diff --git a/README.md b/README.md
index ea5fcfe..462fed3 100644
--- a/README.md
+++ b/README.md
@@ -81,6 +81,17 @@ python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGA
We provide complete training codes for GFPGAN.
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
diff --git a/README_CN.md b/README_CN.md
index ea5fcfe..462fed3 100644
--- a/README_CN.md
+++ b/README_CN.md
@@ -81,6 +81,17 @@ python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGA
We provide complete training codes for GFPGAN.
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
diff --git a/experiments/pretrained_models/README.md b/experiments/pretrained_models/README.md
new file mode 100644
index 0000000..3401a5c
--- /dev/null
+++ b/experiments/pretrained_models/README.md
@@ -0,0 +1,7 @@
+# Pre-trained Models and Other Data
+
+Download pre-trained models and other data. Put them in this 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)
diff --git a/train_gfpgan_v1.yml b/train_gfpgan_v1.yml
index 3c31ba6..010048f 100644
--- a/train_gfpgan_v1.yml
+++ b/train_gfpgan_v1.yml
@@ -9,9 +9,11 @@ datasets:
train:
name: FFHQ
type: FFHQDegradationDataset
- dataroot_gt: datasets/ffhq/ffhq_512.lmdb
+ # dataroot_gt: datasets/ffhq/ffhq_512.lmdb
+ dataroot_gt: datasets/ffhq/ffhq_512
io_backend:
- type: lmdb
+ # type: lmdb
+ type: disk
use_hflip: true
mean: [0.5, 0.5, 0.5]
@@ -44,10 +46,12 @@ datasets:
prefetch_mode: ~
val:
- name: validation1020_512
+ # 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/validation1020_512/input # TODO: modify before release
- dataroot_gt: datasets/faces/validation1020_512/input
+ dataroot_lq: datasets/faces/validation/input
+ dataroot_gt: datasets/faces/validation/reference
io_backend:
type: disk
mean: [0.5, 0.5, 0.5]