v0.2.5.0

update readme
Add comparisons for the soon be released animevideo-v3 model (#301 )
2022-04-24 19:59:55 +08:00 · 2022-04-24 19:58:00 +08:00 · 2022-04-24 17:30:21 +08:00 · 2022-04-24 17:27:15 +08:00 · 2022-04-24 17:22:43 +08:00 · 2022-02-23 20:44:51 +08:00
60 changed files with 1912 additions and 227 deletions
--- a/.github/workflows/no-response.yml
+++ b/.github/workflows/no-response.yml
@@ -1,12 +1,11 @@
 name: No Response

+# TODO: it seems not to work
 # Modified from: https://raw.githubusercontent.com/github/docs/main/.github/workflows/no-response.yaml

-# **What it does**: Closes issues that don't have enough information to be
-#                   actionable.
-# **Why we have it**: To remove the need for maintainers to remember to check
-#                     back on issues periodically to see if contributors have
-#                     responded.
+# **What it does**: Closes issues that don't have enough information to be actionable.
+# **Why we have it**: To remove the need for maintainers to remember to check back on issues periodically
+#                     to see if contributors have responded.
 # **Who does it impact**: Everyone that works on docs or docs-internal.

 on:
--- a/CODE_OF_CONDUCT.md
+++ b/CODE_OF_CONDUCT.md
@@ -0,0 +1,128 @@
+# 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.
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -39,7 +39,3 @@ Here are some TODOs:
 - [ ] 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/FAQ.md
+++ b/FAQ.md
@@ -1,9 +1,7 @@
 # FAQ

-1. **What is the difference of `--netscale` and `outscale`?**
+1. **How to select models?**<br>
+A: Please refer to [docs/model_zoo.md](docs/model_zoo.md)

-A: TODO.
-
-1. **How to select models?**
-
-A: TODO.
+1. **Can `face_enhance` be used for anime images/animation videos?**<br>
+A: No, it can only be used for real faces. It is recommended not to use this option for anime images/animation videos to save GPU memory.
--- a/README.md
+++ b/README.md
@@ -1,4 +1,8 @@
-# Real-ESRGAN
+<p align="center">
+  <img src="assets/realesrgan_logo.png" height=120>
+</p>
+
+## <div align="center"><b><a href="README.md">English</a> | <a href="README_CN.md">简体中文</a></b></div>

 [![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/)
@@ -8,27 +12,20 @@
 [![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)

+:fire: Update the **RealESRGAN AnimeVideo-v3** model **更新动漫视频的小模型**. Please see [anime video models](docs/anime_video_model.md) and [comparisons](docs/anime_comparisons.md) for more details.
+
 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/v0.2.2.4/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).
+2. [Colab Demo](https://colab.research.google.com/drive/1yNl9ORUxxlL4N0keJa2SEPB61imPQd1B?usp=sharing) for Real-ESRGAN (**anime videos**) <a href="https://colab.research.google.com/drive/1yNl9ORUxxlL4N0keJa2SEPB61imPQd1B?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>.
+3. Portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-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>
+Real-ESRGAN aims at developing **Practical Algorithms for General Image/Video 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).
+: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](README.md#hugs-acknowledgement).

 :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). 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).
+:milky_way: Thanks for your valuable feedbacks/suggestions. All the feedbacks are updated in [feedback.md](feedback.md).

 ---

@@ -41,6 +38,52 @@ Other recommended projects:<br>

 ---

+<!---------------------------------- Updates --------------------------->
+<details>
+<summary>🚩<b>Updates</b></summary>
+
+- ✅ Update the **RealESRGAN AnimeVideo-v3** model. Please see [anime video models](docs/anime_video_model.md) and [comparisons](docs/anime_comparisons.md) for more details.
+- ✅ Add small models for anime videos. More details are in [anime video models](docs/anime_video_model.md).
+- ✅ Add the ncnn implementation [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan).
+- ✅ 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)
+- ✅ Support finetuning on your own data or paired data (*i.e.*, finetuning ESRGAN). See [here](Training.md#Finetune-Real-ESRGAN-on-your-own-dataset)
+- ✅ Integrate [GFPGAN](https://github.com/TencentARC/GFPGAN) to support **face enhancement**.
+- ✅ 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)
+- ✅ Support arbitrary scale with `--outscale` (It actually further resizes outputs with `LANCZOS4`). Add *RealESRGAN_x2plus.pth* model.
+- ✅ [The inference code](inference_realesrgan.py) supports: 1) **tile** options; 2) images with **alpha channel**; 3) **gray** images; 4) **16-bit** images.
+- ✅ The training codes have been released. A detailed guide can be found in [Training.md](Training.md).
+
+</details>
+
+<!---------------------------------- Projects that use Real-ESRGAN --------------------------->
+<details>
+<summary>🧩<b>Projects that use Real-ESRGAN</b></summary>
+
+&nbsp;&nbsp;&nbsp;&nbsp;👋 If you develop/use Real-ESRGAN in your projects, welcome to let me know.
+
+- NCNN-Android: [RealSR-NCNN-Android](https://github.com/tumuyan/RealSR-NCNN-Android) by [tumuyan](https://github.com/tumuyan)
+- VapourSynth: [vs-realesrgan](https://github.com/HolyWu/vs-realesrgan) by [HolyWu](https://github.com/HolyWu)
+- NCNN: [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan)
+
+&nbsp;&nbsp;&nbsp;&nbsp;**GUI**
+
+- [Waifu2x-Extension-GUI](https://github.com/AaronFeng753/Waifu2x-Extension-GUI) by [AaronFeng753](https://github.com/AaronFeng753)
+- [Squirrel-RIFE](https://github.com/Justin62628/Squirrel-RIFE) by [Justin62628](https://github.com/Justin62628)
+- [Real-GUI](https://github.com/scifx/Real-GUI) by [scifx](https://github.com/scifx)
+- [Real-ESRGAN_GUI](https://github.com/net2cn/Real-ESRGAN_GUI) by [net2cn](https://github.com/net2cn)
+- [Real-ESRGAN-EGUI](https://github.com/WGzeyu/Real-ESRGAN-EGUI) by [WGzeyu](https://github.com/WGzeyu)
+- [anime_upscaler](https://github.com/shangar21/anime_upscaler) by [shangar21](https://github.com/shangar21)
+
+</details>
+
+<!---------------------------------- Demo videos --------------------------->
+<details open>
+<summary>👀<b>Demo videos</b></summary>
+
+- [大闹天宫片段](https://www.bilibili.com/video/BV1ja41117zb)
+
+</details>
+
 ### :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

 > [[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>
@@ -76,21 +119,22 @@ If you have some images that Real-ESRGAN could not well restored, please also op

 ### Portable executable files

-You can download [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 download [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip) **executable files for Intel/AMD/Nvidia GPU**.

 This executable file is **portable** and includes all the binaries and models required. No CUDA or PyTorch environment is needed.<br>

 You can simply run the following command (the Windows example, more information is in the README.md of each executable files):

 ```bash
-./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png
+./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n model_name
 ```

-We have provided three models:
+We have provided five models:

 1. realesrgan-x4plus  (default)
 2. realesrnet-x4plus
 3. realesrgan-x4plus-anime (optimized for anime images, small model size)
+4. realesr-animevideov3 (animation video)

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

@@ -103,23 +147,21 @@ You can use the `-n` argument for other models, for example, `./realesrgan-ncnn-
 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)
+  -s scale             upscale ratio (can be 2, 3, 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
+  -m model-path        folder path to the pre-trained models. default=models
+  -n model-name        model name (default=realesr-animevideov3, can be realesr-animevideov3 | realesrgan-x4plus | realesrgan-x4plus-anime | realesrnet-x4plus)
+  -g gpu-id            gpu device to use (default=auto) 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
+  -x                   enable tta mode"
  -f format            output image format (jpg/png/webp, default=ext/png)
+  -v                   verbose output
 ```

 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).
-
 ---

 ## :wrench: Dependencies and Installation
@@ -162,7 +204,7 @@ wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_
 Inference!

 ```bash
-python inference_realesrgan.py --model_path experiments/pretrained_models/RealESRGAN_x4plus.pth --input inputs --face_enhance
+python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs --face_enhance
 ```

 Results are in the `results` folder
@@ -180,7 +222,7 @@ Pre-trained models: [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real
 # 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
+python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i inputs
 ```

 Results are in the `results` folder
@@ -190,37 +232,25 @@ Results are in the `results` folder
 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]...
+Usage: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile -o 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
+A common command: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile --outscale 3.5 --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
+  -i --input           Input image or folder. Default: inputs
+  -o --output          Output folder. Default: results
+  -n --model_name      Model name. Default: RealESRGAN_x4plus
+  -s, --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
+  -t, --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
+  --fp32               Use fp32 precision during inference. Default: fp16 (half precision).
  --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): 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)
-
- [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)
+Please see [docs/model_zoo.md](docs/model_zoo.md)

 ## :computer: Training and Finetuning on your own dataset

@@ -238,3 +268,11 @@ A detailed guide can be found in [Training.md](Training.md).
 ## :e-mail: Contact

