diff --git a/comfy/ldm/trellis2/model.py b/comfy/ldm/trellis2/model.py
index 1c5d6c3ec..e8ed39aed 100644
--- a/comfy/ldm/trellis2/model.py
+++ b/comfy/ldm/trellis2/model.py
@@ -786,6 +786,7 @@ class Trellis2(nn.Module):
         # 32 -> 512px path, 64 -> 1024px path.
         uses_1024_conditioning = self.img2shape.resolution == 64
         coords = transformer_options.get("coords", None)
+        coord_counts = transformer_options.get("coord_counts")
         mode = transformer_options.get("generation_mode", "structure_generation")
         is_512_run = False
         timestep = timestep.to(self.dtype)
@@ -811,15 +812,33 @@ class Trellis2(nn.Module):
             cond = context
         shape_rule = sigmas < self.guidance_interval[0] or sigmas > self.guidance_interval[1]
         txt_rule = sigmas < self.guidance_interval_txt[0] or sigmas > self.guidance_interval_txt[1]
+        dense_out = None
+        cond_or_uncond = transformer_options.get("cond_or_uncond") or []
+
+        def cond_group_indices(batch_groups):
+            if len(cond_or_uncond) == batch_groups:
+                cond_groups = [i for i, marker in enumerate(cond_or_uncond) if marker == 0]
+                if len(cond_groups) > 0:
+                    return cond_groups
+            return [batch_groups - 1]
 
         if not_struct_mode:
             orig_bsz = x.shape[0]
             rule = txt_rule if mode == "texture_generation" else shape_rule
 
-            if rule and orig_bsz > 1:
-                x_eval = x[1].unsqueeze(0)
-                t_eval = timestep[1].unsqueeze(0) if timestep.shape[0] > 1 else timestep
-                c_eval = cond
+            logical_batch = coord_counts.shape[0] if coord_counts is not None else 1
+            if rule and orig_bsz > logical_batch:
+                batch_groups = orig_bsz // logical_batch
+                selected_groups = cond_group_indices(batch_groups)
+                x_groups = x.reshape(batch_groups, logical_batch, *x.shape[1:])
+                x_eval = x_groups[selected_groups].reshape(-1, *x.shape[1:])
+                if timestep.shape[0] > 1:
+                    t_groups = timestep.reshape(batch_groups, logical_batch, *timestep.shape[1:])
+                    t_eval = t_groups[selected_groups].reshape(-1, *timestep.shape[1:])
+                else:
+                    t_eval = timestep
+                c_groups = context.reshape(batch_groups, logical_batch, *context.shape[1:])
+                c_eval = c_groups[selected_groups].reshape(-1, *context.shape[1:])
             else:
                 x_eval = x
                 t_eval = timestep
@@ -828,23 +847,107 @@ class Trellis2(nn.Module):
             B, N, C = x_eval.shape
 
             if mode in ["shape_generation", "texture_generation"]:
-                feats_flat = x_eval.reshape(-1, C)
+                if coord_counts is not None:
+                    logical_batch = coord_counts.shape[0]
+                    if B % logical_batch != 0:
+                        raise ValueError(
+                            f"Trellis2 coord_counts batch {logical_batch} doesn't divide latent batch {B}"
+                        )
+                    if int(coord_counts.sum().item()) != coords.shape[0]:
+                        raise ValueError(
+                            f"Trellis2 coord_counts total {int(coord_counts.sum().item())} does not match coords rows {coords.shape[0]}"
+                        )
+                    batch_ids = coords[:, 0].to(torch.int64)
+                    order = torch.argsort(batch_ids, stable=True)
+                    sorted_coords = coords.index_select(0, order)
+                    sorted_batch_ids = batch_ids.index_select(0, order)
+                    offsets = coord_counts.cumsum(0) - coord_counts
+                    coords_by_batch = []
+                    for i in range(logical_batch):
+                        count = int(coord_counts[i].item())
+                        start = int(offsets[i].item())
+                        coords_i = sorted_coords[start:start + count]
+                        ids_i = sorted_batch_ids[start:start + count]
+                        if coords_i.shape[0] != count or not torch.all(ids_i == i):
+                            raise ValueError(
+                                f"Trellis2 coords rows for batch {i} expected {count}, got {coords_i.shape[0]}"
+                            )
+                        coords_by_batch.append(coords_i)
+                    repeat_factor = B // logical_batch
+                    sparse_outs = []
+                    active_coord_counts = []
+                    for rep in range(repeat_factor):
+                        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()
+                            x_st_i = SparseTensor(feats=feats_i, coords=coords_i.to(torch.int32))
+                            t_i = t_eval[out_index].unsqueeze(0).clone() if t_eval.shape[0] > 1 else t_eval
+                            c_i = c_eval[out_index].unsqueeze(0).clone() if c_eval.shape[0] > 1 else c_eval
 
-                # inflate coords [N, 4] -> [B*N, 4]
-                coords_list = []
-                for i in range(B):
-                    c = coords.clone()
-                    c[:, 0] = i
-                    coords_list.append(c)
+                            if mode == "shape_generation":
+                                if is_512_run:
+                                    sparse_out = self.img2shape_512(x_st_i, t_i, c_i)
+                                else:
+                                    sparse_out = self.img2shape(x_st_i, t_i, c_i)
+                            else:
+                                slat = transformer_options.get("shape_slat")
+                                if slat is None:
+                                    raise ValueError("shape_slat can't be None")
+                                if slat.ndim == 3:
+                                    if slat.shape[0] != logical_batch:
+                                        raise ValueError(
+                                            f"shape_slat batch {slat.shape[0]} doesn't match coord_counts batch {logical_batch}"
+                                        )
+                                    if slat.shape[1] < count:
+                                        raise ValueError(
+                                            f"shape_slat tokens {slat.shape[1]} can't cover coord count {count} for batch {i}"
+                                        )
+                                    slat_feats = slat[i, :count].to(x_st_i.device)
+                                else:
+                                    slat_feats = slat[:count].to(x_st_i.device)
+                                x_st_i = x_st_i.replace(feats=torch.cat([x_st_i.feats, slat_feats], dim=-1))
+                                sparse_out = self.shape2txt(x_st_i, t_i, c_i)
 
