import gradio as gr import cv2 import numpy as np import insightface from insightface.app import FaceAnalysis from insightface.model_zoo import get_model import urllib.request import os import tempfile # Download model if not exists MODEL_URL = "https://huggingface.co/ezioruan/inswapper_128.onnx/resolve/main/inswapper_128.onnx" MODEL_PATH = "inswapper_128.onnx" if not os.path.exists(MODEL_PATH): print("📥 Downloading face swap model...") urllib.request.urlretrieve(MODEL_URL, MODEL_PATH) # Initialize face analysis print("🔄 Loading face analysis models...") face_app = FaceAnalysis(name="buffalo_l", providers=["CPUExecutionProvider"]) face_app.prepare(ctx_id=0, det_size=(640, 640)) face_swapper = get_model(MODEL_PATH, providers=["CPUExecutionProvider"]) print("✅ Models ready!") def process_video(source_img, target_video, quality_choice): try: # Quality settings map quality_map = { "320p (Fastest)": {"width": 320, "fps_reduction": 3, "bitrate": "300k"}, "480p (Balanced)": {"width": 480, "fps_reduction": 2, "bitrate": "500k"}, "720p (Good)": {"width": 720, "fps_reduction": 1, "bitrate": "1000k"}, "1080p (Original)": {"width": None, "fps_reduction": 1, "bitrate": "2000k"} } settings = quality_map[quality_choice] # Open video cap = cv2.VideoCapture(target_video) original_fps = cap.get(cv2.CAP_PROP_FPS) original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # Calculate new dimensions if settings["width"]: new_width = settings["width"] aspect = original_height / original_width new_height = int(new_width * aspect) else: new_width = original_width new_height = original_height # Calculate new fps new_fps = original_fps // settings["fps_reduction"] # Setup output temp_output = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name fourcc = cv2.VideoWriter_fourcc(*"mp4v") out = cv2.VideoWriter(temp_output, fourcc, new_fps, (new_width, new_height)) # Get source face source_rgb = cv2.cvtColor(source_img, cv2.COLOR_BGR2RGB) source_faces = face_app.get(source_rgb) if len(source_faces) == 0: return None, "❌ No face detected in source image" source_face = source_faces[0] # Process video frame_count = 0 while True: ret, frame = cap.read() if not ret: break # Resize frame if settings["width"]: frame = cv2.resize(frame, (new_width, new_height)) # Convert and swap face frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) target_faces = face_app.get(frame_rgb) if len(target_faces) > 0: result = face_swapper.get(frame_rgb, target_faces[0], source_face, paste_back=True) frame_rgb = result # Convert back and write frame_bgr = cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2BGR) out.write(frame_bgr) frame_count += 1 cap.release() out.release() return temp_output, f"✅ Video processed at {quality_choice} - {frame_count} frames" except Exception as e: return None, f"❌ Error: {str(e)}" # Create UI with gr.Blocks(theme=gr.themes.Soft(), title="FaceSwapAll") as demo: gr.Markdown("# FaceSwapAll with Quality Control") with gr.Row(): with gr.Column(): source = gr.Image(label="Source Face", type="numpy", height=300) with gr.Column(): target = gr.Video(label="Target Video", height=300) # Quality dropdown quality = gr.Dropdown( label="Video Output Quality", choices=["320p (Fastest)", "480p (Balanced)", "720p (Good)", "1080p (Original)"], value="480p (Balanced)", info="Lower quality = faster processing on free CPU" ) swap_btn = gr.Button("Swap Face in Video", variant="primary", size="lg") with gr.Row(): result = gr.Video(label="Result Video") status = gr.Textbox(label="Status", lines=3) swap_btn.click( fn=process_video, inputs=[source, target, quality], outputs=[result, status] ) gr.Markdown(""" ### ⚡ Speed Tips: - **320p**: Fastest (8-10x speedup) - Best for testing - **480p**: Balanced (4-5x speedup) - Good quality/speed tradeoff - **720p**: Good quality (2x speedup) - **1080p**: Original quality - Slowest on free CPU """) if __name__ == "__main__": demo.launch()