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README.md
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@@ -78,7 +78,7 @@ python basicsr/setup.py develop
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### Quick Inference
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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.
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```
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python scripts/download_pretrained_models.py facelib
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python scripts/download_pretrained_models.py CodeFormer
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```
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You can put the testing images in the `inputs/TestWhole` folder. If you would like to test on cropped and aligned faces, you can put them in the `inputs/cropped_faces` folder.
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```
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# For cropped and aligned faces
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python inference_codeformer.py --w 0.5 --has_aligned --test_path [input folder]
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# For the whole images
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# Add '--bg_upsampler realesrgan' to enhance the background regions with Real-ESRGAN
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# Add '--face_upsample' to further upsample restorated face with Real-ESRGAN
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python inference_codeformer.py --w 0.7 --test_path [input folder]
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```
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The results will be saved in the `results` folder.
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@@ -127,4 +129,4 @@ This project is licensed under <a rel="license" href="https://github.com/sczhou/
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This project is based on [BasicSR](https://github.com/XPixelGroup/BasicSR). Some codes are brought from [Unleashing Transformers](https://github.com/samb-t/unleashing-transformers), [YOLOv5-face](https://github.com/deepcam-cn/yolov5-face), and [FaceXLib](https://github.com/xinntao/facexlib). We also adopt [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) to support background image enhancement. Thanks for their awesome works.
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### Contact
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If you have any question, please feel free to reach me out at `[email protected]`.
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### Quick Inference
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#### Download Pre-trained Models:
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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.
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```
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python scripts/download_pretrained_models.py facelib
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python scripts/download_pretrained_models.py CodeFormer
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```
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#### Prepare Testing Data:
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You can put the testing images in the `inputs/TestWhole` folder. If you would like to test on cropped and aligned faces, you can put them in the `inputs/cropped_faces` folder.
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#### Testing on Face Restoration:
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[Note] when comparing our model in your paper, please run the following command indicating `--has_aligned` (for cropped and aligned faces), as the command for the whole image will involve a process of face-background fusion that may damage hair texture on the boundary, which leads to unfair comparison.
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```
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# For cropped and aligned faces
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python inference_codeformer.py --w 0.5 --has_aligned --test_path [input folder]
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```
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```
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# For the whole images
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# Add '--bg_upsampler realesrgan' to enhance the background regions with Real-ESRGAN
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# Add '--face_upsample' to further upsample restorated face with Real-ESRGAN
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python inference_codeformer.py --w 0.7 --test_path [input folder]
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```
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Fidelity weight *w* lays in [0, 1]. Generally, smaller *w* tends to produce a higher-quality result, while larger *w* yields a higher-fidelity result.
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The results will be saved in the `results` folder.
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This project is based on [BasicSR](https://github.com/XPixelGroup/BasicSR). Some codes are brought from [Unleashing Transformers](https://github.com/samb-t/unleashing-transformers), [YOLOv5-face](https://github.com/deepcam-cn/yolov5-face), and [FaceXLib](https://github.com/xinntao/facexlib). We also adopt [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) to support background image enhancement. Thanks for their awesome works.
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### Contact
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If you have any question, please feel free to reach me out at `[email protected]`.
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