Upscaler-Ultra

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, specifically the RealESRGAN-x4plus model, with additional fine-tuning and optimizations.

API Usage

The model is available through Replicate API:

import replicate

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

Replicate: mohsin-riad/upscaler-ultra

Citation

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

@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:

@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
Model Repository: 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