Fix Trellis seeded sparse batch semantics

comfy/ldm/trellis2/model.py

@@ -813,6 +813,14 @@ class Trellis2(nn.Module):
         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]
@@ -820,10 +828,17 @@ class Trellis2(nn.Module):
 
             logical_batch = coord_counts.shape[0] if coord_counts is not None else 1
             if rule and orig_bsz > logical_batch:
-                half = orig_bsz // 2
+                batch_groups = orig_bsz // logical_batch
-                x_eval = x[half:]
+                selected_groups = cond_group_indices(batch_groups)
-                t_eval = timestep[half:] if timestep.shape[0] > 1 else timestep
+                x_groups = x.reshape(batch_groups, logical_batch, *x.shape[1:])
-                c_eval = cond
+                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
@@ -838,113 +853,62 @@ class Trellis2(nn.Module):
                         raise ValueError(
                             f"Trellis2 coord_counts batch {logical_batch} doesn't divide latent batch {B}"
                         )
+                    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 = []
-                    if mode == "shape_generation" and repeat_factor > 1:
+                    for rep in range(repeat_factor):
-                        grouped_outs = []
-                        grouped_counts = []
                         for i in range(logical_batch):
+                            out_index = rep * logical_batch + i
                             count = int(coord_counts[i].item())
-                            coords_i = coords[coords[:, 0] == i].clone()
+                            coords_i = coords_by_batch[i].clone()
-                            if coords_i.shape[0] != count:
+                            coords_i[:, 0] = 0
-                                raise ValueError(
+                            feats_i = x_eval[out_index, :count].clone()
-                                    f"Trellis2 coords rows for batch {i} expected {count}, got {coords_i.shape[0]}"
+                            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
 
-                            feat_batches = []
+                            if mode == "shape_generation":
-                            coord_batches = []
+                                if is_512_run:
-                            index_batch = []
+                                    sparse_out = self.img2shape_512(x_st_i, t_i, c_i)
-                            for rep in range(repeat_factor):
-                                out_index = rep * logical_batch + i
-                                feat_batches.append(x_eval[out_index, :count])
-                                coords_rep = coords_i.clone()
-                                coords_rep[:, 0] = rep
-                                coord_batches.append(coords_rep)
-                                index_batch.append(out_index)
-
-                            x_st_i = SparseTensor(
-                                feats=torch.cat(feat_batches, dim=0),
-                                coords=torch.cat(coord_batches, dim=0).to(torch.int32),
-                            )
-                            index_tensor = torch.tensor(index_batch, device=x_eval.device, dtype=torch.long)
-                            if t_eval.shape[0] > 1:
-                                t_i = t_eval.index_select(0, index_tensor)
-                            else:
-                                t_i = t_eval
-                            if c_eval.shape[0] > 1:
-                                c_i = c_eval.index_select(0, index_tensor)
-                            else:
-                                c_i = c_eval
-
-                            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)
-
-                            feats_group, coords_group = sparse_out.to_tensor_list()
-                            if len(feats_group) != repeat_factor:
-                                raise ValueError(
-                                    f"Trellis2 expected {repeat_factor} sparse output groups for batch {i}, got {len(feats_group)}"
-                                )
-                            for rep, (feats_rep, coords_rep) in enumerate(zip(feats_group, coords_group)):
-                                if feats_rep.shape[0] != count:
-                                    raise ValueError(
-                                        f"Trellis2 sparse output rows for batch {i} rep {rep} expected {count}, got {feats_rep.shape[0]}"
-                                    )
-                                if coords_rep.shape[0] != count:
-                                    raise ValueError(
-                                        f"Trellis2 sparse output coords for batch {i} rep {rep} expected {count}, got {coords_rep.shape[0]}"
-                                    )
-                            grouped_outs.append(feats_group)
-                            grouped_counts.append(count)
-
-                        for rep in range(repeat_factor):
-                            for i in range(logical_batch):
-                                sparse_outs.append(grouped_outs[i][rep])
-                                active_coord_counts.append(grouped_counts[i])
-                    else:
-                        for rep in range(repeat_factor):
-                            for i in range(logical_batch):
-                                out_index = rep * logical_batch + i
-                                count = int(coord_counts[i].item())
-                                coords_i = coords[coords[:, 0] == i].clone()
-                                if coords_i.shape[0] != count:
-                                    raise ValueError(
-                                        f"Trellis2 coords rows for batch {i} expected {count}, got {coords_i.shape[0]}"
-                                    )
-                                coords_i[:, 0] = 0
-                                feats_i = x_eval[out_index, :count]
-                                x_st_i = SparseTensor(feats=feats_i, coords=coords_i.to(torch.int32))
-                                t_i = t_eval[out_index].unsqueeze(0) if t_eval.shape[0] > 1 else t_eval
-                                c_i = c_eval[out_index].unsqueeze(0) if c_eval.shape[0] > 1 else c_eval
-
-                                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")
+                                    sparse_out = self.img2shape(x_st_i, t_i, c_i)
-                                    if slat is None:
+                            else:
-                                        raise ValueError("shape_slat can't be None")
+                                slat = transformer_options.get("shape_slat")
-                                    if slat.ndim == 3:
+                                if slat is None:
-                                        if slat.shape[0] != logical_batch:
+                                    raise ValueError("shape_slat can't be None")
-                                            raise ValueError(
+                                if slat.ndim == 3:
-                                                f"shape_slat batch {slat.shape[0]} doesn't match coord_counts batch {logical_batch}"
+                                    if slat.shape[0] != logical_batch:
-                                            )
+                                        raise ValueError(
-                                        if slat.shape[1] < count:
+                                            f"shape_slat batch {slat.shape[0]} doesn't match coord_counts batch {logical_batch}"
-                                            raise ValueError(
+                                        )
-                                                f"shape_slat tokens {slat.shape[1]} can't cover coord count {count} for batch {i}"
+                                    if slat.shape[1] < count:
-                                            )
+                                        raise ValueError(
-                                        slat_feats = slat[i, :count].to(x_st_i.device)
+                                            f"shape_slat tokens {slat.shape[1]} can't cover coord count {count} for batch {i}"
-                                    else:
+                                        )
-                                        slat_feats = slat[:count].to(x_st_i.device)
+                                    slat_feats = slat[i, :count].to(x_st_i.device)
-                                    x_st_i = x_st_i.replace(feats=torch.cat([x_st_i.feats, slat_feats], dim=-1))
+                                else:
-                                    sparse_out = self.shape2txt(x_st_i, t_i, c_i)
+                                    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)
 
