First commit.

.gitignore

+# ignored folders
+experiments/*
+results/*
+tb_logger/*
+wandb/*
+tmp/*
+weights/*
+
+version.py
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

CODE_OF_CONDUCT.md

+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+xintao.wang@outlook.com or xintaowang@tencent.com.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.

LICENSE

+BSD 3-Clause License
+
+Copyright (c) 2021, Xintao Wang
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

MANIFEST.in

+include assets/*
+include inputs/*
+include scripts/*.py
+include inference_realesrgan.py
+include VERSION
+include LICENSE
+include requirements.txt
+include weights/README.md

README.md

+# Real-ESRGAN
+## 论文
+[Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://arxiv.org/abs/2107.10833)
+
+## 模型结构
+
+生成网络: 采用ESRGAN的生成网络，对于x4倍的超分辨，网络完全按照ESRGAN的生成器执行；对x2和x1倍的超分辨，网络先进行pixel-unshuffle（pixel-shuffl的反操作，pixel-shuffle可理解为通过压缩图像通道而对图像尺寸进行放大），以降低图像分辨率为前提，对图像通道数进行扩充，然后将处理后的图像输入网络进行超分辨重建。
+<div align=center>
+    <img src="./doc/ESRGAN.png"/>
+</div>
+
+对抗网络: 由于使用的复杂的构建数据集的方式，所以需要使用更先进的判别器对生成图像进行判别。使用U-Net判别器可以在像素角度，对单个生成的像素进行真假判断，这能够在保证生成图像整体真实的情况下，注重生成图像细节。
+<div align=center>
+    <img src="./doc/UNet.png"/>
+</div>
+
+## 算法原理
+通过使用更实用的退化过程合成训练对，扩展强大的ESRGAN以恢复一般的真实世界LR图像。
+<div align=center>
+    <img src="./doc/High-order的pipeline.png"/>
+</div>
+
+## 环境配置
+-v 路径、docker_name和imageID根据实际情况修改
+
+### Docker（方法一）
+
+```bash
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04.1-py38-latest
+
+docker run -it -v /path/your_code_data/:/path/ your_code_data/ --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
+
+cd /your_code_path/real-esrgan_pytorch
+# 推理和训练都用到了basicsr库
+pip install basicsr
+# facexlib 和 gfpgan 用于面部增强
+pip install facexlib
+pip install gfpgan
+pip install -r requirements.txt
+python setup.py develop
+```
+
+### Dockerfile（方法二）
+
+```bash
+cd ./docker
+cp ../requirements.txt requirements.txt
+
+docker build --no-cache -t real_esrgan:latest .
+docker run -it -v /path/your_code_data/:/path/your_code_data/ --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
+
+cd /your_code_path/real-esrgan_pytorch
+# 推理和训练都用到了basicsr库
+pip install basicsr
+# facexlib 和 gfpgan 用于面部增强
+pip install facexlib
+pip install gfpgan
+pip install -r requirements.txt
+python setup.py develop
+```
+
+### Anaconda（方法三）
+
+1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装: https://developer.hpccube.com/tool/
+
+```bash
+DTK软件栈：dtk23.04.1
+python：python3.8
+torch：1.13.1
+torchvision：0.14.1
+```
+
+Tips：以上dtk软件栈、python、torch等DCU相关工具版本需要严格一一对应
+
+2、其他非特殊库直接按照下面步骤进行安装
+
+```bash
+# 推理和训练都用到了basicsr库
+pip install basicsr
+# facexlib 和 gfpgan 用于面部增强
+pip install facexlib
+pip install gfpgan
+pip install -r requirements.txt
+python setup.py develop
+```
+
+## 数据集
+### 准备数据集
+
+所需数据集为: DF2K ( DIV2K 和 Flickr2K ) + OST. 仅需要 HR 图片.
+
+[DIV2K](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip)
