From 90ebb50f00bf89ed8c947a0e4ed4ed0803981ea1 Mon Sep 17 00:00:00 2001
From: John Pollock <pollockjj@gmail.com>
Date: Mon, 20 Apr 2026 16:05:10 -0500
Subject: [PATCH] Harden Trellis sparse latent seeding

---
 comfy/ldm/trellis2/model.py                   |  4 +++
 comfy/sample.py                               |  2 --
 comfy_extras/nodes_trellis2.py                |  4 +--
 .../comfy_extras_test/nodes_trellis2_test.py  | 29 +++++++++++++++++++
 4 files changed, 35 insertions(+), 4 deletions(-)

diff --git a/comfy/ldm/trellis2/model.py b/comfy/ldm/trellis2/model.py
index 7cf3e728e..e8ed39aed 100644
--- a/comfy/ldm/trellis2/model.py
+++ b/comfy/ldm/trellis2/model.py
@@ -880,6 +880,10 @@ class Trellis2(nn.Module):
                         for i in range(logical_batch):
                             out_index = rep * logical_batch + i
                             count = int(coord_counts[i].item())
+                            if count > N:
+                                raise ValueError(
+                                    f"Trellis2 coord count {count} exceeds latent token dimension {N} for batch {i}"
+                                )
                             coords_i = coords_by_batch[i].clone()
                             coords_i[:, 0] = 0
                             feats_i = x_eval[out_index, :count].clone()
diff --git a/comfy/sample.py b/comfy/sample.py
index 8626269a1..a4ce5f56f 100644
--- a/comfy/sample.py
+++ b/comfy/sample.py
@@ -74,8 +74,6 @@ def prepare_noise(latent_image, seed, noise_inds=None):
 def fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None):
     if latent_image.is_nested:
         return latent_image
-    if getattr(latent_image, "trellis_skip_empty_fix", False):
-        return latent_image
     latent_format = model.get_model_object("latent_format") #Resize the empty latent image so it has the right number of channels
     if torch.count_nonzero(latent_image) == 0:
         if latent_format.latent_channels != latent_image.shape[1]:
diff --git a/comfy_extras/nodes_trellis2.py b/comfy_extras/nodes_trellis2.py
index 328cec6e7..d345641b1 100644
--- a/comfy_extras/nodes_trellis2.py
+++ b/comfy_extras/nodes_trellis2.py
@@ -807,6 +807,8 @@ class EmptyTextureLatentTrellis2(IO.ComfyNode):
             )
 
         shape_batch_index = normalize_batch_index(shape_latent.get("batch_index"))
+        if batch_index is None:
+            batch_index = shape_batch_index
         shape_latent = shape_latent["samples"]
         batch_size, inferred_coord_counts, max_tokens = infer_batched_coord_layout(coords)
         if coord_counts is not None:
@@ -844,8 +846,6 @@ class EmptyTextureLatentTrellis2(IO.ComfyNode):
                 latent[i, :, :count] = latent_i[0]
         if coord_counts is not None:
             latent.trellis_coord_counts = coord_counts.clone()
-        if batch_index is None:
-            batch_index = shape_batch_index
         model = model.clone()
         model.model_options = model.model_options.copy()
         if "transformer_options" in model.model_options:
diff --git a/tests-unit/comfy_extras_test/nodes_trellis2_test.py b/tests-unit/comfy_extras_test/nodes_trellis2_test.py
index 43647e793..49e872bc7 100644
--- a/tests-unit/comfy_extras_test/nodes_trellis2_test.py
+++ b/tests-unit/comfy_extras_test/nodes_trellis2_test.py
@@ -233,6 +233,35 @@ class TestTrellisBatchSemantics(unittest.TestCase):
                 13,
             )
 
+    def test_empty_texture_latent_uses_shape_batch_index_for_seed_fallback(self):
+        coords = torch.tensor(
+            [
+                [0, 1, 1, 1],
+                [1, 2, 2, 2],
+                [1, 3, 3, 3],
+            ],
+            dtype=torch.int32,
+        )
+        structure = {"coords": coords}
+        shape_latent = {
+            "samples": torch.zeros(2, 32, 2, 1),
+            "batch_index": [4, 9],
+        }
+
+        output, _ = nodes_trellis2.EmptyTextureLatentTrellis2.execute(
+            structure,
+            shape_latent,
+            DummyCloneModel(),
+            13,
+        )
+
+        expected = torch.zeros(2, 32, 2, 1)
+        expected[0, :, :1, :] = torch.randn(1, 32, 1, 1, generator=torch.Generator(device="cpu").manual_seed(17))[0]
+        expected[1, :, :2, :] = torch.randn(1, 32, 2, 1, generator=torch.Generator(device="cpu").manual_seed(22))[0]
+
+        self.assertTrue(torch.equal(output["samples"], expected))
+        self.assertEqual(output["batch_index"], [4, 9])
+
     def test_flatten_batched_sparse_latent_validates_coord_counts(self):
         samples = torch.zeros(2, 32, 3, 1)
         coords = torch.tensor(