Update app.py

app.py

@@ -24,25 +24,6 @@ repo_id = "black-forest-labs/FLUX.1-Fill-dev"
 if torch.cuda.is_available():
     pipe = FluxFillPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16).to("cuda")
 
-
-def create_mask_image(mask_array):
-    # Convert the mask to a numpy array if it's not already
-    if not isinstance(mask_array, np.ndarray):
-        mask_array = np.array(mask_array)
-    
-    # Create a new array with the same shape as the mask, but only for RGB channels
-    processed_mask = np.zeros((mask_array.shape[0], mask_array.shape[1], 3), dtype=np.uint8)
-    
-    # Set transparent parts (alpha=0) to black (0, 0, 0)
-    transparent_mask = mask_array[:, :, 3] == 0
-    processed_mask[transparent_mask] = [0, 0, 0]
-    
-    # Set black parts (RGB=0, 0, 0 and alpha=255) to white (255, 255, 255)
-    black_mask = (mask_array[:, :, :3] == [0, 0, 0]).all(axis=2) & (mask_array[:, :, 3] == 255)
-    processed_mask[black_mask] = [255, 255, 255]
-    
-    return Image.fromarray(processed_mask)
-
 @spaces.GPU()
 def inpaintGen(
         imgMask,
@@ -67,11 +48,8 @@ def inpaintGen(
 
     source_img = Image.open(source_path).convert("RGB")
     mask_img = Image.open(mask_path)
-    if mask_img.mode != 'L':
-        mask_img = mask_img.convert('L')
-    mask_img = ImageOps.invert(mask_img)
-
-    #mask_img = create_mask_image(mask_img)
+    alpha_channel=mask_img.split()[3]
+    binary_mask = alpha_channel.point(lambda p: p > 0 and 255)
     
     width, height = source_img.size
 
@@ -82,7 +60,7 @@ def inpaintGen(
     result = pipe(
         prompt=inpaint_prompt,
         image=source_img,
-        mask_image=mask_img,
+        mask_image=binary_mask,
         width=width,
         height=height,
         num_inference_steps=num_steps,