update setup.py, V0.2.2.5

update ReadMe
add feedback of anime models
2021-10-22 17:16:43 +08:00 · 2021-10-17 01:33:45 +08:00 · 2021-10-17 01:27:04 +08:00 · 2021-10-17 01:03:55 +08:00 · 2021-10-16 23:30:57 +08:00 · 2021-09-27 15:35:37 +08:00
28 changed files with 936 additions and 62 deletions
--- a/.github/workflows/publish-pip.yml
+++ b/.github/workflows/publish-pip.yml
@@ -18,12 +18,15 @@ jobs:
      - name: Install PyTorch (cpu)
        run: pip install torch==1.7.0+cpu torchvision==0.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
      - name: Install dependencies
-        run: pip install -r requirements.txt
+        run: |
+          pip install basicsr
+          pip install facexlib
+          pip install gfpgan
+          pip install -r requirements.txt
      - name: Build and install
        run: rm -rf .eggs && pip install -e .
      - name: Build for distribution
-        # remove bdist_wheel for pip installation with compiling cuda extensions
-        run: python setup.py sdist
+        run: python setup.py sdist bdist_wheel
      - name: Publish distribution to PyPI
        uses: pypa/gh-action-pypi-publish@master
        with:
--- a/.github/workflows/pylint.yml
+++ b/.github/workflows/pylint.yml
@@ -20,11 +20,12 @@ jobs:
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
-        pip install flake8 yapf isort
+        pip install codespell flake8 isort yapf

    # modify the folders accordingly
    - name: Lint
      run: |
+        codespell
        flake8 .
        isort --check-only --diff realesrgan/ scripts/ inference_realesrgan.py setup.py
        yapf -r -d realesrgan/ scripts/ inference_realesrgan.py setup.py
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -24,6 +24,12 @@ repos:
    hooks:
      - id: yapf

+  # codespell
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.1.0
+    hooks:
+      - id: codespell
+
  # pre-commit-hooks
  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v3.2.0
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -0,0 +1,45 @@
+# Contributing to Real-ESRGAN
+
+We like open-source and want to develop practical algorithms for general image restoration. However, individual strength is limited. So, any kinds of contributions are welcome, such as:
+
+- New features
+- New models (your fine-tuned models)
+- Bug fixes
+- Typo fixes
+- Suggestions
+- Maintenance
+- Documents
+- *etc*
+
+## Workflow
+
+1. Fork and pull the latest Real-ESRGAN repository
+1. Checkout a new branch (do not use master branch for PRs)
+1. Commit your changes
+1. Create a PR
+
+**Note**:
+1. Please check the code style and linting
+    1. The style configuration is specified in [setup.cfg](setup.cfg)
+    1. If you use VSCode, the settings are configured in [.vscode/settings.json](.vscode/settings.json)
+1. Strongly recommend using `pre-commit hook`. It will check your code style and linting before your commit.
+    1. In the root path of project folder, run `pre-commit install`
+    1. The pre-commit configuration is listed in [.pre-commit-config.yaml](.pre-commit-config.yaml)
+1. Better to [open a discussion](https://github.com/xinntao/Real-ESRGAN/discussions) before large changes.
+    1. Welcome to discuss :sunglasses:. I will try my best to join the discussion.
+
+## TODO List
+
+:zero: The most straightforward way of improving model performance is to fine-tune on some specific datasets.
+
+Here are some TODOs:
+
+- [ ] optimize for human faces
+- [ ] optimize for texts
+- [ ] support controllable restoration strength
+
+:one: There are also [several issues](https://github.com/xinntao/Real-ESRGAN/issues) that require helpers to improve. If you can help, please let me know :smile:
+
+## Contributors
+
+- [AK391](https://github.com/AK391): Integrate RealESRGAN to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/Real-ESRGAN).
--- a/README.md
+++ b/README.md
@@ -2,37 +2,50 @@

 [![download](https://img.shields.io/github/downloads/xinntao/Real-ESRGAN/total.svg)](https://github.com/xinntao/Real-ESRGAN/releases)
 [![PyPI](https://img.shields.io/pypi/v/realesrgan)](https://pypi.org/project/realesrgan/)
-[![Open issue](https://isitmaintained.com/badge/open/xinntao/Real-ESRGAN.svg)](https://github.com/xinntao/Real-ESRGAN/issues)
+[![Open issue](https://img.shields.io/github/issues/xinntao/Real-ESRGAN)](https://github.com/xinntao/Real-ESRGAN/issues)
+[![Closed issue](https://img.shields.io/github/issues-closed/xinntao/Real-ESRGAN)](https://github.com/xinntao/Real-ESRGAN/issues)
 [![LICENSE](https://img.shields.io/github/license/xinntao/Real-ESRGAN.svg)](https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE)
 [![python lint](https://github.com/xinntao/Real-ESRGAN/actions/workflows/pylint.yml/badge.svg)](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/pylint.yml)
 [![Publish-pip](https://github.com/xinntao/Real-ESRGAN/actions/workflows/publish-pip.yml/badge.svg)](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/publish-pip.yml)

 1. [Colab Demo](https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing) for Real-ESRGAN <a href="https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>.
-2. Portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.2/realesrgan-ncnn-vulkan-20210801-macos.zip) **executable files for Intel/AMD/Nvidia GPU**. You can find more information [here](#Portable-executable-files).
+2. Portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-macos.zip) **executable files for Intel/AMD/Nvidia GPU**. You can find more information [here](#Portable-executable-files). The ncnn implementation is in [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan).
+
+感谢大家的关注和使用:-) 关于动漫插画的模型，目前还有很多问题，主要有: 1. 视频处理不了; 2. 景深虚化有问题; 3. 不可调节, 效果过了; 4. 改变原来的风格。大家提供了很好的反馈。我会逐步整理这些反馈，更新在 [这个文档](feedback.md)。希望不久之后，有新模型可以使用

 Real-ESRGAN aims at developing **Practical Algorithms for General Image Restoration**.<br>
 We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.

+:art: Real-ESRGAN needs your contributions. Any contributions are welcome, such as new features/models/typo fixes/suggestions/maintenance, *etc*. See [CONTRIBUTING.md](CONTRIBUTING.md). All contributors are list [here](CONTRIBUTING.md#Contributors).
+
 :question: Frequently Asked Questions can be found in [FAQ.md](FAQ.md).

 :triangular_flag_on_post: **Updates**
+- :white_check_mark: Add the ncnn implementation [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan).
+- :white_check_mark: Add [*RealESRGAN_x4plus_anime_6B.pth*](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth), which is optimized for **anime** images with much smaller model size. More details and comparisons with [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan) are in [**anime_model.md**](docs/anime_model.md)
+- :white_check_mark: Support finetuning on your own data or paired data (*i.e.*, finetuning ESRGAN). See [here](Training.md#Finetune-Real-ESRGAN-on-your-own-dataset)
 - :white_check_mark: Integrate [GFPGAN](https://github.com/TencentARC/GFPGAN) to support **face enhancement**.
- :white_check_mark: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/Real-ESRGAN).
+- :white_check_mark: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/Real-ESRGAN). Thanks [@AK391](https://github.com/AK391)
 - :white_check_mark: Support arbitrary scale with `--outscale` (It actually further resizes outputs with `LANCZOS4`). Add *RealESRGAN_x2plus.pth* model.
 - :white_check_mark: [The inference code](inference_realesrgan.py) supports: 1) **tile** options; 2) images with **alpha channel**; 3) **gray** images; 4) **16-bit** images.
 - :white_check_mark: The training codes have been released. A detailed guide can be found in [Training.md](Training.md).

