Merge branch 'main' into main

Author: ahnjj

src/diffusers/models/embeddings_flax.py

@@ -29,11 +29,21 @@ def get_sinusoidal_embeddings(
     """Returns the positional encoding (same as Tensor2Tensor).
 
     Args:
-        timesteps: a 1-D Tensor of N indices, one per batch element.
-        These may be fractional.
-        embedding_dim: The number of output channels.
-        min_timescale: The smallest time unit (should probably be 0.0).
-        max_timescale: The largest time unit.
+        timesteps (`jnp.ndarray` of shape `(N,)`):
+            A 1-D array of N indices, one per batch element. These may be fractional.
+        embedding_dim (`int`):
+            The number of output channels.
+        freq_shift (`float`, *optional*, defaults to `1`):
+            Shift applied to the frequency scaling of the embeddings.
+        min_timescale (`float`, *optional*, defaults to `1`):
+            The smallest time unit used in the sinusoidal calculation (should probably be 0.0).
+        max_timescale (`float`, *optional*, defaults to `1.0e4`):
+            The largest time unit used in the sinusoidal calculation.
+        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
+            Whether to flip the order of sinusoidal components to cosine first.
+        scale (`float`, *optional*, defaults to `1.0`):
+            A scaling factor applied to the positional embeddings.
+
     Returns:
         a Tensor of timing signals [N, num_channels]
     """
@@ -61,9 +71,9 @@ class FlaxTimestepEmbedding(nn.Module):
 
     Args:
         time_embed_dim (`int`, *optional*, defaults to `32`):
-                Time step embedding dimension
-        dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
-                Parameters `dtype`
+            Time step embedding dimension.
+        dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
+            The data type for the embedding parameters.
     """
 
     time_embed_dim: int = 32
@@ -83,7 +93,11 @@ class FlaxTimesteps(nn.Module):
 
     Args:
         dim (`int`, *optional*, defaults to `32`):
-                Time step embedding dimension
+            Time step embedding dimension.
+        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
+            Whether to flip the sinusoidal function from sine to cosine.
+        freq_shift (`float`, *optional*, defaults to `1`):
+            Frequency shift applied to the sinusoidal embeddings.
     """
 
     dim: int = 32