Update app.py

app.py

@@ -164,12 +164,8 @@ def activate_button() -> gr.Button:
 def deactivate_button() -> gr.Button:
     return gr.Button(interactive=False)
 
+
 with gr.Blocks() as demo:
-    pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")                                            
-    try:
-        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
-    except:
-        pass
     gr.Markdown("""
     ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
     * Upload an image and click "Generate" to create a 3D asset. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background.
@@ -208,6 +204,7 @@ with gr.Blocks() as demo:
     trial_id = gr.Textbox(visible=False)
     output_buf = gr.State()
 
+    # Example images at the bottom of the page
 
     # Handlers
     image_prompt.upload(
@@ -251,17 +248,17 @@ with gr.Blocks() as demo:
 
 # Launch the Gradio app
 if __name__ == "__main__":
-    #pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
-    #if torch.cuda.is_available():
-    #    pipeline.cuda()
-    #    print("CUDA is available. Using GPU.")
-    #else:
-    #    print("CUDA not available. Falling back to CPU.")
-    #try:
-    #    pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
-    #except:
-    #    pass
-    #print(f"CUDA Available: {torch.cuda.is_available()}")
-    #print(f"CUDA Version: {torch.version.cuda}")
-    #print(f"Number of GPUs: {torch.cuda.device_count()}")
+    pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
+    if torch.cuda.is_available():
+        pipeline.cuda()
+        print("CUDA is available. Using GPU.")
+    else:
+        print("CUDA not available. Falling back to CPU.")
+    try:
+        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
+    except:
+        pass
+    print(f"CUDA Available: {torch.cuda.is_available()}")
+    print(f"CUDA Version: {torch.version.cuda}")
+    print(f"Number of GPUs: {torch.cuda.device_count()}")
     demo.launch(debug=True)