diff --git a/source/extensions/omni.isaac.lab/omni/isaac/lab/utils/noise/noise_model.py b/source/extensions/omni.isaac.lab/omni/isaac/lab/utils/noise/noise_model.py
index d99da58aae..2eeca16b9d 100644
--- a/source/extensions/omni.isaac.lab/omni/isaac/lab/utils/noise/noise_model.py
+++ b/source/extensions/omni.isaac.lab/omni/isaac/lab/utils/noise/noise_model.py
@@ -30,7 +30,7 @@ def constant_noise(data: torch.Tensor, cfg: noise_cfg.ConstantNoiseCfg) -> torch
 
     # fix tensor device for bias on first call and update config parameters
     if isinstance(cfg.bias, torch.Tensor):
-       cfg.bias =  cfg.bias.to(device=data.device)
+       cfg.bias = cfg.bias.to(device=data.device)
 
     if cfg.operation == "add":
         return data + cfg.bias
@@ -55,12 +55,10 @@ def uniform_noise(data: torch.Tensor, cfg: noise_cfg.UniformNoiseCfg) -> torch.T
 
     # fix tensor device for n_max on first call and update config parameters
     if isinstance(cfg.n_max, torch.Tensor):
-        if cfg.n_max.device is not data.device:
-            cfg.n_max = cfg.n_max.to(data.device)
+        cfg.n_max = cfg.n_max.to(data.device)
     # fix tensor device for n_min on first call and update config parameters
     if isinstance(cfg.n_min, torch.Tensor):
-        if cfg.n_min.device is not data.device:
-            cfg.n_min = cfg.n_min.to(data.device)
+        cfg.n_min = cfg.n_min.to(data.device)
 
     if cfg.operation == "add":
         return data + torch.rand_like(data) * (cfg.n_max - cfg.n_min) + cfg.n_min
@@ -85,12 +83,10 @@ def gaussian_noise(data: torch.Tensor, cfg: noise_cfg.GaussianNoiseCfg) -> torch
 
     # fix tensor device for mean on first call and update config parameters
     if isinstance(cfg.mean, torch.Tensor):
-        if cfg.mean.device is not data.device:
-            cfg.mean = cfg.mean.to(data.device)
+        cfg.mean = cfg.mean.to(data.device)
     # fix tensor device for std on first call and update config parameters
     if isinstance(cfg.std, torch.Tensor):
-        if cfg.std.device is not data.device:
-            cfg.std = cfg.std.to(data.device)
+        cfg.std = cfg.std.to(data.device)
 
     if cfg.operation == "add":
         return data + cfg.mean + cfg.std * torch.randn_like(data)