+
+[Flickr2K](https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar)
+
+[OST](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/datasets/OST_dataset.zip)
+
+
+数据集的目录结构如下:
+
+```bash
+├── datasets
+│   ├── DF2K
+│       ├── DF2K_HR # 将DIV2K和Flickr2K的HR图像放于这里
+│       ├── DF2K_multiscale # 生成的
+│       ├── DF2K_multiscale_sub # 生成的
+│       └── meta_info # 生成的
+│   ├── OST
+```
+
+### 预处理数据集
+#### 1.【可选】生成多尺寸图片
+
+针对 DF2K 数据集，我们使用多尺寸缩放策略,对 HR 图像进行下采样，获得多尺寸的标准参考（Ground-Truth）图像。 <br>
+使用 [scripts/generate_multiscale_DF2K.py](scripts/generate_multiscale_DF2K.py) 脚本快速生成多尺寸的图像。<br>
+如果只想简单试试而不进行准确训练，那么该过程可选。
+
+```bash
+# example
+python scripts/generate_multiscale_DF2K.py --input datasets/DF2K/DF2K_HR --output datasets/DF2K/DF2K_multiscale
+```
+
+#### 2.【可选】裁切为子图像
+
+使用脚本 [scripts/extract_subimages.py](scripts/extract_subimages.py) 将 DF2K 图像裁切为子图像，以加快 IO 和处理速度。<br>
+如果你的 IO 够好或储存空间有限，那么此步骤是可选的。
+```bash
+# example
+ python scripts/extract_subimages.py --input datasets/DF2K/DF2K_multiscale --output datasets/DF2K/DF2K_multiscale_sub --crop_size 400 --step 200
+```
+
+#### 3. 准备元信息 txt
+
+您需要准备一个包含图像路径的 txt 文件。
+使用脚本 [scripts/generate_meta_info.py](scripts/generate_meta_info.py) 生成包含图像路径的 txt 文件。<br>
+你还可以合并多个文件夹的图像路径到一个元信息（meta_info）txt, 示例参考如下:
+
+```bash
+ python scripts/generate_meta_info.py --input datasets/DF2K/DF2K_HR, datasets/DF2K/DF2K_multiscale --root datasets/DF2K, datasets/DF2K --meta_info datasets/DF2K/meta_info/meta_info_DF2Kmultiscale.txt
+```
+
+## 训练
+### 完整训练
+
+1. 下载预先训练的模型 [ESRGAN](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth), 将模型放到 `experiments/pretrained_models`目录下。
+```bash
+wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth -P experiments/pretrained_models
+```
+
+2. 相应地修改选项文件 `options/train_realesrnet_x4plus.yml` 中的内容:
+```yml
+train:
+    name: DF2K+OST
+    type: RealESRGANDataset
+    dataroot_gt: datasets/DF2K  # 修改为你的数据集文件夹根目录
+    meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt  # 修改为你自己生成的元信息txt
+    io_backend:
+        type: disk
+```
+
+如果需要指定预训练路径到其他文件，`pretrain_network_g` 参数对应的值, 当前默认保存为`experiments/train_RealESRNetx4plus_1000k_B12G4_fromESRGAN/model/net_g_1000000.pth`。
+
+3. 如果你想在训练过程中执行验证，就取消注释这些内容并进行相应的修改：
+```yml
+    # 取消注释这些以进行验证
+    # val:
+    #   name: validation
+    #   type: PairedImageDataset
+    #   dataroot_gt: path_to_gt
+    #   dataroot_lq: path_to_lq
+    #   io_backend:
+    #     type: disk
+
+...
+
+    # 取消注释这些以进行验证
+    # 验证设置
+    # val:
+    #   val_freq: !!float 5e3
+    #   save_img: True
+
+    #   metrics:
+    #     psnr: # 指标名称，可以是任意的
+    #       type: calculate_psnr
+    #       crop_border: 4
+    #       test_y_channel: false
+```
+
+### 微调训练
+
+你可以用自己的数据集微调 Real-ESRGAN。一般地，微调（Fine-Tune）程序可以分为两种类型:
+
+1. [动态生成降级图像](#动态生成降级图像)
+2. [使用**已配对**的数据](#使用已配对的数据)
+
+#### 动态生成降级图像
+
+只需要高分辨率图像，在训练过程中，使用 Real-ESRGAN 描述的降级模型生成低质量图像。
+
