diff --git a/dalle2_pytorch/dalle2_pytorch.py b/dalle2_pytorch/dalle2_pytorch.py
index b9aef83..12578a7 100644
--- a/dalle2_pytorch/dalle2_pytorch.py
+++ b/dalle2_pytorch/dalle2_pytorch.py
@@ -44,12 +44,13 @@ def freeze_model_and_make_eval_(model):
 
 # diffusion prior
 
-def FeedForward(dim, mult = 4):
+def FeedForward(dim, mult = 4, dropout = 0.):
     inner_dim = int(mult * dim)
     return nn.Sequential(
         nn.LayerNorm(dim),
         nn.Linear(dim, inner_dim, bias = False),
         nn.GELU(),
+        nn.Dropout(dropout),
         nn.Linear(inner_dim, dim, bias = False)
     )
 
@@ -59,13 +60,16 @@ class Attention(nn.Module):
         *,
         dim,
         dim_head = 64,
-        heads = 8
+        heads = 8,
+        dropout = 0.
     ):
         super().__init__()
         self.scale = dim_head ** -0.5
         inner_dim = dim_head * heads
 
         self.norm = nn.LayerNorm(dim)
+        self.dropout = nn.Dropout(dropout)
+
         self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
         self.to_out = nn.Linear(inner_dim, dim, bias = False)
 
@@ -106,7 +110,9 @@ class Transformer(nn.Module):
         dim_head = 64,
         heads = 8,
         ff_mult = 4,
-        norm_out = False
+        norm_out = False,
+        attn_dropout = 0.,
+        ff_dropout = 0.
     ):
         super().__init__()
         # todo - bring in rotary embeddings or alibi
@@ -114,8 +120,8 @@ class Transformer(nn.Module):
         self.layers = nn.ModuleList([])
         for _ in range(depth):
             self.layers.append(nn.ModuleList([
-                Attention(dim = dim, dim_head = dim_head, heads = heads),
-                FeedForward(dim = dim, mult = ff_mult)
+                Attention(dim = dim, dim_head = dim_head, heads = heads, dropout = attn_dropout),
+                FeedForward(dim = dim, mult = ff_mult, dropout = ff_dropout)
             ]))
 
         self.norm = nn.LayerNorm(dim) if norm_out else nn.Identity()  # unclear in paper whether they projected after the classic layer norm for the final denoised image embedding, or just had the transformer output it directly: plan on offering both options
@@ -168,7 +174,8 @@ class Decoder(nn.Module):
         *,
         image,
         image_embed,
-        text_embed = None  # in paper, text embedding was optional for conditioning decoder
+        cond_drop_prob = 0.2,   # for the classifier free guidance
+        text_embed = None       # in paper, text embedding was optional for conditioning decoder
     ):
         return image