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FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04.1-py38-latest
RUN source /opt/dtk/env.sh
COPY requirements.txt requirements.txt
RUN pip3 install -r requirements.txt
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# Contributing to Real-ESRGAN
:art: Real-ESRGAN needs your contributions. Any contributions are welcome, such as new features/models/typo fixes/suggestions/maintenance, *etc*. See [CONTRIBUTING.md](docs/CONTRIBUTING.md). All contributors are list [here](README.md#hugs-acknowledgement).
We like open-source and want to develop practical algorithms for general image restoration. However, individual strength is limited. So, any kinds of contributions are welcome, such as:
- New features
- New models (your fine-tuned models)
- Bug fixes
- Typo fixes
- Suggestions
- Maintenance
- Documents
- *etc*
## Workflow
1. Fork and pull the latest Real-ESRGAN repository
1. Checkout a new branch (do not use master branch for PRs)
1. Commit your changes
1. Create a PR
**Note**:
1. Please check the code style and linting
1. The style configuration is specified in [setup.cfg](setup.cfg)
1. If you use VSCode, the settings are configured in [.vscode/settings.json](.vscode/settings.json)
1. Strongly recommend using `pre-commit hook`. It will check your code style and linting before your commit.
1. In the root path of project folder, run `pre-commit install`
1. The pre-commit configuration is listed in [.pre-commit-config.yaml](.pre-commit-config.yaml)
1. Better to [open a discussion](https://github.com/xinntao/Real-ESRGAN/discussions) before large changes.
1. Welcome to discuss :sunglasses:. I will try my best to join the discussion.
## TODO List
:zero: The most straightforward way of improving model performance is to fine-tune on some specific datasets.
Here are some TODOs:
- [ ] optimize for human faces
- [ ] optimize for texts
- [ ] support controllable restoration strength
:one: There are also [several issues](https://github.com/xinntao/Real-ESRGAN/issues) that require helpers to improve. If you can help, please let me know :smile:
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# FAQ
1. **Q: How to select models?**<br>
A: Please refer to [docs/model_zoo.md](docs/model_zoo.md)
1. **Q: Can `face_enhance` be used for anime images/animation videos?**<br>
A: No, it can only be used for real faces. It is recommended not to use this option for anime images/animation videos to save GPU memory.
1. **Q: Error "slow_conv2d_cpu" not implemented for 'Half'**<br>
A: In order to save GPU memory consumption and speed up inference, Real-ESRGAN uses half precision (fp16) during inference by default. However, some operators for half inference are not implemented in CPU mode. You need to add **`--fp32` option** for the commands. For example, `python inference_realesrgan.py -n RealESRGAN_x4plus.pth -i inputs --fp32`.
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# Comparisons among different anime models
[English](anime_comparisons.md) **|** [简体中文](anime_comparisons_CN.md)
## Update News
- 2022/04/24: Release **AnimeVideo-v3**. We have made the following improvements:
- **better naturalness**
- **Fewer artifacts**
- **more faithful to the original colors**
- **better texture restoration**
- **better background restoration**
## Comparisons
We have compared our RealESRGAN-AnimeVideo-v3 with the following methods.
Our RealESRGAN-AnimeVideo-v3 can achieve better results with faster inference speed.
- [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan) with the hyperparameters: `tile=0`, `noiselevel=2`
- [Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN): we use the [20220227](https://github.com/bilibili/ailab/releases/tag/Real-CUGAN-add-faster-low-memory-mode) version, the hyperparameters are: `cache_mode=0`, `tile=0`, `alpha=1`.
- our RealESRGAN-AnimeVideo-v3
## Results
You may need to **zoom in** for comparing details, or **click the image** to see in the full size. Please note that the images
in the table below are the resized and cropped patches from the original images, you can download the original inputs and outputs from [Google Drive](https://drive.google.com/drive/folders/1bc_Hje1Nqop9NDkUvci2VACSjL7HZMRp?usp=sharing) .
**More natural results, better background restoration**
| Input | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
| :---: | :---: | :---: | :---: |
|![157083983-bec52c67-9a5e-4eed-afef-01fe6cd2af85_patch](https://user-images.githubusercontent.com/11482921/164452769-5d8cb4f8-1708-42d2-b941-f44a6f136feb.png) | ![](https://user-images.githubusercontent.com/11482921/164452767-c825cdec-f721-4ff1-aef1-fec41f146c4c.png) | ![](https://user-images.githubusercontent.com/11482921/164452755-3be50895-e3d4-432d-a7b9-9085c2a8e771.png) | ![](https://user-images.githubusercontent.com/11482921/164452771-be300656-379a-4323-a755-df8025a8c451.png) |
|![a0010_patch](https://user-images.githubusercontent.com/11482921/164454047-22eeb493-3fa9-4142-9fc2-6f2a1c074cd5.png) | ![](https://user-images.githubusercontent.com/11482921/164454046-d5e79f8f-00a0-4b55-bc39-295d0d69747a.png) | ![](https://user-images.githubusercontent.com/11482921/164454040-87886b11-9d08-48bd-862f-0d4aed72eb19.png) | ![](https://user-images.githubusercontent.com/11482921/164454055-73dc9f02-286e-4d5c-8f70-c13742e08f42.png) |