 If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
+
+## :hugs: Acknowledgement
+
+Thanks for all the 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).
+- [Asiimoviet](https://github.com/Asiimoviet): Translate the README.md to Chinese (中文).
+- [2ji3150](https://github.com/2ji3150): Thanks for the [detailed and valuable feedbacks/suggestions](https://github.com/xinntao/Real-ESRGAN/issues/131).
--- a/README_CN.md
+++ b/README_CN.md
@@ -0,0 +1,275 @@
+<p align="center">
+  <img src="assets/realesrgan_logo.png" height=120>
+</p>
+
+## <div align="center"><b><a href="README.md">English</a> | <a href="README_CN.md">简体中文</a></b></div>
+
+[![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://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)
+
+:fire: 更新动漫视频的小模型 **RealESRGAN AnimeVideo-v3**. 更多信息在 [anime video models](docs/anime_video_model.md) 和 [comparisons](docs/anime_comparisons.md)中.
+
+1. Real-ESRGAN的[Colab Demo](https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing) <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. Real-ESRGAN的 **动漫视频** 的[Colab Demo](https://colab.research.google.com/drive/1yNl9ORUxxlL4N0keJa2SEPB61imPQd1B?usp=sharing) <a href="https://colab.research.google.com/drive/1yNl9ORUxxlL4N0keJa2SEPB61imPQd1B?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>.
+3. **支持Intel/AMD/Nvidia显卡**的绿色版exe文件： [Windows版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [macOS版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip)，详情请移步[这里](#便携版（绿色版）可执行文件)。NCNN的实现在 [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan)。
+
+Real-ESRGAN 的目标是开发出**实用的图像/视频修复算法**。<br>
+我们在 ESRGAN 的基础上使用纯合成的数据来进行训练，以使其能被应用于实际的图片修复的场景（顾名思义：Real-ESRGAN）。
+
+:art: Real-ESRGAN 需要，也很欢迎你的贡献，如新功能、模型、bug修复、建议、维护等等。详情可以查看[CONTRIBUTING.md](CONTRIBUTING.md)，所有的贡献者都会被列在[此处](README_CN.md#hugs-感谢)。
+
+:milky_way: 感谢大家提供了很好的反馈。这些反馈会逐步更新在 [这个文档](feedback.md)。
+
+:question: 常见的问题可以在[FAQ.md](FAQ.md)中找到答案。（好吧，现在还是空白的=-=||）
+
+---
+
+如果 Real-ESRGAN 对你有帮助，可以给本项目一个 Star :star: ，或者推荐给你的朋友们，谢谢！:blush: <br/>
+其他推荐的项目：<br/>
+:arrow_forward: [GFPGAN](https://github.com/TencentARC/GFPGAN): 实用的人脸复原算法 <br>
+:arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR): 开源的图像和视频工具箱<br>
+:arrow_forward: [facexlib](https://github.com/xinntao/facexlib): 提供与人脸相关的工具箱<br>
+:arrow_forward: [HandyView](https://github.com/xinntao/HandyView): 基于PyQt5的图片查看器，方便查看以及比较 <br>
+
+---
+
+<!---------------------------------- Updates --------------------------->
+<details>
+<summary>🚩<b>更新</b></summary>
+
+- ✅ 更新动漫视频的小模型 **RealESRGAN AnimeVideo-v3**. 更多信息在 [anime video models](docs/anime_video_model.md) 和 [comparisons](docs/anime_comparisons.md)中.
+- ✅ 添加了针对动漫视频的小模型, 更多信息在 [anime video models](docs/anime_video_model.md) 中.
+- ✅ 添加了ncnn 实现：[Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan).
+- ✅ 添加了 [*RealESRGAN_x4plus_anime_6B.pth*](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)，对二次元图片进行了优化，并减少了model的大小。详情 以及 与[waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan)的对比请查看[**anime_model.md**](docs/anime_model.md)
+- ✅支持用户在自己的数据上进行微调 (finetune)：[详情](Training.md#Finetune-Real-ESRGAN-on-your-own-dataset)
+- ✅ 支持使用[GFPGAN](https://github.com/TencentARC/GFPGAN)**增强人脸**
+- ✅ 通过[Gradio](https://github.com/gradio-app/gradio)添加到了[Huggingface Spaces](https://huggingface.co/spaces)（一个机器学习应用的在线平台）：[Gradio在线版](https://huggingface.co/spaces/akhaliq/Real-ESRGAN)。感谢[@AK391](https://github.com/AK391)
+- ✅ 支持任意比例的缩放：`--outscale`（实际上使用`LANCZOS4`来更进一步调整输出图像的尺寸）。添加了*RealESRGAN_x2plus.pth*模型
+- ✅ [推断脚本](inference_realesrgan.py)支持: 1) 分块处理**tile**; 2) 带**alpha通道**的图像; 3) **灰色**图像; 4) **16-bit**图像.
+- ✅ 训练代码已经发布，具体做法可查看：[Training.md](Training.md)。
+
+</details>
+
+<!---------------------------------- Projects that use Real-ESRGAN --------------------------->
+<details>
+<summary>🧩<b>使用Real-ESRGAN的项目</b></summary>
+
+&nbsp;&nbsp;&nbsp;&nbsp;👋 如果你开发/使用/集成了Real-ESRGAN, 欢迎联系我添加
+
+- NCNN-Android: [RealSR-NCNN-Android](https://github.com/tumuyan/RealSR-NCNN-Android) by [tumuyan](https://github.com/tumuyan)
+- VapourSynth: [vs-realesrgan](https://github.com/HolyWu/vs-realesrgan) by [HolyWu](https://github.com/HolyWu)
+- NCNN: [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan)
+
+&nbsp;&nbsp;&nbsp;&nbsp;**易用的图形界面**
+
+- [Waifu2x-Extension-GUI](https://github.com/AaronFeng753/Waifu2x-Extension-GUI) by [AaronFeng753](https://github.com/AaronFeng753)
+- [Squirrel-RIFE](https://github.com/Justin62628/Squirrel-RIFE) by [Justin62628](https://github.com/Justin62628)
+- [Real-GUI](https://github.com/scifx/Real-GUI) by [scifx](https://github.com/scifx)
+- [Real-ESRGAN_GUI](https://github.com/net2cn/Real-ESRGAN_GUI) by [net2cn](https://github.com/net2cn)
+- [Real-ESRGAN-EGUI](https://github.com/WGzeyu/Real-ESRGAN-EGUI) by [WGzeyu](https://github.com/WGzeyu)
+- [anime_upscaler](https://github.com/shangar21/anime_upscaler) by [shangar21](https://github.com/shangar21)
+
+</details>
+
+<details>
+<summary>👀<b>Demo视频（B站）</b></summary>
+
+- [大闹天宫片段](https://www.bilibili.com/video/BV1ja41117zb)
+
+</details>
+
+### :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
+
+> [[论文](https://arxiv.org/abs/2107.10833)] &emsp; [项目主页] &emsp; [[YouTube 视频](https://www.youtube.com/watch?v=fxHWoDSSvSc)] &emsp; [[B站视频](https://www.bilibili.com/video/BV1H34y1m7sS/)] &emsp; [[Poster](https://xinntao.github.io/projects/RealESRGAN_src/RealESRGAN_poster.pdf)] &emsp; [[PPT](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>
+> Tencent ARC Lab; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
+
+<p align="center">
+  <img src="assets/teaser.jpg">
+</p>
+
+---
+
+我们提供了一套训练好的模型（*RealESRGAN_x4plus.pth*)，可以进行4倍的超分辨率。<br>
+**现在的 Real-ESRGAN 还是有几率失败的，因为现实生活的降质过程比较复杂。**<br>
+而且，本项目对**人脸以及文字之类**的效果还不是太好，但是我们会持续进行优化的。<br>
+
+Real-ESRGAN 将会被长期支持，我会在空闲的时间中持续维护更新。
+
+这些是未来计划的几个新功能：
+
+- [ ] 优化人脸
+- [ ] 优化文字
+- [x] 优化动画图像
+- [ ] 支持更多的超分辨率比例
+- [ ] 可调节的复原
+
+如果你有好主意或需求，欢迎在 issue 或 discussion 中提出。<br/>
+如果你有一些 Real-ESRGAN 中有问题的照片，你也可以在 issue 或者 discussion 中发出来。我会留意（但是不一定能解决:stuck_out_tongue:）。如果有必要的话，我还会专门开一页来记录那些有待解决的图像。
+
+---
+
+### 便携版（绿色版）可执行文件
+
+你可以下载**支持Intel/AMD/Nvidia显卡**的绿色版exe文件： [Windows版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [macOS版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip)。
+
+绿色版指的是这些exe你可以直接运行（放U盘里拷走都没问题），因为里面已经有所需的文件和模型了。它不需要 CUDA 或者 PyTorch运行环境。<br>
+
+你可以通过下面这个命令来运行（Windows版本的例子，更多信息请查看对应版本的README.md）：
+
+```bash
+./realesrgan-ncnn-vulkan.exe -i 输入图像.jpg -o 输出图像.png -n 模型名字
+```
+
+我们提供了五种模型：
+
+1. realesrgan-x4plus（默认）
+2. reaesrnet-x4plus
+3. realesrgan-x4plus-anime（针对动漫插画图像优化，有更小的体积）
+4. realesr-animevideov3 (针对动漫视频)
+
+你可以通过`-n`参数来使用其他模型，例如`./realesrgan-ncnn-vulkan.exe -i 二次元图片.jpg -o 二刺螈图片.png -n realesrgan-x4plus-anime`
+
+### 可执行文件的用法
+
+1. 更多细节可以参考 [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan#computer-usages).
+2. 注意：可执行文件并没有支持 python 脚本 `inference_realesrgan.py` 中所有的功能，比如 `outscale` 选项) .
+
+```console
+Usage: realesrgan-ncnn-vulkan.exe -i infile -o outfile [options]...
+
+  -h                   show this help
+  -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 (can be 2, 3, 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 the pre-trained models. default=models
+  -n model-name        model name (default=realesr-animevideov3, can be realesr-animevideov3 | realesrgan-x4plus | realesrgan-x4plus-anime | realesrnet-x4plus)
+  -g gpu-id            gpu device to use (default=auto) 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)
+  -v                   verbose output
+```
+
+由于这些exe文件会把图像分成几个板块，然后来分别进行处理，再合成导出，输出的图像可能会有一点割裂感（而且可能跟PyTorch的输出不太一样）
+
+---
+
+## :wrench: 依赖以及安装
+
+- Python >= 3.7 (推荐使用[Anaconda](https://www.anaconda.com/download/#linux)或[Miniconda](https://docs.conda.io/en/latest/miniconda.html))
+- [PyTorch >= 1.7](https://pytorch.org/)
+
+#### 安装
+
+1. 把项目克隆到本地
+
+    ```bash
+    git clone https://github.com/xinntao/Real-ESRGAN.git
+    cd Real-ESRGAN
+    ```
+
+2. 安装各种依赖
+
+    ```bash
+    # 安装 basicsr - https://github.com/xinntao/BasicSR
+    # 我们使用BasicSR来训练以及推断
+    pip install basicsr
+    # facexlib和gfpgan是用来增强人脸的
+    pip install facexlib
+    pip install gfpgan
+    pip install -r requirements.txt
+    python setup.py develop
+    ```
+
+## :zap: 快速上手
+
+### 普通图片
+
+下载我们训练好的模型: [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
+```
+
+推断!
+
+```bash
+python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs --face_enhance
+```
+
+结果在`results`文件夹
+
+### 动画图片
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_1.png">
+</p>
+
+训练好的模型: [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)<br>
+有关[waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan)的更多信息和对比在[**anime_model.md**](docs/anime_model.md)中。
+
+```bash
+# 下载模型
+wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P experiments/pretrained_models
+# 推断
+python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i inputs
+```
+
+结果在`results`文件夹
+
+### Python 脚本的用法
+
+1. 虽然你使用了 X4 模型，但是你可以 **输出任意尺寸比例的图片**，只要实用了 `outscale` 参数. 程序会进一步对模型的输出图像进行缩放。
+
+```console
+Usage: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile -o outfile [options]...
+
+A common command: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile --outscale 3.5 --face_enhance
+
+  -h                   show this help
+  -i --input           Input image or folder. Default: inputs
+  -o --output          Output folder. Default: results
+  -n --model_name      Model name. Default: RealESRGAN_x4plus
+  -s, --outscale       The final upsampling scale of the image. Default: 4
+  --suffix             Suffix of the restored image. Default: out
+  -t, --tile           Tile size, 0 for no tile during testing. Default: 0
+  --face_enhance       Whether to use GFPGAN to enhance face. Default: False
+  --fp32               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: 模型库
+
+请参见 [docs/model_zoo.md](docs/model_zoo.md)
+
+## :computer: 训练，在你的数据上微调（Fine-tune）
+
+这里有一份详细的指南：[Training.md](Training.md).
+
+## BibTeX 引用
+
+    @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}
+    }
+
+## :e-mail: 联系我们
+
+如果你有任何问题，请通过 `xintao.wang@outlook.com` 或 `xintaowang@tencent.com` 联系我们。
+
+## :hugs: 感谢
+
+感谢所有的贡献者大大们~
+
+- [AK391](https://github.com/AK391): 通过[Gradio](https://github.com/gradio-app/gradio)添加到了[Huggingface Spaces](https://huggingface.co/spaces)（一个机器学习应用的在线平台）：[Gradio在线版](https://huggingface.co/spaces/akhaliq/Real-ESRGAN)。
+- [Asiimoviet](https://github.com/Asiimoviet): 把 README.md 文档 翻译成了中文。
+- [2ji3150](https://github.com/2ji3150): 感谢详尽并且富有价值的[反馈、建议](https://github.com/xinntao/Real-ESRGAN/issues/131).
--- a/2
+++ b/2
@@ -1 +1 @@
-0.2.2.5
+0.2.5.0
--- a/assets/realesrgan_logo.png
+++ b/assets/realesrgan_logo.png
--- a/assets/realesrgan_logo_ai.png
+++ b/assets/realesrgan_logo_ai.png
--- a/assets/realesrgan_logo_av.png
+++ b/assets/realesrgan_logo_av.png
--- a/assets/realesrgan_logo_gi.png
+++ b/assets/realesrgan_logo_gi.png
--- a/assets/realesrgan_logo_gv.png
+++ b/assets/realesrgan_logo_gv.png
--- a/docs/anime_comparisons.md
+++ b/docs/anime_comparisons.md
@@ -0,0 +1,65 @@
+# Comparisons among different anime models
+
+## Update News
+
+- 2022/04/24: Release **AnimeVideo-v3**. We have made the following improvements:
+  - **better naturalness**
+  - **Fewer artifacts**
+  - **more faithful to the original colors**
+  - **better texture restoration**
+  - **better background restoration**
+
+## Comparisons
+
+We have compared our RealESRGAN-AnimeVideo-v3 with the following methods.
+Our RealESRGAN-AnimeVideo-v3 can achieve better results with faster inference speed.
+
+- [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan) with the hyperparameters: `tile=0`, `noiselevel=2`
+
+- [Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN):
+  we use the [20220227](https://github.com/bilibili/ailab/releases/tag/Real-CUGAN-add-faster-low-memory-mode) version, the hyperparameters are: `cache_mode=0`, `tile=0`, `alpha=1`.
+- our RealESRGAN-AnimeVideo-v3
+
+## Results
+
+You may need to **zoom in** for comparing details, or **click the image** to see in the full size.
+
+**More natural results, better background restoration**
+| Input | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
+| :---: | :---:        |     :---:      |  :---:      |
+|![157083983-bec52c67-9a5e-4eed-afef-01fe6cd2af85_patch](https://user-images.githubusercontent.com/11482921/164452769-5d8cb4f8-1708-42d2-b941-f44a6f136feb.png) | ![](https://user-images.githubusercontent.com/11482921/164452767-c825cdec-f721-4ff1-aef1-fec41f146c4c.png) | ![](https://user-images.githubusercontent.com/11482921/164452755-3be50895-e3d4-432d-a7b9-9085c2a8e771.png) | ![](https://user-images.githubusercontent.com/11482921/164452771-be300656-379a-4323-a755-df8025a8c451.png) |
+|![a0010_patch](https://user-images.githubusercontent.com/11482921/164454047-22eeb493-3fa9-4142-9fc2-6f2a1c074cd5.png) | ![](https://user-images.githubusercontent.com/11482921/164454046-d5e79f8f-00a0-4b55-bc39-295d0d69747a.png) | ![](https://user-images.githubusercontent.com/11482921/164454040-87886b11-9d08-48bd-862f-0d4aed72eb19.png) | ![](https://user-images.githubusercontent.com/11482921/164454055-73dc9f02-286e-4d5c-8f70-c13742e08f42.png) |
+|![00000044_patch](https://user-images.githubusercontent.com/11482921/164451232-bacf64fc-e55a-44db-afbb-6b31ab0f8973.png) | ![](https://user-images.githubusercontent.com/11482921/164451318-f309b61a-75b8-4b74-b5f3-595725f1cf0b.png) | ![](https://user-images.githubusercontent.com/11482921/164451348-994f8a35-adbe-4a4b-9c61-feaa294af06a.png) | ![](https://user-images.githubusercontent.com/11482921/164451361-9b7d376e-6f75-4648-b752-542b44845d1c.png) |
+
+**Fewer artifacts, better detailed textures**
+| Input | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
+| :---: | :---:        |     :---:      |  :---:      |
+|![00000053_patch](https://user-images.githubusercontent.com/11482921/164448411-148a7e5c-cfcd-4504-8bc7-e318eb883bb6.png) | ![](https://user-images.githubusercontent.com/11482921/164448633-dfc15224-b6d2-4403-a3c9-4bb819979364.png) | ![](https://user-images.githubusercontent.com/11482921/164448771-0d359509-5293-4d4c-8e3c-86a2a314ea88.png) | ![](https://user-images.githubusercontent.com/11482921/164448848-1a4ff99e-075b-4458-9db7-2c89e8160aa0.png) |
+|![Disney_v4_22_018514_s2_patch](https://user-images.githubusercontent.com/11482921/164451898-83311cdf-bd3e-450f-b9f6-34d7fea3ab79.png) | ![](https://user-images.githubusercontent.com/11482921/164451894-6c56521c-6561-40d6-a3a5-8dde2c167b8a.png) | ![](https://user-images.githubusercontent.com/11482921/164451888-af9b47e3-39dc-4f3e-b0d7-d372d8191e2a.png) | ![](https://user-images.githubusercontent.com/11482921/164451901-31ca4dd4-9847-4baa-8cde-ad50f4053dcf.png) |
+|![Japan_v2_0_007261_s2_patch](https://user-images.githubusercontent.com/11482921/164454578-73c77392-77de-49c5-b03c-c36631723192.png) | ![](https://user-images.githubusercontent.com/11482921/164454574-b1ede5f0-4520-4eaa-8f59-086751a34e62.png) | ![](https://user-images.githubusercontent.com/11482921/164454567-4cb3fdd8-6a2d-4016-85b2-a305a8ff80e4.png) | ![](https://user-images.githubusercontent.com/11482921/164454583-7f243f20-eca3-4500-ac43-eb058a4a101a.png) |
+|![huluxiongdi_2_patch](https://user-images.githubusercontent.com/11482921/164453482-0726c842-337e-40ec-bf6c-f902ee956a8b.png) | ![](https://user-images.githubusercontent.com/11482921/164453480-71d5e091-5bfa-4c77-9c57-4e37f66ca0a3.png) | ![](https://user-images.githubusercontent.com/11482921/164453468-c295d3c9-3661-45f0-9ecd-406a1877f76e.png) | ![](https://user-images.githubusercontent.com/11482921/164453486-3091887c-587c-450e-b6fe-905cb518d57e.png) |
+
+**Other better results**
+| Input | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
+| :---: | :---:        |     :---:      |  :---:      |
+|![Japan_v2_1_128525_s1_patch](https://user-images.githubusercontent.com/11482921/164454933-67697f7c-b6ef-47dc-bfca-822a78af8acf.png) | ![](https://user-images.githubusercontent.com/11482921/164454931-9450de7c-f0b3-4638-9c1e-0668e0c41ef0.png) | ![](https://user-images.githubusercontent.com/11482921/164454926-ed746976-786d-41c5-8a83-7693cd774c3a.png) | ![](https://user-images.githubusercontent.com/11482921/164454936-8abdf0f0-fb30-40eb-8281-3b46c0bcb9ae.png) |
+|![tianshuqitan_2_patch](https://user-images.githubusercontent.com/11482921/164456948-807c1476-90b6-4507-81da-cb986d01600c.png) | ![](https://user-images.githubusercontent.com/11482921/164456943-25e89de9-d7e5-4f61-a2e1-96786af6ae9e.png) | ![](https://user-images.githubusercontent.com/11482921/164456954-b468c447-59f5-4594-9693-3683e44ba3e6.png) | ![](https://user-images.githubusercontent.com/11482921/164456957-640f910c-3b04-407c-ac20-044d72e19735.png) |
+|![00000051_patch](https://user-images.githubusercontent.com/11482921/164456044-e9a6b3fa-b24e-4eb7-acf9-1f7746551b1e.png) ![00000051_patch](https://user-images.githubusercontent.com/11482921/164456421-b67245b0-767d-4250-9105-80bbe507ecfc.png) | ![](https://user-images.githubusercontent.com/11482921/164456040-85763cf2-cb28-4ba3-abb6-1dbb48c55713.png) ![](https://user-images.githubusercontent.com/11482921/164456419-59cf342e-bc1e-4044-868c-e1090abad313.png) | ![](https://user-images.githubusercontent.com/11482921/164456031-4244bb7b-8649-4e01-86f4-40c2099c5afd.png) ![](https://user-images.githubusercontent.com/11482921/164456411-b6afcbe9-c054-448d-a6df-96d3ba3047f8.png) | ![](https://user-images.githubusercontent.com/11482921/164456035-12e270be-fd52-46d4-b18a-3d3b680731fe.png) ![](https://user-images.githubusercontent.com/11482921/164456417-dcaa8b62-f497-427d-b2d2-f390f1200fb9.png) |
+|![00000099_patch](https://user-images.githubusercontent.com/11482921/164455312-6411b6e1-5823-4131-a4b0-a6be8a9ae89f.png) | ![](https://user-images.githubusercontent.com/11482921/164455310-f2b99646-3a22-47a4-805b-dc451ac86ddb.png) | ![](https://user-images.githubusercontent.com/11482921/164455294-35471b42-2826-4451-b7ec-6de01344954c.png) | ![](https://user-images.githubusercontent.com/11482921/164455305-fa4c9758-564a-4081-8b4e-f11057a0404d.png) |
+|![00000016_patch](https://user-images.githubusercontent.com/11482921/164455672-447353c9-2da2-4fcb-ba4a-7dd6b94c19c1.png) | ![](https://user-images.githubusercontent.com/11482921/164455669-df384631-baaa-42f8-9150-40f658471558.png) | ![](https://user-images.githubusercontent.com/11482921/164455657-68006bf0-138d-4981-aaca-8aa927d2f78a.png) | ![](https://user-images.githubusercontent.com/11482921/164455664-0342b93e-a62a-4b36-a90e-7118f3f1e45d.png) |
+
+## Inference Speed
+
+### PyTorch
+
+Note that we only report the **model** time, and ignore the IO time.
+
+| GPU | Input Resolution | waifu2x | Real-CUGAN | RealESRGAN-AnimeVideo-v3
+| :---: | :---:         |  :---:        |     :---:      |  :---:      |
+| V100 | 1921 x 1080 | - | 3.4 fps | **10.0** fps |
+| V100 | 1280 x 720 | - | 7.2 fps | **22.6** fps |
+| V100 | 640 x 480 | - | 24.4 fps | **65.9** fps |
+
+### ncnn
+
+- [ ] TODO
--- a/docs/anime_model.md
+++ b/docs/anime_model.md
@@ -1,12 +1,13 @@
-# Anime model
+# 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)
+- [Anime Model](#anime-model)
+  - [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](#comparisons-with-sliding-bars)

