diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index a68cb8439..d4f038a63 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -32,6 +32,11 @@ except ImportError as e:
             raise e
         exit(-1)
 
+try:
+    from sageattention import sageattn_varlen  # sageattention >= 2
+except ImportError:
+    sageattn_varlen = None
+
 SAGE_ATTENTION3_IS_AVAILABLE = False
 try:
     from sageattn3 import sageattn3_blackwell
@@ -48,6 +53,24 @@ except ImportError:
         logging.error(f"\n\nTo use the `--use-flash-attention` feature, the `flash-attn` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install flash-attn")
         exit(-1)
 
+try:
+    from torch.nn.attention.varlen import varlen_attn as _torch_varlen_attn
+except ImportError:
+    _torch_varlen_attn = None
+
+
+def _is_varlen(kwargs):
+    """Varlen mode is opted into by passing cu_seqlens_q in kwargs."""
+    return kwargs.get("cu_seqlens_q") is not None
+
+
+def _varlen_args(kwargs):
+    cu_seqlens_q = kwargs["cu_seqlens_q"]
+    cu_seqlens_kv = kwargs.get("cu_seqlens_kv", cu_seqlens_q)
+    max_seqlen_q = int(kwargs["max_seqlen_q"])
+    max_seqlen_kv = int(kwargs.get("max_seqlen_kv", max_seqlen_q))
+    return cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv
+
 REGISTERED_ATTENTION_FUNCTIONS = {}
 def register_attention_function(name: str, func: Callable):
     # avoid replacing existing functions
@@ -144,6 +167,8 @@ def wrap_attn(func):
 
 @wrap_attn
 def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    if _is_varlen(kwargs):
+        return attention_pytorch(q, k, v, heads, mask=mask, **kwargs)
     attn_precision = get_attn_precision(attn_precision, q.dtype)
 
     if skip_reshape:
@@ -218,6 +243,8 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
 
 @wrap_attn
 def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    if _is_varlen(kwargs):
+        return attention_pytorch(query, key, value, heads, mask=mask, **kwargs)
     attn_precision = get_attn_precision(attn_precision, query.dtype)
 
     if skip_reshape:
@@ -293,6 +320,8 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None,
 
 @wrap_attn
 def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    if _is_varlen(kwargs):
+        return attention_pytorch(q, k, v, heads, mask=mask, **kwargs)
     attn_precision = get_attn_precision(attn_precision, q.dtype)
 
     if skip_reshape:
@@ -424,6 +453,17 @@ except:
 
 @wrap_attn
 def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    if _is_varlen(kwargs):
+        # q, k, v expected as packed [T_total, H, C]. xformers wants per-item
+        # seqlen lists and a leading batch dim of 1.
+        cu_seqlens_q, cu_seqlens_kv, _max_q, _max_kv = _varlen_args(kwargs)
+        q_seqlen = (cu_seqlens_q[1:] - cu_seqlens_q[:-1]).tolist()
+        kv_seqlen = (cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]).tolist()
+        attn_bias = xformers.ops.fmha.BlockDiagonalMask.from_seqlens(q_seqlen, kv_seqlen)
+        return xformers.ops.memory_efficient_attention(
+            q.unsqueeze(0), k.unsqueeze(0), v.unsqueeze(0), attn_bias=attn_bias,
+        )[0]
+
     b = q.shape[0]
     dim_head = q.shape[-1]
     # check to make sure xformers isn't broken
@@ -493,6 +533,22 @@ else:
 
 @wrap_attn
 def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    if _is_varlen(kwargs):
+        # q, k, v expected as packed [T_total, H, C]. cu_seqlens_q / cu_seqlens_kv
+        # describe per-item offsets. Native varlen kernel if available, else
+        # nested-tensor SDPA fallback. mask/attn_precision/etc are ignored here.
+        cu_seqlens_q, cu_seqlens_kv, max_q, max_kv = _varlen_args(kwargs)
+        if _torch_varlen_attn is not None:
+            return _torch_varlen_attn(q, k, v, cu_seqlens_q, cu_seqlens_kv, max_q, max_kv)
+        q_nj = torch.nested.nested_tensor_from_jagged(q, offsets=cu_seqlens_q.long())
+        k_nj = torch.nested.nested_tensor_from_jagged(k, offsets=cu_seqlens_kv.long())
+        v_nj = torch.nested.nested_tensor_from_jagged(v, offsets=cu_seqlens_kv.long())
+        out = comfy.ops.scaled_dot_product_attention(
+            q_nj.transpose(1, 2), k_nj.transpose(1, 2), v_nj.transpose(1, 2),
+            attn_mask=None, dropout_p=0.0, is_causal=False,
+        )
+        return out.transpose(1, 2).values()
+
     if skip_reshape:
         b, _, _, dim_head = q.shape
     else:
@@ -541,6 +597,13 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
 
 @wrap_attn
 def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    if _is_varlen(kwargs):
+        # q, k, v expected as packed [T_total, H, C].
+        if sageattn_varlen is None:
+            # sageattention v1 has no varlen kernel; fall back to attention_pytorch's varlen path.
+            return attention_pytorch(q, k, v, heads, mask=mask, **kwargs)
+        cu_seqlens_q, cu_seqlens_kv, max_q, max_kv = _varlen_args(kwargs)
+        return sageattn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_kv, max_q, max_kv)
     if kwargs.get("low_precision_attention", True) is False:
         return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=skip_reshape, skip_output_reshape=skip_output_reshape, **kwargs)
 
@@ -698,6 +761,12 @@ except AttributeError as error:
 
 @wrap_attn
 def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    if _is_varlen(kwargs):
+        # q, k, v expected as packed [T_total, H, C].
+        from flash_attn import flash_attn_varlen_func
+        cu_seqlens_q, cu_seqlens_kv, max_q, max_kv = _varlen_args(kwargs)
+        return flash_attn_varlen_func(q, k, v, cu_seqlens_q, cu_seqlens_kv, max_q, max_kv)
+
     if skip_reshape:
         b, _, _, dim_head = q.shape
     else:
diff --git a/comfy/ldm/trellis2/attention.py b/comfy/ldm/trellis2/attention.py
index a09a8fca8..07a629c01 100644
--- a/comfy/ldm/trellis2/attention.py
+++ b/comfy/ldm/trellis2/attention.py
@@ -1,125 +1,107 @@
-import torch
 import math
+from typing import Tuple, Union
+
+import torch
+
 from comfy.ldm.modules.attention import optimized_attention
-from typing import Tuple, Union, List
 from comfy.ldm.trellis2.vae import VarLenTensor
-import comfy.ops
-
-try:
-    from torch.nn.attention.varlen import varlen_attn as _varlen_attn
-except ImportError:
-    _varlen_attn = None
 
 
-def var_attn_arg(kwargs):
-    cu_seqlens_q = kwargs.get("cu_seqlens_q", None)
-    max_seqlen_q = kwargs.get("max_seqlen_q", None)
-    cu_seqlens_k = kwargs.get("cu_seqlens_kv", cu_seqlens_q)
-    max_seqlen_k = kwargs.get("max_kv_seqlen", max_seqlen_q)
-    assert cu_seqlens_q is not None, "cu_seqlens_q shouldn't be None when var_length is True"
-    return cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k
+def _build_cu_seqlens(seqlen, device):
+    """Cumulative-offset tensor from a list/tensor of per-item sequence lengths."""
+    if isinstance(seqlen, torch.Tensor):
+        return torch.cat(
+            [torch.zeros(1, dtype=torch.int32, device=seqlen.device), torch.cumsum(seqlen, dim=0).int()],
+            dim=0,
+        ).to(device)
+    return torch.cat(
+        [torch.tensor([0]), torch.cumsum(torch.tensor(seqlen), dim=0)],
+    ).int().to(device)
 
-def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
-    cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k = var_attn_arg(kwargs)
-    if not skip_reshape:
-        total_tokens, embed_dim = q.shape
-        head_dim = embed_dim // heads
-        q = q.view(total_tokens, heads, head_dim)
-        k = k.view(k.shape[0], heads, head_dim)
-        v = v.view(v.shape[0], heads, head_dim)
 
-    if _varlen_attn is not None:
-        return _varlen_attn(
-            q, k, v,
-            cu_seqlens_q, cu_seqlens_k,
-            int(max_seqlen_q), int(max_seqlen_k),
-        )
+def _layout_to_feats(t):
+    """Returns (sparse_template_or_None, seqlen, feats[T, H, C])."""
+    if isinstance(t, VarLenTensor):
+        seqlen = [t.layout[i].stop - t.layout[i].start for i in range(t.shape[0])]
+        return t, seqlen, t.feats
+    N, L = t.shape[:2]
+    return None, [L] * N, t.reshape(-1, *t.shape[-2:])
 
-    # Fallback: nested-tensor SDPA (PyTorch < the version that introduced varlen_attn)
-    q = torch.nested.nested_tensor_from_jagged(q, offsets=cu_seqlens_q.long())
-    k = torch.nested.nested_tensor_from_jagged(k, offsets=cu_seqlens_k.long())
-    v = torch.nested.nested_tensor_from_jagged(v, offsets=cu_seqlens_k.long())
-    q = q.transpose(1, 2)
-    k = k.transpose(1, 2)
-    v = v.transpose(1, 2)
-    out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
-    return out.transpose(1, 2).values()
-
-def scaled_dot_product_attention(*args, **kwargs):
-    num_all_args = len(args) + len(kwargs)
-
-    q = None
-    if num_all_args == 1:
-        qkv = args[0] if len(args) > 0 else kwargs.get('qkv')
-    elif num_all_args == 2:
-        q = args[0] if len(args) > 0 else kwargs.get('q')
-        kv = args[1] if len(args) > 1 else kwargs.get('kv')
-    elif num_all_args == 3:
-        q = args[0] if len(args) > 0 else kwargs.get('q')
-        k = args[1] if len(args) > 1 else kwargs.get('k')
-        v = args[2] if len(args) > 2 else kwargs.get('v')
-
-    if q is not None:
-        heads = q.shape[2]
-    else:
-        heads = qkv.shape[3]
-
-    if num_all_args == 1:
-        q, k, v = qkv.unbind(dim=2)
-    elif num_all_args == 2:
-        k, v = kv.unbind(dim=2)
 
+def dense_attention(q, k, v, **kwargs):
+    """q, k, v: [B, L, H, C]. Permutes for comfy's [B, H, L, C] convention."""
+    heads = q.shape[2]
     q = q.permute(0, 2, 1, 3)
     k = k.permute(0, 2, 1, 3)
     v = v.permute(0, 2, 1, 3)
-
     out = optimized_attention(q, k, v, heads, skip_output_reshape=True, skip_reshape=True, **kwargs)
+    return out.permute(0, 2, 1, 3)
 
-    out = out.permute(0, 2, 1, 3)
 
-    return out
+def sparse_attention(q, k, v, **kwargs):
+    """
+    Varlen attention for SparseTensor inputs. Each of q, k, v may be a VarLenTensor
+    (sparse) or dense [B, L, H, C]. Output type matches q. Backend dispatch lives
+    in comfy.ldm.modules.attention.optimized_attention; we just build cu_seqlens
+    from the layouts.
+    """
+    s, q_seqlen, q_feats = _layout_to_feats(q)
+    _, kv_seqlen, k_feats = _layout_to_feats(k)
+    _, _, v_feats = _layout_to_feats(v)
+    heads = q_feats.shape[1]
 
-def sparse_windowed_scaled_dot_product_self_attention(
-    qkv,
-    window_size: int,
-    shift_window: Tuple[int, int, int] = (0, 0, 0)
-):
+    device = q_feats.device
+    cu_seqlens_q = _build_cu_seqlens(q_seqlen, device)
+    cu_seqlens_kv = _build_cu_seqlens(kv_seqlen, device)
 
-    serialization_spatial_cache_name = f'windowed_attention_{window_size}_{shift_window}'
-    serialization_spatial_cache = qkv.get_spatial_cache(serialization_spatial_cache_name)
-    if serialization_spatial_cache is None:
-        fwd_indices, bwd_indices, seq_lens, attn_func_args = calc_window_partition(qkv, window_size, shift_window)
-        qkv.register_spatial_cache(serialization_spatial_cache_name, (fwd_indices, bwd_indices, seq_lens, attn_func_args))
-    else:
-        fwd_indices, bwd_indices, seq_lens, attn_func_args = serialization_spatial_cache
+    out = optimized_attention(
+        q_feats, k_feats, v_feats, heads,
+        cu_seqlens_q=cu_seqlens_q, cu_seqlens_kv=cu_seqlens_kv,
+        max_seqlen_q=max(q_seqlen), max_seqlen_kv=max(kv_seqlen),
+        skip_reshape=True, skip_output_reshape=True,
+        **kwargs,
+    )
+
+    if s is not None:
+        return s.replace(out)
+    N, L = q.shape[:2]
+    return out.reshape(N, L, heads, -1)
+
+
+def sparse_windowed_self_attention(qkv, window_size: int, shift_window: Tuple[int, int, int] = (0, 0, 0)):
+    """Windowed sparse self-attention. Partitions voxels into windows via spatial
+    sort, then runs varlen attention with one sequence per non-empty window."""
+    cache_name = f'windowed_attention_{window_size}_{shift_window}'
+    cache = qkv.get_spatial_cache(cache_name)
+    if cache is None:
+        cache = calc_window_partition(qkv, window_size, shift_window)
+        qkv.register_spatial_cache(cache_name, cache)
+    fwd_indices, bwd_indices, seq_lens = cache
 
     qkv_feats = qkv.feats[fwd_indices]      # [M, 3, H, C]
-    heads = qkv_feats.shape[2]
+    q, k, v = qkv_feats.unbind(dim=1)       # each [M, H, C]
+    heads = q.shape[1]
+    device = q.device
 
-    if optimized_attention.__name__ == 'attention_xformers':
-        q, k, v = qkv_feats.unbind(dim=1)
-        q = q.unsqueeze(0)                                                              # [1, M, H, C]
-        k = k.unsqueeze(0)                                                              # [1, M, H, C]
-        v = v.unsqueeze(0)                                                              # [1, M, H, C]
-        #out = xops.memory_efficient_attention(q, k, v, **attn_func_args)[0]             # [M, H, C]
-        out = optimized_attention(q, k, v, heads, skip_output_reshape=True, skip_reshape=True)
-    elif optimized_attention.__name__ == 'attention_flash':
-        if 'flash_attn' not in globals():
-            import flash_attn
-        out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv_feats, **attn_func_args)  # [M, H, C]
-    else:
-        out = optimized_attention(q, k, v, heads, skip_output_reshape=True, skip_reshape=True)
-
-    out = out[bwd_indices]      # [T, H, C]
+    cu_seqlens = _build_cu_seqlens(seq_lens, device)
+    max_seqlen = int(seq_lens.max())
 