|![00000044_patch](https://user-images.githubusercontent.com/11482921/164451232-bacf64fc-e55a-44db-afbb-6b31ab0f8973.png) | ![](https://user-images.githubusercontent.com/11482921/164451318-f309b61a-75b8-4b74-b5f3-595725f1cf0b.png) | ![](https://user-images.githubusercontent.com/11482921/164451348-994f8a35-adbe-4a4b-9c61-feaa294af06a.png) | ![](https://user-images.githubusercontent.com/11482921/164451361-9b7d376e-6f75-4648-b752-542b44845d1c.png) |
**Fewer artifacts, better detailed textures**
| Input | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
| :---: | :---: | :---: | :---: |
|![00000053_patch](https://user-images.githubusercontent.com/11482921/164448411-148a7e5c-cfcd-4504-8bc7-e318eb883bb6.png) | ![](https://user-images.githubusercontent.com/11482921/164448633-dfc15224-b6d2-4403-a3c9-4bb819979364.png) | ![](https://user-images.githubusercontent.com/11482921/164448771-0d359509-5293-4d4c-8e3c-86a2a314ea88.png) | ![](https://user-images.githubusercontent.com/11482921/164448848-1a4ff99e-075b-4458-9db7-2c89e8160aa0.png) |
|![Disney_v4_22_018514_s2_patch](https://user-images.githubusercontent.com/11482921/164451898-83311cdf-bd3e-450f-b9f6-34d7fea3ab79.png) | ![](https://user-images.githubusercontent.com/11482921/164451894-6c56521c-6561-40d6-a3a5-8dde2c167b8a.png) | ![](https://user-images.githubusercontent.com/11482921/164451888-af9b47e3-39dc-4f3e-b0d7-d372d8191e2a.png) | ![](https://user-images.githubusercontent.com/11482921/164451901-31ca4dd4-9847-4baa-8cde-ad50f4053dcf.png) |
|![Japan_v2_0_007261_s2_patch](https://user-images.githubusercontent.com/11482921/164454578-73c77392-77de-49c5-b03c-c36631723192.png) | ![](https://user-images.githubusercontent.com/11482921/164454574-b1ede5f0-4520-4eaa-8f59-086751a34e62.png) | ![](https://user-images.githubusercontent.com/11482921/164454567-4cb3fdd8-6a2d-4016-85b2-a305a8ff80e4.png) | ![](https://user-images.githubusercontent.com/11482921/164454583-7f243f20-eca3-4500-ac43-eb058a4a101a.png) |
|![huluxiongdi_2_patch](https://user-images.githubusercontent.com/11482921/164453482-0726c842-337e-40ec-bf6c-f902ee956a8b.png) | ![](https://user-images.githubusercontent.com/11482921/164453480-71d5e091-5bfa-4c77-9c57-4e37f66ca0a3.png) | ![](https://user-images.githubusercontent.com/11482921/164453468-c295d3c9-3661-45f0-9ecd-406a1877f76e.png) | ![](https://user-images.githubusercontent.com/11482921/164453486-3091887c-587c-450e-b6fe-905cb518d57e.png) |
**Other better results**
| Input | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
| :---: | :---: | :---: | :---: |
|![Japan_v2_1_128525_s1_patch](https://user-images.githubusercontent.com/11482921/164454933-67697f7c-b6ef-47dc-bfca-822a78af8acf.png) | ![](https://user-images.githubusercontent.com/11482921/164454931-9450de7c-f0b3-4638-9c1e-0668e0c41ef0.png) | ![](https://user-images.githubusercontent.com/11482921/164454926-ed746976-786d-41c5-8a83-7693cd774c3a.png) | ![](https://user-images.githubusercontent.com/11482921/164454936-8abdf0f0-fb30-40eb-8281-3b46c0bcb9ae.png) |
|![tianshuqitan_2_patch](https://user-images.githubusercontent.com/11482921/164456948-807c1476-90b6-4507-81da-cb986d01600c.png) | ![](https://user-images.githubusercontent.com/11482921/164456943-25e89de9-d7e5-4f61-a2e1-96786af6ae9e.png) | ![](https://user-images.githubusercontent.com/11482921/164456954-b468c447-59f5-4594-9693-3683e44ba3e6.png) | ![](https://user-images.githubusercontent.com/11482921/164456957-640f910c-3b04-407c-ac20-044d72e19735.png) |
|![00000051_patch](https://user-images.githubusercontent.com/11482921/164456044-e9a6b3fa-b24e-4eb7-acf9-1f7746551b1e.png) ![00000051_patch](https://user-images.githubusercontent.com/11482921/164456421-b67245b0-767d-4250-9105-80bbe507ecfc.png) | ![](https://user-images.githubusercontent.com/11482921/164456040-85763cf2-cb28-4ba3-abb6-1dbb48c55713.png) ![](https://user-images.githubusercontent.com/11482921/164456419-59cf342e-bc1e-4044-868c-e1090abad313.png) | ![](https://user-images.githubusercontent.com/11482921/164456031-4244bb7b-8649-4e01-86f4-40c2099c5afd.png) ![](https://user-images.githubusercontent.com/11482921/164456411-b6afcbe9-c054-448d-a6df-96d3ba3047f8.png) | ![](https://user-images.githubusercontent.com/11482921/164456035-12e270be-fd52-46d4-b18a-3d3b680731fe.png) ![](https://user-images.githubusercontent.com/11482921/164456417-dcaa8b62-f497-427d-b2d2-f390f1200fb9.png) |
|![00000099_patch](https://user-images.githubusercontent.com/11482921/164455312-6411b6e1-5823-4131-a4b0-a6be8a9ae89f.png) | ![](https://user-images.githubusercontent.com/11482921/164455310-f2b99646-3a22-47a4-805b-dc451ac86ddb.png) | ![](https://user-images.githubusercontent.com/11482921/164455294-35471b42-2826-4451-b7ec-6de01344954c.png) | ![](https://user-images.githubusercontent.com/11482921/164455305-fa4c9758-564a-4081-8b4e-f11057a0404d.png) |
|![00000016_patch](https://user-images.githubusercontent.com/11482921/164455672-447353c9-2da2-4fcb-ba4a-7dd6b94c19c1.png) | ![](https://user-images.githubusercontent.com/11482921/164455669-df384631-baaa-42f8-9150-40f658471558.png) | ![](https://user-images.githubusercontent.com/11482921/164455657-68006bf0-138d-4981-aaca-8aa927d2f78a.png) | ![](https://user-images.githubusercontent.com/11482921/164455664-0342b93e-a62a-4b36-a90e-7118f3f1e45d.png) |
## Inference Speed
### PyTorch
Note that we only report the **model** time, and ignore the IO time.