-If Real-ESRGAN is helpful in your photos/projects, please help to :star: this repo. Thanks:blush: <br>
-Other recommended projects: &emsp; :arrow_forward: [GFPGAN](https://github.com/TencentARC/GFPGAN) &emsp; :arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR) &emsp; :arrow_forward: [facexlib](https://github.com/xinntao/facexlib)
+---
+
+If Real-ESRGAN is helpful in your photos/projects, please help to :star: this repo or recommend it to your friends. Thanks:blush: <br>
+Other recommended projects:<br>
+:arrow_forward: [GFPGAN](https://github.com/TencentARC/GFPGAN): A practical algorithm for real-world face restoration <br>
+:arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR): An open-source image and video restoration toolbox<br>
+:arrow_forward: [facexlib](https://github.com/xinntao/facexlib): A collection that provides useful face-relation functions.<br>
+:arrow_forward: [HandyView](https://github.com/xinntao/HandyView): A PyQt5-based image viewer that is handy for view and comparison. <br>

 ---

 ### :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

-> [[Paper](https://arxiv.org/abs/2107.10833)] &emsp; [Project Page] &emsp; [Demo] <br>
+> [[Paper](https://arxiv.org/abs/2107.10833)] &emsp; [Project Page] &emsp; [[YouTube Video](https://www.youtube.com/watch?v=fxHWoDSSvSc)] &emsp; [[B站讲解](https://www.bilibili.com/video/BV1H34y1m7sS/)] &emsp; [[Poster](https://xinntao.github.io/projects/RealESRGAN_src/RealESRGAN_poster.pdf)] &emsp; [[PPT slides](https://docs.google.com/presentation/d/1QtW6Iy8rm8rGLsJ0Ldti6kP-7Qyzy6XL/edit?usp=sharing&ouid=109799856763657548160&rtpof=true&sd=true)]<br>
 > [Xintao Wang](https://xinntao.github.io/), Liangbin Xie, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en) <br>
-> Applied Research Center (ARC), Tencent PCG<br>
-> Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
+> Tencent ARC Lab; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences

 <p align="center">
  <img src="assets/teaser.jpg">
@@ -52,7 +65,7 @@ Here is a TODO list in the near future:

 - [ ] optimize for human faces
 - [ ] optimize for texts
- [ ] optimize for anime images [in progress]
+- [x] optimize for anime images
 - [ ] support more scales
 - [ ] support controllable restoration strength

@@ -77,10 +90,32 @@ We have provided three models:

 1. realesrgan-x4plus  (default)
 2. realesrnet-x4plus
-3. esrgan-x4
+3. realesrgan-x4plus-anime (optimized for anime images, small model size)

 You can use the `-n` argument for other models, for example, `./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n realesrnet-x4plus`

+### Usage of executable files
+
+1. Please refer to [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan#computer-usages) for more details.
+1. Note that it does not support all the functions (such as `outscale`) as the python script `inference_realesrgan.py`.
+
+```console
+Usage: realesrgan-ncnn-vulkan.exe -i infile -o outfile [options]...
+
+  -h                   show this help
+  -v                   verbose output
+  -i input-path        input image path (jpg/png/webp) or directory
+  -o output-path       output image path (jpg/png/webp) or directory
+  -s scale             upscale ratio (4, default=4)
+  -t tile-size         tile size (>=32/0=auto, default=0) can be 0,0,0 for multi-gpu
+  -m model-path        folder path to pre-trained models(default=models)
+  -n model-name        model name (default=realesrgan-x4plus, can be realesrgan-x4plus | realesrgan-x4plus-anime | realesrnet-x4plus)
+  -g gpu-id            gpu device to use (default=0) can be 0,1,2 for multi-gpu
+  -j load:proc:save    thread count for load/proc/save (default=1:2:2) can be 1:2,2,2:2 for multi-gpu
+  -x                   enable tta mode
+  -f format            output image format (jpg/png/webp, default=ext/png)
+```
+
 Note that it may introduce block inconsistency (and also generate slightly different results from the PyTorch implementation), because this executable file first crops the input image into several tiles, and then processes them separately, finally stitches together.

 This executable file is based on the wonderful [Tencent/ncnn](https://github.com/Tencent/ncnn) and [realsr-ncnn-vulkan](https://github.com/nihui/realsr-ncnn-vulkan) by [nihui](https://github.com/nihui).
@@ -116,9 +151,9 @@ This executable file is based on the wonderful [Tencent/ncnn](https://github.com

 ## :zap: Quick Inference

-Download pre-trained models: [RealESRGAN_x4plus.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth)
+### Inference general images

