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import gradio as gr |
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from inference import infer |
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def greet(image, prompt): |
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restore_img = infer(img=image, text_prompt=prompt) |
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return restore_img |
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title = "🖼️ ICDR 🖼️" |
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description = ''' ## ICDR: Image Restoration Framework for Composite Degradation following Human Instructions |
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Our Github : https://github.com/kimww42/ICDR |
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Siwon Kim, Donghyeon Yoon |
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Ajou Univ |
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it can take a long time to operate in cpu environment. (30 minutes per sheet), In this case, you can run app.py directly to test demo in a local environment.(https://github.com/kimww42/ICDR) |
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''' |
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article = "<p style='text-align: center'><a href='https://github.com/kimww42/ICDR' target='_blank'>ICDR</a></p>" |
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examples = [['input/00013.png', "Remove the rain as much as possible like the picture taken on a clear day."], |
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['input/00010.png', "I love this photo, could you remove the haze and more brighter?"], |
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['input/00058.png', "I have to post an emotional shot on Instagram, but it was shot too foggy and too dark. Change it like a sunny day and brighten it up!"], |
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['input/00075.png', "Remove the rain from the video, remove the brightness and fog"], |
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] |
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css = """ |
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.image-frame img, .image-container img { |
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width: auto; |
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height: auto; |
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max-width: none; |
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} |
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""" |
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demo = gr.Interface( |
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fn=greet, |
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inputs=[gr.Image(type="pil", label="Input"), |
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gr.Text(label="Prompt") ], |
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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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examples=examples, |
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css=css, |
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) |
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if __name__ == "__main__": |
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demo.launch() |
