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README.md
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@@ -23,6 +23,8 @@ We extend the powerful ESRGAN to a practical restoration application (namely, Re
- :white_check_mark: [The inference code](inference_realesrgan.py) supports: 1) **tile** options; 2) images with **alpha channel**; 3) **gray** images; 4) **16-bit** images. - :white_check_mark: [The inference code](inference_realesrgan.py) supports: 1) **tile** options; 2) images with **alpha channel**; 3) **gray** images; 4) **16-bit** images.
- :white_check_mark: The training codes have been released. A detailed guide can be found in [Training.md](Training.md). - :white_check_mark: The training codes have been released. A detailed guide can be found in [Training.md](Training.md).
---
If Real-ESRGAN is helpful in your photos/projects, please help to :star: this repo. Thanks:blush: <br> If Real-ESRGAN is helpful in your photos/projects, please help to :star: this repo. Thanks:blush: <br>
Other recommended projects: &emsp; :arrow_forward: [GFPGAN](https://github.com/TencentARC/GFPGAN) &emsp; :arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR) &emsp; :arrow_forward: [facexlib](https://github.com/xinntao/facexlib) Other recommended projects: &emsp; :arrow_forward: [GFPGAN](https://github.com/TencentARC/GFPGAN) &emsp; :arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR) &emsp; :arrow_forward: [facexlib](https://github.com/xinntao/facexlib)

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Training.md
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@@ -139,62 +139,62 @@ You can merge several folders into one meta_info txt. Here is the example:
You can finetune Real-ESRGAN on your own dataset. Typically, the fine-tuning process can be divided into two cases: You can finetune Real-ESRGAN on your own dataset. Typically, the fine-tuning process can be divided into two cases:
1. [generate degraded images on the fly](#Generate-degraded-images-on-the-fly) 1. [Generate degraded images on the fly](#Generate-degraded-images-on-the-fly)
1. [use your own **paired** data(#Use-paired-training-data) 1. [Use your own **paired** data](#Use-paired-training-data)
### Generate degraded images on the fly ### Generate degraded images on the fly
Only high-resolution images are required. The low-quality images are generated with the degradation process in Real-ESRGAN during trainig. Only high-resolution images are required. The low-quality images are generated with the degradation process described in Real-ESRGAN during trainig.
**Prepare dataset** **1. Prepare dataset**
See [this section](#dataset-preparation) for more details. See [this section](#dataset-preparation) for more details.
**Download pre-trained models** **2. Download pre-trained models**
Download pre-trained models into `experiments/pretrained_models`. Download pre-trained models into `experiments/pretrained_models`.
*RealESRGAN_x4plus.pth* - *RealESRGAN_x4plus.pth*:
```bash ```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
``` ```
*RealESRGAN_x4plus_netD.pth* - *RealESRGAN_x4plus_netD.pth*:
```bash ```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models
``` ```
**Finetune** **3. Finetune**
Modify [options/finetune_realesrgan_x4plus.yml](options/finetune_realesrgan_x4plus.yml) accordingly, especially the `datasets` part: Modify [options/finetune_realesrgan_x4plus.yml](options/finetune_realesrgan_x4plus.yml) accordingly, especially the `datasets` part:
```yml
train: ```yml
name: DF2K+OST train:
type: RealESRGANDataset name: DF2K+OST
dataroot_gt: datasets/DF2K # modify to the root path of your folder type: RealESRGANDataset
meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt # modify to your own generate meta info txt dataroot_gt: datasets/DF2K # modify to the root path of your folder
io_backend: meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt # modify to your own generate meta info txt
type: disk io_backend:
``` type: disk
```
We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 \
python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/finetune_realesrgan_x4plus.yml --launcher pytorch --auto_resume
```
### Use paired training data ```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 \
python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/finetune_realesrgan_x4plus.yml --launcher pytorch --auto_resume
```
### Use your own paired data
You can also finetune RealESRGAN with your own paired data. It is more similar to fine-tuning ESRGAN. You can also finetune RealESRGAN with your own paired data. It is more similar to fine-tuning ESRGAN.
**Prepare dataset** **1. Prepare dataset**
Assume that you already have two folders: Assume that you already have two folders:
- gt folder (Ground-truth, high-resolution images): datasets/DF2K/DIV2K_train_HR_sub - **gt folder** (Ground-truth, high-resolution images): *datasets/DF2K/DIV2K_train_HR_sub*
- lq folder (Low quality, low-resolution images): datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub - **lq folder** (Low quality, low-resolution images): *datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub*
Then, you can prepare the meta_info txt file using the script [scripts/generate_meta_info_pairdata.py](scripts/generate_meta_info_pairdata.py): Then, you can prepare the meta_info txt file using the script [scripts/generate_meta_info_pairdata.py](scripts/generate_meta_info_pairdata.py):
@@ -202,18 +202,16 @@ Then, you can prepare the meta_info txt file using the script [scripts/generate_
python scripts/generate_meta_info_pairdata.py --input datasets/DF2K/DIV2K_train_HR_sub datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub --meta_info datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt python scripts/generate_meta_info_pairdata.py --input datasets/DF2K/DIV2K_train_HR_sub datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub --meta_info datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt
``` ```
**Download pre-trained models** **2. Download pre-trained models**
Download pre-trained models into `experiments/pretrained_models`. Download pre-trained models into `experiments/pretrained_models`.
*RealESRGAN_x4plus.pth* - *RealESRGAN_x4plus.pth*
```bash ```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
``` ```
*RealESRGAN_x4plus_netD.pth* - *RealESRGAN_x4plus_netD.pth*
```bash ```bash
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models
``` ```
@@ -221,19 +219,21 @@ Download pre-trained models into `experiments/pretrained_models`.
**Finetune** **Finetune**
Modify [options/finetune_realesrgan_x4plus_pairdata.yml](options/finetune_realesrgan_x4plus_pairdata.yml) accordingly, especially the `datasets` part: Modify [options/finetune_realesrgan_x4plus_pairdata.yml](options/finetune_realesrgan_x4plus_pairdata.yml) accordingly, especially the `datasets` part:
```yml
train: ```yml
name: DIV2K train:
type: RealESRGANPairedDataset name: DIV2K
dataroot_gt: datasets/DF2K # modify to the root path of your folder type: RealESRGANPairedDataset
dataroot_lq: datasets/DF2K # modify to the root path of your folder dataroot_gt: datasets/DF2K # modify to the root path of your folder
meta_info: datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt # modify to the root path of your folder dataroot_lq: datasets/DF2K # modify to the root path of your folder
io_backend: meta_info: datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt # modify to the root path of your folder
type: disk io_backend:
``` type: disk
```
We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 \ ```bash
python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/finetune_realesrgan_x4plus_pairdata.yml --launcher pytorch --auto_resume CUDA_VISIBLE_DEVICES=0,1,2,3 \
``` python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/finetune_realesrgan_x4plus_pairdata.yml --launcher pytorch --auto_resume
```