global model

app.py

@@ -143,6 +143,7 @@ def run_segmentation(
     rgb_image = image_prompts["image"].convert("RGB")
 
     global work_space
+    global sam_segmentator
 
     device = "cpu"
     sam_segmentator.to(device=device, dtype=DTYPE if device == 'cuda' else torch.float32)
@@ -188,6 +189,7 @@ def run_depth_estimation(
     )
 
     rgb_image = rgb_image.resize((1024, 1024), Image.Resampling.LANCZOS)
+    global moge_v2_dpt_model
 
     device = 'cuda' if torch.cuda.is_available() else 'cpu'
     dtype = torch.float16 if device == 'cuda' else torch.float32
@@ -852,9 +854,8 @@ with gr.Blocks() as demo:
 
 if __name__ == '__main__':
 
-    work_space = None
-    dpt_pack = None
-    generated_object_map = {}
+    global sam_segmentator
+    global moge_v2_dpt_model
 
     # Prepare models
     ## Grounding SAM