-                                sparse_outs.append(sparse_out.feats)
+                            sparse_outs.append(sparse_out.feats)
-                                active_coord_counts.append(count)
+                            active_coord_counts.append(count)
 
                     out_channels = sparse_outs[0].shape[-1]
                     padded = sparse_outs[0].new_zeros((B, N, out_channels))
@@ -1022,7 +986,6 @@ class Trellis2(nn.Module):
             out = self.shape2txt(x_st, t_eval, c_eval)
         else: # structure
             orig_bsz = x.shape[0]
-            cond_or_uncond = transformer_options.get("cond_or_uncond") or []
             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:
@@ -1034,23 +997,19 @@ class Trellis2(nn.Module):
                 c_groups = context.reshape(batch_groups, logical_batch, *context.shape[1:])
 
                 if shape_rule and batch_groups > 1:
-                    selected_group_indices = [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:
-                        half = orig_bsz // 2
+                        x_i = x_groups[selected_group_indices, sample_index]
-                        x_i = x[half + sample_index].unsqueeze(0)
                         if timestep.shape[0] > 1:
-                            t_i = timestep[half + sample_index].unsqueeze(0)
+                            t_i = t_groups[selected_group_indices, sample_index]
                         else:
                             t_i = timestep
-                        if cond.shape[0] > 1:
+                        c_i = c_groups[selected_group_indices, sample_index]
-                            c_i = cond[sample_index].unsqueeze(0)
-                        else:
-                            c_i = cond
                     else:
                         x_i = x_groups[selected_group_indices, sample_index]
                         if timestep.shape[0] > 1:

comfy/sample.py

@@ -15,32 +15,26 @@ def prepare_noise_inner(latent_image, generator, noise_inds=None):
         else:
             noise_inds = np.asarray(noise_inds, dtype=np.int64)
 