+    out = optimized_attention(
+        q, k, v, heads,
+        cu_seqlens_q=cu_seqlens, cu_seqlens_kv=cu_seqlens,
+        max_seqlen_q=max_seqlen, max_seqlen_kv=max_seqlen,
+        skip_reshape=True, skip_output_reshape=True,
+    )
+    out = out[bwd_indices]
     return qkv.replace(out)
 
+
 def calc_window_partition(
     tensor,
     window_size: Union[int, Tuple[int, ...]],
     shift_window: Union[int, Tuple[int, ...]] = 0,
-) -> Tuple[torch.Tensor, torch.Tensor, List[int], List[int]]:
-
+):
+    """Returns (fwd_indices, bwd_indices, seq_lens) for window partitioning."""
     DIM = tensor.coords.shape[1] - 1
     shift_window = (shift_window,) * DIM if isinstance(shift_window, int) else shift_window
     window_size = (window_size,) * DIM if isinstance(window_size, int) else window_size
@@ -136,139 +118,6 @@ def calc_window_partition(
     bwd_indices = torch.empty_like(fwd_indices)
     bwd_indices[fwd_indices] = torch.arange(fwd_indices.shape[0], device=tensor.device)
     seq_lens = torch.bincount(shifted_indices)
-    mask = seq_lens != 0
-    seq_lens = seq_lens[mask]
+    seq_lens = seq_lens[seq_lens != 0]
 
-    if optimized_attention.__name__ == 'attention_xformers':
-        if 'xops' not in globals():
-            import xformers.ops as xops
-        attn_func_args = {
-            'attn_bias': xops.fmha.BlockDiagonalMask.from_seqlens(seq_lens)
-        }
-    elif optimized_attention.__name__ == 'attention_flash':
-        attn_func_args = {
-            'cu_seqlens': torch.cat([torch.tensor([0], device=tensor.device), torch.cumsum(seq_lens, dim=0)], dim=0).int(),
-            'max_seqlen': torch.max(seq_lens)
-        }
-
-    return fwd_indices, bwd_indices, seq_lens, attn_func_args
-
-
-def sparse_scaled_dot_product_attention(*args, **kwargs):
-    q=None
-    arg_names_dict = {
-        1: ['qkv'],
-        2: ['q', 'kv'],
-        3: ['q', 'k', 'v']
-    }
-    num_all_args = len(args) + len(kwargs)
-    for key in arg_names_dict[num_all_args][len(args):]:
-        assert key in kwargs, f"Missing argument {key}"
-
-    if num_all_args == 1:
-        qkv = args[0] if len(args) > 0 else kwargs['qkv']
-        device = qkv.device
-
-        s = qkv
-        q_seqlen = [qkv.layout[i].stop - qkv.layout[i].start for i in range(qkv.shape[0])]
-        kv_seqlen = q_seqlen
-        qkv = qkv.feats     # [T, 3, H, C]
-
-    elif num_all_args == 2:
-        q = args[0] if len(args) > 0 else kwargs['q']
-        kv = args[1] if len(args) > 1 else kwargs['kv']
-        device = q.device
-
-        if isinstance(q, VarLenTensor):
-            s = q
-            q_seqlen = [q.layout[i].stop - q.layout[i].start for i in range(q.shape[0])]
-            q = q.feats     # [T_Q, H, C]
-        else:
-            s = None
-            N, L, H, C = q.shape
-            q_seqlen = [L] * N
-            q = q.reshape(N * L, H, C)   # [T_Q, H, C]
-
-        if isinstance(kv, VarLenTensor):
-            kv_seqlen = [kv.layout[i].stop - kv.layout[i].start for i in range(kv.shape[0])]
-            kv = kv.feats     # [T_KV, 2, H, C]
-        else:
-            N, L, _, H, C = kv.shape
-            kv_seqlen = [L] * N
-            kv = kv.reshape(N * L, 2, H, C)   # [T_KV, 2, H, C]
-
-    elif num_all_args == 3:
-        q = args[0] if len(args) > 0 else kwargs['q']
-        k = args[1] if len(args) > 1 else kwargs['k']
-        v = args[2] if len(args) > 2 else kwargs['v']
-        device = q.device
-
-        if isinstance(q, VarLenTensor):
-            s = q
-            q_seqlen = [q.layout[i].stop - q.layout[i].start for i in range(q.shape[0])]
-            q = q.feats     # [T_Q, H, Ci]
-        else:
-            s = None
-            N, L, H, CI = q.shape
-            q_seqlen = [L] * N
-            q = q.reshape(N * L, H, CI)  # [T_Q, H, Ci]
-
-        if isinstance(k, VarLenTensor):
-            kv_seqlen = [k.layout[i].stop - k.layout[i].start for i in range(k.shape[0])]
-            k = k.feats     # [T_KV, H, Ci]
-            v = v.feats     # [T_KV, H, Co]
-        else:
-            N, L, H, CI, CO = *k.shape, v.shape[-1]
-            kv_seqlen = [L] * N
-            k = k.reshape(N * L, H, CI)     # [T_KV, H, Ci]
-            v = v.reshape(N * L, H, CO)     # [T_KV, H, Co]
-
-    # TODO: change
-    if q is not None:
-        heads = q
-    else:
-        heads = qkv
-    heads = heads.shape[2]
-    if optimized_attention.__name__ == 'attention_xformers':
-        if 'xops' not in globals():
-            import xformers.ops as xops
-        if num_all_args == 1:
-            q, k, v = qkv.unbind(dim=1)
-        elif num_all_args == 2:
-            k, v = kv.unbind(dim=1)
-        q = q.unsqueeze(0)
-        k = k.unsqueeze(0)
-        v = v.unsqueeze(0)
-        mask = xops.fmha.BlockDiagonalMask.from_seqlens(q_seqlen, kv_seqlen)
-        out = xops.memory_efficient_attention(q, k, v, mask)[0]
-    elif optimized_attention.__name__ == 'attention_flash':
-        if 'flash_attn' not in globals():
-            import flash_attn
-        cu_seqlens_q = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(q_seqlen), dim=0)]).int().to(device)
-        if num_all_args in [2, 3]:
-            cu_seqlens_kv = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(kv_seqlen), dim=0)]).int().to(device)
-        if num_all_args == 1:
-            out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv, cu_seqlens_q, max(q_seqlen))
-        elif num_all_args == 2:
-            out = flash_attn.flash_attn_varlen_kvpacked_func(q, kv, cu_seqlens_q, cu_seqlens_kv, max(q_seqlen), max(kv_seqlen))
-        elif num_all_args == 3:
-            out = flash_attn.flash_attn_varlen_func(q, k, v, cu_seqlens_q, cu_seqlens_kv, max(q_seqlen), max(kv_seqlen))
-
-    elif optimized_attention.__name__ == "attention_pytorch":
-        cu_seqlens_q = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(q_seqlen), dim=0)]).int().to(device)
-        if num_all_args in [2, 3]:
-            cu_seqlens_kv = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(kv_seqlen), dim=0)]).int().to(device)
-        else:
-            cu_seqlens_kv = cu_seqlens_q
-        if num_all_args == 1:
-            q, k, v = qkv.unbind(dim=1)
-        elif num_all_args == 2:
-            k, v = kv.unbind(dim=1)
-        out = attention_pytorch(q, k, v, heads=heads,cu_seqlens_q=cu_seqlens_q,
-                                cu_seqlens_kv=cu_seqlens_kv, max_seqlen_q=max(q_seqlen), max_kv_seqlen=max(kv_seqlen),
-                                skip_reshape=True, skip_output_reshape=True)
-
-    if s is not None:
-        return s.replace(out)
-    else:
-        return out.reshape(N, L, H, -1)
+    return fwd_indices, bwd_indices, seq_lens
diff --git a/comfy/ldm/trellis2/model.py b/comfy/ldm/trellis2/model.py
index 0b4181ad2..7e263169a 100644
--- a/comfy/ldm/trellis2/model.py
+++ b/comfy/ldm/trellis2/model.py
@@ -4,11 +4,12 @@ import torch.nn as nn
 from comfy.ldm.trellis2.vae import SparseTensor, SparseLinear, sparse_cat, VarLenTensor
 from typing import Optional, Tuple, Literal, Union, List
 from comfy.ldm.trellis2.attention import (
-    sparse_windowed_scaled_dot_product_self_attention, sparse_scaled_dot_product_attention, scaled_dot_product_attention
+    sparse_windowed_self_attention, sparse_attention, dense_attention
 )
 from comfy.ldm.genmo.joint_model.layers import TimestepEmbedder
 from comfy.ldm.flux.math import apply_rope, apply_rope1
 
+
 class SparseGELU(nn.GELU):
     def forward(self, input: VarLenTensor) -> VarLenTensor:
         return input.replace(super().forward(input.feats))
@@ -31,22 +32,16 @@ class LayerNorm32(nn.LayerNorm):
         x = x.to(dtype=torch.float32)
         o = super().forward(x)
         return o.to(dtype=x_dtype)
-
-
+    
 class SparseMultiHeadRMSNorm(nn.Module):
     def __init__(self, dim: int, heads: int, device, dtype):
         super().__init__()
-        self.scale = dim ** 0.5
         self.gamma = nn.Parameter(torch.ones(heads, dim, device=device, dtype=dtype))
 
     def forward(self, x: Union[VarLenTensor, torch.Tensor]) -> Union[VarLenTensor, torch.Tensor]:
-        x_type = x.dtype
-        x = x.float()
         if isinstance(x, VarLenTensor):
-            x = x.replace(F.normalize(x.feats, dim=-1) * self.gamma * self.scale)
-        else:
-            x = F.normalize(x, dim=-1) * self.gamma * self.scale
-        return x.to(x_type)
+            return x.replace(F.rms_norm(x.feats, (x.feats.shape[-1],)) * self.gamma)
+        return F.rms_norm(x, (x.shape[-1],)) * self.gamma
 
 class SparseRotaryPositionEmbedder(nn.Module):
     def __init__(
@@ -84,12 +79,6 @@ class SparseRotaryPositionEmbedder(nn.Module):
 
         return freqs_cis
 
-    def _get_phases(self, indices: torch.Tensor) -> torch.Tensor:
-        self.freqs = self.freqs.to(indices.device)
-        phases = torch.outer(indices, self.freqs)
-        phases = torch.polar(torch.ones_like(phases), phases)
-        return phases
-
     def forward(self, q, k=None):
         cache_name = f'rope_cis_{self.dim}d_f{self.rope_freq[1]}_hd{self.head_dim}'
         freqs_cis = q.get_spatial_cache(cache_name)
@@ -110,27 +99,10 @@ class SparseRotaryPositionEmbedder(nn.Module):
         q_feats, k_feats = apply_rope(q.feats, k.feats, f_cis)
         return q.replace(q_feats), k.replace(k_feats)
 
-    @staticmethod
-    def apply_rotary_embedding(x: torch.Tensor, phases: torch.Tensor) -> torch.Tensor:
-        x_complex = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
-        x_rotated = x_complex * phases.unsqueeze(-2)
-        x_embed = torch.view_as_real(x_rotated).reshape(*x_rotated.shape[:-1], -1).to(x.dtype)
-        return x_embed
 
 class RotaryPositionEmbedder(SparseRotaryPositionEmbedder):
-    def forward(self, indices: torch.Tensor) -> torch.Tensor:
-        phases = self._get_phases(indices.reshape(-1)).reshape(*indices.shape[:-1], -1)
-        if torch.is_complex(phases):
-            phases = phases.to(torch.complex64)
-        else:
-            phases = phases.to(torch.float32)
-        if phases.shape[-1] < self.head_dim // 2:
-                padn = self.head_dim // 2 - phases.shape[-1]
-                phases = torch.cat([phases, torch.polar(
-                    torch.ones(*phases.shape[:-1], padn, device=phases.device, dtype=torch.float32),
-                    torch.zeros(*phases.shape[:-1], padn, device=phases.device, dtype=torch.float32)
-                )], dim=-1)
-        return phases
+    def forward(self, coords: torch.Tensor) -> torch.Tensor:
+        return self._get_freqs_cis(coords) # [L, head_dim/2, 2, 2]
 
 class SparseMultiHeadAttention(nn.Module):
     def __init__(
@@ -198,50 +170,53 @@ class SparseMultiHeadAttention(nn.Module):
         x_feats = x_feats.reshape(*x_feats.shape[:2], num_fused, self.num_heads, -1)
         return x.replace(x_feats.squeeze(0)) if isinstance(x, VarLenTensor) else x_feats
 