| GPU | Input Resolution | waifu2x | Real-CUGAN | RealESRGAN-AnimeVideo-v3
| :---: | :---: | :---: | :---: | :---: |
| V100 | 1921 x 1080 | - | 3.4 fps | **10.0** fps |
| V100 | 1280 x 720 | - | 7.2 fps | **22.6** fps |
| V100 | 640 x 480 | - | 24.4 fps | **65.9** fps |
### ncnn
- [ ] TODO
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# 动漫视频模型比较
[English](anime_comparisons.md) **|** [简体中文](anime_comparisons_CN.md)
## 更新
- 2022/04/24: 发布 **AnimeVideo-v3**. 主要做了以下更新：
- **更自然**
- **更少瑕疵**
- **颜色保持得更好**
- **更好的纹理恢复**
- **虚化背景处理**
## 比较
我们将 RealESRGAN-AnimeVideo-v3 与以下方法进行了比较。我们的 RealESRGAN-AnimeVideo-v3 可以以更快的推理速度获得更好的结果。
- [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan). 超参数: `tile=0`, `noiselevel=2`
- [Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN): 我们使用了[20220227](https://github.com/bilibili/ailab/releases/tag/Real-CUGAN-add-faster-low-memory-mode)版本, 超参: `cache_mode=0`, `tile=0`, `alpha=1`.
- 我们的 RealESRGAN-AnimeVideo-v3
## 结果
您可能需要**放大**以比较详细信息, 或者**单击图像**以查看完整尺寸。 请注意下面表格的图片是从原图里裁剪patch并且resize后的结果，您可以从
[Google Drive](https://drive.google.com/drive/folders/1bc_Hje1Nqop9NDkUvci2VACSjL7HZMRp?usp=sharing) 里下载原始的输入和输出。
**更自然的结果，更好的虚化背景恢复**
| 输入 | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
| :---: | :---: | :---: | :---: |
|![157083983-bec52c67-9a5e-4eed-afef-01fe6cd2af85_patch](https://user-images.githubusercontent.com/11482921/164452769-5d8cb4f8-1708-42d2-b941-f44a6f136feb.png) | ![](https://user-images.githubusercontent.com/11482921/164452767-c825cdec-f721-4ff1-aef1-fec41f146c4c.png) | ![](https://user-images.githubusercontent.com/11482921/164452755-3be50895-e3d4-432d-a7b9-9085c2a8e771.png) | ![](https://user-images.githubusercontent.com/11482921/164452771-be300656-379a-4323-a755-df8025a8c451.png) |
|![a0010_patch](https://user-images.githubusercontent.com/11482921/164454047-22eeb493-3fa9-4142-9fc2-6f2a1c074cd5.png) | ![](https://user-images.githubusercontent.com/11482921/164454046-d5e79f8f-00a0-4b55-bc39-295d0d69747a.png) | ![](https://user-images.githubusercontent.com/11482921/164454040-87886b11-9d08-48bd-862f-0d4aed72eb19.png) | ![](https://user-images.githubusercontent.com/11482921/164454055-73dc9f02-286e-4d5c-8f70-c13742e08f42.png) |
|![00000044_patch](https://user-images.githubusercontent.com/11482921/164451232-bacf64fc-e55a-44db-afbb-6b31ab0f8973.png) | ![](https://user-images.githubusercontent.com/11482921/164451318-f309b61a-75b8-4b74-b5f3-595725f1cf0b.png) | ![](https://user-images.githubusercontent.com/11482921/164451348-994f8a35-adbe-4a4b-9c61-feaa294af06a.png) | ![](https://user-images.githubusercontent.com/11482921/164451361-9b7d376e-6f75-4648-b752-542b44845d1c.png) |
**更少瑕疵，更好的细节纹理**
| 输入 | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
| :---: | :---: | :---: | :---: |
|![00000053_patch](https://user-images.githubusercontent.com/11482921/164448411-148a7e5c-cfcd-4504-8bc7-e318eb883bb6.png) | ![](https://user-images.githubusercontent.com/11482921/164448633-dfc15224-b6d2-4403-a3c9-4bb819979364.png) | ![](https://user-images.githubusercontent.com/11482921/164448771-0d359509-5293-4d4c-8e3c-86a2a314ea88.png) | ![](https://user-images.githubusercontent.com/11482921/164448848-1a4ff99e-075b-4458-9db7-2c89e8160aa0.png) |
|![Disney_v4_22_018514_s2_patch](https://user-images.githubusercontent.com/11482921/164451898-83311cdf-bd3e-450f-b9f6-34d7fea3ab79.png) | ![](https://user-images.githubusercontent.com/11482921/164451894-6c56521c-6561-40d6-a3a5-8dde2c167b8a.png) | ![](https://user-images.githubusercontent.com/11482921/164451888-af9b47e3-39dc-4f3e-b0d7-d372d8191e2a.png) | ![](https://user-images.githubusercontent.com/11482921/164451901-31ca4dd4-9847-4baa-8cde-ad50f4053dcf.png) |
|![Japan_v2_0_007261_s2_patch](https://user-images.githubusercontent.com/11482921/164454578-73c77392-77de-49c5-b03c-c36631723192.png) | ![](https://user-images.githubusercontent.com/11482921/164454574-b1ede5f0-4520-4eaa-8f59-086751a34e62.png) | ![](https://user-images.githubusercontent.com/11482921/164454567-4cb3fdd8-6a2d-4016-85b2-a305a8ff80e4.png) | ![](https://user-images.githubusercontent.com/11482921/164454583-7f243f20-eca3-4500-ac43-eb058a4a101a.png) |
|![huluxiongdi_2_patch](https://user-images.githubusercontent.com/11482921/164453482-0726c842-337e-40ec-bf6c-f902ee956a8b.png) | ![](https://user-images.githubusercontent.com/11482921/164453480-71d5e091-5bfa-4c77-9c57-4e37f66ca0a3.png) | ![](https://user-images.githubusercontent.com/11482921/164453468-c295d3c9-3661-45f0-9ecd-406a1877f76e.png) | ![](https://user-images.githubusercontent.com/11482921/164453486-3091887c-587c-450e-b6fe-905cb518d57e.png) |
**其他更好的结果**
| 输入 | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
| :---: | :---: | :---: | :---: |