 <p align="center">
  <img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_1.png">
@@ -14,7 +15,7 @@

 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
+<https://user-images.githubusercontent.com/17445847/131535127-613250d4-f754-4e20-9720-2f9608ad0675.mp4>

 ## How to Use

@@ -26,12 +27,12 @@ Pre-trained models: [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real
 # 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
+python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i 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**.
+Download the latest portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip) **executable files for Intel/AMD/Nvidia GPU**.

 Taking the Windows as example, run:

@@ -63,6 +64,6 @@ We compare Real-ESRGAN-anime with [waifu2x](https://github.com/nihui/waifu2x-ncn

 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/17445847/131536647-a2fbf896-b495-4a9f-b1dd-ca7bbc90101a.mp4>

-https://user-images.githubusercontent.com/17445847/131536742-6d9d82b6-9765-4296-a15f-18f9aeaa5465.mp4
+<https://user-images.githubusercontent.com/17445847/131536742-6d9d82b6-9765-4296-a15f-18f9aeaa5465.mp4>
--- a/docs/anime_video_model.md
+++ b/docs/anime_video_model.md
@@ -0,0 +1,123 @@
+# Anime Video Models
+
+:white_check_mark: We add small models that are optimized for anime videos :-)<br>
+More comparisons can be found in [anime_comparisons.md](docs/anime_comparisons.md)
+
+- [How to Use](#how-to-use)
+- [PyTorch Inference](#pytorch-inference)
+- [ncnn Executable File](#ncnn-executable-file)
+  - [Step 1: Use ffmpeg to extract frames from video](#step-1-use-ffmpeg-to-extract-frames-from-video)
+  - [Step 2: Inference with Real-ESRGAN executable file](#step-2-inference-with-real-esrgan-executable-file)
+  - [Step 3: Merge the enhanced frames back into a video](#step-3-merge-the-enhanced-frames-back-into-a-video)
+- [More Demos](#more-demos)
+
+| Models                                                                                                                             | Scale | Description                    |
+| ---------------------------------------------------------------------------------------------------------------------------------- | :---- | :----------------------------- |
+| [realesr-animevideov3](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth) | X4 <sup>1</sup>   | Anime video model with XS size |
+
+Note: <br>
+<sup>1</sup> This model can also be used for X1, X2, X3.
+
+---
+
+The following are some demos (best view in the full screen mode).
+
+<https://user-images.githubusercontent.com/17445847/145706977-98bc64a4-af27-481c-8abe-c475e15db7ff.MP4>
+
+<https://user-images.githubusercontent.com/17445847/145707055-6a4b79cb-3d9d-477f-8610-c6be43797133.MP4>
+
+<https://user-images.githubusercontent.com/17445847/145783523-f4553729-9f03-44a8-a7cc-782aadf67b50.MP4>
+
+## How to Use
+
+### PyTorch Inference
+
+```bash
+# download model
+wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth -P realesrgan/weights
+# inference
+python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2
+```
+
+### NCNN Executable File
+
+#### Step 1: Use ffmpeg to extract frames from video
+
+```bash
+ffmpeg -i onepiece_demo.mp4 -qscale:v 1 -qmin 1 -qmax 1 -vsync 0 tmp_frames/frame%08d.png
+```
+
+- Remember to create the folder `tmp_frames` ahead
+
+#### Step 2: Inference with Real-ESRGAN executable file
+
+1. Download the latest portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip) **executable files for Intel/AMD/Nvidia GPU**
+
+1. Taking the Windows as example, run:
+
+    ```bash
+    ./realesrgan-ncnn-vulkan.exe -i tmp_frames -o out_frames -n realesr-animevideov3 -s 2 -f jpg
+    ```
+
+    - Remember to create the folder `out_frames` ahead
+
+#### Step 3: Merge the enhanced frames back into a video
+
+1. First obtain fps from input videos by
+
+    ```bash
+    ffmpeg -i onepiece_demo.mp4
+    ```
+
+    ```console
+    Usage:
+    -i                   input video path
+    ```
+
+    You will get the output similar to the following screenshot.
+
+    <p align="center">
+        <img src="https://user-images.githubusercontent.com/17445847/145710145-c4f3accf-b82f-4307-9f20-3803a2c73f57.png">
+    </p>
+
+2. Merge frames
+
+    ```bash
+    ffmpeg -r 23.98 -i out_frames/frame%08d.jpg -c:v libx264 -r 23.98 -pix_fmt yuv420p output.mp4
+    ```
+
+    ```console
+    Usage:
+    -i                   input video path
+    -c:v                 video encoder (usually we use libx264)
+    -r                   fps, remember to modify it to meet your needs
+    -pix_fmt             pixel format in video
+    ```
+
+    If you also want to copy audio from the input videos, run:
+
+     ```bash
+    ffmpeg -r 23.98 -i out_frames/frame%08d.jpg -i onepiece_demo.mp4 -map 0:v:0 -map 1:a:0 -c:a copy -c:v libx264 -r 23.98 -pix_fmt yuv420p output_w_audio.mp4
+    ```
+
+    ```console
+    Usage:
+    -i                   input video path, here we use two input streams
+    -c:v                 video encoder (usually we use libx264)
+    -r                   fps, remember to modify it to meet your needs
+    -pix_fmt             pixel format in video
+    ```
+
+## More Demos
+
+- Input video for One Piece:
+
+    <https://user-images.githubusercontent.com/17445847/145706822-0e83d9c4-78ef-40ee-b2a4-d8b8c3692d17.mp4>
+
+- Out video for One Piece
+
+    <https://user-images.githubusercontent.com/17445847/164960481-759658cf-fcb8-480c-b888-cecb606e8744.mp4>
+
+**More comparisons**
+
+<https://user-images.githubusercontent.com/17445847/145707458-04a5e9b9-2edd-4d1f-b400-380a72e5f5e6.MP4>
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -0,0 +1,48 @@
+# :european_castle: Model Zoo
+
+- [For General Images](#for-general-images)
+- [For Anime Images](#for-anime-images)
+- [For Anime Videos](#for-anime-videos)
+
+---
+
+## For General Images
+
+| Models                                                                                                                          | Scale | Description                                  |
+| ------------------------------------------------------------------------------------------------------------------------------- | :---- | :------------------------------------------- |
+| [RealESRGAN_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth)                      | X4    | X4 model for general images                  |
+| [RealESRGAN_x2plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth)                      | X2    | X2 model for general images                  |
+| [RealESRNet_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth)                      | X4    | X4 model with MSE loss (over-smooth effects) |
+| [official ESRGAN_x4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth) | X4    | official ESRGAN model                        |
+
+The following models are **discriminators**, which are usually used for fine-tuning.
+
+| Models                                                                                                                 | Corresponding model |
+| ---------------------------------------------------------------------------------------------------------------------- | :------------------ |
+| [RealESRGAN_x4plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth) | RealESRGAN_x4plus   |
+| [RealESRGAN_x2plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x2plus_netD.pth) | RealESRGAN_x2plus   |
+
+## For Anime Images / Illustrations
+
+| Models                                                                                                                         | Scale | Description                                                 |
+| ------------------------------------------------------------------------------------------------------------------------------ | :---- | :---------------------------------------------------------- |
+| [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth) | X4    | Optimized for anime images; 6 RRDB blocks (smaller network) |
+
+The following models are **discriminators**, which are usually used for fine-tuning.
+
+| Models                                                                                                                                   | Corresponding model        |
+| ---------------------------------------------------------------------------------------------------------------------------------------- | :------------------------- |
+| [RealESRGAN_x4plus_anime_6B_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B_netD.pth) | RealESRGAN_x4plus_anime_6B |
+
+## For Animation Videos
+
+| Models                                                                                                                             | Scale | Description                    |
+| ---------------------------------------------------------------------------------------------------------------------------------- | :---- | :----------------------------- |
+| [realesr-animevideov3](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth) | X4<sup>1</sup>    | Anime video model with XS size |
+
+Note: <br>
+<sup>1</sup> This model can also be used for X1, X2, X3.
+
+The following models are **discriminators**, which are usually used for fine-tuning.
+
+TODO
--- a/feedback.md
+++ b/feedback.md
@@ -7,3 +7,5 @@
 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
+
+Thanks for the [detailed and valuable feedbacks/suggestions](https://github.com/xinntao/Real-ESRGAN/issues/131) by [2ji3150](https://github.com/2ji3150).
--- a/inference_realesrgan.py
+++ b/inference_realesrgan.py
@@ -5,26 +5,30 @@ import os
 from basicsr.archs.rrdbnet_arch import RRDBNet

