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401cf51a73
74
PaperModel.md
Normal file
74
PaperModel.md
Normal file
@ -0,0 +1,74 @@
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# Installation
|
||||
|
||||
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>
|
||||
If you want want to use the original model in our paper, please follow the instructions below.
|
||||
|
||||
1. Clone repo
|
||||
|
||||
```bash
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git clone https://github.com/xinntao/GFPGAN.git
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cd GFPGAN
|
||||
```
|
||||
|
||||
1. Install dependent packages
|
||||
|
||||
As StyleGAN2 uses customized PyTorch C++ extensions, you need to **compile them during installation** or **load them 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
|
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pip install basicsr
|
||||
|
||||
# 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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||||
|
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pip install -r requirements.txt
|
||||
|
||||
# remember to set BASICSR_JIT=True before your running commands
|
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```
|
||||
|
||||
**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
|
||||
# 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
|
||||
pip install facexlib
|
||||
|
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pip install -r requirements.txt
|
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```
|
||||
|
||||
## :zap: Quick Inference
|
||||
|
||||
Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
|
||||
|
||||
```bash
|
||||
wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
|
||||
```
|
||||
|
||||
- Option 1: Load extensions just-in-time(JIT)
|
||||
|
||||
```bash
|
||||
BASICSR_JIT=True python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
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||||
|
||||
# for aligned images
|
||||
BASICSR_JIT=True python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1 --aligned
|
||||
```
|
||||
|
||||
- Option 2: Have successfully compiled extensions during installation
|
||||
|
||||
```bash
|
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python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
|
||||
|
||||
# for aligned images
|
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python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1 --aligned
|
||||
```
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110
README.md
110
README.md
@ -5,31 +5,23 @@
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[](https://github.com/TencentARC/GFPGAN/blob/master/LICENSE)
|
||||
[](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/pylint.yml)
|
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|
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[**Paper**](https://arxiv.org/abs/2101.04061) **|** [**Project Page**](https://xinntao.github.io/projects/gfpgan)    [English](README.md) **|** [简体中文](README_CN.md)
|
||||
1. [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo) for GFPGAN <a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
|
||||
1. We provide a *clean* version of GFPGAN, which can run without CUDA extensions. So that it can run in **Windows** or on **CPU mode**.
|
||||
|
||||
GFPGAN is a blind face restoration algorithm towards real-world face images.
|
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GFPGAN aims at developing **Practical Algorithm for Real-world Face Restoration**.<br>
|
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It leverages rich and diverse priors encapsulated in a pretrained face GAN (*e.g.*, StyleGAN2) for blind face restoration.
|
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|
||||
<a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
|
||||
[Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo)
|
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:triangular_flag_on_post: **Updates**
|
||||
|
||||
- :white_check_mark: We provide a *clean* version of GFPGAN, which does not require CUDA extensionts.
|
||||
- :white_check_mark: We provide an updated model without colorizing faces.
|
||||
|
||||
### :book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior
|
||||
|
||||
> [[Paper](https://arxiv.org/abs/2101.04061)]   [[Project Page](https://xinntao.github.io/projects/gfpgan)]   [Demo] <br>
|
||||
> [Xintao Wang](https://xinntao.github.io/), [Yu Li](https://yu-li.github.io/), [Honglun Zhang](https://scholar.google.com/citations?hl=en&user=KjQLROoAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en) <br>
|
||||
> Applied Research Center (ARC), Tencent PCG
|
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|
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#### Abstract
|
||||
|
||||
Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages **rich and diverse priors encapsulated in a pretrained face GAN** for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.
