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[ICLR 2024] Controlling Vision-Language Models for Universal Image Restoration. 5th place in the NTIRE 2024 Restore Any Image Model in the Wild Challenge.
| Date | Stars |
|---|---|
| 2026-07-31 | 816 |
| 2026-08-06 | 817 |
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## Controlling Vision-Language Models for Universal Image Restoration <br><sub>Official PyTorch Implementation of DA-CLIP. </sub> [Project Page](https://algolzw.github.io/daclip-uir) | [Paper](https://arxiv.org/abs/2310.01018) | [Model Card 🤗](https://huggingface.co/weblzw/daclip-uir-ViT-B-32-irsde) [](https://colab.research.google.com/github/camenduru/daclip-uir-colab/blob/main/daclip_uir_gradio_colab.ipynb) [](https://huggingface.co/spaces/fffiloni/DA-CLIP) [](https://replicate.com/cjwbw/daclip-uir)  Our follow-up work [Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models](https://arxiv.org/abs/2404.09732) (CVPRW 2024) presents a [posterior sampling](https://github.com/Algolzw/daclip-uir/blob/74b7851827b485287971300e4b2a56ea0f8f1d75/universal-image-restoration/utils/sde_utils.py#L297) for better image generation and handles real-world mixed-degradation images similar to [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN). ### Updates [**2024.04.16**] Our follow-up paper "[Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models](https://arxiv.org/abs/2404.09732)" is on [ArXiv](https://arxiv.org/abs/2404.09732) now! <br> [**2024.04.15**] Updated a [wild-IR](https://github.com/Algolzw/daclip-uir/tree/main/universal-image-restoration/config/wild-ir) model for real-world degradations and the [posterior sampling](https://github.com/Algolzw/daclip-uir/blob/74b7851827b485287971300e4b2a56ea0f8f1d75/universal-image-restoration/utils/sde_utils.py#L297) for better image generation. The pretrained weights [wild-ir.pth](https://drive.google.com/file/d/1DqHL61gZJI-3EGsXPJrhERrrst7ZR6gP/view?usp=sharing) and [wild-daclip_ViT-L-14.pt](https://drive.google.com/file/d/1eVxgvwzwLl5oWSVIgnA2gycV6ewLEVJd/view?usp=sharing) are also provided for wild-ir. <br> [**2024.01.20**] 🎉🎉🎉 Our DA-CLIP paper was accepted by ICLR 2024 🎉🎉🎉 We further provide a more robust model in the [model card](https://huggingface.co/weblzw/daclip-uir-ViT-B-32-irsde/tree/main). <br> [**2023.10.25**] Added [dataset links](https://github.com/Algolzw/daclip-uir#dataset-links) for training and testing. <br> [**2023.10.13**] Added the Replicate [demo](https://replicate.com/cjwbw/daclip-uir) and [api](https://replicate.com/cjwbw/daclip-uir/api)🔥. Thanks to [@chenxwh](https://github.com/chenxwh)!!! We updated the Hugging Face [demo](https://huggingface.co/spaces/fffiloni/DA-CLIP)🔥 and online Colab [demo](https://colab.research.google.com/github/camenduru/daclip-uir-colab/blob/main/daclip_uir_gradio_colab.ipynb)🔥. Thanks to [@fffiloni](https://github.com/fffiloni) and [@camenduru](https://github.com/camenduru) !!! We also made a [Model Card](https://huggingface.co/weblzw/daclip-uir-ViT-B-32-irsde) in Hugging Face 🤗 and provided more [examples](https://drive.google.com/file/d/1C1nmP5kJXzxrULxTMVWF5P30qezqP6kn/view?usp=sharing) for testing.<br> [**2023.10.09**] The **pretrained weights** of DA-CLIP and the Universal IR model are released in [link1](https://drive.google.com/file/d/1A6u4CaVrcpcZckGUNzEXqMF8x_JXsZdX/view?usp=sharing) and [link2](https://drive.google.com/file/d/1eXsyrmAbWOvhIY4Wbt5v4IxaggA5aZMG/view?usp=sharing), respectively. In addition, we also provide a [Gradio](https://gradio.app/) app file for the case that you want to [test your own images](https://github.com/Algolzw/daclip-uir#Gradio).<br> ## How to Run the Code? ### Dependencies * OS: Ubuntu 20.04 * nvidia: - cuda: 11.4 * python 3.8 ### Install We advise you first create a virtual environment with: ```bash python3 -m venv .env source .env/bin/activate pip install -U pip pip install -r requirements.txt ``` ### DA-CLIP Usage Get into the `universal-image-restoration` directory and
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