diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py
index 1f29622bdf20..5e9863ab0d0a 100644
--- a/src/diffusers/models/embeddings.py
+++ b/src/diffusers/models/embeddings.py
@@ -545,11 +545,14 @@ def get_1d_rotary_pos_embed(
     assert dim % 2 == 0
 
     if isinstance(pos, int):
-        pos = np.arange(pos)
+        pos = torch.arange(pos)
+    if isinstance(pos, np.ndarray):
+        pos = torch.from_numpy(pos)  # type: ignore  # [S]
+
     theta = theta * ntk_factor
     freqs = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype)[: (dim // 2)] / dim)) / linear_factor  # [D/2]
-    t = torch.from_numpy(pos).to(freqs.device)  # type: ignore  # [S]
-    freqs = torch.outer(t, freqs)  # type: ignore   # [S, D/2]
+    freqs = freqs.to(pos.device)
+    freqs = torch.outer(pos, freqs)  # type: ignore   # [S, D/2]
     if use_real and repeat_interleave_real:
         # flux, hunyuan-dit, cogvideox
         freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float()  # [S, D]
@@ -626,7 +629,7 @@ def forward(self, ids: torch.Tensor) -> torch.Tensor:
         n_axes = ids.shape[-1]
         cos_out = []
         sin_out = []
-        pos = ids.squeeze().float().cpu().numpy()
+        pos = ids.squeeze().float()
         is_mps = ids.device.type == "mps"
         freqs_dtype = torch.float32 if is_mps else torch.float64
         for i in range(n_axes):