diff --git a/src/diffusers/models/unet_2d_condition.py b/src/diffusers/models/unet_2d_condition.py
index 3fb4202ed119..9243dc66d3e8 100644
--- a/src/diffusers/models/unet_2d_condition.py
+++ b/src/diffusers/models/unet_2d_condition.py
@@ -16,6 +16,7 @@
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 import torch.utils.checkpoint
 
 from ..configuration_utils import ConfigMixin, register_to_config
@@ -101,6 +102,9 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
             class conditioning with `class_embed_type` equal to `None`.
         time_embedding_type (`str`, *optional*, default to `positional`):
             The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
+        time_embedding_act_fn (`str`, *optional*, default to `None`):
+            Optional activation function to use on the time embeddings only one time before they as passed to the rest
+            of the unet. Choose from `silu`, `mish`, `gelu`, and `swish`.
         timestep_post_act (`str, *optional*, default to `None`):
             The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
         time_cond_proj_dim (`int`, *optional*, default to `None`):
@@ -157,6 +161,7 @@ def __init__(
         resnet_skip_time_act: bool = False,
         resnet_out_scale_factor: int = 1.0,
         time_embedding_type: str = "positional",
+        time_embedding_act_fn: Optional[str] = None,
         timestep_post_act: Optional[str] = None,
         time_cond_proj_dim: Optional[int] = None,
         conv_in_kernel: int = 3,
@@ -267,6 +272,19 @@ def __init__(
         else:
             self.class_embedding = None
 
+        if time_embedding_act_fn is None:
+            self.time_embed_act = None
+        elif time_embedding_act_fn == "swish":
+            self.time_embed_act = lambda x: F.silu(x)
+        elif time_embedding_act_fn == "mish":
+            self.time_embed_act = nn.Mish()
+        elif time_embedding_act_fn == "silu":
+            self.time_embed_act = nn.SiLU()
+        elif time_embedding_act_fn == "gelu":
+            self.time_embed_act = nn.GELU()
+        else:
+            raise ValueError(f"Unsupported activation function: {time_embedding_act_fn}")
+
         self.down_blocks = nn.ModuleList([])
         self.up_blocks = nn.ModuleList([])
 
@@ -657,6 +675,9 @@ def forward(
             else:
                 emb = emb + class_emb
 
+        if self.time_embed_act is not None:
+            emb = self.time_embed_act(emb)
+
         if self.encoder_hid_proj is not None:
             encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
 
diff --git a/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py b/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py
index 51d1c62c926b..cc8cde4daa3b 100644
--- a/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py
+++ b/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py
@@ -3,6 +3,7 @@
 import numpy as np
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...models import ModelMixin
@@ -182,6 +183,9 @@ class UNetFlatConditionModel(ModelMixin, ConfigMixin):
             class conditioning with `class_embed_type` equal to `None`.
         time_embedding_type (`str`, *optional*, default to `positional`):
             The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
+        time_embedding_act_fn (`str`, *optional*, default to `None`):
+            Optional activation function to use on the time embeddings only one time before they as passed to the rest
+            of the unet. Choose from `silu`, `mish`, `gelu`, and `swish`.
         timestep_post_act (`str, *optional*, default to `None`):
             The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
         time_cond_proj_dim (`int`, *optional*, default to `None`):
@@ -243,6 +247,7 @@ def __init__(
         resnet_skip_time_act: bool = False,
         resnet_out_scale_factor: int = 1.0,
         time_embedding_type: str = "positional",
+        time_embedding_act_fn: Optional[str] = None,
         timestep_post_act: Optional[str] = None,
         time_cond_proj_dim: Optional[int] = None,
         conv_in_kernel: int = 3,
@@ -359,6 +364,19 @@ def __init__(
         else:
             self.class_embedding = None
 
+        if time_embedding_act_fn is None:
+            self.time_embed_act = None
+        elif time_embedding_act_fn == "swish":
+            self.time_embed_act = lambda x: F.silu(x)
+        elif time_embedding_act_fn == "mish":
+            self.time_embed_act = nn.Mish()
+        elif time_embedding_act_fn == "silu":
+            self.time_embed_act = nn.SiLU()
+        elif time_embedding_act_fn == "gelu":
+            self.time_embed_act = nn.GELU()
+        else:
+            raise ValueError(f"Unsupported activation function: {time_embedding_act_fn}")
+
         self.down_blocks = nn.ModuleList([])
         self.up_blocks = nn.ModuleList([])
 
@@ -752,6 +770,9 @@ def forward(
             else:
                 emb = emb + class_emb
 
+        if self.time_embed_act is not None:
+            emb = self.time_embed_act(emb)
+
         if self.encoder_hid_proj is not None:
             encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)