update README.md.

README.md

@@ -64,14 +64,12 @@ python basicsr/setup.py develop
 ### Quick Inference
 
 ##### Download Pre-trained Models:
-Download the facelib pretrained models from [[Google Drive](https://drive.google.com/drive/folders/1b_3qwrzY_kTQh0-SnBoGBgOrJ_PLZSKm?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EvDxR7FcAbZMp_MA9ouq7aQB8XTppMb3-T0uGZ_2anI2mg?e=DXsJFo)] to the `weights/facelib` folder. 
-You can download by run the following command OR manually download the pretrained models.
+Download the facelib pretrained models from [[Google Drive](https://drive.google.com/drive/folders/1b_3qwrzY_kTQh0-SnBoGBgOrJ_PLZSKm?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EvDxR7FcAbZMp_MA9ouq7aQB8XTppMb3-T0uGZ_2anI2mg?e=DXsJFo)] to the `weights/facelib` folder. You can manually download the pretrained models OR download by runing the following command.
 ```
 python scripts/download_pretrained_models.py facelib
 ```
 
-Download the CodeFormer pretrained models from [[Google Drive](https://drive.google.com/drive/folders/1CNNByjHDFt0b95q54yMVp6Ifo5iuU6QS?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EoKFj4wo8cdIn2-TY2IV6CYBhZ0pIG4kUOeHdPR_A5nlbg?e=AO8UN9)] to the `weights/CodeFormer` folder. 
-You can download by run the following command OR manually download the pretrained models.
+Download the CodeFormer pretrained models from [[Google Drive](https://drive.google.com/drive/folders/1CNNByjHDFt0b95q54yMVp6Ifo5iuU6QS?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EoKFj4wo8cdIn2-TY2IV6CYBhZ0pIG4kUOeHdPR_A5nlbg?e=AO8UN9)] to the `weights/CodeFormer` folder. You can manually download the pretrained models OR download by runing the following command.
 ```
 python scripts/download_pretrained_models.py CodeFormer
 ```

inference_codeformer.py

@@ -64,6 +64,7 @@ if __name__ == '__main__':
     net.load_state_dict(checkpoint)
     net.eval()
 
+    # ------------------ set up FaceRestoreHelper -------------------
     # large det_model: 'YOLOv5l', 'retinaface_resnet50'
     # small det_model: 'YOLOv5n', 'retinaface_mobile0.25'
     face_helper = FaceRestoreHelper(
@@ -75,9 +76,7 @@ if __name__ == '__main__':
         use_parse=True,
         device=device)
 
-
-    # face_helper.init_dlib(args.detection_path, args.landmark5_path, args.landmark68_path)
-
+    # -------------------- start to processing ---------------------
     # scan all the jpg and png images
     for img_path in sorted(glob.glob(os.path.join(args.test_path, '*.[jp][pn]g'))):
         # clean all the intermediate results to process the next image