Update app.py

app.py

@@ -147,63 +147,52 @@ def check_input_image(input_image):
 
 def preprocess(input_image, do_remove_background, foreground_ratio):
     def fill_background(image):
-        # torch.cuda.synchronize()
-        # torch.cuda.empty_cache()
+        torch.cuda.synchronize()  # Ensure previous CUDA operations are complete
         image = np.array(image).astype(np.float32) / 255.0
         image = image[:, :, :3] * image[:, :, 3:4] + (1 - image[:, :, 3:4]) * 0.5
         image = Image.fromarray((image * 255.0).astype(np.uint8))
         return image
 
     if do_remove_background:
-        # torch.cuda.synchronize()
-        # torch.cuda.empty_cache()
+        torch.cuda.synchronize()
         image = input_image.convert("RGB")
         image = remove_background(image, rembg_session)
         image = resize_foreground(image, foreground_ratio)
         image = fill_background(image)
         
-        # torch.cuda.synchronize()
-        # torch.cuda.empty_cache()
-        print("Background Removed")
+        torch.cuda.synchronize()
     else:
         image = input_image
         if image.mode == "RGBA":
             image = fill_background(image)
-    # torch.cuda.synchronize()  # Wait for all CUDA operations to complete
-    # torch.cuda.empty_cache()
+    torch.cuda.synchronize()  # Wait for all CUDA operations to complete
+    torch.cuda.empty_cache()
     return image
 
-# @spaces.GPU
+
+
 def generate(image, mc_resolution, formats=["obj", "glb"]):
-    # torch.cuda.synchronize()
+    torch.cuda.synchronize()
     scene_codes = model(image, device=device)
-    # torch.cuda.synchronize()
+    torch.cuda.synchronize()
     mesh = model.extract_mesh(scene_codes, resolution=mc_resolution)[0]
-    # torch.cuda.synchronize()
+    torch.cuda.synchronize()
     mesh = to_gradio_3d_orientation(mesh)
-    # torch.cuda.synchronize()
-
+    torch.cuda.synchronize()
+    
     mesh_path_glb = tempfile.NamedTemporaryFile(suffix=f".glb", delete=False)
-    # torch.cuda.synchronize()
+    torch.cuda.synchronize()
     mesh.export(mesh_path_glb.name)
-    # torch.cuda.synchronize()
-
+    torch.cuda.synchronize()
+    
     mesh_path_obj = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False)
-    # torch.cuda.synchronize()
-    mesh.apply_scale([-1, 1, 1])  # Otherwise the visualized .obj will be flipped
+    torch.cuda.synchronize()
+    mesh.apply_scale([-1, 1, 1])
     mesh.export(mesh_path_obj.name)
-    # torch.cuda.synchronize()  # Ensure all CUDA operations are complete before clearing cache
-    # torch.cuda.empty_cache()
+    torch.cuda.synchronize()
+    torch.cuda.empty_cache()
     return mesh_path_obj.name, mesh_path_glb.name
 
-def upload_file_to_s3(file_path, bucket_name, object_name=None):    
-    s3_client.upload_file(file_path, bucket_name, object_name)
-    
-    # print(f"File {file_path} uploaded successfully to {bucket_name}/{object_name}.")
-    # torch.cuda.synchronize()  # Wait for all CUDA operations to complete
-    # torch.cuda.empty_cache()
-    return True
-
     
 
 @app.post("/process_image/")