做网站创业,如何查询网站服务器地址,杭州网站建设服务公司,湖南发展最新消息公告Python Anaconda 的安装等请参阅#xff1a;
Python开源项目CodeFormer——人脸重建#xff08;Face Restoration#xff09;#xff0c;模糊清晰、划痕修复及黑白上色的实践https://blog.csdn.net/beijinghorn/article/details/134334021
VQFR也是 腾讯 LAB 的作品…Python Anaconda 的安装等请参阅
Python开源项目CodeFormer——人脸重建Face Restoration模糊清晰、划痕修复及黑白上色的实践https://blog.csdn.net/beijinghorn/article/details/134334021
VQFR也是 腾讯 LAB 的作品比较忠于德国 VQGAN 思想的速度虽然慢一点效果凑合。用于修正国人脸效果一般。代码比较精炼笔者用 Python2Sharp 转为 C/C# 复现速度与效果尚可。 6 VQFR (ECCV 2022 Oral) https://github.com/TencentARC/VQFR https://github.com/TencentARC/VQFR/releases 模型下载
download Open issue Closed issue LICENSE google colab logo
Colab Demo for VQFR https://colab.research.google.com/drive/1Nd_PUrHaYmeEAOF5f_Zi0VuOxlJ62gLr?uspsharing Online demo: Replicate.ai (may need to sign in, return the whole image) https://replicate.com/tencentarc/vqfr
6.1 进化史Updates 2022.10.16 Clean research codes Update VQFR-v2. In this version, we emphasize the restoration quality of the texture branch and balance fidelity with user control. google colab logo Support enhancing non-face regions (background) with Real-ESRGAN. The Colab Demo of VQFR is created. The training/inference codes and pretrained models in paper are released.
This paper aims at investigating the potential and limitation of Vector-Quantized (VQ) dictionary for blind face restoration. We propose a new framework VQFR – incoporating the Vector-Quantized Dictionary and the Parallel Decoder. Compare with previous arts, VQFR produces more realistic facial details and keep the comparable fidelity.
VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder [Paper] [Project Page] [Video] [B站] [Poster] [Slides] Yuchao Gu, Xintao Wang, Liangbin Xie, Chao Dong, Gen Li, Ying Shan, Ming-Ming Cheng Nankai University; Tencent ARC Lab; Tencent Online Video; Shanghai AI Laboratory; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Release
去这里下载模型。 6.2 依赖项与安装Dependencies and Installation 6.2.1 依赖项Dependencies Python 3.7 (Recommend to use Anaconda or Miniconda) PyTorch 1.7 Option: NVIDIA GPU CUDA Option: Linux
6.2.2 安装Installation 6.2.2.1 Clone repo
git clone https://github.com/TencentARC/VQFR.git cd VQFR
6.2.2.2 Install dependent packages
# Build VQFR with extension pip install -r requirements.txt VQFR_EXTTrue python setup.py develop
# Following packages are required to run demo.py
# Install basicsr - https://github.com/xinntao/BasicSR pip install basicsr
# Install facexlib - https://github.com/xinntao/facexlib # We use face detection and face restoration helper in the facexlib package pip install facexlib
# If you want to enhance the background (non-face) regions with Real-ESRGAN, # you also need to install the realesrgan package pip install realesrgan
6.3 快速指南Quick Inference Download pre-trained VQFRv1/v2 models [Google Drive]. https://drive.google.com/drive/folders/1lczKYEbARwe27FJlKoFdng7UnffGDjO2?uspsharing
https://github.com/TencentARC/VQFR/releases/download/v2.0.0/VQFR_v2.pth https://github.com/TencentARC/VQFR/releases/download/v2.0.0/VQ_Codebook_FFHQ512_v2.pth
Inference
# for real-world image python demo.py -i inputs/whole_imgs -o results -v 2.0 -s 2 -f 0.1
# for cropped face python demo.py -i inputs/cropped_faces/ -o results -v 2.0 -s 1 -f 0.1 --aligned Usage: python demo.py -i inputs/whole_imgs -o results -v 2.0 -s 2 -f 0.1 [options]... -h show this help -i input Input image or folder. Default: inputs/whole_imgs -o output Output folder. Default: results -v version VQFR model version. Option: 1.0. Default: 1.0 -f fidelity_ratio VQFRv2 model supports user control fidelity ratio, range from [0,1]. 0 for the best quality and 1 for the best fidelity. Default: 0 -s upscale The final upsampling scale of the image. Default: 2 -bg_upsampler background upsampler. Default: realesrgan -bg_tile Tile size for background sampler, 0 for no tile during testing. Default: 400 -suffix Suffix of the restored faces -only_center_face Only restore the center face -aligned Input are aligned faces -ext Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto
6.4 自我训练Training We provide the training codes for VQFR (used in our paper).
