diff --git a/comfy/ldm/ace/attention.py b/comfy/ldm/ace/attention.py
index 631d13647..c81801747 100644
--- a/comfy/ldm/ace/attention.py
+++ b/comfy/ldm/ace/attention.py
@@ -19,6 +19,7 @@ import torch.nn.functional as F
 from torch import nn
 
 import comfy.model_management
+from comfy.ldm.modules.attention import optimized_attention
 
 class Attention(nn.Module):
     def __init__(
@@ -326,10 +327,6 @@ class CustomerAttnProcessor2_0:
     Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
     """
 
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
     def apply_rotary_emb(
         self,
         x: torch.Tensor,
@@ -435,9 +432,8 @@ class CustomerAttnProcessor2_0:
             attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
 
         # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        hidden_states = optimized_attention(
+            query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, skip_output_reshape=True,
         )
 
         hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)