-    def forward(self, x: SparseTensor, context: Optional[Union[VarLenTensor, torch.Tensor]] = None) -> SparseTensor:
+    def forward(self, x: SparseTensor, context: Optional[Union[VarLenTensor, torch.Tensor]] = None,
+                transformer_options=None) -> SparseTensor:
         if self._type == "self":
-            dtype = next(self.to_qkv.parameters()).dtype
-            x = x.to(dtype)
             qkv = self._linear(self.to_qkv, x)
             qkv = self._fused_pre(qkv, num_fused=3)
-            if self.qk_rms_norm or self.use_rope:
+            if self.attn_mode == "full":
                 q, k, v = qkv.unbind(dim=-3)
                 if self.qk_rms_norm:
                     q = self.q_rms_norm(q)
                     k = self.k_rms_norm(k)
                 if self.use_rope:
                     q, k = self.rope(q, k)
-                qkv = qkv.replace(torch.stack([q.feats, k.feats, v.feats], dim=1))
-            if self.attn_mode == "full":
-                h = sparse_scaled_dot_product_attention(qkv)
-            elif self.attn_mode == "windowed":
-                h = sparse_windowed_scaled_dot_product_self_attention(
-                    qkv, self.window_size, shift_window=self.shift_window
-                )
-            elif self.attn_mode == "double_windowed":
-                qkv0 = qkv.replace(qkv.feats[:, :, self.num_heads//2:])
-                qkv1 = qkv.replace(qkv.feats[:, :, :self.num_heads//2])
-                h0 = sparse_windowed_scaled_dot_product_self_attention(
-                    qkv0, self.window_size, shift_window=(0, 0, 0)
-                )
-                h1 = sparse_windowed_scaled_dot_product_self_attention(
-                    qkv1, self.window_size, shift_window=tuple([self.window_size//2] * 3)
-                )
-                h = qkv.replace(torch.cat([h0.feats, h1.feats], dim=1))
+                h = sparse_attention(q, k, v, transformer_options=transformer_options)
+            else:
+                # Windowed paths take packed qkv; preserve any per-head norm/rope.
+                if self.qk_rms_norm or self.use_rope:
+                    q, k, v = qkv.unbind(dim=-3)
+                    if self.qk_rms_norm:
+                        q = self.q_rms_norm(q)
+                        k = self.k_rms_norm(k)
+                    if self.use_rope:
+                        q, k = self.rope(q, k)
+                    qkv = qkv.replace(torch.stack([q.feats, k.feats, v.feats], dim=1))
+                if self.attn_mode == "windowed":
+                    h = sparse_windowed_self_attention(
+                        qkv, self.window_size, shift_window=self.shift_window
+                    )
+                elif self.attn_mode == "double_windowed":
+                    qkv0 = qkv.replace(qkv.feats[:, :, self.num_heads//2:])
+                    qkv1 = qkv.replace(qkv.feats[:, :, :self.num_heads//2])
+                    h0 = sparse_windowed_self_attention(
+                        qkv0, self.window_size, shift_window=(0, 0, 0)
+                    )
+                    h1 = sparse_windowed_self_attention(
+                        qkv1, self.window_size, shift_window=tuple([self.window_size//2] * 3)
+                    )
+                    h = qkv.replace(torch.cat([h0.feats, h1.feats], dim=1))
         else:
             q = self._linear(self.to_q, x)
             q = self._reshape_chs(q, (self.num_heads, -1))
-            dtype = next(self.to_kv.parameters()).dtype
-            context = context.to(dtype)
             kv = self._linear(self.to_kv, context)
             kv = self._fused_pre(kv, num_fused=2)
+            k, v = kv.unbind(dim=-3)
             if self.qk_rms_norm:
                 q = self.q_rms_norm(q)
-                k, v = kv.unbind(dim=-3)
                 k = self.k_rms_norm(k)
-                h = sparse_scaled_dot_product_attention(q, k, v)
-            else:
-                h = sparse_scaled_dot_product_attention(q, kv)
+            h = sparse_attention(q, k, v, transformer_options=transformer_options)
         h = self._reshape_chs(h, (-1,))
         h = self._linear(self.to_out, h)
         return h
@@ -265,9 +240,9 @@ class ProjectAttentionSparse(nn.Module):
         self.proj_linear = operations.Linear(proj_in_channels, channels, bias=True,
                                              device=device, dtype=dtype)
 
-    def forward(self, x: SparseTensor, context) -> SparseTensor:
+    def forward(self, x: SparseTensor, context, transformer_options=None) -> SparseTensor:
         global_ctx, proj_in = _split_proj_context(context)
-        global_out = self.cross_attn_block(x, global_ctx)
+        global_out = self.cross_attn_block(x, global_ctx, transformer_options=transformer_options)
         if isinstance(proj_in, tuple):
             proj_in = torch.cat([proj_in[0], proj_in[1]], dim=-1)
         proj_out = self.proj_linear(proj_in.to(self.proj_linear.weight.dtype))
@@ -282,9 +257,9 @@ class ProjectAttentionDense(nn.Module):
         self.proj_linear = operations.Linear(proj_in_channels, channels, bias=True,
                                              device=device, dtype=dtype)
 
-    def forward(self, x: torch.Tensor, context) -> torch.Tensor:
+    def forward(self, x: torch.Tensor, context, transformer_options=None) -> torch.Tensor:
         global_ctx, proj_in = _split_proj_context(context)
-        global_out = self.cross_attn_block(x, global_ctx)
+        global_out = self.cross_attn_block(x, global_ctx, transformer_options=transformer_options)
         if isinstance(proj_in, tuple):
             proj_in = torch.cat([proj_in[0], proj_in[1]], dim=-1)
         proj_out = self.proj_linear(proj_in.to(self.proj_linear.weight.dtype))
@@ -367,32 +342,36 @@ class ModulatedSparseTransformerCrossBlock(nn.Module):
         else:
             self.modulation = nn.Parameter(torch.randn(6 * channels, device=device, dtype=dtype) / channels ** 0.5)
 
-    def _forward(self, x: SparseTensor, mod: torch.Tensor, context) -> SparseTensor:
+    def _forward(self, x: SparseTensor, mod: torch.Tensor, context, transformer_options=None) -> SparseTensor:
         if self.share_mod:
             shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.modulation + mod).type(mod.dtype).chunk(6, dim=1)
         else:
             shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1)
-        h = x.replace(self.norm1(x.feats))
-        h = h * (1 + scale_msa) + shift_msa
-        h = self.self_attn(h)
-        h = h * gate_msa
-        x = x + h
+        # Fuse the (mul + add) and (mul + residual) pairs into addcmul so the
+        # mod/shift broadcasts hit one kernel each instead of two.
+        b_map = x.batch_boardcast_map
+
+        h_feats = self.norm1(x.feats)
+        h_feats = torch.addcmul(shift_msa[b_map], h_feats, (1 + scale_msa)[b_map])
+        h = self.self_attn(x.replace(h_feats), transformer_options=transformer_options)
+        x = x.replace(torch.addcmul(x.feats, h.feats, gate_msa[b_map]))
+
         h = x.replace(self.norm2(x.feats))
         if self.image_attn_mode == "global":
             global_ctx, _ = _split_proj_context(context)
-            h = self.cross_attn(h, global_ctx)
+            h = self.cross_attn(h, global_ctx, transformer_options=transformer_options)
         else:
-            h = self.cross_attn(h, context)
-        x = x + h
-        h = x.replace(self.norm3(x.feats))
-        h = h * (1 + scale_mlp) + shift_mlp
-        h = self.mlp(h)
-        h = h * gate_mlp
+            h = self.cross_attn(h, context, transformer_options=transformer_options)
         x = x + h
+
+        h_feats = self.norm3(x.feats)
+        h_feats = torch.addcmul(shift_mlp[b_map], h_feats, (1 + scale_mlp)[b_map])
+        h = self.mlp(x.replace(h_feats))
+        x = x.replace(torch.addcmul(x.feats, h.feats, gate_mlp[b_map]))
         return x
 
-    def forward(self, x: SparseTensor, mod: torch.Tensor, context) -> SparseTensor:
-        return self._forward(x, mod, context)
+    def forward(self, x: SparseTensor, mod: torch.Tensor, context, transformer_options=None) -> SparseTensor:
+        return self._forward(x, mod, context, transformer_options=transformer_options)
 
 
 class SLatFlowModel(nn.Module):
@@ -480,24 +459,21 @@ class SLatFlowModel(nn.Module):
         t: torch.Tensor,
         cond: Union[torch.Tensor, List[torch.Tensor]],
         concat_cond: Optional[SparseTensor] = None,
-        **kwargs
+        transformer_options=None,
+        **kwargs,
     ) -> SparseTensor:
         if concat_cond is not None:
             x = sparse_cat([x, concat_cond], dim=-1)
         if isinstance(cond, list):
             cond = VarLenTensor.from_tensor_list(cond)
 
-        dtype = next(self.input_layer.parameters()).dtype
-        x = x.to(dtype)
         h = self.input_layer(x)
-        t = t.to(dtype)
-        t_embedder = self.t_embedder.to(dtype)
-        t_emb = t_embedder(t, out_dtype = t.dtype)
+        t_emb = self.t_embedder(t, out_dtype=t.dtype)
         if self.share_mod:
             t_emb = self.adaLN_modulation(t_emb)
 
         for block in self.blocks:
-            h = block(h, t_emb, cond)
+            h = block(h, t_emb, cond, transformer_options=transformer_options)
 
         h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:]))
         h = self.out_layer(h)
@@ -566,40 +542,34 @@ class MultiHeadAttention(nn.Module):
 
         self.to_out = operations.Linear(channels, channels, device=device, dtype=dtype)
 
-    def forward(self, x: torch.Tensor, context: Optional[torch.Tensor] = None, phases: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def forward(self, x: torch.Tensor, context: Optional[torch.Tensor] = None,
+                phases: Optional[torch.Tensor] = None, transformer_options=None) -> torch.Tensor:
         B, L, C = x.shape
         if self._type == "self":
-            x = x.to(next(self.to_qkv.parameters()).dtype)
             qkv = self.to_qkv(x)
             qkv = qkv.reshape(B, L, 3, self.num_heads, -1)
-
-            if self.attn_mode == "full":
-                if self.qk_rms_norm or self.use_rope:
-                    q, k, v = qkv.unbind(dim=2)
-                    if self.qk_rms_norm:
-                        q = self.q_rms_norm(q)
-                        k = self.k_rms_norm(k)
-                    if self.use_rope:
-                        assert phases is not None, "Phases must be provided for RoPE"
-                        q = RotaryPositionEmbedder.apply_rotary_embedding(q, phases)
-                        k = RotaryPositionEmbedder.apply_rotary_embedding(k, phases)
-                    h = scaled_dot_product_attention(q, k, v)
-                else:
-                    h = scaled_dot_product_attention(qkv)
+            q, k, v = qkv.unbind(dim=2)
+            if self.qk_rms_norm:
+                q = self.q_rms_norm(q)
+                k = self.k_rms_norm(k)
+            if self.use_rope:
+                assert phases is not None, "Phases must be provided for RoPE"
+                # phases is [L, head_dim/2, 2, 2]; broadcast to [1, L, 1, ...]
+                # to align with q/k of shape [B, L, H, head_dim].
+                f_cis = phases.unsqueeze(0).unsqueeze(2)
+                q, k = apply_rope(q, k, f_cis)
+            h = dense_attention(q, k, v, transformer_options=transformer_options)
         else:
             Lkv = context.shape[1]
             q = self.to_q(x)
-            context = context.to(next(self.to_kv.parameters()).dtype)
             kv = self.to_kv(context)
             q = q.reshape(B, L, self.num_heads, -1)
             kv = kv.reshape(B, Lkv, 2, self.num_heads, -1)
+            k, v = kv.unbind(dim=2)
             if self.qk_rms_norm:
                 q = self.q_rms_norm(q)
-                k, v = kv.unbind(dim=2)
                 k = self.k_rms_norm(k)
-                h = scaled_dot_product_attention(q, k, v)
-            else:
-                h = scaled_dot_product_attention(q, kv)
+            h = dense_attention(q, k, v, transformer_options=transformer_options)
         h = h.reshape(B, L, -1)
         h = self.to_out(h)
         return h
@@ -677,32 +647,39 @@ class ModulatedTransformerCrossBlock(nn.Module):
         else:
             self.modulation = nn.Parameter(torch.randn(6 * channels, device=device, dtype=dtype) / channels ** 0.5)
 
-    def _forward(self, x: torch.Tensor, mod: torch.Tensor, context, phases: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def _forward(self, x: torch.Tensor, mod: torch.Tensor, context,
+                 phases: Optional[torch.Tensor] = None, transformer_options=None) -> torch.Tensor:
         if self.share_mod:
             shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.modulation + mod).type(mod.dtype).chunk(6, dim=1)
         else:
             shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1)
-        h = self.norm1(x)
-        h = h * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1)
-        h = self.self_attn(h, phases=phases)
-        h = h * gate_msa.unsqueeze(1)
-        x = x + h
+        shift_msa = shift_msa.unsqueeze(1)
+        scale_msa = scale_msa.unsqueeze(1)
+        gate_msa = gate_msa.unsqueeze(1)
+        shift_mlp = shift_mlp.unsqueeze(1)
+        scale_mlp = scale_mlp.unsqueeze(1)
+        gate_mlp = gate_mlp.unsqueeze(1)
+
+        h = torch.addcmul(shift_msa, self.norm1(x), 1 + scale_msa)
+        h = self.self_attn(h, phases=phases, transformer_options=transformer_options)
+        x = torch.addcmul(x, h, gate_msa)
+
         h = self.norm2(x)
         if self.image_attn_mode == "global":
             global_ctx, _ = _split_proj_context(context)
-            h = self.cross_attn(h, global_ctx)
+            h = self.cross_attn(h, global_ctx, transformer_options=transformer_options)
         else:
-            h = self.cross_attn(h, context)
+            h = self.cross_attn(h, context, transformer_options=transformer_options)
         x = x + h
-        h = self.norm3(x)
-        h = h * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1)
+
+        h = torch.addcmul(shift_mlp, self.norm3(x), 1 + scale_mlp)
         h = self.mlp(h)
-        h = h * gate_mlp.unsqueeze(1)
-        x = x + h
+        x = torch.addcmul(x, h, gate_mlp)
         return x
 
-    def forward(self, x: torch.Tensor, mod: torch.Tensor, context, phases: Optional[torch.Tensor] = None) -> torch.Tensor:
-        return self._forward(x, mod, context, phases)
+    def forward(self, x: torch.Tensor, mod: torch.Tensor, context,
+                phases: Optional[torch.Tensor] = None, transformer_options=None) -> torch.Tensor:
+        return self._forward(x, mod, context, phases, transformer_options=transformer_options)
 
 
 class SparseStructureFlowModel(nn.Module):
@@ -792,18 +769,18 @@ class SparseStructureFlowModel(nn.Module):
 
         self.out_layer = operations.Linear(model_channels, out_channels, device=device, dtype=dtype)
 
-    def forward(self, x: torch.Tensor, t: torch.Tensor, cond: torch.Tensor) -> torch.Tensor:
+    def forward(self, x: torch.Tensor, t: torch.Tensor, cond: torch.Tensor,
+                transformer_options=None) -> torch.Tensor:
         x = x.view(x.shape[0], self.in_channels, *[self.resolution] * 3)
 
         h = x.view(*x.shape[:2], -1).permute(0, 2, 1).contiguous()
 
-        h = h.to(next(self.input_layer.parameters()).dtype)
         h = self.input_layer(h)
-        t_emb = self.t_embedder(t, out_dtype = t.dtype)
+        t_emb = self.t_embedder(t, out_dtype=t.dtype)
         if self.share_mod:
             t_emb = self.adaLN_modulation(t_emb)
         for block in self.blocks:
-            h = block(h, t_emb, cond, self.rope_phases)
+            h = block(h, t_emb, cond, self.rope_phases, transformer_options=transformer_options)
         h = F.layer_norm(h, h.shape[-1:])
         h = self.out_layer(h)
 
@@ -820,7 +797,7 @@ def timestep_reshift(t_shifted, old_shift=3.0, new_shift=5.0):
 
 
 # Pixal3D ProjGrid math — port of upstream's ProjGrid + project_points_to_image_batch.
-# World frame uses world Y as depth (Blender convention), camera looks along -Z local;
+# World frame uses world Y as depth, camera looks along -Z local;
 # transform_matrix is camera-to-world (inverted internally). Intrinsics: fx = 16 / tan(fov/2)
 # with sensor_width = 32mm.
 