|![Japan_v2_1_128525_s1_patch](https://user-images.githubusercontent.com/11482921/164454933-67697f7c-b6ef-47dc-bfca-822a78af8acf.png) | ![](https://user-images.githubusercontent.com/11482921/164454931-9450de7c-f0b3-4638-9c1e-0668e0c41ef0.png) | ![](https://user-images.githubusercontent.com/11482921/164454926-ed746976-786d-41c5-8a83-7693cd774c3a.png) | ![](https://user-images.githubusercontent.com/11482921/164454936-8abdf0f0-fb30-40eb-8281-3b46c0bcb9ae.png) |
|![tianshuqitan_2_patch](https://user-images.githubusercontent.com/11482921/164456948-807c1476-90b6-4507-81da-cb986d01600c.png) | ![](https://user-images.githubusercontent.com/11482921/164456943-25e89de9-d7e5-4f61-a2e1-96786af6ae9e.png) | ![](https://user-images.githubusercontent.com/11482921/164456954-b468c447-59f5-4594-9693-3683e44ba3e6.png) | ![](https://user-images.githubusercontent.com/11482921/164456957-640f910c-3b04-407c-ac20-044d72e19735.png) |
|![00000051_patch](https://user-images.githubusercontent.com/11482921/164456044-e9a6b3fa-b24e-4eb7-acf9-1f7746551b1e.png) ![00000051_patch](https://user-images.githubusercontent.com/11482921/164456421-b67245b0-767d-4250-9105-80bbe507ecfc.png) | ![](https://user-images.githubusercontent.com/11482921/164456040-85763cf2-cb28-4ba3-abb6-1dbb48c55713.png) ![](https://user-images.githubusercontent.com/11482921/164456419-59cf342e-bc1e-4044-868c-e1090abad313.png) | ![](https://user-images.githubusercontent.com/11482921/164456031-4244bb7b-8649-4e01-86f4-40c2099c5afd.png) ![](https://user-images.githubusercontent.com/11482921/164456411-b6afcbe9-c054-448d-a6df-96d3ba3047f8.png) | ![](https://user-images.githubusercontent.com/11482921/164456035-12e270be-fd52-46d4-b18a-3d3b680731fe.png) ![](https://user-images.githubusercontent.com/11482921/164456417-dcaa8b62-f497-427d-b2d2-f390f1200fb9.png) |
|![00000099_patch](https://user-images.githubusercontent.com/11482921/164455312-6411b6e1-5823-4131-a4b0-a6be8a9ae89f.png) | ![](https://user-images.githubusercontent.com/11482921/164455310-f2b99646-3a22-47a4-805b-dc451ac86ddb.png) | ![](https://user-images.githubusercontent.com/11482921/164455294-35471b42-2826-4451-b7ec-6de01344954c.png) | ![](https://user-images.githubusercontent.com/11482921/164455305-fa4c9758-564a-4081-8b4e-f11057a0404d.png) |
|![00000016_patch](https://user-images.githubusercontent.com/11482921/164455672-447353c9-2da2-4fcb-ba4a-7dd6b94c19c1.png) | ![](https://user-images.githubusercontent.com/11482921/164455669-df384631-baaa-42f8-9150-40f658471558.png) | ![](https://user-images.githubusercontent.com/11482921/164455657-68006bf0-138d-4981-aaca-8aa927d2f78a.png) | ![](https://user-images.githubusercontent.com/11482921/164455664-0342b93e-a62a-4b36-a90e-7118f3f1e45d.png) |
## 推理速度比较
### PyTorch
请注意，我们只报告了**模型推理**的时间, 而忽略了读写硬盘的时间.
| GPU | 输入尺寸 | waifu2x | Real-CUGAN | RealESRGAN-AnimeVideo-v3
| :---: | :---: | :---: | :---: | :---: |
| V100 | 1921 x 1080 | - | 3.4 fps | **10.0** fps |
| V100 | 1280 x 720 | - | 7.2 fps | **22.6** fps |
| V100 | 640 x 480 | - | 24.4 fps | **65.9** fps |
### ncnn
- [ ] TODO
docs/anime_model.md 0 → 100644
View file @ d118e789
# 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](#comparisons-with-sliding-bars)
<p align="center">
<img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_1.png">
</p>
The following is a video comparison with sliding bar. You may need to use the full-screen mode for better visual quality, as the original image is large; otherwise, you may encounter aliasing issue.
<https://user-images.githubusercontent.com/17445847/131535127-613250d4-f754-4e20-9720-2f9608ad0675.mp4>
## How to Use
### PyTorch Inference
Pre-trained models: [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)
```bash
# download model
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P weights
# inference
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/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip) **executable files for Intel/AMD/Nvidia GPU**.
Taking the Windows as example, run:
```bash
./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n realesrgan-x4plus-anime
```
## Comparisons with waifu2x
We compare Real-ESRGAN-anime with [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan). We use the `-n 2 -s 4` for waifu2x.
<p align="center">
<img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_1.png">
</p>
<p align="center">
<img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_2.png">
</p>
<p align="center">
<img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_3.png">
</p>
<p align="center">
<img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_4.png">
</p>
<p align="center">
<img src="https://raw.githubusercontent.com/xinntao/public-figures/master/Real-ESRGAN/cmp_realesrgan_anime_5.png">
</p>
## Comparisons with Sliding Bars
The following are video comparisons with sliding bar. You may need to use the full-screen mode for better visual quality, as the original image is large; otherwise, you may encounter aliasing issue.