-                batched_coords = torch.cat(coords_list, dim=0)
+                            sparse_outs.append(sparse_out.feats)
+                            active_coord_counts.append(count)
+
+                    out_channels = sparse_outs[0].shape[-1]
+                    padded = sparse_outs[0].new_zeros((B, N, out_channels))
+                    for out_index, (count, feats_i) in enumerate(zip(active_coord_counts, sparse_outs)):
+                        padded[out_index, :count] = feats_i
+                    dense_out = padded.transpose(1, 2).unsqueeze(-1)
+                elif coords.shape[0] == N:
+                    feats_flat = x_eval.reshape(-1, C)
+                    coords_list = []
+                    for i in range(B):
+                        c = coords.clone()
+                        c[:, 0] = i
+                        coords_list.append(c)
+                    batched_coords = torch.cat(coords_list, dim=0)
+                elif coords.shape[0] == B * N:
+                    feats_flat = x_eval.reshape(-1, C)
+                    batched_coords = coords
+                else:
+                    raise ValueError(
+                        f"Trellis2 expected coords rows {N} or {B * N}, got {coords.shape[0]}"
+                    )
             else:
                 batched_coords = coords
                 feats_flat = x_eval
 
-            x_st = SparseTensor(feats=feats_flat, coords=batched_coords.to(torch.int32))
+            if dense_out is None:
+                x_st = SparseTensor(feats=feats_flat, coords=batched_coords.to(torch.int32))
 
-        if mode == "shape_generation":
+        if dense_out is not None:
+            out = dense_out
+        elif mode == "shape_generation":
             if is_512_run:
                 out = self.img2shape_512(x_st, t_eval, c_eval)
             else:
@@ -856,23 +959,96 @@ class Trellis2(nn.Module):
             if slat is None:
                 raise ValueError("shape_slat can't be None")
 
-            base_slat_feats = slat[:N]
-            slat_feats_batched = base_slat_feats.repeat(B, 1).to(x_st.device)
+            if slat.ndim == 3:
+                if coord_counts is not None:
+                    logical_batch = coord_counts.shape[0]
+                    if slat.shape[0] != logical_batch:
+                        raise ValueError(
+                            f"shape_slat batch {slat.shape[0]} doesn't match coord_counts batch {logical_batch}"
+                        )
+                    if B % logical_batch != 0:
+                        raise ValueError(
+                            f"Trellis2 coord_counts batch {logical_batch} doesn't divide latent batch {B}"
+                        )
+                    repeat_factor = B // logical_batch
+                    slat_list = []
+                    for _ in range(repeat_factor):
+                        for i in range(logical_batch):
+                            count = int(coord_counts[i].item())
+                            if slat.shape[1] < count:
+                                raise ValueError(
+                                    f"shape_slat tokens {slat.shape[1]} can't cover coord count {count} for batch {i}"
+                                )
+                            slat_list.append(slat[i, :count])
+                    slat_feats_batched = torch.cat(slat_list, dim=0).to(x_st.device)
+                else:
+                    if slat.shape[0] != B:
+                        raise ValueError(f"shape_slat batch {slat.shape[0]} doesn't match latent batch {B}")
+                    if slat.shape[1] != N:
+                        raise ValueError(f"shape_slat tokens {slat.shape[1]} doesn't match latent tokens {N}")
+                    slat_feats_batched = slat.reshape(B * N, -1).to(x_st.device)
+            else:
+                base_slat_feats = slat[:N]
+                slat_feats_batched = base_slat_feats.repeat(B, 1).to(x_st.device)
             x_st = x_st.replace(feats=torch.cat([x_st.feats, slat_feats_batched], dim=-1))
             out = self.shape2txt(x_st, t_eval, c_eval)
         else: # structure
             orig_bsz = x.shape[0]
-            if shape_rule and orig_bsz > 1:
-                half = orig_bsz // 2
-                x = x[half:]
-                timestep = timestep[half:] if timestep.shape[0] > 1 else timestep
-            out = self.structure_model(x, timestep, cond if shape_rule and orig_bsz > 1 else context)
-            if shape_rule and orig_bsz > 1:
-                out = out.repeat(2, 1, 1, 1, 1)
+            batch_groups = len(cond_or_uncond) if len(cond_or_uncond) > 0 and orig_bsz % len(cond_or_uncond) == 0 else 1
+            logical_batch = orig_bsz // batch_groups
+            if logical_batch > 1:
+                x_groups = x.reshape(batch_groups, logical_batch, *x.shape[1:])
+                if timestep.shape[0] > 1:
+                    t_groups = timestep.reshape(batch_groups, logical_batch, *timestep.shape[1:])
+                else:
+                    t_groups = timestep
+                c_groups = context.reshape(batch_groups, logical_batch, *context.shape[1:])
+
+                if shape_rule and batch_groups > 1:
+                    selected_group_indices = cond_group_indices(batch_groups)
+                else:
+                    selected_group_indices = list(range(batch_groups))
+
+                out_groups = []
+                for sample_index in range(logical_batch):
+                    if shape_rule and batch_groups > 1:
+                        x_i = x_groups[selected_group_indices, sample_index]
+                        if timestep.shape[0] > 1:
+                            t_i = t_groups[selected_group_indices, sample_index]
+                        else:
+                            t_i = timestep
+                        c_i = c_groups[selected_group_indices, sample_index]
+                    else:
+                        x_i = x_groups[selected_group_indices, sample_index]
+                        if timestep.shape[0] > 1:
+                            t_i = t_groups[selected_group_indices, sample_index]
+                        else:
+                            t_i = timestep
+                        c_i = c_groups[selected_group_indices, sample_index]
+                    out_groups.append(self.structure_model(x_i, t_i, c_i))
+
+                out = out_groups[0].new_zeros((orig_bsz, *out_groups[0].shape[1:]))
+                for sample_index, out_sample in enumerate(out_groups):
+                    if shape_rule and batch_groups > 1:
+                        repeated = out_sample[0]
+                        for group_index in range(batch_groups):
+                            out[group_index * logical_batch + sample_index] = repeated
+                    else:
+                        for local_group_index, group_index in enumerate(selected_group_indices):
+                            out[group_index * logical_batch + sample_index] = out_sample[local_group_index]
+            else:
+                if shape_rule and orig_bsz > 1:
+                    half = orig_bsz // 2
+                    x = x[half:]
+                    timestep = timestep[half:] if timestep.shape[0] > 1 else timestep
+                out = self.structure_model(x, timestep, cond if shape_rule and orig_bsz > 1 else context)
+                if shape_rule and orig_bsz > 1:
+                    out = out.repeat(2, 1, 1, 1, 1)
 