@@ -927,7 +904,7 @@ def _back_project_to_tokens(
             if not mask.any():
                 continue
             p = coords_world[mask].unsqueeze(0)
-            uv, depth, valid = _project_points_to_image(
+            uv, _, _ = _project_points_to_image(
                 p, transform_matrix[b:b+1], camera_angle_x[b:b+1], image_resolution)
             uv_ndc = (uv + 0.5) / image_resolution * 2.0 - 1.0
             # padding_mode='border' is load-bearing: masking out-of-frame voxels confuses
@@ -937,7 +914,7 @@ def _back_project_to_tokens(
             out[mask] = sampled
         return out
     else:
-        uv, depth, valid = _project_points_to_image(
+        uv, _, _ = _project_points_to_image(
             coords_world, transform_matrix, camera_angle_x, image_resolution)
         uv_ndc = (uv + 0.5) / image_resolution * 2.0 - 1.0
         sampled = _sample_features(feature_map, uv_ndc)
@@ -945,21 +922,6 @@ def _back_project_to_tokens(
         return out
 
 
-def _pack_per_voxel_scalar(proj_pack: Optional[dict], key: str, eval_batch: int, device) -> torch.Tensor:
-    if proj_pack is None or key not in proj_pack:
-        return torch.ones((eval_batch,), device=device, dtype=torch.float32)
-    t = proj_pack[key].to(device=device, dtype=torch.float32)
-    if t.ndim == 0:
-        return t.expand(eval_batch).clone()
-    return _expand_pack(t, eval_batch)
-
-
-def _expand_pack(t: torch.Tensor, eval_batch: int) -> torch.Tensor:
-    if eval_batch == t.shape[0]:
-        return t
-    if eval_batch % t.shape[0] != 0:
-        raise ValueError(f"eval batch {eval_batch} is not a multiple of pack batch {t.shape[0]}")
-    return t.repeat((eval_batch // t.shape[0],) + (1,) * (t.ndim - 1))
 
 
 def _select_stage_entry(proj_pack: dict, stage: Optional[str]):
@@ -973,53 +935,96 @@ def _select_stage_entry(proj_pack: dict, stage: Optional[str]):
     raise ValueError(f"proj_feat_pack has no usable feature_map (stage={stage!r})")
 
 
-def _build_proj_cond(global_cond: torch.Tensor, image_attn_mode: str, proj_pack: Optional[dict],
-                     coords_world: torch.Tensor, batch_ids: Optional[torch.Tensor] = None,
-                     eval_batch: Optional[int] = None,
-                     proj_in_channels: Optional[int] = None,
-                     stage: Optional[str] = None,
-                     cond_or_uncond: Optional[list] = None):
-    if image_attn_mode == "global":
-        return global_cond
-    if proj_pack is None:
-        raise ValueError(f"image_attn_mode={image_attn_mode!r} but proj_feat_pack is missing")
-    device = coords_world.device
+def compute_stage_proj_feats(
+    proj_pack: dict,
+    stage: str,
+    coords: Optional[torch.Tensor] = None,
+    coord_resolution: Optional[int] = None,
+    dense_grid_resolution: Optional[int] = None,
+    batch_size: Optional[int] = None,
+    device=None,
+) -> torch.Tensor:
+    """Back-project a Pixal3D stage's feature maps onto its target voxel/grid coords.
+
+    For sparse (shape / texture) stages: pass ``coords`` (with ``coord_resolution``).
+    Returns ``[N_voxels, C]`` per-voxel features with channel count =
+    LR channels + optional HR channels.
+
+    For the dense SS stage: pass ``dense_grid_resolution`` (16) + ``batch_size``.
+    Returns ``[B, R^3, C]`` features for the dense grid.
+
+    """
+    if device is None:
+        device = coords.device if coords is not None else proj_pack["mesh_scale"].device
+    mesh_scale = proj_pack["mesh_scale"].to(device)
     T = proj_pack["transform_matrix"].to(device)
     cam_angle = proj_pack["camera_angle_x"].to(device)
     feat_map_lr, feat_map_hr, image_resolution = _select_stage_entry(proj_pack, stage)
     feat_map_lr = feat_map_lr.to(device)
     if feat_map_hr is not None:
         feat_map_hr = feat_map_hr.to(device)
-    if eval_batch is not None:
-        T = _expand_pack(T, eval_batch)
-        cam_angle = _expand_pack(cam_angle, eval_batch) if cam_angle.ndim >= 1 else cam_angle
-        feat_map_lr = _expand_pack(feat_map_lr, eval_batch)
-        if feat_map_hr is not None:
-            feat_map_hr = _expand_pack(feat_map_hr, eval_batch)
-    # Channel-count check against the trained proj_linear input. If HR is present, the
-    # block expects (LR_channels + HR_channels) since we concat the sampled features.
-    expected_channels = feat_map_lr.shape[1] + (feat_map_hr.shape[1] if feat_map_hr is not None else 0)
-    if proj_in_channels is not None and expected_channels != proj_in_channels:
+
+    if coords is not None:
+        if coord_resolution is None:
+            raise ValueError("compute_stage_proj_feats: coord_resolution required when coords is given")
+        coords_world, batch_ids = _coords_to_proj_world(coords, coord_resolution, mesh_scale)
+    else:
+        if dense_grid_resolution is None or batch_size is None:
+            raise ValueError("compute_stage_proj_feats: dense_grid_resolution + batch_size required for dense path")
+        coords_world = _dense_grid_proj_world(dense_grid_resolution, mesh_scale, batch_size,
+                                              device=device, dtype=torch.float32)
+        batch_ids = None
+
+    proj_lr = _back_project_to_tokens(coords_world, feat_map_lr, T, cam_angle,
+                                       image_resolution=image_resolution, batch_ids=batch_ids)
+    if feat_map_hr is not None:
+        proj_hr = _back_project_to_tokens(coords_world, feat_map_hr, T, cam_angle,
+                                           image_resolution=image_resolution, batch_ids=batch_ids)
+        return torch.cat([proj_lr, proj_hr], dim=-1)
+    return proj_lr
+
+
+def _shape_proj_cond(global_cond: torch.Tensor, image_attn_mode: str,
+                     proj_feats: Optional[torch.Tensor],
+                     batch_ids: Optional[torch.Tensor] = None,
+                     eval_batch: Optional[int] = None,
+                     logical_batch: Optional[int] = None,
+                     proj_in_channels: Optional[int] = None,
+                     stage: Optional[str] = None,
+                     cond_or_uncond: Optional[list] = None,
+                     has_hr: bool = False):
+    """Take pre-computed per-token proj features (from compute_stage_proj_feats),
+    apply CFG-batch duplication + uncond-slot zeroing, and wrap into the
+    ``{"global", "proj"}`` context dict consumed by ProjectAttention.
+
+    proj_feats shape:
+      sparse (shape/texture): [N_voxels, C]   (batch_ids gives per-voxel batch)
+      dense  (SS):            [B, N, C]
+    """
+    if image_attn_mode == "global":
+        return global_cond
+    if proj_feats is None:
+        raise ValueError(f"image_attn_mode={image_attn_mode!r} but trellis2_proj_feats is missing — "
+                         f"the stage setup node (or Pixal3DConditioning for SS) should have computed it.")
+    if proj_in_channels is not None and proj_feats.shape[-1] != proj_in_channels:
         hint = ""
-        if feat_map_hr is None and expected_channels < proj_in_channels:
+        if not has_hr and proj_feats.shape[-1] < proj_in_channels:
             hint = (" — feature_map_hr is missing for this stage. Connect a NAFModel "
                     "input to Pixal3DConditioning; the shape/texture stages of this "
                     "checkpoint need a NAF-upsampled HR feature map.")
         raise ValueError(
-            f"proj_feat_pack[{stage!r}] has LR={feat_map_lr.shape[1]} "
-            f"+ HR={feat_map_hr.shape[1] if feat_map_hr is not None else 0} "
-            f"= {expected_channels} channels, sub-model expects {proj_in_channels}.{hint}"
+            f"proj_feats for stage {stage!r} has {proj_feats.shape[-1]} channels, "
+            f"sub-model expects {proj_in_channels}.{hint}"
         )
-    proj_feats_lr = _back_project_to_tokens(coords_world, feat_map_lr, T, cam_angle,
-                                            image_resolution=image_resolution,
-                                            batch_ids=batch_ids)
-    if feat_map_hr is not None:
-        proj_feats_hr = _back_project_to_tokens(coords_world, feat_map_hr, T, cam_angle,
-                                                image_resolution=image_resolution,
-                                                batch_ids=batch_ids)
-        proj_feats = torch.cat([proj_feats_lr, proj_feats_hr], dim=-1)
-    else:
-        proj_feats = proj_feats_lr
+
+    # CFG-duplicate proj_feats to match the model's eval batch.
+    if eval_batch is not None and logical_batch is not None and eval_batch > logical_batch:
+        repeats = eval_batch // logical_batch
+        if batch_ids is None:
+            proj_feats = proj_feats.repeat((repeats,) + (1,) * (proj_feats.ndim - 1))
+        else:
+            proj_feats = proj_feats.repeat((repeats, 1))
+
     # Mirror upstream's neg_cond by zeroing proj for any uncond batch slot.
     if cond_or_uncond is not None and eval_batch is not None:
         uncond_slots = [i for i, v in enumerate(cond_or_uncond) if v == 1]
@@ -1056,10 +1061,6 @@ class Trellis2(nn.Module):
 
         super().__init__()
         self.dtype = dtype
-        operations = operations or nn
-        # for some reason it passes num_heads = -1
-        if num_heads == -1:
-            num_heads = 12
         args = {
             "out_channels":out_channels, "num_blocks":num_blocks, "cond_channels" :cond_channels,
             "model_channels":model_channels, "num_heads":num_heads, "mlp_ratio": mlp_ratio, "share_mod": share_mod,
@@ -1082,101 +1083,79 @@ class Trellis2(nn.Module):
             self.structure_model = SparseStructureFlowModel(resolution=16, in_channels=8, out_channels=8, **struct_proj_kwargs, **args)
         else:
             self.shape2txt = SLatFlowModel(resolution=resolution, in_channels=in_channels*2, **tex_proj_kwargs, **args)
-        self.guidance_interval = [0.6, 1.0]
-        self.guidance_interval_txt = [0.6, 0.9]
 
     def forward(self, x, timestep, context, **kwargs):
         transformer_options = kwargs.get("transformer_options", {})
         cond_or_uncond = transformer_options.get("cond_or_uncond")
-        model_options = {}
-        if hasattr(self, "meta"):
-            model_options = self.meta
         timestep = timestep.to(x.dtype)
         embeds = kwargs.get("embeds")
         if embeds is None:
             raise ValueError("Trellis2.forward requires 'embeds' in kwargs")
 
-        is_1024 = True#self.img2shape.resolution == 1024
-        coords = model_options.get("coords", None)
-        coord_counts = model_options.get("coord_counts", None)
-        mode = model_options.get("generation_mode", "structure_generation")
-        proj_feat_pack = model_options.get("proj_feat_pack", None)
-        coord_resolution = model_options.get("coord_resolution", None)
+        # Per-stage cascade metadata
+        coords = kwargs.get("trellis2_coords")
+        coord_counts = kwargs.get("trellis2_coord_counts")
+        mode = kwargs.get("trellis2_generation_mode", "structure_generation")
+        proj_feat_pack = kwargs.get("proj_feat_pack")
+        # Pre-computed per-stage back-projected features
+        proj_feats = kwargs.get("trellis2_proj_feats")
 
-        is_512_run = False
+        is_first_shape_pass = False
         if mode == "shape_generation_512":
-            is_512_run = True
+            is_first_shape_pass = True
             mode = "shape_generation"
 
         if coords is not None:
-            if x.ndim == 4:
-                x = x.squeeze(-1).transpose(1, 2)
-            not_struct_mode = True
+            x = x.squeeze(-1).transpose(1, 2)
+            is_sparse_mode = True
         else:
             mode = "structure_generation"
-            not_struct_mode = False
+            is_sparse_mode = False
 
         if x.size(-1) == 16 and x.size(-2) == 16:
             mode = "structure_generation"
-            not_struct_mode = False
+            is_sparse_mode = False
 
-        if not not_struct_mode:
+        if not is_sparse_mode:
             bsz = x.size(0)
             x = x[:, :8]
             x = x.view(bsz, 8, 16, 16, 16)
 
-        if is_1024 and not_struct_mode and not is_512_run:
+        if is_sparse_mode and not is_first_shape_pass:
             context = embeds
 
-        sigmas = transformer_options.get("sigmas")[0].item()
-        if sigmas < 1.00001:
-            timestep *= 1000.0
+        if is_sparse_mode:
+            t_eval = timestep
+            c_eval = context
 
-        if context.size(0) > 1:
-            cond = context.chunk(2)[1]
-        else:
-            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]
-
-        if not_struct_mode:
-            orig_bsz = x.shape[0]
-            rule = txt_rule if mode == "texture_generation" else shape_rule
-
-            # CFG Bypass Slicing
-            if rule and orig_bsz > 1:
-                half = orig_bsz // 2
-                x_eval = x[half:]
-                t_eval = timestep[half:] if timestep.shape[0] > 1 else timestep
-                c_eval = cond
-            else:
-                x_eval = x
-                t_eval = timestep
-                c_eval = context
-
-            B, N, C = x_eval.shape
+            B, N, C = x.shape
 
             # Vectorized SparseTensor Construction
             if mode in ["shape_generation", "texture_generation"]:
                 if coord_counts is not None:
                     logical_batch = coord_counts.shape[0]
-                    # Duplicate coords if CFG is active
+                    # Duplicate sparse coords when the sampler asks for >1 cond
+                    # (CFG or otherwise). Each duplicate is offset along col 0
+                    # so SparseTensor sees a fresh logical batch.
                     if B > logical_batch:
-                        c_pos = coords.clone()
-                        c_pos[:, 0] += logical_batch
-                        batched_coords = torch.cat([coords, c_pos], dim=0)
-                        counts_eval = torch.cat([coord_counts, coord_counts], dim=0)
+                        reps = B // logical_batch
+                        c_copies = []
+                        for i in range(reps):
+                            c = coords.clone()
+                            c[:, 0] += i * logical_batch
+                            c_copies.append(c)
+                        batched_coords = torch.cat(c_copies, dim=0)
+                        counts_eval = coord_counts.repeat(reps)
                     else:
                         batched_coords = coords
                         counts_eval = coord_counts
 
