- 💥 Updated online demo:
. Here is the backup.
- 💥 Updated online demo:
- Colab Demo for GFPGAN
; (Another Colab Demo for the original paper model)
🚀 Thanks for your interest in our work. You may also want to check our new updates on the tiny models for anime images and videos in Real-ESRGAN 😊
GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration.
It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration.
❓ Frequently Asked Questions can be found in FAQ.md.
🚩 Updates
- ✅ Add RestoreFormer inference codes.
- ✅ Add V1.4 model, which produces slightly more details and better identity than V1.3.
- ✅ Add V1.3 model, which produces more natural restoration results, and better results on very low-quality / high-quality inputs. See more in Model zoo, Comparisons.md
- ✅ Integrated to Huggingface Spaces with Gradio. See Gradio Web Demo.
- ✅ Support enhancing non-face regions (background) with Real-ESRGAN.
- ✅ We provide a clean version of GFPGAN, which does not require CUDA extensions.
- ✅ We provide an updated model without colorizing faces.
If GFPGAN is helpful in your photos/projects, please help to ⭐ this repo or recommend it to your friends. Thanks😊
Other recommended projects:
▶️ Real-ESRGAN: A practical algorithm for general image restoration
▶️ BasicSR: An open-source image and video restoration toolbox
▶️ facexlib: A collection that provides useful face-relation functions
▶️ HandyView: A PyQt5-based image viewer that is handy for view and comparison
[Paper] [Project Page] [Demo]
Xintao Wang, Yu Li, Honglun Zhang, Ying Shan
Applied Research Center (ARC), Tencent PCG
- Python >= 3.7 (Recommend to use Anaconda or Miniconda)
- PyTorch >= 1.7
- Option: NVIDIA GPU + CUDA
- Option: Linux
We now provide a clean version of GFPGAN, which does not require customized CUDA extensions.