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-# InstructIR: High-Quality Image Restoration Following Human Instructions (ECCV 2024)
-[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2401.16468)
-<a href="https://colab.research.google.com/drive/1OrTvS-i6uLM2Y8kIkq8ZZRwEQxQFchfq?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
-[![Hugging Face](https://img.shields.io/badge/Demo-%F0%9F%A4%97%20Hugging%20Face-blue)](https://huggingface.co/spaces/marcosv/InstructIR)
-[![Replicate](https://img.shields.io/badge/Demo-%F0%9F%9A%80%20Replicate-blue)](https://replicate.com/mv-lab/instructir)
-[![Paper page](https://huggingface.co/datasets/huggingface/badges/resolve/main/paper-page-sm.svg)](https://huggingface.co/papers/2401.16468)
-[Marcos V. Conde](https://mv-lab.github.io/), [Gregor Geigle](https://scholar.google.com/citations?user=uIlyqRwAAAAJ&hl=en), [Radu Timofte](https://scholar.google.com/citations?user=u3MwH5kAAAAJ&hl=en)
-Computer Vision Lab, University of Wuerzburg | Sony PlayStation, FTG
-<a href="https://mv-lab.github.io/InstructIR/"><img src="images/instructir.gif" alt="InstructIR" width=100%></a>
-Video courtesy of Gradio ([see their post about InstructIR](https://twitter.com/Gradio/status/1752776176811041049)). Also shoutout to AK -- [see his tweet](https://twitter.com/_akhaliq/status/1752551364566126798).
-### TL;DR: quickstart
-InstructIR takes as input an image and a human-written instruction for how to improve that image. The neural model performs all-in-one image restoration. InstructIR achieves state-of-the-art results on several restoration tasks including image denoising, deraining, deblurring, dehazing, and (low-light) image enhancement.
-**🚀 You can start with the [demo tutorial](demo.ipynb)**
-<details>
-<summary> <b> Abstract</b> (click me to read)</summary>
-<p>
-Image restoration is a fundamental problem that involves recovering a high-quality clean image from its degraded observation. All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model. In this work, we present the first approach that uses human-written instructions to guide the image restoration model. Given natural language prompts, our model can recover high-quality images from their degraded counterparts, considering multiple degradation types. Our method, InstructIR, achieves state-of-the-art results on several restoration tasks including image denoising, deraining, deblurring, dehazing, and (low-light) image enhancement. InstructIR improves +1dB over previous all-in-one restoration methods. Moreover, our dataset and results represent a novel benchmark for new research on text-guided image restoration and enhancement.
-</p>
-</details>
-### TODO / News 🔥
-- [ ] Upload Model weights and results for other InstructIR variants (3D, 5D).
-- [x] [download all the test datasets](https://drive.google.com/file/d/11wGsKOMDVrBlsle4xtzORPLZAsGhel8c/view?usp=sharing) for all-in-one restoration.
-- [x] check the instructions below to run `eval_instructir.py` and get all the metrics and results for all-in-one restoration.
-- [x] You can download all the qualitative results here [instructir_results.zip](https://github.com/mv-lab/InstructIR/releases/download/instructir-results/instructir_results.zip)
-- [x] Upload models to HF 🤗 [(download the models here)](https://huggingface.co/marcosv/InstructIR)
-- [x] 🤗 [Hugging Face Demo](https://huggingface.co/spaces/marcosv/InstructIR) try it now
-- [x] [Google Colab Tutorial](https://colab.research.google.com/drive/1OrTvS-i6uLM2Y8kIkq8ZZRwEQxQFchfq?usp=sharing) (check [demo.ipynb](demo.ipynb))
-### Try it / Tutorial
-[Try it]((https://huggingface.co/spaces/marcosv/InstructIR)) directly on 🤗 Hugging Face at no cost, no code.
-🚀 You can start with the [demo tutorial](demo.ipynb). We also host the same tutorial on [google colab](https://colab.research.google.com/drive/1OrTvS-i6uLM2Y8kIkq8ZZRwEQxQFchfq?usp=sharing) so you can run it using free GPUs!.
-<a href="https://mv-lab.github.io/InstructIR/"><img src="images/instructir_teaser.png" alt="InstructIR" width=100%></a>
-## Results
-Check `test.py` and `eval_instructir.py`. The following command provides all the metric for all the benchmarks using the pre-trained models in `models/`. The results from InstructIR are saved in the indicated folder `results/`
-```
-python eval_instructir.py --model models/im_instructir-7d.pt --lm models/lm_instructir-7d.pt --device 0 --config configs/eval5d.yml --save results/
-```
-An example of the output log is:
-```
->>> Eval on CBSD68_15 noise 0
-CBSD68_15_base 24.84328738380881
-CBSD68_15_psnr 33.98722295200123 68
-CBSD68_15_ssim 0.9315137801801457
-....
-```
-You can **[download all the test datasets](https://drive.google.com/file/d/11wGsKOMDVrBlsle4xtzORPLZAsGhel8c/view?usp=sharing)**, and locate them in `test-data/`. Make sure the paths are updated in the config file `configs/eval5d.yml`.