-                    # Create boolean mask [B, N] to drop the padded zeros instantly
+                    # Boolean mask [B, N] to drop the padded zeros instantly
                     mask = torch.arange(N, device=x.device).unsqueeze(0) < counts_eval.unsqueeze(1)
-                    feats_flat = x_eval[mask]
+                    feats_flat = x[mask]
                 else:
-                    feats_flat = x_eval.reshape(-1, C)
-                    coords_list =[]
+                    feats_flat = x.reshape(-1, C)
+                    coords_list = []
                     for i in range(B):
                         c = coords.clone()
                         c[:, 0] = i
@@ -1185,35 +1164,42 @@ class Trellis2(nn.Module):
                     mask = None
             else:
                 batched_coords = coords
-                feats_flat = x_eval
+                feats_flat = x
                 mask = None
 
-            x_st = SparseTensor(feats=feats_flat, coords=batched_coords.to(torch.int32))
+            x_st = SparseTensor(
+                feats=feats_flat,
+                coords=batched_coords.to(torch.int32),
+                shape=torch.Size([B] + list(feats_flat.shape[1:])),
+            )
 
         if mode == "shape_generation":
             shape_attn = self.image_attn_mode_shape
             if shape_attn != "global":
-                if coord_resolution is None:
-                    raise ValueError("Pixal3D shape_generation requires coord_resolution in model_options; "
-                                     "EmptyTrellis2ShapeLatent should set it from the input voxel.")
-                mesh_scale = _pack_per_voxel_scalar(proj_feat_pack, "mesh_scale", B, batched_coords.device)
-                xyz_world, batch_ids = _coords_to_proj_world(batched_coords, coord_resolution, mesh_scale)
-                sub_model = self.img2shape_512 if is_512_run else self.img2shape
-                stage_name = "shape_512" if is_512_run else "shape_1024"
-                c_eval = _build_proj_cond(c_eval, shape_attn, proj_feat_pack, xyz_world, batch_ids,
-                                          eval_batch=B,
+                sub_model = self.img2shape_512 if is_first_shape_pass else self.img2shape
+                stage_name = "shape_512" if is_first_shape_pass else "shape_1024"
+                # batched_coords carries CFG-doubled batch ids in col 0; per-voxel
+                # batch_ids drive uncond-slot masking inside _shape_proj_cond.
+                batch_ids = batched_coords[:, 0].long()
+                logical_batch = coord_counts.shape[0] if coord_counts is not None else B
+                has_hr = bool(proj_feat_pack and proj_feat_pack.get("stages", {})
+                              .get(stage_name, {}).get("feature_map_hr") is not None)
+                c_eval = _shape_proj_cond(c_eval, shape_attn, proj_feats,
+                                          batch_ids=batch_ids,
+                                          eval_batch=B, logical_batch=logical_batch,
                                           proj_in_channels=sub_model.proj_in_channels,
                                           stage=stage_name,
-                                          cond_or_uncond=cond_or_uncond)
-            if is_512_run:
-                out = self.img2shape_512(x_st, t_eval, c_eval)
+                                          cond_or_uncond=cond_or_uncond,
+                                          has_hr=has_hr)
+            if is_first_shape_pass:
+                out = self.img2shape_512(x_st, t_eval, c_eval, transformer_options=transformer_options)
             else:
-                out = self.img2shape(x_st, t_eval, c_eval)
+                out = self.img2shape(x_st, t_eval, c_eval, transformer_options=transformer_options)
 
         elif mode == "texture_generation":
             if self.shape2txt is None:
                 raise ValueError("Checkpoint for Trellis2 doesn't include texture generation!")
-            slat = model_options.get("shape_slat")
+            slat = kwargs.get("trellis2_shape_slat")
             if slat is None:
                 raise ValueError("shape_slat can't be None")
 
@@ -1227,49 +1213,40 @@ class Trellis2(nn.Module):
             x_st = x_st.replace(feats=torch.cat([x_st.feats, slat_feats.to(x_st.feats.device)], dim=-1))
             tex_attn = self.image_attn_mode_texture
             if tex_attn != "global":
-                if coord_resolution is None:
-                    raise ValueError("Pixal3D texture_generation requires coord_resolution in model_options; "
-                                     "EmptyTrellis2LatentTexture should set it from the input voxel.")
-                mesh_scale = _pack_per_voxel_scalar(proj_feat_pack, "mesh_scale", B, batched_coords.device)
-                xyz_world, batch_ids = _coords_to_proj_world(batched_coords, coord_resolution, mesh_scale)
-                c_eval = _build_proj_cond(c_eval, tex_attn, proj_feat_pack, xyz_world, batch_ids,
-                                          eval_batch=B,
+                batch_ids = batched_coords[:, 0].long()
+                logical_batch = coord_counts.shape[0] if coord_counts is not None else B
+                has_hr = bool(proj_feat_pack and proj_feat_pack.get("stages", {})
+                              .get("tex_1024", {}).get("feature_map_hr") is not None)
+                c_eval = _shape_proj_cond(c_eval, tex_attn, proj_feats,
+                                          batch_ids=batch_ids,
+                                          eval_batch=B, logical_batch=logical_batch,
                                           proj_in_channels=self.shape2txt.proj_in_channels,
                                           stage="tex_1024",
-                                          cond_or_uncond=cond_or_uncond)
-            out = self.shape2txt(x_st, t_eval, c_eval)
+                                          cond_or_uncond=cond_or_uncond,
+                                          has_hr=has_hr)
+            out = self.shape2txt(x_st, t_eval, c_eval, transformer_options=transformer_options)
 
         else: # structure
-            orig_bsz = x.shape[0]
             struct_attn = self.image_attn_mode_structure
-            if shape_rule and orig_bsz > 1:
-                half = orig_bsz // 2
-                x_eval = x[half:]
-                t_eval = timestep[half:] if timestep.shape[0] > 1 else timestep
-                struct_cond = cond
-                if struct_attn != "global":
-                    mesh_scale = _pack_per_voxel_scalar(proj_feat_pack, "mesh_scale", half, x.device)
-                    grid_xyz = _dense_grid_proj_world(16, mesh_scale, half, device=x.device)
-                    struct_cond = _build_proj_cond(cond, struct_attn, proj_feat_pack, grid_xyz,
-                                                   eval_batch=half,
-                                                   proj_in_channels=self.structure_model.proj_in_channels,
-                                                   stage="ss",
-                                                   cond_or_uncond=cond_or_uncond)
-                out = self.structure_model(x_eval, t_eval, struct_cond)
-                out = out.repeat(2, 1, 1, 1, 1)
-            else:
-                struct_cond = context
-                if struct_attn != "global":
-                    mesh_scale = _pack_per_voxel_scalar(proj_feat_pack, "mesh_scale", orig_bsz, x.device)
-                    grid_xyz = _dense_grid_proj_world(16, mesh_scale, orig_bsz, device=x.device)
-                    struct_cond = _build_proj_cond(context, struct_attn, proj_feat_pack, grid_xyz,
-                                                   eval_batch=orig_bsz,
-                                                   proj_in_channels=self.structure_model.proj_in_channels,
-                                                   stage="ss",
-                                                   cond_or_uncond=cond_or_uncond)
-                out = self.structure_model(x, timestep, struct_cond)
+            has_hr_ss = bool(proj_feat_pack and proj_feat_pack.get("stages", {})
+                             .get("ss", {}).get("feature_map_hr") is not None)
+            logical_batch_ss = (
+                proj_feat_pack["mesh_scale"].shape[0]
+                if (proj_feat_pack is not None and torch.is_tensor(proj_feat_pack.get("mesh_scale")))
+                else x.shape[0]
+            )
+            struct_cond = context
+            if struct_attn != "global":
+                struct_cond = _shape_proj_cond(context, struct_attn, proj_feats,
+                                               batch_ids=None,
+                                               eval_batch=x.shape[0], logical_batch=logical_batch_ss,
+                                               proj_in_channels=self.structure_model.proj_in_channels,
+                                               stage="ss",
+                                               cond_or_uncond=cond_or_uncond,
+                                               has_hr=has_hr_ss)
+            out = self.structure_model(x, timestep, struct_cond, transformer_options=transformer_options)
 
-        if not_struct_mode:
+        if is_sparse_mode:
             if mask is not None:
                 # Instantly scatter the valid tokens back into a padded rectangular tensor
                 padded_out = torch.zeros((B, N, out.feats.shape[-1]), device=x.device, dtype=out.feats.dtype)
@@ -1277,9 +1254,6 @@ class Trellis2(nn.Module):
                 out_tensor = padded_out.transpose(1, 2).unsqueeze(-1)
             else:
                 out_tensor = out.feats.view(B, N, -1).transpose(1, 2).unsqueeze(-1)
-
-            if rule and orig_bsz > 1:
-                out_tensor = out_tensor.repeat(2, 1, 1, 1)
             return out_tensor
         else:
             out = torch.nn.functional.pad(out, (0, 0, 0, 0, 0, 0, 0, 24))
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 2a27e7cca..142ec530a 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -1646,6 +1646,17 @@ class Trellis2(BaseModel):
         out = super().extra_conds(**kwargs)
         embeds = kwargs.get("embeds")
         out["embeds"] = comfy.conds.CONDRegular(embeds)
+        # CONDConstant: shared across pos/neg
+        for k in ("trellis2_coords", "trellis2_coord_counts",
+                  "trellis2_generation_mode", "trellis2_shape_slat",
+                  "trellis2_proj_feats"):
+            v = kwargs.get(k)
+            if v is not None:
+                out[k] = comfy.conds.CONDConstant(v)
+        # Pixal3D's per-stage feature maps + camera params travel as a dict
+        proj_feat_pack = kwargs.get("proj_feat_pack")
+        if proj_feat_pack is not None:
+            out["proj_feat_pack"] = comfy.conds.CONDConstant(proj_feat_pack)
         return out
 
 class WAN21_FlowRVS(WAN21):
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 86338f1d7..34b8b0d95 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -1322,10 +1322,10 @@ class Trellis2(supported_models_base.BASE):
     unet_config = {
         "image_model": "trellis2"
     }
+    unet_extra_config = {}
 
     sampling_settings = {
         "shift": 3.0,
-        "multiplier": 1.0
     }
 
     memory_usage_factor = 3.5
diff --git a/comfy_extras/nodes_trellis2.py b/comfy_extras/nodes_trellis2.py
index 3484d6d6d..7bc22074e 100644
--- a/comfy_extras/nodes_trellis2.py
+++ b/comfy_extras/nodes_trellis2.py
@@ -1,7 +1,9 @@
 from typing_extensions import override
 from comfy_api.latest import ComfyExtension, IO, Types, io
 from comfy.ldm.trellis2.vae import SparseTensor
-from comfy.ldm.trellis2.model import _build_proj_transform_matrix, _project_points_to_image
+from comfy.ldm.trellis2.model import (
+    _build_proj_transform_matrix, _project_points_to_image, compute_stage_proj_feats,
+)
 from comfy.ldm.trellis2.naf.model import build_naf_from_state_dict
 from comfy_extras.nodes_mesh_postprocess import pack_variable_mesh_batch
 import comfy.model_management
@@ -14,48 +16,9 @@ import math
 import torch
 
 ShapeSubdivides = io.Custom("SHAPE_SUBDIVIDES")
-Pixal3DProjPack = io.Custom("PIXAL3D_PROJ_PACK")
 NAFModel = io.Custom("NAF_MODEL")
 
 
-# Pixal3D trains in a 90°-X-rotated grid frame (F_p). We un-rotate decoder outputs for
-# user-facing previews/meshes, then re-rotate before feeding coords back to the shape DiT.
-
-def _pixal3d_unrotate_voxel_data(data: torch.Tensor) -> torch.Tensor:
-    if data.ndim == 4:
-        return data.flip(-1).permute(0, 1, 3, 2).contiguous()
-    if data.ndim == 5:
-        return data.flip(-1).permute(0, 1, 2, 4, 3).contiguous()
-    raise ValueError(f"unexpected voxel shape {tuple(data.shape)}")
-
-
-def _pixal3d_rerotate_voxel_data(data: torch.Tensor) -> torch.Tensor:
-    if data.ndim == 4:
-        return data.permute(0, 1, 3, 2).flip(-1).contiguous()
-    if data.ndim == 5:
-        return data.permute(0, 1, 2, 4, 3).flip(-1).contiguous()
-    raise ValueError(f"unexpected voxel shape {tuple(data.shape)}")
-
-
-def _pixal3d_unrotate_vertices(vertices: torch.Tensor) -> torch.Tensor:
-    if vertices.numel() == 0:
-        return vertices
-    x, y, z = vertices.unbind(-1)
-    return torch.stack([-x, y, -z], dim=-1).contiguous()
-
-
-def _pixal3d_unrotate_sparse_coords(coords: torch.Tensor, resolution: int) -> torch.Tensor:
-    if coords.numel() == 0:
-        return coords
-    R1 = resolution - 1
-    if coords.shape[-1] == 4:
-        b, i, j, k = coords.unbind(-1)
-        return torch.stack([b, R1 - i, j, R1 - k], dim=-1).contiguous()
-    if coords.shape[-1] == 3:
-        i, j, k = coords.unbind(-1)
-        return torch.stack([R1 - i, j, R1 - k], dim=-1).contiguous()
-    raise ValueError(f"unexpected coord shape {tuple(coords.shape)}")
-
 def prepare_trellis_vae_for_decode(vae, sample_shape):
     memory_required = vae.memory_used_decode(sample_shape, vae.vae_dtype)
     if len(sample_shape) == 5:
@@ -202,15 +165,21 @@ class VaeDecodeShapeTrellis(IO.ComfyNode):
 
     @classmethod
     def execute(cls, samples, vae):
-
-        resolution = int(vae.first_stage_model.resolution.item())
+        # Mesh grid_size must match the actual coord resolution the upstream
+        # stage was run at (1024 cascade -> 64, 1536 cascade -> 96). The VAE's
+        # built-in `.resolution` buffer defaults to 1024 and is otherwise stale;
+        # take coord_resolution from the latent dict if the stage node set it.
+        coord_resolution = samples.get("coord_resolution")
+        if coord_resolution is not None:
+            resolution = int(coord_resolution) * 16
+        else:
+            resolution = int(vae.first_stage_model.resolution.item())
         sample_tensor = samples["samples"]
         device = comfy.model_management.get_torch_device()
         coords = samples["coords"]
         prepare_trellis_vae_for_decode(vae, sample_tensor.shape)
         trellis_vae = vae.first_stage_model
         coord_counts = samples.get("coord_counts")
-        pixal3d_mode = samples.get("model_options", {}).get("proj_feat_pack") is not None
 
         samples = samples["samples"]
         if coord_counts is None:
@@ -236,10 +205,6 @@ class VaeDecodeShapeTrellis(IO.ComfyNode):
                 coords_list = [stage_tensor.coords for stage_tensor in stage_tensors]
                 subs.append(SparseTensor.from_tensor_list(feats_list, coords_list))
 