-Download pretrained models:
+Download pre-trained models: [RealESRGAN_x4plus.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth)

 ```bash
 wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
@@ -132,24 +167,72 @@ python inference_realesrgan.py --model_path experiments/pretrained_models/RealES

 Results are in the `results` folder

+### Inference anime images
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_1.png">
+</p>
+
+Pre-trained models: [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)<br>
+ More details and comparisons with [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan) are in [**anime_model.md**](docs/anime_model.md)
+
+```bash
+# download model
+wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P experiments/pretrained_models
+# inference
+python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus_anime_6B.pth --input inputs
+```
+
+Results are in the `results` folder
+
+### Usage of python script
+
+1. You can use X4 model for **arbitrary output size** with the argument `outscale`. The program will further perform cheap resize operation after the Real-ESRGAN output.
+
+```console
+Usage: python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input infile --output outfile [options]...
+
+A common command: python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input infile --netscale 4 --outscale 3.5 --half --face_enhance
+
+  -h                   show this help
+  --input              Input image or folder. Default: inputs
+  --output             Output folder. Default: results
+  --model_path         Path to the pre-trained model. Default: experiments/pretrained_models/RealESRGAN_x4plus.pth
+  --netscale           Upsample scale factor of the network. Default: 4
+  --outscale           The final upsampling scale of the image. Default: 4
+  --suffix             Suffix of the restored image. Default: out
+  --tile               Tile size, 0 for no tile during testing. Default: 0
+  --face_enhance       Whether to use GFPGAN to enhance face. Default: False
+  --half               Whether to use half precision during inference. Default: False
+  --ext                Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto
+```
+
 ## :european_castle: Model Zoo

- [RealESRGAN-x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth)
- [RealESRNet-x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth)
- [RealESRGAN-x2plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth)
- [official ESRGAN-x4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth)
+- [RealESRGAN_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth): X4 model for general images
+- [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth): Optimized for anime images; 6 RRDB blocks (slightly smaller network)
+- [RealESRGAN_x2plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth): X2 model for general images
+- [RealESRNet_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth): X4 model with MSE loss (over-smooth effects)

-## :computer: Training
+- [official ESRGAN_x4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth): official ESRGAN model (X4)
+
+The following models are **discriminators**, which are usually used for fine-tuning.
+
+- [RealESRGAN_x4plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth)
+- [RealESRGAN_x2plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x2plus_netD.pth)
+- [RealESRGAN_x4plus_anime_6B_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B_netD.pth)
+
+## :computer: Training and Finetuning on your own dataset

 A detailed guide can be found in [Training.md](Training.md).

 ## BibTeX

-    @Article{wang2021realesrgan,
-        title={Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
-        author={Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
-        journal={arXiv:2107.10833},
-        year={2021}
+    @InProceedings{wang2021realesrgan,
+        author    = {Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
+        title     = {Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
+        booktitle = {International Conference on Computer Vision Workshops (ICCVW)},
+        date      = {2021}
    }

 ## :e-mail: Contact
--- a/Training.md
+++ b/Training.md
@@ -1,16 +1,24 @@
-# :computer: How to Train Real-ESRGAN
+# :computer: How to Train/Finetune Real-ESRGAN

-The training codes have been released. <br>
-Note that the codes have a lot of refactoring. So there may be some bugs/performance drops. Welcome to report bugs/issues.
+- [Train Real-ESRGAN](#train-real-esrgan)
+  - [Overview](#overview)
+  - [Dataset Preparation](#dataset-preparation)
+  - [Train Real-ESRNet](#Train-Real-ESRNet)
+  - [Train Real-ESRGAN](#Train-Real-ESRGAN)
+- [Finetune Real-ESRGAN on your own dataset](#Finetune-Real-ESRGAN-on-your-own-dataset)
+  - [Generate degraded images on the fly](#Generate-degraded-images-on-the-fly)
+  - [Use paired training data](#use-your-own-paired-data)

-## Overview
+## Train Real-ESRGAN
+
+### Overview

 The training has been divided into two stages. These two stages have the same data synthesis process and training pipeline, except for the loss functions. Specifically,

 1. We first train Real-ESRNet with L1 loss from the pre-trained model ESRGAN.
 1. We then use the trained Real-ESRNet model as an initialization of the generator, and train the Real-ESRGAN with a combination of L1 loss, perceptual loss and GAN loss.

-## Dataset Preparation
+### Dataset Preparation

 We use DF2K (DIV2K and Flickr2K) + OST datasets for our training. Only HR images are required. <br>
 You can download from :
@@ -19,9 +27,30 @@ You can download from :
 2. Flickr2K: https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar
 3. OST: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/datasets/OST_dataset.zip

-For the DF2K dataset, we use a multi-scale strategy, *i.e.*, we downsample HR images to obtain several Ground-Truth images with different scales.
+Here are steps for data preparation.

-We then crop DF2K images into sub-images for faster IO and processing.
+#### Step 1: [Optional] Generate multi-scale images
+
+For the DF2K dataset, we use a multi-scale strategy, *i.e.*, we downsample HR images to obtain several Ground-Truth images with different scales. <br>
+You can use the [scripts/generate_multiscale_DF2K.py](scripts/generate_multiscale_DF2K.py) script to generate multi-scale images. <br>
+Note that this step can be omitted if you just want to have a fast try.
+
+```bash
+python scripts/generate_multiscale_DF2K.py --input datasets/DF2K/DF2K_HR --output datasets/DF2K/DF2K_multiscale
+```
+
+#### Step 2: [Optional] Crop to sub-images
+
+We then crop DF2K images into sub-images for faster IO and processing.<br>
+This step is optional if your IO is enough or your disk space is limited.
+
+You can use the [scripts/extract_subimages.py](scripts/extract_subimages.py) script. Here is the example:
+
+```bash
+ python scripts/extract_subimages.py --input datasets/DF2K/DF2K_multiscale --output datasets/DF2K/DF2K_multiscale_sub --crop_size 400 --step 200
+```
+
+#### Step 3: Prepare a txt for meta information

 You need to prepare a txt file containing the image paths. The following are some examples in `meta_info_DF2Kmultiscale+OST_sub.txt` (As different users may have different sub-images partitions, this file is not suitable for your purpose and you need to prepare your own txt file):

@@ -32,7 +61,14 @@ DF2K_HR_sub/000001_s003.png
 ...
 ```

-## Train Real-ESRNet
+You can use the [scripts/generate_meta_info.py](scripts/generate_meta_info.py) script to generate the txt file. <br>
+You can merge several folders into one meta_info txt. Here is the example:
+
+```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
+```
+
+### Train Real-ESRNet

 1. Download pre-trained model [ESRGAN](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth) into `experiments/pretrained_models`.
    ```bash
@@ -78,13 +114,23 @@ DF2K_HR_sub/000001_s003.png
    CUDA_VISIBLE_DEVICES=0,1,2,3 \
    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --debug
    ```
+
+    Train with **a single GPU** in the *debug* mode:
+    ```bash
+    python realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --debug
+    ```
 1. The formal training. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
    ```bash
    CUDA_VISIBLE_DEVICES=0,1,2,3 \
    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --auto_resume
    ```

-## Train Real-ESRGAN
+    Train with **a single GPU**:
+    ```bash
+    python realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --auto_resume
+    ```
+
+### Train Real-ESRGAN

