teaser2.mp4
This repository provides the official PyTorch implementation for the following paper:
StyleGANEX: StyleGAN-Based Manipulation Beyond Cropped Aligned Faces
Shuai Yang, Liming Jiang, Ziwei Liu and Chen Change Loy
In ICCV 2023.
Project Page | Paper | Supplementary Video
Abstract: Recent advances in face manipulation using StyleGAN have produced impressive results. However, StyleGAN is inherently limited to cropped aligned faces at a fixed image resolution it is pre-trained on. In this paper, we propose a simple and effective solution to this limitation by using dilated convolutions to rescale the receptive fields of shallow layers in StyleGAN, without altering any model parameters. This allows fixed-size small features at shallow layers to be extended into larger ones that can accommodate variable resolutions, making them more robust in characterizing unaligned faces. To enable real face inversion and manipulation, we introduce a corresponding encoder that provides the first-layer feature of the extended StyleGAN in addition to the latent style code. We validate the effectiveness of our method using unaligned face inputs of various resolutions in a diverse set of face manipulation tasks, including facial attribute editing, super-resolution, sketch/mask-to-face translation, and face toonification.
Features:
- Support for Unaligned Faces: StyleGANEX can manipulate normal field-of-view face images and videos.
- Compatibility: StyleGANEX can directly load pre-trained StyleGAN parameters without retraining.
- Flexible Manipulation: StyleGANEX retains the style representation and editing ability of StyleGAN.
- [07/2023] Training code is released.
- [07/2023] The paper is accepted to ICCV 2023 😁!
- [03/2023] Integrated to 🤗 Hugging Face. Enjoy the web demo!
- [03/2023] Inference code is released.
- [03/2023] This website is created.
Clone this repo:
git clone https://github.com/williamyang1991/StyleGANEX.git
cd StyleGANEX
Dependencies:
We have tested on:
- CUDA 10.1
- PyTorch 1.7.1
- Pillow 8.3.1; Matplotlib 3.4.2; opencv-python 4.5.3; tqdm 4.61.2; Ninja 1.10.2; dlib 19.24.0; gradio 3.4
To help users get started, we provide a Jupyter notebook found in ./inference_playground.ipynb
that allows one to visualize the performance of StyleGANEX.
The notebook will download the necessary pretrained models and run inference on the images found in ./data/
.
We also provide a UI for testing StyleGANEX that is built with gradio. Running the following command in a terminal will launch the demo:
python app_gradio.py
This demo is also hosted on Hugging Face.
Pre-trained models can be downloaded from Google Drive, Baidu Cloud (access code: luck) or Hugging Face:
Task | Model | Description |
---|---|---|
Inversion | styleganex_inversion.pt | pre-trained model for StyleGANEX inversion |
Image translation | styleganex_sr32.pt | pre-trained model specially for 32x face super resolution |
styleganex_sr.pt | pre-trained model for 4x-48x face super resolution | |
styleganex_sketch2face.pt | pre-trained model for skech-to-face translation | |
styleganex_mask2face.pt | pre-trained model for parsing map-to-face translation | |
Video editing | styleganex_edit_hair.pt | pre-trained model for hair color editing on videos |
styleganex_edit_age.pt | pre-trained model for age editing on videos | |
styleganex_toonify_cartoon.pt | pre-trained Cartoon model for video face toonification | |
styleganex_toonify_arcane.pt | pre-trained Arcane model for video face toonification | |
styleganex_toonify_pixar.pt | pre-trained Pixar model for video face toonification | |
Supporting model | ||
faceparsing.pth | BiSeNet for face parsing from face-parsing.PyTorch |
The downloaded models are suggested to be put into ./pretrained_models/
We can embed a face image into the latent space of StyleGANEX to obtain its w+ latent code and the first-layer feature f with inversion.py
.
python inversion.py --ckpt STYLEGANEX_MODEL_PATH --data_path FACE_IMAGE_PATH
The results are saved in the folder ./output/
.
The results contain a reconstructed image FILE_NAME_inversion.jpg
and a FILE_NAME_inversion.pt
file.
You can obtain w+ latent code and the first-layer feature f by
latents = torch.load('./output/FILE_NAME_inversion.pt')
wplus_hat = latents['wplus'].to(device) # w+
f_hat = [latents['f'][0].to(device)] # f
The ./inference_playground.ipynb
provides some face editing examples based on wplus_hat
and f_hat
.
image_translation.py
supports face super-resolution, sketch-to-face translation and parsing map-to-face translation.
python image_translation.py --ckpt STYLEGANEX_MODEL_PATH --data_path FACE_INPUT_PATH
The results are saved in the folder ./output/
.
