fix: Normalize mask input to float

adetailer/ultralytics.py

@@ -22,7 +22,7 @@ def ultralytics_predict(
     device: str = "",
     classes: str = "",
 ) -> PredictOutput[float]:
-    from ultralytics import YOLO
+    from ultralytics import YOLO  # noqa: PLC0415
 
     model = YOLO(model_path)
     apply_classes(model, model_path, classes)
@@ -61,11 +61,12 @@ def mask_to_pil(masks: torch.Tensor, shape: tuple[int, int]) -> list[Image.Image
     """
     Parameters
     ----------
-    masks: torch.Tensor, dtype=torch.float32, shape=(N, H, W).
-        The device can be CUDA, but `to_pil_image` takes care of that.
+    masks: torch.Tensor, dtype=torch.float32 or torch.uint8, shape=(N, H, W).
+        uint8 tensor is expected to have values 0 or 1 (not 0-255).
 
     shape: tuple[int, int]
         (W, H) of the original image
     """
+    masks = masks.float()
     n = masks.shape[0]
     return [to_pil_image(masks[i], mode="L").resize(shape) for i in range(n)]