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| title: CCSR Upscaler | |
| emoji: 👁 | |
| colorFrom: gray | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 4.42.0 | |
| app_file: app.py | |
| pinned: true | |
| short_description: Upscale an image using CCSR | |
| # CCSR Upscaler | |
| This Gradio space implements the Continuous Contrastive Super-Resolution (CCSR) model for image upscaling. CCSR is a state-of-the-art super-resolution method that can upscale images while preserving details and enhancing quality. | |
| ## Features | |
| - Upload any image for upscaling | |
| - Adjust super-resolution scale (1x to 8x) | |
| - Fine-tune parameters like t_max and t_min | |
| - Choose from different color fixing methods | |
| ## How to Use | |
| 1. Upload an image you want to upscale | |
| 2. Adjust the SR Scale slider to set the upscaling factor | |
| 3. Fine-tune t_max and t_min values if desired | |
| 4. Select a color fixing method from the dropdown | |
| 5. Click "Submit" to generate the upscaled image | |
| ## Model Details | |
| This space uses the CCSR model trained on real-world images. The model checkpoint and configuration are loaded from: | |
| - Checkpoint: `weights/real-world_ccsr.ckpt` | |
| - Config: `configs/model/ccsr_stage2.yaml` | |
| ## Requirements | |
| The main dependencies for this project are listed in the `requirements.txt` file, including: | |
| - torch | |
| - torchvision | |
| - gradio | |
| - einops | |
| - pytorch-lightning | |
| - omegaconf | |
| - open-clip-torch | |
| - xformers | |
| - taming-transformers | |
| ## Acknowledgements | |
| This implementation is based on the CCSR model. For more details about the original work, please refer to the [CCSR GitHub repository](https://github.com/camenduru/CCSR). |