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README.md

+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/ArcticHare105/S3Diff.git
+cd S3Diff/
+pip install -r requirements.tx    # 安装环境中缺少的依赖，已有的进行注释，open-clip-torch、peft、diffusers、huggingface_hub要安装指定版本！！！
+# 安装diffusers
+pip install diffusers==0.25.1
+```
+
+## 推理
+
+#### Step1: 下载推理数据
+
+推理数据没有开源，需要自行准备数据集
+
+需将下载的数据集按以下结构整理（以 Set5 为例）
+
+```plaintext
+dataset/
+├── Set5/
+│   ├── LR (低分辨率图像，作为输入)
+│   └── HR (高分辨率图像，作为GT，用于评估)
+```
+
+#### Step2: 下载预训练模型
+
+We enable automatic model download in our code, if you need to conduct offline inference, download the pretrained model [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo) and S3Diff [[HuggingFace](https://huggingface.co/zhangap/S3Diff) | [GoogleDrive](https://drive.google.com/drive/folders/1cWYQYRFpadC4K2GuH8peg_hWEoFddZtj?usp=sharing)]
+
+You can put the weight into `pretrained_weight/`
+
+#### Step3: 推理
+
+Please add the paths to evaluate datasets in `configs/sr_test.yaml` and the path of GT folder in `run_inference.sh` Then run:
+
+```
+sh run_inference.sh
+```
+
+脚本中代码如下：
+
+```
+accelerate launch --num_processes=1 --gpu_ids="0," --main_process_port 29300 src/inference_s3diff.py \
+    --de_net_path="./pretrained_weight/zhangap/S3Diff/de_net.pth" \
+    --output_dir="./output" \
+    --ref_path="./datasets/Set5/image_SRF_2/HR_1024/" \
+    --align_method="wavelet"
+```
+
+src/inference_s3diff.py脚本中预训练模型改成自己的路径，如下：
+
+```
+# pretrained_path = hf_hub_download(repo_id="zhangap/S3Diff", filename="s3diff.pkl")   # 源代码
+pretrained_path = "./pretrained_weight/zhangap/S3Diff/s3diff.pkl"                      ###
+
+
+# from huggingface_hub import snapshot_download
+# sd_path = snapshot_download(repo_id="stabilityai/sd-turbo")
+# 直接使用本地已下载的 sd-turbo路径
+sd_path = "./pretrained_weight/stabilityai/sd-turbo/"
+```
\ No newline at end of file

basicsr/__init__.py

+# https://github.com/xinntao/BasicSR
+# flake8: noqa
+from .archs import *
+from .data import *
+from .losses import *
+from .metrics import *
+from .models import *
+from .ops import *
+from .test import *
+from .train import *
+from .utils import *
+# from .version import __gitsha__, __version__

basicsr/archs/__init__.py

+import importlib
+from copy import deepcopy
+from os import path as osp
+
+from basicsr.utils import get_root_logger, scandir
+from basicsr.utils.registry import ARCH_REGISTRY
+
+__all__ = ['build_network']
+
+# automatically scan and import arch modules for registry
+# scan all the files under the 'archs' folder and collect files ending with '_arch.py'
+arch_folder = osp.dirname(osp.abspath(__file__))
+arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
+# import all the arch modules
+_arch_modules = [importlib.import_module(f'basicsr.archs.{file_name}') for file_name in arch_filenames]
+
+
+def build_network(opt):
+    opt = deepcopy(opt)
+    network_type = opt.pop('type')
+    net = ARCH_REGISTRY.get(network_type)(**opt)
+    logger = get_root_logger()
+    logger.info(f'Network [{net.__class__.__name__}] is created.')
+    return net