Awesome
DCFace: Synthetic Face Generation with Dual Condition Diffusion Model
Official repository for the paper DCFace: Synthetic Face Generation with Dual Condition Diffusion Model (CVPR 2023).
- Arxiv: https://arxiv.org/abs/2304.07060
- Main paper: main.pdf
- Supplementary: supp.pdf
Pipepline
ID Consistent Generation
Installation
- one-liner :
install.sh
If above fails, then
pip install -r requirements.txt
# and download model weights from the link below
Image generation
Sample Code with given example images
We provide the sample code to generate images with the pretrained weights. The sample aligned images are provided in the repository.
- Download the pretrained weights from the link
- Place the
pretrained_models
directory underdcface
(same level assrc
) - Run
cd dcface/src
python synthesis.py --id_images_root sample_images/id_images/sample_57.png --style_images_root sample_images/style_images/woman
Optional
One can also generate new subject images and prepare custom style images.
1. Creating ID Images (New Subjects)
Unconditional ID image generation is done in dcface/stage1/unconditional_generation
Take a look at the README.md
in that directory for instructions on how to generate new ID images.
2. Creating Style Images
Any aligned images can serve as style images. We provive some sample images in sample_images/style_images
directory.
For anyone who wants to use their own style images, one should align the images first.
Take a look at the README.md
in dcface/stage1/style_bank
directory for instructions on how to align images.
3. Using 1. and 2. to generate dataset
Assuming that you followed 1. and 2. you will have an id_image
and style_images
directory.
For the sake of explaination, let's say
- ID image is
<Project_root>/dcface/stage1/unconditional_generation/unconditional_samples_aligned/00011.png
- Style directory is
<Project_root>/dcface/stage1/style_bank/style_images/raw_aligned
Then to combine these run by pointing at these paths,
cd dcface/src
python synthesis.py \
--id_images_root <Project_root>/dcface/stage1/unconditional_generation/unconditional_samples_aligned/00011.png \
--style_images_root <Project_root>/dcface/stage1/style_bank/style_images/raw_aligned
The result will be saved at <Project_root>/dcface/generated_images/
Training generator
Dataset preparation
- Download casia webface dataset from insightface
- Place it under
$DATA_ROOT
(ex:/data/
). - ex:
/data/faces_webface_112x112
- Place it under
Download model weights
- Download all pretrained weights from the link
- Place the
pretrained_models
directory underdcface
(same level assrc
)
Run
cd dcface/src/
bash train.sh
Dataset Release
DCFace synthetic dataset can be downloaded from link
The format of the downloaded file is in rec
format.
- you can convert it to
png
using the script. - rec file will be useful for the face recognition training script provided in the repository. (to be released soon)
cd dcface/convert
python record.py --rec_path <path_to_rec_file> --save_path <path_to_save_png>
# ex
# <path_to_rec_file> : dcface_0.5m_oversample_xid/record
# <path_to_save_png> : dcface_0.5m_oversample_xid/images
Training Face Recognition Model
- to be released soon