Upload app.py

app.py

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+import gradio as gr
+from refacer import Refacer
+import argparse
+import os
+import requests
+
+# Hugging Face URL to download the model
+model_url = "https://huggingface.co/ofter/4x-UltraSharp/resolve/main/inswapper_128.onnx"
+model_path = "./inswapper_128.onnx"
+
+# Function to download the model
+def download_model():
+    if not os.path.exists(model_path):
+        print("Downloading inswapper_128.onnx...")
+        response = requests.get(model_url)
+        if response.status_code == 200:
+            with open(model_path, 'wb') as f:
+                f.write(response.content)
+            print("Model downloaded successfully!")
+        else:
+            raise Exception(f"Failed to download the model. Status code: {response.status_code}")
+    else:
+        print("Model already exists.")
+
+# Download the model when the script runs
+download_model()
+
+# Argument parser
+parser = argparse.ArgumentParser(description='Refacer')
+parser.add_argument("--max_num_faces", type=int, help="Max number of faces on UI", default=5)
+parser.add_argument("--force_cpu", help="Force CPU mode", default=False, action="store_true")
+parser.add_argument("--share_gradio", help="Share Gradio", default=False, action="store_true")
+parser.add_argument("--server_name", type=str, help="Server IP address", default="127.0.0.1")
+parser.add_argument("--server_port", type=int, help="Server port", default=7860)
+parser.add_argument("--colab_performance", help="Use in colab for better performance", default=False, action="store_true")
+args = parser.parse_args()
+
+# Initialize the Refacer class
+refacer = Refacer(force_cpu=args.force_cpu, colab_performance=args.colab_performance)
+
+num_faces = args.max_num_faces
+
+# Run function for refacing video
+def run(*vars):
+    video_path = vars[0]
+    origins = vars[1:(num_faces+1)]
+    destinations = vars[(num_faces+1):(num_faces*2)+1]
+    thresholds = vars[(num_faces*2)+1:]
+
+    faces = []
+    for k in range(0, num_faces):
+        if origins[k] is not None and destinations[k] is not None:
+            faces.append({
+                'origin': origins[k],
+                'destination': destinations[k],
+                'threshold': thresholds[k]
+            })
+
+    # Call refacer to process video and get file path
+    refaced_video_path = refacer.reface(video_path, faces)  # refaced video path
+    print(f"Refaced video can be found at {refaced_video_path}")
+
+    return refaced_video_path  # Return the file path to show in Gradio output
+
+# Prepare Gradio components
+origin = []
+destination = []
+thresholds = []
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Markdown("# Refacer")
+    with gr.Row():
+        video = gr.Video(label="Original video", format="mp4")
+        video2 = gr.Video(label="Refaced video", interactive=False, format="mp4")
+
+    for i in range(0, num_faces):
+        with gr.Tab(f"Face #{i+1}"):
+            with gr.Row():
+                origin.append(gr.Image(label="Face to replace"))
+                destination.append(gr.Image(label="Destination face"))
+            with gr.Row():
+                thresholds.append(gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.2))
+    
+    with gr.Row():
+        button = gr.Button("Reface", variant="primary")
+
+    button.click(fn=run, inputs=[video] + origin + destination + thresholds, outputs=[video2])
+
+# Launch the Gradio app
+demo.queue().launch(show_error=True, share=args.share_gradio, server_name="0.0.0.0", server_port=args.server_port)