+        base_seed = int(generator.initial_seed())
         unique_inds = np.unique(noise_inds)
-        first_indices = {int(unique_index): int(np.flatnonzero(noise_inds == unique_index)[0]) for unique_index in unique_inds.tolist()}
+        sample_noises = {}
-        index_states = {}
+        for noise_index in unique_inds.tolist():
-        for unique_index in sorted(first_indices):
+            rows = np.flatnonzero(noise_inds == noise_index)
-            index_states[unique_index] = generator.get_state().clone()
+            max_count = max(int(coord_counts[row].item()) for row in rows.tolist())
-            count = int(coord_counts[first_indices[unique_index]].item())
-            torch.randn(
-                [1, latent_image.size(1), count, latent_image.size(3)],
-                dtype=torch.float32,
-                layout=latent_image.layout,
-                generator=generator,
-                device="cpu",
-            )
-
-        for batch_index, noise_index in enumerate(noise_inds.tolist()):
-            count = int(coord_counts[batch_index].item())
             local_generator = torch.Generator(device="cpu")
-            local_generator.set_state(index_states[int(noise_index)].clone())
+            local_generator.manual_seed(base_seed + int(noise_index))
-            sample_noise = torch.randn(
+            sample_noises[int(noise_index)] = torch.randn(
-                [1, latent_image.size(1), count, latent_image.size(3)],
+                [1, latent_image.size(1), max_count, latent_image.size(3)],
                 dtype=torch.float32,
                 layout=latent_image.layout,
                 generator=local_generator,
                 device="cpu",
             )
-            noise[batch_index:batch_index + 1, :, :count, :] = sample_noise
+
+        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:
@@ -76,6 +70,8 @@ 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]:

comfy_extras/nodes_trellis2.py

@@ -115,18 +115,54 @@ def infer_batched_coord_layout(coords):
     return batch_size, counts, max_tokens
 
 
+def split_batched_coords(coords, coord_counts):
+    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 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 in range(coord_counts.shape[0]):
+    for i, coords_i in enumerate(coords_items):
         count = int(coord_counts[i].item())
-        coords_i = coords[coords[:, 0] == i]
-        if coords_i.shape[0] != count:
-            raise ValueError(f"Trellis2 coords rows for batch {i} expected {count}, got {coords_i.shape[0]}")
         feat_list.append(samples[i, :count])
         coord_list.append(coords_i)
 
@@ -138,12 +174,10 @@ def split_batched_sparse_latent(samples, coords, coord_counts):
     if coord_counts is None:
         return [(samples.reshape(-1, samples.shape[-1]), coords)]
 
+    coords_items = split_batched_coords(coords, coord_counts)
     items = []
-    for i in range(coord_counts.shape[0]):
+    for i, coords_i in enumerate(coords_items):
         count = int(coord_counts[i].item())
-        coords_i = coords[coords[:, 0] == i]
-        if coords_i.shape[0] != count:
-            raise ValueError(f"Trellis2 coords rows for batch {i} expected {count}, got {coords_i.shape[0]}")
         items.append((samples[i, :count], coords_i))
     return items
 
@@ -345,6 +379,7 @@ 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)
         if samples.shape[0] > 1:
@@ -361,6 +396,8 @@ 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):
@@ -386,6 +423,7 @@ class Trellis2UpsampleCascade(IO.ComfyNode):
         comfy.model_management.load_model_gpu(vae.patcher)
 
         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
         lr_resolution = 512
         target_resolution = int(target_resolution)
@@ -424,40 +462,48 @@ class Trellis2UpsampleCascade(IO.ComfyNode):
         )
         decoder_dtype = next(decoder.parameters()).dtype
 
-        final_coords_list = []
+        sample_hr_coords = []
-        output_resolutions = []
+        for feats_i, coords_i in items:
-        output_coord_counts = []
-        for batch_index, (feats_i, coords_i) in enumerate(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)
-            hr_coords_i = decoder.upsample(slat_i, upsample_times=4)
+            sample_hr_coords.append(decoder.upsample(slat_i, upsample_times=4))
 
-            hr_resolution = target_resolution
+        hr_resolution = target_resolution
-            while True:
+        while True:
+            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)
-                final_coords_i = quant_coords_i.unique(dim=0)
+                if quant_coords_i.unique(dim=0).shape[0] >= max_tokens:
-                num_tokens = final_coords_i.shape[0]
+                    exceeds_limit = True
-
-                if num_tokens < max_tokens or hr_resolution <= 1024:
                     break
-                hr_resolution -= 128
+            if not exceeds_limit or hr_resolution <= 1024:
+                break
+            hr_resolution -= 128
 
