01 克隆官方的源码仓库
git clone https://github.com/RosettaCommons/RFdiffusion.git02 下载模型权重
cd RFdiffusion
mkdir models && cd models
wget http://files.ipd.uw.edu/pub/RFdiffusion/6f5902ac237024bdd0c176cb93063dc4/Base_ckpt.pt
wget http://files.ipd.uw.edu/pub/RFdiffusion/e29311f6f1bf1af907f9ef9f44b8328b/Complex_base_ckpt.pt
wget http://files.ipd.uw.edu/pub/RFdiffusion/60f09a193fb5e5ccdc4980417708dbab/Complex_Fold_base_ckpt.pt
wget http://files.ipd.uw.edu/pub/RFdiffusion/74f51cfb8b440f50d70878e05361d8f0/InpaintSeq_ckpt.pt
wget http://files.ipd.uw.edu/pub/RFdiffusion/76d00716416567174cdb7ca96e208296/InpaintSeq_Fold_ckpt.pt
wget http://files.ipd.uw.edu/pub/RFdiffusion/5532d2e1f3a4738decd58b19d633b3c3/ActiveSite_ckpt.pt
wget http://files.ipd.uw.edu/pub/RFdiffusion/12fc204edeae5b57713c5ad7dcb97d39/Base_epoch8_ckpt.pt
Optional:
wget http://files.ipd.uw.edu/pub/RFdiffusion/f572d396fae9206628714fb2ce00f72e/Complex_beta_ckpt.pt
# original structure prediction weights
wget http://files.ipd.uw.edu/pub/RFdiffusion/1befcb9b28e2f778f53d47f18b7597fa/RF_structure_prediction_weights.pt03 根据yml文件创建conda环境 （这里把yml文件设定的环境名称从SE3nv改成了lmk_RFdiffusion）
conda env create -f /data/lmk/RFdiffusion/env/SE3nv.yml
conda activate lmk_RFdiffusion04 安装 RFdiffusion 的核心几何深度学习组件 —— SE3Transformer
cd env/SE3Transformer
pip install --no-cache-dir -r requirements.txt # 安装 SE3-Transformer 需要的 Python 依赖
python setup.py install # 把 SE3-Transformer 编译并安装到当前环境
cd ../..
pip install -e . # 以可编辑模式安装RFdiffusion包，这样改动源码后无需重新安装，Python会直接引用工作区的代码，便于开发与调试。