<https://user-images.githubusercontent.com/17445847/131536647-a2fbf896-b495-4a9f-b1dd-ca7bbc90101a.mp4>
<https://user-images.githubusercontent.com/17445847/131536742-6d9d82b6-9765-4296-a15f-18f9aeaa5465.mp4>
docs/anime_video_model.md 0 → 100644
View file @ d118e789
# Anime Video Models
:white_check_mark: We add small models that are optimized for anime videos :-)<br>
More comparisons can be found in [anime_comparisons.md](anime_comparisons.md)
- [How to Use](#how-to-use)
- [PyTorch Inference](#pytorch-inference)
- [ncnn Executable File](#ncnn-executable-file)
- [Step 1: Use ffmpeg to extract frames from video](#step-1-use-ffmpeg-to-extract-frames-from-video)
- [Step 2: Inference with Real-ESRGAN executable file](#step-2-inference-with-real-esrgan-executable-file)
- [Step 3: Merge the enhanced frames back into a video](#step-3-merge-the-enhanced-frames-back-into-a-video)
- [More Demos](#more-demos)
| Models | Scale | Description |
| ---------------------------------------------------------------------------------------------------------------------------------- | :---- | :----------------------------- |
| [realesr-animevideov3](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth) | X4 <sup>1</sup> | Anime video model with XS size |
Note: <br>
<sup>1</sup> This model can also be used for X1, X2, X3.
---
The following are some demos (best view in the full screen mode).
<https://user-images.githubusercontent.com/17445847/145706977-98bc64a4-af27-481c-8abe-c475e15db7ff.MP4>
<https://user-images.githubusercontent.com/17445847/145707055-6a4b79cb-3d9d-477f-8610-c6be43797133.MP4>
<https://user-images.githubusercontent.com/17445847/145783523-f4553729-9f03-44a8-a7cc-782aadf67b50.MP4>
## How to Use
### PyTorch Inference
```bash
# download model
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth -P weights
# single gpu and single process inference
CUDA_VISIBLE_DEVICES=0 python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2
# single gpu and multi process inference (you can use multi-processing to improve GPU utilization)
CUDA_VISIBLE_DEVICES=0 python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2 --num_process_per_gpu 2
# multi gpu and multi process inference
CUDA_VISIBLE_DEVICES=0,1,2,3 python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2 --num_process_per_gpu 2
```
```console
Usage:
--num_process_per_gpu The total number of process is num_gpu * num_process_per_gpu. The bottleneck of
the program lies on the IO, so the GPUs are usually not fully utilized. To alleviate
this issue, you can use multi-processing by setting this parameter. As long as it
does not exceed the CUDA memory
--extract_frame_first If you encounter ffmpeg error when using multi-processing, you can turn this option on.
```
### NCNN Executable File
#### Step 1: Use ffmpeg to extract frames from video
```bash
ffmpeg -i onepiece_demo.mp4 -qscale:v 1 -qmin 1 -qmax 1 -vsync 0 tmp_frames/frame%08d.png
```
- Remember to create the folder `tmp_frames` ahead
#### Step 2: Inference with Real-ESRGAN executable file
1. Download the latest portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip) **executable files for Intel/AMD/Nvidia GPU**
1. Taking the Windows as example, run:
```bash
./realesrgan-ncnn-vulkan.exe -i tmp_frames -o out_frames -n realesr-animevideov3 -s 2 -f jpg
```
- Remember to create the folder `out_frames` ahead
#### Step 3: Merge the enhanced frames back into a video
1. First obtain fps from input videos by
```bash
ffmpeg -i onepiece_demo.mp4
```
```console
Usage:
-i input video path
```
You will get the output similar to the following screenshot.
<p align="center">
<img src="https://user-images.githubusercontent.com/17445847/145710145-c4f3accf-b82f-4307-9f20-3803a2c73f57.png">
</p>
2. Merge frames
```bash
ffmpeg -r 23.98 -i out_frames/frame%08d.jpg -c:v libx264 -r 23.98 -pix_fmt yuv420p output.mp4
```
```console
Usage:
-i input video path
-c:v video encoder (usually we use libx264)
-r fps, remember to modify it to meet your needs
-pix_fmt pixel format in video
```
If you also want to copy audio from the input videos, run:
```bash
ffmpeg -r 23.98 -i out_frames/frame%08d.jpg -i onepiece_demo.mp4 -map 0:v:0 -map 1:a:0 -c:a copy -c:v libx264 -r 23.98 -pix_fmt yuv420p output_w_audio.mp4
```
```console
Usage:
-i input video path, here we use two input streams
-c:v video encoder (usually we use libx264)
-r fps, remember to modify it to meet your needs
-pix_fmt pixel format in video
```
## More Demos
- Input video for One Piece:
<https://user-images.githubusercontent.com/17445847/145706822-0e83d9c4-78ef-40ee-b2a4-d8b8c3692d17.mp4>
- Out video for One Piece
<https://user-images.githubusercontent.com/17445847/164960481-759658cf-fcb8-480c-b888-cecb606e8744.mp4>
**More comparisons**
<https://user-images.githubusercontent.com/17445847/145707458-04a5e9b9-2edd-4d1f-b400-380a72e5f5e6.MP4>
docs/feedback.md 0 → 100644
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# Feedback 反馈
## 动漫插画模型
1. 视频处理不了: 目前的模型，不是针对视频的，所以视频效果很很不好。我们在探究针对视频的模型了
1. 景深虚化有问题: 现在的模型把一些景深 和 特意的虚化 都复原了，感觉不好。这个后面我们会考虑把这个信息结合进入。一个简单的做法是识别景深和虚化，然后作为条件告诉神经网络，哪些地方复原强一些，哪些地方复原要弱一些
1. 不可以调节: 像 Waifu2X 可以调节。可以根据自己的喜好，做调整，但是 Real-ESRGAN-anime 并不可以。导致有些恢复效果过了
1. 把原来的风格改变了: 不同的动漫插画都有自己的风格，现在的 Real-ESRGAN-anime 倾向于恢复成一种风格（这是受到训练数据集影响的）。风格是动漫很重要的一个要素，所以要尽可能保持
1. 模型太大: 目前的模型处理太慢，能够更快。这个我们有相关的工作在探究，希望能够尽快有结果，并应用到 Real-ESRGAN 这一系列的模型上
Thanks for the [detailed and valuable feedbacks/suggestions](https://github.com/xinntao/Real-ESRGAN/issues/131) by [2ji3150](https://github.com/2ji3150).
