Compare commits
4 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
|
f07aaffda0 | ||
|
|
20355e0c79 | ||
|
|
192f672f91 | ||
|
|
696e1a6741 |
4
FAQ.md
4
FAQ.md
@@ -1,9 +1,5 @@
|
||||
# FAQ
|
||||
|
||||
1. **What is the difference of `--netscale` and `outscale`?**
|
||||
|
||||
A: TODO.
|
||||
|
||||
1. **How to select models?**
|
||||
|
||||
A: TODO.
|
||||
|
||||
52
README.md
52
README.md
@@ -10,12 +10,10 @@
|
||||
|
||||
[English](README.md) **|** [简体中文](README_CN.md)
|
||||
|
||||
: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).
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
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`
|
||||
|
||||
@@ -166,7 +169,7 @@ 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
|
||||
@@ -184,7 +187,7 @@ 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
|
||||
@@ -194,37 +197,26 @@ 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 --half --face_enhance
|
||||
|
||||
-h show this help
|
||||
--input Input image or folder. Default: inputs
|
||||
--output Output folder. Default: results
|
||||
--model_path Path to the pre-trained model. Default: experiments/pretrained_models/RealESRGAN_x4plus.pth
|
||||
--netscale Upsample scale factor of the network. Default: 4
|
||||
--outscale The final upsampling scale of the image. Default: 4
|
||||
-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
|
||||
--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
|
||||
|
||||
|
||||
51
README_CN.md
51
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`
|
||||
|
||||
@@ -162,7 +167,7 @@ wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_
|
||||
推断!
|
||||
|
||||
```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
|
||||
```
|
||||
|
||||
结果在`results`文件夹
|
||||
@@ -180,28 +185,27 @@ python inference_realesrgan.py --model_path experiments/pretrained_models/RealES
|
||||
# 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 --model_path experiments/pretrained_models/RealESRGAN_x4plus_anime_6B.pth --input inputs
|
||||
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. 虽然你实用了 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。
|
||||
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 --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 --half --face_enhance
|
||||
|
||||
-h show this help
|
||||
--input Input image or folder. Default: inputs
|
||||
--output Output folder. Default: results
|
||||
--model_path Path to the pre-trained model. Default: experiments/pretrained_models/RealESRGAN_x4plus.pth
|
||||
--netscale Upsample scale factor of the network. Default: 4
|
||||
--outscale The final upsampling scale of the image. Default: 4
|
||||
-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
|
||||
--ext Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto
|
||||
@@ -209,18 +213,7 @@ A common command: python inference_realesrgan.py --model_path experiments/pretra
|
||||
|
||||
## :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）
|
||||
|
||||
|
||||
@@ -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">
|
||||
@@ -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/v0.2.3.0/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:
|
||||
|
||||
|
||||
121
docs/anime_video_model.md
Normal file
121
docs/anime_video_model.md
Normal file
@@ -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
|
||||
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
|
||||
@@ -5,28 +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'
|
||||
'RealESRGANv2-anime-xsx2 | RealESRGANv2-animevideo-xsx2-nousm | RealESRGANv2-animevideo-xsx2'
|
||||
'RealESRGANv2-anime-xsx4 | RealESRGANv2-animevideo-xsx4-nousm | RealESRGANv2-animevideo-xsx4'))
|
||||
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(
|
||||
'--alpha_upsampler',
|
||||
type=str,
|
||||
@@ -39,16 +41,39 @@ 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 [
|
||||
'RealESRGANv2-anime-xsx2', 'RealESRGANv2-animevideo-xsx2-nousm', 'RealESRGANv2-animevideo-xsx2'
|
||||
]: # x2 VGG-style model (XS size)
|
||||
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=2, act_type='prelu')
|
||||
netscale = 2
|
||||
elif args.model_name in [
|
||||
'RealESRGANv2-anime-xsx4', 'RealESRGANv2-animevideo-xsx4-nousm', 'RealESRGANv2-animevideo-xsx4'
