update README

README.md

@@ -5,7 +5,7 @@
 [![LICENSE](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/TencentARC/GFPGAN/blob/master/LICENSE)
 [![python lint](https://github.com/TencentARC/GFPGAN/actions/workflows/pylint.yml/badge.svg)](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/pylint.yml)
 
-1. [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo) for GFPGAN <a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><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 GFPGAN <a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>; (Another [Colab Demo](https://colab.research.google.com/drive/1Oa1WwKB4M4l1GmR7CtswDVgOCOeSLChA?usp=sharing) for the original paper model)
 1. We provide a *clean* version of GFPGAN, which can run without CUDA extensions. So that it can run in **Windows** or on **CPU mode**.
 
 GFPGAN aims at developing **Practical Algorithm for Real-world Face Restoration**.<br>
@@ -13,7 +13,7 @@ It leverages rich and diverse priors encapsulated in a pretrained face GAN (*e.g
 
 :triangular_flag_on_post: **Updates**
 
-- :white_check_mark: We provide a *clean* version of GFPGAN, which does not require CUDA extensionts.
+- :white_check_mark: We provide a *clean* version of GFPGAN, which does not require CUDA extensions.
 - :white_check_mark: We provide an updated model without colorizing faces.
 
 ### :book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior
@@ -77,8 +77,8 @@ python inference_gfpgan_full.py --upscale_factor 2 --test_path inputs/whole_imgs
 
 ## :european_castle: Model Zoo
 
-- [GFPGANCleanv1-NoCE-C2.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth)
-- [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
+- [GFPGANCleanv1-NoCE-C2.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth): No colorization; no CUDA extensions are required. It is still in training. Trained with more data with pre-processing.
+- [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth): The paper Model, with colorization.
 
 ## :computer: Training