The demo of different style with age edit.
Various applications based on Stylegan2 Style mixing that can be inference on cpu.
This code is heavily based on stylegan2-pytorch. Thanks rosinality so much to make his work available 🙏🙏🙏
The demo of different style of psp-mobile-256p.
The demo of different style of psp-res50-256p.
The demo of different style of e4e-mobile-1024p.
The demo of different style of e4e-res50-1024p.
The demo of different style with age edit of e4e-res50-1024p
The demo of different style with gender edit of e4e-res50-1024p
The demo of different style with pose edit of e4e-res50-1024p
The demo of different style with smile edit of e4e-res50-1024p
I provide some of the model to test.
| Google Drive | 百度云 | Description |
|---|---|---|
| torch weight | torch weight 密码:0mgk | cartoon of e4e-mbv3 1024p and psp-mbv3 256p |
| onnx weight | onnx weight 密码:inn8 | stylegan2 onnx, cartoon of e4e-mbv3 1024p and psp-mbv3 256p |
| openvino weight | openvino weight 密码:759q | openvino cartoon of e4e-mbv3 1024p and psp-mbv3 |
All test on MacOs 11.4 | 2.6 GHz Intel Core i7 | 32 GB 2667 MHz DDR4
pSp-mbv3-256p
| Name | Time(s) |
|---|---|
| torch | 1.9860 |
| onnx | 0.5869 |
| openvino | 0.4533 |
e4e-mbv3-1024p
| Name | Time(s) |
|---|---|
| torch | 4.8058 |
| onnx | 1.5155 |
| openvino | 0.8690 |
python scripts/test.py \
--ckpt ./best_model.pt \
--network psp \
--platform torch \
--align \
--images_path ./test_images
python scripts/test.py \
--ckpt ./cartoon_psp_mobile_256p.onnx \
--network psp \
--platform onnx \
--align \
--images_path ./test_images
python scripts/test.py \
--ckpt_encoder ./art_mobile_encoder_1024p \
--ckpt_decoder ./art_decoder_1024p \
--network e4e \
--platform openvino \
--align \
--images_path ./test_images \
--edit \
--edit_direction ./editings/smile.npy
function can find in tools/torch2onnx.py
Inference Engine version: 2021.4.0-3839-cd81789d294-releases/2021/4 Model Optimizer version: 2021.4.0-3839-cd81789d294-releases/2021/4
python3 mo.py \
--input_model psp_mobile_256p.onnx \
--output_dir ./openvino \
--data_type FP16 \
--mean_values [127.5,127.5,127.5] \
--scale_values [127.5,127.5,127.5] \
--move_to_preprocess