diff --git a/adetailer/common.py b/adetailer/common.py
index 731b081..f12b682 100644
--- a/adetailer/common.py
+++ b/adetailer/common.py
@@ -38,6 +38,7 @@ def get_models(model_dir: Union[str, Path]) -> OrderedDict[str, Optional[str]]:
             "mediapipe_face_full": None,
             "mediapipe_face_short": None,
             "hand_yolov8n.pt": hf_hub_download(repo_id, "hand_yolov8n.pt"),
+            "person_yolov8n-seg.pt": hf_hub_download(repo_id, "person_yolov8n-seg.pt"),
         }
     )
 
diff --git a/adetailer/ultralytics.py b/adetailer/ultralytics.py
index b7d2916..80e03d8 100644
--- a/adetailer/ultralytics.py
+++ b/adetailer/ultralytics.py
@@ -32,7 +32,10 @@ def ultralytics_predict(
         return PredictOutput()
     bboxes = bboxes.tolist()
 
-    masks = create_mask_from_bbox(image, bboxes)
+    if pred[0].masks is None:
+        masks = create_mask_from_bbox(image, bboxes)
+    else:
+        masks = mask_to_pil(pred[0].masks.data)
     preview = pred[0].plot()
     preview = cv2.cvtColor(preview, cv2.COLOR_BGR2RGB)
     preview = Image.fromarray(preview)
@@ -51,3 +54,16 @@ def ultralytics_check():
     if p == "C:\\":
         message = "[-] ADetailer: if you get stuck here, try moving the stable-diffusion-webui to a different directory, or try running as administrator."
         print(message)
+
+
+def mask_to_pil(masks) -> 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.
+    """
+    from torchvision.transforms.functional import to_pil_image
+
+    n = masks.shape[0]
+    return [to_pil_image(masks[i], mode="L") for i in range(n)]