Haiyu Wu commited on
Commit
d1455ac
·
1 Parent(s): 96c5656
Files changed (1) hide show
  1. app.py +12 -9
app.py CHANGED
@@ -17,6 +17,12 @@ from time import time
17
  MAX_SEED = np.iinfo(np.int32).max
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  device = "cuda"
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  def clear_image():
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  return None
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@@ -86,13 +92,10 @@ def image_generation(input_image, quality, random_perturbation, sigma, dimension
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  generator, id_model, pose_model, quality_model = initialize_models()
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  generated_images = []
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- if input_image is None:
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- raise gr.Error("No image uploaded!")
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- else:
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- input_image = np.transpose(input_image, (2, 0, 1))
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- input_image = torch.from_numpy(input_image).unsqueeze(0).float().to(device)
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- input_image.div_(255).sub_(0.5).div_(0.5)
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- feature = id_model(input_image).clone().detach().cpu().numpy()
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  if not random_perturbation:
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  features = []
@@ -175,7 +178,7 @@ def main():
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  gr.Markdown(title)
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  gr.Markdown(description)
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- with gr.Row():
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  with gr.Column():
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  image_file = gr.Image(label="Upload an image (optional)", type="filepath")
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@@ -243,7 +246,7 @@ def main():
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  fn=clear_image,
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  inputs=[],
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  outputs=[gallery]
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- ).then(
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  fn=generating,
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  inputs=[],
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  outputs=[generation_time]
 
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  MAX_SEED = np.iinfo(np.int32).max
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  device = "cuda"
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+
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+ def check_input_image(input_image):
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+ if input_image is None:
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+ raise gr.Error("No image uploaded!")
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+
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+
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  def clear_image():
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  return None
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  generator, id_model, pose_model, quality_model = initialize_models()
93
 
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  generated_images = []
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+ input_image = np.transpose(input_image, (2, 0, 1))
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+ input_image = torch.from_numpy(input_image).unsqueeze(0).float().to(device)
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+ input_image.div_(255).sub_(0.5).div_(0.5)
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+ feature = id_model(input_image).clone().detach().cpu().numpy()
 
 
 
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  if not random_perturbation:
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  features = []
 
178
 
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  gr.Markdown(title)
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  gr.Markdown(description)
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+ with gr.Row(variant="panel"):
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  with gr.Column():
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  image_file = gr.Image(label="Upload an image (optional)", type="filepath")
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  fn=clear_image,
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  inputs=[],
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  outputs=[gallery]
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+ ).then(fn=check_input_image, inputs=[image_file]).success(
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  fn=generating,
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  inputs=[],
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  outputs=[generation_time]