diff --git a/comfy/ldm/trellis2/attention.py b/comfy/ldm/trellis2/attention.py
deleted file mode 100644
index 571101863..000000000
--- a/comfy/ldm/trellis2/attention.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from comfy.ldm.modules.attention import optimized_attention
-from comfy.ldm.trellis2.vae import VarLenTensor
-
-
-def dense_attention(q, k, v, **kwargs):
-    """q, k, v: [B, L, H, C]. Permutes for comfy's [B, H, L, C] convention."""
-    heads = q.shape[2]
-    q = q.permute(0, 2, 1, 3)
-    k = k.permute(0, 2, 1, 3)
-    v = v.permute(0, 2, 1, 3)
-    out = optimized_attention(q, k, v, heads, skip_output_reshape=True, skip_reshape=True, **kwargs)
-    return out.permute(0, 2, 1, 3)
-
-
-def _to_rect(t):
-    """Fold a VarLenTensor packed as [sum(L_i), H, C] into a dense [B, L, H, C].
-
-    The sparse generation stages run a single object per call (optionally
-    CFG-duplicated, which keeps every batch entry the same length), so the
-    packed layout is rectangular and attention is ordinary dense attention over
-    a batch dim — no variable-length kernel needed. A dense [B, L, H, C] tensor
-    (e.g. cross-attention context) passes through unchanged.
-    """
-    if not isinstance(t, VarLenTensor):
-        return t
-    B = t.shape[0]
-    seqlens = [t.layout[i].stop - t.layout[i].start for i in range(B)]
-    if len(set(seqlens)) != 1:
-        raise ValueError(
-            "trellis2 sparse attention expects equal sequence lengths per batch "
-            f"(single object, optionally CFG-duplicated); got {seqlens}. "
-            "Multi-object batching is not supported."
-        )
-    return t.feats.view(B, seqlens[0], *t.feats.shape[1:])
-
-
-def sparse_attention(q, k, v, **kwargs):
-    """Full attention over a SparseTensor's voxels.
-
-    Single object (optionally CFG-duplicated) => the packed layout is
-    rectangular, so we fold it into a batch dim and run ordinary dense
-    attention. Output type matches q.
-    """
-    out = dense_attention(_to_rect(q), _to_rect(k), _to_rect(v), **kwargs)  # [B, Lq, H, C]
-    if isinstance(q, VarLenTensor):
-        return q.replace(out.reshape(-1, *out.shape[2:]))
-    return out
diff --git a/comfy/ldm/trellis2/model.py b/comfy/ldm/trellis2/model.py
index 3f3751052..2beb389e7 100644
--- a/comfy/ldm/trellis2/model.py
+++ b/comfy/ldm/trellis2/model.py
@@ -3,10 +3,42 @@ import torch.nn.functional as F
 import torch.nn as nn
 from comfy.ldm.trellis2.vae import SparseTensor, SparseLinear, sparse_cat, VarLenTensor
 from typing import Optional, Tuple, Literal, Union, List
-from comfy.ldm.trellis2.attention import sparse_attention, dense_attention
+from comfy.ldm.modules.attention import optimized_attention
 from comfy.ldm.genmo.joint_model.layers import TimestepEmbedder
 from comfy.ldm.flux.math import apply_rope, apply_rope1
 
+
+def dense_attention(q, k, v, **kwargs):
+    heads = q.shape[2]
+    q = q.permute(0, 2, 1, 3)
+    k = k.permute(0, 2, 1, 3)
+    v = v.permute(0, 2, 1, 3)
+    out = optimized_attention(q, k, v, heads, skip_output_reshape=True, skip_reshape=True, **kwargs)
+    return out.permute(0, 2, 1, 3)
+
+
+def _to_rect(t):
+    # Single object (optionally CFG-duplicated) => packed layout is rectangular,
+    # so we can fold it into a batch dim and use dense attention.
+    if not isinstance(t, VarLenTensor):
+        return t
+    B = t.shape[0]
+    seqlens = [t.layout[i].stop - t.layout[i].start for i in range(B)]
+    if len(set(seqlens)) != 1:
+        raise ValueError(
+            "trellis2 sparse attention expects equal sequence lengths per batch "
+            f"(single object, optionally CFG-duplicated); got {seqlens}."
+        )
+    return t.feats.view(B, seqlens[0], *t.feats.shape[1:])
+
+
+def sparse_attention(q, k, v, **kwargs):
+    out = dense_attention(_to_rect(q), _to_rect(k), _to_rect(v), **kwargs)
+    if isinstance(q, VarLenTensor):
+        return q.replace(out.reshape(-1, *out.shape[2:]))
+    return out
+
+
 class SparseGELU(nn.GELU):
     def forward(self, input: VarLenTensor) -> VarLenTensor:
         return input.replace(super().forward(input.feats))