AI 修复模糊图片



cd C:\Windows\System32\CodeFormer

python -w 0.5 –has_aligned –input_path C:\Windows\System32\CodeFormer\inputs\cropped_faces


python -w 0.5 --has_aligned --input_path空格C:\Windows\System32\CodeFormer\inputs\cropped_faces\

Dependencies and Installation

  • Pytorch >= 1.7.1
  • CUDA >= 10.1
  • Other required packages in requirements.txt
# git clone this repository
git clone
cd CodeFormer

# create new anaconda env
conda create -n codeformer python=3.8 -y
conda activate codeformer

# install python dependencies
pip3 install -r requirements.txt
python basicsr/ develop
conda install -c conda-forge dlib (only for face detection or cropping with dlib)

Quick Inference

Download Pre-trained Models:

Download the facelib and dlib pretrained models from [Releases | Google Drive | OneDrive] to the weights/facelib folder. You can manually download the pretrained models OR download by running the following command:

python scripts/ facelib
python scripts/ dlib (only for dlib face detector)

Download the CodeFormer pretrained models from [Releases | Google Drive | OneDrive] to the weights/CodeFormer folder. You can manually download the pretrained models OR download by running the following command:

python scripts/ CodeFormer

Prepare Testing Data:

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. You can get the cropped and aligned faces by running the following command:

# you may need to install dlib via: conda install -c conda-forge dlib
python scripts/ -i [input folder] -o [output folder]


[Note] If you want to compare CodeFormer in your paper, please run the following command indicating --has_aligned (for cropped and aligned face), 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.

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. The results will be saved in the results folder.

🧑🏻 Face Restoration (cropped and aligned face)

# For cropped and aligned faces (512x512)
python -w 0.5 --has_aligned --input_path [image folder]|[image path]

🖼️ Whole Image Enhancement

# For whole image
# Add '--bg_upsampler realesrgan' to enhance the background regions with Real-ESRGAN
# Add '--face_upsample' to further upsample restorated face with Real-ESRGAN
python -w 0.7 --input_path [image folder]|[image path]

🎬 Video Enhancement

# For Windows/Mac users, please install ffmpeg first
conda install -c conda-forge ffmpeg
# For video clips
# Video path should end with '.mp4'|'.mov'|'.avi'
python --bg_upsampler realesrgan --face_upsample -w 1.0 --input_path

🌈 Face Colorization (cropped and aligned face)

# For cropped and aligned faces (512x512)
# Colorize black and white or faded photo
python --input_path [image folder]|[image path]

🎨 Face Inpainting (cropped and aligned face)

# For cropped and aligned faces (512x512)
# Inputs could be masked by white brush using an image editing app (e.g., Photoshop) 
# (check out the examples in inputs/masked_faces)
python --input_path [image folder]|[image path]


The training commands can be found in the documents: English | 简体中文.


If our work is useful for your research, please consider citing:

    author = {Zhou, Shangchen and Chan, Kelvin C.K. and Li, Chongyi and Loy, Chen Change},
    title = {Towards Robust Blind Face Restoration with Codebook Lookup TransFormer},
    booktitle = {NeurIPS},
    year = {2022}


This project is licensed under NTU S-Lab License 1.0. Redistribution and use should follow this license.


This project is based on BasicSR. Some codes are brought from Unleashing TransformersYOLOv5-face, and FaceXLib. We also adopt Real-ESRGAN to support background image enhancement. Thanks for their awesome works.


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