• [arXiv] •
![](assets/teaser.png)
PGDiff builds a versatile framework that is applicable to a broad range of face restoration tasks.
📕 Supported Applications
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- 2023.12.04: Add an option to speed up the inference process by adjusting the number of denoising steps.
- 2023.10.10: Release our codes and models. Have fun! 😋
- 2023.08.16: This repo is created.
# git clone this repository
git clone https://github.com/pq-yang/PGDiff.git
cd PGDiff
# create new anaconda env
conda create -n pgdiff python=3.8 -y
conda activate pgdiff
# install python dependencies
conda install mpi4py
pip3 install -r requirements.txt
pip install -e .
Download the pretrained face diffusion model from [Google Drive | BaiduPan (pw: pgdf)] to the models
folder (credit to DifFace).
🚀 🚀 If you want to speed up the inference process, you may choose to shorten the number of DDPM denoising steps by specifying a smaller --timestep_respacing
argument (e.g., --timestep_respacing ddpm200
for 200 timesteps). We recommend at least 100 steps for a decent result.
To extract smooth semantics from the input images, download the pretrained restorer from [Google Drive | BaiduPan (pw: pgdf)] to the models/restorer
folder. The pretrained restorer provided here is modified from the create_restorer
function and specifying your own --restorer_path
accordingly.
Guidance scale
For cropped and aligned faces (512x512):
python inference_pgdiff.py --task restoration --in_dir [image folder] --out_dir [result folder] --restorer_path [restorer path] --guidance_scale [s] --timestep_respacing ddpm[steps]
Example:
python inference_pgdiff.py --task restoration --in_dir testdata/cropped_faces --out_dir results/blind_restoration --guidance_scale 0.05
We provide a set of color statistics in the adaptive_instance_normalization
function as the default colorization style. One may change the colorization style by running the script scripts/color_stat_calculation.py
to obtain target statistics (avg mean & avg std) and replace those in the adaptive_instance_normalization
function.
For cropped and aligned faces (512x512):
python inference_pgdiff.py --task colorization --in_dir [image folder] --out_dir [result folder] --lightness_weight [w_l] --color_weight [w_c] --guidance_scale [s] --timestep_respacing ddpm[steps]
Example:
🌈 Try different color styles for various outputs!
# style 0 (default)
python inference_pgdiff.py --task colorization --in_dir testdata/grayscale_faces --out_dir results/colorization --guidance_scale 0.01
# style 1 (uncomment line 272-273 in `guided_diffusion/script_util.py`)
python inference_pgdiff.py --task colorization --in_dir testdata/grayscale_faces --out_dir results/colorization_style1 --guidance_scale 0.01
# style 3 (uncomment line 278-279 in `guided_diffusion/script_util.py`)
python inference_pgdiff.py --task colorization --in_dir testdata/grayscale_faces --out_dir results/colorization_style3 --guidance_scale 0.01
A folder for mask(s) mask_dir
must be specified with each mask image name corresponding to each input (masked) image. Each input mask shoud be a binary map with white pixels representing masked regions (refer to testdata/append_masks
). We also provide a script scripts/irregular_mask_gen.py
to randomly generate irregular stroke masks on input images.
❗ Note: If you don't specify mask_dir
, we will automatically treat the input image as if there are no missing pixels.
For cropped and aligned faces (512x512):
python inference_pgdiff.py --task inpainting --in_dir [image folder] --mask_dir [mask folder] --out_dir [result folder] --unmasked_weight [w_um] --guidance_scale [s] --timestep_respacing ddpm[steps]
Example:
🌈 Try different seeds for various outputs!
python inference_pgdiff.py --task inpainting --in_dir testdata/masked_faces --mask_dir testdata/append_masks --out_dir results/inpainting --guidance_scale 0.01 --seed 4321
To extract identity features from both the reference image and the intermediate results, download the pretrained ArcFace model from [Google Drive | BaiduPan (pw: pgdf)] to the models
folder.
A folder for reference image(s) ref_dir
must be specified with each reference image name corresponding to each input image. A reference image is suggested to be a high-quality image from the same identity as the input low-quality image. Test image pairs we provided here are from the CelebRef-HQ dataset.
