Fabrice-TIERCELIN commited on
Commit
a780a8d
1 Parent(s): 2c1c611

Fix moving code

Browse files
Files changed (1) hide show
  1. app.py +5 -8
app.py CHANGED
@@ -210,8 +210,6 @@ def restore_in_Xmin(
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  denoise_image = HWC3(np.array(denoise_image))
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- torch.cuda.set_device(SUPIR_device)
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-
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  if model_select != model.current_model:
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  print('load ' + model_select)
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  if model_select == 'v0-Q':
@@ -220,9 +218,6 @@ def restore_in_Xmin(
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  model.load_state_dict(ckpt_F, strict=False)
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  model.current_model = model_select
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- model.ae_dtype = convert_dtype(ae_dtype)
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- model.model.dtype = convert_dtype(diff_dtype)
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-
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  # Allocation
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  if allocation == 1:
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  return restore_in_1min(
@@ -338,6 +333,11 @@ def restore(
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  start = time.time()
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  print('restore ==>>')
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  input_image = upscale_image(input_image, upscale, unit_resolution=32, min_size=min_size)
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  LQ = np.array(input_image) / 255.0
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  LQ = np.power(LQ, gamma_correction)
@@ -378,9 +378,6 @@ def restore(
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  " pixels large and " + str(result_height) + \
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  " pixels high, so a resolution of " + f'{result_width * result_height:,}' + " pixels."
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  print(information)
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- unique_name = str(uuid.uuid4()) + ".png"
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- results[0].save(unique_name)
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- print(unique_name)
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  # Only one image can be shown in the slider
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  return [noisy_image] + [results[0]], gr.update(label="Downloadable results in *." + output_format + " format", format = output_format, value = results), gr.update(value = information, visible = True)
 
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  denoise_image = HWC3(np.array(denoise_image))
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  if model_select != model.current_model:
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  print('load ' + model_select)
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  if model_select == 'v0-Q':
 
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  model.load_state_dict(ckpt_F, strict=False)
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  model.current_model = model_select
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  # Allocation
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  if allocation == 1:
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  return restore_in_1min(
 
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  start = time.time()
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  print('restore ==>>')
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+ torch.cuda.set_device(SUPIR_device)
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+
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+ model.ae_dtype = convert_dtype(ae_dtype)
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+ model.model.dtype = convert_dtype(diff_dtype)
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+
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  input_image = upscale_image(input_image, upscale, unit_resolution=32, min_size=min_size)
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  LQ = np.array(input_image) / 255.0
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  LQ = np.power(LQ, gamma_correction)
 
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  " pixels large and " + str(result_height) + \
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  " pixels high, so a resolution of " + f'{result_width * result_height:,}' + " pixels."
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  print(information)
 
 
 
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  # Only one image can be shown in the slider
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  return [noisy_image] + [results[0]], gr.update(label="Downloadable results in *." + output_format + " format", format = output_format, value = results), gr.update(value = information, visible = True)