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app.py

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+
+import gradio as gr
+from PIL import Image
+from run_faceswap import run_faceswap
+
+def swap_faces(base_image, face_image):
+    try:
+        result = run_faceswap(base_image, face_image)
+        result_img = Image.fromarray(result)
+        return result_img
+    except Exception as e:
+        return f"Error al generar imagen: {str(e)}"
+
+iface = gr.Interface(
+    fn=swap_faces,
+    inputs=[
+        gr.Image(type="pil", label="Imagen base (cuerpo en pista)"),
+        gr.Image(type="pil", label="Foto de la cara (webcam)")
+    ],
+    outputs=gr.Image(type="pil", label="Resultado final"),
+    title="SimSwap - Reemplazo realista de rostro",
+    description="Sube una imagen con cuerpo/fondo y una con el rostro a reemplazar. El sistema mantendrá todo igual salvo el rostro."
+)
+
+if __name__ == "__main__":
+    iface.launch()

run_faceswap.py

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+
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = ""
+
+import cv2
+import torch
+import numpy as np
+from PIL import Image
+from torchvision import transforms
+from types import SimpleNamespace
+
+from SimSwap.models.models import create_model
+from SimSwap.arcface.model import Backbone
+
+DEVICE = "cpu"
+
+opt_dict = {
+    "isTrain": False,
+    "Arc_path": "arcface_model.tar",
+    "which_epoch": "latest",
+    "load_pretrain": "checkpoints/simswap",
+    "crop_size": 224,
+    "resize_or_crop": "none",
+    "gan_mode": "hinge",
+    "no_ganFeat_loss": True,
+    "no_vgg_loss": True,
+    "lambda_feat": 10.0,
+    "lambda_rec": 10.0,
+    "beta1": 0.5,
+    "lr": 0.0002,
+    "continue_train": False,
+    "name": "simswap",
+    "checkpoints_dir": "./checkpoints",
+    "gpu_ids": [],
+    "use_mask": True,
+    "dataset_mode": "Swap",
+    "model": "fs_swap"
+}
+opt = SimpleNamespace(**opt_dict)
+
+model = create_model(opt)
+model.setup(opt)
+model.device = torch.device("cpu")
+if hasattr(model, 'netG'):
+    model.netG = model.netG.cpu()
+model.eval()
+
+arcface = Backbone(50, 0.6, 'ir_se')
+arcface.load_state_dict(torch.load(opt.Arc_path, map_location=DEVICE))
+arcface.eval().to(DEVICE)
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor()
+])
+
+def run_faceswap(base_image_pil, face_image_pil):
+    base_img = cv2.cvtColor(np.array(base_image_pil), cv2.COLOR_RGB2BGR)
+    id_img = face_image_pil.convert("RGB").resize((112, 112))
+    id_tensor = transform(id_img).unsqueeze(0).to(DEVICE)
+
+    with torch.no_grad():
+        id_embedding = arcface(id_tensor)
+        id_embedding = id_embedding / id_embedding.norm(dim=-1, keepdim=True)
+
+    temp_input_path = 'temp_base.jpg'
+    cv2.imwrite(temp_input_path, base_img)
+
+    opt.pic_a_path = temp_input_path
+    opt.pic_b_path = None
+    model.set_input(opt, id_embedding)
+    model.test()
+    swapped = model.get_current_visuals()['synthesized_image']
+
+    result_np = swapped.squeeze().permute(1, 2, 0).cpu().numpy() * 255
+    result_np = result_np.astype(np.uint8)
+    return result_np