+1. 下载预训练模型到 `experiments/pretrained_models` 目录下：
+
+- *RealESRGAN_x4plus.pth*:
+    ```bash
+    wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
+    ```
+
+- *RealESRGAN_x4plus_netD.pth*:
+    ```bash
+    wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models
+    ```
+
+2. 修改选项文件 [options/finetune_realesrgan_x4plus.yml](options/finetune_realesrgan_x4plus.yml)，特别是 `datasets` 部分：
+
+```yml
+train:
+    name: DF2K+OST
+    type: RealESRGANDataset
+    dataroot_gt: datasets/DF2K   # 修改为你的数据集文件夹根目录
+    meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt  # 修改为你自己生成的元信息txt
+    io_backend:
+        type: disk
+```
+
+#### 使用已配对的数据
+
+你还可以用自己已经配对的数据微调 RealESRGAN，这个过程更类似于微调 ESRGAN。
+
+1. 数据准备
+
+假设你已经有两个文件夹（folder）:
+
+- **gt folder**（标准参考，高分辨率图像）：*datasets/DF2K/DIV2K_train_HR_sub*
+- **lq folder**（低质量，低分辨率图像）：*datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub*
+
+然后，使用脚本 [scripts/generate_meta_info_pairdata.py](scripts/generate_meta_info_pairdata.py) 生成元信息（meta_info）txt 文件。
+
+```bash
+python scripts/generate_meta_info_pairdata.py --input datasets/DF2K/DIV2K_train_HR_sub datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub --meta_info datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt
+```
+
+2. 下载预训练模型
+
+下载所需预训练模型到 `experiments/pretrained_models` 目录下。
+
+- *RealESRGAN_x4plus.pth*:
+    ```bash
+    wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
+    ```
+
+- *RealESRGAN_x4plus_netD.pth*:
+    ```bash
+    wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models
+    ```
+
+3. finetune
+
+修改选项文件 [options/finetune_realesrgan_x4plus_pairdata.yml](options/finetune_realesrgan_x4plus_pairdata.yml) ，特别是 `datasets` 部分：
+
+```yml
+train:
+    name: DIV2K
+    type: RealESRGANPairedDataset
+    dataroot_gt: datasets/DF2K  # 修改为你的 gt folder 文件夹根目录
+    dataroot_lq: datasets/DF2K  # 修改为你的 lq folder 文件夹根目录
+    meta_info: datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt  # 修改为你自己生成的元信息txt
+    io_backend:
+        type: disk
+```
+### 训练命令
+#### 单机多卡
+默认auto_resume模式，根据完整训练或者微调训练，修改-opt参数对应的yml文件。当前默认为完整训练
+```bash
+bash train_multi.sh
+```
+
+#### 单机单卡
+默认auto_resume模式
+```bash
+bash train.sh
+```
+
+## 推理
+下载预训练模型[RealESRGAN_x4plus.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth), 将其放入 weights文件夹下, 测试结果存默认保存在results文件夹下。
+```bash
+# 下载预训练模型
+wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P weights
+# 执行推理
+python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs --face_enhance
+```
+
+## result
+
+<div align=center>
+    <img src="./doc/00017_gray.jpg"/>
+    <img src="./doc/00017_gray_out.jpg"/>
+</div>
+
+### 精度
+
+| NIQE | ADE20K val | OST300 |
+| :------: | :------: | :------: |
+| our | xxx | xxx |
+| paper | 3.7886 | 2.8659 |
+
+## 应用场景
+### 算法类别
+图像超分
+
+### 热点应用行业
+网安,交通,政府,工业
+
+## 源码仓库及问题反馈
+https://developer.hpccube.com/codes/modelzoo/real-esrgan_pytorch
+
+## 参考资料
+https://github.com/xinntao/Real-ESRGAN

