diff --git a/nodes/patch_lib/LTXVideoPatch.py b/nodes/patch_lib/LTXVideoPatch.py
index 60e8853..68cd200 100644
--- a/nodes/patch_lib/LTXVideoPatch.py
+++ b/nodes/patch_lib/LTXVideoPatch.py
@@ -3,7 +3,10 @@ import math
 import torch
 from torch import Tensor
 
-from comfy.ldm.lightricks.model import precompute_freqs_cis
+try:
+  from comfy.ldm.lightricks.model import LTXBaseModel
+except:
+    from comfy.ldm.lightricks.model import precompute_freqs_cis
 from comfy.ldm.lightricks.symmetric_patchifier import latent_to_pixel_coords
 from ..patch_util import PatchKeys
 
@@ -60,7 +63,10 @@ def ltx_forward_orig(
     if attention_mask is not None and not torch.is_floating_point(attention_mask):
         attention_mask = (attention_mask - 1).to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) * torch.finfo(x.dtype).max
 
-    pe = precompute_freqs_cis(fractional_coords, dim=self.inner_dim, out_dtype=x.dtype)
+    try:
+        pe = LTXBaseModel.precompute_freqs_cis(fractional_coords, dim=self.inner_dim, out_dtype=x.dtype)
+    except:
+        pe = precompute_freqs_cis(fractional_coords, dim=self.inner_dim, out_dtype=x.dtype)
 
     batch_size = x.shape[0]
     timestep, embedded_timestep = self.adaln_single(
diff --git a/nodes/patch_lib/old/LTXVideoPatch.py b/nodes/patch_lib/old/LTXVideoPatch.py
index f47b4b0..79f8a71 100644
--- a/nodes/patch_lib/old/LTXVideoPatch.py
+++ b/nodes/patch_lib/old/LTXVideoPatch.py
@@ -3,7 +3,10 @@ import math
 import torch
 from torch import Tensor
 
-from comfy.ldm.lightricks.model import precompute_freqs_cis
+try:
+  from comfy.ldm.lightricks.model import LTXBaseModel
+except:
+    from comfy.ldm.lightricks.model import precompute_freqs_cis
 from ...patch_util import PatchKeys
 
 # changed in comfyui hash commit 93fedd92fe0eb67a09e29069b05adebb40678639 (between comfyui version 0.3.19 and 1.3.20)
@@ -77,7 +80,10 @@ def ltx_forward_orig(
     if attention_mask is not None and not torch.is_floating_point(attention_mask):
         attention_mask = (attention_mask - 1).to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) * torch.finfo(x.dtype).max
 
-    pe = precompute_freqs_cis(indices_grid, dim=self.inner_dim, out_dtype=x.dtype)
+    try:
+        pe = LTXBaseModel.precompute_freqs_cis(indices_grid, dim=self.inner_dim, out_dtype=x.dtype)
+    except:
+        pe = precompute_freqs_cis(indices_grid, dim=self.inner_dim, out_dtype=x.dtype)
 
     batch_size = x.shape[0]
     timestep, embedded_timestep = self.adaln_single(