diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py
index 0890842f5775..7684fdf9cd6c 100644
--- a/src/diffusers/models/embeddings.py
+++ b/src/diffusers/models/embeddings.py
@@ -319,12 +319,16 @@ def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):
     assert embed_dim % 4 == 0
 
     # use half of dimensions to encode grid_h
-    emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real)  # (H*W, D/4)
-    emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real)  # (H*W, D/4)
+    emb_h = get_1d_rotary_pos_embed(
+        embed_dim // 2, grid[0].reshape(-1), use_real=use_real
+    )  # (H*W, D/2) if use_real else (H*W, D/4)
+    emb_w = get_1d_rotary_pos_embed(
+        embed_dim // 2, grid[1].reshape(-1), use_real=use_real
+    )  # (H*W, D/2) if use_real else (H*W, D/4)
 
     if use_real:
-        cos = torch.cat([emb_h[0], emb_w[0]], dim=1)  # (H*W, D/2)
-        sin = torch.cat([emb_h[1], emb_w[1]], dim=1)  # (H*W, D/2)
+        cos = torch.cat([emb_h[0], emb_w[0]], dim=1)  # (H*W, D)
+        sin = torch.cat([emb_h[1], emb_w[1]], dim=1)  # (H*W, D)
         return cos, sin
     else:
         emb = torch.cat([emb_h, emb_w], dim=1)  # (H*W, D/2)
@@ -371,6 +375,8 @@ def get_1d_rotary_pos_embed(
     Returns:
         `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]
     """
+    assert dim % 2 == 0
+
     if isinstance(pos, int):
         pos = np.arange(pos)
     theta = theta * ntk_factor