---
language: en
tags:
- mohsin-riad
- image-processing
- super-resolution
- upscaling
- real-esrgan
license: apache-2.0
base_model: xinntao/realesrgan-x4plus
datasets:
- DIV2K
- Flickr2K
library_name: pytorch
pipeline_tag: image-to-image
---

# Upscaler-Ultra

![](https://replicate.delivery/pbxt/N5xUyx5jJ9DOFRm1dQaKbPM3CBaovTL2V04xwCPBhsQmMORp/Screenshot%202025-05-30%20at%203.17.55%E2%80%AFAM.png)

## Model Description

Upscaler-Ultra is a high-performance image upscaling model built upon RealESRGAN architecture. This model is designed to enhance image resolution while maintaining high quality and preserving fine details. The model specializes in upscaling low-resolution images to higher resolutions with minimal artifacts and maximum clarity, leveraging the proven effectiveness of Real-ESRGAN for practical image restoration tasks.

### Model Architecture

This model is based on RealESRGAN (Real-Enhanced Super-Resolution Generative Adversarial Networks), which utilizes:
- Enhanced ESRGAN architecture optimized for real-world image degradation
- Adversarial training with improved discriminator networks
- Perceptual loss functions for better visual quality
- Specialized training techniques for handling complex real-world artifacts

## Intended Uses & Limitations

### Intended Uses

- Image upscaling and enhancement
- Photo restoration and quality improvement
- Digital art enhancement
- Low-resolution image improvement
- Professional photography post-processing
- Real-world image super-resolution tasks

### Limitations

- Performance may vary depending on input image quality and degradation type
- Very low-resolution inputs might not achieve optimal results
- Processing time increases with input image size
- May not preserve extremely fine details in heavily compressed images
- Best suited for natural images rather than synthetic graphics

### Base Model
Built upon [RealESRGAN](https://github.com/xinntao/Real-ESRGAN), specifically the RealESRGAN-x4plus model, with additional fine-tuning and optimizations.

### API Usage

The model is available through Replicate API:
```python
import replicate

output = replicate.run(
    "mohsin-riad/upscaler-ultra",
    input={"image": "path_to_your_image.jpg"}
)
```

Replicate: [mohsin-riad/upscaler-ultra](https://replicate.com/mohsin-riad/upscaler-ultra)
## Citation

If you use this model in your research, please cite:

```bibtex
@misc{upscaler-ultra,
  author = {Mohsin Riad},
  title = {Upscaler-Ultra: High-Quality Image Upscaling Model Based on RealESRGAN},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
  howpublished = {\url{https://huggingface.co/mohsin-riad/upscaler-ultra}}
}
```

Please also cite the original RealESRGAN work:

```bibtex
@InProceedings{wang2021realesrgan,
    author    = {Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
    title     = {Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
    booktitle = {International Conference on Computer Vision Workshops (ICCVW)},
    date      = {2021}
}
```

## Additional Information

For questions and feedback, please contact:
- GitHub: [mohsin-riad](http://github.com/mohsin-riad)
- Model Repository: [upscaler-ultra](http://github.com/mohsin-riad/upscaler-ultra)

### License

This model is released under the Apache License 2.0. 

### Acknowledgments

- Special thanks to the RealESRGAN team for the foundational architecture
- Thanks to the open-source community and all contributors who have helped in the development of this model
- Built upon the excellent work of Xintao Wang et al. on Real-ESRGAN