 from realesrgan import RealESRGANer
+from realesrgan.archs.srvgg_arch import SRVGGNetCompact


 def main():
+    """Inference demo for Real-ESRGAN.
+    """
    parser = argparse.ArgumentParser()
-    parser.add_argument('--input', type=str, default='inputs', help='Input image or folder')
+    parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder')
    parser.add_argument(
-        '--model_path',
+        '-n',
+        '--model_name',
        type=str,
-        default='experiments/pretrained_models/RealESRGAN_x4plus.pth',
-        help='Path to the pre-trained model')
-    parser.add_argument('--output', type=str, default='results', help='Output folder')
-    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')
+        default='RealESRGAN_x4plus',
+        help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus | '
+              'realesr-animevideov3'))
+    parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
+    parser.add_argument('-s', '--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=0, help='Tile size, 0 for no tile during testing')
+    parser.add_argument('-t', '--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(
+        '--fp32', action='store_true', help='Use fp32 precision during inference. Default: fp16 (half precision).')
    parser.add_argument(
        '--alpha_upsampler',
        type=str,
@@ -37,26 +41,42 @@ 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
+    # determine models according to model names
+    args.model_name = args.model_name.split('.')[0]
+    if args.model_name in ['RealESRGAN_x4plus', 'RealESRNet_x4plus']:  # x4 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+    elif args.model_name in ['RealESRGAN_x4plus_anime_6B']:  # x4 RRDBNet model with 6 blocks
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
+        netscale = 4
+    elif args.model_name in ['RealESRGAN_x2plus']:  # x2 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
+        netscale = 2
+    elif args.model_name in ['realesr-animevideov3']:  # x4 VGG-style model (XS size)
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
+        netscale = 4

-    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=args.block, num_grow_ch=32, scale=args.netscale)
+    # determine model paths
+    model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth')
+    if not os.path.isfile(model_path):
+        model_path = os.path.join('realesrgan/weights', args.model_name + '.pth')
+    if not os.path.isfile(model_path):
+        raise ValueError(f'Model {args.model_name} does not exist.')

+    # restorer
    upsampler = RealESRGANer(
-        scale=args.netscale,
-        model_path=args.model_path,
+        scale=netscale,
+        model_path=model_path,
        model=model,
        tile=args.tile,
        tile_pad=args.tile_pad,
        pre_pad=args.pre_pad,
-        half=args.half)
+        half=not args.fp32)

-    if args.face_enhance:
+    if args.face_enhance:  # Use GFPGAN for face enhancement
        from gfpgan import GFPGANer
        face_enhancer = GFPGANer(
-            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth',
+            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
            upscale=args.outscale,
            arch='clean',
            channel_multiplier=2,
@@ -78,20 +98,12 @@ def main():
        else:
            img_mode = None

-        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 performance.')
-        if max(h, w) < 500 and args.netscale == 2:
-            import warnings
-            warnings.warn('The input image is small, try X4 model for better performance.')
-
        try:
            if args.face_enhance:
                _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
            else:
                output, _ = upsampler.enhance(img, outscale=args.outscale)
-        except Exception as error:
+        except RuntimeError as error:
            print('Error', error)
            print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
        else:
@@ -101,6 +113,9 @@ def main():
                extension = args.ext
            if img_mode == 'RGBA':  # RGBA images should be saved in png format
                extension = 'png'
+            if args.suffix == '':
+                save_path = os.path.join(args.output, f'{imgname}.{extension}')
+            else:
                save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}')
            cv2.imwrite(save_path, output)

--- a/inference_realesrgan_video.py
+++ b/inference_realesrgan_video.py
@@ -0,0 +1,185 @@
+import argparse
+import glob
+import mimetypes
+import os
+import queue
+import shutil
+import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from basicsr.utils.logger import AvgTimer
+from tqdm import tqdm
+
+from realesrgan import IOConsumer, PrefetchReader, RealESRGANer
+from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+
+
+def main():
+    """Inference demo for Real-ESRGAN.
+    It mainly for restoring anime videos.
+
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i', '--input', type=str, default='inputs', help='Input video, image or folder')
+    parser.add_argument(
+        '-n',
+        '--model_name',
+        type=str,
+        default='realesr-animevideov3',
+        help=('Model names: realesr-animevideov3 | RealESRGAN_x4plus_anime_6B | RealESRGAN_x4plus | RealESRNet_x4plus |'
+              ' RealESRGAN_x2plus | '
+              'Default:realesr-animevideov3'))
+    parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
+    parser.add_argument('-s', '--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 video')
+    parser.add_argument('-t', '--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(
+        '--fp32', action='store_true', help='Use fp32 precision during inference. Default: fp16 (half precision).')
+    parser.add_argument('--fps', type=float, default=None, help='FPS of the output video')
+    parser.add_argument('--consumer', type=int, default=4, help='Number of IO consumers')
+
+    parser.add_argument(
+        '--alpha_upsampler',
+        type=str,
+        default='realesrgan',
+        help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
+    parser.add_argument(
+        '--ext',
+        type=str,
+        default='auto',
+        help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
+    args = parser.parse_args()
+
+    # ---------------------- determine models according to model names ---------------------- #
+    args.model_name = args.model_name.split('.pth')[0]
+    if args.model_name in ['RealESRGAN_x4plus', 'RealESRNet_x4plus']:  # x4 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+    elif args.model_name in ['RealESRGAN_x4plus_anime_6B']:  # x4 RRDBNet model with 6 blocks
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
+        netscale = 4
+    elif args.model_name in ['RealESRGAN_x2plus']:  # x2 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
+        netscale = 2
+    elif args.model_name in ['realesr-animevideov3']:  # x4 VGG-style model (XS size)
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
+        netscale = 4
+
+    # ---------------------- determine model paths ---------------------- #
+    model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth')
+    if not os.path.isfile(model_path):
+        model_path = os.path.join('realesrgan/weights', args.model_name + '.pth')
+    if not os.path.isfile(model_path):
+        raise ValueError(f'Model {args.model_name} does not exist.')
+
+    # restorer
+    upsampler = RealESRGANer(
+        scale=netscale,
+        model_path=model_path,
+        model=model,
+        tile=args.tile,
+        tile_pad=args.tile_pad,
+        pre_pad=args.pre_pad,
+        half=not args.fp32)
+
+    if args.face_enhance:  # Use GFPGAN for face enhancement
+        from gfpgan import GFPGANer
+        face_enhancer = GFPGANer(
+            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
+            upscale=args.outscale,
+            arch='clean',
+            channel_multiplier=2,
+            bg_upsampler=upsampler)
+    os.makedirs(args.output, exist_ok=True)
+    # for saving restored frames
+    save_frame_folder = os.path.join(args.output, 'frames_tmpout')
+    os.makedirs(save_frame_folder, exist_ok=True)
+
+    # input can be a video file / a folder of frames / an image
+    if mimetypes.guess_type(args.input)[0].startswith('video'):  # is a video file
+        video_name = os.path.splitext(os.path.basename(args.input))[0]
+        frame_folder = os.path.join('tmp_frames', video_name)
+        os.makedirs(frame_folder, exist_ok=True)
+        # use ffmpeg to extract frames
+        os.system(f'ffmpeg -i {args.input} -qscale:v 1 -qmin 1 -qmax 1 -vsync 0  {frame_folder}/frame%08d.png')
+        # get image path list
+        paths = sorted(glob.glob(os.path.join(frame_folder, '*')))
+        # get input video fps
+        if args.fps is None:
+
+            import ffmpeg
+            probe = ffmpeg.probe(args.input)
+            video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video']
+            args.fps = eval(video_streams[0]['avg_frame_rate'])
+    elif mimetypes.guess_type(args.input)[0].startswith('image'):  # is an image file
+        paths = [args.input]
+        video_name = 'video'
+    else:
+        paths = sorted(glob.glob(os.path.join(args.input, '*')))
+        video_name = 'video'
+
+    timer = AvgTimer()
+    timer.start()
+    pbar = tqdm(total=len(paths), unit='frame', desc='inference')
+    # set up prefetch reader
+    reader = PrefetchReader(paths, num_prefetch_queue=4)
+    reader.start()
+
+    que = queue.Queue()
+    consumers = [IOConsumer(args, que, f'IO_{i}') for i in range(args.consumer)]
+    for consumer in consumers:
+        consumer.start()
+
+    for idx, (path, img) in enumerate(zip(paths, reader)):
+        imgname, extension = os.path.splitext(os.path.basename(path))
+        if len(img.shape) == 3 and img.shape[2] == 4:
+            img_mode = 'RGBA'
+        else:
+            img_mode = None
+
+        try:
+            if args.face_enhance:
+                _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+            else:
+                output, _ = upsampler.enhance(img, outscale=args.outscale)
+        except RuntimeError as error:
+            print('Error', error)
+            print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
+
+        else:
+            if args.ext == 'auto':
+                extension = extension[1:]
+            else:
+                extension = args.ext
+            if img_mode == 'RGBA':  # RGBA images should be saved in png format
+                extension = 'png'
+            save_path = os.path.join(save_frame_folder, f'{imgname}_out.{extension}')
+
+            que.put({'output': output, 'save_path': save_path})
+
+        pbar.update(1)
+        torch.cuda.synchronize()
+        timer.record()
+        avg_fps = 1. / (timer.get_avg_time() + 1e-7)
+        pbar.set_description(f'idx {idx}, fps {avg_fps:.2f}')
+
+    for _ in range(args.consumer):
+        que.put('quit')
+    for consumer in consumers:
+        consumer.join()
+    pbar.close()
+
+    # merge frames to video
+    video_save_path = os.path.join(args.output, f'{video_name}_{args.suffix}.mp4')
+    os.system(f'ffmpeg -r {args.fps} -i {save_frame_folder}/frame%08d_out.{extension} -i {args.input}'
+              f' -map 0:v:0 -map 1:a:0 -c:a copy -c:v libx264 -r {args.fps} -pix_fmt yuv420p  {video_save_path}')
+    # delete tmp file
+    shutil.rmtree(save_frame_folder)
+    if os.path.isdir(frame_folder):
+        shutil.rmtree(frame_folder)
+
+
+if __name__ == '__main__':
+    main()
--- a/inputs/video/onepiece_demo.mp4
+++ b/inputs/video/onepiece_demo.mp4
--- a/options/finetune_realesrgan_x4plus.yml
+++ b/options/finetune_realesrgan_x4plus.yml
@@ -90,7 +90,6 @@ network_g:
  num_block: 23
  num_grow_ch: 32

-
 network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
@@ -169,7 +168,7 @@ train:
 #   save_img: True

 #   metrics:
-#     psnr: # metric name, can be arbitrary
+#     psnr: # metric name
 #       type: calculate_psnr
 #       crop_border: 4
 #       test_y_channel: false
--- a/options/finetune_realesrgan_x4plus_pairdata.yml
+++ b/options/finetune_realesrgan_x4plus_pairdata.yml
@@ -52,7 +52,6 @@ network_g:
  num_block: 23
  num_grow_ch: 32

