use ZeroGPU

app.py

@@ -5,6 +5,7 @@
 #
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
+import spaces
 import os
 import cv2
 import torch
@@ -35,9 +36,8 @@ path = hf_hub_download(
     force_download=True
 )
 model = StreamVGGT()
-ckpt = torch.load(path, map_location=device)
+ckpt = torch.load(path, map_location="cpu")
 model.load_state_dict(ckpt, strict=True)
-model = model.to(device)
 model.eval() 
 del ckpt
 
@@ -45,6 +45,7 @@ del ckpt
 # -------------------------------------------------------------------------
 # 1) Core model inference
 # -------------------------------------------------------------------------
+@spaces.GPU(duration=120)
 def run_model(target_dir, model) -> dict:
     """
     Run the VGGT model on images in the 'target_dir/images' folder and return predictions.
@@ -187,6 +188,7 @@ def update_gallery_on_upload(input_video, input_images):
 # -------------------------------------------------------------------------
 # 4) Reconstruction: uses the target_dir plus any viz parameters
 # -------------------------------------------------------------------------
+@spaces.GPU(duration=180)
 def gradio_demo(
     target_dir,
     conf_thres=3.0,
@@ -524,6 +526,7 @@ with gr.Blocks(
     # ---------------------- Examples section ----------------------
     examples = get_examples_from_folder(building_folder)
 
+    @spaces.GPU(duration=180)
     def example_pipeline(
         input_video,
         num_images_str,