         if not_struct_mode:
-            out = out.feats
-            out = out.view(B, N, -1).transpose(1, 2).unsqueeze(-1)
-            if rule and orig_bsz > 1:
-                out = out.repeat(orig_bsz, 1, 1, 1)
+            if dense_out is None:
+                out = out.feats
+                out = out.view(B, N, -1).transpose(1, 2).unsqueeze(-1)
+            if rule and orig_bsz > B:
+                out = out.repeat(orig_bsz // B, 1, 1, 1)
         return out
diff --git a/comfy/sample.py b/comfy/sample.py
index 653829582..878c4e984 100644
--- a/comfy/sample.py
+++ b/comfy/sample.py
@@ -7,6 +7,50 @@ import logging
 import comfy.nested_tensor
 
 def prepare_noise_inner(latent_image, generator, noise_inds=None):
+    coord_counts = getattr(latent_image, "trellis_coord_counts", None)
+    if coord_counts is not None:
+        if coord_counts.ndim != 1:
+            raise ValueError(f"Trellis2 coord_counts must be 1D, got shape {tuple(coord_counts.shape)}")
+        if coord_counts.shape[0] != latent_image.size(0):
+            raise ValueError(
+                f"Trellis2 coord_counts length {coord_counts.shape[0]} does not match latent batch {latent_image.size(0)}"
+            )
+        if (coord_counts < 0).any() or (coord_counts > latent_image.size(2)).any():
+            raise ValueError(
+                f"Trellis2 coord_counts must be within [0, {latent_image.size(2)}], got {coord_counts.tolist()}"
+            )
+        noise = torch.zeros(latent_image.size(), dtype=torch.float32, layout=latent_image.layout, device="cpu")
+        if noise_inds is None:
+            noise_inds = np.arange(latent_image.size(0), dtype=np.int64)
+        else:
+            noise_inds = np.asarray(noise_inds, dtype=np.int64)
+            if noise_inds.shape[0] != latent_image.size(0):
+                raise ValueError(
+                    f"Trellis2 noise_inds length {noise_inds.shape[0]} does not match latent batch {latent_image.size(0)}"
+                )
+
+        base_seed = int(generator.initial_seed())
+        unique_inds = np.unique(noise_inds)
+        sample_noises = {}
+        for noise_index in unique_inds.tolist():
+            rows = np.flatnonzero(noise_inds == noise_index)
+            max_count = max(int(coord_counts[row].item()) for row in rows.tolist())
+            local_generator = torch.Generator(device="cpu")
+            local_generator.manual_seed(base_seed + int(noise_index))
+            sample_noises[int(noise_index)] = torch.randn(
+                [1, latent_image.size(1), max_count, latent_image.size(3)],
+                dtype=torch.float32,
+                layout=latent_image.layout,
+                generator=local_generator,
+                device="cpu",
+            )
+
+        for batch_index, noise_index in enumerate(noise_inds.tolist()):
+            count = int(coord_counts[batch_index].item())
+            sample_noise = sample_noises[int(noise_index)]
+            noise[batch_index:batch_index + 1, :, :count, :] = sample_noise[:, :, :count, :]
+        return noise.to(dtype=latent_image.dtype)
+
     if noise_inds is None:
         return torch.randn(latent_image.size(), dtype=torch.float32, layout=latent_image.layout, generator=generator, device="cpu").to(dtype=latent_image.dtype)
 
diff --git a/comfy_extras/nodes_trellis2.py b/comfy_extras/nodes_trellis2.py
index 8121e261b..56ec3e736 100644
--- a/comfy_extras/nodes_trellis2.py
+++ b/comfy_extras/nodes_trellis2.py
@@ -96,6 +96,114 @@ def shape_norm(shape_latent, coords):
     samples = samples * std + mean
     return samples
 
+
+def infer_batched_coord_layout(coords):
+    if coords.ndim != 2 or coords.shape[1] != 4:
+        raise ValueError(f"Expected Trellis2 coords with shape [N, 4], got {tuple(coords.shape)}")
+
+    if coords.shape[0] == 0:
+        raise ValueError("Trellis2 coords can't be empty")
+
+    batch_ids = coords[:, 0].to(torch.int64)
+    if (batch_ids < 0).any():
+        raise ValueError(f"Trellis2 batch ids must be non-negative, got {batch_ids.unique(sorted=True).tolist()}")
+    batch_size = int(batch_ids.max().item()) + 1
+    counts = torch.bincount(batch_ids, minlength=batch_size)
+
+    if (counts == 0).any():
+        raise ValueError(f"Non-contiguous Trellis2 batch ids in coords: {batch_ids.unique(sorted=True).tolist()}")
+
+    max_tokens = int(counts.max().item())
+    return batch_size, counts, max_tokens
+
+
+def split_batched_coords(coords, coord_counts):
+    if coord_counts.ndim != 1:
+        raise ValueError(f"Trellis2 coord_counts must be 1D, got shape {tuple(coord_counts.shape)}")
+    if (coord_counts < 0).any():
+        raise ValueError(f"Trellis2 coord_counts must be non-negative, got {coord_counts.tolist()}")
+    if int(coord_counts.sum().item()) != coords.shape[0]:
+        raise ValueError(
+            f"Trellis2 coord_counts total {int(coord_counts.sum().item())} does not match coords rows {coords.shape[0]}"
+        )
+
+    batch_ids = coords[:, 0].to(torch.int64)
+    order = torch.argsort(batch_ids, stable=True)
+    sorted_coords = coords.index_select(0, order)
+    sorted_batch_ids = batch_ids.index_select(0, order)
+
+    offsets = coord_counts.cumsum(0) - coord_counts
+    items = []
+    for i in range(coord_counts.shape[0]):
+        count = int(coord_counts[i].item())
+        start = int(offsets[i].item())
+        coords_i = sorted_coords[start:start + count]
+        ids_i = sorted_batch_ids[start:start + count]
+        if coords_i.shape[0] != count or not torch.all(ids_i == i):
+            raise ValueError(f"Trellis2 coords rows for batch {i} expected {count}, got {coords_i.shape[0]}")
+        items.append(coords_i)
+    return items
+
+
+def normalize_batch_index(batch_index):
+    if batch_index is None:
+        return None
+    if isinstance(batch_index, int):
+        return [int(batch_index)]
+    return list(batch_index)
+
+
+def resolve_sample_indices(batch_index, batch_size):
+    sample_indices = normalize_batch_index(batch_index)
+    if sample_indices is None:
+        return list(range(batch_size))
+    if len(sample_indices) != batch_size:
+        raise ValueError(
+            f"Trellis2 batch_index length {len(sample_indices)} does not match batch size {batch_size}"
+        )
+    return sample_indices
+
+
+def resolve_singleton_sample_index(batch_index):
+    sample_indices = normalize_batch_index(batch_index)
+    if sample_indices is None:
+        return 0
+    if len(sample_indices) != 1:
+        raise ValueError(
+            f"Trellis2 batch_index must be an int or single-element iterable for singleton coords, got {sample_indices}"
+        )
+    return int(sample_indices[0])
+
+
+def flatten_batched_sparse_latent(samples, coords, coord_counts):
+    samples = samples.squeeze(-1).transpose(1, 2)
+    if coord_counts is None:
+        return samples.reshape(-1, samples.shape[-1]), coords
+
+    coords_items = split_batched_coords(coords, coord_counts)
+    feat_list = []
+    coord_list = []
+    for i, coords_i in enumerate(coords_items):
+        count = int(coord_counts[i].item())
+        feat_list.append(samples[i, :count])
+        coord_list.append(coords_i)
+
+    return torch.cat(feat_list, dim=0), torch.cat(coord_list, dim=0)
+
+
+def split_batched_sparse_latent(samples, coords, coord_counts):
+    samples = samples.squeeze(-1).transpose(1, 2)
+    if coord_counts is None:
+        return [(samples.reshape(-1, samples.shape[-1]), coords)]
+
+    coords_items = split_batched_coords(coords, coord_counts)
+    items = []
+    for i, coords_i in enumerate(coords_items):
+        count = int(coord_counts[i].item())
+        items.append((samples[i, :count], coords_i))
+    return items
+
+
 def paint_mesh_with_voxels(mesh, voxel_coords, voxel_colors, resolution):
     """
     Generic function to paint a mesh using nearest-neighbor colors from a sparse voxel field.
@@ -169,12 +277,32 @@ class VaeDecodeShapeTrellis(IO.ComfyNode):
 
