---
title: DicFace_model
canonical_url: "https://www.modelscope.cn/models/fudan-generative-vision/DicFace_model"
md_url: "https://www.modelscope.cn/models/fudan-generative-vision/DicFace_model.md"
repository: fudan-generative-vision/DicFace_model
last_updated: 2025-06-26
license: mit
library_name:
- pytorch
frameworks:
- pytorch
downloads: 281
stars: 0
---
# DicFace_model
> DicFace_model - fudan-generative-vision 在 ModelScope 开源的模型。DicFace: Dirichlet-Constrained Variational Codebook Learning for Temporally Coherent Video Face Restoration
fudan-generative-vision/DicFace_model 是 ModelScope 魔搭社区上的机器学习模型，采用 mit 许可。
- **Repository**: fudan-generative-vision/DicFace_model
- **License**: mit
- **Downloads**: 281
- **Stars**: 0
- **Last updated**: 2025-06-26
Source: https://www.modelscope.cn/models/fudan-generative-vision/DicFace_model
---
DicFace: Dirichlet-Constrained Variational Codebook Learning for Temporally Coherent Video Face Restoration
1Fudan University
2Alibaba Group
3Nanjing University
## 📸 Showcase
### Blind Face Restoration
### Face Inpainting
### Face Colorization
## 📰 News
- **`2025/06/23`**: Release our pretrained model on huggingface [repo](https://huggingface.co/fudan-generative-ai/DicFace).
- **`2025/06/17`**: Paper submitted on Arixiv. [paper](https://arxiv.org/abs/2506.13355)
- **`2025/06/16`**: 🎉🎉🎉 Release inference scripts
## 📅️ Roadmap
| Status | Milestone | ETA |
| :----: | :------------------------------------------------------------------------------------------- | :--------: |
| ✅ | **[Inference Code release](https://github.com/fudan-generative-vision/DicFace)** | 2025-6-16 |
| ✅ | **[Model Weight release， baidu-link](https://pan.baidu.com/s/1VTNbdtZDvgY0163a1T8ITw?pwd=dicf)** |2025-6-16|
| ✅ | **[Paper submitted on Arixiv](https://arxiv.org/abs/2506.13355)** | 2025-6-17 |
| 🚀 | **[Test data release]()** | 2025-6-24 |
| 🚀 | **[Training Code release]()** | 2025-6-24 |
## ⚙️ Installation
- System requirement: PyTorch version >=2.4.1, python == 3.10
- Tested on GPUs: A800, python version == 3.10, PyTorch version == 2.4.1, cuda version == 12.1
Download the codes:
```bash
git clone https://github.com/fudan-generative-vision/DicFace
cd DicFace
```
Create conda environment:
```bash
conda create -n DicFace python=3.10
conda activate DicFace
```
Install PyTorch
```bash
conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 pytorch-cuda=12.1 -c pytorch -c nvidia
```
Install packages with `pip`
```bash
pip install -r requirements.txt
python basicsr/setup.py develop
conda install -c conda-forge dlib
```
### 📥 Download Pretrained Models
The pre-trained weights have been uploaded to Baidu Netdisk. Please download them from the [link](https://pan.baidu.com/s/1VTNbdtZDvgY0163a1T8ITw?pwd=dicf)
Now you can easily get all pretrained models required by inference from our HuggingFace [repo](https://huggingface.co/fudan-generative-ai/DicFace).
**File Structure of Pretrained Models**
The downloaded .ckpts directory contains the following pre-trained models:
```
.ckpts
|-- CodeFormer # CodeFormer-related models
| |-- bfr_100k.pth # Blind Face Restoration model
| |-- color_100k.pth # Color Restoration model
| `-- inpainting_100k.pth # Image Inpainting model
|-- dlib # dlib face-related models
| |-- mmod_human_face_detector.dat # Human face detector
| `-- shape_predictor_5_face_landmarks.dat # 5-point face landmark predictor
|-- facelib # Face processing library models
| |-- detection_Resnet50_Final.pth # ResNet50 face detector
| |-- detection_mobilenet0.25_Final.pth # MobileNet0.25 face detector
| |-- parsing_parsenet.pth # Face parsing model
| |-- yolov5l-face.pth # YOLOv5l face detection model
| `-- yolov5n-face.pth # YOLOv5n face detection model
|-- realesrgan # Real-ESRGAN super-resolution model
| `-- RealESRGAN_x2plus.pth # 2x super-resolution enhancement model
`-- vgg # VGG feature extraction model
`-- vgg.pth # VGG network pre-trained weights
```
### 🎮 Run Inference
#### for blind face restoration
```bash
python scripts/inference.py \
-i /path/to/video \
-o /path/to/output_folder \
--max_length 10 \
--save_video_fps 24 \
--ckpt_path /bfr/bfr_weight.pth \
--bg_upsampler realesrgan \
--save_video
# or your videos has been aligned
python scripts/inference.py \
-i /path/to/video \
-o /path/to/output_folder \
--max_length 10 \
--save_video_fps 24 \
--ckpt_path /bfr/bfr_weight.pth \
--save_video \
--has_aligned
```
#### for colorization & inpainting task
**The current colorization & inpainting tasks only supports input of aligned faces. If a non-aligned face is input, it may lead to unsatisfactory final results.**
``` bash
# for colorization task
python scripts/inference_color_and_inpainting.py \
-i /path/to/video_warped \
-o /path/to/output_folder \
--max_length 10 \
--save_video_fps 24 \
--ckpt_path /colorization/colorization_weight.pth \
--bg_upsampler realesrgan \
--save_video \
--has_aligned
# for inpainting task
python scripts/inference_color_and_inpainting.py \
-i /path/to/video_warped \
-o /path/to/output_folder \
--max_length 10 \
--save_video_fps 24 \
--ckpt_path /inpainting/inpainting_weight.pth \
--bg_upsampler realesrgan \
--save_video \
--has_aligned
```
## test data
our test data link: https://pan.baidu.com/s/1zMp3fnf6LvlRT9CAoL1OUw?pwd=drhh
**TBD**
## 📝 Citation
If you find our work useful for your research, please consider citing the paper:
```
@misc{chen2025dicfacedirichletconstrainedvariationalcodebook,
title={DicFace: Dirichlet-Constrained Variational Codebook Learning for Temporally Coherent Video Face Restoration},
author={Yan Chen and Hanlin Shang and Ce Liu and Yuxuan Chen and Hui Li and Weihao Yuan and Hao Zhu and Zilong Dong and Siyu Zhu},
year={2025},
eprint={2506.13355},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.13355},
}
```
