update README.md

README.md

@@ -5,30 +5,27 @@
 [![LICENSE](https://img.shields.io/github/license/xinntao/Real-ESRGAN.svg)](https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE)
 [![python lint](https://github.com/xinntao/Real-ESRGAN/actions/workflows/pylint.yml/badge.svg)](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/pylint.yml)
 
-We also provide a [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo) 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>.
+1. [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo) 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 executable file](https://github.com/xinntao/Real-ESRGAN/releases). You can find more information [here](#Portable-executable-files).
+
+Real-ESRGAN aims for **Practical Image Restoration**.<br>
+We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.
 
 ### :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
 
 > [[Paper](https://arxiv.org/abs/2107.10833)] &emsp; [Project Page] &emsp; [Demo] <br>
 > [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) <br>
-> Applied Research Center (ARC), Tencent PCG; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
+> Applied Research Center (ARC), Tencent PCG<br>
+> Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
 
 <p align="center">
   <img src="assets/teaser.jpg">
 </p>
 
-#### Abstract
-
-Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images. In this work, we extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data. Specifically, a high-order degradation modeling process is introduced to better simulate complex real-world degradations. We also consider the common ringing and overshoot artifacts in the synthesis process. In addition, we employ a U-Net discriminator with spectral normalization to increase discriminator capability and stabilize the training dynamics. Extensive comparisons have shown its superior visual performance than prior works on various real datasets. We also provide efficient implementations to synthesize training pairs on the fly.
-
-#### 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}
-    }
+We provide a pretrained model (*RealESRGAN_x4plus.pth*) with upsampling X4.<br>
+**Note that RealESRGAN may still fail in some cases as the real-world degradations are really too complex.**<br>
+Moreover, it **may not** perform well on **human faces, text**, *etc*, which will be optimized later.
+<br>
 
 ---
 
@@ -36,16 +33,20 @@ We are cleaning the training codes. It will be finished on 23 or 24, July.
 
 ---
 
+### Portable executable files
+
 You can download **Windows executable files** from https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN-ncnn-vulkan.zip
 
+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:
 ```bash
 ./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png
 ```
 
-Note that it may introduce block artifacts (and also generate slightly different results from the PyTorch implementation), because this executable file first crops the input image into several tiles, and then processes them separately, finally stitches together.
+Note that it may introduce block inconsistency (and also generate slightly different results from the PyTorch implementation), because this executable file first crops the input image into several tiles, and then processes them separately, finally stitches together.
 
-This executable file is based on the wonderful [ncnn project](https://github.com/Tencent/ncnn) and [realsr-ncnn-vulkan](https://github.com/nihui/realsr-ncnn-vulkan).
+This executable file is based on the wonderful [Tecent/ncnn](https://github.com/Tencent/ncnn) and [realsr-ncnn-vulkan](https://github.com/nihui/realsr-ncnn-vulkan) by [nihui](https://github.com/nihui).
 
 ---
 
@@ -69,7 +70,7 @@ This executable file is based on the wonderful [ncnn project](https://github.com
     # Install basicsr - https://github.com/xinntao/BasicSR
     # We use BasicSR for both training and inference
     pip install basicsr
-    pip install -r requirements.txt
+    # pip install -r requirements.txt
     ```
 
 ## :zap: Quick Inference
@@ -90,6 +91,15 @@ python inference_realesrgan.py --model_path experiments/pretrained_models/RealES
 
 Results are in the `results` folder
 
+## 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}
+    }
+
 ## :e-mail: Contact
 
 If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`.