Similar to blind face restoration, reference-based restoration requires to tune the guidance scale
For cropped and aligned faces (512x512):
python inference_pgdiff.py --task ref_restoration --in_dir [image folder] --ref_dir [reference folder] --out_dir [result folder] --ss_weight [w_ss] --ref_weight [w_ref] --guidance_scale [s] --timestep_respacing ddpm[steps]
Example:
# Choice 1: MSE Loss (default)
python inference_pgdiff.py --task ref_restoration --in_dir testdata/ref_cropped_faces --ref_dir testdata/ref_faces --out_dir results/ref_restoration_mse --guidance_scale 0.05 --ref_weight 25
# Choice 2: Cosine Similarity Loss (uncomment line 71-72)
python inference_pgdiff.py --task ref_restoration --in_dir testdata/ref_cropped_faces --ref_dir testdata/ref_faces --out_dir results/ref_restoration_cos --guidance_scale 0.05 --ref_weight 1e4
-
If scratches exist, a folder for mask(s)
mask_dir
must be specified with the name of each mask image corresponding to that of each input image. Each input mask shoud be a binary map with white pixels representing masked regions. To obtain a scratch map automatically, we recommend using the scratch detection model from Bringing Old Photo Back to Life. One may also generate or adjust the scratch map with an image editing app (e.g., Photoshop). -
If scratches don't exist, set the
mask_dir
augment asNone
(default). As a result, if you don't specifymask_dir
, we will automatically treat the input image as if there are no missing pixels.
For cropped and aligned faces (512x512):
python inference_pgdiff.py --task old_photo_restoration --in_dir [image folder] --mask_dir [mask folder] --out_dir [result folder] --op_lightness_weight [w_op_l] --op_color_weight [w_op_c] --guidance_scale [s] --timestep_respacing ddpm[steps]
Similar to blind face restoration, old photo restoration is a more complex task (restoration + colorization + inpainting) that requires to tune the guidance scale
# no scratches (don't specify mask_dir)
python inference_pgdiff.py --task old_photo_restoration --in_dir testdata/op_cropped_faces/lg --out_dir results/op_restoration/lg --guidance_scale 0.004 --seed 4321
# no scratches (don't specify mask_dir)
python inference_pgdiff.py --task old_photo_restoration --in_dir testdata/op_cropped_faces/med --out_dir results/op_restoration/med --guidance_scale 0.002 --seed 1234
# with scratches
python inference_pgdiff.py --task old_photo_restoration --in_dir testdata/op_cropped_faces/med_scratch --mask_dir testdata/op_mask --out_dir results/op_restoration/med_scratch --guidance_scale 0.002 --seed 1111
python inference_pgdiff.py --task old_photo_restoration --in_dir testdata/op_cropped_faces/hv --mask_dir testdata/op_mask --out_dir results/op_restoration/hv --guidance_scale 0.0015 --seed 4321
🌈 Customize your results with different color styles!
# style 1 (uncomment line 272-273 in `guided_diffusion/script_util.py`)
python inference_pgdiff.py --task old_photo_restoration --in_dir testdata/op_cropped_faces/hv --mask_dir testdata/op_mask --out_dir results/op_restoration/hv_style1 --guidance_scale 0.0015 --seed 4321
# style 2 (uncomment line 275-276 in `guided_diffusion/script_util.py`)
python inference_pgdiff.py --task old_photo_restoration --in_dir testdata/op_cropped_faces/hv --mask_dir testdata/op_mask --out_dir results/op_restoration/hv_style2 --guidance_scale 0.0015 --seed 4321
If you find our work useful for your research, please consider citing:
@inproceedings{yang2023pgdiff,
title={{PGDiff}: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidance},
author={Yang, Peiqing and Zhou, Shangchen and Tao, Qingyi and Loy, Chen Change},
booktitle={NeurIPS},
year={2023}
}
This project is licensed under NTU S-Lab License 1.0. Redistribution and use should follow this license.
This study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).
This implementation is based on guided-diffusion. We also adopt the pretrained face diffusion model from DifFace, the pretrained identity feature extraction model from ArcFace, and the restorer backbone from Real-ESRGAN. Thanks for their awesome works!
If you have any questions, please feel free to reach out at [email protected]
.