docs/model_zoo.md 0 → 100644
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# :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 |
| [realesr-general-x4v3](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth) | X4 (can also be used for X1, X2, X3) | A tiny small model (consume much fewer GPU memory and time); not too strong deblur and denoise capacity |
The following models are **discriminators**, which are usually used for fine-tuning.
| Models | Corresponding model |
| ---------------------------------------------------------------------------------------------------------------------- | :------------------ |
| [RealESRGAN_x4plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth) | RealESRGAN_x4plus |
| [RealESRGAN_x2plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x2plus_netD.pth) | RealESRGAN_x2plus |
## For Anime Images / Illustrations
| Models | Scale | Description |
| ------------------------------------------------------------------------------------------------------------------------------ | :---- | :---------------------------------------------------------- |
| [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth) | X4 | Optimized for anime images; 6 RRDB blocks (smaller network) |
The following models are **discriminators**, which are usually used for fine-tuning.
| Models | Corresponding model |
| ---------------------------------------------------------------------------------------------------------------------------------------- | :------------------------- |
| [RealESRGAN_x4plus_anime_6B_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B_netD.pth) | RealESRGAN_x4plus_anime_6B |
## For Animation Videos
| Models | Scale | Description |
| ---------------------------------------------------------------------------------------------------------------------------------- | :---- | :----------------------------- |
| [realesr-animevideov3](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth) | X4<sup>1</sup> | Anime video model with XS size |
Note: <br>
<sup>1</sup> This model can also be used for X1, X2, X3.
The following models are **discriminators**, which are usually used for fine-tuning.
TODO
docs/ncnn_conversion.md 0 → 100644
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# Instructions on converting to NCNN models
1. Convert to onnx model with `scripts/pytorch2onnx.py`. Remember to modify codes accordingly
1. Convert onnx model to ncnn model
1. `cd ncnn-master\ncnn\build\tools\onnx`
1. `onnx2ncnn.exe realesrgan-x4.onnx realesrgan-x4-raw.param realesrgan-x4-raw.bin`
1. Optimize ncnn model
1. fp16 mode
1. `cd ncnn-master\ncnn\build\tools`
1. `ncnnoptimize.exe realesrgan-x4-raw.param realesrgan-x4-raw.bin realesrgan-x4.param realesrgan-x4.bin 1`
1. Modify the blob name in `realesrgan-x4.param`: `data` and `output`
gen_data_txt.sh 0 → 100644
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#!/bin/bash
python scripts/generate_meta_info.py --input datasets/DF2K/DF2K_HR datasets/DF2K/DF2K_multiscale datasets/OST_datasets/train_sub/ \
--root datasets/DF2K datasets/DF2K datasets/DF2K \
--meta_info datasets/DF2K/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt
inference_realesrgan.py 0 → 100644
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import argparse
import cv2
import glob
import os
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.download_util import load_file_from_url
from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
def main():
"""Inference demo for Real-ESRGAN.
"""
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 | '
'realesr-animevideov3 | realesr-general-x4v3'))
parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
parser.add_argument(
'-dn',
'--denoise_strength',
type=float,
default=0.5,
help=('Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. '
'Only used for the realesr-general-x4v3 model'))
parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
parser.add_argument(
'--model_path', type=str, default=None, help='[Option] Model path. Usually, you do not need to specify it')
parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image')
parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
parser.add_argument(
'--fp32', action='store_true', help='Use fp32 precision during inference. Default: fp16 (half precision).')
parser.add_argument(
'--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')
parser.add_argument(
'-g', '--gpu-id', type=int, default=None, help='gpu device to use (default=None) can be 0,1,2 for multi-gpu')
args = parser.parse_args()
# determine models according to model names
args.model_name = args.model_name.split('.')[0]
if args.model_name == 'RealESRGAN_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
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
elif args.model_name == '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
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
elif args.model_name == '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
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
elif args.model_name == '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
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
elif args.model_name == 'realesr-animevideov3': # x4 VGG-style model (XS size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
elif args.model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
netscale = 4
file_url = [
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
]
# determine model paths
if args.model_path is not None:
model_path = args.model_path
else:
model_path = os.path.join('weights', args.model_name + '.pth')
if not os.path.isfile(model_path):
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
for url in file_url:
# model_path will be updated
model_path = load_file_from_url(
url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
# use dni to control the denoise strength
dni_weight = None
if args.model_name == 'realesr-general-x4v3' and args.denoise_strength != 1:
wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
model_path = [model_path, wdn_model_path]
dni_weight = [args.denoise_strength, 1 - args.denoise_strength]
# restorer
upsampler = RealESRGANer(
scale=netscale,
model_path=model_path,
dni_weight=dni_weight,
model=model,
tile=args.tile,
tile_pad=args.tile_pad,
pre_pad=args.pre_pad,
half=not args.fp32,
gpu_id=args.gpu_id)
if args.face_enhance: # Use GFPGAN for face enhancement
from gfpgan import GFPGANer
face_enhancer = GFPGANer(
model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
upscale=args.outscale,
arch='clean',
channel_multiplier=2,
bg_upsampler=upsampler)
os.makedirs(args.output, exist_ok=True)
if os.path.isfile(args.input):
paths = [args.input]
else:
paths = sorted(glob.glob(os.path.join(args.input, '*')))
for idx, path in enumerate(paths):
if os.path.isdir(path):
continue
imgname, extension = os.path.splitext(os.path.basename(path))
print('Testing', idx, imgname)
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
if len(img.shape) == 3 and img.shape[2] == 4:
img_mode = 'RGBA'
else:
img_mode = None
try:
if args.face_enhance:
if len(img.shape) == 2:
img_mode = 'L'
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
_, _, 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'
if args.suffix == '':
save_path = os.path.join(args.output, f'{imgname}.{extension}')
else:
