需要用到xformers,所以使用的镜像是 image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-ubuntu22.04-dtk24.04.3-py3.10 dtk25.04.1和dtk25.04.2的镜像中没有适配安装xformers ``` # 拉取镜像 docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-ubuntu22.04-dtk24.04.3-py3.10 # 创建容器 docker run -it --network=host --name=dtk24043_torch23 -v /opt/hyhal:/opt/hyhal:ro -v /usr/local/hyhal:/usr/local/hyhal:ro -v /public:/public:ro --privileged --device=/dev/kfd --device=/dev/dri --ipc=host --shm-size=128G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root --ulimit stack=-1:-1 --ulimit memlock=-1:-1 image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-ubuntu22.04-dtk24.04.3-py3.10 ``` ``` git clone https://github.com/NJU-PCALab/STAR.git cd STAR pip install -r requirements.txt # 安装环境中缺少的依赖,已有的进行注释,open-clip-torch要安装指定版本!!! # 安装diffusers git clone -b v0.30.0-release http://developer.sourcefind.cn/codes/OpenDAS/diffusers.git cd diffusers/ python3 setup.py install sudo apt-get update && sudo apt-get install ffmpeg libsm6 libxext6 -y ``` #### Step 1: 下载预训练模型 [HuggingFace](https://huggingface.co/SherryX/STAR). We provide two versions for I2VGen-XL-based model, `heavy_deg.pt` for heavy degraded videos and `light_deg.pt` for light degraded videos (e.g., the low-resolution video downloaded from video websites). You can put the weight into `pretrained_weight/`. #### Step 2: 准备测试数据(pr中有,此步跳过) You can put the testing videos in the `input/video/`. As for the prompt, there are three options: 1. No prompt. 2. Automatically generate a prompt (e.g., [using Pllava](https://github.com/hpcaitech/Open-Sora/tree/main/tools/caption#pllava-captioning)). 3. Manually write the prompt. You can put the txt file in the `input/text/`. #### Step 3: 修改为自己的本地路径 You need to change the paths in `video_super_resolution/scripts/inference_sr.sh` to your local corresponding paths, including `video_folder_path`, `txt_file_path`, `model_path`, and `save_dir`. #### Step 4: 运行推理命令 ``` bash video_super_resolution/scripts/inference_sr.sh ```