diff --git a/tiled_diffusion.py b/tiled_diffusion.py
index 816e099..e0d4f95 100644
--- a/tiled_diffusion.py
+++ b/tiled_diffusion.py
@@ -385,17 +385,19 @@ def process_controlnet(self, x_shape, x_dtype, c_in: dict, cond_or_uncond: List,
                 #
                 # Below can be in this if clause because self.refresh will trigger on resolution change,
                 # e.g. cause of ConditioningSetArea, so that particular case isn't cached atm.
+                cf = control.compression_ratio
                 if cns.shape[0] != batch_size:
                     cns = repeat_to_batch_size(cns, batch_size)
                 if shifts is not None:
                     control.cns = cns
                     cns = cns.roll(shifts=tuple(x * cf for x in shifts), dims=(-2,-1))
-                control.cond_hint = torch.cat([cns[:, :, bbox[1]*cf:bbox[3]*cf, bbox[0]*cf:bbox[2]*cf] for bbox in bboxes], dim=0)
+                control.cond_hint = torch.cat([cns[:, :, bbox[1]*cf:bbox[3]*cf, bbox[0]*cf:bbox[2]*cf] for bbox in bboxes], dim=0).to(device=cns.device)
                 self.control_params[tuple_key][param_id][batch_id] = control.cond_hint
             else:
                 if hasattr(control,'cns') and shifts is not None:
+                    cf = control.compression_ratio
                     cns = control.cns.roll(shifts=tuple(x * cf for x in shifts), dims=(-2,-1))
-                    control.cond_hint = torch.cat([cns[:, :, bbox[1]*cf:bbox[3]*cf, bbox[0]*cf:bbox[2]*cf] for bbox in bboxes], dim=0)
+                    control.cond_hint = torch.cat([cns[:, :, bbox[1]*cf:bbox[3]*cf, bbox[0]*cf:bbox[2]*cf] for bbox in bboxes], dim=0).to(device=cns.device)
                 else:
                     control.cond_hint = self.control_params[tuple_key][param_id][batch_id]
             control = control.previous_controlnet