 1. After the training of Real-ESRNet, you now have the file `experiments/train_RealESRNetx4plus_1000k_B12G4_fromESRGAN/model/net_g_1000000.pth`. If you need to specify the pre-trained path to other files, modify the `pretrain_network_g` value in the option file `train_realesrgan_x4plus.yml`.
 1. Modify the option file `train_realesrgan_x4plus.yml` accordingly. Most modifications are similar to those listed above.
@@ -93,8 +139,131 @@ DF2K_HR_sub/000001_s003.png
    CUDA_VISIBLE_DEVICES=0,1,2,3 \
    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --debug
    ```
+
+    Train with **a single GPU** in the *debug* mode:
+    ```bash
+    python realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --debug
+    ```
 1. The formal training. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
    ```bash
    CUDA_VISIBLE_DEVICES=0,1,2,3 \
    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --auto_resume
    ```
+
+    Train with **a single GPU**:
+    ```bash
+    python realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --auto_resume
+    ```
+
+## Finetune Real-ESRGAN on your own dataset
+
+You can finetune Real-ESRGAN on your own dataset. Typically, the fine-tuning process can be divided into two cases:
+
+1. [Generate degraded images on the fly](#Generate-degraded-images-on-the-fly)
+1. [Use your own **paired** data](#Use-paired-training-data)
+
+### Generate degraded images on the fly
+
+Only high-resolution images are required. The low-quality images are generated with the degradation process described in Real-ESRGAN during trainig.
+
+**1. Prepare dataset**
+
+See [this section](#dataset-preparation) for more details.
+
+**2. Download pre-trained models**
+
+Download pre-trained models into `experiments/pretrained_models`.
+
+- *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**
+
+Modify [options/finetune_realesrgan_x4plus.yml](options/finetune_realesrgan_x4plus.yml) accordingly, especially the `datasets` part:
+
+```yml
+train:
+    name: DF2K+OST
+    type: RealESRGANDataset
+    dataroot_gt: datasets/DF2K  # modify to the root path of your folder
+    meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt  # modify to your own generate meta info txt
+    io_backend:
+        type: disk
+```
+
+We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/finetune_realesrgan_x4plus.yml --launcher pytorch --auto_resume
+```
+
+Finetune with **a single GPU**:
+```bash
+python realesrgan/train.py -opt options/finetune_realesrgan_x4plus.yml --auto_resume
+```
+
+### Use your own paired data
+
+You can also finetune RealESRGAN with your own paired data. It is more similar to fine-tuning ESRGAN.
+
+**1. Prepare dataset**
+
+Assume that you already have two folders:
+
+- **gt folder** (Ground-truth, high-resolution images): *datasets/DF2K/DIV2K_train_HR_sub*
+- **lq folder** (Low quality, low-resolution images): *datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub*
+
+Then, you can prepare the meta_info txt file using the script [scripts/generate_meta_info_pairdata.py](scripts/generate_meta_info_pairdata.py):
+
+```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. Download pre-trained models**
+
+Download pre-trained models into `experiments/pretrained_models`.
+
+- *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**
+
+Modify [options/finetune_realesrgan_x4plus_pairdata.yml](options/finetune_realesrgan_x4plus_pairdata.yml) accordingly, especially the `datasets` part:
+
+```yml
+train:
+    name: DIV2K
+    type: RealESRGANPairedDataset
+    dataroot_gt: datasets/DF2K  # modify to the root path of your folder
+    dataroot_lq: datasets/DF2K  # modify to the root path of your folder
+    meta_info: datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt  # modify to the root path of your folder
+    io_backend:
+    type: disk
+```
+
+We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/finetune_realesrgan_x4plus_pairdata.yml --launcher pytorch --auto_resume
+```
+
+Finetune with **a single GPU**:
+```bash
+python realesrgan/train.py -opt options/finetune_realesrgan_x4plus_pairdata.yml --auto_resume
+```
--- a/2
+++ b/2
@@ -1 +1 @@
-0.2.2.1
+0.2.2.5
--- a/docs/anime_model.md
+++ b/docs/anime_model.md
@@ -0,0 +1,68 @@
+# Anime model
+
+:white_check_mark: We add [*RealESRGAN_x4plus_anime_6B.pth*](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth), which is optimized for **anime** images with much smaller model size.
+
+- [How to Use](#How-to-Use)
+  - [PyTorch Inference](#PyTorch-Inference)
+  - [ncnn Executable File](#ncnn-Executable-File)
+- [Comparisons with waifu2x](#Comparisons-with-waifu2x)
+- [Comparisons with Sliding Bars](#Comparions-with-Sliding-Bars)
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_1.png">
+</p>
+
+The following is a video comparison with sliding bar. You may need to use the full-screen mode for better visual quality, as the original image is large; otherwise, you may encounter aliasing issue.
+
+https://user-images.githubusercontent.com/17445847/131535127-613250d4-f754-4e20-9720-2f9608ad0675.mp4
+
+## How to Use
+
+### PyTorch Inference
+
+Pre-trained models: [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)
+
+```bash
+# download model
+wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P experiments/pretrained_models
+# inference
+python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus_anime_6B.pth --input inputs
+```
+
+### ncnn Executable File
+
+Download the latest portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-macos.zip) **executable files for Intel/AMD/Nvidia GPU**.
+
+Taking the Windows as example, run:
+
+```bash
+./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n realesrgan-x4plus-anime
+```
+
+## Comparisons with waifu2x
+
+We compare Real-ESRGAN-anime with [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan). We use the `-n 2 -s 4` for waifu2x.
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_1.png">
+</p>
+<p align="center">
+  <img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_2.png">
+</p>
+<p align="center">
+  <img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_3.png">
+</p>
+<p align="center">
+  <img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_4.png">
+</p>
+<p align="center">
+  <img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_5.png">
+</p>
+
+## Comparisons with Sliding Bars
+
+The following are video comparisons with sliding bar. You may need to use the full-screen mode for better visual quality, as the original image is large; otherwise, you may encounter aliasing issue.
+
+https://user-images.githubusercontent.com/17445847/131536647-a2fbf896-b495-4a9f-b1dd-ca7bbc90101a.mp4
+
+https://user-images.githubusercontent.com/17445847/131536742-6d9d82b6-9765-4296-a15f-18f9aeaa5465.mp4
--- a/docs/ncnn_conversion.md
+++ b/docs/ncnn_conversion.md
@@ -0,0 +1,11 @@
+# Instructions on converting to NCNN models
+
+1. Convert to onnx model with `scripts/pytorch2onnx.py`. Remember to modify codes accordingly
+1. Convert onnx model to ncnn model
+    1. `cd ncnn-master\ncnn\build\tools\onnx`
+    1. `onnx2ncnn.exe realesrgan-x4.onnx realesrgan-x4-raw.param realesrgan-x4-raw.bin`
+1. Optimize ncnn model
+    1. fp16 mode
+        1. `cd ncnn-master\ncnn\build\tools`
+        1. `ncnnoptimize.exe realesrgan-x4-raw.param realesrgan-x4-raw.bin realesrgan-x4.param realesrgan-x4.bin 1`
+1. Modify the blob name in `realesrgan-x4.param`: `data` and `output`
--- a/feedback.md
+++ b/feedback.md
@@ -0,0 +1,9 @@
+# Feedback 反馈
+
+## 动漫插画模型
+
+1. 视频处理不了: 目前的模型，不是针对视频的，所以视频效果很很不好。我们在探究针对视频的模型了
+1. 景深虚化有问题: 现在的模型把一些景深 和 特意的虚化 都复原了，感觉不好。这个后面我们会考虑把这个信息结合进入。一个简单的做法是识别景深和虚化，然后作为条件告诉神经网络，哪些地方复原强一些，哪些地方复原要弱一些
+1. 不可以调节: 像 Waifu2X 可以调节。可以根据自己的喜好，做调整，但是 Real-ESRGAN-anime 并不可以。导致有些恢复效果过了
+1. 把原来的风格改变了: 不同的动漫插画都有自己的风格，现在的 Real-ESRGAN-anime 倾向于恢复成一种风格（这是受到训练数据集影响的）。风格是动漫很重要的一个要素，所以要尽可能保持
+1. 模型太大: 目前的模型处理太慢，能够更快。这个我们有相关的工作在探究，希望能够尽快有结果，并应用到 Real-ESRGAN 这一系列的模型上
--- a/inference_realesrgan.py
+++ b/inference_realesrgan.py
@@ -2,6 +2,7 @@ import argparse
 import cv2
 import glob
 import os
+from basicsr.archs.rrdbnet_arch import RRDBNet

 from realesrgan import RealESRGANer

@@ -18,11 +19,12 @@ def main():
    parser.add_argument('--netscale', type=int, default=4, help='Upsample scale factor of the network')
    parser.add_argument('--outscale', type=float, default=4, help='The final upsampling scale of the image')
    parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image')
-    parser.add_argument('--tile', type=int, default=800, help='Tile size, 0 for no tile during testing')
+    parser.add_argument('--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
    parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
    parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
    parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
    parser.add_argument('--half', action='store_true', help='Use half precision during inference')
+    parser.add_argument('--block', type=int, default=23, help='num_block in RRDB')
    parser.add_argument(
        '--alpha_upsampler',
        type=str,
@@ -35,9 +37,17 @@ def main():
        help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
    args = parser.parse_args()

