diff --git a/src/diffusers/models/transformers/transformer_z_image.py b/src/diffusers/models/transformers/transformer_z_image.py
index 5983c34ab640..a3e50132eb38 100644
--- a/src/diffusers/models/transformers/transformer_z_image.py
+++ b/src/diffusers/models/transformers/transformer_z_image.py
@@ -25,6 +25,7 @@
 from ...models.attention_processor import Attention
 from ...models.modeling_utils import ModelMixin
 from ...models.normalization import RMSNorm
+from ...utils import is_torch_npu_available
 from ...utils.torch_utils import maybe_allow_in_graph
 from ..attention_dispatch import dispatch_attention_fn
 from ..modeling_outputs import Transformer2DModelOutput
@@ -323,37 +324,72 @@ def __init__(
         self.axes_lens = axes_lens
         assert len(axes_dims) == len(axes_lens), "axes_dims and axes_lens must have the same length"
         self.freqs_cis = None
+        self.freqs_real = None
+        self.freqs_imag = None
 
     @staticmethod
     def precompute_freqs_cis(dim: List[int], end: List[int], theta: float = 256.0):
         with torch.device("cpu"):
-            freqs_cis = []
-            for i, (d, e) in enumerate(zip(dim, end)):
-                freqs = 1.0 / (theta ** (torch.arange(0, d, 2, dtype=torch.float64, device="cpu") / d))
-                timestep = torch.arange(e, device=freqs.device, dtype=torch.float64)
-                freqs = torch.outer(timestep, freqs).float()
-                freqs_cis_i = torch.polar(torch.ones_like(freqs), freqs).to(torch.complex64)  # complex64
-                freqs_cis.append(freqs_cis_i)
-
-            return freqs_cis
+            if is_torch_npu_available:
+                freqs_real_list = []
+                freqs_imag_list = []
+                for i, (d, e) in enumerate(zip(dim, end)):
+                    freqs = 1.0 / (theta ** (torch.arange(0, d, 2, dtype=torch.float64, device="cpu") / d))
+                    timestep = torch.arange(e, device=freqs.device, dtype=torch.float64)
+                    freqs = torch.outer(timestep, freqs).float()
+                    freqs_real = torch.cos(freqs)
+                    freqs_imag = torch.sin(freqs)
+                    freqs_real_list.append(freqs_real.to(torch.float32))
+                    freqs_imag_list.append(freqs_imag.to(torch.float32))
+
+                return freqs_real_list, freqs_imag_list
+            else:
+                freqs_cis = []
+                for i, (d, e) in enumerate(zip(dim, end)):
+                    freqs = 1.0 / (theta ** (torch.arange(0, d, 2, dtype=torch.float64, device="cpu") / d))
+                    timestep = torch.arange(e, device=freqs.device, dtype=torch.float64)
+                    freqs = torch.outer(timestep, freqs).float()
+                    freqs_cis_i = torch.polar(torch.ones_like(freqs), freqs).to(torch.complex64)  # complex64
+                    freqs_cis.append(freqs_cis_i)
+                return freqs_cis
 
     def __call__(self, ids: torch.Tensor):
         assert ids.ndim == 2
         assert ids.shape[-1] == len(self.axes_dims)
         device = ids.device
 
-        if self.freqs_cis is None:
-            self.freqs_cis = self.precompute_freqs_cis(self.axes_dims, self.axes_lens, theta=self.theta)
-            self.freqs_cis = [freqs_cis.to(device) for freqs_cis in self.freqs_cis]
+        if is_torch_npu_available:
+            if self.freqs_real is None or self.freqs_imag is None:
+                freqs_real, freqs_imag = self.precompute_freqs_cis(self.axes_dims, self.axes_lens, theta=self.theta)
+                self.freqs_real = [fr.to(device) for fr in freqs_real]
+                self.freqs_imag = [fi.to(device) for fi in freqs_imag]
+            else:
+                # Ensure freqs_cis are on the same device as ids
+                if self.freqs_real[0].device != device:
+                    self.freqs_real = [fr.to(device) for fr in freqs_real]
+                    self.freqs_imag = [fi.to(device) for fi in freqs_imag]
+
+            result = []
+            for i in range(len(self.axes_dims)):
+                index = ids[:, i]
+                real_part = self.freqs_real[i][index]
+                imag_part = self.freqs_imag[i][index]
+                complex_part = torch.complex(real_part, imag_part)
+                result.append(complex_part)
         else:
-            # Ensure freqs_cis are on the same device as ids
-            if self.freqs_cis[0].device != device:
+            if self.freqs_cis is None:
+                self.freqs_cis = self.precompute_freqs_cis(self.axes_dims, self.axes_lens, theta=self.theta)
                 self.freqs_cis = [freqs_cis.to(device) for freqs_cis in self.freqs_cis]
+            else:
+                # Ensure freqs_cis are on the same device as ids
+                if self.freqs_cis[0].device != device:
+                    self.freqs_cis = [freqs_cis.to(device) for freqs_cis in self.freqs_cis]
+
+            result = []
+            for i in range(len(self.axes_dims)):
+                index = ids[:, i]
+                result.append(self.freqs_cis[i][index])
 
-        result = []
-        for i in range(len(self.axes_dims)):
-            index = ids[:, i]
-            result.append(self.freqs_cis[i][index])
         return torch.cat(result, dim=-1)