-        if pixal3d_mode:
-            for m in mesh:
-                m.vertices = _pixal3d_unrotate_vertices(m.vertices)
-
         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):
@@ -276,7 +241,6 @@ class VaeDecodeTextureTrellis(IO.ComfyNode):
         prepare_trellis_vae_for_decode(vae, sample_tensor.shape)
         trellis_vae = vae.first_stage_model
         coord_counts = samples.get("coord_counts")
-        pixal3d_mode = samples.get("model_options", {}).get("proj_feat_pack") is not None
 
         samples = samples["samples"]
         samples, coords = flatten_batched_sparse_latent(samples, coords, coord_counts)
@@ -288,7 +252,7 @@ class VaeDecodeTextureTrellis(IO.ComfyNode):
 
         voxel = trellis_vae.decode_tex_slat(samples, shape_subdivides)
         color_feats = voxel.feats[:, :3]
-        voxel_coords = voxel.coords#[:, 1:]
+        voxel_coords = voxel.coords
 
         if voxel_coords.numel() > 0 and voxel_coords.shape[-1] >= 3:
             spatial = voxel_coords[:, -3:] if voxel_coords.shape[-1] == 4 else voxel_coords
@@ -297,9 +261,6 @@ class VaeDecodeTextureTrellis(IO.ComfyNode):
         else:
             tex_resolution = 1024
 
-        if pixal3d_mode:
-            voxel_coords = _pixal3d_unrotate_sparse_coords(voxel_coords, resolution=tex_resolution)
-
         voxel = Types.VOXEL(voxel_coords, color_feats, tex_resolution)
         return IO.NodeOutput(voxel)
 
@@ -312,7 +273,7 @@ class VaeDecodeStructureTrellis2(IO.ComfyNode):
             inputs=[
                 IO.Latent.Input("samples"),
                 IO.Vae.Input("vae"),
-                IO.Combo.Input("resolution", options=["32", "64"], default="32")
+                IO.Combo.Input("resolution", options=["32", "64"], default="32"),
             ],
             outputs=[
                 IO.Voxel.Output("voxel"),
@@ -338,129 +299,132 @@ class VaeDecodeStructureTrellis2(IO.ComfyNode):
             ratio = current_res // resolution
             decoded = torch.nn.functional.max_pool3d(decoded.float(), ratio, ratio, 0) > 0.5
         voxel_data = decoded.squeeze(1).float()
-        if samples.get("model_options", {}).get("proj_feat_pack") is not None:
-            voxel_data = _pixal3d_unrotate_voxel_data(voxel_data)
-        out = Types.VOXEL(voxel_data)
-        return IO.NodeOutput(out)
+        return IO.NodeOutput(Types.VOXEL(voxel_data))
 
-class Trellis2UpsampleCascade(IO.ComfyNode):
+class Trellis2UpsampleStage(IO.ComfyNode):
+    """Cascade-upsamples a 512-resolution shape latent into high-resolution
+    sparse coords and sets up the second shape-stage sampling pass at the
+    target resolution, attaching per-stage metadata to the conditioning for
+    the model to consume via extra_conds. target_resolution is reduced in
+    128-step decrements until the unique upsampled coord count fits under
+    max_tokens (floor 1024)."""
     @classmethod
     def define_schema(cls):
         return IO.Schema(
-            node_id="Trellis2UpsampleCascade",
+            node_id="Trellis2UpsampleStage",
             category="latent/3d",
-            display_name="Trellis2 Upsample Cascade",
-            description="Upsamples low-resolution Trellis2 shape latents into higher resolution coordinates while respecting the maximum token budget.",
+            display_name="Trellis2 Upsample Stage",
             inputs=[
-                IO.Latent.Input("shape_latent"),
+                IO.Conditioning.Input("positive"),
+                IO.Conditioning.Input("negative"),
+                IO.Latent.Input("shape_latent", tooltip="The 512-resolution shape latent output from the first shape-stage KSampler."),
                 IO.Vae.Input("vae"),
                 IO.Combo.Input("target_resolution", options=["1024", "1536"], default="1024", tooltip="Controls output detail level for upsampling."),
                 IO.Int.Input("max_tokens", default=49152, min=1024, max=100000,
                              tooltip=(
                                 "Maximum number of output elements (coordinates) allowed after upsampling. "
                                 "Used to limit memory usage and control mesh density."
-                            ))
+                            )),
             ],
             outputs=[
-                IO.Voxel.Output(
-                "high_res_voxel",
-                tooltip=(
-                    "High-resolution sparse coordinates produced after cascade upsampling. "
-                    "Represents the refined 3D structure at target resolution."
-                )
-            )
+                IO.Conditioning.Output(display_name="positive"),
+                IO.Conditioning.Output(display_name="negative"),
+                IO.Latent.Output(),
             ]
         )
 
-    @classmethod
-    def execute(cls, shape_latent, vae, target_resolution, max_tokens):
-        shape_latent_512 = shape_latent
-        device = comfy.model_management.get_torch_device()
-        prepare_trellis_vae_for_decode(vae, shape_latent_512["samples"].shape)
+    @staticmethod
+    def _quantize_unique(hr_coords: torch.Tensor, lr_resolution: int, hr_resolution: int) -> torch.Tensor:
+        # Fold the two scalar divisions into one and chain the float math in-place
+        # to avoid 3 full M*3 fp32 transients per call.
+        scale = (hr_resolution // 16) / lr_resolution
+        spatial = hr_coords[:, 1:].float()
+        spatial.add_(0.5).mul_(scale)
+        quant = torch.cat([hr_coords[:, :1], spatial.int()], dim=1)
+        return quant.unique(dim=0)
 
-        coord_counts = shape_latent_512.get("coord_counts")
+    @classmethod
+    def execute(cls, positive, negative, shape_latent, vae, target_resolution, max_tokens):
+        device = comfy.model_management.get_torch_device()
+        prepare_trellis_vae_for_decode(vae, shape_latent["samples"].shape)
+
+        coord_counts = shape_latent.get("coord_counts")
         decoder = vae.first_stage_model.shape_dec
         lr_resolution = 512
         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))
+        # Decode each sample's HR coords, then search for the largest hr_resolution
+        # that fits under max_tokens across all samples.
+        if coord_counts is None:
+            feats, coords_512 = flatten_batched_sparse_latent(
+                shape_latent["samples"], shape_latent["coords"], coord_counts,
+            )
+            slat = shape_norm(feats.to(device), coords_512.to(device))
+            slat.feats = slat.feats.to(decoder_dtype)
+            sample_hr_coords = [decoder.upsample(slat, upsample_times=4)]
+        else:
+            items = split_batched_sparse_latent(
+                shape_latent["samples"], shape_latent["coords"], coord_counts,
+            )
+            sample_hr_coords = []
+            for feats_i, coords_i in items:
+                coords_i = coords_i.to(device).clone()
+                coords_i[:, 0] = 0
+                slat_i = shape_norm(feats_i.to(device), coords_i)
+                slat_i.feats = slat_i.feats.to(decoder_dtype)
+                sample_hr_coords.append(decoder.upsample(slat_i, upsample_times=4))
 
+        # Resolution search — cache the final iteration's quantized unique tensors
+        # so we don't recompute .unique() per sample after picking hr_resolution.
         hr_resolution = target_resolution
+        quant_unique_list = []
         while True:
+            quant_unique_list = []
             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:
+                qu = cls._quantize_unique(hr_coords_i, lr_resolution, hr_resolution)
+                quant_unique_list.append(qu)
+                if qu.shape[0] >= max_tokens:
                     exceeds_limit = True
                     break
-            if not exceeds_limit or hr_resolution <= 1024:
+            if not exceeds_limit:
+                break
+            if hr_resolution <= 1024:
+                for k in range(len(quant_unique_list), len(sample_hr_coords)):
+                    quant_unique_list.append(
+                        cls._quantize_unique(sample_hr_coords[k], lr_resolution, hr_resolution)
+                    )
                 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] = sample_offset
-            final_coords_list.append(final_coords_i)
-            output_coord_counts.append(int(final_coords_i.shape[0]))
+        # Rewrite batch column to match per-sample offset and concat.
+        per_sample_counts = []
+        for sample_offset, qu in enumerate(quant_unique_list):
+            qu[:, 0] = sample_offset
+            per_sample_counts.append(int(qu.shape[0]))
+        coords = torch.cat(quant_unique_list, dim=0)
+        counts = torch.tensor(per_sample_counts, dtype=torch.int64)
+        coord_resolution = hr_resolution // 16
 
-        coords = torch.cat(final_coords_list, dim=0)
-        output = Types.VOXEL(coords)
-        output.coord_counts = torch.tensor(output_coord_counts, dtype=torch.int64)
-        output.resolutions = torch.full((len(final_coords_list),), int(hr_resolution), dtype=torch.int64)
-        output.upsampled = True
+        batch_size, _, max_tokens_out = infer_batched_coord_layout(coords)
+        latent = torch.zeros(batch_size, 32, max_tokens_out, 1)
 
-        return IO.NodeOutput(output,)
+        extras = {
+            "trellis2_generation_mode": "shape_generation",
+            "trellis2_coords": coords,
+            "trellis2_coord_counts": counts,
+        }
+        proj_pack = _proj_pack_from_conditioning(positive)
+        if proj_pack is not None:
+            extras["trellis2_proj_feats"] = compute_stage_proj_feats(
+                proj_pack, "shape_1024", coords=coords, coord_resolution=coord_resolution,
+            )
+        positive_out = _conditioning_set_extras(positive, extras)
+        negative_out = _conditioning_set_extras(negative, extras)
+        out_latent = {"samples": latent, "coords": coords, "coord_counts": counts,
+                      "coord_resolution": coord_resolution, "type": "trellis2"}
+        return IO.NodeOutput(positive_out, negative_out, out_latent)
 
 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)
@@ -639,130 +603,152 @@ class Trellis2Conditioning(IO.ComfyNode):
         negative = [[neg_cond_batched, {"embeds": neg_embeds_batched}]]
         return IO.NodeOutput(positive, negative)
 
-class EmptyTrellis2ShapeLatent(IO.ComfyNode):
+def _proj_pack_from_conditioning(conditioning):
+    """Return the proj_feat_pack dict embedded in a Pixal3D conditioning (or None
+    for vanilla Trellis2 / no conditioning connected). Pixal3DConditioning ships
+    the pack in cond[0][1]["proj_feat_pack"]; Trellis2Conditioning doesn't set it."""
+    if not conditioning:
+        return None
+    entry = conditioning[0]
+    if not isinstance(entry, (list, tuple)) or len(entry) < 2 or not isinstance(entry[1], dict):
+        return None
+    return entry[1].get("proj_feat_pack")
+
+
+def _conditioning_set_extras(conditioning, extras: dict):
+    """Return a copy of `conditioning` with `extras` merged into each entry's
+    dict — same shallow-copy pattern ControlNetApplyAdvanced uses. The dicts
+    are copied so we don't mutate upstream conditioning."""
+    out = []
+    for entry in conditioning:
+        if isinstance(entry, (list, tuple)) and len(entry) >= 2 and isinstance(entry[1], dict):
+            new_dict = entry[1].copy()
+            new_dict.update(extras)
+            out.append([entry[0], new_dict])
+        else:
+            out.append(entry)
+    return out
+
+
+class Trellis2ShapeStage(IO.ComfyNode):
+    """Sets up the first shape-stage sampling pass: extracts sparse coords from
+    the dense structure voxel produced by VaeDecodeStructureTrellis2, builds an
+    empty sparse latent, and attaches per-stage metadata to the conditioning so
+    the model reads it via extra_conds at sample time. For the second shape pass
+    (post-upsample), use Trellis2UpsampleStage instead — it combines the cascade
+    and the second-pass stage setup."""
     @classmethod
     def define_schema(cls):
         return IO.Schema(
-            node_id="EmptyTrellis2ShapeLatent",
+            node_id="Trellis2ShapeStage",
             category="latent/3d",
             inputs=[
+                IO.Conditioning.Input("positive"),
+                IO.Conditioning.Input("negative"),
                 IO.Voxel.Input(
                     "voxel",
-                    tooltip=(
-                        "Shape structure input. Accepts either a voxel structure "
-                        "or upsampled voxel coordinates from a previous cascade stage."
-                    )
-            ),
-                Pixal3DProjPack.Input(
-                    "proj_feat_pack",
-                    optional=True,
-                    tooltip="Pixal3D pixel-aligned projection pack from Pixal3DConditioning. Leave empty for vanilla Trellis2.",
+                    tooltip="Dense structure voxel from VaeDecodeStructureTrellis2.",
                 ),
             ],
             outputs=[
+                IO.Conditioning.Output(display_name="positive"),
+                IO.Conditioning.Output(display_name="negative"),
                 IO.Latent.Output(),
             ]
         )
 
     @classmethod
-    def execute(cls, voxel, proj_feat_pack=None):
-        is_512_pass = False
-        coord_resolution = None
-        upsampled = hasattr(voxel, "upsampled")
-        if upsampled:
-            if hasattr(voxel, "resolutions") and voxel.resolutions is not None:
-                coord_resolution = int(voxel.resolutions[0].item()) // 16
-            voxel = voxel.data
-
-        if not upsampled:
-            voxel_data = voxel.data
-            if proj_feat_pack is not None:
-                voxel_data = _pixal3d_rerotate_voxel_data(voxel_data)
-            decoded = voxel_data.unsqueeze(1)
-            coords = torch.argwhere(decoded.bool())[:, [0, 2, 3, 4]].int()
-            is_512_pass = True
-            coord_resolution = int(decoded.shape[-1])
+    def execute(cls, positive, negative, voxel):
+        decoded = voxel.data.unsqueeze(1)
+        coords = torch.argwhere(decoded.bool())[:, [0, 2, 3, 4]].int()
+        coord_resolution = int(decoded.shape[-1])
 
+        # Dispatch based on the upstream voxel resolution, mirroring upstream's
+        # pipeline_type → ss_res table:
+        #   coord_res == 32 → first cascade shape pass OR pure-512 pipeline
+        #                     (img2shape_512 + shape_512 proj stage, 512 DINO).
+        #   coord_res >  32 → pure-1024 non-cascade pipeline
+        #                     (img2shape + shape_1024 proj stage, 1024 DINO).
+        if coord_resolution <= 32:
+            mode = "shape_generation_512"
+            stage = "shape_512"
         else:
-            coords = voxel.int()
+            mode = "shape_generation"
+            stage = "shape_1024"
 
         batch_size, counts, max_tokens = infer_batched_coord_layout(coords)
-        in_channels = 32
-        # image like format
-        latent = torch.zeros(batch_size, in_channels, max_tokens, 1)
+        latent = torch.zeros(batch_size, 32, max_tokens, 1)
 
