diff --git a/comfy/conds.py b/comfy/conds.py
index 5af3e93ea..55d8cdd78 100644
--- a/comfy/conds.py
+++ b/comfy/conds.py
@@ -4,6 +4,25 @@ import comfy.utils
 import logging
 
 
+def is_equal(x, y):
+    if torch.is_tensor(x) and torch.is_tensor(y):
+        return torch.equal(x, y)
+    elif isinstance(x, dict) and isinstance(y, dict):
+        if x.keys() != y.keys():
+            return False
+        return all(is_equal(x[k], y[k]) for k in x)
+    elif isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)):
+        if type(x) is not type(y) or len(x) != len(y):
+            return False
+        return all(is_equal(a, b) for a, b in zip(x, y))
+    else:
+        try:
+            return x == y
+        except Exception:
+            logging.warning("comparison issue with COND")
+            return False
+
+
 class CONDRegular:
     def __init__(self, cond):
         self.cond = cond
@@ -84,7 +103,7 @@ class CONDConstant(CONDRegular):
         return self._copy_with(self.cond)
 
     def can_concat(self, other):
-        if self.cond != other.cond:
+        if not is_equal(self.cond, other.cond):
             return False
         return True
 
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 04695c079..8f852e3c6 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -65,42 +65,6 @@ from typing import TYPE_CHECKING
 if TYPE_CHECKING:
     from comfy.model_patcher import ModelPatcher
 
-
-class _CONDGuideEntries(comfy.conds.CONDConstant):
-    """CONDConstant subclass that safely compares guide_attention_entries.
-
-    guide_attention_entries may contain ``pixel_mask`` tensors.  The default
-    ``CONDConstant.can_concat`` uses ``!=`` which triggers a ``ValueError``
-    on tensors.  This subclass performs a structural comparison instead.
-    """
-
-    def can_concat(self, other):
-        if not isinstance(other, _CONDGuideEntries):
-            return False
-        a, b = self.cond, other.cond
-        if len(a) != len(b):
-            return False
-        for ea, eb in zip(a, b):
-            if ea["pre_filter_count"] != eb["pre_filter_count"]:
-                return False
-            if ea["strength"] != eb["strength"]:
-                return False
-            if ea.get("latent_shape") != eb.get("latent_shape"):
-                return False
-            a_has = ea.get("pixel_mask") is not None
-            b_has = eb.get("pixel_mask") is not None
-            if a_has != b_has:
-                return False
-            if a_has:
-                pm_a, pm_b = ea["pixel_mask"], eb["pixel_mask"]
-                if pm_a is not pm_b:
-                    if (pm_a.shape != pm_b.shape
-                            or pm_a.device != pm_b.device
-                            or pm_a.dtype != pm_b.dtype
-                            or not torch.equal(pm_a, pm_b)):
-                        return False
-        return True
-
 class ModelType(Enum):
     EPS = 1
     V_PREDICTION = 2
@@ -1012,7 +976,7 @@ class LTXV(BaseModel):
 
         guide_attention_entries = kwargs.get("guide_attention_entries", None)
         if guide_attention_entries is not None:
-            out['guide_attention_entries'] = _CONDGuideEntries(guide_attention_entries)
+            out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries)
 
         return out
 
@@ -1068,7 +1032,7 @@ class LTXAV(BaseModel):
 
         guide_attention_entries = kwargs.get("guide_attention_entries", None)
         if guide_attention_entries is not None:
-            out['guide_attention_entries'] = _CONDGuideEntries(guide_attention_entries)
+            out['guide_attention_entries'] = comfy.conds.CONDConstant(guide_attention_entries)
 
         return out