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Commit
5c36eb3
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1 Parent(s): ce7ee61

Update app.py

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Files changed (1) hide show
  1. app.py +20 -25
app.py CHANGED
@@ -8,7 +8,7 @@ import requests
8
  model_url = "https://huggingface.co/ofter/4x-UltraSharp/resolve/main/inswapper_128.onnx"
9
  model_path = "./inswapper_128.onnx"
10
 
11
- # Function to download the model if it doesn't exist
12
  def download_model():
13
  if not os.path.exists(model_path):
14
  print("Downloading inswapper_128.onnx...")
@@ -25,41 +25,42 @@ def download_model():
25
  # Download the model when the script runs
26
  download_model()
27
 
28
- # Argument parser for command-line options
29
- parser = argparse.ArgumentParser(description="Refacer")
30
  parser.add_argument("--max_num_faces", type=int, help="Max number of faces on UI", default=5)
31
  parser.add_argument("--force_cpu", help="Force CPU mode", default=False, action="store_true")
32
- parser.add_argument("--share_gradio", help="Share Gradio", default=True, action="store_true")
33
- parser.add_argument("--server_name", type=str, help="Server IP address", default="0.0.0.0")
34
- parser.add_argument("--server_port", type=int, help="Server port", default=int(os.getenv("PORT", 7860)))
35
  parser.add_argument("--colab_performance", help="Use in colab for better performance", default=False, action="store_true")
36
- args = parser.parse_args([])
37
 
38
  # Initialize the Refacer class
39
  refacer = Refacer(force_cpu=args.force_cpu, colab_performance=args.colab_performance)
 
40
  num_faces = args.max_num_faces
41
 
42
  # Run function for refacing video
43
  def run(*vars):
44
  video_path = vars[0]
45
- origins = vars[1:(num_faces + 1)]
46
- destinations = vars[(num_faces + 1):(num_faces * 2) + 1]
47
- thresholds = vars[(num_faces * 2) + 1:]
48
 
49
  faces = []
50
  for k in range(0, num_faces):
51
  if origins[k] is not None and destinations[k] is not None:
52
  faces.append({
53
- "origin": origins[k],
54
- "destination": destinations[k],
55
- "threshold": thresholds[k]
56
  })
57
 
58
  # Call refacer to process video and get file path
59
- refaced_video_path = refacer.reface(video_path, faces)
60
  print(f"Refaced video can be found at {refaced_video_path}")
61
 
62
- return refaced_video_path
63
 
64
  # Prepare Gradio components
65
  origin = []
@@ -74,23 +75,17 @@ with gr.Blocks() as demo:
74
  video2 = gr.Video(label="Refaced video", interactive=False, format="mp4")
75
 
76
  for i in range(0, num_faces):
77
- with gr.Tab(f"Face #{i + 1}"):
78
  with gr.Row():
79
  origin.append(gr.Image(label="Face to replace"))
80
  destination.append(gr.Image(label="Destination face"))
81
  with gr.Row():
82
  thresholds.append(gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.2))
83
-
84
  with gr.Row():
85
  button = gr.Button("Reface", variant="primary")
86
 
87
  button.click(fn=run, inputs=[video] + origin + destination + thresholds, outputs=[video2])
88
 
89
- # Launch the Gradio app with optional share functionality
90
- demo.queue().launch(
91
- show_error=True,
92
- share=args.share_gradio, # Share option for public access
93
- server_name=args.server_name, # Server IP address
94
- server_port=args.server_port, # Port number
95
- debug=True # Enable debug mode for better error handling
96
- )
 
8
  model_url = "https://huggingface.co/ofter/4x-UltraSharp/resolve/main/inswapper_128.onnx"
9
  model_path = "./inswapper_128.onnx"
10
 
11
+ # Function to download the model
12
  def download_model():
13
  if not os.path.exists(model_path):
14
  print("Downloading inswapper_128.onnx...")
 
25
  # Download the model when the script runs
26
  download_model()
27
 
28
+ # Argument parser
29
+ parser = argparse.ArgumentParser(description='Refacer')
30
  parser.add_argument("--max_num_faces", type=int, help="Max number of faces on UI", default=5)
31
  parser.add_argument("--force_cpu", help="Force CPU mode", default=False, action="store_true")
32
+ parser.add_argument("--share_gradio", help="Share Gradio", default=False, action="store_true")
33
+ parser.add_argument("--server_name", type=str, help="Server IP address", default="127.0.0.1")
34
+ parser.add_argument("--server_port", type=int, help="Server port", default=7860)
35
  parser.add_argument("--colab_performance", help="Use in colab for better performance", default=False, action="store_true")
36
+ args = parser.parse_args()
37
 
38
  # Initialize the Refacer class
39
  refacer = Refacer(force_cpu=args.force_cpu, colab_performance=args.colab_performance)
40
+
41
  num_faces = args.max_num_faces
42
 
43
  # Run function for refacing video
44
  def run(*vars):
45
  video_path = vars[0]
46
+ origins = vars[1:(num_faces+1)]
47
+ destinations = vars[(num_faces+1):(num_faces*2)+1]
48
+ thresholds = vars[(num_faces*2)+1:]
49
 
50
  faces = []
51
  for k in range(0, num_faces):
52
  if origins[k] is not None and destinations[k] is not None:
53
  faces.append({
54
+ 'origin': origins[k],
55
+ 'destination': destinations[k],
56
+ 'threshold': thresholds[k]
57
  })
58
 
59
  # Call refacer to process video and get file path
60
+ refaced_video_path = refacer.reface(video_path, faces) # refaced video path
61
  print(f"Refaced video can be found at {refaced_video_path}")
62
 
63
+ return refaced_video_path # Return the file path to show in Gradio output
64
 
65
  # Prepare Gradio components
66
  origin = []
 
75
  video2 = gr.Video(label="Refaced video", interactive=False, format="mp4")
76
 
77
  for i in range(0, num_faces):
78
+ with gr.Tab(f"Face #{i+1}"):
79
  with gr.Row():
80
  origin.append(gr.Image(label="Face to replace"))
81
  destination.append(gr.Image(label="Destination face"))
82
  with gr.Row():
83
  thresholds.append(gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.2))
84
+
85
  with gr.Row():
86
  button = gr.Button("Reface", variant="primary")
87
 
88
  button.click(fn=run, inputs=[video] + origin + destination + thresholds, outputs=[video2])
89
 
90
+ # Launch the Gradio app
91
+ demo.queue().launch(show_error=True, share=args.share_gradio, server_name="0.0.0.0", server_port=args.server_port)