Additional notes to consider:
--parsing_model_ckpt
(default:pretrained_models/faceparsing.pth
): path to the pre-trained parsing model--resize_factor
(default: 32): super resolution resize factor- For styleganex_sr.pt, should be in [4, 48]
- For styleganex_sr32.pt, should be 32
--number
(default: 4): output number of multi-modal translation (for sketch/mask-to-face translation task)--use_raw_data
(default: False):- if not specified, apply possible pre-processing to the input data
- For styleganex_sr/sr32.pt, the input face image, e.g.,
./data/ILip77SbmOE.png
will be downsampled based on--resize_factor
. The downsampled image will be also saved in./output/
. - For styleganex_sketch2face.pt, no pre-processing will be applied.
- For styleganex_mask2face.pt, the input face image, e.g.,
./data/ILip77SbmOE.png
will be transformed into a parsing map. The parsing map and its visualization version will be also saved in./output/
.
- For styleganex_sr/sr32.pt, the input face image, e.g.,
- if specified, directly load input data without pre-processing
- For styleganex_sr/sr32.pt, the input should be downsampled face images, e.g.,
./data/ILip77SbmOE_45x45.png
- For styleganex_sketch2face.pt, the input should be a one-channel sketch image e.g.,
./data/234_sketch.jpg
- For styleganex_mask2face.pt, the input should be a one-channel parsing map e.g.,
./data/ILip77SbmOE_mask.png
- For styleganex_sr/sr32.pt, the input should be downsampled face images, e.g.,
- if not specified, apply possible pre-processing to the input data
video_editing.py
supports video facial attribute editing and video face toonification.
python video_editing.py --ckpt STYLEGANEX_MODEL_PATH --data_path FACE_INPUT_PATH
The results are saved in the folder ./output/
.
Additional notes to consider:
--data_path
: the input can be either an image or a video.--scale_factor
: for attribute editing task (styleganex_edit_hair/age), control the editing degree.
- As with pSp, we provide support for numerous datasets and experiments (encoding, translation, etc.).
- Refer to
configs/paths_config.py
to define the necessary data paths and model paths for training and evaluation. - Refer to
configs/transforms_config.py
for the transforms defined for each dataset/experiment. - Finally, refer to
configs/data_configs.py
for the source/target data paths for the train and test sets as well as the transforms.
- Refer to
- If you wish to experiment with your own dataset, you can simply make the necessary adjustments in
data_configs.py
to define your data paths.transforms_configs.py
to define your own data transforms.
As an example, assume we wish to run encoding using ffhq (dataset_type=ffhq_encode
).
We first go to configs/paths_config.py
and define:
dataset_paths = {
'ffhq': '/path/to/ffhq/realign320x320'
'ffhq_test': '/path/to/ffhq/realign320x320_test'
}
The transforms for the experiment are defined in the class EncodeTransforms
in configs/transforms_config.py
.
Finally, in configs/data_configs.py
, we define:
DATASETS = {
'ffhq_encode': {
'transforms': transforms_config.EncodeTransforms,
'train_source_root': dataset_paths['ffhq'],
'train_target_root': dataset_paths['ffhq'],
'test_source_root': dataset_paths['ffhq_test'],
'test_target_root': dataset_paths['ffhq_test'],
},
}
When defining our datasets, we will take the values in the above dictionary.
The 1280x1280 ffhq images can be obtain by the modified script of official ffhq:
- Download the in-the-wild images with
python script/download_ffhq1280.py --wilds
- Reproduce the aligned 1280×1280 images wiht
python script/download_ffhq1280.py --align
- 320x320 ffhq images can be obtained by setting
output_size=320, transform_size=1280
in Line 272 of download_ffhq1280.py
Please download the pre-trained models to support the training of StyleGANEX
Path | Description |
---|---|
original_stylegan | StyleGAN trained with the FFHQ dataset |
toonify_model | StyleGAN finetuned on cartoon dataset for image toonification (cartoon, pixar, arcane) |
original_psp_encoder | pSp trained with the FFHQ dataset for StyleGAN inversion. |
pretrained_encoder | StyleGANEX encoder pretrained with the synthetic data for StyleGAN inversion. |
styleganex_encoder | StyleGANEX encoder trained with the FFHQ dataset for StyleGANEX inversion. |
editing_vector | Editing vectors for editing face attributes (age, hair color) |
augmentation_vector | Editing vectors for data augmentation |
The main training script can be found in scripts/train.py
.
Intermediate training results are saved to opts.exp_dir
. This includes checkpoints, train outputs, and test outputs.
Note: Our default code is a CPU-compatible version. You can switch to a more efficient version by using cpp extention.