-
 network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
@@ -131,7 +130,7 @@ train:
 #   save_img: True

 #   metrics:
-#     psnr: # metric name, can be arbitrary
+#     psnr: # metric name
 #       type: calculate_psnr
 #       crop_border: 4
 #       test_y_channel: false
--- a/options/train_realesrgan_x2plus.yml
+++ b/options/train_realesrgan_x2plus.yml
@@ -91,7 +91,6 @@ network_g:
  num_grow_ch: 32
  scale: 2

-
 network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
@@ -167,7 +166,7 @@ train:
 #   save_img: True

 #   metrics:
-#     psnr: # metric name, can be arbitrary
+#     psnr: # metric name
 #       type: calculate_psnr
 #       crop_border: 4
 #       test_y_channel: false
--- a/options/train_realesrgan_x4plus.yml
+++ b/options/train_realesrgan_x4plus.yml
@@ -90,7 +90,6 @@ network_g:
  num_block: 23
  num_grow_ch: 32

-
 network_d:
  type: UNetDiscriminatorSN
  num_in_ch: 3
@@ -166,7 +165,7 @@ train:
 #   save_img: True

 #   metrics:
-#     psnr: # metric name, can be arbitrary
+#     psnr: # metric name
 #       type: calculate_psnr
 #       crop_border: 4
 #       test_y_channel: false
--- a/options/train_realesrnet_x2plus.yml
+++ b/options/train_realesrnet_x2plus.yml
@@ -125,7 +125,7 @@ train:
 #   save_img: True

 #   metrics:
-#     psnr: # metric name, can be arbitrary
+#     psnr: # metric name
 #       type: calculate_psnr
 #       crop_border: 4
 #       test_y_channel: false
--- a/options/train_realesrnet_x4plus.yml
+++ b/options/train_realesrnet_x4plus.yml
@@ -124,7 +124,7 @@ train:
 #   save_img: True

 #   metrics:
-#     psnr: # metric name, can be arbitrary
+#     psnr: # metric name
 #       type: calculate_psnr
 #       crop_border: 4
 #       test_y_channel: false
--- a/realesrgan/init.py
+++ b/realesrgan/init.py
@@ -3,4 +3,4 @@ from .archs import *
 from .data import *
 from .models import *
 from .utils import *
-from .version import __gitsha__, __version__
+from .version import *
--- a/realesrgan/archs/discriminator_arch.py
+++ b/realesrgan/archs/discriminator_arch.py
@@ -6,15 +6,23 @@ from torch.nn.utils import spectral_norm

@ARCH_REGISTRY.register()
 class UNetDiscriminatorSN(nn.Module):
-    """Defines a U-Net discriminator with spectral normalization (SN)"""
+    """Defines a U-Net discriminator with spectral normalization (SN)
+
+    It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
+
+    Arg:
+        num_in_ch (int): Channel number of inputs. Default: 3.
+        num_feat (int): Channel number of base intermediate features. Default: 64.
+        skip_connection (bool): Whether to use skip connections between U-Net. Default: True.
+    """

    def __init__(self, num_in_ch, num_feat=64, skip_connection=True):
        super(UNetDiscriminatorSN, self).__init__()
        self.skip_connection = skip_connection
        norm = spectral_norm
-
+        # the first convolution
        self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1)
-
+        # downsample
        self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False))
        self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False))
        self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False))
@@ -22,14 +30,13 @@ class UNetDiscriminatorSN(nn.Module):
        self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False))
        self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False))
        self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False))
-
-        # extra
+        # extra convolutions
        self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
        self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
-
        self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1)

    def forward(self, x):
+        # downsample
        x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True)
        x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True)
        x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True)
@@ -52,7 +59,7 @@ class UNetDiscriminatorSN(nn.Module):
        if self.skip_connection:
            x6 = x6 + x0

-        # extra
+        # extra convolutions
        out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True)
        out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True)
        out = self.conv9(out)
--- a/realesrgan/archs/srvgg_arch.py
+++ b/realesrgan/archs/srvgg_arch.py
@@ -0,0 +1,69 @@
+from basicsr.utils.registry import ARCH_REGISTRY
+from torch import nn as nn
+from torch.nn import functional as F
+
+
+@ARCH_REGISTRY.register()
+class SRVGGNetCompact(nn.Module):
+    """A compact VGG-style network structure for super-resolution.
+
+    It is a compact network structure, which performs upsampling in the last layer and no convolution is
+    conducted on the HR feature space.
+
+    Args:
+        num_in_ch (int): Channel number of inputs. Default: 3.
+        num_out_ch (int): Channel number of outputs. Default: 3.
+        num_feat (int): Channel number of intermediate features. Default: 64.
+        num_conv (int): Number of convolution layers in the body network. Default: 16.
+        upscale (int): Upsampling factor. Default: 4.
+        act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
+    """
+
+    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
+        super(SRVGGNetCompact, self).__init__()
+        self.num_in_ch = num_in_ch
+        self.num_out_ch = num_out_ch
+        self.num_feat = num_feat
+        self.num_conv = num_conv
+        self.upscale = upscale
+        self.act_type = act_type
+
+        self.body = nn.ModuleList()
+        # the first conv
+        self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
+        # the first activation
+        if act_type == 'relu':
+            activation = nn.ReLU(inplace=True)
+        elif act_type == 'prelu':
+            activation = nn.PReLU(num_parameters=num_feat)
+        elif act_type == 'leakyrelu':
+            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+        self.body.append(activation)
+
+        # the body structure
+        for _ in range(num_conv):
+            self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
+            # activation
+            if act_type == 'relu':
+                activation = nn.ReLU(inplace=True)
+            elif act_type == 'prelu':
+                activation = nn.PReLU(num_parameters=num_feat)
+            elif act_type == 'leakyrelu':
+                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+            self.body.append(activation)
+
+        # the last conv
+        self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
+        # upsample
+        self.upsampler = nn.PixelShuffle(upscale)
+
+    def forward(self, x):
+        out = x
+        for i in range(0, len(self.body)):
+            out = self.body[i](out)
+
+        out = self.upsampler(out)
+        # add the nearest upsampled image, so that the network learns the residual
+        base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
+        out += base
+        return out
--- a/realesrgan/data/realesrgan_dataset.py
+++ b/realesrgan/data/realesrgan_dataset.py
@@ -15,18 +15,31 @@ from torch.utils import data as data

@DATASET_REGISTRY.register()
 class RealESRGANDataset(data.Dataset):
-    """
-    Dataset used for Real-ESRGAN model.
+    """Dataset used for Real-ESRGAN model:
+    Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
+
+    It loads gt (Ground-Truth) images, and augments them.
+    It also generates blur kernels and sinc kernels for generating low-quality images.
+    Note that the low-quality images are processed in tensors on GPUS for faster processing.
+
+    Args:
+        opt (dict): Config for train datasets. It contains the following keys:
+            dataroot_gt (str): Data root path for gt.
+            meta_info (str): Path for meta information file.
+            io_backend (dict): IO backend type and other kwarg.
+            use_hflip (bool): Use horizontal flips.
+            use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
+            Please see more options in the codes.
    """

    def __init__(self, opt):
        super(RealESRGANDataset, self).__init__()
        self.opt = opt
-        # file client (io backend)
        self.file_client = None
        self.io_backend_opt = opt['io_backend']
        self.gt_folder = opt['dataroot_gt']

+        # file client (lmdb io backend)
        if self.io_backend_opt['type'] == 'lmdb':
            self.io_backend_opt['db_paths'] = [self.gt_folder]
            self.io_backend_opt['client_keys'] = ['gt']
@@ -35,18 +48,20 @@ class RealESRGANDataset(data.Dataset):
            with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
                self.paths = [line.split('.')[0] for line in fin]
        else:
+            # disk backend with meta_info
+            # Each line in the meta_info describes the relative path to an image
            with open(self.opt['meta_info']) as fin:
-                paths = [line.strip() for line in fin]
+                paths = [line.strip().split(' ')[0] for line in fin]
                self.paths = [os.path.join(self.gt_folder, v) for v in paths]

        # blur settings for the first degradation
        self.blur_kernel_size = opt['blur_kernel_size']
        self.kernel_list = opt['kernel_list']
-        self.kernel_prob = opt['kernel_prob']
+        self.kernel_prob = opt['kernel_prob']  # a list for each kernel probability
        self.blur_sigma = opt['blur_sigma']
-        self.betag_range = opt['betag_range']
-        self.betap_range = opt['betap_range']
-        self.sinc_prob = opt['sinc_prob']
+        self.betag_range = opt['betag_range']  # betag used in generalized Gaussian blur kernels
+        self.betap_range = opt['betap_range']  # betap used in plateau blur kernels
+        self.sinc_prob = opt['sinc_prob']  # the probability for sinc filters

        # blur settings for the second degradation
        self.blur_kernel_size2 = opt['blur_kernel_size2']
@@ -61,6 +76,7 @@ class RealESRGANDataset(data.Dataset):
        self.final_sinc_prob = opt['final_sinc_prob']

        self.kernel_range = [2 * v + 1 for v in range(3, 11)]  # kernel size ranges from 7 to 21
+        # TODO: kernel range is now hard-coded, should be in the configure file
        self.pulse_tensor = torch.zeros(21, 21).float()  # convolving with pulse tensor brings no blurry effect
        self.pulse_tensor[10, 10] = 1

@@ -76,7 +92,7 @@ class RealESRGANDataset(data.Dataset):
        while retry > 0:
            try:
                img_bytes = self.file_client.get(gt_path, 'gt')
-            except Exception as e:
+            except (IOError, OSError) as e:
                logger = get_root_logger()
                logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}')
                # change another file to read
@@ -89,10 +105,11 @@ class RealESRGANDataset(data.Dataset):
                retry -= 1
        img_gt = imfrombytes(img_bytes, float32=True)

-        # -------------------- augmentation for training: flip, rotation -------------------- #
+        # -------------------- Do augmentation for training: flip, rotation -------------------- #
        img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot'])

-        # crop or pad to 400: 400 is hard-coded. You may change it accordingly
+        # crop or pad to 400
+        # TODO: 400 is hard-coded. You may change it accordingly
        h, w = img_gt.shape[0:2]
        crop_pad_size = 400
        # pad
@@ -154,7 +171,7 @@ class RealESRGANDataset(data.Dataset):
        pad_size = (21 - kernel_size) // 2
        kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size)))

-        # ------------------------------------- sinc kernel ------------------------------------- #
+        # ------------------------------------- the final sinc kernel ------------------------------------- #
        if np.random.uniform() < self.opt['final_sinc_prob']:
            kernel_size = random.choice(self.kernel_range)
            omega_c = np.random.uniform(np.pi / 3, np.pi)
--- a/realesrgan/data/realesrgan_paired_dataset.py
+++ b/realesrgan/data/realesrgan_paired_dataset.py
@@ -11,8 +11,7 @@ from torchvision.transforms.functional import normalize
 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.
+    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.
@@ -28,8 +27,8 @@ class RealESRGANPairedDataset(data.Dataset):
            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: '{}'.
+            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
@@ -42,23 +41,23 @@ class RealESRGANPairedDataset(data.Dataset):
    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']
+        # mean and std for normalizing the input images
        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 = '{}'
+        self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}'

+        # file client (lmdb io backend)
        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:
+            # disk backend with meta_info
+            # Each line in the meta_info describes the relative path to an image
            with open(self.opt['meta_info']) as fin:
                paths = [line.strip() for line in fin]
            self.paths = []
@@ -68,6 +67,9 @@ class RealESRGANPairedDataset(data.Dataset):
                lq_path = os.path.join(self.lq_folder, lq_path)
                self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)]))
        else:
+            # disk backend
+            # it will scan the whole folder to get meta info
+            # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file
            self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)

    def __getitem__(self, index):
--- a/realesrgan/models/realesrgan_model.py
+++ b/realesrgan/models/realesrgan_model.py
@@ -13,35 +13,45 @@ from torch.nn import functional as F

@MODEL_REGISTRY.register()
 class RealESRGANModel(SRGANModel):
-    """RealESRGAN Model"""
+    """RealESRGAN Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
+
+    It mainly performs:
+    1. randomly synthesize LQ images in GPU tensors
+    2. optimize the networks with GAN training.
+    """

    def __init__(self, opt):
        super(RealESRGANModel, self).__init__(opt)
-        self.jpeger = DiffJPEG(differentiable=False).cuda()
-        self.usm_sharpener = USMSharp().cuda()
+        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
+        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
        self.queue_size = opt.get('queue_size', 180)