|
||||
|
||||
#### BibTeX
|
||||
|
||||
@InProceedings{wang2021gfpgan,
|
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author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan},
|
||||
title = {Towards Real-World Blind Face Restoration with Generative Facial Prior},
|
||||
booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
|
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year = {2021}
|
||||
}
|
||||
|
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<p align="center">
|
||||
<img src="https://xinntao.github.io/projects/GFPGAN_src/gfpgan_teaser.jpg">
|
||||
</p>
|
||||
@ -40,25 +32,23 @@ 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/)
|
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- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
|
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- Linux (We have not tested on Windows)
|
||||
- Option: NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
|
||||
- Option: Linux (We have not tested on Windows)
|
||||
|
||||
### Installation
|
||||
|
||||
We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions. <br>
|
||||
If you want want to use the original model in our paper, please see [PaperModel.md](PaperModel.md) for installation.
|
||||
|
||||
1. Clone repo
|
||||
|
||||
```bash
|
||||
git clone https://github.com/xinntao/GFPGAN.git
|
||||
git clone https://github.com/TencentARC/GFPGAN.git
|
||||
cd GFPGAN
|
||||
```
|
||||
|
||||
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
|
||||
@ -68,58 +58,43 @@ Blind face restoration usually relies on facial priors, such as facial geometry
|
||||
# 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
|
||||
# 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
|
||||
pip install facexlib
|
||||
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## :zap: Quick Inference
|
||||
|
||||
Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
|
||||
Download pre-trained models: [GFPGANCleanv1-NoCE-C2.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth)
|
||||
|
||||
```bash
|
||||
wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
|
||||
wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P experiments/pretrained_models
|
||||
```
|
||||
|
||||
- Option 1: Load extensions just-in-time(JIT)
|
||||
**Inference!**
|
||||
|
||||
```bash
|
||||
BASICSR_JIT=True python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs
|
||||
```bash
|
||||
python inference_gfpgan_full.py --upscale_factor 2 --test_path inputs/whole_imgs --save_root results
|
||||
```
|
||||
|
||||
# for aligned images
|
||||
BASICSR_JIT=True python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
|
||||
```
|
||||
## :european_castle: Model Zoo
|
||||
|
||||
- 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
|
||||
```
|
||||
- [GFPGANCleanv1-NoCE-C2.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth)
|
||||
- [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
|
||||
|
||||
## :computer: Training
|
||||
|
||||
We provide complete training codes for GFPGAN. <br>
|
||||
We provide the training codes for GFPGAN (used in our paper). <br>
|
||||
You could improve it according to your own needs.
|
||||
|
||||
**Tips**
|
||||
|
||||
1. More high quality faces can improve the restoration quality.
|
||||
2. You may need to perform some pre-processing, such as beauty makeup.
|
||||
|
||||
|
||||
**Procedures**
|
||||
|
||||
(You can try a simple version ( `train_gfpgan_v1_simple.yml`) that does not require face component landmarks.)
|
||||
|
||||
1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset)
|
||||
|
||||
1. Download pre-trained models and other data. Put them in the `experiments/pretrained_models` folder.
|
||||
@ -133,13 +108,18 @@ 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.
|
||||
GFPGAN is released under Apache License Version 2.0.
|
||||
|
||||
## BibTeX
|
||||
|
||||
@InProceedings{wang2021gfpgan,
|
||||
author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan},
|
||||
title = {Towards Real-World Blind Face Restoration with Generative Facial Prior},
|
||||
booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
|
||||
year = {2021}
|
||||
}
|
||||
|
||||
## :e-mail: Contact
|
||||
|
||||
|
||||
103
README_CN.md
103
README_CN.md
@ -1,103 +0,0 @@
|
||||
# GFPGAN (CVPR 2021)
|
||||
|
||||
[**Paper**](https://arxiv.org/abs/2101.04061) **|** [**Project Page**](https://xinntao.github.io/projects/gfpgan)    [English](README.md) **|** [简体中文](README_CN.md)
|
||||
|
||||
GFPGAN is a blind face restoration algorithm towards real-world face images.
