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--- |
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language: en |
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tags: |
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- mohsin-riad |
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- image-processing |
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- super-resolution |
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- upscaling |
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- real-esrgan |
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license: apache-2.0 |
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base_model: xinntao/realesrgan-x4plus |
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datasets: |
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- DIV2K |
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- Flickr2K |
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library_name: pytorch |
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pipeline_tag: image-to-image |
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--- |
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# Upscaler-Ultra |
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## Model Description |
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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. |
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### Model Architecture |
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This model is based on RealESRGAN (Real-Enhanced Super-Resolution Generative Adversarial Networks), which utilizes: |
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- Enhanced ESRGAN architecture optimized for real-world image degradation |
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- Adversarial training with improved discriminator networks |
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- Perceptual loss functions for better visual quality |
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- Specialized training techniques for handling complex real-world artifacts |
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## Intended Uses & Limitations |
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### Intended Uses |
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- Image upscaling and enhancement |
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- Photo restoration and quality improvement |
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- Digital art enhancement |
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- Low-resolution image improvement |
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- Professional photography post-processing |
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- Real-world image super-resolution tasks |
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### Limitations |
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- Performance may vary depending on input image quality and degradation type |
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- Very low-resolution inputs might not achieve optimal results |
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- Processing time increases with input image size |
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- May not preserve extremely fine details in heavily compressed images |
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- Best suited for natural images rather than synthetic graphics |
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### Base Model |
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Built upon [RealESRGAN](https://github.com/xinntao/Real-ESRGAN), specifically the RealESRGAN-x4plus model, with additional fine-tuning and optimizations. |
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### API Usage |
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The model is available through Replicate API: |
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```python |
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import replicate |
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output = replicate.run( |
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"mohsin-riad/upscaler-ultra", |
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input={"image": "path_to_your_image.jpg"} |
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) |
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``` |
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Replicate: [mohsin-riad/upscaler-ultra](https://replicate.com/mohsin-riad/upscaler-ultra) |
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## Citation |
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If you use this model in your research, please cite: |
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```bibtex |
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@misc{upscaler-ultra, |
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author = {Mohsin Riad}, |
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title = {Upscaler-Ultra: High-Quality Image Upscaling Model Based on RealESRGAN}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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journal = {Hugging Face Hub}, |
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howpublished = {\url{https://huggingface.co/mohsin-riad/upscaler-ultra}} |
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} |
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``` |
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Please also cite the original RealESRGAN work: |
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```bibtex |
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@InProceedings{wang2021realesrgan, |
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author = {Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan}, |
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title = {Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data}, |
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booktitle = {International Conference on Computer Vision Workshops (ICCVW)}, |
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date = {2021} |
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} |
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``` |
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## Additional Information |
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For questions and feedback, please contact: |
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- GitHub: [mohsin-riad](http://github.com/mohsin-riad) |
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- Model Repository: [upscaler-ultra](http://github.com/mohsin-riad/upscaler-ultra) |
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### License |
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This model is released under the Apache License 2.0. |
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### Acknowledgments |
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- Special thanks to the RealESRGAN team for the foundational architecture |
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- Thanks to the open-source community and all contributors who have helped in the development of this model |
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- Built upon the excellent work of Xintao Wang et al. on Real-ESRGAN |
