diff --git a/README.md b/README.md
index 424fc1f..8058531 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@
[](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/publish-pip.yml)
1. [Colab Demo](https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing) for Real-ESRGAN .
-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).
+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).
Real-ESRGAN aims at developing **Practical Algorithms for General Image Restoration**.
We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.
@@ -19,6 +19,7 @@ We extend the powerful ESRGAN to a practical restoration application (namely, Re
:question: Frequently Asked Questions can be found in [FAQ.md](FAQ.md).
:triangular_flag_on_post: **Updates**
+- :white_check_mark: Add the ncnn implementation [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan).
- :white_check_mark: Add [*RealESRGAN_x4plus_anime_6B.pth*](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth), which is optimized for **anime** images with much smaller model size. More details and comparisons with [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan) are in [**anime_model.md**](docs/anime_model.md)
- :white_check_mark: Support finetuning on your own data or paired data (*i.e.*, finetuning ESRGAN). See [here](Training.md#Finetune-Real-ESRGAN-on-your-own-dataset)
- :white_check_mark: Integrate [GFPGAN](https://github.com/TencentARC/GFPGAN) to support **face enhancement**.
@@ -40,10 +41,9 @@ Other recommended projects:
### :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
-> [[Paper](https://arxiv.org/abs/2107.10833)] [Project Page] [Demo]
+> [[Paper](https://arxiv.org/abs/2107.10833)] [Project Page] [[YouTube Video](https://www.youtube.com/watch?v=fxHWoDSSvSc)] [[Poster](https://xinntao.github.io/projects/RealESRGAN_src/RealESRGAN_poster.pdf)] [[PPT slides](https://docs.google.com/presentation/d/1QtW6Iy8rm8rGLsJ0Ldti6kP-7Qyzy6XL/edit?usp=sharing&ouid=109799856763657548160&rtpof=true&sd=true)]
> [Xintao Wang](https://xinntao.github.io/), Liangbin Xie, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en)
-> Applied Research Center (ARC), Tencent PCG
-> Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
+> Tencent ARC Lab; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
@@ -164,25 +164,29 @@ Results are in the `results` folder
## :european_castle: Model Zoo
- [RealESRGAN_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth)
-- [RealESRGAN_x4plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth)
- [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)
-- [RealESRGAN_x4plus_anime_6B_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B_netD.pth)
-- [RealESRNet_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth)
- [RealESRGAN_x2plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth)
-- [RealESRGAN_x2plus_netD](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x2plus_netD.pth)
+- [RealESRNet_x4plus](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth)
+
- [official ESRGAN_x4](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth)
+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)
+
## :computer: Training and Finetuning on your own dataset
A detailed guide can be found in [Training.md](Training.md).
## BibTeX
- @Article{wang2021realesrgan,
- title={Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
- author={Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
- journal={arXiv:2107.10833},
- year={2021}
+ @InProceedings{wang2021realesrgan,
+ author = {Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
+ title = {Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
+ booktitle = {International Conference on Computer Vision Workshops (ICCVW)},
+ date = {2021}
}
## :e-mail: Contact