save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}')
cv2.imwrite(save_path, output)
if __name__ == '__main__':
main()
inference_realesrgan_video.py 0 → 100644
View file @ d118e789
import argparse
import cv2
import glob
import mimetypes
import numpy as np
import os
import shutil
import subprocess
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.download_util import load_file_from_url
from os import path as osp
from tqdm import tqdm
from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
try:
import ffmpeg
except ImportError:
import pip
pip.main(['install', '--user', 'ffmpeg-python'])
import ffmpeg
def get_video_meta_info(video_path):
ret = {}
probe = ffmpeg.probe(video_path)
video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video']
has_audio = any(stream['codec_type'] == 'audio' for stream in probe['streams'])
ret['width'] = video_streams[0]['width']
ret['height'] = video_streams[0]['height']
ret['fps'] = eval(video_streams[0]['avg_frame_rate'])
ret['audio'] = ffmpeg.input(video_path).audio if has_audio else None
ret['nb_frames'] = int(video_streams[0]['nb_frames'])
return ret
def get_sub_video(args, num_process, process_idx):
if num_process == 1:
return args.input
meta = get_video_meta_info(args.input)
duration = int(meta['nb_frames'] / meta['fps'])
part_time = duration // num_process
print(f'duration: {duration}, part_time: {part_time}')
os.makedirs(osp.join(args.output, f'{args.video_name}_inp_tmp_videos'), exist_ok=True)
out_path = osp.join(args.output, f'{args.video_name}_inp_tmp_videos', f'{process_idx:03d}.mp4')
cmd = [
args.ffmpeg_bin, f'-i {args.input}', '-ss', f'{part_time * process_idx}',
f'-to {part_time * (process_idx + 1)}' if process_idx != num_process - 1 else '', '-async 1', out_path, '-y'
]
print(' '.join(cmd))
subprocess.call(' '.join(cmd), shell=True)
return out_path
class Reader:
def __init__(self, args, total_workers=1, worker_idx=0):
self.args = args
input_type = mimetypes.guess_type(args.input)[0]
self.input_type = 'folder' if input_type is None else input_type
self.paths = [] # for image&folder type
self.audio = None
self.input_fps = None
if self.input_type.startswith('video'):
video_path = get_sub_video(args, total_workers, worker_idx)
self.stream_reader = (
ffmpeg.input(video_path).output('pipe:', format='rawvideo', pix_fmt='bgr24',
loglevel='error').run_async(
pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin))
meta = get_video_meta_info(video_path)
self.width = meta['width']
self.height = meta['height']
self.input_fps = meta['fps']
self.audio = meta['audio']
self.nb_frames = meta['nb_frames']
else:
if self.input_type.startswith('image'):
self.paths = [args.input]
else:
paths = sorted(glob.glob(os.path.join(args.input, '*')))
tot_frames = len(paths)
num_frame_per_worker = tot_frames // total_workers + (1 if tot_frames % total_workers else 0)
self.paths = paths[num_frame_per_worker * worker_idx:num_frame_per_worker * (worker_idx + 1)]
self.nb_frames = len(self.paths)
assert self.nb_frames > 0, 'empty folder'
from PIL import Image
tmp_img = Image.open(self.paths[0])
self.width, self.height = tmp_img.size
self.idx = 0
def get_resolution(self):
return self.height, self.width
def get_fps(self):
if self.args.fps is not None:
return self.args.fps
elif self.input_fps is not None:
return self.input_fps
return 24
def get_audio(self):
return self.audio
def __len__(self):
return self.nb_frames
def get_frame_from_stream(self):
img_bytes = self.stream_reader.stdout.read(self.width * self.height * 3) # 3 bytes for one pixel
if not img_bytes:
return None
img = np.frombuffer(img_bytes, np.uint8).reshape([self.height, self.width, 3])
return img
def get_frame_from_list(self):
if self.idx >= self.nb_frames:
return None
img = cv2.imread(self.paths[self.idx])
self.idx += 1
return img
def get_frame(self):
if self.input_type.startswith('video'):
return self.get_frame_from_stream()
else:
return self.get_frame_from_list()
def close(self):
if self.input_type.startswith('video'):
self.stream_reader.stdin.close()
self.stream_reader.wait()
class Writer:
def __init__(self, args, audio, height, width, video_save_path, fps):
out_width, out_height = int(width * args.outscale), int(height * args.outscale)
if out_height > 2160:
print('You are generating video that is larger than 4K, which will be very slow due to IO speed.',
'We highly recommend to decrease the outscale(aka, -s).')
if audio is not None:
self.stream_writer = (
ffmpeg.input('pipe:', format='rawvideo', pix_fmt='bgr24', s=f'{out_width}x{out_height}',
framerate=fps).output(
audio,
video_save_path,
pix_fmt='yuv420p',
vcodec='libx264',
loglevel='error',
acodec='copy').overwrite_output().run_async(
pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin))
else:
self.stream_writer = (
ffmpeg.input('pipe:', format='rawvideo', pix_fmt='bgr24', s=f'{out_width}x{out_height}',
framerate=fps).output(
video_save_path, pix_fmt='yuv420p', vcodec='libx264',
loglevel='error').overwrite_output().run_async(
pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin))
def write_frame(self, frame):
frame = frame.astype(np.uint8).tobytes()
self.stream_writer.stdin.write(frame)
def close(self):
self.stream_writer.stdin.close()
self.stream_writer.wait()
def inference_video(args, video_save_path, device=None, total_workers=1, worker_idx=0):
# ---------------------- determine models according to model names ---------------------- #
args.model_name = args.model_name.split('.pth')[0]
if args.model_name == 'RealESRGAN_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
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
elif args.model_name == '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
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
elif args.model_name == '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
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
elif args.model_name == '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
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
elif args.model_name == 'realesr-animevideov3': # x4 VGG-style model (XS size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
elif args.model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
netscale = 4
file_url = [
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
]
# ---------------------- determine model paths ---------------------- #
model_path = os.path.join('weights', args.model_name + '.pth')
if not os.path.isfile(model_path):
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
for url in file_url:
# model_path will be updated
model_path = load_file_from_url(
url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
# use dni to control the denoise strength
dni_weight = None
if args.model_name == 'realesr-general-x4v3' and args.denoise_strength != 1:
wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
model_path = [model_path, wdn_model_path]
dni_weight = [args.denoise_strength, 1 - args.denoise_strength]
# restorer
upsampler = RealESRGANer(
scale=netscale,
model_path=model_path,
dni_weight=dni_weight,
model=model,
tile=args.tile,
tile_pad=args.tile_pad,
pre_pad=args.pre_pad,
half=not args.fp32,
device=device,
)
if 'anime' in args.model_name and args.face_enhance:
print('face_enhance is not supported in anime models, we turned this option off for you. '
'if you insist on turning it on, please manually comment the relevant lines of code.')