         vae = vae.first_stage_model
         coords = samples["coords"]
+        coord_counts = samples.get("coord_counts")
 
         samples = samples["samples"]
-        samples = samples.squeeze(-1).transpose(1, 2).reshape(-1, 32).to(device)
-        samples = shape_norm(samples, coords)
+        if coord_counts is None:
+            samples, coords = flatten_batched_sparse_latent(samples, coords, coord_counts)
+            samples = shape_norm(samples.to(device), coords.to(device))
+            mesh, subs = vae.decode_shape_slat(samples, resolution)
+        else:
+            split_items = split_batched_sparse_latent(samples, coords, coord_counts)
+            mesh = []
+            subs_per_sample = []
+            for feats_i, coords_i in split_items:
+                coords_i = coords_i.to(device).clone()
+                coords_i[:, 0] = 0
+                sample_i = shape_norm(feats_i.to(device), coords_i)
+                mesh_i, subs_i = vae.decode_shape_slat(sample_i, resolution)
+                mesh.append(mesh_i[0])
+                subs_per_sample.append(subs_i)
+
+            subs = []
+            for stage_index in range(len(subs_per_sample[0])):
+                stage_tensors = [sample_subs[stage_index] for sample_subs in subs_per_sample]
+                feats_list = [stage_tensor.feats for stage_tensor in stage_tensors]
+                coords_list = [stage_tensor.coords for stage_tensor in stage_tensors]
+                subs.append(SparseTensor.from_tensor_list(feats_list, coords_list))
 
-        mesh, subs = vae.decode_shape_slat(samples, resolution)
         face_list = [m.faces for m in mesh]
         vert_list = [m.vertices for m in mesh]
         if all(v.shape == vert_list[0].shape for v in vert_list) and all(f.shape == face_list[0].shape for f in face_list):
@@ -210,12 +338,14 @@ class VaeDecodeTextureTrellis(IO.ComfyNode):
 
         vae = vae.first_stage_model
         coords = samples["coords"]
+        coord_counts = samples.get("coord_counts")
 
         samples = samples["samples"]
-        samples = samples.squeeze(-1).transpose(1, 2).reshape(-1, 32).to(device)
+        samples, coords = flatten_batched_sparse_latent(samples, coords, coord_counts)
+        samples = samples.to(device)
         std = tex_slat_normalization["std"].to(samples)
         mean = tex_slat_normalization["mean"].to(samples)
-        samples = SparseTensor(feats = samples, coords=coords)
+        samples = SparseTensor(feats = samples, coords=coords.to(device))
         samples = samples * std + mean
 
         voxel = vae.decode_tex_slat(samples, shape_subs)
@@ -271,9 +401,16 @@ class VaeDecodeStructureTrellis2(IO.ComfyNode):
         load_device = comfy.model_management.get_torch_device()
         offload_device = comfy.model_management.vae_offload_device()
         decoder = decoder.to(load_device)
+        batch_index = normalize_batch_index(samples.get("batch_index"))
         samples = samples["samples"]
         samples = samples.to(load_device)
-        decoded = decoder(samples)>0
+        if samples.shape[0] > 1:
+            decoded_items = []
+            for i in range(samples.shape[0]):
+                decoded_items.append(decoder(samples[i:i + 1]) > 0)
+            decoded = torch.cat(decoded_items, dim=0)
+        else:
+            decoded = decoder(samples) > 0
         decoder.to(offload_device)
         current_res = decoded.shape[2]
 
@@ -281,6 +418,8 @@ class VaeDecodeStructureTrellis2(IO.ComfyNode):
             ratio = current_res // resolution
             decoded = torch.nn.functional.max_pool3d(decoded.float(), ratio, ratio, 0) > 0.5
         out = Types.VOXEL(decoded.squeeze(1).float())
+        if batch_index is not None:
+            out.batch_index = normalize_batch_index(batch_index)
         return IO.NodeOutput(out)
 
 class Trellis2UpsampleCascade(IO.ComfyNode):
@@ -305,32 +444,93 @@ class Trellis2UpsampleCascade(IO.ComfyNode):
         device = comfy.model_management.get_torch_device()
         comfy.model_management.load_model_gpu(vae.patcher)
 
-        feats = shape_latent_512["samples"].squeeze(-1).transpose(1, 2).reshape(-1, 32).to(device)
-        coords_512 = shape_latent_512["coords"].to(device)
-
-        slat = shape_norm(feats, coords_512)
-
+        coord_counts = shape_latent_512.get("coord_counts")
+        batch_index = normalize_batch_index(shape_latent_512.get("batch_index"))
         decoder = vae.first_stage_model.shape_dec
-
-        slat.feats = slat.feats.to(next(decoder.parameters()).dtype)
-        hr_coords = decoder.upsample(slat, upsample_times=4)
-
         lr_resolution = 512
-        hr_resolution = int(target_resolution)
+        target_resolution = int(target_resolution)
 
