Make mlp args match the one from timm

PixArt/model/blocks.py

@@ -419,20 +419,25 @@ class LabelEmbedder(nn.Module):
         embeddings = self.embedding_table(labels)
         return embeddings
 
+
 class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, dtype=None, device=None, operations=None) -> None:
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=None, dtype=None, device=None, operations=None) -> None:
         super().__init__()
         out_features = out_features or in_features
         hidden_features = hidden_features or in_features
 
-        self.fc1 = operations.Linear(in_features, hidden_features, bias=True, dtype=dtype, device=device)
+        self.fc1 = operations.Linear(in_features, hidden_features, bias=bias, dtype=dtype, device=device)
         self.act = act_layer()
-        self.fc2 = operations.Linear(hidden_features, out_features, bias=True, dtype=dtype, device=device)
+        self.fc2 = operations.Linear(hidden_features, out_features, bias=bias, dtype=dtype, device=device)
+
+        self.drop1 = nn.Identity()
+        self.drop2 = nn.Identity()
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = self.act(self.fc1(x))
         return self.fc2(x)
 
+
 class CaptionEmbedder(nn.Module):
     """
     Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.