plz

app.py

@@ -26,7 +26,7 @@ from sam2.sam2_image_predictor import SAM2ImagePredictor
 #         DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 #         self.predictor = SAM2ImagePredictor.from_pretrained(MODEL, device=DEVICE)
 
-
+PREDICTOR = None
 
 MODELS = {
     "RealVisXL V5.0 Lightning": "SG161222/RealVisXL_V5.0_Lightning",
@@ -64,9 +64,6 @@ pipe = StableDiffusionXLFillPipeline.from_pretrained(
 pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
 pipe.to("cuda")
 print(pipe)
-DEVICE = torch.device("cuda")
-SAM_MODEL = "facebook/sam2.1-hiera-large"
-PREDICTOR = SAM2ImagePredictor.from_pretrained(SAM_MODEL, device=DEVICE)
 
 def load_default_pipeline():
     global pipe
@@ -80,6 +77,15 @@ def load_default_pipeline():
 
 @spaces.GPU()
 def predict_masks(prompts):
+
+    DEVICE = torch.device("cuda")
+    SAM_MODEL = "facebook/sam2.1-hiera-large"
+    if PREDICTOR is None:
+        PREDICTOR = SAM2ImagePredictor.from_pretrained(SAM_MODEL, device=DEVICE)
+    else:
+        PREDICTOR = PREDICTOR
+
+
     """Predict a single mask from the image based on selected points."""
     image = np.array(prompts["image"])  # Convert the image to a numpy array
     points = prompts["points"]  # Get the points from prompts