|
||||
]: # 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,
|
||||
@@ -80,15 +105,6 @@ def main():
|
||||
else:
|
||||
img_mode = None
|
||||
|
||||
# give warnings for too large/small images
|
||||
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)
|
||||
|
||||
199
inference_realesrgan_video.py
Normal file
199
inference_realesrgan_video.py
Normal file
@@ -0,0 +1,199 @@
|
||||
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 image or folder')
|
||||
parser.add_argument(
|
||||
'-n',
|
||||
'--model_name',
|
||||
type=str,
|
||||
default='RealESRGAN_x4plus',
|
||||
help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus'
|
||||
'RealESRGANv2-anime-xsx2 | RealESRGANv2-animevideo-xsx2-nousm | RealESRGANv2-animevideo-xsx2'
|
||||
'RealESRGANv2-anime-xsx4 | RealESRGANv2-animevideo-xsx4-nousm | RealESRGANv2-animevideo-xsx4'))
|
||||
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('--half', action='store_true', help='Use half precision during inference')
|
||||
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,
|
||||
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('.')[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 [
|
||||
'RealESRGANv2-anime-xsx2', 'RealESRGANv2-animevideo-xsx2-nousm', 'RealESRGANv2-animevideo-xsx2'
|
||||
]: # x2 VGG-style model (XS size)
|
||||
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=2, act_type='prelu')
|
||||
netscale = 2
|
||||
elif args.model_name in [
|
||||
'RealESRGANv2-anime-xsx4', 'RealESRGANv2-animevideo-xsx4-nousm', 'RealESRGANv2-animevideo-xsx4'
|
||||
]: # 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=args.half)
|
||||
|
||||
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',
|
||||
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)
|
||||
|
||||
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, '*')))
|
||||
if args.video:
|
||||
if args.fps is None:
|
||||
# get input video fps
|
||||
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
|
||||
if args.video:
|
||||
video_save_path = os.path.join(args.output, f'{video_name}_{args.suffix}.mp4')
|
||||
if args.audio:
|
||||
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}')
|
||||
else:
|
||||
os.system(f'ffmpeg -i {save_frame_folder}/frame%08d_out.{extension} '
|
||||
f'-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()
|
||||
BIN
inputs/video/onepiece_demo.mp4
Normal file
BIN
inputs/video/onepiece_demo.mp4
Normal file
Binary file not shown.
@@ -3,4 +3,4 @@ from .archs import *
|
||||
from .data import *
|
||||
from .models import *
|
||||
from .utils import *
|
||||
from .version import __version__
|
||||
from .version import *
|
||||
|
||||
69
realesrgan/archs/srvgg_arch.py
Normal file
69
realesrgan/archs/srvgg_arch.py
Normal file
@@ -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
|
||||
@@ -2,8 +2,9 @@ import cv2
|
||||
import math
|
||||
import numpy as np
|
||||
import os
|
||||
import queue
|
||||
import threading
|
||||
import torch
|
||||
from basicsr.archs.rrdbnet_arch import RRDBNet
|
||||
from basicsr.utils.download_util import load_file_from_url
|
||||
from torch.nn import functional as F
|
||||
|
||||
@@ -16,7 +17,7 @@ class RealESRGANer():
|
||||
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. If None, the model will be constructed here. Default: None.
|
||||
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.
|
||||
@@ -35,14 +36,11 @@ 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=os.path.join(ROOT_DIR, 'realesrgan/weights'), progress=True, file_name=None)
|
||||
loadnet = torch.load(model_path)
|
||||
loadnet = torch.load(model_path, map_location=torch.device('cpu'))
|
||||
# prefer to use params_ema
|
||||
if 'params_ema' in loadnet:
|
||||
keyname = 'params_ema'
|
||||
@@ -230,3 +228,53 @@ class RealESRGANer():
|
||||
), interpolation=cv2.INTER_LANCZOS4)
|
||||
|
||||
return output, img_mode
|
||||
|
||||
|
||||
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.
|
||||
"""
|
||||
|
||||
def __init__(self, img_list, num_prefetch_queue):
|
||||
super().__init__()
|
||||
self.que = queue.Queue(num_prefetch_queue)
|
||||
self.img_list = img_list
|
||||
|
||||
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.')
|
||||
|
||||
Reference in New Issue
Block a user