diff --git a/trellis_sparse_mlx/convert.py b/trellis_sparse_mlx/convert.py
new file mode 100644
index 0000000..55f67ab
--- /dev/null
+++ b/trellis_sparse_mlx/convert.py
@@ -0,0 +1,71 @@
+"""Weight-layout helpers shared by every TRELLIS-lineage port.
+
+The one genuinely tricky part of converting these checkpoints is that spconv kernels and
+dense torch conv kernels are BOTH rank 5 and cannot be told apart by rank alone:
+
+    spconv SubMConv3d : [out, kz, ky, kx, in]    (KRSC)
+    torch  nn.Conv3d  : [out, in, kz, ky, kx]
+
+Models in this family contain both — LATO.2's V-VAE uses sparse convs while its voxel
+encoder uses dense `nn.Conv3d`, and Pixal3D's decoders mix them likewise. Treating rank 5
+as automatically sparse silently mangles whichever one guessed wrong: the reshape
+succeeds, the shapes stay plausible, and the output is quietly garbage.
+"""
+
+from __future__ import annotations
+
+from typing import Tuple
+
+import numpy as np
+
+
+def classify_5d(name: str, shape: Tuple[int, ...]) -> str:
+    """Return 'krsc' (spconv) or 'conv3d' (dense torch) for a rank-5 weight."""
+    krsc = shape[1] == shape[2] == shape[3]
+    conv3d = shape[2] == shape[3] == shape[4]
+    if krsc and not conv3d:
+        return "krsc"
+    if conv3d and not krsc:
+        return "conv3d"
+    if krsc and conv3d:
+        # e.g. (O,3,3,3,3): genuinely ambiguous by shape. spconv weights always sit under
+        # the wrapper's `.conv.weight`; fail loudly rather than coin-flip.
+        if name.endswith(".conv.weight"):
+            return "krsc"
+        raise ValueError(
+            f"{name}: ambiguous 5-D layout {shape} — cannot tell KRSC from Conv3d"
+        )
+    raise ValueError(f"{name}: unrecognised 5-D layout {shape}")
+
+
+def krsc_to_mlx(arr: np.ndarray, flip_kernel: bool = False) -> np.ndarray:
+    """[O, kz, ky, kx, I] -> [K^3, I, O], the layout SubMConv3d expects.
+
+    The C-order flatten of (kz,ky,kx) matches `conv._kernel_offsets`, which meshgrids
+    `arange(k) - k//2` with indexing="ij" — so index 13 is the centre tap at k=3.
+
+    `flip_kernel` mirrors every offset (d -> -d), i.e. swaps cross-correlation for true
+    convolution. Which one spconv means cannot be checked without spconv, and a flipped
+    kernel is numerically silent, so this stays a runtime experiment rather than a guess
+    baked into the converter.
+    """
+    o, kz, ky, kx, i = arr.shape
+    if not (kz == ky == kx):
+        raise ValueError(f"non-cubic kernel {arr.shape}")
+    w = arr.transpose(1, 2, 3, 4, 0).reshape(kz * ky * kx, i, o)
+    return w[::-1].copy() if flip_kernel else w
+
+
+def conv3d_to_mlx(arr: np.ndarray) -> np.ndarray:
+    """[O, I, kz, ky, kx] -> [O, kz, ky, kx, I]; mlx.nn.Conv3d is channels-last."""
+    return arr.transpose(0, 2, 3, 4, 1).copy()
+
+
+def convert_tensor(name: str, arr: np.ndarray, flip_kernel: bool = False):
+    """Returns (array, kind) where kind is 'dense' | 'krsc' | 'conv3d'."""
+    if arr.ndim != 5:
+        return arr, "dense"
+    kind = classify_5d(name, arr.shape)
+    if kind == "conv3d":
+        return conv3d_to_mlx(arr), "conv3d"
+    return krsc_to_mlx(arr, flip_kernel), "krsc"