Dataset preparation: FFHQ https://github.com/NVlabs/ffhq-dataset
Download lpips weights [Google Drive] into experiments/pretrained_models/ https://drive.google.com/drive/folders/1weXfn5mdIwp2dEfDbNNUkauQgo8fx-2D?uspsharing
Codebook Training
Pre-train VQ codebook on FFHQ datasets. python -m torch.distributed.launch --nproc_per_node8 --master_port2022 vqfr/train.py -opt options/train/VQGAN/train_vqgan_v1_B16_800K.yml --launcher pytorch Or download our pretrained VQ codebook Google Drive and put them in the experiments/pretrained_models folder. https://drive.google.com/drive/folders/1lczKYEbARwe27FJlKoFdng7UnffGDjO2?uspsharing
Restoration Training
Modify the configuration file options/train/VQFR/train_vqfr_v1_B16_200K.yml accordingly.
Training
python -m torch.distributed.launch --nproc_per_node8 --master_port2022 vqfr/train.py -opt options/train/VQFR/train_vqfr_v1_B16_200K.yml --launcher pytorch
6.5 评估Evaluation We evaluate VQFR on one synthetic dataset CelebA-Test, and three real-world datasets LFW-Test, CelebChild and Webphoto-Test. For reproduce our evaluation results, you need to perform the following steps:
1 Download testing datasets (or VQFR results) by the following links: Name Datasets Short Description Download VQFR Results Testing Datasets CelebA-Test(LQ/HQ) 3000 (LQ, HQ) synthetic images for testing Google Drive Google Drive LFW-Test(LQ) 1711 real-world images for testing CelebChild(LQ) 180 real-world images for testing Webphoto-Test(LQ) 469 real-world images for testing
2 Install related package and download pretrained models for different metrics: # LPIPS pip install lpips # Deg. cd metric_paper/ git clone https://github.com/ronghuaiyang/arcface-pytorch.git mv arcface-pytorch/ arcface/ rm arcface/config/__init__.py arcface/models/__init__.py # put pretrained models of different metrics to experiments/pretrained_models/metric_weights/
Metrics Pretrained Weights Download FID inception_FFHQ_512.pth Google Drive Deg resnet18_110.pth LMD alignment_WFLW_4HG.pth
Generate restoration results: Specify the dataset_lq/dataset_gt to the testing dataset root in test_vqfr_v1.yml.
Then run the following command: python vqfr/test.py -opt options/test/VQFR/test_vqfr_v1.yml
Run evaluation: # LPIPS|PSNR/SSIM|LMD|Deg. python metric_paper/[calculate_lpips.py|calculate_psnr_ssim.py|calculate_landmark_distance.py|calculate_cos_dist.py] -restored_folder folder_to_results -gt_folder folder_to_gt # FID|NIQE python metric_paper/[calculate_fid_folder.py|calculate_niqe.py] -restored_folder folder_to_results
6.6 权利License VQFR is released under Apache License Version 2.0. 6.7 知识Acknowledgement Thanks to the following open-source projects:
Taming-transformers https://github.com/CompVis/taming-transformers GFPGAN https://github.com/TencentARC/GFPGAN DistSup https://github.com/distsup/DistSup
6.8 引用Citation inproceedings{gu2022vqfr, title{VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder}, author{Gu, Yuchao and Wang, Xintao and Xie, Liangbin and Dong, Chao and Li, Gen and Shan, Ying and Cheng, Ming-Ming}, year{2022}, booktitle{ECCV} }
6.9 联系Contact If you have any question, please email yuchaogu9710gmail.com.
