JoPmt commited on
Commit
8d13cb3
1 Parent(s): 70923ee

Update app.py

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Files changed (1) hide show
  1. app.py +8 -10
app.py CHANGED
@@ -97,6 +97,10 @@ def plex(qr_code_value, text, neg_prompt, modil, one, two, three):
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  original.thumbnail((512, 512))
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  cannyimage = load_image(original).resize((512,512))
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  cannyimage = np.array(cannyimage)
 
 
 
 
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  low_threshold = 100
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  high_threshold = 200
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  cannyimage = cv2.Canny(cannyimage, low_threshold, high_threshold)
@@ -105,20 +109,14 @@ def plex(qr_code_value, text, neg_prompt, modil, one, two, three):
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  cannyimage = Image.fromarray(cannyimage)
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  images = [cannyimage]
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  generator = torch.Generator(device="cpu").manual_seed(random.randint(1, 4836923))
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-
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- image = pipe(
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- [prompt]*2,
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- images,
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- num_inference_steps=one,
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- generator=generator,
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- strength=two,
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- negative_prompt=[neg_prompt]*2,
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- controlnet_conditioning_scale=three,
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- )
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  for i, imge in enumerate(image["images"]):
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  apol.append(imge)
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  apol.append(original)
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  apol.append(cannyimage)
 
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  return apol
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  iface = gr.Interface(fn=plex, inputs=[gr.Textbox(label="QR Code URL"),gr.Textbox(label="prompt"),gr.Textbox(label="neg prompt"),gr.Dropdown(choices=models, label="some sd models", value=models[0], type="value"), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=5, value=5), gr.Slider(label="prompt strength", minimum=0.01, step=0.01, maximum=0.99, value=0.20), gr.Slider(label="controlnet scale", minimum=0.01, step=0.01, maximum=0.99, value=0.80)], outputs=gr.Gallery(label="out", columns=1))
 
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  original.thumbnail((512, 512))
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  cannyimage = load_image(original).resize((512,512))
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  cannyimage = np.array(cannyimage)
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+ pannyimage = load_image(original).resize((512,512))
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+ pannyimage = np.array(pannyimage)
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+ pannyimage = np.inverse(pannyimage)
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+ pannyimage = Image.fromarray(pannyimage)
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  low_threshold = 100
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  high_threshold = 200
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  cannyimage = cv2.Canny(cannyimage, low_threshold, high_threshold)
 
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  cannyimage = Image.fromarray(cannyimage)
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  images = [cannyimage]
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  generator = torch.Generator(device="cpu").manual_seed(random.randint(1, 4836923))
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+ imzge = pipe(prompt,original,num_inference_steps=one,generator=generator,strength=two,negative_prompt=neg_prompt,controlnet_conditioning_scale=three,).images[0]
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+ apol.append(imzge)
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+ image = pipe([prompt]*2,images,num_inference_steps=one,generator=generator,strength=two,negative_prompt=[neg_prompt]*2,controlnet_conditioning_scale=three,)
 
 
 
 
 
 
 
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  for i, imge in enumerate(image["images"]):
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  apol.append(imge)
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  apol.append(original)
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  apol.append(cannyimage)
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+ apol.append(pannyimage)
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  return apol
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  iface = gr.Interface(fn=plex, inputs=[gr.Textbox(label="QR Code URL"),gr.Textbox(label="prompt"),gr.Textbox(label="neg prompt"),gr.Dropdown(choices=models, label="some sd models", value=models[0], type="value"), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=5, value=5), gr.Slider(label="prompt strength", minimum=0.01, step=0.01, maximum=0.99, value=0.20), gr.Slider(label="controlnet scale", minimum=0.01, step=0.01, maximum=0.99, value=0.80)], outputs=gr.Gallery(label="out", columns=1))