VERSION

+0.3.0

cog.yaml

+# This file is used for constructing replicate env
+image: "r8.im/tencentarc/realesrgan"
+
+build:
+  gpu: true
+  python_version: "3.8"
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+  python_packages:
+    - "torch==1.7.1"
+    - "torchvision==0.8.2"
+    - "numpy==1.21.1"
+    - "lmdb==1.2.1"
+    - "opencv-python==4.5.3.56"
+    - "PyYAML==5.4.1"
+    - "tqdm==4.62.2"
+    - "yapf==0.31.0"
+    - "basicsr==1.4.2"
+    - "facexlib==0.2.5"
+
+predict: "cog_predict.py:Predictor"

cog_predict.py

+# flake8: noqa
+# This file is used for deploying replicate models
+# running: cog predict -i img=@inputs/00017_gray.png -i version='General - v3' -i scale=2 -i face_enhance=True -i tile=0
+# push: cog push r8.im/xinntao/realesrgan
+
+import os
+
+os.system('pip install gfpgan')
+os.system('python setup.py develop')
+
+import cv2
+import shutil
+import tempfile
+import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from basicsr.archs.srvgg_arch import SRVGGNetCompact
+
+from realesrgan.utils import RealESRGANer
+
+try:
+    from cog import BasePredictor, Input, Path
+    from gfpgan import GFPGANer
+except Exception:
+    print('please install cog and realesrgan package')
+
+
+class Predictor(BasePredictor):
+
+    def setup(self):
+        os.makedirs('output', exist_ok=True)
+        # download weights
+        if not os.path.exists('weights/realesr-general-x4v3.pth'):
+            os.system(
+                'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P ./weights'
+            )
+        if not os.path.exists('weights/GFPGANv1.4.pth'):
+            os.system('wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P ./weights')
+        if not os.path.exists('weights/RealESRGAN_x4plus.pth'):
+            os.system(
+                'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./weights'
+            )
+        if not os.path.exists('weights/RealESRGAN_x4plus_anime_6B.pth'):
+            os.system(
+                'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P ./weights'
+            )
+        if not os.path.exists('weights/realesr-animevideov3.pth'):
+            os.system(
+                'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth -P ./weights'
+            )
+
+    def choose_model(self, scale, version, tile=0):
+        half = True if torch.cuda.is_available() else False
+        if version == 'General - RealESRGANplus':
+            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+            model_path = 'weights/RealESRGAN_x4plus.pth'
+            self.upsampler = RealESRGANer(
+                scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
+        elif version == 'General - v3':
+            model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+            model_path = 'weights/realesr-general-x4v3.pth'
+            self.upsampler = RealESRGANer(
+                scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
+        elif version == 'Anime - anime6B':
+            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
+            model_path = 'weights/RealESRGAN_x4plus_anime_6B.pth'
+            self.upsampler = RealESRGANer(
+                scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
+        elif version == 'AnimeVideo - v3':
+            model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
+            model_path = 'weights/realesr-animevideov3.pth'
+            self.upsampler = RealESRGANer(
+                scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
+
+        self.face_enhancer = GFPGANer(
+            model_path='weights/GFPGANv1.4.pth',
+            upscale=scale,
+            arch='clean',
+            channel_multiplier=2,
+            bg_upsampler=self.upsampler)
+
+    def predict(
+        self,
+        img: Path = Input(description='Input'),
+        version: str = Input(
+            description='RealESRGAN version. Please see [Readme] below for more descriptions',
+            choices=['General - RealESRGANplus', 'General - v3', 'Anime - anime6B', 'AnimeVideo - v3'],
+            default='General - v3'),
+        scale: float = Input(description='Rescaling factor', default=2),
+        face_enhance: bool = Input(
+            description='Enhance faces with GFPGAN. Note that it does not work for anime images/vidoes', default=False),
+        tile: int = Input(
+            description=
+            'Tile size. Default is 0, that is no tile. When encountering the out-of-GPU-memory issue, please specify it, e.g., 400 or 200',
+            default=0)
+    ) -> Path:
+        if tile <= 100 or tile is None:
+            tile = 0
+        print(f'img: {img}. version: {version}. scale: {scale}. face_enhance: {face_enhance}. tile: {tile}.')
+        try:
+            extension = os.path.splitext(os.path.basename(str(img)))[1]
+            img = cv2.imread(str(img), cv2.IMREAD_UNCHANGED)
+            if len(img.shape) == 3 and img.shape[2] == 4:
+                img_mode = 'RGBA'
+            elif len(img.shape) == 2:
+                img_mode = None
+                img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+            else:
+                img_mode = None
+
+            h, w = img.shape[0:2]
+            if h < 300:
+                img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
+
+            self.choose_model(scale, version, tile)
+
+            try:
+                if face_enhance:
+                    _, _, output = self.face_enhancer.enhance(
+                        img, has_aligned=False, only_center_face=False, paste_back=True)
+                else:
+                    output, _ = self.upsampler.enhance(img, outscale=scale)
+            except RuntimeError as error:
+                print('Error', error)
+                print('If you encounter CUDA out of memory, try to set "tile" to a smaller size, e.g., 400.')
+
+            if img_mode == 'RGBA':  # RGBA images should be saved in png format
+                extension = 'png'
+            # save_path = f'output/out.{extension}'
+            # cv2.imwrite(save_path, output)
+            out_path = Path(tempfile.mkdtemp()) / f'out.{extension}'
+            cv2.imwrite(str(out_path), output)
+        except Exception as error:
+            print('global exception: ', error)
+        finally:
+            clean_folder('output')
+        return out_path
+
+
+def clean_folder(folder):
+    for filename in os.listdir(folder):
+        file_path = os.path.join(folder, filename)
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)
+            elif os.path.isdir(file_path):
+                shutil.rmtree(file_path)
+        except Exception as e:
+            print(f'Failed to delete {file_path}. Reason: {e}')