To do so, please change models.stylegan2.op
to models.stylegan2.op_old
StyleGANEX/models/stylegan2/model.py
Line 8 in 73b580c
First pretrain encoder on synthetic 1024x1024 images. You can download our pretrained encoder here
python scripts/pretrain.py \
--exp_dir=/path/to/experiment \
--ckpt=/path/to/original_psp_encoder \
--max_steps=2000
Then finetune encoder on real 1280x1280 ffhq images based on the pretrained encoder
python scripts/train.py \
--dataset_type=ffhq_encode \
--exp_dir=/path/to/experiment \
--checkpoint_path=/path/to/pretrained_encoder \
--max_steps=100000 \
--workers=8 \
--batch_size=8 \
--val_interval=2500 \
--save_interval=50000 \
--start_from_latent_avg \
--id_lambda=0.1 \
--w_norm_lambda=0.001 \
--affine_augment \
--random_crop \
--crop_face
python scripts/train.py \
--dataset_type=ffhq_sketch_to_face \
--exp_dir=/path/to/experiment \
--stylegan_weights=/path/to/original_stylegan \
--max_steps=100000 \
--workers=8 \
--batch_size=8 \
--val_interval=2500 \
--save_interval=10000 \
--start_from_latent_avg \
--w_norm_lambda=0.005 \
--affine_augment \
--random_crop \
--crop_face \
--use_skip \
--skip_max_layer=1 \
--label_nc=1 \
--input_nc=1 \
--use_latent_mask
python scripts/train.py \
--dataset_type=ffhq_seg_to_face \
--exp_dir=/path/to/experiment \
--stylegan_weights=/path/to/original_stylegan \
--max_steps=100000 \
--workers=8 \
--batch_size=8 \
--val_interval=2500 \
--save_interval=10000 \
--start_from_latent_avg \
--w_norm_lambda=0.005 \
--affine_augment \
--random_crop \
--crop_face \
--use_skip \
--skip_max_layer=2 \
--label_nc=19 \
--input_nc=19 \
--use_latent_mask
python scripts/train.py \
--dataset_type=ffhq_super_resolution \
--exp_dir=/path/to/experiment \
--checkpoint_path=/path/to/styleganex_encoder \
--max_steps=100000 \
--workers=4 \
--batch_size=4 \
--val_interval=2500 \
--save_interval=10000 \
--start_from_latent_avg \
--adv_lambda=0.1 \
--affine_augment \
--random_crop \
--crop_face \
--use_skip \
--skip_max_layer=4 \
--resize_factors=8
For one model supporting multiple resize factors, set --skip_max_layer=2
and --resize_factors=1,2,4,8,16
python scripts/train.py \
--dataset_type=ffhq_edit \
--exp_dir=/path/to/experiment \
--checkpoint_path=/path/to/styleganex_encoder \
--max_steps=100000 \
--workers=2 \
--batch_size=2 \
--val_interval=2500 \
--save_interval=10000 \
--start_from_latent_avg \
--adv_lambda=0.1 \
--tmp_lambda=30 \
--affine_augment \
--crop_face \
--use_skip \
--skip_max_layer=7 \
--editing_w_path=/path/to/editing_vector \
--direction_path=/path/to/augmentation_vector \
--use_att=1 \
--generate_training_data
python scripts/train.py \
--dataset_type=toonify \
--exp_dir=/path/to/experiment \
--checkpoint_path=/path/to/styleganex_encoder \
--max_steps=55000 \
--workers=2 \
--batch_size=2 \
--val_interval=2500 \
--save_interval=10000 \
--start_from_latent_avg \
--adv_lambda=0.1 \
--tmp_lambda=30 \
--affine_augment \
--crop_face \
--use_skip \
--skip_max_layer=7 \
--toonify_weights=/path/to/toonify_model
- See
options/train_options.py
for all training-specific flags. - If you wish to generate images from segmentation maps, please specify
--label_nc=N
and--input_nc=N
whereN
is the number of semantic categories. - Similarly, for generating images from sketches, please specify
--label_nc=1
and--input_nc=1
. - Specifying
--label_nc=0
(the default value), will directly use the RGB colors as input.
Overview of StyleGANEX inversion and facial attribute/style editing on unaligned faces:
Video facial attribute editing:
part2.mp4
Video face toonification:
part3.mp4
If you find this work useful for your research, please consider citing our paper:
@inproceedings{yang2023styleganex,
title = {StyleGANEX: StyleGAN-Based Manipulation Beyond Cropped Aligned Faces},
author = {Yang, Shuai and Jiang, Liming and Liu, Ziwei and and Loy, Chen Change},
booktitle = {ICCV},
year = {2023},
}
The code is mainly developed based on stylegan2-pytorch, pixel2style2pixel and VToonify.