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Default: inputs/whole_imgs -o output Output folder. h show this help -i input Input image or folder. Usage: python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2. If you want to use the original model in our paper, please see PaperModel.md for installation. We now provide a clean version of GFPGAN, which does not require customized CUDA extensions. Python >= 3.7 (Recommend to use Anaconda or Miniconda).Xintao Wang, Yu Li, Honglun Zhang, Ying ShanĪpplied Research Center (ARC), Tencent PCG 📖 GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior ▶️ HandyView: A PyQt5-based image viewer that is handy for view and comparison ▶️ facexlib: A collection that provides useful face-relation functions ▶️ BasicSR: An open-source image and video restoration toolbox ▶️ Real-ESRGAN: A practical algorithm for general image restoration If GFPGAN is helpful in your photos/projects, please help to ⭐ this repo or recommend it to your friends.

✅ We provide an updated model without colorizing faces.✅ We provide a clean version of GFPGAN, which does not require CUDA extensions.

✅ Support enhancing non-face regions (background) with Real-ESRGAN.

✅ Integrated to Huggingface Spaces with Gradio.

✅ Add V1.3 model, which produces more natural restoration results, and better results on very low-quality / high-quality inputs.

✅ Add V1.4 model, which produces slightly more details and better identity than V1.3.

❓ Frequently Asked Questions can be found in FAQ.md. It leverages rich and diverse priors encapsulated in a pretrained face GAN ( e.g., StyleGAN2) for blind face restoration. GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration. You may also want to check our new updates on the tiny models for anime images and videos in Real-ESRGAN 😊

Colab Demo for GFPGAN (Another Colab Demo for the original paper model).