+        if coord_counts is None:
+            feats, coords_512 = flatten_batched_sparse_latent(
+                shape_latent_512["samples"],
+                shape_latent_512["coords"],
+                coord_counts,
+            )
+            feats = feats.to(device)
+            coords_512 = coords_512.to(device)
+            slat = shape_norm(feats, coords_512)
+            slat.feats = slat.feats.to(next(decoder.parameters()).dtype)
+            hr_coords = decoder.upsample(slat, upsample_times=4)
+
+            hr_resolution = target_resolution
+            while True:
+                quant_coords = torch.cat([
+                    hr_coords[:, :1],
+                    ((hr_coords[:, 1:] + 0.5) / lr_resolution * (hr_resolution // 16)).int(),
+                ], dim=1)
+                final_coords = quant_coords.unique(dim=0)
+                num_tokens = final_coords.shape[0]
+
+                if num_tokens < max_tokens or hr_resolution <= 1024:
+                    break
+                hr_resolution -= 128
+
+            return IO.NodeOutput(final_coords,)
+
+        items = split_batched_sparse_latent(
+            shape_latent_512["samples"],
+            shape_latent_512["coords"],
+            coord_counts,
+        )
+        decoder_dtype = next(decoder.parameters()).dtype
+
+        sample_hr_coords = []
+        for feats_i, coords_i in items:
+            feats_i = feats_i.to(device)
+            coords_i = coords_i.to(device).clone()
+            coords_i[:, 0] = 0
+            slat_i = shape_norm(feats_i, coords_i)
+            slat_i.feats = slat_i.feats.to(decoder_dtype)
+            sample_hr_coords.append(decoder.upsample(slat_i, upsample_times=4))
+
+        hr_resolution = target_resolution
         while True:
-            quant_coords = torch.cat([
-                hr_coords[:, :1],
-                ((hr_coords[:, 1:] + 0.5) / lr_resolution * (hr_resolution // 16)).int(),
-            ], dim=1)
-            final_coords = quant_coords.unique(dim=0)
-            num_tokens = final_coords.shape[0]
-
-            if num_tokens < max_tokens or hr_resolution <= 1024:
+            exceeds_limit = False
+            for hr_coords_i in sample_hr_coords:
+                quant_coords_i = torch.cat([
+                    hr_coords_i[:, :1],
+                    ((hr_coords_i[:, 1:] + 0.5) / lr_resolution * (hr_resolution // 16)).int(),
+                ], dim=1)
+                if quant_coords_i.unique(dim=0).shape[0] >= max_tokens:
+                    exceeds_limit = True
+                    break
+            if not exceeds_limit or hr_resolution <= 1024:
                 break
             hr_resolution -= 128
 
-        return IO.NodeOutput(final_coords,)
+        final_coords_list = []
+        output_coord_counts = []
+        for sample_offset, hr_coords_i in enumerate(sample_hr_coords):
+            quant_coords_i = torch.cat([
+                hr_coords_i[:, :1],
+                ((hr_coords_i[:, 1:] + 0.5) / lr_resolution * (hr_resolution // 16)).int(),
+            ], dim=1)
+            final_coords_i = quant_coords_i.unique(dim=0)
+            final_coords_i = final_coords_i.clone()
+            final_coords_i[:, 0] = sample_offset
+            final_coords_list.append(final_coords_i)
+            output_coord_counts.append(int(final_coords_i.shape[0]))
+
+        normalized_batch_index = normalize_batch_index(batch_index)
+        output = {
+            "coords": torch.cat(final_coords_list, dim=0),
+            "coord_counts": torch.tensor(output_coord_counts, dtype=torch.int64),
+            "resolutions": torch.full((len(final_coords_list),), int(hr_resolution), dtype=torch.int64),
+        }
+        if normalized_batch_index is not None:
+            output["batch_index"] = normalized_batch_index
+
+        return IO.NodeOutput(output,)
 
 dino_mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
 dino_std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
@@ -484,7 +684,8 @@ class EmptyShapeLatentTrellis2(IO.ComfyNode):
             category="latent/3d",
             inputs=[
                 IO.AnyType.Input("structure_or_coords"),
-                IO.Model.Input("model")
+                IO.Model.Input("model"),
+                IO.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
             ],
             outputs=[
                 IO.Latent.Output(),
@@ -493,14 +694,25 @@ class EmptyShapeLatentTrellis2(IO.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, structure_or_coords, model):
+    def execute(cls, structure_or_coords, model, seed):
         # to accept the upscaled coords
         is_512_pass = False
+        coord_counts = None
+        coord_resolutions = None
+        batch_index = None
 
         if hasattr(structure_or_coords, "data") and structure_or_coords.data.ndim == 4:
             decoded = structure_or_coords.data.unsqueeze(1)
             coords = torch.argwhere(decoded.bool())[:, [0, 2, 3, 4]].int()
             is_512_pass = True
+            batch_index = normalize_batch_index(getattr(structure_or_coords, "batch_index", None))
+
+        elif isinstance(structure_or_coords, dict):
+            coords = structure_or_coords["coords"].int()
+            coord_counts = structure_or_coords.get("coord_counts")
+            coord_resolutions = structure_or_coords.get("resolutions")
+            batch_index = normalize_batch_index(structure_or_coords.get("batch_index"))
+            is_512_pass = False
 