-        if is_512_pass:
-            generation_mode = "shape_generation_512"
-        else:
-            generation_mode = "shape_generation"
-        model_options = {"generation_mode": generation_mode, "coords": coords, "coord_counts": counts}
-        if coord_resolution is not None:
-            model_options["coord_resolution"] = coord_resolution
-        if proj_feat_pack is not None:
-            model_options["proj_feat_pack"] = proj_feat_pack
-        return IO.NodeOutput({"samples": latent, "coords": coords, "coord_counts": counts, "type": "trellis2",
-                              "model_options": model_options})
+        extras = {
+            "trellis2_generation_mode": mode,
+            "trellis2_coords": coords,
+            "trellis2_coord_counts": counts,
+        }
+        proj_pack = _proj_pack_from_conditioning(positive)
+        if proj_pack is not None:
+            extras["trellis2_proj_feats"] = compute_stage_proj_feats(
+                proj_pack, stage, coords=coords, coord_resolution=coord_resolution,
+            )
+        positive_out = _conditioning_set_extras(positive, extras)
+        negative_out = _conditioning_set_extras(negative, extras)
+        out_latent = {"samples": latent, "coords": coords, "coord_counts": counts,
+                      "coord_resolution": coord_resolution, "type": "trellis2"}
+        return IO.NodeOutput(positive_out, negative_out, out_latent)
 
-class EmptyTrellis2LatentTexture(IO.ComfyNode):
+class Trellis2TextureStage(IO.ComfyNode):
+    """Sets up the texture-stage sampling pass. Reads coords / coord_counts /
+    coord_resolution and the shape_slat (the per-voxel shape latent) from the
+    incoming shape_latent dict — set there by Trellis2ShapeStage or
+    Trellis2UpsampleStage. Builds an empty sparse latent at the same coord
+    layout and attaches per-stage metadata to the conditioning."""
     @classmethod
     def define_schema(cls):
         return IO.Schema(
-            node_id="EmptyTrellis2LatentTexture",
+            node_id="Trellis2TextureStage",
             category="latent/3d",
             inputs=[
-                IO.Voxel.Input(
-                    "voxel",
-                    tooltip=(
-                        "Shape structure input. Accepts either a voxel structure "
-                        "or upsampled voxel coordinates from a previous cascade stage."
-                    )
-                ),
+                IO.Conditioning.Input("positive"),
+                IO.Conditioning.Input("negative"),
                 IO.Latent.Input("shape_latent"),
-                Pixal3DProjPack.Input(
-                    "proj_feat_pack",
-                    optional=True,
-                    tooltip="Pixal3D pixel-aligned projection pack from Pixal3DConditioning. Leave empty for vanilla Trellis2.",
-                ),
             ],
             outputs=[
+                IO.Conditioning.Output(display_name="positive"),
+                IO.Conditioning.Output(display_name="negative"),
                 IO.Latent.Output(),
             ]
         )
 
     @classmethod
-    def execute(cls, voxel, shape_latent, proj_feat_pack=None):
+    def execute(cls, positive, negative, shape_latent):
         channels = 32
-        coord_resolution = None
-        upsampled = hasattr(voxel, "upsampled")
-        if upsampled:
-            if hasattr(voxel, "resolutions") and voxel.resolutions is not None:
-                coord_resolution = int(voxel.resolutions[0].item()) // 16
-            voxel = voxel.data
-
-        if not upsampled:
-            voxel_data = voxel.data
-            if proj_feat_pack is not None:
-                voxel_data = _pixal3d_rerotate_voxel_data(voxel_data)
-            decoded = voxel_data.unsqueeze(1)
-            coords = torch.argwhere(decoded.bool())[:, [0, 2, 3, 4]].int()
-            coord_resolution = int(decoded.shape[-1])
-        else:
-            coords = voxel.int()
+        coords = shape_latent["coords"]
+        coord_resolution = shape_latent.get("coord_resolution")
 
         batch_size, counts, max_tokens = infer_batched_coord_layout(coords)
 
-        shape_latent = shape_latent["samples"]
-        if shape_latent.ndim == 4:
-            shape_latent = shape_latent.squeeze(-1).transpose(1, 2).reshape(-1, channels)
+        shape_slat = shape_latent["samples"]
+        if shape_slat.ndim == 4:
+            shape_slat = shape_slat.squeeze(-1).transpose(1, 2).reshape(-1, channels)
 
         latent = torch.zeros(batch_size, channels, max_tokens, 1)
-        model_options = {"generation_mode": "texture_generation", "coords": coords, "coord_counts": counts, "shape_slat": shape_latent}
+        extras = {
+            "trellis2_generation_mode": "texture_generation",
+            "trellis2_coords": coords,
+            "trellis2_coord_counts": counts,
+            "trellis2_shape_slat": shape_slat,
+        }
+        proj_pack = _proj_pack_from_conditioning(positive)
+        if proj_pack is not None and coord_resolution is not None:
+            extras["trellis2_proj_feats"] = compute_stage_proj_feats(
+                proj_pack, "tex_1024", coords=coords, coord_resolution=coord_resolution,
+            )
+
+        positive_out = _conditioning_set_extras(positive, extras)
+        negative_out = _conditioning_set_extras(negative, extras)
+        out_latent = {"samples": latent, "type": "trellis2", "coords": coords, "coord_counts": counts}
         if coord_resolution is not None:
-            model_options["coord_resolution"] = coord_resolution
-        if proj_feat_pack is not None:
-            model_options["proj_feat_pack"] = proj_feat_pack
-        return IO.NodeOutput({"samples": latent, "type": "trellis2", "coords": coords, "coord_counts": counts,
-                              "model_options": model_options})
+            out_latent["coord_resolution"] = coord_resolution
+        return IO.NodeOutput(positive_out, negative_out, out_latent)
 
 
 class EmptyTrellis2LatentStructure(IO.ComfyNode):
@@ -773,30 +759,17 @@ class EmptyTrellis2LatentStructure(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."),
-                Pixal3DProjPack.Input(
-                    "proj_feat_pack",
-                    optional=True,
-                    tooltip="Pixal3D pixel-aligned projection pack. Leave empty for vanilla Trellis2.",
-                ),
             ],
             outputs=[
                 IO.Latent.Output(),
             ]
         )
     @classmethod
-    def execute(cls, batch_size, proj_feat_pack=None):
-        # Trellis2.forward slices x[:, :8] and pads out to 32; KSampler residual math
-        # needs the empty latent to match latent_format (32-channel).
+    def execute(cls, batch_size):
         in_channels = 32
         resolution = 16
         latent = torch.zeros(batch_size, in_channels, resolution, resolution, resolution)
-        output = {
-            "samples": latent,
-            "type": "trellis2",
-        }
-        if proj_feat_pack is not None:
-            output["model_options"] = {"proj_feat_pack": proj_feat_pack}
-        return IO.NodeOutput(output)
+        return IO.NodeOutput({"samples": latent, "type": "trellis2"})
 
 def _dinov3_patches_to_2d(tokens, image_size, patch_size=16):
     h_p = w_p = image_size // patch_size
@@ -813,20 +786,11 @@ def _dinov3_patches_to_2d(tokens, image_size, patch_size=16):
     return patches.transpose(1, 2).reshape(tokens.shape[0], -1, h_p, w_p).contiguous()
 
 
-def _fov_from_moge_intrinsics(moge_intrinsics: torch.Tensor) -> float:
-    fx = moge_intrinsics[..., 0, 0].float()
-    fov = 2.0 * torch.atan(0.5 / fx.clamp(min=1e-4))
-    return float(fov.mean().item())
-
-
-def _run_dinov3_with_patches(model, cropped_pil, image_size):
-    # Pixal3D's cross-attn was trained against CLS + registers only (~5 tokens), not the
-    # full patch grid. The patch grid goes to the proj branch via patches_2d.
+def _run_dinov3_with_patches(model, composite, image_size):
     model_internal = model.model
     torch_device = comfy.model_management.get_torch_device()
-    resized = cropped_pil.resize((image_size, image_size), Image.Resampling.LANCZOS)
-    img_np = np.array(resized).astype(np.float32) / 255.0
-    img_t = torch.from_numpy(img_np).permute(2, 0, 1).unsqueeze(0).to(torch_device)
+    img_t = comfy.utils.common_upscale(composite, image_size, image_size, "lanczos", "disabled")
+    img_t = img_t.to(torch_device)
     img_t = (img_t - dino_mean.to(torch_device)) / dino_std.to(torch_device)
     model_internal.image_size = image_size
     tokens = model_internal(img_t, skip_norm_elementwise=True)[0]
@@ -838,48 +802,59 @@ def _run_dinov3_with_patches(model, cropped_pil, image_size):
 
 
 def _crop_image_with_mask(item_image, item_mask, max_image_size=1024):
-    img_np = (item_image.cpu().numpy() * 255).clip(0, 255).astype(np.uint8)
-    mask_np = (item_mask.cpu().numpy() * 255).clip(0, 255).astype(np.uint8)
-    pil_img = Image.fromarray(img_np)
-    pil_mask = Image.fromarray(mask_np)
-    max_size = max(pil_img.size)
-    scale = min(1.0, max_image_size / max_size)
-    if scale < 1.0:
-        new_w, new_h = int(pil_img.width * scale), int(pil_img.height * scale)
-        pil_img = pil_img.resize((new_w, new_h), Image.Resampling.LANCZOS)
-        pil_mask = pil_mask.resize((new_w, new_h), Image.Resampling.NEAREST)
-    scene_size = (pil_img.width, pil_img.height)
-    rgba_np = np.zeros((pil_img.height, pil_img.width, 4), dtype=np.uint8)
-    rgba_np[:, :, :3] = np.array(pil_img)
-    rgba_np[:, :, 3] = np.array(pil_mask)
-    alpha = rgba_np[:, :, 3]
-    bbox_coords = np.argwhere(alpha > 0.8 * 255)
-    if len(bbox_coords) > 0:
-        y_min, x_min = np.min(bbox_coords[:, 0]), np.min(bbox_coords[:, 1])
-        y_max, x_max = np.max(bbox_coords[:, 0]), np.max(bbox_coords[:, 1])
+    img = item_image.permute(2, 0, 1).unsqueeze(0).cpu().float()
+    mask = item_mask.unsqueeze(0).unsqueeze(0).cpu().float()
+    # Upstream went float→PIL uint8 implicitly; match that to keep composite bit-exact.
+    img = (img.clamp(0, 1) * 255.0).to(torch.uint8).float() / 255.0
+    mask = (mask.clamp(0, 1) * 255.0).to(torch.uint8).float() / 255.0
+
+    H, W = img.shape[-2:]
+    if max(H, W) > max_image_size:
+        scale = max_image_size / max(H, W)
+        new_w, new_h = int(W * scale), int(H * scale)
+        img  = comfy.utils.common_upscale(img,  new_w, new_h, "lanczos",       "disabled")
+        mask = comfy.utils.common_upscale(mask, new_w, new_h, "nearest-exact", "disabled")
+        H, W = new_h, new_w
+    scene_size = (W, H)
+
+    alpha_u8 = (mask[0, 0].clamp(0, 1) * 255.0).to(torch.uint8)
+    fg_pixels = (alpha_u8 > 204).nonzero()
+    if fg_pixels.numel() > 0:
+        y_min, x_min = fg_pixels.min(dim=0).values.tolist()
+        y_max, x_max = fg_pixels.max(dim=0).values.tolist()
         center_y, center_x = (y_min + y_max) / 2.0, (x_min + x_max) / 2.0
-        # Upstream pads the bbox by 10% — encoders were trained with that breathing room.
-        size = max(y_max - y_min, x_max - x_min)
-        size = int(size * 1.1)
+        size = int(max(y_max - y_min, x_max - x_min) * 1.1)
         half = size // 2
         crop_x1 = int(center_x - half)
         crop_y1 = int(center_y - half)
         crop_x2 = crop_x1 + 2 * half
         crop_y2 = crop_y1 + 2 * half
-        crop_bbox = (crop_x1, crop_y1, crop_x2, crop_y2)
-        rgba_pil = Image.fromarray(rgba_np)
-        cropped_rgba = rgba_pil.crop(crop_bbox)
-        cropped_np = np.array(cropped_rgba).astype(np.float32) / 255.0
     else:
         logging.warning("Mask for the image is empty. Pixal3D requires a clean foreground mask.")
-        cropped_np = rgba_np.astype(np.float32) / 255.0
-        crop_bbox = (0, 0, scene_size[0], scene_size[1])
-    fg = cropped_np[:, :, :3]
-    alpha_float = cropped_np[:, :, 3:4]
-    composite_np = fg * alpha_float
-    composite_uint8 = (composite_np * 255.0).round().clip(0, 255).astype(np.uint8)
-    return Image.fromarray(composite_uint8), crop_bbox, scene_size
+        crop_x1, crop_y1, crop_x2, crop_y2 = 0, 0, W, H
+    crop_bbox = (crop_x1, crop_y1, crop_x2, crop_y2)
 
+    # Zero-pad out-of-bounds slice (PIL.crop semantics).
+    pad_l = max(0, -crop_x1)
+    pad_t = max(0, -crop_y1)
+    pad_r = max(0, crop_x2 - W)
+    pad_b = max(0, crop_y2 - H)
+    if pad_l or pad_t or pad_r or pad_b:
+        img  = torch.nn.functional.pad(img,  (pad_l, pad_r, pad_t, pad_b), value=0.0)
+        mask = torch.nn.functional.pad(mask, (pad_l, pad_r, pad_t, pad_b), value=0.0)
+        crop_x1 += pad_l; crop_x2 += pad_l
+        crop_y1 += pad_t; crop_y2 += pad_t
+    cropped_img  = img [..., crop_y1:crop_y2, crop_x1:crop_x2]
+    cropped_mask = mask[..., crop_y1:crop_y2, crop_x1:crop_x2]
+
+    composite = (cropped_img * cropped_mask).clamp(0, 1)
+    composite = (composite * 255.0).round().clamp(0, 255).to(torch.uint8).float() / 255.0
+    return composite, crop_bbox, scene_size
+
+def _fov_from_moge_intrinsics(moge_intrinsics: torch.Tensor) -> float:
+    fx = moge_intrinsics[..., 0, 0].float()
+    fov = 2.0 * torch.atan(0.5 / fx.clamp(min=1e-4))
+    return float(fov.mean().item())
 
 class Pixal3DConditioning(IO.ComfyNode):
 
