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
This commit is contained in:
Xintao
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README.md
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README.md
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[English](README.md) **|** [简体中文](README_CN.md)
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: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).
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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>.
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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).
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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.
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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.
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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).
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Real-ESRGAN aims at developing **Practical Algorithms for General Image Restoration**.<br>
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We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.
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@@ -24,7 +22,10 @@ We extend the powerful ESRGAN to a practical restoration application (namely, Re
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:question: Frequently Asked Questions can be found in [FAQ.md](FAQ.md) (Well, it is still empty there =-=||).
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:milky_way: Thanks for your valuable feedbacks/suggestions. All the feedbacks are updated in [feedback.md](feedback.md).
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:triangular_flag_on_post: **Updates**
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- :white_check_mark: Add small models for anime videos. More details are in [anime video models](docs/anime_video_model.md).
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- :white_check_mark: Add the ncnn implementation [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan).
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- :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)
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- :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)
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@@ -80,21 +81,23 @@ If you have some images that Real-ESRGAN could not well restored, please also op
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### Portable executable files
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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**.
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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**.
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This executable file is **portable** and includes all the binaries and models required. No CUDA or PyTorch environment is needed.<br>
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You can simply run the following command (the Windows example, more information is in the README.md of each executable files):
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```bash
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./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png
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./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n model_name
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```
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We have provided three models:
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We have provided five models:
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1. realesrgan-x4plus (default)
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2. realesrnet-x4plus
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3. realesrgan-x4plus-anime (optimized for anime images, small model size)
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4. RealESRGANv2-animevideo-xsx2 (anime video, X2)
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5. RealESRGANv2-animevideo-xsx4 (anime video, X4)
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You can use the `-n` argument for other models, for example, `./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n realesrnet-x4plus`
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@@ -213,18 +216,7 @@ A common command: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile
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## :european_castle: Model Zoo
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- [RealESRGAN_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth): X4 model for general images
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- [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)
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- [RealESRGAN_x2plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth): X2 model for general images
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- [RealESRNet_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth): X4 model with MSE loss (over-smooth effects)
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- [official ESRGAN_x4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth): official ESRGAN model (X4)
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The following models are **discriminators**, which are usually used for fine-tuning.
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- [RealESRGAN_x4plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth)
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- [RealESRGAN_x2plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x2plus_netD.pth)
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- [RealESRGAN_x4plus_anime_6B_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B_netD.pth)
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Please see [docs/model_zoo.md](docs/model_zoo.md)
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## :computer: Training and Finetuning on your own dataset
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README_CN.md
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@@ -10,19 +10,22 @@
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[English](README.md) **|** [简体中文](README_CN.md)
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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>.
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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)。
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: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.
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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.
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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>.
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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)。
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Real-ESRGAN 的目标是开发出**实用的图像修复算法**。<br>
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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）。
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: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)。
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: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)。
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: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=-=||）
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:triangular_flag_on_post: **更新**
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- :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.
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- :white_check_mark: 添加了ncnn 实现：[Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan).
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- :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)
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- :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)
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@@ -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
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### 便携版（绿色版）可执行文件
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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)。
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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)。
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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>
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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）：
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```bash
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./realesrgan-ncnn-vulkan.exe -i 输入图像.jpg -o 输出图像.png
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./realesrgan-ncnn-vulkan.exe -i 输入图像.jpg -o 输出图像.png -n 模型名字
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```
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我们提供了三种模型：
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我们提供了五种模型：
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1. realesrgan-x4plus（默认）
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2. reaesrnet-x4plus
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3. realesrgan-x4plus-anime（针对动漫插画图像优化，有更小的体积）
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4. RealESRGANv2-animevideo-xsx2 (针对动漫视频, X2)
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5. RealESRGANv2-animevideo-xsx4 (针对动漫视频, X4)
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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`
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## :european_castle: 模型库
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- [RealESRGAN_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth): X4 model for general images
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- [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)
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- [RealESRGAN_x2plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth): X2 model for general images
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- [RealESRNet_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth): X4 model with MSE loss (over-smooth effects)
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- [official ESRGAN_x4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth): official ESRGAN model (X4)
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下面是 **判别器** 模型, 他们经常被用来微调（fine-tune）模型.
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- [RealESRGAN_x4plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth)
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- [RealESRGAN_x2plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x2plus_netD.pth)
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- [RealESRGAN_x4plus_anime_6B_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B_netD.pth)
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请参见 [docs/model_zoo.md](docs/model_zoo.md)
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## :computer: 训练，在你的数据上微调（Fine-tune）
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@@ -32,7 +32,7 @@ python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i inputs
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### ncnn Executable File
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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**.
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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**.
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Taking the Windows as example, run:
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# Anime Video Models
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:white_check_mark: We add small models that are optimized for anime videos :-)
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| Models | Scale | Description |
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| ---------------------------------------------------------------------------------------------------------------------------------- | :---- | :----------------------------- |
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| [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 |
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| [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 |
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- [Anime Video Models](#anime-video-models)
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- [How to Use](#how-to-use)
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- [PyTorch Inference](#pytorch-inference)
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- [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
|
||||
47
docs/model_zoo.md
Normal file
47
docs/model_zoo.md
Normal file
@@ -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
|
||||
@@ -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()
|
||||
|
||||
BIN
inputs/video/onepiece_demo.mp4
Normal file
BIN
inputs/video/onepiece_demo.mp4
Normal file
Binary file not shown.
Reference in New Issue
Block a user