+    if 'RealESRGAN_x4plus_anime_6B.pth' in args.model_path:
+        args.block = 6
+    elif 'RealESRGAN_x2plus.pth' in args.model_path:
+        args.netscale = 2
+
+    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=args.block, num_grow_ch=32, scale=args.netscale)
+
    upsampler = RealESRGANer(
        scale=args.netscale,
        model_path=args.model_path,
+        model=model,
        tile=args.tile,
        tile_pad=args.tile_pad,
        pre_pad=args.pre_pad,
@@ -71,10 +81,10 @@ def main():
        h, w = img.shape[0:2]
        if max(h, w) > 1000 and args.netscale == 4:
            import warnings
-            warnings.warn('The input image is large, try X2 model for better performace.')
+            warnings.warn('The input image is large, try X2 model for better performance.')
        if max(h, w) < 500 and args.netscale == 2:
            import warnings
-            warnings.warn('The input image is small, try X4 model for better performace.')
+            warnings.warn('The input image is small, try X4 model for better performance.')

        try:
            if args.face_enhance:
--- a/options/finetune_realesrgan_x4plus.yml
+++ b/options/finetune_realesrgan_x4plus.yml
@@ -1,8 +1,8 @@
 # general settings
-name: finetune_RealESRGANx4plus_400k_B12G4
+name: finetune_RealESRGANx4plus_400k
 model_type: RealESRGANModel
 scale: 4
-num_gpu: 4
+num_gpu: auto
 manual_seed: 0

 # ----------------- options for synthesizing training data in RealESRGANModel ----------------- #
--- a/options/finetune_realesrgan_x4plus_pairdata.yml
+++ b/options/finetune_realesrgan_x4plus_pairdata.yml
@@ -0,0 +1,151 @@
+# general settings
+name: finetune_RealESRGANx4plus_400k_pairdata
+model_type: RealESRGANModel
+scale: 4
+num_gpu: auto
+manual_seed: 0
+
+# USM the ground-truth
+l1_gt_usm: True
+percep_gt_usm: True
+gan_gt_usm: False
+
+high_order_degradation: False # do not use the high-order degradation generation process
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: DIV2K
+    type: RealESRGANPairedDataset
+    dataroot_gt: datasets/DF2K
+    dataroot_lq: datasets/DF2K
+    meta_info: datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt
+    io_backend:
+      type: disk
+
+    gt_size: 256
+    use_hflip: True
+    use_rot: False
+
+    # data loader
+    use_shuffle: true
+    num_worker_per_gpu: 5
+    batch_size_per_gpu: 12
+    dataset_enlarge_ratio: 1
+    prefetch_mode: ~
+
+  # Uncomment these for validation
+  # val:
+  #   name: validation
+  #   type: PairedImageDataset
+  #   dataroot_gt: path_to_gt
+  #   dataroot_lq: path_to_lq
+  #   io_backend:
+  #     type: disk
+
+# network structures
+network_g:
+  type: RRDBNet
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 64
+  num_block: 23
+  num_grow_ch: 32
+
+
+network_d:
+  type: UNetDiscriminatorSN
+  num_in_ch: 3
+  num_feat: 64
+  skip_connection: True
+
+# path
+path:
+  # use the pre-trained Real-ESRNet model
+  pretrain_network_g: experiments/pretrained_models/RealESRNet_x4plus.pth
+  param_key_g: params_ema
+  strict_load_g: true
+  pretrain_network_d: experiments/pretrained_models/RealESRGAN_x4plus_netD.pth
+  param_key_d: params
+  strict_load_d: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+  optim_d:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [400000]
+    gamma: 0.5
+
+  total_iter: 400000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1.0
+    reduction: mean
+  # perceptual loss (content and style losses)
+  perceptual_opt:
+    type: PerceptualLoss
+    layer_weights:
+      # before relu
+      'conv1_2': 0.1
+      'conv2_2': 0.1
+      'conv3_4': 1
+      'conv4_4': 1
+      'conv5_4': 1
+    vgg_type: vgg19
+    use_input_norm: true
+    perceptual_weight: !!float 1.0
+    style_weight: 0
+    range_norm: false
+    criterion: l1
+  # gan loss
+  gan_opt:
+    type: GANLoss
+    gan_type: vanilla
+    real_label_val: 1.0
+    fake_label_val: 0.0
+    loss_weight: !!float 1e-1
+
+  net_d_iters: 1
+  net_d_init_iters: 0
+
+# Uncomment these for validation
+# validation settings
+# val:
+#   val_freq: !!float 5e3
+#   save_img: True
+
+#   metrics:
+#     psnr: # metric name, can be arbitrary
+#       type: calculate_psnr
+#       crop_border: 4
+#       test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 100
+  save_checkpoint_freq: !!float 5e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500
--- a/options/train_realesrgan_x2plus.yml
+++ b/options/train_realesrgan_x2plus.yml
@@ -2,7 +2,7 @@
 name: train_RealESRGANx2plus_400k_B12G4
 model_type: RealESRGANModel
 scale: 2
-num_gpu: 4
+num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
 manual_seed: 0

 # ----------------- options for synthesizing training data in RealESRGANModel ----------------- #
--- a/options/train_realesrgan_x4plus.yml
+++ b/options/train_realesrgan_x4plus.yml
@@ -2,7 +2,7 @@
 name: train_RealESRGANx4plus_400k_B12G4
 model_type: RealESRGANModel
 scale: 4
-num_gpu: 4
+num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
 manual_seed: 0

 # ----------------- options for synthesizing training data in RealESRGANModel ----------------- #
--- a/options/train_realesrnet_x2plus.yml
+++ b/options/train_realesrnet_x2plus.yml
@@ -2,7 +2,7 @@
 name: train_RealESRNetx2plus_1000k_B12G4
 model_type: RealESRNetModel
 scale: 2
-num_gpu: 4
+num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
 manual_seed: 0

 # ----------------- options for synthesizing training data in RealESRNetModel ----------------- #
--- a/options/train_realesrnet_x4plus.yml
+++ b/options/train_realesrnet_x4plus.yml
@@ -2,7 +2,7 @@
 name: train_RealESRNetx4plus_1000k_B12G4
 model_type: RealESRNetModel
 scale: 4
-num_gpu: 4
+num_gpu: auto  # auto: can infer from your visible devices automatically. official: 4 GPUs
 manual_seed: 0

