---
pipeline_tag: image-to-image
tags:
- ESRGAN
- super-resolution
- upscaling
- x4
- image-enhancement
- safetensors
---
# ESRGAN 4x Models

## Overview
This repository contains two powerful 4x super-resolution models based on ESRGAN
(Enhanced Super-Resolution Generative Adversarial Network). These models are specifically
designed to upscale images to four times their original resolution while maintaining or
enhancing visual fidelity.

## Model Descriptions

1. **Nickelback_70000G.safetensors**
   - **Architecture**: ESRGAN
   - **Strengths**   : Photorealistic upscaling of real-world images.
   - **Use Cases**   : Best suited for scenarios requiring photorealistic output, such as enhancing
                       rofessional photography or increasing resolution in images where realistic
                       content is crucial.

2. **foolhardy_Remacri.safetensors**
   - **Architecture**: ESRGAN
   - **Strengths**   : Enhances the quality of comics and anime images by improving detailed texture and edge sharpness.
   - **Use Cases**   : Ideal for users who need high-quality outputs in anime and comic book styles,
                       where fine details are important for narrative or artistic value.

## Usage Instructions

1. **Installation**: Ensure you have `PyTorch` installed along with any other necessary libraries in your environment.
2. **Loading Models**: Load the models using a script that supports ESRGAN model inference (such as `Real-ESRGAN` or custom scripts).
3. **Inference**: Run your images through one of these models to upscale them by 4x.

## Evaluation

These models were part of an evaluation process similar to "Hires.fix":
1. The image was first generated artificially using Z-Image for 8 steps at a resolution of 1088x1600 px.
2. The image was then downsampled by a factor of 2 using bilinear interpolation.
3. One of the ESRGAN models (x4) was applied to upscale the image.
4. The image was downsampled again by a factor of 2 using bilinear interpolation.
5. Finally, 2 steps of Z-Image were applied.

The resulting images had the same resolution as the original but with improved quality.
Among these checkpoints, Nickelback_70000G.safetensors and foolhardy_Remacri.safetensors
produced the best image quality for photographic and illustrative/comic/anime content
respectively, according to my evaluation.

## Limitations

Both checkpoints are based on ESRGAN and therefore inherit its limitations:
- They should not be used in applications where accuracy and consistency are critical, such as medical imaging or legal document processing.