+        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] = batch_index
+            final_coords_i[:, 0] = sample_offset
             final_coords_list.append(final_coords_i)
-            output_resolutions.append(int(hr_resolution))
             output_coord_counts.append(int(final_coords_i.shape[0]))
 
         return IO.NodeOutput({
             "coords": torch.cat(final_coords_list, dim=0),
             "coord_counts": torch.tensor(output_coord_counts, dtype=torch.int64),
-            "resolutions": torch.tensor(output_resolutions, dtype=torch.int64),
+            "resolutions": torch.full((len(final_coords_list),), int(hr_resolution), dtype=torch.int64),
+            "batch_index": normalize_batch_index(batch_index),
         },)
 
 dino_mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
@@ -612,7 +658,8 @@ 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(),
@@ -621,21 +668,24 @@ 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:
@@ -655,15 +705,17 @@ class EmptyShapeLatentTrellis2(IO.ComfyNode):
         else:
             coord_counts = inferred_coord_counts
         if batch_size == 1:
-            coord_counts = None
+            sample_indices = normalize_batch_index(batch_index) or [0]
-            latent = torch.randn(1, in_channels, coords.shape[0], 1)
+            generator = torch.Generator(device="cpu")
+            generator.manual_seed(int(seed) + int(sample_indices[0]))
+            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)
-            base_state = torch.random.get_rng_state()
+            for i, sample_index in enumerate(sample_indices):
-            for i in range(batch_size):
                 count = int(coord_counts[i].item())
                 generator = torch.Generator(device="cpu")
-                generator.set_state(base_state.clone())
+                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:
@@ -685,11 +737,12 @@ class EmptyShapeLatentTrellis2(IO.ComfyNode):
         else:
             model.model_options["transformer_options"]["generation_mode"] = "shape_generation"
         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["coord_resolutions"] = coord_resolutions
-            output["batch_index"] = [0] * batch_size
         return IO.NodeOutput(output, model)
 
 class EmptyTextureLatentTrellis2(IO.ComfyNode):
@@ -701,7 +754,8 @@ 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(),
@@ -710,20 +764,24 @@ 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()
 
+        shape_batch_index = normalize_batch_index(shape_latent.get("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:
@@ -746,19 +804,23 @@ class EmptyTextureLatentTrellis2(IO.ComfyNode):
                 )
 
         if batch_size == 1:
-            coord_counts = None
+            sample_indices = normalize_batch_index(batch_index) or [0]
-            latent = torch.randn(1, channels, coords.shape[0], 1)
+            generator = torch.Generator(device="cpu")
+            generator.manual_seed(int(seed) + int(sample_indices[0]))
+            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)
-            base_state = torch.random.get_rng_state()
+            for i, sample_index in enumerate(sample_indices):
-            for i in range(batch_size):
                 count = int(coord_counts[i].item())
                 generator = torch.Generator(device="cpu")
-                generator.set_state(base_state.clone())
+                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()
+        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:
@@ -772,9 +834,10 @@ class EmptyTextureLatentTrellis2(IO.ComfyNode):
         model.model_options["transformer_options"]["generation_mode"] = "texture_generation"
         model.model_options["transformer_options"]["shape_slat"] = shape_latent
         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
-            output["batch_index"] = [0] * batch_size
         return IO.NodeOutput(output, model)
 
 
@@ -786,19 +849,29 @@ 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(1, in_channels, resolution, resolution, resolution).repeat(batch_size, 1, 1, 1, 1)
+        sample_indices = [int(batch_index_start) + i for i in range(batch_size)]
-        output = {"samples": latent, "type": "trellis2"}
+        latent = torch.zeros(batch_size, in_channels, resolution, resolution, resolution)
-        if batch_size > 1:
+        for i, sample_index in enumerate(sample_indices):
-            output["batch_index"] = [0] * batch_size
+            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):

tests-unit/comfy_extras_test/nodes_trellis2_test.py

@@ -123,5 +123,88 @@ 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_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)
+
+
 if __name__ == "__main__":
     unittest.main()

tests-unit/comfy_test/sample_test.py

@@ -0,0 +1,47 @@
+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:, :])))
+
+
+if __name__ == "__main__":
+    unittest.main()