SparseStructureDecoder verified at correlation 1.00000000

74/74 params, max abs diff 4.6e-4 on values around -163 (~3e-6 relative), 60ms for a
16^3 latent -> 64^3 occupancy grid.

Fully dense, so no sparse ops involved - but MLX's Conv3d is channels-LAST where torch
is NCDHW, so tensors are carried channels-last throughout and transposed only at the
boundaries. The converter already emits [O,kz,ky,kx,I] to match. pixel_shuffle_3d had
to be rewritten for that layout: the (H,s)(W,s)(D,s) interleave order is what matters
and getting it wrong scrambles the grid while preserving its shape.

This completes the structure stage end to end: image -> ss_flow -> latent -> ss_dec.

README.md

@@ -75,9 +75,12 @@ Weights: 24.04 GB across 19 files (1.3B DiTs at 512/1024 + shape/tex decoders).
 - [x] **All four flow models verified against upstream at correlation 1.00000000**
       — `ss_flow` (max diff 1.2e-5) and `slat_flow` (9.3e-6), 700/700 params each,
       on the real 1.3B checkpoints
-- [ ] `SparseConvNeXtBlock3d`, `SparseResBlockC2S3d`, `SparseSpatial2Channel` (decoders)
+- [x] `SparseConvNeXtBlock3d`, `SparseResBlockC2S3d`, `spatial2channel`/`channel2spatial`
-- [ ] Model graphs
+      — all in the shared core, round-trip and selective-growth tested
-- [ ] End-to-end
+- [x] **`ss_dec` verified at correlation 1.00000000** (74/74), completing the whole
+      structure stage: image -> ss_flow -> latent -> ss_dec -> 64^3 occupancy grid
+- [ ] `shape_dec` / `tex_dec` model graphs (the two sparse decoders)
+- [ ] End-to-end pipeline wiring
 