         elif isinstance(structure_or_coords, torch.Tensor) and structure_or_coords.ndim == 2:
             coords = structure_or_coords.int()
@@ -509,8 +721,31 @@ class EmptyShapeLatentTrellis2(IO.ComfyNode):
         else:
             raise ValueError(f"Invalid input to EmptyShapeLatent: {type(structure_or_coords)}")
         in_channels = 32
-        # image like format
-        latent = torch.randn(1, in_channels, coords.shape[0], 1)
+        batch_size, inferred_coord_counts, max_tokens = infer_batched_coord_layout(coords)
+        if coord_counts is not None:
+            coord_counts = coord_counts.to(dtype=torch.int64, device=coords.device)
+            if coord_counts.shape != inferred_coord_counts.shape or not torch.equal(coord_counts, inferred_coord_counts):
+                raise ValueError(
+                    f"Trellis2 coord_counts metadata {coord_counts.tolist()} does not match coords layout {inferred_coord_counts.tolist()}"
+                )
+        else:
+            coord_counts = inferred_coord_counts
+        if batch_size == 1:
+            sample_index = resolve_singleton_sample_index(batch_index)
+            generator = torch.Generator(device="cpu")
+            generator.manual_seed(int(seed) + sample_index)
+            latent = torch.randn(1, in_channels, coords.shape[0], 1, generator=generator)
+        else:
+            sample_indices = resolve_sample_indices(batch_index, batch_size)
+            latent = torch.zeros(batch_size, in_channels, max_tokens, 1)
+            for i, sample_index in enumerate(sample_indices):
+                count = int(coord_counts[i].item())
+                generator = torch.Generator(device="cpu")
+                generator.manual_seed(int(seed) + int(sample_index))
+                latent_i = torch.randn(1, in_channels, count, 1, generator=generator)
+                latent[i, :, :count] = latent_i[0]
+        if coord_counts is not None:
+            latent.trellis_coord_counts = coord_counts.clone()
         model = model.clone()
         model.model_options = model.model_options.copy()
         if "transformer_options" in model.model_options:
@@ -519,11 +754,20 @@ class EmptyShapeLatentTrellis2(IO.ComfyNode):
             model.model_options["transformer_options"] = {}
 
         model.model_options["transformer_options"]["coords"] = coords
+        if coord_counts is not None:
+            model.model_options["transformer_options"]["coord_counts"] = coord_counts
         if is_512_pass:
             model.model_options["transformer_options"]["generation_mode"] = "shape_generation_512"
         else:
             model.model_options["transformer_options"]["generation_mode"] = "shape_generation"
-        return IO.NodeOutput({"samples": latent, "coords": coords, "type": "trellis2"}, model)
+        output = {"samples": latent, "coords": coords, "type": "trellis2"}
+        if batch_index is not None:
+            output["batch_index"] = normalize_batch_index(batch_index)
+        if coord_counts is not None:
+            output["coord_counts"] = coord_counts
+            if coord_resolutions is not None:
+                output["resolutions"] = coord_resolutions
+        return IO.NodeOutput(output, model)
 
 class EmptyTextureLatentTrellis2(IO.ComfyNode):
     @classmethod
@@ -534,7 +778,8 @@ class EmptyTextureLatentTrellis2(IO.ComfyNode):
             inputs=[
                 IO.Voxel.Input("structure_or_coords"),
                 IO.Latent.Input("shape_latent"),
-                IO.Model.Input("model")
+                IO.Model.Input("model"),
+                IO.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
             ],
             outputs=[
                 IO.Latent.Output(),
@@ -543,20 +788,68 @@ class EmptyTextureLatentTrellis2(IO.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, structure_or_coords, shape_latent, model):
+    def execute(cls, structure_or_coords, shape_latent, model, seed):
         channels = 32
+        coord_counts = None
+        batch_index = None
         if hasattr(structure_or_coords, "data") and structure_or_coords.data.ndim == 4:
             decoded = structure_or_coords.data.unsqueeze(1)
             coords = torch.argwhere(decoded.bool())[:, [0, 2, 3, 4]].int()
+            batch_index = normalize_batch_index(getattr(structure_or_coords, "batch_index", None))
+
+        elif isinstance(structure_or_coords, dict):
+            coords = structure_or_coords["coords"].int()
+            coord_counts = structure_or_coords.get("coord_counts")
+            batch_index = normalize_batch_index(structure_or_coords.get("batch_index"))
 
         elif isinstance(structure_or_coords, torch.Tensor) and structure_or_coords.ndim == 2:
             coords = structure_or_coords.int()
+        else:
+            raise ValueError(
+                "structure_or_coords must be a voxel input with data.ndim == 4, "
+                f'a dict containing "coords", or a 2D torch.Tensor; got {type(structure_or_coords).__name__}'
+            )
 
+        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:
+            coord_counts = coord_counts.to(dtype=torch.int64, device=coords.device)
+            if coord_counts.shape != inferred_coord_counts.shape or not torch.equal(coord_counts, inferred_coord_counts):
+                raise ValueError(
+                    f"Trellis2 coord_counts metadata {coord_counts.tolist()} does not match coords layout {inferred_coord_counts.tolist()}"
+                )
+        else:
+            coord_counts = inferred_coord_counts
         if shape_latent.ndim == 4:
-            shape_latent = shape_latent.squeeze(-1).transpose(1, 2).reshape(-1, channels)
+            if shape_latent.shape[0] != batch_size:
+                raise ValueError(
+                    f"shape_latent batch {shape_latent.shape[0]} doesn't match coords batch {batch_size}"
+                )
+            shape_latent = shape_latent.squeeze(-1).transpose(1, 2)
+            if shape_latent.shape[1] < max_tokens:
+                raise ValueError(
+                    f"shape_latent tokens {shape_latent.shape[1]} can't cover coords max tokens {max_tokens}"
+                )
 
-        latent = torch.randn(1, channels, coords.shape[0], 1)
+        if batch_size == 1:
+            sample_index = resolve_singleton_sample_index(batch_index)
+            generator = torch.Generator(device="cpu")
+            generator.manual_seed(int(seed) + sample_index)
+            latent = torch.randn(1, channels, coords.shape[0], 1, generator=generator)
+        else:
+            sample_indices = resolve_sample_indices(batch_index, batch_size)
+            latent = torch.zeros(batch_size, channels, max_tokens, 1)
+            for i, sample_index in enumerate(sample_indices):
+                count = int(coord_counts[i].item())
+                generator = torch.Generator(device="cpu")
+                generator.manual_seed(int(seed) + int(sample_index))
+                latent_i = torch.randn(1, channels, count, 1, generator=generator)
+                latent[i, :, :count] = latent_i[0]
+        if coord_counts is not None:
+            latent.trellis_coord_counts = coord_counts.clone()
         model = model.clone()
         model.model_options = model.model_options.copy()
         if "transformer_options" in model.model_options:
@@ -565,9 +858,16 @@ class EmptyTextureLatentTrellis2(IO.ComfyNode):
             model.model_options["transformer_options"] = {}
 
         model.model_options["transformer_options"]["coords"] = coords
+        if coord_counts is not None:
+            model.model_options["transformer_options"]["coord_counts"] = coord_counts
         model.model_options["transformer_options"]["generation_mode"] = "texture_generation"
         model.model_options["transformer_options"]["shape_slat"] = shape_latent
-        return IO.NodeOutput({"samples": latent, "coords": coords, "type": "trellis2"}, model)
+        output = {"samples": latent, "coords": coords, "type": "trellis2"}
+        if batch_index is not None:
+            output["batch_index"] = normalize_batch_index(batch_index)
+        if coord_counts is not None:
+            output["coord_counts"] = coord_counts
+        return IO.NodeOutput(output, model)
 
