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Towards Robust Blind Face Restoration with Codebook Lookup Transformer.

CodeFormer is an AI-powered face restoration tool that utilizes a codebook lookup transformer to enhance and restore degraded or blurry face images. It leverages a pre-trained model trained on a vast dataset of faces to accurately reconstruct facial details, even in low-resolution or heavily distorted images. The tool is designed to be robust against various types of degradation, including blur, noise, and compression artifacts. It supports both whole image enhancement and restoration of cropped and aligned faces. CodeFormer uses PyTorch and offers options for face detection using dlib. It can be integrated into various applications through its Python API and also supports video input for enhancing video quality. The architecture involves a transformer network that learns a codebook representation of faces, enabling it to generate high-fidelity reconstructions. CodeFormer has gained significant traction in both academic research and practical applications due to its effectiveness and ease of use.
CodeFormer is an AI-powered face restoration tool that utilizes a codebook lookup transformer to enhance and restore degraded or blurry face images.
Explore all tools that specialize in image enhancement. This domain focus ensures CodeFormer delivers optimized results for this specific requirement.
Uses a transformer architecture with a codebook to learn and reconstruct high-fidelity face details, robust to various degradations.
Option to integrate Real-ESRGAN for background upsampling and face upsampling, enhancing the overall image quality.
Utilizes dlib for accurate face detection, improving the precision of face cropping and alignment.
Ability to colorize black and white or faded photos of faces using a dedicated colorization script.
Supports face inpainting, allowing users to fill in missing or damaged parts of a face using an image editing application.
Install Pytorch >= 1.7.1 and CUDA >= 10.1.
Clone the CodeFormer repository from GitHub: `git clone https://github.com/sczhou/CodeFormer`.
Create a new Anaconda environment: `conda create -n codeformer python=3.8 -y`.
Activate the environment: `conda activate codeformer`.
Install Python dependencies: `pip3 install -r requirements.txt`.
Install basicsr: `python basicsr/setup.py develop`.
Install dlib (optional for face detection/cropping): `conda install -c conda-forge dlib`.
Download pre-trained models for facelib and dlib to the `weights/facelib` folder using `python scripts/download_pretrained_models.py facelib` and `python scripts/download_pretrained_models.py dlib`.
Download the CodeFormer pre-trained models to the `weights/CodeFormer` folder using `python scripts/download_pretrained_models.py CodeFormer`.
Prepare testing data by placing images in the `inputs/TestWhole` folder for whole images or `inputs/cropped_faces` for cropped faces.
Run inference using the appropriate script (e.g., `python inference_codeformer.py -w 0.7 --input_path [image path]` for whole image enhancement).
All Set
Ready to go
Verified feedback from other users.
"CodeFormer is highly regarded for its ability to restore and enhance faces with impressive accuracy and detail, although some users note the need for technical expertise to set up."
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