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Overview

Real-ESRGAN integrates with GFPGAN (Generative Facial Prior GAN) to provide enhanced face restoration. This combination uses Real-ESRGAN for overall image super-resolution and GFPGAN to specifically enhance facial details.

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How It Works

When face enhancement is enabled:
  1. Detection: GFPGAN detects faces in the image
  2. Enhancement: Each detected face is enhanced using GFPGANv1.3
  3. Background: Real-ESRGAN upscales the background
  4. Composition: Enhanced faces are pasted back into the upscaled background
This approach ensures both high-quality face restoration and overall image super-resolution.

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Quick Start

1

Install Dependencies

Install GFPGAN and facexlib:
pip install facexlib
pip install gfpgan
2

Run with Face Enhancement

python inference_realesrgan.py -n RealESRGAN_x4plus -i input.jpg --face_enhance
3

Check Results

The output image will have enhanced faces with upscaled background.

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Usage Examples

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Basic Face Enhancement

python inference_realesrgan.py -n RealESRGAN_x4plus -i portrait.jpg --face_enhance

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Advanced Usage

python inference_realesrgan.py -n RealESRGAN_x4plus -i large_photo.jpg \
  --face_enhance --tile 400 --tile_pad 10

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Compatible Models

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GFPGAN Integration Details

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Model Information

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Model
GFPGANv1.3
GFPGAN version 1.3 is automatically downloaded and used for face enhancement.Download URL:
https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth
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Architecture
clean
Uses the ‘clean’ architecture variant of GFPGAN.
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Channel Multiplier
2
Set to 2 for optimal quality.
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Background Upsampler
RealESRGAN
Real-ESRGAN is used as the background upsampler, ensuring consistent quality across the entire image.

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Face Detection Parameters

# Internal GFPGAN parameters used by Real-ESRGAN
has_aligned=False       # Faces are not pre-aligned
only_center_face=False  # Detect all faces in the image
paste_back=True         # Paste enhanced faces back to the image
  • has_aligned: Set to False as input faces are not pre-aligned
  • only_center_face: Set to False to enhance all detected faces
  • paste_back: Set to True to composite faces back into the upscaled image

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Use Cases

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Portrait Photography

python inference_realesrgan.py -n RealESRGAN_x4plus -i portraits --face_enhance
Enhance professional or casual portraits with improved facial details.

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Old Family Photos

python inference_realesrgan.py -n RealESRGAN_x4plus -i old_photos \
  --face_enhance --tile 400
Restore and upscale vintage family photographs. Use tiling for large scans.

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Group Photos

python inference_realesrgan.py -n RealESRGAN_x4plus -i group_photo.jpg --face_enhance
Enhance all faces in group photos - GFPGAN detects and enhances each face individually.

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Low-Resolution Photos

python inference_realesrgan.py -n RealESRGAN_x4plus -i low_res.jpg \
  --face_enhance --outscale 4
Upscale and enhance low-resolution images from older cameras or compressed sources.

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Video Frames with Faces

# Note: Face enhancement works but is slow for videos
python inference_realesrgan_video.py -i video.mp4 -n RealESRGAN_x4plus \
  -s 2 --face_enhance
Face enhancement significantly increases processing time for videos. Consider processing only key frames or using face enhancement selectively.

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Performance Considerations

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Processing Time

Face enhancement adds overhead:
  • Without face enhancement: ~0.1-0.5s per image (depends on size and GPU)
  • With face enhancement: ~0.5-2s per image (depends on number of faces)
Batch ProcessingProcess multiple images in batch mode for better efficiency:
python inference_realesrgan.py -n RealESRGAN_x4plus -i photos_folder \
  --face_enhance -o enhanced_folder

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Memory Usage

CUDA Out of MemoryFace enhancement increases memory usage. If you encounter memory errors:
  1. Use tiling: 
    python inference_realesrgan.py -n RealESRGAN_x4plus -i photo.jpg \
  --face_enhance --tile 400
  2. Use a smaller model: 
    python inference_realesrgan.py -n realesr-general-x4v3 -i photo.jpg \
  --face_enhance
  3. Process smaller batches or individual images

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GPU Selection

For multi-GPU systems: 
# Use specific GPU
python inference_realesrgan.py -n RealESRGAN_x4plus -i photo.jpg \
  --face_enhance --gpu-id 0

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Comparison Examples

python inference_realesrgan.py -n RealESRGAN_x4plus -i portrait.jpg
Results:
  • Without: Good overall upscaling, but faces may lack fine details
  • With: Sharper facial features, better skin texture, enhanced eye details

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Tips for Best Results

Input Image QualityBetter source images = better results:
  • Use the highest resolution source available
  • Avoid heavily compressed images when possible
  • Ensure faces are reasonably visible in the input
Face SizeFace enhancement works best when:
  • Faces are at least 64x64 pixels in the input
  • Faces are clearly visible (not too dark or blurry)
  • Faces are front-facing or at slight angles
For very small faces, upscale without face enhancement first, then apply face enhancement to the upscaled result.
Multiple FacesGFPGAN automatically detects and enhances all faces in the image:
python inference_realesrgan.py -n RealESRGAN_x4plus -i group_photo.jpg --face_enhance
No special configuration needed for multiple faces.
Output FormatUse PNG for best quality preservation:
python inference_realesrgan.py -n RealESRGAN_x4plus -i photo.jpg \
  --face_enhance --ext png

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Troubleshooting

Install the required dependencies:
pip install facexlib
pip install gfpgan
If you still encounter issues:
pip install --upgrade gfpgan facexlib
Check if you’re using a compatible model:Compatible:
  • RealESRGAN_x4plus ✓
  • RealESRNet_x4plus ✓
  • RealESRGAN_x2plus ✓
  • realesr-general-x4v3 ✓
Not Compatible:
  • RealESRGAN_x4plus_anime_6B ✗ (automatically disabled)
  • realesr-animevideov3 ✗ (automatically disabled)
This can happen with heavily compressed or very low-quality inputs. Try:
  1. Use a higher quality source image
  2. Try without face enhancement if the result is too aggressive
  3. Use RealESRNet_x4plus for smoother results: 
    python inference_realesrgan.py -n RealESRNet_x4plus -i photo.jpg --face_enhance
GFPGAN may miss faces that are:
  • Too small (< 64x64 pixels)
  • At extreme angles
  • Partially occluded
  • Very dark or blurry
Solutions:
  1. Pre-crop and process faces individually
  2. Manually adjust brightness/contrast before processing
  3. Try upscaling first without face enhancement, then apply face enhancement
Face enhancement requires additional memory. Solutions:
  1. Use tiling: 
    python inference_realesrgan.py -n RealESRGAN_x4plus -i photo.jpg \
  --face_enhance --tile 400
  2. Use smaller model: 
    python inference_realesrgan.py -n realesr-general-x4v3 -i photo.jpg \
  --face_enhance
  3. Process images individually instead of batch processing

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Standalone GFPGAN

For more control over face enhancement, you can use GFPGAN directly: 
# Install GFPGAN
pip install gfpgan

# Use GFPGAN standalone
python -m gfpgan.inference_gfpgan -i inputs -o results
See the GFPGAN repository for more options.

GFPGAN Project

Official GFPGAN repository

General Images

Learn about models for real-world photos

Basic Inference

Complete reference for inference options

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