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--- |
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license: apache-2.0 |
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base_model: Wan-AI/Wan2.1-T2V-14B |
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tags: |
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- text-to-video |
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- diffusion |
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- video-generation |
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- turbodiffusion |
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- wan2.1 |
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pipeline_tag: text-to-video |
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--- |
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<p align="center"> |
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<img src="assets/TurboDiffusion_Logo.png" width="300"/> |
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<p> |
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# TurboWan2.1-T2V-14B-480P |
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- This HuggingFace repo contains the `TurboWan2.1-T2V-14B-480P` model. |
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- For RTX 5090 or similar GPUs, please use the `TurboWan2.1-T2V-14B-480P-quant`. For other GPUs with a bigger GPU memory than 40GB, we recommend using `TurboWan2.1-T2V-14B-480P`. |
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- For usage instructions, please see **https://github.com/thu-ml/TurboDiffusion** |
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- Paper: [TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times](https://arxiv.org/pdf/2512.16093) |
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# Citation |
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``` |
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@article{zhang2025turbodiffusion, |
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title={TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times}, |
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author={Zhang, Jintao and Zheng, Kaiwen and Jiang, Kai and Wang, Haoxu and Stoica, Ion and Gonzalez, Joseph E and Chen, Jianfei and Zhu, Jun}, |
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journal={arXiv preprint arXiv:2512.16093}, |
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year={2025} |
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} |
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@software{turbodiffusion2025, |
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title={TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times}, |
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author={The TurboDiffusion Team}, |
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url={https://github.com/thu-ml/TurboDiffusion}, |
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year={2025} |
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} |
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@inproceedings{zhang2025sageattention, |
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title={SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration}, |
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author={Zhang, Jintao and Wei, Jia and Zhang, Pengle and Zhu, Jun and Chen, Jianfei}, |
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booktitle={International Conference on Learning Representations (ICLR)}, |
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year={2025} |
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} |
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@article{zhang2025sla, |
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title={SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention}, |
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author={Zhang, Jintao and Wang, Haoxu and Jiang, Kai and Yang, Shuo and Zheng, Kaiwen and Xi, Haocheng and Wang, Ziteng and Zhu, Hongzhou and Zhao, Min and Stoica, Ion and others}, |
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journal={arXiv preprint arXiv:2509.24006}, |
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year={2025} |
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} |
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@article{zheng2025rcm, |
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title={Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency}, |
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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}, |
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journal={arXiv preprint arXiv:2510.08431}, |
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year={2025} |
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} |
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@inproceedings{zhang2024sageattention2, |
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title={Sageattention2: Efficient attention with thorough outlier smoothing and per-thread int4 quantization}, |
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author={Zhang, Jintao and Huang, Haofeng and Zhang, Pengle and Wei, Jia and Zhu, Jun and Chen, Jianfei}, |
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booktitle={International Conference on Machine Learning (ICML)}, |
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year={2025} |
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} |
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@article{zhang2025sageattention2++, |
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title={Sageattention2++: A more efficient implementation of sageattention2}, |
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author={Zhang, Jintao and Xu, Xiaoming and Wei, Jia and Huang, Haofeng and Zhang, Pengle and Xiang, Chendong and Zhu, Jun and Chen, Jianfei}, |
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journal={arXiv preprint arXiv:2505.21136}, |
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year={2025} |
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} |
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@article{zhang2025sageattention3, |
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title={SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training}, |
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author={Zhang, Jintao and Wei, Jia and Zhang, Pengle and Xu, Xiaoming and Huang, Haofeng and Wang, Haoxu and Jiang, Kai and Zhu, Jun and Chen, Jianfei}, |
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journal={arXiv preprint arXiv:2505.11594}, |
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year={2025} |
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} |
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``` |
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