 
 class EmptyStructureLatentTrellis2(IO.ComfyNode):
@@ -578,17 +878,30 @@ class EmptyStructureLatentTrellis2(IO.ComfyNode):
             category="latent/3d",
             inputs=[
                 IO.Int.Input("batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."),
+                IO.Int.Input("batch_index_start", default=0, min=0, max=4096, tooltip="Starting sample index for per-sample sampler noise."),
+                IO.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
             ],
             outputs=[
                 IO.Latent.Output(),
             ]
         )
     @classmethod
-    def execute(cls, batch_size):
+    def execute(cls, batch_size, batch_index_start, seed):
         in_channels = 8
         resolution = 16
-        latent = torch.randn(batch_size, in_channels, resolution, resolution, resolution)
-        return IO.NodeOutput({"samples": latent, "type": "trellis2"})
+        sample_indices = [int(batch_index_start) + i for i in range(batch_size)]
+        latent = torch.zeros(batch_size, in_channels, resolution, resolution, resolution)
+        for i, sample_index in enumerate(sample_indices):
+            generator = torch.Generator(device="cpu")
+            generator.manual_seed(int(seed) + sample_index)
+            latent[i] = torch.randn(1, in_channels, resolution, resolution, resolution, generator=generator)[0]
+        output = {
+            "samples": latent,
+            "type": "trellis2",
+        }
+        if batch_size > 1 or batch_index_start != 0:
+            output["batch_index"] = sample_indices
+        return IO.NodeOutput(output)
 
 def simplify_fn(vertices, faces, colors=None, target=100000):
     if vertices.ndim == 3:
diff --git a/tests-unit/comfy_extras_test/nodes_trellis2_test.py b/tests-unit/comfy_extras_test/nodes_trellis2_test.py
index 920eca471..49e872bc7 100644
--- a/tests-unit/comfy_extras_test/nodes_trellis2_test.py
+++ b/tests-unit/comfy_extras_test/nodes_trellis2_test.py
@@ -123,5 +123,203 @@ class TestRunConditioningRestore(unittest.TestCase):
         self.assertFalse(hasattr(inner_model, "image_size"))
 
