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gradio app is ready!
Browse files- app.py +20 -26
- images/real_fog.png +0 -0
app.py
CHANGED
@@ -6,7 +6,7 @@ import os
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import torch
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import numpy as np
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import yaml
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#from gradio_imageslider import ImageSlider
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## local code
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@@ -25,9 +25,12 @@ def dict2namespace(config):
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return namespace
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CONFIG = "configs/eval5d.yml"
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LM_MODEL = "
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MODEL_NAME = "
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# parse config file
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with open(os.path.join(CONFIG), "r") as f:
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@@ -89,9 +92,9 @@ description = ''' ## [High-Quality Image Restoration Following Human Instruction
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Computer Vision Lab, University of Wuerzburg | Sony PlayStation, FTG
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### TL;DR: quickstart
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InstructIR takes as input an image and a human-written instruction for how to improve that image
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**π You can start with the [demo tutorial](https://github.com/mv-lab/InstructIR/blob/main/demo.ipynb)**
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<details>
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<summary> <b> Abstract</b> (click me to read)</summary>
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</p>
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</details>
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> Disclaimer
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**This demo expects an image with some degradations (blur, noise, rain, low-light, haze) and a prompt requesting what should be done.**
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Due to the GPU memory limitations, the app might crash if you feed a high-resolution image (2K, 4K).
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<br>
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'''
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article = "<p style='text-align: center'><a href='https://github.com/mv-lab/InstructIR' target='_blank'>High-Quality Image Restoration Following Human Instructions</a></p>"
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examples = [['images/rain-020.png', "I love this photo, could you remove the raindrops? please keep the content intact"],
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['images/gradio_demo_images/city.jpg', "I took this photo during a foggy day, can you improve it?"],
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['images/gradio_demo_images/frog.png', "can you remove the tiny dots in the image? it is very unpleasant"],
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["images/lol_748.png", "my image is too dark, I cannot see anything, can you fix it?"],
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["images/gopro.png", "I took this photo while I was running, can you stabilize the image? it is too blurry"],
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["images/a0010.jpg", "please I want this image for my photo album, can you edit it as a photographer"]
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css = """
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.image-frame img, .image-container img {
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gr.Image(type="pil", label="Input"),
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gr.Text(label="Prompt")
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],
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outputs=[gr.Image(type="pil", label="Ouput")],
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title=title,
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description=description,
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article=article,
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)
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if __name__ == "__main__":
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demo.launch()
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# with gr.Blocks() as demo:
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# with gr.Row(equal_height=True):
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# with gr.Column(scale=1):
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# input = gr.Image(type="pil", label="Input")
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# with gr.Column(scale=1):
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# prompt = gr.Text(label="Prompt")
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# process_btn = gr.Button("Process")
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# with gr.Row(equal_height=True):
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# output = gr.Image(type="pil", label="Ouput")
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# slider = ImageSlider(position=0.5, type="pil", label="SideBySide")
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# process_btn.click(fn=process_img, inputs=[input, prompt], outputs=[output, slider])
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# demo.launch(share=True)
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import torch
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import numpy as np
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import yaml
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from huggingface_hub import hf_hub_download
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#from gradio_imageslider import ImageSlider
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## local code
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return namespace
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hf_hub_download(repo_id="marcosv/InstructIR", filename="im_instructir-7d.pt", local_dir="./")
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hf_hub_download(repo_id="marcosv/InstructIR", filename="lm_instructir-7d.pt", local_dir="./")
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CONFIG = "configs/eval5d.yml"
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LM_MODEL = "lm_instructir-7d.pt"
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MODEL_NAME = "im_instructir-7d.pt"
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# parse config file
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with open(os.path.join(CONFIG), "r") as f:
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Computer Vision Lab, University of Wuerzburg | Sony PlayStation, FTG
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### TL;DR: quickstart
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***InstructIR takes as input an image and a human-written instruction for how to improve that image.***
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The (single) 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.
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**π You can start with the [demo tutorial.](https://github.com/mv-lab/InstructIR/blob/main/demo.ipynb)** Check [our github](https://github.com/mv-lab/InstructIR) for more information
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<details>
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<summary> <b> Abstract</b> (click me to read)</summary>
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</p>
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</details>
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> **Disclaimer:** please remember this is not a product, thus, you will notice some limitations.
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**This demo expects an image with some degradations (blur, noise, rain, low-light, haze) and a prompt requesting what should be done.**
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Due to the GPU memory limitations, the app might crash if you feed a high-resolution image (2K, 4K). <br>
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The model was trained using mostly synthetic data, thus it might not work great on real-world complex images.
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However, it works surprisingly well on real-world foggy and low-light images.
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You can also try general image enhancement prompts (e.g., "retouch this image", "enhance the colors") and see how it improves the colors.
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<br>
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'''
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article = "<p style='text-align: center'><a href='https://github.com/mv-lab/InstructIR' target='_blank'>High-Quality Image Restoration Following Human Instructions</a></p>"
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#### Image,Prompts examples
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examples = [['images/rain-020.png', "I love this photo, could you remove the raindrops? please keep the content intact"],
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['images/gradio_demo_images/city.jpg', "I took this photo during a foggy day, can you improve it?"],
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['images/gradio_demo_images/frog.png', "can you remove the tiny dots in the image? it is very unpleasant"],
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["images/lol_748.png", "my image is too dark, I cannot see anything, can you fix it?"],
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["images/gopro.png", "I took this photo while I was running, can you stabilize the image? it is too blurry"],
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["images/a0010.jpg", "please I want this image for my photo album, can you edit it as a photographer"],
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["images/real_fog.png", "How can I remove the fog and mist from this photo?"]]
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css = """
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.image-frame img, .image-container img {
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gr.Image(type="pil", label="Input"),
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gr.Text(label="Prompt")
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],
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outputs=[gr.Image(type="pil", label="Ouput")],
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title=title,
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description=description,
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article=article,
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)
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if __name__ == "__main__":
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demo.launch()
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images/real_fog.png
ADDED