Add unet act fn to other model components (#3136)

Adding act fn config to the unet timestep class embedding and conv
activation.

The custom activation defaults to silu which is the default
activation function for both the conv act and the timestep class
embeddings so default behavior is not changed.

The only unet which use the custom activation is the stable diffusion
latent upscaler https://huggingface.co/stabilityai/sd-x2-latent-upscaler/blob/main/unet/config.json
(I ran a script against the hub to confirm).
The latent upscaler does not use the conv activation nor the timestep
class embeddings so we don't change its behavior.

Author: williamberman

src/diffusers/models/unet_2d_condition.py

@@ -248,7 +248,7 @@ def __init__(
         if class_embed_type is None and num_class_embeds is not None:
             self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
         elif class_embed_type == "timestep":
-            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
         elif class_embed_type == "identity":
             self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
         elif class_embed_type == "projection":
@@ -437,7 +437,18 @@ def __init__(
             self.conv_norm_out = nn.GroupNorm(
                 num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
             )
-            self.conv_act = nn.SiLU()
+
+            if act_fn == "swish":
+                self.conv_act = lambda x: F.silu(x)
+            elif act_fn == "mish":
+                self.conv_act = nn.Mish()
+            elif act_fn == "silu":
+                self.conv_act = nn.SiLU()
+            elif act_fn == "gelu":
+                self.conv_act = nn.GELU()
+            else:
+                raise ValueError(f"Unsupported activation function: {act_fn}")
+
         else:
             self.conv_norm_out = None
             self.conv_act = None

src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py

@@ -345,7 +345,7 @@ def __init__(
         if class_embed_type is None and num_class_embeds is not None:
             self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
         elif class_embed_type == "timestep":
-            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
         elif class_embed_type == "identity":
             self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
         elif class_embed_type == "projection":
@@ -534,7 +534,18 @@ def __init__(
             self.conv_norm_out = nn.GroupNorm(
                 num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
             )
-            self.conv_act = nn.SiLU()
+
+            if act_fn == "swish":
+                self.conv_act = lambda x: F.silu(x)
+            elif act_fn == "mish":
+                self.conv_act = nn.Mish()
+            elif act_fn == "silu":
+                self.conv_act = nn.SiLU()
+            elif act_fn == "gelu":
+                self.conv_act = nn.GELU()
+            else:
+                raise ValueError(f"Unsupported activation function: {act_fn}")
+
         else:
             self.conv_norm_out = None
             self.conv_act = None