@@ -901,10 +876,6 @@ class Pixal3DConditioning(IO.ComfyNode):
                     "mesh_scale", default=1.0, min=0.1, max=4.0, step=0.01,
                     tooltip="Mesh scale; 1.0 means unit cube.",
                 ),
-                IO.Float.Input(
-                    "distance_override", default=0.0, min=0.0, max=10.0, step=0.001,
-                    tooltip="Override camera distance directly. 0 = auto-derive from FOV.",
-                ),
                 io.Custom("MOGE_GEOMETRY").Input(
                     "moge_geometry",
                     optional=True,
@@ -920,13 +891,11 @@ class Pixal3DConditioning(IO.ComfyNode):
             outputs=[
                 IO.Conditioning.Output(display_name="positive"),
                 IO.Conditioning.Output(display_name="negative"),
-                Pixal3DProjPack.Output(display_name="proj_feat_pack"),
             ],
         )
 
     @classmethod
     def execute(cls, clip_vision_model, image, mask, camera_angle_x, mesh_scale,
-                distance_override=0.0,
                 moge_geometry=None, naf_model=None) -> IO.NodeOutput:
         if image.ndim == 3:
             image = image.unsqueeze(0)
@@ -945,21 +914,21 @@ class Pixal3DConditioning(IO.ComfyNode):
 
         cond_512_list, cond_1024_list = [], []
         patches_512_list, patches_1024_list = [], []
-        cropped_pil_list = []
+        composite_list = []
         crop_bbox_list, scene_size_list = [], []
 
         torch_device = comfy.model_management.get_torch_device()
         for b in range(batch_size):
             item_image = image[b]
             item_mask = mask[b] if mask.size(0) > 1 else mask[0]
-            cropped_pil, crop_bbox, scene_size = _crop_image_with_mask(
+            composite, crop_bbox, scene_size = _crop_image_with_mask(
                 item_image, item_mask, max_image_size=1024)
             crop_bbox_list.append(crop_bbox)
             scene_size_list.append(scene_size)
-            cropped_pil_list.append(cropped_pil)
+            composite_list.append(composite)
 
-            cond_512 = _run_dinov3_with_patches(clip_vision_model, cropped_pil, 512)
-            cond_1024 = _run_dinov3_with_patches(clip_vision_model, cropped_pil, 1024)
+            cond_512 = _run_dinov3_with_patches(clip_vision_model, composite, 512)
+            cond_1024 = _run_dinov3_with_patches(clip_vision_model, composite, 1024)
             cond_512_list.append(cond_512["tokens"].to(device))
             cond_1024_list.append(cond_1024["tokens"].to(device))
             patches_512_list.append(cond_512["patches_2d"].to(device))
@@ -971,42 +940,28 @@ class Pixal3DConditioning(IO.ComfyNode):
         fm_512_dino = torch.cat(patches_512_list, dim=0)
         fm_1024_dino = torch.cat(patches_1024_list, dim=0)
 
-        # Upstream samples the LR DINO grid AND the NAF HR grid separately at projected
-        # 3D points, then cats sampled features along channels. Back-projection (in model.py)
-        # mirrors that — here we just stash LR + optional HR per stage.
+        # The LR DINO grid AND the NAF HR grid are sampled separately
         # NAF targets per stage: shape_512=512, shape_1024=512, tex_1024=1024.
-        def _naf_hr(lr_feat, image_pil_list, image_size, naf_target):
+        def _naf_hr(lr_feat, composites, image_size, naf_target):
             if naf_model is None or naf_target is None:
                 return None
-            # Run NAF in the input feature dtype (typically fp16 since DINO/ClipVision
-            # loads that way). The previous .float() cast doubled NAF memory by forcing
-            # full fp32 — at tex_1024/target=1024 that's ~10 GB on its own. Model
-            # weights need to match input dtype since PyTorch conv ops error out on
-            # mixed fp16-input/fp32-weight.
             target_dtype = lr_feat.dtype
             if next(naf_model.parameters()).dtype != target_dtype:
                 naf_model.to(dtype=target_dtype)
-            imgs = torch.stack([
-                torch.from_numpy(
-                    np.array(p.resize((image_size, image_size), Image.Resampling.LANCZOS))
-                       .astype(np.float32) / 255.0
-                ).permute(2, 0, 1)
-                for p in image_pil_list
+            imgs = torch.cat([
+                comfy.utils.common_upscale(c, image_size, image_size, "lanczos", "disabled")
+                for c in composites
             ], dim=0).to(torch_device).to(target_dtype)
-            
             hr = naf_model(imgs, lr_feat.to(torch_device).to(target_dtype), naf_target)
             return hr.to(device)
 
-        hr_shape_512  = _naf_hr(fm_512_dino,  cropped_pil_list, 512,  (512, 512))
-        hr_shape_1024 = _naf_hr(fm_1024_dino, cropped_pil_list, 1024, (512, 512))
-        hr_tex_1024   = _naf_hr(fm_1024_dino, cropped_pil_list, 1024, (1024, 1024))
+        hr_shape_512  = _naf_hr(fm_512_dino,  composite_list, 512,  (512, 512))
+        hr_shape_1024 = _naf_hr(fm_1024_dino, composite_list, 1024, (512, 512))
+        hr_tex_1024   = _naf_hr(fm_1024_dino, composite_list, 1024, (1024, 1024))
 
         # distance_from_fov: grid_point (-1, 0, 0) projects to pixel (0, image_resolution-1).
         camera_angle_x = float(camera_angle_x)
-        if distance_override > 0:
-            distance = float(distance_override)
-        else:
-            distance = 0.5 / math.tan(camera_angle_x / 2.0) / float(mesh_scale)
+        distance = 0.5 / math.tan(camera_angle_x / 2.0) / float(mesh_scale)
         cam_angle_t = torch.tensor([camera_angle_x] * batch_size, device=device, dtype=torch.float32)
         dist_t = torch.tensor([distance] * batch_size, device=device, dtype=torch.float32)
         scale_t = torch.tensor([float(mesh_scale)] * batch_size, device=device, dtype=torch.float32)
@@ -1028,13 +983,33 @@ class Pixal3DConditioning(IO.ComfyNode):
             "scene_sizes": scene_size_list,
         }
 
-        # global_512 → SS/shape_512 cross-attn; global_1024 → shape_1024/tex_1024
-        # (Trellis2.forward swaps context↔embeds for non-structure HR stages).
+        # global_512 → SS/shape_512 cross-attn; global_1024 → shape_1024/tex_1024.
+        # proj_feat_pack rides in the conditioning dict (same place embeds, ControlNet
+        # hints etc. live); the sampler auto-promotes it to a model.forward kwarg via
+        # Trellis2.extra_conds. The same pack object is shared between pos/neg —
+        # CONDConstant.can_concat sees them equal and concats to a single dict, then
+        # Trellis2.forward zeros proj for the uncond slots via cond_or_uncond.
+        # Pre-compute the SS-stage proj features (dense 16³ grid) once here — the
+        # shape/texture stages do their own computes in their respective stage nodes.
+        # proj_pack lives on intermediate (CPU); force the compute onto cuda so
+        # the bilinear-sampling step doesn't run on CPU.
+        ss_proj_feats = compute_stage_proj_feats(
+            proj_pack, "ss", dense_grid_resolution=16, batch_size=batch_size,
+            device=torch_device,
+        )
         neg_global = torch.zeros_like(global_512)
         neg_embeds = torch.zeros_like(global_1024)
-        positive = [[global_512, {"embeds": global_1024}]]
-        negative = [[neg_global, {"embeds": neg_embeds}]]
-        return IO.NodeOutput(positive, negative, proj_pack)
+        base_extras = {
+            "embeds": global_1024, "proj_feat_pack": proj_pack,
+            "trellis2_proj_feats": ss_proj_feats,
+        }
+        neg_extras = {
+            "embeds": neg_embeds, "proj_feat_pack": proj_pack,
+            "trellis2_proj_feats": ss_proj_feats,
+        }
+        positive = [[global_512, base_extras]]
+        negative = [[neg_global, neg_extras]]
+        return IO.NodeOutput(positive, negative)
 
 
 def _project_vertices_to_image_uv(vertices_world, transform_matrix, camera_angle_x, image_resolution):
@@ -1069,7 +1044,7 @@ class Pixal3DAlignObject(IO.ComfyNode):
             category="latent/3d",
             inputs=[
                 IO.Mesh.Input("mesh"),
-                Pixal3DProjPack.Input("proj_feat_pack", tooltip="The proj pack produced by Pixal3DConditioning for this object."),
+                IO.Conditioning.Input("positive", tooltip="The positive conditioning from Pixal3DConditioning for this object — Pixal3DAlignObject reads transform_matrix / camera_angle_x / mesh_scale / crop_bboxes out of its proj_feat_pack."),
                 io.Custom("MOGE_GEOMETRY").Input("moge_geometry", tooltip="MoGe geometry computed on the original scene image."),
                 IO.Mask.Input(
                     "object_mask",
@@ -1089,7 +1064,10 @@ class Pixal3DAlignObject(IO.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, mesh, proj_feat_pack, moge_geometry, object_mask=None, batch_index=0) -> IO.NodeOutput:
+    def execute(cls, mesh, positive, moge_geometry, object_mask=None, batch_index=0) -> IO.NodeOutput:
+        proj_feat_pack = _proj_pack_from_conditioning(positive)
+        if proj_feat_pack is None:
+            raise ValueError("Pixal3DAlignObject: positive conditioning has no proj_feat_pack — connect a Pixal3DConditioning output.")
         vertices = mesh.vertices
         faces = mesh.faces
         if vertices.ndim == 3:
@@ -1117,10 +1095,11 @@ class Pixal3DAlignObject(IO.ComfyNode):
                 "image. Run MoGe on the same resized scene image Pixal3DConditioning used."
             )
 
-        # Compose VaeDecodeShapeTrellis's R_y(180°) inverse with R_proj to map user mesh
-        # space to ProjGrid world: (X, Y, Z) -> (-X, Z, Y).
+        # Vertices come out of VaeDecodeShapeTrellis in the Pixal3D model frame
+        # (no un-rotation). Apply _PROJ_GRID_ROTATION = R_x(-90°) to map model
+        # frame → ProjGrid world: (X, Y, Z) -> (X, -Z, Y).
         v = vertices_one.float()
-        verts_world = torch.stack([-v[..., 0], v[..., 2], v[..., 1]], dim=-1)
+        verts_world = torch.stack([v[..., 0], -v[..., 2], v[..., 1]], dim=-1)
         verts_world = verts_world / float(mesh_scale.item())
         uv_crop, _depth, valid = _project_vertices_to_image_uv(
             verts_world, T[0], cam_angle[0], image_resolution)
@@ -1200,6 +1179,74 @@ class LoadNAFModel(IO.ComfyNode):
         return IO.NodeOutput(model)
 
 
+class CFGGuidanceInterval(IO.ComfyNode):
+    """Generic model patch: apply CFG only during [start_percent, end_percent] of
+    the sampling schedule. Outside that window, skip the uncond computation and
+    collapse to effective cfg=1 — same idea as upstream Trellis2 / Pixal3D's
+    guidance_interval_mixin, but lives at the sampler level (via
+    sampler_calc_cond_batch_function) so it works for any model.
+
+    Percents use ComfyUI's standard convention: 0.0 = start of sampling
+    (max-noise step), 1.0 = end of sampling (clean step). Conversion to sigma
+    is done via model_sampling.percent_to_sigma so the window is portable
+    across schedules (flow / EDM / discrete) and shift settings.
+
+    Defaults are full-range (no bypass). For Trellis2's upstream behavior,
+    wire (start_percent=0.0, end_percent=0.667) on the SS / shape KSamplers;
+    texture defaults to cfg=1 so the node is moot there."""
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="CFGGuidanceInterval",
+            category="model_patches/sampling",
+            inputs=[
+                IO.Model.Input("model"),
+                IO.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001,
+                               tooltip="Fraction of sampling at which CFG turns ON (0 = beginning)."),
+                IO.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001,
+                               tooltip="Fraction of sampling at which CFG turns OFF (1 = end)."),
+            ],
+            outputs=[IO.Model.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model, start_percent, end_percent):
+        import comfy.samplers
+
+        model_sampling = model.get_model_object("model_sampling")
+        # percent_to_sigma is monotonically decreasing: percent=0 -> sigma_max,
+        # percent=1 -> sigma_min. So start_percent < end_percent in user space
+        # means sigma_start > sigma_end. "Inside the window" is sigma in
+        # [sigma_end, sigma_start].
+        sigma_start = float(model_sampling.percent_to_sigma(start_percent))
+        sigma_end = float(model_sampling.percent_to_sigma(end_percent))
+
+        def calc_cond_batch_with_interval(args):
+            sigma_val = args["sigma"][0].item()
+            conds = args["conds"]
+            input_x = args["input"]
+            timestep = args["sigma"]
+            model_ref = args["model"]
+            model_opts = args["model_options"]
+
+            # conds is typically [cond, uncond]; uncond may be None when ComfyUI's
+            # global cfg=1 optimization has already pruned it.
+            cond = conds[0]
+            uncond = conds[1] if len(conds) > 1 else None
+            inside = sigma_end <= sigma_val <= sigma_start
+
+            if uncond is None or inside:
+                return comfy.samplers.calc_cond_batch(model_ref, conds, input_x, timestep, model_opts)
+            # Outside the window: compute cond only, mirror it into the uncond slot
+            # so the downstream cfg_function collapses to `cond` (effective cfg=1).
+            out = comfy.samplers.calc_cond_batch(model_ref, [cond], input_x, timestep, model_opts)
+            return [out[0], out[0]]
+
+        m = model.clone()
+        m.model_options["sampler_calc_cond_batch_function"] = calc_cond_batch_with_interval
+        return IO.NodeOutput(m)
+
+
 class Trellis2Extension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1208,13 +1255,14 @@ class Trellis2Extension(ComfyExtension):
             Pixal3DConditioning,
             Pixal3DAlignObject,
             LoadNAFModel,
-            EmptyTrellis2ShapeLatent,
+            Trellis2ShapeStage,
             EmptyTrellis2LatentStructure,
-            EmptyTrellis2LatentTexture,
+            Trellis2TextureStage,
             VaeDecodeTextureTrellis,
             VaeDecodeShapeTrellis,
             VaeDecodeStructureTrellis2,
-            Trellis2UpsampleCascade,
+            Trellis2UpsampleStage,
+            CFGGuidanceInterval,
         ]
 
 
diff --git a/nodes.py b/nodes.py
index 65413206a..f3fcb6656 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1537,10 +1537,6 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
     if "noise_mask" in latent:
         noise_mask = latent["noise_mask"]
 
-    if "model_options" in latent:
-        inner = model.model.diffusion_model
-        inner.meta = latent["model_options"]
-
     callback = latent_preview.prepare_callback(model, steps)
     disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
     samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,