    @torch.no_grad()
    def _dequeue_and_enqueue(self):
-        # training pair pool
+        """It is the training pair pool for increasing the diversity in a batch.
+
+        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
+        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
+        to increase the degradation diversity in a batch.
+        """
        # initialize
        b, c, h, w = self.lq.size()
        if not hasattr(self, 'queue_lr'):
-            assert self.queue_size % b == 0, 'queue size should be divisible by batch size'
+            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
            _, c, h, w = self.gt.size()
            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
            self.queue_ptr = 0
-        if self.queue_ptr == self.queue_size:  # full
+        if self.queue_ptr == self.queue_size:  # the pool is full
            # do dequeue and enqueue
            # shuffle
            idx = torch.randperm(self.queue_size)
            self.queue_lr = self.queue_lr[idx]
            self.queue_gt = self.queue_gt[idx]
-            # get
+            # get first b samples
            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
-            # update
+            # update the queue
            self.queue_lr[0:b, :, :, :] = self.lq.clone()
            self.queue_gt[0:b, :, :, :] = self.gt.clone()

@@ -55,6 +65,8 @@ class RealESRGANModel(SRGANModel):

    @torch.no_grad()
    def feed_data(self, data):
+        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
+        """
        if self.is_train and self.opt.get('high_order_degradation', True):
            # training data synthesis
            self.gt = data['gt'].to(self.device)
@@ -79,7 +91,7 @@ class RealESRGANModel(SRGANModel):
                scale = 1
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(out, scale_factor=scale, mode=mode)
-            # noise
+            # add noise
            gray_noise_prob = self.opt['gray_noise_prob']
            if np.random.uniform() < self.opt['gaussian_noise_prob']:
                out = random_add_gaussian_noise_pt(
@@ -93,7 +105,7 @@ class RealESRGANModel(SRGANModel):
                    rounds=False)
            # JPEG compression
            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
-            out = torch.clamp(out, 0, 1)
+            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
            out = self.jpeger(out, quality=jpeg_p)

            # ----------------------- The second degradation process ----------------------- #
@@ -111,7 +123,7 @@ class RealESRGANModel(SRGANModel):
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(
                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
-            # noise
+            # add noise
            gray_noise_prob = self.opt['gray_noise_prob2']
            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
                out = random_add_gaussian_noise_pt(
@@ -162,7 +174,9 @@ class RealESRGANModel(SRGANModel):
            self._dequeue_and_enqueue()
            # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue
            self.gt_usm = self.usm_sharpener(self.gt)
+            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
        else:
+            # for paired training or validation
            self.lq = data['lq'].to(self.device)
            if 'gt' in data:
                self.gt = data['gt'].to(self.device)
@@ -175,6 +189,7 @@ class RealESRGANModel(SRGANModel):
        self.is_train = True

    def optimize_parameters(self, current_iter):
+        # usm sharpening
        l1_gt = self.gt_usm
        percep_gt = self.gt_usm
        gan_gt = self.gt_usm
--- a/realesrgan/models/realesrnet_model.py
+++ b/realesrgan/models/realesrnet_model.py
@@ -12,35 +12,46 @@ from torch.nn import functional as F

@MODEL_REGISTRY.register()
 class RealESRNetModel(SRModel):
-    """RealESRNet Model"""
+    """RealESRNet Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
+
+    It is trained without GAN losses.
+    It mainly performs:
+    1. randomly synthesize LQ images in GPU tensors
+    2. optimize the networks with GAN training.
+    """

    def __init__(self, opt):
        super(RealESRNetModel, self).__init__(opt)
-        self.jpeger = DiffJPEG(differentiable=False).cuda()
-        self.usm_sharpener = USMSharp().cuda()
+        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
+        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
        self.queue_size = opt.get('queue_size', 180)

    @torch.no_grad()
    def _dequeue_and_enqueue(self):
-        # training pair pool
+        """It is the training pair pool for increasing the diversity in a batch.
+
+        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
+        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
+        to increase the degradation diversity in a batch.
+        """
        # initialize
        b, c, h, w = self.lq.size()
        if not hasattr(self, 'queue_lr'):
-            assert self.queue_size % b == 0, 'queue size should be divisible by batch size'
+            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
            _, c, h, w = self.gt.size()
            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
            self.queue_ptr = 0
-        if self.queue_ptr == self.queue_size:  # full
+        if self.queue_ptr == self.queue_size:  # the pool is full
            # do dequeue and enqueue
            # shuffle
            idx = torch.randperm(self.queue_size)
            self.queue_lr = self.queue_lr[idx]
            self.queue_gt = self.queue_gt[idx]
-            # get
+            # get first b samples
            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
-            # update
+            # update the queue
            self.queue_lr[0:b, :, :, :] = self.lq.clone()
            self.queue_gt[0:b, :, :, :] = self.gt.clone()

@@ -54,10 +65,12 @@ class RealESRNetModel(SRModel):

    @torch.no_grad()
    def feed_data(self, data):
+        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
+        """
        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
+            # USM sharpen the GT images
            if self.opt['gt_usm'] is True:
                self.gt = self.usm_sharpener(self.gt)

@@ -80,7 +93,7 @@ class RealESRNetModel(SRModel):
                scale = 1
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(out, scale_factor=scale, mode=mode)
-            # noise
+            # add noise
            gray_noise_prob = self.opt['gray_noise_prob']
            if np.random.uniform() < self.opt['gaussian_noise_prob']:
                out = random_add_gaussian_noise_pt(
@@ -94,7 +107,7 @@ class RealESRNetModel(SRModel):
                    rounds=False)
            # JPEG compression
            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
-            out = torch.clamp(out, 0, 1)
+            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
            out = self.jpeger(out, quality=jpeg_p)

            # ----------------------- The second degradation process ----------------------- #
@@ -112,7 +125,7 @@ class RealESRNetModel(SRModel):
            mode = random.choice(['area', 'bilinear', 'bicubic'])
            out = F.interpolate(
                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
-            # noise
+            # add noise
            gray_noise_prob = self.opt['gray_noise_prob2']
            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
                out = random_add_gaussian_noise_pt(
@@ -160,7 +173,9 @@ class RealESRNetModel(SRModel):

            # training pair pool
            self._dequeue_and_enqueue()
+            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
        else:
+            # for paired training or validation
            self.lq = data['lq'].to(self.device)
            if 'gt' in data:
                self.gt = data['gt'].to(self.device)
--- a/realesrgan/utils.py
+++ b/realesrgan/utils.py
@@ -2,16 +2,29 @@ import cv2
 import math
 import numpy as np
 import os
+import queue
+import threading
 import torch
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from torch.hub import download_url_to_file, get_dir
+from basicsr.utils.download_util import load_file_from_url
 from torch.nn import functional as F
-from urllib.parse import urlparse

 ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))


 class RealESRGANer():
+    """A helper class for upsampling images with RealESRGAN.
+
+    Args:
+        scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
+        model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
+        model (nn.Module): The defined network. Default: None.
+        tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
+            input images into tiles, and then process each of them. Finally, they will be merged into one image.
+            0 denotes for do not use tile. Default: 0.
+        tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
+        pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
+        half (float): Whether to use half precision during inference. Default: False.
+    """

    def __init__(self, scale, model_path, model=None, tile=0, tile_pad=10, pre_pad=10, half=False):
        self.scale = scale
@@ -23,13 +36,12 @@ class RealESRGANer():

        # initialize model
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        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 the model_path starts with https, it will first download models to the folder: realesrgan/weights
        if model_path.startswith('https://'):
            model_path = load_file_from_url(
-                url=model_path, model_dir='realesrgan/weights', progress=True, file_name=None)
-        loadnet = torch.load(model_path)
+                url=model_path, model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'), progress=True, file_name=None)
+        loadnet = torch.load(model_path, map_location=torch.device('cpu'))
+        # prefer to use params_ema
        if 'params_ema' in loadnet:
            keyname = 'params_ema'
        else:
@@ -41,6 +53,8 @@ class RealESRGANer():
            self.model = self.model.half()

    def pre_process(self, img):
+        """Pre-process, such as pre-pad and mod pad, so that the images can be divisible
+        """
        img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
        self.img = img.unsqueeze(0).to(self.device)
        if self.half:
@@ -49,7 +63,7 @@ class RealESRGANer():
        # pre_pad
        if self.pre_pad != 0:
            self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
-        # mod pad
+        # mod pad for divisible borders
        if self.scale == 2:
            self.mod_scale = 2
        elif self.scale == 1:
@@ -64,10 +78,14 @@ class RealESRGANer():
            self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')

    def process(self):
+        # model inference
        self.output = self.model(self.img)

    def tile_process(self):
-        """Modified from: https://github.com/ata4/esrgan-launcher
+        """It will first crop input images to tiles, and then process each tile.
+        Finally, all the processed tiles are merged into one images.
+
+        Modified from: https://github.com/ata4/esrgan-launcher
        """
        batch, channel, height, width = self.img.shape
        output_height = height * self.scale
@@ -107,7 +125,7 @@ class RealESRGANer():
                try:
                    with torch.no_grad():
                        output_tile = self.model(input_tile)
-                except Exception as error:
+                except RuntimeError as error:
                    print('Error', error)
                print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')

@@ -188,7 +206,7 @@ class RealESRGANer():
                output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
                output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
                output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
-            else:
+            else:  # use the cv2 resize for alpha channel
                h, w = alpha.shape[0:2]
                output_alpha = cv2.resize(alpha, (w * self.scale, h * self.scale), interpolation=cv2.INTER_LINEAR)

@@ -212,21 +230,51 @@ class RealESRGANer():
        return output, img_mode


-def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
-    """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
+class PrefetchReader(threading.Thread):
+    """Prefetch images.
+
+    Args:
+        img_list (list[str]): A image list of image paths to be read.
+        num_prefetch_queue (int): Number of prefetch queue.
    """
-    if model_dir is None:
-        hub_dir = get_dir()
-        model_dir = os.path.join(hub_dir, 'checkpoints')

-    os.makedirs(os.path.join(ROOT_DIR, model_dir), exist_ok=True)
+    def __init__(self, img_list, num_prefetch_queue):
+        super().__init__()
+        self.que = queue.Queue(num_prefetch_queue)
+        self.img_list = img_list

-    parts = urlparse(url)
-    filename = os.path.basename(parts.path)
-    if file_name is not None:
-        filename = file_name
-    cached_file = os.path.abspath(os.path.join(ROOT_DIR, model_dir, filename))
-    if not os.path.exists(cached_file):
-        print(f'Downloading: "{url}" to {cached_file}\n')
-        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
-    return cached_file
+    def run(self):
+        for img_path in self.img_list:
+            img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
+            self.que.put(img)
+
+        self.que.put(None)
+
+    def __next__(self):
+        next_item = self.que.get()
+        if next_item is None:
+            raise StopIteration
+        return next_item
+
+    def __iter__(self):
+        return self
+
+
+class IOConsumer(threading.Thread):
+
+    def __init__(self, opt, que, qid):
+        super().__init__()
+        self._queue = que
+        self.qid = qid
+        self.opt = opt
+
+    def run(self):
+        while True:
+            msg = self._queue.get()
+            if isinstance(msg, str) and msg == 'quit':
+                break
+
+            output = msg['output']
+            save_path = msg['save_path']
+            cv2.imwrite(save_path, output)
+        print(f'IO worker {self.qid} is done.')
--- a/scripts/extract_subimages.py
+++ b/scripts/extract_subimages.py
@@ -14,34 +14,24 @@ def main(args):

    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.
-
+        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.
+        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.
+        Typically, there are GT folder and LQ folder to be processed for DIV2K dataset.
+        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
@@ -68,6 +58,7 @@ def extract_subimages(opt):
        print(f'Folder {save_folder} already exists. Exit.')
        sys.exit(1)

+    # scan all images
    img_list = list(scandir(input_folder, full_path=True))

    pbar = tqdm(total=len(img_list), unit='image', desc='Extract')
@@ -88,8 +79,7 @@ def worker(path, opt):
        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.
+            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.

