| --- |
| 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 |
|
|
|  |
|
|
| ## 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 |