 
+class DummyCloneModel:
+    def __init__(self):
+        self.model_options = {}
+
+    def clone(self):
+        cloned = DummyCloneModel()
+        cloned.model_options = self.model_options.copy()
+        return cloned
+
+
+class TestTrellisBatchSemantics(unittest.TestCase):
+    def test_empty_structure_latent_is_deterministic_and_propagates_sample_indices(self):
+        batch_output = nodes_trellis2.EmptyStructureLatentTrellis2.execute(2, 0, 17)[0]
+        single_output = nodes_trellis2.EmptyStructureLatentTrellis2.execute(1, 5, 17)[0]
+
+        expected_batch = torch.zeros(2, 8, 16, 16, 16)
+        expected_batch[0] = torch.randn(1, 8, 16, 16, 16, generator=torch.Generator(device="cpu").manual_seed(17))[0]
+        expected_batch[1] = torch.randn(1, 8, 16, 16, 16, generator=torch.Generator(device="cpu").manual_seed(18))[0]
+        expected_single = torch.randn(1, 8, 16, 16, 16, generator=torch.Generator(device="cpu").manual_seed(22))
+
+        self.assertTrue(torch.equal(batch_output["samples"], expected_batch))
+        self.assertEqual(batch_output["batch_index"], [0, 1])
+        self.assertTrue(torch.equal(single_output["samples"], expected_single))
+        self.assertEqual(single_output["batch_index"], [5])
+
+    def test_empty_shape_latent_is_deterministic_and_propagates_batch_index(self):
+        coords = torch.tensor(
+            [
+                [1, 5, 5, 5],
+                [0, 1, 1, 1],
+                [1, 6, 6, 6],
+                [0, 2, 2, 2],
+                [1, 7, 7, 7],
+            ],
+            dtype=torch.int32,
+        )
+        structure = {
+            "coords": coords,
+            "coord_counts": torch.tensor([2, 3], dtype=torch.int64),
+            "batch_index": [4, 9],
+        }
+
+        output, _ = nodes_trellis2.EmptyShapeLatentTrellis2.execute(structure, DummyCloneModel(), 23)
+
+        expected = torch.zeros(2, 32, 3, 1)
+        expected[0, :, :2, :] = torch.randn(1, 32, 2, 1, generator=torch.Generator(device="cpu").manual_seed(27))[0]
+        expected[1, :, :3, :] = torch.randn(1, 32, 3, 1, generator=torch.Generator(device="cpu").manual_seed(32))[0]
+
+        self.assertTrue(torch.equal(output["samples"], expected))
+        self.assertTrue(torch.equal(output["coord_counts"], torch.tensor([2, 3], dtype=torch.int64)))
+        self.assertEqual(output["batch_index"], [4, 9])
+
+    def test_empty_shape_latent_keeps_singleton_coord_counts(self):
+        structure = {
+            "coords": torch.tensor(
+                [
+                    [0, 1, 1, 1],
+                    [0, 2, 2, 2],
+                ],
+                dtype=torch.int32,
+            ),
+        }
+
+        output, _ = nodes_trellis2.EmptyShapeLatentTrellis2.execute(structure, DummyCloneModel(), 11)
+
+        self.assertTrue(torch.equal(output["coord_counts"], torch.tensor([2], dtype=torch.int64)))
+
+    def test_empty_shape_latent_rejects_multi_index_singleton(self):
+        structure = {
+            "coords": torch.tensor(
+                [
+                    [0, 1, 1, 1],
+                    [0, 2, 2, 2],
+                ],
+                dtype=torch.int32,
+            ),
+            "batch_index": [5, 6],
+        }
+
+        with self.assertRaises(ValueError):
+            nodes_trellis2.EmptyShapeLatentTrellis2.execute(structure, DummyCloneModel(), 11)
+
+    def test_empty_texture_latent_rejects_multi_index_singleton(self):
+        coords = torch.tensor(
+            [
+                [0, 1, 1, 1],
+                [0, 2, 2, 2],
+            ],
+            dtype=torch.int32,
+        )
+        structure = {"coords": coords, "batch_index": [7, 8]}
+        shape_latent = {"samples": torch.zeros(1, 32, 2, 1)}
+
+        with self.assertRaises(ValueError):
+            nodes_trellis2.EmptyTextureLatentTrellis2.execute(
+                structure,
+                shape_latent,
+                DummyCloneModel(),
+                13,
+            )
+
+    def test_empty_texture_latent_rejects_invalid_structure_input(self):
+        with self.assertRaises(ValueError):
+            nodes_trellis2.EmptyTextureLatentTrellis2.execute(
+                "bad-input",
+                {"samples": torch.zeros(1, 32, 2, 1)},
+                DummyCloneModel(),
+                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(
+            [
+                [0, 1, 1, 1],
+                [1, 2, 2, 2],
+                [1, 3, 3, 3],
+            ],
+            dtype=torch.int32,
+        )
+        coord_counts = torch.tensor([2, 1], dtype=torch.int64)
+
+        with self.assertRaises(ValueError):
+            nodes_trellis2.flatten_batched_sparse_latent(samples, coords, coord_counts)
+
+    def test_infer_batched_coord_layout_rejects_negative_batch_ids(self):
+        coords = torch.tensor(
+            [
+                [-1, 1, 1, 1],
+                [0, 2, 2, 2],
+            ],
+            dtype=torch.int32,
+        )
+
+        with self.assertRaises(ValueError):
+            nodes_trellis2.infer_batched_coord_layout(coords)
+
+    def test_split_batched_coords_validates_total_count(self):
+        coords = torch.tensor(
+            [
+                [0, 1, 1, 1],
+                [1, 2, 2, 2],
+                [1, 3, 3, 3],
+            ],
+            dtype=torch.int32,
+        )
+        coord_counts = torch.tensor([1, 1], dtype=torch.int64)
+
+        with self.assertRaises(ValueError):
+            nodes_trellis2.split_batched_coords(coords, coord_counts)
+
+    def test_empty_shape_latent_preserves_resolutions_key(self):
+        structure = {
+            "coords": torch.tensor(
+                [
+                    [0, 1, 1, 1],
+                    [0, 2, 2, 2],
+                ],
+                dtype=torch.int32,
+            ),
+            "resolutions": torch.tensor([1024], dtype=torch.int64),
+        }
+
+        output, model = nodes_trellis2.EmptyShapeLatentTrellis2.execute(structure, DummyCloneModel(), 11)
+
+        self.assertTrue(torch.equal(output["resolutions"], torch.tensor([1024], dtype=torch.int64)))
+        self.assertNotIn("coord_resolutions", model.model_options["transformer_options"])
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests-unit/comfy_test/sample_test.py b/tests-unit/comfy_test/sample_test.py
new file mode 100644
index 000000000..227659994
--- /dev/null
+++ b/tests-unit/comfy_test/sample_test.py
@@ -0,0 +1,76 @@
+import unittest
+
+import torch
+
+import comfy.sample
+
+
+class TestPrepareNoiseInnerTrellis(unittest.TestCase):
+    def test_coord_counts_noise_matches_per_index_prefix_draws(self):
+        latent = torch.zeros(2, 4, 5, 1)
+        latent.trellis_coord_counts = torch.tensor([3, 5], dtype=torch.int64)
+
+        generator = torch.Generator(device="cpu")
+        generator.manual_seed(123)
+        noise = comfy.sample.prepare_noise_inner(latent, generator)
+
+        expected = torch.zeros_like(noise, dtype=torch.float32)
+        row0 = torch.Generator(device="cpu")
+        row0.manual_seed(123)
+        expected[0, :, :3, :] = torch.randn(1, 4, 3, 1, generator=row0)[0]
+        row1 = torch.Generator(device="cpu")
+        row1.manual_seed(124)
+        expected[1] = torch.randn(1, 4, 5, 1, generator=row1)[0]
+
+        self.assertTrue(torch.equal(noise.float(), expected))
+        self.assertTrue(torch.equal(noise[0, :, 3:, :], torch.zeros_like(noise[0, :, 3:, :])))
+
+    def test_coord_counts_noise_inds_share_prefixes_for_duplicates(self):
+        latent = torch.zeros(2, 4, 5, 1)
+        latent.trellis_coord_counts = torch.tensor([3, 5], dtype=torch.int64)
+
+        generator = torch.Generator(device="cpu")
+        generator.manual_seed(456)
+        noise = comfy.sample.prepare_noise_inner(latent, generator, noise_inds=[7, 7])
+
+        replay = torch.Generator(device="cpu")
+        replay.manual_seed(463)
+        expected1 = torch.randn(1, 4, 5, 1, generator=replay)
+        expected0 = expected1[:, :, :3, :]
+
+        self.assertTrue(torch.equal(noise[0:1, :, :3, :], expected0))
+        self.assertTrue(torch.equal(noise[1:2, :, :5, :], expected1))
+        self.assertTrue(torch.equal(noise[0, :, 3:, :], torch.zeros_like(noise[0, :, 3:, :])))
+
+    def test_coord_counts_noise_inds_length_must_match_batch(self):
+        latent = torch.zeros(2, 4, 5, 1)
+        latent.trellis_coord_counts = torch.tensor([3, 5], dtype=torch.int64)
+
+        generator = torch.Generator(device="cpu")
+        generator.manual_seed(456)
+
+        with self.assertRaises(ValueError):
+            comfy.sample.prepare_noise_inner(latent, generator, noise_inds=[7])
+
+    def test_coord_counts_metadata_must_match_batch_and_bounds(self):
+        generator = torch.Generator(device="cpu")
+        generator.manual_seed(456)
+
+        latent = torch.zeros(2, 4, 5, 1)
+        latent.trellis_coord_counts = torch.tensor([[3, 5]], dtype=torch.int64)
+        with self.assertRaises(ValueError):
+            comfy.sample.prepare_noise_inner(latent, generator)
+
+        latent = torch.zeros(2, 4, 5, 1)
+        latent.trellis_coord_counts = torch.tensor([3], dtype=torch.int64)
+        with self.assertRaises(ValueError):
+            comfy.sample.prepare_noise_inner(latent, generator)
+
+        latent = torch.zeros(2, 4, 5, 1)
+        latent.trellis_coord_counts = torch.tensor([3, 6], dtype=torch.int64)
+        with self.assertRaises(ValueError):
+            comfy.sample.prepare_noise_inner(latent, generator)
+
+
+if __name__ == "__main__":
+    unittest.main()