diff --git a/src/refiners/fluxion/layers/attentions.py b/src/refiners/fluxion/layers/attentions.py
index 8676bff..3d356b0 100644
--- a/src/refiners/fluxion/layers/attentions.py
+++ b/src/refiners/fluxion/layers/attentions.py
@@ -1,3 +1,5 @@
+import math
+import torch
 from jaxtyping import Float
 from torch.nn.functional import scaled_dot_product_attention as _scaled_dot_product_attention  # type: ignore
 from torch import Tensor, device as Device, dtype as DType
@@ -18,11 +20,31 @@ def scaled_dot_product_attention(
     return _scaled_dot_product_attention(query, key, value, is_causal=is_causal)  # type: ignore
 
 
+def sparse_dot_product_attention_non_optimized(
+    query: Float[Tensor, "batch source_sequence_length dim"],
+    key: Float[Tensor, "batch target_sequence_length dim"],
+    value: Float[Tensor, "batch target_sequence_length dim"],
+    is_causal: bool = False,
+) -> Float[Tensor, "batch source_sequence_length dim"]:
+    if is_causal:
+        # TODO: implement causal attention
+        raise NotImplementedError("Causal attention for non_optimized attention is not yet implemented")
+    _, _, _, dim = query.shape
+    attention = query @ key.permute(0, 1, 3, 2)
+    attention = attention / math.sqrt(dim)
+    attention = torch.softmax(input=attention, dim=-1)
+    return attention @ value
+
+
 class ScaledDotProductAttention(Module):
-    def __init__(self, num_heads: int = 1, is_causal: bool | None = None) -> None:
+    def __init__(self, num_heads: int = 1, is_causal: bool | None = None, is_optimized: bool = True) -> None:
         super().__init__()
         self.num_heads = num_heads
         self.is_causal = is_causal
+        self.is_optimized = is_optimized
+        self.dot_product = (
+            scaled_dot_product_attention if self.is_optimized else sparse_dot_product_attention_non_optimized
+        )
 
     def forward(
         self,
@@ -32,7 +54,7 @@ class ScaledDotProductAttention(Module):
         is_causal: bool | None = None,
     ) -> Float[Tensor, "batch num_queries dim"]:
         return self.merge_multi_head(
-            scaled_dot_product_attention(
+            x=self.dot_product(
                 query=self.split_to_multi_head(query),
                 key=self.split_to_multi_head(key),
                 value=self.split_to_multi_head(value),
@@ -69,6 +91,7 @@ class Attention(Chain):
         "inner_dim",
         "use_bias",
         "is_causal",
+        "is_optimized",
     ]
 
     def __init__(
@@ -80,6 +103,7 @@ class Attention(Chain):
         inner_dim: int | None = None,
         use_bias: bool = True,
         is_causal: bool | None = None,
+        is_optimized: bool = True,
         device: Device | str | None = None,
         dtype: DType | None = None,
     ) -> None:
@@ -94,6 +118,7 @@ class Attention(Chain):
         self.inner_dim = inner_dim or embedding_dim
         self.use_bias = use_bias
         self.is_causal = is_causal
+        self.is_optimized = is_optimized
         super().__init__(
             Distribute(
                 Linear(
@@ -118,7 +143,7 @@ class Attention(Chain):
                     dtype=dtype,
                 ),
             ),
-            ScaledDotProductAttention(num_heads=num_heads, is_causal=is_causal),
+            ScaledDotProductAttention(num_heads=num_heads, is_causal=is_causal, is_optimized=is_optimized),
             Linear(
                 in_features=self.inner_dim,
                 out_features=self.embedding_dim,
@@ -137,6 +162,7 @@ class SelfAttention(Attention):
         num_heads: int = 1,
         use_bias: bool = True,
         is_causal: bool | None = None,
+        is_optimized: bool = True,
         device: Device | str | None = None,
         dtype: DType | None = None,
     ) -> None:
@@ -146,6 +172,7 @@ class SelfAttention(Attention):
             num_heads=num_heads,
             use_bias=use_bias,
             is_causal=is_causal,
+            is_optimized=is_optimized,
             device=device,
             dtype=dtype,
         )
@@ -153,7 +180,7 @@ class SelfAttention(Attention):
 
 
 class SelfAttention2d(SelfAttention):
-    structural_attrs = ["channels"]
+    structural_attrs = Attention.structural_attrs + ["channels"]
 
     def __init__(
         self,
@@ -161,6 +188,7 @@ class SelfAttention2d(SelfAttention):
         num_heads: int = 1,
         use_bias: bool = True,
         is_causal: bool | None = None,
+        is_optimized: bool = True,
         device: Device | str | None = None,
         dtype: DType | None = None,
     ) -> None:
@@ -171,6 +199,7 @@ class SelfAttention2d(SelfAttention):
             num_heads=num_heads,
             use_bias=use_bias,
             is_causal=is_causal,
+            is_optimized=is_optimized,
             device=device,
             dtype=dtype,
         )