|
||||
|
||||
<a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
|
||||
[Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo)
|
||||
|
||||
### :book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior
|
||||
> [[Paper](https://arxiv.org/abs/2101.04061)]   [[Project Page](https://xinntao.github.io/projects/gfpgan)]   [Demo] <br>
|
||||
> [Xintao Wang](https://xinntao.github.io/), [Yu Li](https://yu-li.github.io/), [Honglun Zhang](https://scholar.google.com/citations?hl=en&user=KjQLROoAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en) <br>
|
||||
> Applied Research Center (ARC), Tencent PCG
|
||||
|
||||
#### Abstract
|
||||
|
||||
Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages **rich and diverse priors encapsulated in a pretrained face GAN** for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.
|
||||
|
||||
#### BibTeX
|
||||
|
||||
@InProceedings{wang2021gfpgan,
|
||||
author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan},
|
||||
title = {Towards Real-World Blind Face Restoration with Generative Facial Prior},
|
||||
booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
|
||||
year = {2021}
|
||||
}
|
||||
|
||||
<p align="center">
|
||||
<img src="https://xinntao.github.io/projects/GFPGAN_src/gfpgan_teaser.jpg">
|
||||
</p>
|
||||
|
||||
---
|
||||
|
||||
## :wrench: Dependencies and Installation
|
||||
|
||||
- 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)
|
||||
|
||||
### Installation
|
||||
|
||||
1. Clone repo
|
||||
|
||||
```bash
|
||||
git clone https://github.com/xinntao/GFPGAN.git
|
||||
cd GFPGAN
|
||||
```
|
||||
|
||||
1. Install dependent packages
|
||||
|
||||
```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
|
||||
|
||||
# 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
|
||||
```
|
||||
|
||||
## :zap: Quick Inference
|
||||
|
||||
Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
|
||||
|
||||
```bash
|
||||
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
|
||||
|
||||
# for aligned images
|
||||
python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
|
||||
```
|
||||
|
||||
## :computer: Training
|
||||
|
||||
We provide complete training codes for GFPGAN. <br>
|
||||
You could improve it according to your own needs.
|
||||
|
||||
1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset)
|
||||
|
||||
1. Download pre-trained models and other data. Put them in the `experiments/pretrained_models` folder.
|
||||
1. [Pretrained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth)
|
||||
1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth)
|
||||
1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth)
|
||||
|
||||
1. Modify the configuration file `train_gfpgan_v1.yml` accordingly.
|
||||
|
||||
1. Training
|
||||
|
||||
> 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.
|
||||
|
||||
## :e-mail: Contact
|
||||
|
||||
If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
|
||||
@ -34,6 +34,12 @@ datasets:
|
||||
color_jitter_pt_prob: 0.3
|
||||
gray_prob: 0.01
|
||||
|
||||
# If you do not want colorization, please set
|
||||
