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README.md

@@ -1,3 +1,68 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: Wan-AI/Wan2.2-I2V-A14B
+tags:
+- image-to-video
+- diffusion
+- video-generation
+- turbodiffusion
+- wan2.2
+pipeline_tag: image-to-video
+---
+
+<p align="center">
+    <img src="assets/TurboDiffusion_Logo.png" width="300"/>
+<p>
+
+# TurboDiffusion-Wan2.2-I2V-A14B-720P
+
+- This HuggingFace repo contains the `TurboDiffusion-Wan2.2-I2V-A14B-720P` model.
+
+- For RTX 5090 or similar GPUs, please use the quantized versions (`TurboDiffusion-Wan2.2-I2V-A14B-high-720P-quant` and `TurboDiffusion-Wan2.2-I2V-A14B-low-720P-quant`). For other GPUs with a bigger GPU memory than 40GB, we recommend using (`TurboDiffusion-Wan2.2-I2V-A14B-high-720P` and `TurboDiffusion-Wan2.2-I2V-A14B-low-720P`).
+
+- For usage instructions, please see **https://github.com/thu-ml/TurboDiffusion**
+
+
+## Citation
+```
+@inproceedings{zhang2025sageattention,
+  title={SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration}, 
+  author={Zhang, Jintao and Wei, Jia and Zhang, Pengle and Zhu, Jun and Chen, Jianfei},
+  booktitle={International Conference on Learning Representations (ICLR)},
+  year={2025}
+}
+
+@article{zhang2025sla,
+  title={SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention},
+  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 Gonzalez, Joseph E. and Zhu, Jun and Chen, Jianfei},
+  journal={arXiv preprint arXiv:2509.24006},
+  year={2025}
+}
+
+@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}
+}
+
+@inproceedings{zhang2024sageattention2,
+  title={Sageattention2: Efficient attention with thorough outlier smoothing and per-thread int4 quantization},
+  author={Zhang, Jintao and Huang, Haofeng and Zhang, Pengle and Wei, Jia and Zhu, Jun and Chen, Jianfei},
+  booktitle={International Conference on Machine Learning (ICML)},
+  year={2025}
+}
+
+@article{zhang2025sageattention2++,
+  title={Sageattention2++: A more efficient implementation of sageattention2},
+  author={Zhang, Jintao and Xu, Xiaoming and Wei, Jia and Huang, Haofeng and Zhang, Pengle and Xiang, Chendong and Zhu, Jun and Chen, Jianfei},
+  journal={arXiv preprint arXiv:2505.21136},
+  year={2025}
+}
+@article{zhang2025sageattention3,
+  title={SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training},
+  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},
+  journal={arXiv preprint arXiv:2505.11594},
+  year={2025}
+}
+```