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app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-import spaces
+# import spaces  # Not needed on dedicated GPU
 from gradio_litmodel3d import LitModel3D
 
 import os
@@ -107,7 +107,6 @@ def get_seed(randomize_seed: bool, seed: int) -> int:
     return np.random.randint(0, MAX_SEED) if randomize_seed else seed
 
 
-@spaces.GPU
 def image_to_3d(
     image: Image.Image,
     multiimages: List[Tuple[Image.Image, str]],
@@ -180,7 +179,6 @@ def image_to_3d(
     return state, video_path
 
 
-@spaces.GPU(duration=90)
 def extract_glb(
     state: dict,
     mesh_simplify: float,
@@ -207,7 +205,6 @@ def extract_glb(
     return glb_path, glb_path
 
 
-@spaces.GPU
 def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
     """
     Extract a Gaussian file from the 3D model.
@@ -255,6 +252,17 @@ def split_image(image: Image.Image) -> List[Image.Image]:
     return [preprocess_image(image) for image in images]
 
 
+
+# Initialize pipeline (dedicated GPU - load at startup)
+pipeline = TrellisImageTo3DPipeline.from_pretrained("microsoft/TRELLIS-image-large")
+pipeline.cuda()
+try:
+    pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
+except:
+    pass
+print("Pipeline loaded successfully")
+
+
 with gr.Blocks(delete_cache=(600, 600)) as demo:
     gr.Markdown("""
     ## Image to 3D Asset with [TRELLIS](https://github.com/microsoft/TRELLIS)
@@ -406,10 +414,5 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
 
 # Launch the Gradio app
 if __name__ == "__main__":
-    pipeline = TrellisImageTo3DPipeline.from_pretrained("microsoft/TRELLIS-image-large")
-    pipeline.cuda()
-    try:
-        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
-    except:
         pass
     demo.launch()