--- a/scripts/generate_meta_info.py
+++ b/scripts/generate_meta_info.py
@@ -11,15 +11,17 @@ def main(args):
        for img_path in img_paths:
            status = True
            if args.check:
+                # read the image once for check, as some images may have errors
                try:
                    img = cv2.imread(img_path)
-                except Exception as error:
+                except (IOError, OSError) 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:
+                # get the relative path
                img_name = os.path.relpath(img_path, root)
                print(img_name)
                txt_file.write(f'{img_name}\n')
--- a/scripts/generate_meta_info_pairdata.py
+++ b/scripts/generate_meta_info_pairdata.py
@@ -5,6 +5,7 @@ import os

 def main(args):
    txt_file = open(args.meta_info, 'w')
+    # sca images
    img_paths_gt = sorted(glob.glob(os.path.join(args.input[0], '*')))
    img_paths_lq = sorted(glob.glob(os.path.join(args.input[1], '*')))

@@ -12,6 +13,7 @@ def main(args):
                                                    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):
+        # get the relative paths
        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}')
@@ -19,7 +21,7 @@ def main(args):


 if __name__ == '__main__':
-    """Generate meta info (txt file) for paired images.
+    """This script is used to generate meta info (txt file) for paired images.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument(
--- a/scripts/generate_multiscale_DF2K.py
+++ b/scripts/generate_multiscale_DF2K.py
@@ -5,7 +5,6 @@ from PIL import Image


 def main(args):
-
    # For DF2K, we consider the following three scales,
    # and the smallest image whose shortest edge is 400
    scale_list = [0.75, 0.5, 1 / 3]
@@ -37,6 +36,9 @@ def main(args):


 if __name__ == '__main__':
+    """Generate multi-scale versions for GT images with LANCZOS resampling.
+    It is now used for DF2K dataset (DIV2K + Flickr 2K)
+    """
    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_multiscale', help='Output folder')
--- a/scripts/pytorch2onnx.py
+++ b/scripts/pytorch2onnx.py
@@ -1,17 +1,36 @@
+import argparse
 import torch
 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, 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)
-model.cpu().eval()

-# An example input you would normally provide to your model's forward() method
-x = torch.rand(1, 3, 64, 64)
+def main(args):
+    # An instance of the model
+    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+    if args.params:
+        keyname = 'params'
+    else:
+        keyname = 'params_ema'
+    model.load_state_dict(torch.load(args.input)[keyname])
+    # set the train mode to false since we will only run the forward pass.
+    model.train(False)
+    model.cpu().eval()

-# Export the model
-with torch.no_grad():
-    torch_out = torch.onnx._export(model, x, 'realesrgan-x4.onnx', opset_version=11, export_params=True)
+    # An example input
+    x = torch.rand(1, 3, 64, 64)
+    # Export the model
+    with torch.no_grad():
+        torch_out = torch.onnx._export(model, x, args.output, opset_version=11, export_params=True)
+    print(torch_out.shape)
+
+
+if __name__ == '__main__':
+    """Convert pytorch model to onnx models"""
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--input', type=str, default='experiments/pretrained_models/RealESRGAN_x4plus.pth', help='Input model path')
+    parser.add_argument('--output', type=str, default='realesrgan-x4.onnx', help='Output onnx path')
+    parser.add_argument('--params', action='store_false', help='Use params instead of params_ema')
+    args = parser.parse_args()
+
+    main(args)
--- a/setup.cfg
+++ b/setup.cfg
@@ -17,7 +17,7 @@ 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,torchvision,tqdm
+known_third_party = PIL,basicsr,cv2,numpy,pytest,torch,torchvision,tqdm,yaml
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY

