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Overview

Real-ESRGAN provides a specialized model optimized for anime images, illustrations, and artwork. The RealESRGAN_x4plus_anime_6B model is significantly smaller than the general model while delivering excellent results for anime content.

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RealESRGAN_x4plus_anime_6B Model

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Key Features

  • Optimized for Anime: Trained specifically on anime and illustration data
  • Smaller Size: Only 6 RRDB blocks vs. 23 blocks in the general model (~17MB vs ~64MB)
  • Fast Processing: Faster inference due to smaller architecture
  • High Quality: Preserves anime-style details and aesthetics

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Download and Install

1

Download Model

wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P weights
2

Run Inference

python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i inputs
3

Check Results

Enhanced anime images are saved in the results folder with 4x upscaling.

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Usage Examples

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Basic Upscaling

python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i anime_image.jpg

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Advanced Options

python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i large_anime.png --tile 400

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Model Specifications

PropertyValue
ArchitectureRRDBNet with 6 blocks
Upscale Factor4x
Model Size~17MB
Input Channels3 (RGB)
Output Channels3 (RGB)
Feature Channels64
Growth Channels32
Training DataAnime images and illustrations

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Comparison with Other Models

FeatureAnime 6Bx4plus General
Anime QualityExcellentGood
General ImagesGoodExcellent
Model Size17MB64MB
SpeedFasterSlower
Best ForAnime/IllustrationsPhotos/Natural images

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Common Use Cases

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Upscaling Anime Screenshots

python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i screenshots -o upscaled
Perfect for enhancing low-resolution anime screenshots from streaming services.

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Processing Manga Panels

python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i manga_pages -o enhanced --ext png
Preserves fine line details in manga artwork.

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Enhancing Anime Artwork

python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i artwork.jpg --outscale 4 --ext png
Upscale digital artwork and illustrations while maintaining anime aesthetic.

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Processing Game Assets

python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i game_assets -o enhanced_assets --tile 400
Upscale anime-style game sprites and textures. Use tiling for large textures.

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Tips for Best Results

Image FormatUse PNG format for anime images when possible to avoid JPEG compression artifacts:
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i input.jpg --ext png
Transparent ImagesThe anime model handles RGBA images with transparency:
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i anime_with_alpha.png
Output is automatically saved as PNG to preserve the alpha channel.
Face Enhancement Not RecommendedThe --face_enhance option is designed for realistic faces and may not work well with anime faces. It’s recommended to not use face enhancement with anime models:
# Don't use --face_enhance with anime images
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i anime.jpg
Choosing Output ScaleFor anime images:
  • 2x: Moderate enhancement, good for already decent quality
  • 4x: Standard upscaling for most anime content
  • Higher: Use for very low resolution sources
# For moderate quality anime
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i anime.jpg --outscale 2

# For low quality anime
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i anime.jpg --outscale 4

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Performance Optimization

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Memory Usage

The anime model uses less memory than the general model: 
# No tiling needed for most images
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i anime.jpg

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Batch Processing

Process multiple anime images efficiently: 
# Process entire folder
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i anime_collection -o upscaled_collection

# With custom settings
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i anime_collection \
  -o upscaled_collection --ext png --suffix enhanced

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GPU Selection

Use specific GPU for processing: 
# Use GPU 0
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i anime.jpg --gpu-id 0

# Use GPU 1
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i anime.jpg --gpu-id 1

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NCNN Executable Alternative

For users who prefer the portable NCNN executable:
1

Download NCNN Executable

Download the appropriate version for your platform:
2

Run NCNN Version

./realesrgan-ncnn-vulkan.exe -i anime.jpg -o output.png -n realesrgan-x4plus-anime
The NCNN version doesn’t require CUDA or PyTorch installation.

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Troubleshooting

This is normal for the anime model as it’s trained to reduce noise. If you want to preserve more texture:
  1. Use the general model instead: 
    python inference_realesrgan.py -n RealESRGAN_x4plus -i anime.jpg
  2. Or use a lower output scale: 
    python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i anime.jpg --outscale 2
Ensure you’re using PNG output for best color preservation:
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i anime.jpg --ext png
Use tiling to reduce memory usage:
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i anime.png --tile 400
Try smaller tile sizes (256, 300) if still encountering issues.

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Next Steps

Anime Videos

Learn to upscale anime videos with realesr-animevideov3

General Images

Explore models for real-world photos

NCNN Executable

Use the portable executable without Python

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