan2.1_T2V_1.3B_480p_TF-sCM-init_SF-DMD_c3-3_step4.pt

pt · 2.64 GB · SHA-256 870950258ef7…2f23 · Hugging Face

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Causal_rCM_Wan2.1_T2V_14B_480p_TF_Diffusion_c1-1.pt

pt · 26.6 GB · SHA-256 447dede9017f…a577 · Hugging Face

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Causal_rCM_Wan2.1_T2V_14B_480p_TF_Diffusion_c3-3.pt

pt · 26.6 GB · SHA-256 551f62e159b4…3f00 · Hugging Face

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rCM_Wan2.1_T2V_1.3B_480p.pt

pt · 2.64 GB · SHA-256 3baa20e8e64c…370a · Hugging Face

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rCM_Wan2.1_T2V_14B_480p.pt

pt · 26.6 GB · SHA-256 68cfc511dcbb…1967 · Hugging Face

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rCM_Wan2.1_T2V_14B_720p.pt

pt · 26.6 GB · SHA-256 585d9187c8ca…80fb · Hugging Face

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Wan2.2-I2V-A14B-high-rCM4.0-merged.pt

pt · 26.6 GB · SHA-256 4a16a635ee52…7ab1 · Hugging Face

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Wan2.2-I2V-A14B-low-rCM1.0-merged.pt

pt · 26.6 GB · SHA-256 2bbadb228b3c…6b97 · Hugging Face

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Source excerpts

3 excerpts

--- license: apache-2.0 base_model: - Wan-AI/Wan2.1-T2V-1.3B - Wan-AI/Wan2.1-T2V-14B - Wan-AI/Wan2.2-I2V-A14B pipeline_tag: text-to-video tags: - Streaming-Video-Generation - Interactive-World-Model --- # rCM: Score-Regularized Continuous-Time Consistency Model # Causal-rCM: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models [**Paper**](https://arxiv.org/abs/2510.08431) [**Paper2**](https://arxiv.org/abs/2606.25473) | [**Website**](https://research.nvidia.com/labs/dir/rcm) | [**Code**](https://github.com/NVlabs/rcm) This repo holds unofficial rCM and Causal-rCM models for Wan, reproduced by Tsinghua University. The Wan2.2 rCM checkpoints are obtained by merging Wan2.1 rCM weights to Wan2.2 checkpoints, no extra training included. Should have the same effect as directly using the Wan2.1 rCM LoRAs (https://huggingface.co/Kijai/WanVideo_comfy/tree/main/LoRAs/rCM) and adjusting the strength. ## 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: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models}, author={Zheng, Kaiwen and He, Guande and Zhao, Min and Zhang, Jintao and Chen, Huayu and Chen, Jianfei and Lin, Chen-Hsuan and Liu, Ming-Yu and Zhu, Jun and Ma, Qianli}, journal={arXiv preprint arXiv:2606.25473}, year={2026} } ```

Source context: 0 downloads · 23 likes · Pipeline text-to-video · Repo worstcoder/rcm-Wan

Source context: 0 downloads · 21 likes · Pipeline text-to-video · Repo worstcoder/rcm-Wan