diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py
index 8bc30f7cabcf..ec1c68b86c89 100644
--- a/src/diffusers/models/embeddings.py
+++ b/src/diffusers/models/embeddings.py
@@ -35,10 +35,21 @@ def get_timestep_embedding(
     """
     This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
 
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
-    embeddings. :return: an [N x dim] Tensor of positional embeddings.
+    Args
+        timesteps (torch.Tensor):
+            a 1-D Tensor of N indices, one per batch element. These may be fractional.
+        embedding_dim (int):
+            the dimension of the output.
+        flip_sin_to_cos (bool):
+            Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)
+        downscale_freq_shift (float):
+            Controls the delta between frequencies between dimensions
+        scale (float):
+            Scaling factor applied to the embeddings.
+        max_period (int):
+            Controls the maximum frequency of the embeddings
+    Returns
+        torch.Tensor: an [N x dim] Tensor of positional embeddings.
     """
     assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"