 # ----------------- options for synthesizing training data in RealESRNetModel ----------------- #
--- a/realesrgan/data/realesrgan_paired_dataset.py
+++ b/realesrgan/data/realesrgan_paired_dataset.py
@@ -0,0 +1,106 @@
+import os
+from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb
+from basicsr.data.transforms import augment, paired_random_crop
+from basicsr.utils import FileClient, imfrombytes, img2tensor
+from basicsr.utils.registry import DATASET_REGISTRY
+from torch.utils import data as data
+from torchvision.transforms.functional import normalize
+
+
+@DATASET_REGISTRY.register()
+class RealESRGANPairedDataset(data.Dataset):
+    """Paired image dataset for image restoration.
+
+    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and
+    GT image pairs.
+
+    There are three modes:
+    1. 'lmdb': Use lmdb files.
+        If opt['io_backend'] == lmdb.
+    2. 'meta_info': Use meta information file to generate paths.
+        If opt['io_backend'] != lmdb and opt['meta_info'] is not None.
+    3. 'folder': Scan folders to generate paths.
+        The rest.
+
+    Args:
+        opt (dict): Config for train datasets. It contains the following keys:
+            dataroot_gt (str): Data root path for gt.
+            dataroot_lq (str): Data root path for lq.
+            meta_info (str): Path for meta information file.
+            io_backend (dict): IO backend type and other kwarg.
+            filename_tmpl (str): Template for each filename. Note that the
+                template excludes the file extension. Default: '{}'.
+            gt_size (int): Cropped patched size for gt patches.
+            use_hflip (bool): Use horizontal flips.
+            use_rot (bool): Use rotation (use vertical flip and transposing h
+                and w for implementation).
+
+            scale (bool): Scale, which will be added automatically.
+            phase (str): 'train' or 'val'.
+    """
+
+    def __init__(self, opt):
+        super(RealESRGANPairedDataset, self).__init__()
+        self.opt = opt
+        # file client (io backend)
+        self.file_client = None
+        self.io_backend_opt = opt['io_backend']
+        self.mean = opt['mean'] if 'mean' in opt else None
+        self.std = opt['std'] if 'std' in opt else None
+
+        self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
+        if 'filename_tmpl' in opt:
+            self.filename_tmpl = opt['filename_tmpl']
+        else:
+            self.filename_tmpl = '{}'
+
+        if self.io_backend_opt['type'] == 'lmdb':
+            self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
+            self.io_backend_opt['client_keys'] = ['lq', 'gt']
+            self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt'])
+        elif 'meta_info' in self.opt and self.opt['meta_info'] is not None:
+            with open(self.opt['meta_info']) as fin:
+                paths = [line.strip() for line in fin]
+            self.paths = []
+            for path in paths:
+                gt_path, lq_path = path.split(', ')
+                gt_path = os.path.join(self.gt_folder, gt_path)
+                lq_path = os.path.join(self.lq_folder, lq_path)
+                self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)]))
+        else:
+            self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)
+
+    def __getitem__(self, index):
+        if self.file_client is None:
+            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
+
+        scale = self.opt['scale']
+
+        # Load gt and lq images. Dimension order: HWC; channel order: BGR;
+        # image range: [0, 1], float32.
+        gt_path = self.paths[index]['gt_path']
+        img_bytes = self.file_client.get(gt_path, 'gt')
+        img_gt = imfrombytes(img_bytes, float32=True)
+        lq_path = self.paths[index]['lq_path']
+        img_bytes = self.file_client.get(lq_path, 'lq')
+        img_lq = imfrombytes(img_bytes, float32=True)
+
+        # augmentation for training
+        if self.opt['phase'] == 'train':
+            gt_size = self.opt['gt_size']
+            # random crop
+            img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path)
+            # flip, rotation
+            img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot'])
+
+        # BGR to RGB, HWC to CHW, numpy to tensor
+        img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
+        # normalize
+        if self.mean is not None or self.std is not None:
+            normalize(img_lq, self.mean, self.std, inplace=True)
+            normalize(img_gt, self.mean, self.std, inplace=True)
+
+        return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path}
+
+    def __len__(self):
+        return len(self.paths)
--- a/realesrgan/models/realesrgan_model.py
+++ b/realesrgan/models/realesrgan_model.py
@@ -19,7 +19,7 @@ class RealESRGANModel(SRGANModel):
        super(RealESRGANModel, self).__init__(opt)
        self.jpeger = DiffJPEG(differentiable=False).cuda()
        self.usm_sharpener = USMSharp().cuda()
-        self.queue_size = opt['queue_size']
+        self.queue_size = opt.get('queue_size', 180)

    @torch.no_grad()
    def _dequeue_and_enqueue(self):
@@ -55,7 +55,7 @@ class RealESRGANModel(SRGANModel):

    @torch.no_grad()
    def feed_data(self, data):
-        if self.is_train:
+        if self.is_train and self.opt.get('high_order_degradation', True):
            # training data synthesis
            self.gt = data['gt'].to(self.device)
            self.gt_usm = self.usm_sharpener(self.gt)
@@ -166,6 +166,7 @@ class RealESRGANModel(SRGANModel):
            self.lq = data['lq'].to(self.device)
            if 'gt' in data:
                self.gt = data['gt'].to(self.device)
+                self.gt_usm = self.usm_sharpener(self.gt)

    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
        # do not use the synthetic process during validation
--- a/realesrgan/models/realesrnet_model.py
+++ b/realesrgan/models/realesrnet_model.py
@@ -18,7 +18,7 @@ class RealESRNetModel(SRModel):
        super(RealESRNetModel, self).__init__(opt)
        self.jpeger = DiffJPEG(differentiable=False).cuda()
        self.usm_sharpener = USMSharp().cuda()
-        self.queue_size = opt['queue_size']
+        self.queue_size = opt.get('queue_size', 180)

    @torch.no_grad()
    def _dequeue_and_enqueue(self):
@@ -54,7 +54,7 @@ class RealESRNetModel(SRModel):

    @torch.no_grad()
    def feed_data(self, data):
-        if self.is_train:
+        if self.is_train and self.opt.get('high_order_degradation', True):
            # training data synthesis
            self.gt = data['gt'].to(self.device)
            # USM the GT images
@@ -164,6 +164,7 @@ class RealESRNetModel(SRModel):
            self.lq = data['lq'].to(self.device)
            if 'gt' in data:
                self.gt = data['gt'].to(self.device)
+                self.gt_usm = self.usm_sharpener(self.gt)

    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
        # do not use the synthetic process during validation
--- a/realesrgan/utils.py
+++ b/realesrgan/utils.py
@@ -13,7 +13,7 @@ ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))

 class RealESRGANer():

-    def __init__(self, scale, model_path, tile=0, tile_pad=10, pre_pad=10, half=False):
+    def __init__(self, scale, model_path, model=None, tile=0, tile_pad=10, pre_pad=10, half=False):
        self.scale = scale
        self.tile_size = tile
        self.tile_pad = tile_pad
@@ -23,7 +23,8 @@ class RealESRGANer():

