diff --git a/examples/community/lpw_stable_diffusion_xl.py b/examples/community/lpw_stable_diffusion_xl.py
index cb955a688643..dfe60d9794e1 100644
--- a/examples/community/lpw_stable_diffusion_xl.py
+++ b/examples/community/lpw_stable_diffusion_xl.py
@@ -250,6 +250,7 @@ def get_weighted_text_embeddings_sdxl(
     neg_prompt: str = "",
     neg_prompt_2: str = None,
     num_images_per_prompt: int = 1,
+    device: Optional[torch.device] = None,
 ):
     """
     This function can process long prompt with weights, no length limitation
@@ -262,10 +263,13 @@ def get_weighted_text_embeddings_sdxl(
         neg_prompt (str)
         neg_prompt_2 (str)
         num_images_per_prompt (int)
+        device (torch.device)
     Returns:
         prompt_embeds (torch.Tensor)
         neg_prompt_embeds (torch.Tensor)
     """
+    device = device or pipe._execution_device
+
     if prompt_2:
         prompt = f"{prompt} {prompt_2}"
 
@@ -330,17 +334,17 @@ def get_weighted_text_embeddings_sdxl(
     # get prompt embeddings one by one is not working.
     for i in range(len(prompt_token_groups)):
         # get positive prompt embeddings with weights
-        token_tensor = torch.tensor([prompt_token_groups[i]], dtype=torch.long, device=pipe.device)
-        weight_tensor = torch.tensor(prompt_weight_groups[i], dtype=torch.float16, device=pipe.device)
+        token_tensor = torch.tensor([prompt_token_groups[i]], dtype=torch.long, device=device)
+        weight_tensor = torch.tensor(prompt_weight_groups[i], dtype=torch.float16, device=device)
 
-        token_tensor_2 = torch.tensor([prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device)
+        token_tensor_2 = torch.tensor([prompt_token_groups_2[i]], dtype=torch.long, device=device)
 
         # use first text encoder
-        prompt_embeds_1 = pipe.text_encoder(token_tensor.to(pipe.device), output_hidden_states=True)
+        prompt_embeds_1 = pipe.text_encoder(token_tensor.to(device), output_hidden_states=True)
         prompt_embeds_1_hidden_states = prompt_embeds_1.hidden_states[-2]
 
         # use second text encoder
-        prompt_embeds_2 = pipe.text_encoder_2(token_tensor_2.to(pipe.device), output_hidden_states=True)
+        prompt_embeds_2 = pipe.text_encoder_2(token_tensor_2.to(device), output_hidden_states=True)
         prompt_embeds_2_hidden_states = prompt_embeds_2.hidden_states[-2]
         pooled_prompt_embeds = prompt_embeds_2[0]
 
@@ -357,16 +361,16 @@ def get_weighted_text_embeddings_sdxl(
         embeds.append(token_embedding)
 
         # get negative prompt embeddings with weights
-        neg_token_tensor = torch.tensor([neg_prompt_token_groups[i]], dtype=torch.long, device=pipe.device)
-        neg_token_tensor_2 = torch.tensor([neg_prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device)
-        neg_weight_tensor = torch.tensor(neg_prompt_weight_groups[i], dtype=torch.float16, device=pipe.device)
+        neg_token_tensor = torch.tensor([neg_prompt_token_groups[i]], dtype=torch.long, device=device)
+        neg_token_tensor_2 = torch.tensor([neg_prompt_token_groups_2[i]], dtype=torch.long, device=device)
+        neg_weight_tensor = torch.tensor(neg_prompt_weight_groups[i], dtype=torch.float16, device=device)
 
         # use first text encoder
-        neg_prompt_embeds_1 = pipe.text_encoder(neg_token_tensor.to(pipe.device), output_hidden_states=True)
+        neg_prompt_embeds_1 = pipe.text_encoder(neg_token_tensor.to(device), output_hidden_states=True)
         neg_prompt_embeds_1_hidden_states = neg_prompt_embeds_1.hidden_states[-2]
 
         # use second text encoder
-        neg_prompt_embeds_2 = pipe.text_encoder_2(neg_token_tensor_2.to(pipe.device), output_hidden_states=True)
+        neg_prompt_embeds_2 = pipe.text_encoder_2(neg_token_tensor_2.to(device), output_hidden_states=True)
         neg_prompt_embeds_2_hidden_states = neg_prompt_embeds_2.hidden_states[-2]
         negative_pooled_prompt_embeds = neg_prompt_embeds_2[0]