Update readme for anime video models; add video demo (#181)

* update readme

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README.md

@@ -10,12 +10,10 @@
 
 [English](README.md) **|** [简体中文](README_CN.md)
 
-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).
-
-Thanks for your interests and use:-) There are still many problems about the anime/illustration model, mainly including: 1. It cannot deal with videos; 2. It cannot be aware of depth/depth-of-field; 3. It is not adjustable; 4. May change the original style. Thanks for your valuable feedbacks/suggestions. All the feedbacks are updated in [feedback.md](feedback.md). Hopefully, a new model will be available soon.
+:fire: :fire: :fire: Add **small video models** for anime videos (**针对动漫视频的小模型**). Please see [anime video models](docs/anime_video_model.md).
 
-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。这些反馈会逐步更新在 [这个文档](feedback.md)。希望不久之后，有新模型可以使用.
+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/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-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).
 
 Real-ESRGAN aims at developing **Practical Algorithms for General Image Restoration**.<br>
 We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.
@@ -24,7 +22,10 @@ We extend the powerful ESRGAN to a practical restoration application (namely, Re
 
 :question: Frequently Asked Questions can be found in [FAQ.md](FAQ.md) (Well, it is still empty there =-=||).
 
+:milky_way: Thanks for your valuable feedbacks/suggestions. All the feedbacks are updated in [feedback.md](feedback.md).
+
 :triangular_flag_on_post: **Updates**
+- :white_check_mark: Add small models for anime videos. More details are in [anime video models](docs/anime_video_model.md).
 - :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)
@@ -80,21 +81,23 @@ 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/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-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. RealESRGANv2-animevideo-xsx2 (anime video, X2)
+5. RealESRGANv2-animevideo-xsx4 (anime video, X4)
 
 You can use the `-n` argument for other models, for example, `./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n realesrnet-x4plus`
 
@@ -213,18 +216,7 @@ A common command: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile
 
 ## :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
 

README_CN.md

@@ -10,19 +10,22 @@
 
 [English](README.md) **|** [简体中文](README_CN.md)
 
-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. **支持Intel/AMD/Nvidia显卡**的绿色版exe文件： [Windows版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-windows.zip) / [Linux版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-ubuntu.zip) / [macOS版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-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)。
+:fire: :fire: :fire: 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.
 
-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。这些反馈会逐步更新在 [这个文档](feedback.md)。希望不久之后，有新模型可以使用.
+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. **支持Intel/AMD/Nvidia显卡**的绿色版exe文件： [Windows版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-windows.zip) / [Linux版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-ubuntu.zip) / [macOS版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-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=-=||）
 
 :triangular_flag_on_post: **更新**
+- :white_check_mark: 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.
 - :white_check_mark: 添加了ncnn 实现：[Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan).
 - :white_check_mark: 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)
 - :white_check_mark: 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)
@@ -76,21 +79,23 @@ Real-ESRGAN 将会被长期支持，我会在空闲的时间中持续维护更
 
 ### 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/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-windows.zip) / [Linux版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-ubuntu.zip) / [macOS版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/realesrgan-ncnn-vulkan-20210901-macos.zip)。
+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/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-windows.zip) / [Linux版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-ubuntu.zip) / [macOS版](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-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
+./realesrgan-ncnn-vulkan.exe -i 输入图像.jpg -o 输出图像.png -n 模型名字
 ```
 
-我们提供了三种模型：
+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. RealESRGANv2-animevideo-xsx2 (针对动漫视频, X2)
+5. RealESRGANv2-animevideo-xsx4 (针对动漫视频, X4)
 
 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`
 
@@ -208,18 +213,7 @@ A common command: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile
 
 ## :european_castle: 模型库
 
-- [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)
-
-下面是 **判别器** 模型, 他们经常被用来微调（fine-tune）模型.
-
-- [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)
+请参见 [docs/model_zoo.md](docs/model_zoo.md)
 
 ## :computer: 训练，在你的数据上微调（Fine-tune）
 

docs/anime_model.md

@@ -32,7 +32,7 @@ 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/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-macos.zip) **executable files for Intel/AMD/Nvidia GPU**.
 
 Taking the Windows as example, run:
 

docs/anime_video_model.md

@@ -0,0 +1,121 @@
+# Anime Video Models
+
+:white_check_mark: We add small models that are optimized for anime videos :-)
+
+| Models                                                                                                                             | Scale | Description                    |
+| ---------------------------------------------------------------------------------------------------------------------------------- | :---- | :----------------------------- |
+| [RealESRGANv2-animevideo-xsx2](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/RealESRGANv2-animevideo-xsx2.pth) | X2    | Anime video model with XS size |
+| [RealESRGANv2-animevideo-xsx4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/RealESRGANv2-animevideo-xsx4.pth) | X4    | Anime video model with XS size |
+
+- [Anime Video Models](#anime-video-models)
+  - [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)
+
+---
+
+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/17445847/145707046-8702a17c-a194-4471-8a53-a4cc44c9594c.MP4
+
+## How to Use
+
+### PyTorch Inference
+
+```bash
+# download model
+wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/RealESRGANv2-animevideo-xsx2.pth -P experiments/pretrained_models
+# inference
+python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n RealESRGANv2-animevideo-xsx2 -s 2 -v -a --half --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/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/realesrgan-ncnn-vulkan-20211212-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 RealESRGANv2-animevideo-xsx2 -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 -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 -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/17445847/145706827-384108c0-78f6-4aa7-9621-99d1aaf65682.mp4
+
+**More comparisons**
+
+https://user-images.githubusercontent.com/17445847/145707458-04a5e9b9-2edd-4d1f-b400-380a72e5f5e6.MP4

docs/model_zoo.md

@@ -0,0 +1,47 @@
+# :european_castle: Model Zoo
+
+- [:european_castle: Model Zoo](#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
+
+| 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 Anime Videos
+
+| Models                                                                                                                             | Scale | Description                    |
+| ---------------------------------------------------------------------------------------------------------------------------------- | :---- | :----------------------------- |
+| [RealESRGANv2-animevideo-xsx2](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/RealESRGANv2-animevideo-xsx2.pth) | X2    | Anime video model with XS size |
+| [RealESRGANv2-animevideo-xsx4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.3.0/RealESRGANv2-animevideo-xsx4.pth) | X4    | Anime video model with XS size |
+
+The following models are **discriminators**, which are usually used for fine-tuning.
+
+TODO

inference_realesrgan_video.py

@@ -39,6 +39,8 @@ def main():
     parser.add_argument('-v', '--video', action='store_true', help='Output a video using ffmpeg')
     parser.add_argument('-a', '--audio', action='store_true', help='Keep audio')
     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,
@@ -133,8 +135,7 @@ def main():
     reader.start()
 
     que = queue.Queue()
-    num_consumer = 4
-    consumers = [IOConsumer(args, que, f'IO_{i}') for i in range(num_consumer)]
+    consumers = [IOConsumer(args, que, f'IO_{i}') for i in range(args.consumer)]
     for consumer in consumers:
         consumer.start()
 
@@ -171,7 +172,7 @@ def main():
         avg_fps = 1. / (timer.get_avg_time() + 1e-7)
         pbar.set_description(f'idx {idx}, fps {avg_fps:.2f}')
 
-    for _ in range(num_consumer):
+    for _ in range(args.consumer):
         que.put('quit')
     for consumer in consumers:
         consumer.join()