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77 lines
2.8 KiB
Markdown
77 lines
2.8 KiB
Markdown
# Installation
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We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions. See [here](README.md#installation) for this easier installation.<br>
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If you want want to use the original model in our paper, please follow the instructions below.
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1. Clone repo
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```bash
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git clone https://github.com/xinntao/GFPGAN.git
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cd GFPGAN
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```
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1. Install dependent packages
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As StyleGAN2 uses customized PyTorch C++ extensions, you need to **compile them during installation** or **load them just-in-time(JIT)**.
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You can refer to [BasicSR-INSTALL.md](https://github.com/xinntao/BasicSR/blob/master/INSTALL.md) for more details.
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**Option 1: Load extensions just-in-time(JIT)** (For those just want to do simple inferences, may have less issues)
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```bash
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# Install basicsr - https://github.com/xinntao/BasicSR
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# We use BasicSR for both training and inference
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pip install basicsr
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# Install facexlib - https://github.com/xinntao/facexlib
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# We use face detection and face restoration helper in the facexlib package
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pip install facexlib
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pip install -r requirements.txt
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python setup.py develop
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# remember to set BASICSR_JIT=True before your running commands
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```
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**Option 2: Compile extensions during installation** (For those need to train/inference for many times)
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```bash
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# Install basicsr - https://github.com/xinntao/BasicSR
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# We use BasicSR for both training and inference
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# Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient
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# Add -vvv for detailed log prints
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BASICSR_EXT=True pip install basicsr -vvv
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# Install facexlib - https://github.com/xinntao/facexlib
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# We use face detection and face restoration helper in the facexlib package
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pip install facexlib
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pip install -r requirements.txt
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python setup.py develop
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```
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## :zap: Quick Inference
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Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
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```bash
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wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
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```
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- Option 1: Load extensions just-in-time(JIT)
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```bash
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BASICSR_JIT=True python inference_gfpgan.py --input inputs/whole_imgs --output results --version 1
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# for aligned images
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BASICSR_JIT=True python inference_gfpgan.py --input inputs/whole_imgs --output results --version 1 --aligned
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```
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- Option 2: Have successfully compiled extensions during installation
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```bash
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python inference_gfpgan.py --input inputs/whole_imgs --output results --version 1
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# for aligned images
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python inference_gfpgan.py --input inputs/whole_imgs --output results --version 1 --aligned
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```
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