# color_jitter_prob: ~
|
||||
# color_jitter_pt_prob: ~
|
||||
# gray_prob: 0.01
|
||||
# gt_gray: True
|
||||
|
||||
crop_components: true
|
||||
component_path: experiments/pretrained_models/FFHQ_eye_mouth_landmarks_512.pth
|
||||
eye_enlarge_ratio: 1.4
|
||||
@ -42,7 +48,7 @@ datasets:
|
||||
use_shuffle: true
|
||||
num_worker_per_gpu: 6
|
||||
batch_size_per_gpu: 3
|
||||
dataset_enlarge_ratio: 100
|
||||
dataset_enlarge_ratio: 1
|
||||
prefetch_mode: ~
|
||||
|
||||
val:
|
||||
|
||||
216
train_gfpgan_v1_simple.yml
Normal file
216
train_gfpgan_v1_simple.yml
Normal file
@ -0,0 +1,216 @@
|
||||
# general settings
|
||||
name: train_GFPGANv1_512_simple
|
||||
model_type: GFPGANModel
|
||||
num_gpu: 4
|
||||
manual_seed: 0
|
||||
|
||||
# dataset and data loader settings
|
||||
datasets:
|
||||
train:
|
||||
name: FFHQ
|
||||
type: FFHQDegradationDataset
|
||||
# dataroot_gt: datasets/ffhq/ffhq_512.lmdb
|
||||
dataroot_gt: datasets/ffhq/ffhq_512
|
||||
io_backend:
|
||||
# type: lmdb
|
||||
type: disk
|
||||
|
||||
use_hflip: true
|
||||
mean: [0.5, 0.5, 0.5]
|
||||
std: [0.5, 0.5, 0.5]
|
||||
out_size: 512
|
||||
|
||||
blur_kernel_size: 41
|
||||
kernel_list: ['iso', 'aniso']
|
||||
kernel_prob: [0.5, 0.5]
|
||||
blur_sigma: [0.1, 10]
|
||||
downsample_range: [0.8, 8]
|
||||
noise_range: [0, 20]
|
||||
jpeg_range: [60, 100]
|
||||
|
||||
# color jitter and gray
|
||||
color_jitter_prob: 0.3
|
||||
color_jitter_shift: 20
|
||||
color_jitter_pt_prob: 0.3
|
||||
gray_prob: 0.01
|
||||
|
||||
# If you do not want colorization, please set
|
||||
# color_jitter_prob: ~
|
||||
# color_jitter_pt_prob: ~
|
||||
# gray_prob: 0.01
|
||||
# gt_gray: True
|
||||
|
||||
# crop_components: false
|
||||
# component_path: experiments/pretrained_models/FFHQ_eye_mouth_landmarks_512.pth
|
||||
# eye_enlarge_ratio: 1.4
|
||||
|
||||
# data loader
|
||||
use_shuffle: true
|
||||
num_worker_per_gpu: 6
|
||||
batch_size_per_gpu: 3
|
||||
dataset_enlarge_ratio: 1
|
||||
prefetch_mode: ~
|
||||
|
||||
val:
|
||||
# Please modify accordingly to use your own validation
|
||||
# Or comment the val block if do not need validation during training
|
||||
name: validation
|
||||
type: PairedImageDataset
|
||||
dataroot_lq: datasets/faces/validation/input
|
||||
dataroot_gt: datasets/faces/validation/reference
|
||||
io_backend:
|
||||
type: disk
|
||||
mean: [0.5, 0.5, 0.5]
|
||||
std: [0.5, 0.5, 0.5]
|
||||
scale: 1
|
||||
|
||||
# network structures
|
||||
network_g:
|
||||
type: GFPGANv1
|
||||
out_size: 512
|
||||
num_style_feat: 512
|
||||
channel_multiplier: 1
|
||||
resample_kernel: [1, 3, 3, 1]
|
||||
decoder_load_path: experiments/pretrained_models/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth
|
||||
fix_decoder: true
|
||||
num_mlp: 8
|
||||
lr_mlp: 0.01
|
||||
input_is_latent: true
|
||||
different_w: true
|
||||
narrow: 1
|
||||
sft_half: true
|
||||
|
||||
network_d:
|
||||
type: StyleGAN2Discriminator
|
||||
out_size: 512
|
||||
channel_multiplier: 1
|
||||
resample_kernel: [1, 3, 3, 1]
|
||||
|
||||
# network_d_left_eye:
|
||||
# type: FacialComponentDiscriminator
|
||||
