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update README: update installation

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Xintao 2021-07-18 01:50:06 +08:00
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@ -36,6 +36,7 @@ Blind face restoration usually relies on facial priors, such as facial geometry
- Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
- [PyTorch >= 1.7](https://pytorch.org/)
- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
- Linux (We have not tested on Windows)
### Installation
@ -48,11 +49,33 @@ Blind face restoration usually relies on facial priors, such as facial geometry
1. Install dependent packages
As StyleGAN2 uses customized PyTorch C++ extensions, you need to **compile them during installation** or **load then just-in-time(JIT)**.
You can refer to [BasicSR-INSTALL.md](https://github.com/xinntao/BasicSR/blob/master/INSTALL.md) for more details.
**Option 1: Load extensions just-in-time(JIT)** (For those just want to do simple inferences, may have less issues)
```bash
# Install basicsr - https://github.com/xinntao/BasicSR
# We use BasicSR for both training and inference
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
pip install -r requirements.txt
# remember to set BASICSR_JIT=True before your running commands
```
**Option 2: Compile extensions during installation** (For those need to train/inference for many times)
```bash
# Install basicsr - https://github.com/xinntao/BasicSR
# We use BasicSR for both training and inference
# Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient
BASICSR_EXT=True pip install basicsr
# Add -vvv for detailed log prints
BASICSR_EXT=True pip install basicsr -vvv
# Install facexlib - https://github.com/xinntao/facexlib
# We use face detection and face restoration helper in the facexlib package
@ -69,12 +92,23 @@ Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN
wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
```
```bash
python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs
- Option 1: Load extensions just-in-time(JIT)
# for aligned images
python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
```
```bash
BASICSR_JIT=True python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs
# for aligned images
BASICSR_JIT=True python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
```
- Option 2: Have successfully compiled extensions during installation
```bash
python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs
# for aligned images
python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
```
## :computer: Training
@ -94,6 +128,10 @@ You could improve it according to your own needs.
> python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch
or load extensions just-in-time(JIT)
> BASICSR_JIT=True python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch
## :scroll: License and Acknowledgement
GFPGAN is realeased under Apache License Version 2.0.