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Update app.py
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app.py
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@@ -7,12 +7,13 @@ client = Client("radames/Enhance-This-HiDiffusion-SDXL")
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# Define your interface function
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def my_interface(input_image, prompt="This is a beautiful scenery", negative_prompt="blurry, ugly, duplicate, poorly drawn, deformed, mosaic", seed=1415926535897932, guidance_scale=8.5, scale=2, controlnet_conditioning_scale=0.5, strength=1.0, controlnet_start=0.0, controlnet_end=1.0, guassian_sigma=2.0, intensity_threshold=3):
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# Call the other space's predict function
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result = client.predict(input_image, prompt, negative_prompt, seed, guidance_scale, scale, controlnet_conditioning_scale, strength, controlnet_start, controlnet_end, guassian_sigma, intensity_threshold)
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# Define your Gradio interface
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iface = gr.Interface(fn=my_interface,
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inputs=gr.Image(),
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outputs=gr.Image())
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# Launch your Gradio interface
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# Define your interface function
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def my_interface(input_image, prompt="This is a beautiful scenery", negative_prompt="blurry, ugly, duplicate, poorly drawn, deformed, mosaic", seed=1415926535897932, guidance_scale=8.5, scale=2, controlnet_conditioning_scale=0.5, strength=1.0, controlnet_start=0.0, controlnet_end=1.0, guassian_sigma=2.0, intensity_threshold=3):
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# Call the other space's predict function
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result = client.predict(input_image, prompt, negative_prompt, seed, guidance_scale, scale, controlnet_conditioning_scale, strength, controlnet_start, controlnet_end, guassian_sigma, intensity_threshold, api_name="predict")
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# Return only the first image from the result
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return result[0][0]
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# Define your Gradio interface
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iface = gr.Interface(fn=my_interface,
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inputs=gr.Image(shape=(1024,1024), source="upload"),
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outputs=gr.Image())
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# Launch your Gradio interface
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