update

README.md

@@ -82,15 +82,98 @@ To use these weights, please refer to the official [GitHub Repository](https://g
 ### Installation
 
 ```bash
-git clone [https://github.com/knightyxp/VideoCoF](https://github.com/knightyxp/VideoCoF)
+git clone https://github.com/knightyxp/VideoCoF
 cd VideoCoF
 
-# Create environment
+# 1. Create and activate a conda environment
 conda create -n videocof python=3.10
 conda activate videocof
 
-# Install PyTorch (adjust for your CUDA version)
-pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url [https://download.pytorch.org/whl/cu121](https://download.pytorch.org/whl/cu121)
+# 2. Install PyTorch (Choose version compatible with your CUDA)
+# For standard GPUs (CUDA 12.1):
+pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
 
-# Install dependencies
-pip install -r requirements.txt
+# For Hopper GPUs (e.g., H100/H800) requiring fast inference:
+# pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu128
+
+# 3. Install other dependencies
+pip install -r requirements.txt
+```
+
+**Note on Flash Attention:**
+We recommend using **FlashAttention-3** (currently beta) for optimal performance, especially on NVIDIA H100/H800 GPUs. 
+If you are using these GPUs, please follow the [official FlashAttention-3 installation guide](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#flashattention-3-beta-release) after installing the compatible PyTorch version (e.g., PyTorch 2.8 + CUDA 12.8).
+
+### Download Models
+
+*   **Wan-2.1-T2V-14B Pretrained Weights:**
+    
+    ```bash
+    git lfs install
+    git clone https://huggingface.co/Wan-AI/Wan2.1-T2V-14B
+    
+    # Or using huggingface-cli:
+    # hf download Wan-AI/Wan2.1-T2V-14B --local-dir Wan2.1-T2V-14B
+    ```
+
+*   **VideoCoF Checkpoint:**
+    
+    ```bash
+    git lfs install
+    git clone https://huggingface.co/XiangpengYang/VideoCoF videocof_weight
+
+    # Or using huggingface-cli:
+    # hf download XiangpengYang/VideoCoF --local-dir videocof_weight
+    ```
+
+### Inference
+
+```bash
+export CUDA_VISIBLE_DEVICES=0
+torchrun --nproc_per_node=1 inference.py \
+  --video_path assets/two_man.mp4 \
+  --prompt "Remove the young man with short black hair wearing black shirt on the left." \
+  --output_dir results/obj_rem \
+  --model_name /scratch3/yan204/models/Wan2.1-T2V-14B \
+  --seed 0 \
+  --num_frames 33 \
+  --source_frames 33 \
+  --reasoning_frames 4 \
+  --repeat_rope \
+  --videocof_path videocof_weight/videocof.safetensors
+```
+
+For parallel inference:
+
+```bash
+sh scripts/parallel_infer.sh
+```
+
+## 🙏 Acknowledgments
+
+We thank the authors of related works and the open-source community [VideoX-Fun](https://github.com/aigc-apps/VideoX-Fun) and [Wan](https://github.com/Wan-Video/Wan2.1) for their contributions.
+
+## 📜 License
+
+This project is licensed under the [Apache License 2.0](LICENSE).
+
+## 📮 Contact
+
+For any questions, please feel free to reach out to the author Xiangpeng Yang [@knightyxp](https://github.com/knightyxp), email: knightyxp@gmail.com/Xiangpeng.Yang@student.uts.edu.au
+
+## 📄 Citation
+
+If you find this work useful for your research, please consider citing:
+
+```bibtex
+@article{yang2025videocof,
+  title={Unified Video Editing with Temporal Reasoner},
+  author={Yang, Xiangpeng and Xie, Ji and Yang, Yiyuan and Huang, Yan and Xu, Min and Wu, Qiang},
+  journal={arXiv preprint arXiv:2400.00000},
+  year={2025}
+}
+```
+
+<div align="center">
+  ❤️ **If you find this project helpful, please consider giving it a like!** ❤️
+</div>

config.json

@@ -0,0 +1,5 @@
+{
+    "name": [
+      "VideoCoF"
+    ]
+}