@@ -25,3 +25,9 @@ default_section = THIRDPARTY
 skip = .git,./docs/build
 count =
 quiet-level = 3
+
+[aliases]
+test=pytest
+
+[tool:pytest]
+addopts=tests/
--- a/tests/data/gt.lmdb/data.mdb
+++ b/tests/data/gt.lmdb/data.mdb
--- a/tests/data/gt.lmdb/lock.mdb
+++ b/tests/data/gt.lmdb/lock.mdb
--- a/tests/data/gt.lmdb/meta_info.txt
+++ b/tests/data/gt.lmdb/meta_info.txt
@@ -0,0 +1,2 @@
+baboon.png (480,500,3) 1
+comic.png (360,240,3) 1
--- a/tests/data/gt/baboon.png
+++ b/tests/data/gt/baboon.png
--- a/tests/data/gt/comic.png
+++ b/tests/data/gt/comic.png
--- a/tests/data/lq.lmdb/data.mdb
+++ b/tests/data/lq.lmdb/data.mdb
--- a/tests/data/lq.lmdb/lock.mdb
+++ b/tests/data/lq.lmdb/lock.mdb
--- a/tests/data/lq.lmdb/meta_info.txt
+++ b/tests/data/lq.lmdb/meta_info.txt
@@ -0,0 +1,2 @@
+baboon.png (120,125,3) 1
+comic.png (80,60,3) 1
--- a/tests/data/lq/baboon.png
+++ b/tests/data/lq/baboon.png
--- a/tests/data/lq/comic.png
+++ b/tests/data/lq/comic.png
--- a/tests/data/meta_info_gt.txt
+++ b/tests/data/meta_info_gt.txt
@@ -0,0 +1,2 @@
+baboon.png
+comic.png
--- a/tests/data/meta_info_pair.txt
+++ b/tests/data/meta_info_pair.txt
@@ -0,0 +1,2 @@
+gt/baboon.png, lq/baboon.png
+gt/comic.png, lq/comic.png
--- a/tests/data/test_realesrgan_dataset.yml
+++ b/tests/data/test_realesrgan_dataset.yml
@@ -0,0 +1,28 @@
+name: Demo
+type: RealESRGANDataset
+dataroot_gt: tests/data/gt
+meta_info: tests/data/meta_info_gt.txt
+io_backend:
+  type: disk
+
+blur_kernel_size: 21
+kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
+kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
+sinc_prob: 1
+blur_sigma: [0.2, 3]
+betag_range: [0.5, 4]
+betap_range: [1, 2]
+
+blur_kernel_size2: 21
+kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
+kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
+sinc_prob2: 1
+blur_sigma2: [0.2, 1.5]
+betag_range2: [0.5, 4]
+betap_range2: [1, 2]
+
+final_sinc_prob: 1
+
+gt_size: 128
+use_hflip: True
+use_rot: False
--- a/tests/data/test_realesrgan_model.yml
+++ b/tests/data/test_realesrgan_model.yml
@@ -0,0 +1,115 @@
+scale: 4
+num_gpu: 1
+manual_seed: 0
+is_train: True
+dist: False
+
+# ----------------- options for synthesizing training data ----------------- #
+# USM the ground-truth
+l1_gt_usm: True
+percep_gt_usm: True
+gan_gt_usm: False
+
+# the first degradation process
+resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
+resize_range: [0.15, 1.5]
+gaussian_noise_prob: 1
+noise_range: [1, 30]
+poisson_scale_range: [0.05, 3]
+gray_noise_prob: 1
+jpeg_range: [30, 95]
+
+# the second degradation process
+second_blur_prob: 1
+resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
+resize_range2: [0.3, 1.2]
+gaussian_noise_prob2: 1
+noise_range2: [1, 25]
+poisson_scale_range2: [0.05, 2.5]
+gray_noise_prob2: 1
+jpeg_range2: [30, 95]
+
+gt_size: 32
+queue_size: 1
+
+# network structures
+network_g:
+  type: RRDBNet
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 4
+  num_block: 1
+  num_grow_ch: 2
+
+network_d:
+  type: UNetDiscriminatorSN
+  num_in_ch: 3
+  num_feat: 2
+  skip_connection: True
+
+# path
+path:
+  pretrain_network_g: ~
+  param_key_g: params_ema
+  strict_load_g: 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
+
+
+# validation settings
+val:
+  val_freq: !!float 5e3
+  save_img: False
--- a/tests/data/test_realesrgan_paired_dataset.yml
+++ b/tests/data/test_realesrgan_paired_dataset.yml
@@ -0,0 +1,13 @@
+name: Demo
+type: RealESRGANPairedDataset
+scale: 4
+dataroot_gt: tests/data
+dataroot_lq: tests/data
+meta_info: tests/data/meta_info_pair.txt
+io_backend:
+  type: disk
+
+phase: train
+gt_size: 128
+use_hflip: True
+use_rot: False
--- a/tests/data/test_realesrnet_model.yml
+++ b/tests/data/test_realesrnet_model.yml
@@ -0,0 +1,75 @@
+scale: 4
+num_gpu: 1
+manual_seed: 0
+is_train: True
+dist: False
+
+# ----------------- options for synthesizing training data ----------------- #
+gt_usm: True  # USM the ground-truth
+
+# the first degradation process
+resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
+resize_range: [0.15, 1.5]
+gaussian_noise_prob: 1
+noise_range: [1, 30]
+poisson_scale_range: [0.05, 3]
+gray_noise_prob: 1
+jpeg_range: [30, 95]
+
+# the second degradation process
+second_blur_prob: 1
+resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
+resize_range2: [0.3, 1.2]
+gaussian_noise_prob2: 1
+noise_range2: [1, 25]
+poisson_scale_range2: [0.05, 2.5]
+gray_noise_prob2: 1
+jpeg_range2: [30, 95]
+
+gt_size: 32
+queue_size: 1
+
+# network structures
+network_g:
+  type: RRDBNet
+  num_in_ch: 3
+  num_out_ch: 3
+  num_feat: 4
+  num_block: 1
+  num_grow_ch: 2
+
+# path
+path:
+  pretrain_network_g: ~
+  param_key_g: params_ema
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 2e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: MultiStepLR
+    milestones: [1000000]
+    gamma: 0.5
+
+  total_iter: 1000000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1.0
+    reduction: mean
+
+
+# validation settings
+val:
+  val_freq: !!float 5e3
+  save_img: False
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -0,0 +1,151 @@
+import pytest
+import yaml
+
+from realesrgan.data.realesrgan_dataset import RealESRGANDataset
+from realesrgan.data.realesrgan_paired_dataset import RealESRGANPairedDataset
+
+
+def test_realesrgan_dataset():
+
+    with open('tests/data/test_realesrgan_dataset.yml', mode='r') as f:
+        opt = yaml.load(f, Loader=yaml.FullLoader)
+
+    dataset = RealESRGANDataset(opt)
+    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
+    assert len(dataset) == 2  # whether to read correct meta info
+    assert dataset.kernel_list == [
+        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
+    ]  # correct initialization the degradation configurations
+    assert dataset.betag_range2 == [0.5, 4]
+
+    # test __getitem__
+    result = dataset.__getitem__(0)
+    # check returned keys
+    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 400, 400)
+    assert result['kernel1'].shape == (21, 21)
+    assert result['kernel2'].shape == (21, 21)
+    assert result['sinc_kernel'].shape == (21, 21)
+    assert result['gt_path'] == 'tests/data/gt/baboon.png'
+
+    # ------------------ test lmdb backend -------------------- #
+    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
+    opt['io_backend']['type'] = 'lmdb'
+
+    dataset = RealESRGANDataset(opt)
+    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
+    assert len(dataset.paths) == 2  # whether to read correct meta info
+    assert dataset.kernel_list == [
+        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
+    ]  # correct initialization the degradation configurations
+    assert dataset.betag_range2 == [0.5, 4]
+
+    # test __getitem__
+    result = dataset.__getitem__(1)
+    # check returned keys
+    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 400, 400)
+    assert result['kernel1'].shape == (21, 21)
+    assert result['kernel2'].shape == (21, 21)
+    assert result['sinc_kernel'].shape == (21, 21)
+    assert result['gt_path'] == 'comic'
+
+    # ------------------ test with sinc_prob = 0 -------------------- #
+    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
+    opt['io_backend']['type'] = 'lmdb'
+    opt['sinc_prob'] = 0
+    opt['sinc_prob2'] = 0
+    opt['final_sinc_prob'] = 0
+    dataset = RealESRGANDataset(opt)
+    result = dataset.__getitem__(0)
+    # check returned keys
+    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 400, 400)
+    assert result['kernel1'].shape == (21, 21)
+    assert result['kernel2'].shape == (21, 21)
+    assert result['sinc_kernel'].shape == (21, 21)
+    assert result['gt_path'] == 'baboon'
+
+    # ------------------ lmdb backend should have paths ends with lmdb -------------------- #
+    with pytest.raises(ValueError):
+        opt['dataroot_gt'] = 'tests/data/gt'
+        opt['io_backend']['type'] = 'lmdb'
+        dataset = RealESRGANDataset(opt)
+
+
+def test_realesrgan_paired_dataset():
+
+    with open('tests/data/test_realesrgan_paired_dataset.yml', mode='r') as f:
+        opt = yaml.load(f, Loader=yaml.FullLoader)
+
+    dataset = RealESRGANPairedDataset(opt)
+    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
+    assert len(dataset) == 2  # whether to read correct meta info
+
+    # test __getitem__
+    result = dataset.__getitem__(0)
+    # check returned keys
+    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 128, 128)
+    assert result['lq'].shape == (3, 32, 32)
+    assert result['gt_path'] == 'tests/data/gt/baboon.png'
+    assert result['lq_path'] == 'tests/data/lq/baboon.png'
+
+    # ------------------ test lmdb backend -------------------- #
+    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
+    opt['dataroot_lq'] = 'tests/data/lq.lmdb'
+    opt['io_backend']['type'] = 'lmdb'
+
+    dataset = RealESRGANPairedDataset(opt)
+    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
+    assert len(dataset) == 2  # whether to read correct meta info
+
+    # test __getitem__
+    result = dataset.__getitem__(1)
+    # check returned keys
+    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 128, 128)
+    assert result['lq'].shape == (3, 32, 32)
+    assert result['gt_path'] == 'comic'
+    assert result['lq_path'] == 'comic'
+
+    # ------------------ test paired_paths_from_folder -------------------- #
+    opt['dataroot_gt'] = 'tests/data/gt'
+    opt['dataroot_lq'] = 'tests/data/lq'
+    opt['io_backend'] = dict(type='disk')
+    opt['meta_info'] = None
+
+    dataset = RealESRGANPairedDataset(opt)
+    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
+    assert len(dataset) == 2  # whether to read correct meta info
+
+    # test __getitem__
+    result = dataset.__getitem__(0)
+    # check returned keys
+    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 128, 128)
+    assert result['lq'].shape == (3, 32, 32)
+
+    # ------------------ test normalization -------------------- #
+    dataset.mean = [0.5, 0.5, 0.5]
+    dataset.std = [0.5, 0.5, 0.5]
+    # test __getitem__
+    result = dataset.__getitem__(0)
+    # check returned keys
+    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
+    assert set(expected_keys).issubset(set(result.keys()))
+    # check shape and contents
+    assert result['gt'].shape == (3, 128, 128)
+    assert result['lq'].shape == (3, 32, 32)
--- a/tests/test_discriminator_arch.py
+++ b/tests/test_discriminator_arch.py
@@ -0,0 +1,19 @@
+import torch
+
+from realesrgan.archs.discriminator_arch import UNetDiscriminatorSN
+
+
+def test_unetdiscriminatorsn():
+    """Test arch: UNetDiscriminatorSN."""
+
+    # model init and forward (cpu)
+    net = UNetDiscriminatorSN(num_in_ch=3, num_feat=4, skip_connection=True)
+    img = torch.rand((1, 3, 32, 32), dtype=torch.float32)
+    output = net(img)
+    assert output.shape == (1, 1, 32, 32)
+
+    # model init and forward (gpu)
+    if torch.cuda.is_available():
+        net.cuda()
+        output = net(img.cuda())
+        assert output.shape == (1, 1, 32, 32)
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -0,0 +1,126 @@
+import torch
+import yaml
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from basicsr.data.paired_image_dataset import PairedImageDataset
+from basicsr.losses.losses import GANLoss, L1Loss, PerceptualLoss
+
+from realesrgan.archs.discriminator_arch import UNetDiscriminatorSN
+from realesrgan.models.realesrgan_model import RealESRGANModel
+from realesrgan.models.realesrnet_model import RealESRNetModel
+
+
+def test_realesrnet_model():
+    with open('tests/data/test_realesrnet_model.yml', mode='r') as f:
+        opt = yaml.load(f, Loader=yaml.FullLoader)
+
+    # build model
+    model = RealESRNetModel(opt)
+    # test attributes
+    assert model.__class__.__name__ == 'RealESRNetModel'
+    assert isinstance(model.net_g, RRDBNet)
+    assert isinstance(model.cri_pix, L1Loss)
+    assert isinstance(model.optimizers[0], torch.optim.Adam)
+
+    # prepare data
+    gt = torch.rand((1, 3, 32, 32), dtype=torch.float32)
+    kernel1 = torch.rand((1, 5, 5), dtype=torch.float32)
+    kernel2 = torch.rand((1, 5, 5), dtype=torch.float32)
+    sinc_kernel = torch.rand((1, 5, 5), dtype=torch.float32)
+    data = dict(gt=gt, kernel1=kernel1, kernel2=kernel2, sinc_kernel=sinc_kernel)
+    model.feed_data(data)
+    # check dequeue
+    model.feed_data(data)
+    # check data shape
+    assert model.lq.shape == (1, 3, 8, 8)
+    assert model.gt.shape == (1, 3, 32, 32)
+
+    # change probability to test if-else
+    model.opt['gaussian_noise_prob'] = 0
+    model.opt['gray_noise_prob'] = 0
+    model.opt['second_blur_prob'] = 0
+    model.opt['gaussian_noise_prob2'] = 0
+    model.opt['gray_noise_prob2'] = 0
+    model.feed_data(data)
+    # check data shape
+    assert model.lq.shape == (1, 3, 8, 8)
+    assert model.gt.shape == (1, 3, 32, 32)
+
+    # ----------------- test nondist_validation -------------------- #
+    # construct dataloader
+    dataset_opt = dict(
+        name='Demo',
+        dataroot_gt='tests/data/gt',
+        dataroot_lq='tests/data/lq',
+        io_backend=dict(type='disk'),
+        scale=4,
+        phase='val')
+    dataset = PairedImageDataset(dataset_opt)
+    dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
+    assert model.is_train is True
+    model.nondist_validation(dataloader, 1, None, False)
+    assert model.is_train is True
+
+
+def test_realesrgan_model():
+    with open('tests/data/test_realesrgan_model.yml', mode='r') as f:
+        opt = yaml.load(f, Loader=yaml.FullLoader)
+
+    # build model
+    model = RealESRGANModel(opt)
+    # test attributes
+    assert model.__class__.__name__ == 'RealESRGANModel'
+    assert isinstance(model.net_g, RRDBNet)  # generator
+    assert isinstance(model.net_d, UNetDiscriminatorSN)  # discriminator
+    assert isinstance(model.cri_pix, L1Loss)
+    assert isinstance(model.cri_perceptual, PerceptualLoss)
+    assert isinstance(model.cri_gan, GANLoss)
+    assert isinstance(model.optimizers[0], torch.optim.Adam)
+    assert isinstance(model.optimizers[1], torch.optim.Adam)
+
+    # prepare data
+    gt = torch.rand((1, 3, 32, 32), dtype=torch.float32)
+    kernel1 = torch.rand((1, 5, 5), dtype=torch.float32)
+    kernel2 = torch.rand((1, 5, 5), dtype=torch.float32)
+    sinc_kernel = torch.rand((1, 5, 5), dtype=torch.float32)
+    data = dict(gt=gt, kernel1=kernel1, kernel2=kernel2, sinc_kernel=sinc_kernel)
+    model.feed_data(data)
+    # check dequeue
+    model.feed_data(data)
+    # check data shape
+    assert model.lq.shape == (1, 3, 8, 8)
+    assert model.gt.shape == (1, 3, 32, 32)
+
+    # change probability to test if-else
+    model.opt['gaussian_noise_prob'] = 0
+    model.opt['gray_noise_prob'] = 0
+    model.opt['second_blur_prob'] = 0
+    model.opt['gaussian_noise_prob2'] = 0
+    model.opt['gray_noise_prob2'] = 0
+    model.feed_data(data)
+    # check data shape
+    assert model.lq.shape == (1, 3, 8, 8)
+    assert model.gt.shape == (1, 3, 32, 32)
+
+    # ----------------- test nondist_validation -------------------- #
+    # construct dataloader
+    dataset_opt = dict(
+        name='Demo',
+        dataroot_gt='tests/data/gt',
+        dataroot_lq='tests/data/lq',
+        io_backend=dict(type='disk'),
+        scale=4,
+        phase='val')
+    dataset = PairedImageDataset(dataset_opt)
+    dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
+    assert model.is_train is True
+    model.nondist_validation(dataloader, 1, None, False)
+    assert model.is_train is True
+
+    # ----------------- test optimize_parameters -------------------- #
+    model.feed_data(data)
+    model.optimize_parameters(1)
+    assert model.output.shape == (1, 3, 32, 32)
+    assert isinstance(model.log_dict, dict)
+    # check returned keys
+    expected_keys = ['l_g_pix', 'l_g_percep', 'l_g_gan', 'l_d_real', 'out_d_real', 'l_d_fake', 'out_d_fake']
+    assert set(expected_keys).issubset(set(model.log_dict.keys()))
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -0,0 +1,87 @@
+import numpy as np
+from basicsr.archs.rrdbnet_arch import RRDBNet
+
+from realesrgan.utils import RealESRGANer
+
+
+def test_realesrganer():
+    # initialize with default model
+    restorer = RealESRGANer(
+        scale=4,
+        model_path='experiments/pretrained_models/RealESRGAN_x4plus.pth',
+        model=None,
+        tile=10,
+        tile_pad=10,
+        pre_pad=2,
+        half=False)
+    assert isinstance(restorer.model, RRDBNet)
+    assert restorer.half is False
+    # initialize with user-defined model
+    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
+    restorer = RealESRGANer(
+        scale=4,
+        model_path='experiments/pretrained_models/RealESRGAN_x4plus_anime_6B.pth',
+        model=model,
+        tile=10,
+        tile_pad=10,
+        pre_pad=2,
+        half=True)
+    # test attribute
+    assert isinstance(restorer.model, RRDBNet)
+    assert restorer.half is True
+
+    # ------------------ test pre_process ---------------- #
+    img = np.random.random((12, 12, 3)).astype(np.float32)
+    restorer.pre_process(img)
+    assert restorer.img.shape == (1, 3, 14, 14)
+    # with modcrop
+    restorer.scale = 1
+    restorer.pre_process(img)
+    assert restorer.img.shape == (1, 3, 16, 16)
+
+    # ------------------ test process ---------------- #
+    restorer.process()
+    assert restorer.output.shape == (1, 3, 64, 64)
+
+    # ------------------ test post_process ---------------- #
+    restorer.mod_scale = 4
+    output = restorer.post_process()
+    assert output.shape == (1, 3, 60, 60)
+
+    # ------------------ test tile_process ---------------- #
+    restorer.scale = 4
+    img = np.random.random((12, 12, 3)).astype(np.float32)
+    restorer.pre_process(img)
+    restorer.tile_process()
+    assert restorer.output.shape == (1, 3, 64, 64)
+
+    # ------------------ test enhance ---------------- #
+    img = np.random.random((12, 12, 3)).astype(np.float32)
+    result = restorer.enhance(img, outscale=2)
+    assert result[0].shape == (24, 24, 3)
+    assert result[1] == 'RGB'
+
+    # ------------------ test enhance with 16-bit image---------------- #
+    img = np.random.random((4, 4, 3)).astype(np.uint16) + 512
+    result = restorer.enhance(img, outscale=2)
+    assert result[0].shape == (8, 8, 3)
+    assert result[1] == 'RGB'
+
+    # ------------------ test enhance with gray image---------------- #
+    img = np.random.random((4, 4)).astype(np.float32)
+    result = restorer.enhance(img, outscale=2)
+    assert result[0].shape == (8, 8)
+    assert result[1] == 'L'
+
+    # ------------------ test enhance with RGBA---------------- #
+    img = np.random.random((4, 4, 4)).astype(np.float32)
+    result = restorer.enhance(img, outscale=2)
+    assert result[0].shape == (8, 8, 4)
+    assert result[1] == 'RGBA'
+
+    # ------------------ test enhance with RGBA, alpha_upsampler---------------- #
+    restorer.tile_size = 0
+    img = np.random.random((4, 4, 4)).astype(np.float32)
+    result = restorer.enhance(img, outscale=2, alpha_upsampler=None)
+    assert result[0].shape == (8, 8, 4)
+    assert result[1] == 'RGBA'
Author	SHA1	Message	Date
Xintao	685d429c81	v0.2.5.0	2022-04-24 19:59:55 +08:00
Xintao	13c95fe094	update readme	2022-04-24 19:58:00 +08:00
wyz	82cf0e8e4a	Add comparisons for the soon be released animevideo-v3 model (#301 ) * add comparisons for animevideo-v3 model * fix markdown table format Co-authored-by: yanzewu <yanzewu@tencent.com>	2022-04-24 17:30:21 +08:00
Xintao	cddc2ff658	update readme	2022-04-24 17:27:15 +08:00
Xintao	98add035f2	support realesr-animevideov3	2022-04-24 17:22:43 +08:00
Xintao	9ff1944d06	use GFPGAN v1.3	2022-02-23 20:44:51 +08:00
Xintao	3d96c8ab9f	update logo size	2022-02-16 00:39:03 +08:00
Xintao	f115f40a77	V0.2.4.0	2022-02-15 23:57:21 +08:00
Xintao	2b4e485eb0	Update ReadMe (#259 ) * add logo * update readme * update readme * update readme * update updates * update updates * update updates * update updates * update updates * update readme * update readme * update readme * update readme	2022-02-15 23:50:51 +08:00
Xintao	01aeba2f7a	Add CODE_OF_CONDUCT.md	2022-01-08 20:07:51 +08:00
Xintao	3e65d21817	fix ffmpeg framerate bug	2021-12-14 00:12:36 +08:00
Xintao	b7f191a9f5	update demo video	2021-12-13 17:12:34 +08:00
Xintao	e83bf0e1e4	add realesrgan anime video colab demo	2021-12-12 21:26:43 +08:00
Xintao	f07aaffda0	V0.2.3.0: add anime video models	2021-12-12 20:19:09 +08:00
Xintao	20355e0c79	Update readme for anime video models; add video demo (#181 ) * update readme * update readme * update readme * update readme * update readme * update readme * update readme * update readme * update readme	2021-12-12 20:17:30 +08:00
Xintao	192f672f91	add inference_realesrgan_video	2021-12-12 16:49:35 +08:00
Xintao	696e1a6741	add SRVGGNetCompact arch, update inference	2021-12-12 13:29:21 +08:00
Xintao	3e0085aeda	V0.2.2.6	2021-12-09 17:41:19 +08:00
Xintao	42110857ef	add unittest for model and utils	2021-11-28 19:54:19 +08:00
Xintao	1d180efaf3	add unittest for dataset and archs	2021-11-28 15:59:14 +08:00
Xintao	7dd860a881	catch more specific errors	2021-11-24 00:14:05 +08:00
Xintao	35ee6f781e	improve codes comments	2021-11-23 00:52:00 +08:00
Xintao	c9023b3d7a	Update README_CN.md (#142 ) * update contribution * updte readme * updte readme * update readme-cn * update readme-cn * update readme-cn * update readme-cn * update readme-cn	2021-11-01 19:16:48 +08:00
Asiimoviet	fb79d65ff3	Added Chinese README (#126 ) * Added Chinese README * Update README_CN.md * Create README_CN.md	2021-11-01 17:00:06 +08:00
@@ -1 +1 @@
 .2.2.5
 .2.5.0