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General models are designed for natural images, photographs, and real-world content. These models excel at upscaling portraits, landscapes, and everyday photography.

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Available Models

RealESRGAN_x4plus

Primary model with best quality for 4x upscaling

RealESRGAN_x2plus

Optimized for 2x upscaling

realesr-general-x4v3

Lightweight model for fast processing

RealESRNet_x4plus

MSE-based model for smooth results

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Model Specifications

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RealESRGAN_x4plus

Recommended: This is the flagship model for general images, providing the best quality for 4x upscaling.
PropertyValue
Scale4x
ArchitectureRRDBNet
RRDB Blocks23
Features64
Growth Channels32
DownloadRealESRGAN_x4plus.pth
# Download and use
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P weights
python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs -o outputs

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RealESRGAN_x2plus

PropertyValue
Scale2x
ArchitectureRRDBNet
RRDB Blocks23
Features64
Growth Channels32
DownloadRealESRGAN_x2plus.pth
# Download and use
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth -P weights
python inference_realesrgan.py -n RealESRGAN_x2plus -i inputs -o outputs

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realesr-general-x4v3

Lightweight: This model consumes much less GPU memory and processing time, making it ideal for resource-constrained environments.
PropertyValue
Scale1x, 2x, 3x, or 4x (variable)
ArchitectureSRVGGNetCompact
Convolution Layers32
Features64
SizeSmall (S)
Downloadrealesr-general-x4v3.pth
# Download and use with custom scale
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P weights
python inference_realesrgan.py -n realesr-general-x4v3 -i inputs -s 2  # 2x upscaling
The realesr-general-x4v3 model supports adjustable denoise strength:
# Weak denoise (keep more original texture)
python inference_realesrgan.py -n realesr-general-x4v3 -i inputs -dn 0.2

# Strong denoise (smoother result)
python inference_realesrgan.py -n realesr-general-x4v3 -i inputs -dn 0.8
The -dn parameter ranges from 0 (weak denoise) to 1 (strong denoise). Default is 0.5.

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RealESRNet_x4plus

This model uses MSE loss instead of GAN training, resulting in over-smooth effects. Use this if you prefer less sharp, more natural-looking results.
PropertyValue
Scale4x
ArchitectureRRDBNet
RRDB Blocks23
Training LossMSE (no GAN)
DownloadRealESRNet_x4plus.pth
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth -P weights
python inference_realesrgan.py -n RealESRNet_x4plus -i inputs -o outputs

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Legacy Model

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ESRGAN (Official)

The original ESRGAN model trained on DF2K and OST datasets:
PropertyValue
Scale4x
DownloadESRGAN_SRx4_DF2KOST_official-ff704c30.pth

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Discriminator Models

Discriminator models are used for fine-tuning and training. You don’t need these for inference.
DiscriminatorCorresponding Generator Model
RealESRGAN_x4plus_netD.pthRealESRGAN_x4plus
RealESRGAN_x2plus_netD.pthRealESRGAN_x2plus

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Model Comparison

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Quality vs Speed

Quality:  RealESRGAN_x4plus > RealESRGAN_x2plus > realesr-general-x4v3 > RealESRNet_x4plus
Speed:    realesr-general-x4v3 > RealESRNet_x4plus > RealESRGAN_x2plus > RealESRGAN_x4plus
Memory:   realesr-general-x4v3 < RealESRNet_x4plus < RealESRGAN_x2plus < RealESRGAN_x4plus

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Use Case Recommendations

Best Quality

RealESRGAN_x4plusUse for final production work where quality is paramount.

Fast Processing

realesr-general-x4v3Use for batch processing or when speed matters.

2x Upscaling

RealESRGAN_x2plusUse when you only need 2x resolution increase.

Smooth Results

RealESRNet_x4plusUse for artistic or stylized results without sharpening.

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Usage Examples

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Basic Inference

# Using RealESRGAN_x4plus (recommended)
python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs

# Using lightweight model
python inference_realesrgan.py -n realesr-general-x4v3 -i inputs

# Custom output scale
python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs -s 2  # 2x instead of 4x

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Advanced Options

# With tiling for large images
python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs -t 400

# With face enhancement
python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs --face_enhance

# Custom output folder and format
python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs -o results --ext jpg

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Next Steps

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