| | --- |
| | 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 |
| | [**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 rCM/TurboDiffusion style. |
| |
|
| | Specifically, 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} |
| | } |
| | ``` |
