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Overview

Real-ESRGAN provides two training model classes:
  • RealESRGANModel: Full GAN training with discriminator
  • RealESRNetModel: Training without GAN losses (generator only)
Both models implement realistic degradation synthesis on GPU and support high-order degradation for training Real-World Blind Super-Resolution.

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RealESRGANModel

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Class Definition

from realesrgan.models.realesrgan_model import RealESRGANModel

model = RealESRGANModel(opt)

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Description

RealESRGANModel extends SRGANModel from BasicSR and is designed for training Real-ESRGAN with adversarial losses. It performs:
  1. Random synthesis of low-quality (LQ) images on GPU using realistic degradations
  2. Network optimization with GAN training (generator and discriminator)

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Constructor Parameters

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opt
dict
required
Configuration dictionary containing training options. Key parameters include:
  • queue_size (int, default: 180): Size of training pair pool for increasing degradation diversity
  • high_order_degradation (bool): Enable two-order degradation synthesis
  • scale (int): Upsampling scale factor
  • gt_size (int): Ground truth patch size for training
  • l1_gt_usm (bool): Whether to use USM-sharpened GT for L1 loss
  • percep_gt_usm (bool): Whether to use USM-sharpened GT for perceptual loss
  • gan_gt_usm (bool): Whether to use USM-sharpened GT for GAN loss

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Key Methods

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feed_data()

Accepts data from dataloader and applies two-order degradations to synthesize LQ images. 
model.feed_data(data)
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data
dict
required
Dictionary containing:
  • gt: Ground truth high-resolution images
  • kernel1: First blur kernel
  • kernel2: Second blur kernel
  • sinc_kernel: Final sinc filter kernel
  • lq: Low-quality images (for validation/paired training)
During training with high_order_degradation=True, this method synthesizes realistic degradations including blur, resize, noise, and JPEG compression applied twice in sequence.

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optimize_parameters()

Performs one optimization step for both generator and discriminator. 
model.optimize_parameters(current_iter)
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current_iter
int
required
Current training iteration number.
This method:
  1. Optimizes the generator with pixel loss, perceptual loss, and adversarial loss
  2. Optimizes the discriminator to distinguish real vs. fake images
  3. Updates EMA (Exponential Moving Average) model if enabled

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nondist_validation()

Runs validation without synthetic degradation process. 
model.nondist_validation(dataloader, current_iter, tb_logger, save_img)

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Degradation Pipeline

The model applies a two-order degradation pipeline during training: First Degradation:
  1. Blur with kernel1
  2. Random resize (up/down/keep)
  3. Add Gaussian or Poisson noise
  4. JPEG compression
Second Degradation:
  1. Optional blur with kernel2
  2. Random resize
  3. Add Gaussian or Poisson noise
  4. JPEG compression + sinc filter

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Training Pair Pool

The model uses _dequeue_and_enqueue() to maintain a training pair pool that increases degradation diversity across batches.

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RealESRNetModel

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Class Definition

from realesrgan.models.realesrnet_model import RealESRNetModel

model = RealESRNetModel(opt)

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Description

RealESRNetModel extends SRModel from BasicSR and is designed for training Real-ESRGAN without GAN losses. It’s useful for:
  • Pre-training the generator before GAN training
  • Training models without adversarial losses
  • Faster convergence for initial training stages
RealESRNetModel is trained without GAN losses but uses the same degradation synthesis pipeline as RealESRGANModel.

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Constructor Parameters

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opt
dict
required
Configuration dictionary. Key parameters:
  • queue_size (int, default: 180): Training pair pool size
  • high_order_degradation (bool): Enable two-order degradation synthesis
  • scale (int): Upsampling scale factor
  • gt_size (int): Ground truth patch size
  • gt_usm (bool): Apply USM sharpening to ground truth images

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Key Methods

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feed_data()

Same degradation synthesis as RealESRGANModel, but with optional USM sharpening on GT. 
model.feed_data(data)
Unlike RealESRGANModel, this version applies USM sharpening directly to GT if gt_usm=True, rather than maintaining separate GT and GT_USM versions.

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nondist_validation()

Validation without synthetic degradations. 
model.nondist_validation(dataloader, current_iter, tb_logger, save_img)

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Differences from RealESRGANModel

FeatureRealESRGANModelRealESRNetModel
Base ClassSRGANModelSRModel
GAN TrainingYes (G + D)No (G only)
USM GT VariantsMultiple (GT, GT_USM)Single GT with optional USM
Training SpeedSlowerFaster
Image QualityHigher (with GAN)Good (no adversarial artifacts)

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Usage Example

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Training Configuration

import torch
from realesrgan.models.realesrgan_model import RealESRGANModel

# Define training options
opt = {
    'name': 'RealESRGAN_x4plus',
    'scale': 4,
    'queue_size': 180,
    'high_order_degradation': True,
    'gt_size': 256,
    
    # First degradation
    'resize_prob': [0.2, 0.7, 0.1],
    'resize_range': [0.15, 1.5],
    'gaussian_noise_prob': 0.5,
    'noise_range': [1, 30],
    'poisson_scale_range': [0.05, 3],
    'gray_noise_prob': 0.4,
    'jpeg_range': [30, 95],
    
    # Second degradation
    'second_blur_prob': 0.8,
    'resize_prob2': [0.3, 0.4, 0.3],
    'resize_range2': [0.3, 1.2],
    'gaussian_noise_prob2': 0.5,
    'noise_range2': [1, 25],
    'poisson_scale_range2': [0.05, 2.5],
    'gray_noise_prob2': 0.4,
    'jpeg_range2': [30, 95],
    
    # Loss options
    'l1_gt_usm': False,
    'percep_gt_usm': False,
    'gan_gt_usm': False,
    
    # Network and training
    'network_g': {...},
    'network_d': {...},
    'train': {...}
}

# Initialize model
model = RealESRGANModel(opt)

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Pre-training with RealESRNetModel

from realesrgan.models.realesrnet_model import RealESRNetModel

# First stage: Train without GAN
opt_psnr = opt.copy()
opt_psnr['name'] = 'RealESRNet_x4plus'
model_psnr = RealESRNetModel(opt_psnr)

# Train for 200k iterations...
# Then switch to GAN training

model_gan = RealESRGANModel(opt)
model_gan.load_network(model_psnr.net_g, 'net_g')

# Continue training with GAN losses...

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Source References

  • RealESRGANModel: realesrgan/models/realesrgan_model.py:14
  • RealESRNetModel: realesrgan/models/realesrnet_model.py:13

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