diff --git a/examples/community/rerender_a_video.py b/examples/community/rerender_a_video.py
index b28145ae561d..5b16d58961dc 100644
--- a/examples/community/rerender_a_video.py
+++ b/examples/community/rerender_a_video.py
@@ -119,11 +119,11 @@ def forward_backward_consistency_check(fwd_flow, bwd_flow, alpha=0.01, beta=0.5)
 
 
 @torch.no_grad()
-def get_warped_and_mask(flow_model, image1, image2, image3=None, pixel_consistency=False):
+def get_warped_and_mask(flow_model, image1, image2, image3=None, pixel_consistency=False, device=None):
     if image3 is None:
         image3 = image1
     padder = InputPadder(image1.shape, padding_factor=8)
-    image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
+    image1, image2 = padder.pad(image1[None].to(device), image2[None].to(device))
     results_dict = flow_model(
         image1, image2, attn_splits_list=[2], corr_radius_list=[-1], prop_radius_list=[-1], pred_bidir_flow=True
     )
@@ -307,6 +307,7 @@ def __init__(
         feature_extractor: CLIPImageProcessor,
         image_encoder=None,
         requires_safety_checker: bool = True,
+        device=None,
     ):
         super().__init__(
             vae,
@@ -320,6 +321,7 @@ def __init__(
             image_encoder,
             requires_safety_checker,
         )
+        self.to(device)
 
         if safety_checker is None and requires_safety_checker:
             logger.warning(
@@ -374,7 +376,7 @@ def __init__(
             attention_type="swin",
             ffn_dim_expansion=4,
             num_transformer_layers=6,
-        ).to("cuda")
+        ).to(self.device)
 
         checkpoint = torch.utils.model_zoo.load_url(
             "https://huggingface.co/Anonymous-sub/Rerender/resolve/main/models/gmflow_sintel-0c07dcb3.pth",
@@ -928,13 +930,13 @@ def __call__(
             prev_image = self.image_processor.preprocess(prev_image).to(dtype=torch.float32)
 
             warped_0, bwd_occ_0, bwd_flow_0 = get_warped_and_mask(
-                self.flow_model, first_image, image[0], first_result, False
+                self.flow_model, first_image, image[0], first_result, False, self.device
             )
             blend_mask_0 = blur(F.max_pool2d(bwd_occ_0, kernel_size=9, stride=1, padding=4))
             blend_mask_0 = torch.clamp(blend_mask_0 + bwd_occ_0, 0, 1)
 
             warped_pre, bwd_occ_pre, bwd_flow_pre = get_warped_and_mask(
-                self.flow_model, prev_image[0], image[0], prev_result, False
+                self.flow_model, prev_image[0], image[0], prev_result, False, self.device
             )
             blend_mask_pre = blur(F.max_pool2d(bwd_occ_pre, kernel_size=9, stride=1, padding=4))
             blend_mask_pre = torch.clamp(blend_mask_pre + bwd_occ_pre, 0, 1)