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RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs

paper_RestroeForemer++   paere_RestroeForemer   code_RestroeForemer++   code_RestroeForemer   demo

This repo is an official implementation of "RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs".

RestoreFormer++ is an extension of our RestoreFormer. It proposes to restore a degraded face image with both fidelity and realness by using the powerful fully-spacial attention mechanisms to model the abundant contextual information in the face and its interplay with our reconstruction-oriented high-quality priors. Besides, it introduces an extending degrading model (EDM) that contains more realistic degraded scenarios for training data synthesizing, which helps to enhance its robustness and generalization towards real-world scenarios. Our results compared with the state-of-the-art methods and performance with/without EDM are in following:

images/fig1.png

images/fig3.png

ToDo List

  • 20240122 Provided local gradio demo.
  • 20230915 Update an online demo Huggingface Gradio
  • 20230915 Provide a user-friendly method for inference.
    • It is avaliable for background SR with RealESRGAN.
    • basicsr should be upgraded to 1.4.2.
  • 20230914 Upload model
  • 20230914 Realse Code
  • 20221120 Introducing the project.

Environment

  • python>=3.7
  • pytorch>=1.7.1
  • pytorch-lightning==1.0.8
  • omegaconf==2.0.0
  • basicsr==1.3.3.4 basicsr>=1.4.2
  • realesrgan==0.3.0
pip install -r RF_requirements.txt

❗❗❗ Warning Different versions of pytorch-lightning and omegaconf may lead to errors or different results.

Preparations of dataset and models

Dataset:

  • Training data: Both ROHQD and RestoreFormer++ in our work are trained with FFHQ which attained from FFHQ repository. The original size of the images in FFHQ are 1024x1024. We resize them to 512x512 with bilinear interpolation in our work. Link this dataset to ./data/FFHQ/image512x512.
  • Test data: CelebA-Test, LFW-Test, WebPhoto-Test, and CelebChild-Test

Model: Both pretrained models used for training and the trained model of our RestoreFormer and RestoreFormer++ can be attained from Google Driver. Link these models to ./experiments.

Gradio Demo

python gradio_demo/app.py

Quick Inference

python inference.py -i data/aligned -o results/RF++/aligned -v RestoreFormer++ -s 2 --aligned --save
python inference.py -i data/raw -o results/RF++/raw -v RestoreFormer++ -s 2 --save
python inference.py -i data/aligned -o results/RF/aligned -v RestoreFormer -s 2 --aligned --save
python inference.py -i data/raw -o results/RF/raw -v RestoreFormer -s 2 --save

Note: Related codes are borrowed from GFPGAN.

Test

sh scripts/test.sh

scripts/test.sh

exp_name='RestoreFormer'
exp_name='RestoreFormerPlusPlus'

root_path='experiments'
out_root_path='results'
align_test_path='data/aligned'
# unalign_test_path='data/raw'
tag='test'

outdir=$out_root_path'/'$exp_name'_'$tag

if [ ! -d $outdir ];then
    mkdir -m 777 $outdir
fi

CUDA_VISIBLE_DEVICES=0 python -u scripts/test.py \
--outdir $outdir \
-r $root_path'/'$exp_name'/last.ckpt' \
-c 'configs/'$exp_name'.yaml' \
--test_path $align_test_path \
--aligned
  • This codebase is available for both RestoreFormer and RestoreFormerPlusPlus. Determinate the specific model with exp_name.
  • Setting the model path with root_path
  • Restored results are save in out_root_path
  • Put the degraded face images in test_path
  • If the degraded face images are aligned, set --aligned, else remove it from the script. The provided test images in data/aligned are aligned, while images in data/raw are unaligned and contain several faces.

Training

sh scripts/run.sh

scripts/run.sh

export BASICSR_JIT=True

# For RestoreFormer
# conf_name='HQ_Dictionary'
# conf_name='RestoreFormer'

# For RestoreFormer++
conf_name='ROHQD'
conf_name='RestoreFormerPlusPlus'

# gpus='0,1,2,3,4,5,6,7'
# node_n=1
# ntasks_per_node=8

root_path='PATH_TO_CHECKPOINTS'

gpus='0,'
node_n=1
ntasks_per_node=1

gpu_n=$(expr $node_n \* $ntasks_per_node)

python -u main.py \
--root-path $root_path \
--base 'configs/'$conf_name'.yaml' \
-t True \
--postfix $conf_name'_gpus'$gpu_n \
--gpus $gpus \
--num-nodes $node_n \
--random-seed True \
  • This codebase is available for both RestoreFormer and RestoreFormerPlusPlus. Determinate the training model with conf_name. 'HQ_Dictionary' and 'RestoreFormer' are for RestoreFormer, while 'ROHQD' and 'RestoreFormerPlusPlus' are for RestoreFormerPlusPlus.
  • While training 'RestoreFormer' or 'RestoreFormerPlusPlus', 'ckpt_path' in the corresponding configure files in configs/ sholud be updated with the path of the trained model of 'HQ_Dictionary' or 'ROHQD'.

Metrics

sh scripts/metrics/run.sh

Note.

  • You need to add the path of CelebA-Test dataset in the script if you want get IDD, PSRN, SSIM, LIPIS.
  • Related metric models are in ./experiments/pretrained_models/

Citation

@article{wang2023restoreformer++,
  title={RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs},
  author={Wang, Zhouxia and Zhang, Jiawei and Chen, Tianshui and Wang, Wenping and Luo, Ping},
  booktitle={IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)},
  year={2023}
}

@article{wang2022restoreformer,
  title={RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs},
  author={Wang, Zhouxia and Zhang, Jiawei and Chen, Runjian and Wang, Wenping and Luo, Ping},
  booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

Contact

For any question, feel free to email [email protected] or [email protected].