--------
-You can **[download all the paper results](https://github.com/mv-lab/InstructIR/releases/download/instructir-results/instructir_results.zip)** -check releases-. We test InstructIR in the following benchmarks:
-| Dataset           | Task     | Test Results |
-| :---------------- | :------ | ----: |
-| BSD68             |   Denoising   | [Download](https://github.com/mv-lab/InstructIR/releases/download/instructir-results/instructir_results.zip) |
-| Urban100          |   Denoising   | [Download](https://github.com/mv-lab/InstructIR/releases/download/instructir-results/instructir_results.zip) |
-| Rain100           |   Deraining   | [Download](https://github.com/mv-lab/InstructIR/releases/download/instructir-results/instructir_results.zip) |
-| [GoPro](https://seungjunnah.github.io/Datasets/gopro)             |   Deblurring  | [Download](https://github.com/mv-lab/InstructIR/releases/download/instructir-results/instructir_results.zip) |
-| [LOL](https://daooshee.github.io/BMVC2018website/)               |   Lol Image Enhancement   | [Download](https://github.com/mv-lab/InstructIR/releases/download/instructir-results/instructir_results.zip) |
-| [MIT5K](https://data.csail.mit.edu/graphics/fivek/)             |   Image Enhancement   | [Download](https://github.com/mv-lab/InstructIR/releases/download/instructir-results/instructir_results.zip) |
-In releases or clicking the link above you can download [instructir_results.zip](https://github.com/mv-lab/InstructIR/releases/download/instructir-results/instructir_results.zip) which includes all the qualitative results for those datasets [1.9 Gbs].
-<img src="static/tables/table1.png" width=100%>
-<br>
-<details>
-<summary> <b> Multi-task Results on Dehazing, Deraining, Denoising </b> </summary>
-<img src="static/tables/table-3d.png" width=100%>
-</details>
-<details>
-<summary> <b> Denoising Results (click to read) </b> </summary>
-<img src="static/tables/table-dn.png" width=100%>
-</details>
-<details>
-<summary> <b> Low-light Image Enhancement (LOL) Results (click to read) </b> </summary>
-<img src="static/tables/table-lol.png" width=100%>
-</details>
-<details>
-<summary> <b> Color Image Enhancement (MIT5K) Results (click to read) </b> </summary>
-<img src="static/tables/table-mit5k.png" width=100%>
-</details>
-<br>
---------
-### Control and Interact
-Sometimes the blur, rain, or film grain noise are pleasant effects and part of the **"aesthetics"**. Here we show a simple example on how to interact with InstructIR.
-| Input       |(1) I love this photo, could you remove the raindrops? please keep the content intact | (2) Can you make it look stunning? like a professional photo     |
-| ---        |    :----   |          :--- |
-| <img src="images/rain-020.png" width=100%>      | <img src="images/results/result1.png" width=95%>       | <img src="images/results/result2.png"  width=100%>   |
-|   Input     |(1) my image is too dark, I cannot see anything, can you fix it? | (2) Great it looks nice! can you apply tone mapping?     |
-| <img src="images/lol_748.png" width=100%>      | <img src="images/results/resultlol1.png" width=95%>       | <img src="images/results/resultlol2.png" width=100%>   |
-|   Input     |(1) can you remove the tiny dots in the image? it is very unpleasant | (2) now please inprove the quality and resolution of the picture |
-| <img src="images/frog.png" width=100%>      | <img src="images/results/resultns1.png" width=95%>       | <img src="images/results/resultns2.png" width=100%>   |
-As you can see our model accepts diverse humman-written prompts, from ambiguous to precise instructions. *How does it work?* Imagine we have the following image as input:
-<img src="images/rain-020.png" width=50%>
-Now we can use InstructIR. with the following prompt (1):
-> I love this photo, could you remove the raindrops? please keep the content intact
-<img src="images/results/result1.png" width=50%>
-Now, let's enhance the image a bit further (2).
-> Can you make it look stunning? like a professional photo
-<img src="images/results/result2.png" width=50%>
-The final result looks indeed stunning 🤗 You can do it yourself in the [demo tutorial]().
-### FAQS
-> Disclaimer: please remember this is not a product, thus, you will notice some limitations. As most all-in-one restoration methods, it struggles to generalize on real-world images -- we are working on improving it.
-- ***How should I start?*** Check our [demo Tutorial](demo.ipynb) and also our [google collab](https://colab.research.google.com/drive/1OrTvS-i6uLM2Y8kIkq8ZZRwEQxQFchfq?usp=sharing) notebook.
-- ***How can I compare with your method?*** You can download the results for several benchmarks above on [Results](###Results).
-- ***How can I test the model? I just want to play with it***: Visit our 🤗 [Hugging Face demo](https://huggingface.co/spaces/marcosv/InstructIR) and test ir for free,
-- ***Why aren't you using diffusion-based models?*** (1) We want to keep the solution simple and efficient. (2) Our priority is high-fidelity --as in many industry scenarios realted to computational photography--.
-### Gradio Demo <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a>
-We made a simple [Gradio demo](app.py) you can run (locally) on your machine [here](app.py). You need Python>=3.9 and [these requirements](requirements_gradio.txt) for it: `pip install -r requirements_gradio.txt`
-```
-python app.py
-```
-<br>
-<a href="https://huggingface.co/spaces/marcosv/InstructIR">
-<img src="images/gradio.png" alt="InstructIR Gradio">
-</a>
-### Acknowledgments
-This work was partly supported by the The Humboldt Foundation (AvH). Marcos Conde is also supported by Sony Interactive Entertainment, FTG.
-This work is inspired in [InstructPix2Pix](https://arxiv.org/abs/2211.09800).
-### Contacts
-For any inquiries contact Marcos V. Conde: <a href="mailto:[email protected]">marcos.conde [at] uni-wuerzburg.de</a>
-### Citation BibTeX
-```
-@inproceedings{conde2024high,
-  title={InstructIR: High-Quality Image Restoration Following Human Instructions},
-  author={Conde, Marcos V and Geigle, Gregor and Timofte, Radu},
-  booktitle    = {Proceedings of the European Conference on Computer Vision (ECCV)},
-  year={2024}
-}
-```

+title: swiftlens
+emoji: 🚀
+sdk: Gradio
+app_file: app.py
+pinned: false