return mask

app.py

@@ -2,7 +2,6 @@ import cv2
 import numpy as np
 from PIL import Image
 import os
-from torchvision import transforms
 import gradio as gr
 from huggingface_hub import hf_hub_download, snapshot_download
 
@@ -85,14 +84,6 @@ def create_face_mask(pil_image):
         print(f"Error: {e}")
         raise ValueError('A very specific bad thing happened.')
 
-def pil_to_tensor(image):
-    # Convert a PIL image to a torch tensor scaled to [-1, 1]
-    transform = transforms.Compose([
-        transforms.ToTensor(),            # Converts PIL to tensor with values in [0,1]
-        transforms.Lambda(lambda t: 2 * t - 1)  # Scale to [-1, 1] if required by your model
-    ])
-    return transform(image)
-
 def face_swap_app(target_img, face_img):
     if target_img is None or face_img is None:
         raise ValueError("Both a target image and a face image must be provided.")
@@ -101,14 +92,11 @@ def face_swap_app(target_img, face_img):
     target_img = target_img.convert("RGB")
     face_img = face_img.convert("RGB")
 
-    target_tensor = pil_to_tensor(target_img)
-    face_tensor = pil_to_tensor(face_img)
-
     edit_mask = create_face_mask(face_img)
 
     output_img, edit_image, change_image, mask, seed = ace_infer(
-        reference_image=target_tensor,
-        edit_image=face_tensor,
+        reference_image=target_img,
+        edit_image=face_img,
         edit_mask=edit_mask,
         prompt="Face swap",
         output_height=1024,