|
||||
# network_d_right_eye:
|
||||
# type: FacialComponentDiscriminator
|
||||
|
||||
# network_d_mouth:
|
||||
# type: FacialComponentDiscriminator
|
||||
|
||||
network_identity:
|
||||
type: ResNetArcFace
|
||||
block: IRBlock
|
||||
layers: [2, 2, 2, 2]
|
||||
use_se: False
|
||||
|
||||
# path
|
||||
path:
|
||||
pretrain_network_g: ~
|
||||
param_key_g: params_ema
|
||||
strict_load_g: ~
|
||||
pretrain_network_d: ~
|
||||
# pretrain_network_d_left_eye: ~
|
||||
# pretrain_network_d_right_eye: ~
|
||||
# pretrain_network_d_mouth: ~
|
||||
pretrain_network_identity: experiments/pretrained_models/arcface_resnet18.pth
|
||||
# resume
|
||||
resume_state: ~
|
||||
ignore_resume_networks: ['network_identity']
|
||||
|
||||
# training settings
|
||||
train:
|
||||
optim_g:
|
||||
type: Adam
|
||||
lr: !!float 2e-3
|
||||
optim_d:
|
||||
type: Adam
|
||||
lr: !!float 2e-3
|
||||
optim_component:
|
||||
type: Adam
|
||||
lr: !!float 2e-3
|
||||
|
||||
scheduler:
|
||||
type: MultiStepLR
|
||||
milestones: [600000, 700000]
|
||||
gamma: 0.5
|
||||
|
||||
total_iter: 800000
|
||||
warmup_iter: -1 # no warm up
|
||||
|
||||
# losses
|
||||
# pixel loss
|
||||
pixel_opt:
|
||||
type: L1Loss
|
||||
loss_weight: !!float 1e-1
|
||||
reduction: mean
|
||||
# L1 loss used in pyramid loss, component style loss and identity loss
|
||||
L1_opt:
|
||||
type: L1Loss
|
||||
loss_weight: 1
|
||||
reduction: mean
|
||||
|
||||
# image pyramid loss
|
||||
pyramid_loss_weight: 1
|
||||
remove_pyramid_loss: 50000
|
||||
# perceptual loss (content and style losses)
|
||||
perceptual_opt:
|
||||
type: PerceptualLoss
|
||||
layer_weights:
|
||||
# before relu
|
||||
'conv1_2': 0.1
|
||||
'conv2_2': 0.1
|
||||
'conv3_4': 1
|
||||
'conv4_4': 1
|
||||
'conv5_4': 1
|
||||
vgg_type: vgg19
|
||||
use_input_norm: true
|
||||
perceptual_weight: !!float 1
|
||||
style_weight: 50
|
||||
range_norm: true
|
||||
criterion: l1
|
||||
# gan loss
|
||||
gan_opt:
|
||||
type: GANLoss
|
||||
gan_type: wgan_softplus
|
||||
loss_weight: !!float 1e-1
|
||||
# r1 regularization for discriminator
|
||||
r1_reg_weight: 10
|
||||
# facial component loss
|
||||
# gan_component_opt:
|
||||
# type: GANLoss
|
||||
# gan_type: vanilla
|
||||
# real_label_val: 1.0
|
||||
# fake_label_val: 0.0
|
||||
# loss_weight: !!float 1
|
||||
# comp_style_weight: 200
|
||||
# identity loss
|
||||
identity_weight: 10
|
||||
|
||||
net_d_iters: 1
|
||||
net_d_init_iters: 0
|
||||
net_d_reg_every: 16
|
||||
|
||||
# validation settings
|
||||
val:
|
||||
val_freq: !!float 5e3
|
||||
save_img: true
|
||||
|
||||
metrics:
|
||||
psnr: # metric name, can be arbitrary
|
||||
type: calculate_psnr
|
||||
crop_border: 0
|
||||
test_y_channel: false
|
||||
|
||||
# logging settings
|
||||
logger:
|
||||
print_freq: 100
|
||||
save_checkpoint_freq: !!float 5e3
|
||||
use_tb_logger: true
|
||||
wandb:
|
||||
project: ~
|
||||
resume_id: ~
|
||||
|
||||
# dist training settings
|
||||
dist_params:
|
||||
backend: nccl
|
||||
port: 29500
|
||||
|
||||
find_unused_parameters: true
|
||||
Loading…
x
Reference in New Issue
Block a user