        # initialize model
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=scale)
+        if model is None:
+            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=scale)

        if model_path.startswith('https://'):
            model_path = load_file_from_url(
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,3 +5,5 @@ numpy
 opencv-python
 Pillow
 torch>=1.7
+torchvision
+tqdm
--- a/scripts/extract_subimages.py
+++ b/scripts/extract_subimages.py
@@ -0,0 +1,145 @@
+import argparse
+import cv2
+import numpy as np
+import os
+import sys
+from basicsr.utils import scandir
+from multiprocessing import Pool
+from os import path as osp
+from tqdm import tqdm
+
+
+def main(args):
+    """A multi-thread tool to crop large images to sub-images for faster IO.
+
+    opt (dict): Configuration dict. It contains:
+        n_thread (int): Thread number.
+        compression_level (int):  CV_IMWRITE_PNG_COMPRESSION from 0 to 9.
+            A higher value means a smaller size and longer compression time.
+            Use 0 for faster CPU decompression. Default: 3, same in cv2.
+
+        input_folder (str): Path to the input folder.
+        save_folder (str): Path to save folder.
+        crop_size (int): Crop size.
+        step (int): Step for overlapped sliding window.
+        thresh_size (int): Threshold size. Patches whose size is lower
+            than thresh_size will be dropped.
+
+    Usage:
+        For each folder, run this script.
+        Typically, there are four folders to be processed for DIV2K dataset.
+            DIV2K_train_HR
+            DIV2K_train_LR_bicubic/X2
+            DIV2K_train_LR_bicubic/X3
+            DIV2K_train_LR_bicubic/X4
+        After process, each sub_folder should have the same number of
+        subimages.
+        Remember to modify opt configurations according to your settings.
+    """
+
+    opt = {}
+    opt['n_thread'] = args.n_thread
+    opt['compression_level'] = args.compression_level
+
+    # HR images
+    opt['input_folder'] = args.input
+    opt['save_folder'] = args.output
+    opt['crop_size'] = args.crop_size
+    opt['step'] = args.step
+    opt['thresh_size'] = args.thresh_size
+    extract_subimages(opt)
+
+
+def extract_subimages(opt):
+    """Crop images to subimages.
+
+    Args:
+        opt (dict): Configuration dict. It contains:
+            input_folder (str): Path to the input folder.
+            save_folder (str): Path to save folder.
+            n_thread (int): Thread number.
+    """
+    input_folder = opt['input_folder']
+    save_folder = opt['save_folder']
+    if not osp.exists(save_folder):
+        os.makedirs(save_folder)
+        print(f'mkdir {save_folder} ...')
+    else:
+        print(f'Folder {save_folder} already exists. Exit.')
+        sys.exit(1)
+
+    img_list = list(scandir(input_folder, full_path=True))
+
+    pbar = tqdm(total=len(img_list), unit='image', desc='Extract')
+    pool = Pool(opt['n_thread'])
+    for path in img_list:
+        pool.apply_async(worker, args=(path, opt), callback=lambda arg: pbar.update(1))
+    pool.close()
+    pool.join()
+    pbar.close()
+    print('All processes done.')
+
+
+def worker(path, opt):
+    """Worker for each process.
+
+    Args:
+        path (str): Image path.
+        opt (dict): Configuration dict. It contains:
+            crop_size (int): Crop size.
+            step (int): Step for overlapped sliding window.
+            thresh_size (int): Threshold size. Patches whose size is lower
+                than thresh_size will be dropped.
+            save_folder (str): Path to save folder.
+            compression_level (int): for cv2.IMWRITE_PNG_COMPRESSION.
+
+    Returns:
+        process_info (str): Process information displayed in progress bar.
+    """
+    crop_size = opt['crop_size']
+    step = opt['step']
+    thresh_size = opt['thresh_size']
+    img_name, extension = osp.splitext(osp.basename(path))
+
+    # remove the x2, x3, x4 and x8 in the filename for DIV2K
+    img_name = img_name.replace('x2', '').replace('x3', '').replace('x4', '').replace('x8', '')
+
+    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
+
+    h, w = img.shape[0:2]
+    h_space = np.arange(0, h - crop_size + 1, step)
+    if h - (h_space[-1] + crop_size) > thresh_size:
+        h_space = np.append(h_space, h - crop_size)
+    w_space = np.arange(0, w - crop_size + 1, step)
+    if w - (w_space[-1] + crop_size) > thresh_size:
+        w_space = np.append(w_space, w - crop_size)
+
+    index = 0
+    for x in h_space:
+        for y in w_space:
+            index += 1
+            cropped_img = img[x:x + crop_size, y:y + crop_size, ...]
+            cropped_img = np.ascontiguousarray(cropped_img)
+            cv2.imwrite(
+                osp.join(opt['save_folder'], f'{img_name}_s{index:03d}{extension}'), cropped_img,
+                [cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])
+    process_info = f'Processing {img_name} ...'
+    return process_info
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input', type=str, default='datasets/DF2K/DF2K_HR', help='Input folder')
+    parser.add_argument('--output', type=str, default='datasets/DF2K/DF2K_HR_sub', help='Output folder')
+    parser.add_argument('--crop_size', type=int, default=480, help='Crop size')
+    parser.add_argument('--step', type=int, default=240, help='Step for overlapped sliding window')
+    parser.add_argument(
+        '--thresh_size',
+        type=int,
+        default=0,
+        help='Threshold size. Patches whose size is lower than thresh_size will be dropped.')
+    parser.add_argument('--n_thread', type=int, default=20, help='Thread number.')
+    parser.add_argument('--compression_level', type=int, default=3, help='Compression level')
+    args = parser.parse_args()
+
+    main(args)
--- a/scripts/generate_meta_info.py
+++ b/scripts/generate_meta_info.py
@@ -1,4 +1,5 @@
 import argparse
+import cv2
 import glob
 import os

