diff --git a/CrossAttentionPatch.py b/CrossAttentionPatch.py
index adb2079..6dbb273 100644
--- a/CrossAttentionPatch.py
+++ b/CrossAttentionPatch.py
@@ -1,7 +1,7 @@
 import torch
 import math
 import torch.nn.functional as F
-from comfy.ldm.modules.attention import optimized_attention
+from comfy.ldm.modules.attention import attention_sage, optimized_attention
 from .utils import tensor_to_size
 
 class Attn2Replace:
@@ -64,7 +64,10 @@ def to(self, device, *args, **kwargs):
 
 def ipadapter_attention(out, q, k, v, extra_options, module_key='', ipadapter=None, weight=1.0, cond=None, cond_alt=None, uncond=None, weight_type="linear", mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False, embeds_scaling='V only', **kwargs):
     ipadapter = ipadapter.get_multigpu_clone(q.device)
-    
+
+    epsilon = 0.0
+    if optimized_attention == attention_sage:
+        epsilon = 1e-5
     dtype = q.dtype
     cond_or_uncond = extra_options["cond_or_uncond"]
     block_type = extra_options["block"][0]
@@ -105,17 +108,17 @@ def ipadapter_attention(out, q, k, v, extra_options, module_key='', ipadapter=No
         if weight_type == "style transfer precise":
             if layers == 11 and t_idx == 3:
                 uncond = cond
-                cond = cond * 0
+                cond = cond * epsilon
             elif layers == 16 and (t_idx == 4 or t_idx == 5):
                 uncond = cond
-                cond = cond * 0
+                cond = cond * epsilon
         elif weight_type == "composition precise":
             if layers == 11 and t_idx != 3:
                 uncond = cond
-                cond = cond * 0
+                cond = cond * epsilon
             elif layers == 16 and (t_idx != 4 and t_idx != 5):
                 uncond = cond
-                cond = cond * 0
+                cond = cond * epsilon
 
         weight = weight[t_idx]
 
@@ -170,7 +173,7 @@ def ipadapter_attention(out, q, k, v, extra_options, module_key='', ipadapter=No
             weight = weight.repeat(len(cond_or_uncond), 1, 1) # repeat for cond and uncond
         elif weight == 0:
             return 0
-        
+
         k_cond = ipadapter.ip_layers.to_kvs[k_key](cond).repeat(batch_prompt, 1, 1)
         k_uncond = ipadapter.ip_layers.to_kvs[k_key](uncond).repeat(batch_prompt, 1, 1)
         v_cond = ipadapter.ip_layers.to_kvs[v_key](cond).repeat(batch_prompt, 1, 1)