upscaler-ultra

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