args.face_enhance = False
if args.face_enhance: # Use GFPGAN for face enhancement
from gfpgan import GFPGANer
face_enhancer = GFPGANer(
model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
upscale=args.outscale,
arch='clean',
channel_multiplier=2,
bg_upsampler=upsampler) # TODO support custom device
else:
face_enhancer = None
reader = Reader(args, total_workers, worker_idx)
audio = reader.get_audio()
height, width = reader.get_resolution()
fps = reader.get_fps()
writer = Writer(args, audio, height, width, video_save_path, fps)
pbar = tqdm(total=len(reader), unit='frame', desc='inference')
while True:
img = reader.get_frame()
if img is None:
break
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:
writer.write_frame(output)
torch.cuda.synchronize(device)
pbar.update(1)
reader.close()
writer.close()
def run(args):
args.video_name = osp.splitext(os.path.basename(args.input))[0]
video_save_path = osp.join(args.output, f'{args.video_name}_{args.suffix}.mp4')
if args.extract_frame_first:
tmp_frames_folder = osp.join(args.output, f'{args.video_name}_inp_tmp_frames')
os.makedirs(tmp_frames_folder, exist_ok=True)
os.system(f'ffmpeg -i {args.input} -qscale:v 1 -qmin 1 -qmax 1 -vsync 0 {tmp_frames_folder}/frame%08d.png')
args.input = tmp_frames_folder
num_gpus = torch.cuda.device_count()
num_process = num_gpus * args.num_process_per_gpu
if num_process == 1:
inference_video(args, video_save_path)
return
ctx = torch.multiprocessing.get_context('spawn')
pool = ctx.Pool(num_process)
os.makedirs(osp.join(args.output, f'{args.video_name}_out_tmp_videos'), exist_ok=True)
pbar = tqdm(total=num_process, unit='sub_video', desc='inference')
for i in range(num_process):
sub_video_save_path = osp.join(args.output, f'{args.video_name}_out_tmp_videos', f'{i:03d}.mp4')
pool.apply_async(
inference_video,
args=(args, sub_video_save_path, torch.device(i % num_gpus), num_process, i),
callback=lambda arg: pbar.update(1))
pool.close()
pool.join()
# combine sub videos
# prepare vidlist.txt
with open(f'{args.output}/{args.video_name}_vidlist.txt', 'w') as f:
for i in range(num_process):
f.write(f'file \'{args.video_name}_out_tmp_videos/{i:03d}.mp4\'\n')
cmd = [
args.ffmpeg_bin, '-f', 'concat', '-safe', '0', '-i', f'{args.output}/{args.video_name}_vidlist.txt', '-c',
'copy', f'{video_save_path}'
]
print(' '.join(cmd))
subprocess.call(cmd)
shutil.rmtree(osp.join(args.output, f'{args.video_name}_out_tmp_videos'))
if osp.exists(osp.join(args.output, f'{args.video_name}_inp_tmp_videos')):
shutil.rmtree(osp.join(args.output, f'{args.video_name}_inp_tmp_videos'))
os.remove(f'{args.output}/{args.video_name}_vidlist.txt')
def main():
"""Inference demo for Real-ESRGAN.
It mainly for restoring anime videos.
"""
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, default='inputs', help='Input video, image or folder')
parser.add_argument(
'-n',
'--model_name',
type=str,
default='realesr-animevideov3',
help=('Model names: realesr-animevideov3 | RealESRGAN_x4plus_anime_6B | RealESRGAN_x4plus | RealESRNet_x4plus |'
' RealESRGAN_x2plus | realesr-general-x4v3'
'Default:realesr-animevideov3'))
parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
parser.add_argument(
'-dn',
'--denoise_strength',
type=float,
default=0.5,
help=('Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. '
'Only used for the realesr-general-x4v3 model'))
parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored video')
parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
parser.add_argument(
'--fp32', action='store_true', help='Use fp32 precision during inference. Default: fp16 (half precision).')
parser.add_argument('--fps', type=float, default=None, help='FPS of the output video')
parser.add_argument('--ffmpeg_bin', type=str, default='ffmpeg', help='The path to ffmpeg')
parser.add_argument('--extract_frame_first', action='store_true')
parser.add_argument('--num_process_per_gpu', type=int, default=1)
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()
args.input = args.input.rstrip('/').rstrip('\\')
os.makedirs(args.output, exist_ok=True)
if mimetypes.guess_type(args.input)[0] is not None and mimetypes.guess_type(args.input)[0].startswith('video'):
is_video = True
else:
is_video = False
if is_video and args.input.endswith('.flv'):
mp4_path = args.input.replace('.flv', '.mp4')
os.system(f'ffmpeg -i {args.input} -codec copy {mp4_path}')
args.input = mp4_path
if args.extract_frame_first and not is_video:
args.extract_frame_first = False
run(args)
if args.extract_frame_first:
tmp_frames_folder = osp.join(args.output, f'{args.video_name}_inp_tmp_frames')
shutil.rmtree(tmp_frames_folder)
if __name__ == '__main__':
main()
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