@@ -8,9 +9,20 @@ def main(args):
    for folder, root in zip(args.input, args.root):
        img_paths = sorted(glob.glob(os.path.join(folder, '*')))
        for img_path in img_paths:
-            img_name = os.path.relpath(img_path, root)
-            print(img_name)
-            txt_file.write(f'{img_name}\n')
+            status = True
+            if args.check:
+                try:
+                    img = cv2.imread(img_path)
+                except Exception as error:
+                    print(f'Read {img_path} error: {error}')
+                    status = False
+                if img is None:
+                    status = False
+                    print(f'Img is None: {img_path}')
+            if status:
+                img_name = os.path.relpath(img_path, root)
+                print(img_name)
+                txt_file.write(f'{img_name}\n')


 if __name__ == '__main__':
@@ -34,7 +46,11 @@ if __name__ == '__main__':
        type=str,
        default='datasets/DF2K/meta_info/meta_info_DF2Kmultiscale.txt',
        help='txt path for meta info')
+    parser.add_argument('--check', action='store_true', help='Read image to check whether it is ok')
    args = parser.parse_args()
+
    assert len(args.input) == len(args.root), ('Input folder and folder root should have the same length, but got '
                                               f'{len(args.input)} and {len(args.root)}.')
+    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
+
    main(args)
--- a/scripts/generate_meta_info_pairdata.py
+++ b/scripts/generate_meta_info_pairdata.py
@@ -0,0 +1,47 @@
+import argparse
+import glob
+import os
+
+
+def main(args):
+    txt_file = open(args.meta_info, 'w')
+    img_paths_gt = sorted(glob.glob(os.path.join(args.input[0], '*')))
+    img_paths_lq = sorted(glob.glob(os.path.join(args.input[1], '*')))
+
+    assert len(img_paths_gt) == len(img_paths_lq), ('GT folder and LQ folder should have the same length, but got '
+                                                    f'{len(img_paths_gt)} and {len(img_paths_lq)}.')
+
+    for img_path_gt, img_path_lq in zip(img_paths_gt, img_paths_lq):
+        img_name_gt = os.path.relpath(img_path_gt, args.root[0])
+        img_name_lq = os.path.relpath(img_path_lq, args.root[1])
+        print(f'{img_name_gt}, {img_name_lq}')
+        txt_file.write(f'{img_name_gt}, {img_name_lq}\n')
+
+
+if __name__ == '__main__':
+    """Generate meta info (txt file) for paired images.
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--input',
+        nargs='+',
+        default=['datasets/DF2K/DIV2K_train_HR_sub', 'datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub'],
+        help='Input folder, should be [gt_folder, lq_folder]')
+    parser.add_argument('--root', nargs='+', default=[None, None], help='Folder root, will use the ')
+    parser.add_argument(
+        '--meta_info',
+        type=str,
+        default='datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt',
+        help='txt path for meta info')
+    args = parser.parse_args()
+
+    assert len(args.input) == 2, 'Input folder should have two elements: gt folder and lq folder'
+    assert len(args.root) == 2, 'Root path should have two elements: root for gt folder and lq folder'
+    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
+    for i in range(2):
+        if args.input[i].endswith('/'):
+            args.input[i] = args.input[i][:-1]
+        if args.root[i] is None:
+            args.root[i] = os.path.dirname(args.input[i])
+
+    main(args)
--- a/scripts/pytorch2onnx.py
+++ b/scripts/pytorch2onnx.py
@@ -3,7 +3,7 @@ import torch.onnx
 from basicsr.archs.rrdbnet_arch import RRDBNet

 # An instance of your model
-model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32)
+model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
 model.load_state_dict(torch.load('experiments/pretrained_models/RealESRGAN_x4plus.pth')['params_ema'])
 # set the train mode to false since we will only run the forward pass.
 model.train(False)
--- a/setup.cfg
+++ b/setup.cfg
@@ -17,6 +17,11 @@ line_length = 120
 multi_line_output = 0
 known_standard_library = pkg_resources,setuptools
 known_first_party = realesrgan
-known_third_party = PIL,basicsr,cv2,numpy,torch
+known_third_party = PIL,basicsr,cv2,numpy,torch,torchvision,tqdm
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY
+
+[codespell]
+skip = .git,./docs/build
+count =
+quiet-level = 3
--- a/setup.py
+++ b/setup.py
@@ -43,12 +43,6 @@ def get_git_hash():
 def get_hash():
    if os.path.exists('.git'):
        sha = get_git_hash()[:7]
-    elif os.path.exists(version_file):
-        try:
-            from facexlib.version import __version__
-            sha = __version__.split('+')[-1]
-        except ImportError:
-            raise ImportError('Unable to get git version')
    else:
        sha = 'unknown'
Author	SHA1	Message	Date
Xintao	3338b31f48	update setup.py, V0.2.2.5	2021-10-22 17:16:43 +08:00
Xintao	501efe3da6	update ReadMe	2021-10-17 01:33:45 +08:00
Xintao	8beb7ed17d	add feedback of anime models	2021-10-17 01:27:04 +08:00
Xintao	e2d30f9ea4	update readme: add usage guidance	2021-10-17 01:03:55 +08:00
Xintao	d715e3d26a	update readme	2021-10-16 23:30:57 +08:00
Xintao	772923e207	add codespell to pre-commit hook	2021-09-27 15:35:37 +08:00
Christian Clauss	14247a89d9	Fix typos discovered by codespell (#95 ) * Improve performance * !fixup Fix typo discovered by codespell * fixup! Fix typo discovered by codespell * fixup! Add codespell to lint process	2021-09-27 14:53:03 +08:00
Xintao	aa584e05bc	minor updates on Training.md	2021-09-17 10:30:52 +08:00
Xintao	b525d1793b	add trainining with one gpu	2021-09-17 10:13:25 +08:00
Xintao	0ad2e9c61e	set num_gpu to auto in options	2021-09-17 10:07:09 +08:00
Xintao	90ddf13b5e	Merge branch 'master' of github.com:xinntao/Real-ESRGAN	2021-09-07 21:28:03 +08:00
Xintao	8675208bc9	update: format and standard	2021-09-07 21:27:45 +08:00
Pratik Goyal	8f8536b6d1	Minor spelling correction (#67 )	2021-09-03 14:38:03 +08:00
Xintao	f83472d011	version 0.2.2.4	2021-09-01 00:19:21 +08:00
Xintao	e1b8832f1b	Update README for Real-ESRGAN-anime model (#62 ) * try to add video mp4 * update * update readme * update readme * update readme * update readme * update readme	2021-09-01 00:18:07 +08:00
Xintao	6ff747174d	adapt Real-ESRGAN-anime model	2021-08-31 22:28:30 +08:00
Xintao	c1669c4b0a	support model config during inference	2021-08-31 19:58:40 +08:00
Xintao	18a9c386a8	update readme	2021-08-30 00:02:27 +08:00
Xintao	b659608fb3	add contributing	2021-08-29 23:55:39 +08:00
Xintao	ee5f7b34cb	update open issues	2021-08-29 11:28:15 +08:00
Xintao	3ce826cabe	fix import bug in setup.py	2021-08-28 13:26:09 +08:00
Xintao	2c20a354b6	add check arg	2021-08-28 13:20:10 +08:00
Xintao	3e1f780f51	update readme	2021-08-27 16:25:38 +08:00
Xintao	f5ccd64ce5	support finetune with paired data	2021-08-27 16:14:48 +08:00
Xintao	194c2c14b3	updte readme	2021-08-26 23:12:00 +08:00
Xintao	0fcb49a299	add extract_subimages	2021-08-26 22:55:56 +08:00
Xintao	9976a34454	update pypi, version 0.2.2.3	2021-08-26 22:27:19 +08:00
Xintao	424a09457b	v0.2.2.2	2021-08-26 22:16:20 +08:00
Xintao	52f77e74a8	update publish-pip	2021-08-26 22:13:45 +08:00
@@ -1 +1 @@
 .2.2.1
 .2.2.5