 
 ## Numerical verification

pixal3d_mlx/ss_dec.py

@@ -0,0 +1,206 @@
+"""Pixal3D SparseStructureDecoder in MLX — latent -> occupancy grid.
+
+Fully dense: 3D convs on a small voxel grid, no sparse ops at all. Pairs with `ss_flow`
+to complete the structure stage (image -> latent -> occupancy), which is what decides
+which voxels exist before the SLAT stage runs.
+
+Config (from the sibling JSON):
+    out_channels 1, latent_channels 8, channels [512, 128, 32],
+    num_res_blocks 2, num_res_blocks_middle 2, norm_type "layer"
+
+MLX's Conv3d is channels-LAST (NDHWC) where torch is NCDHW, so tensors are carried in
+channels-last form throughout and only transposed at the boundaries. The weight
+converter already emits `[O, kz, ky, kx, I]` to match.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import List, Optional
+
+import mlx.core as mx
+import mlx.nn as nn
+
+
+def _cln(x: mx.array, weight: mx.array, bias: mx.array, eps: float = 1e-5) -> mx.array:
+    """ChannelLayerNorm32 — LayerNorm over channels, computed in fp32.
+
+    Upstream permutes channels to last, applies LayerNorm, permutes back. Data here is
+    already channels-last, so it is a plain LayerNorm over the final axis.
+    """
+    dt = x.dtype
+    f = x.astype(mx.float32)
+    f = (f - mx.mean(f, -1, keepdims=True)) * mx.rsqrt(mx.var(f, -1, keepdims=True) + eps)
+    return (f * weight + bias).astype(dt)
+
+
+def pixel_shuffle_3d(x: mx.array, scale: int) -> mx.array:
+    """Channels-last 3D pixel shuffle: [B,H,W,D,C*s^3] -> [B,H*s,W*s,D*s,C].
+
+    Upstream works in NCDHW and permutes (0,1,5,2,6,3,7,4) after splitting the channel
+    axis into (C_, s, s, s). Here the channel axis is last, so the split factors sit
+    next to it and the interleave order must still be (H,s)(W,s)(D,s) — getting that
+    order wrong scrambles the grid while preserving its shape.
+    """
+    b, h, w, d, c = x.shape
+    c_ = c // scale**3
+    x = x.reshape(b, h, w, d, c_, scale, scale, scale)
+    x = x.transpose(0, 1, 5, 2, 6, 3, 7, 4)  # b, h, s, w, s, d, s, c_
+    return x.reshape(b, h * scale, w * scale, d * scale, c_)
+
+
+class ResBlock3d(nn.Module):
+    def __init__(self, channels: int, out_channels: Optional[int] = None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.norm1_w = mx.ones((channels,))
+        self.norm1_b = mx.zeros((channels,))
+        self.norm2_w = mx.ones((self.out_channels,))
+        self.norm2_b = mx.zeros((self.out_channels,))
+        self.conv1 = nn.Conv3d(channels, self.out_channels, 3, padding=1)
+        self.conv2 = nn.Conv3d(self.out_channels, self.out_channels, 3, padding=1)
+        self.skip_connection = (
+            nn.Conv3d(channels, self.out_channels, 1)
+            if channels != self.out_channels
+            else None
+        )
+
+    def __call__(self, x: mx.array) -> mx.array:
+        h = _cln(x, self.norm1_w, self.norm1_b)
+        h = self.conv1(nn.silu(h))
+        h = _cln(h, self.norm2_w, self.norm2_b)
+        h = self.conv2(nn.silu(h))
+        return h + (self.skip_connection(x) if self.skip_connection else x)
+
+
+class UpsampleBlock3d(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int):
+        super().__init__()
+        self.conv = nn.Conv3d(in_channels, out_channels * 8, 3, padding=1)
+
+    def __call__(self, x: mx.array) -> mx.array:
+        return pixel_shuffle_3d(self.conv(x), 2)
+
+
+class SparseStructureDecoder(nn.Module):
+    def __init__(
+        self,
+        out_channels: int = 1,
+        latent_channels: int = 8,
+        num_res_blocks: int = 2,
+        channels: List[int] = (512, 128, 32),
+        num_res_blocks_middle: int = 2,
+        **_ignored,
+    ):
+        super().__init__()
+        channels = list(channels)
+        self.channels = channels
+        self.input_layer = nn.Conv3d(latent_channels, channels[0], 3, padding=1)
+        self.middle_block = [
+            ResBlock3d(channels[0], channels[0]) for _ in range(num_res_blocks_middle)
+        ]
+        blocks = []
+        for i, ch in enumerate(channels):
+            blocks += [ResBlock3d(ch, ch) for _ in range(num_res_blocks)]
+            if i < len(channels) - 1:
+                blocks.append(UpsampleBlock3d(ch, channels[i + 1]))
+        self.blocks = blocks
+        self.out_norm_w = mx.ones((channels[-1],))
+        self.out_norm_b = mx.zeros((channels[-1],))
+        self.out_conv = nn.Conv3d(channels[-1], out_channels, 3, padding=1)
+
+    def __call__(self, z: mx.array) -> mx.array:
+        """z: [B, C, D, H, W] (torch order, for a familiar interface)."""
+        x = z.transpose(0, 2, 3, 4, 1)  # -> channels-last
+        x = self.input_layer(x)
+        for b in self.middle_block:
+            x = b(x)
+        for b in self.blocks:
+            x = b(x)
+        x = _cln(x, self.out_norm_w, self.out_norm_b)
+        x = self.out_conv(nn.silu(x))
+        return x.transpose(0, 4, 1, 2, 3)  # back to [B, C, D, H, W]
+
+
+# ------------------------------------------------------------------ loading
+
+def _remap(k: str) -> str:
+    """Upstream nests norms/convs inside Sequential and nn.Module attrs."""
+    k = k.replace("out_layer.0.weight", "out_norm_w").replace("out_layer.0.bias", "out_norm_b")
+    k = k.replace("out_layer.2.", "out_conv.")
+    k = k.replace(".norm1.weight", ".norm1_w").replace(".norm1.bias", ".norm1_b")
+    k = k.replace(".norm2.weight", ".norm2_w").replace(".norm2.bias", ".norm2_b")
+    return k
+
+
+def load(weights_path: str | Path, config_path: str | Path | None = None):
+    wp = Path(weights_path)
+    cp = Path(config_path) if config_path else wp.with_suffix(".json")
+    cfg = json.loads(cp.read_text())
+    args = dict(cfg.get("args", {}))
+    args.pop("use_fp16", None)
+    model = SparseStructureDecoder(**args)
+
+    w = mx.load(str(wp))
+    flat = dict(_flatten(model.parameters()))
+    mapped, unmapped = {}, []
+    for k, v in w.items():
+        m = _remap(k)
+        if m in flat:
+            if flat[m].shape != v.shape:
+                raise ValueError(f"shape mismatch {k} -> {m}: {flat[m].shape} vs {v.shape}")
+            mapped[m] = v
+        else:
+            unmapped.append(f"{k} -> {m} {tuple(v.shape)}")
+    missing = [k for k in flat if k not in mapped]
+    if mapped:
+        model.update(_unflatten(mapped))
+    return model, {
+        "config": cfg.get("name"),
+        "loaded": len(mapped),
+        "params": len(flat),
+        "missing": missing,
+        "unmapped": unmapped,
+    }
+
+
+def _flatten(tree, prefix=""):
+    if isinstance(tree, dict):
+        for k, v in tree.items():
+            yield from _flatten(v, f"{prefix}{k}.")
+    elif isinstance(tree, list):
+        for i, v in enumerate(tree):
+            yield from _flatten(v, f"{prefix}{i}.")
+    elif isinstance(tree, mx.array):
+        yield prefix[:-1], tree
+
+
+def _unflatten(flat: dict):
+    root: dict = {}
+    for key, val in flat.items():
+        parts = key.split(".")
+        node = root
+        for i, p in enumerate(parts[:-1]):
+            nxt = parts[i + 1]
+            default = [] if nxt.isdigit() else {}
+            if isinstance(node, list):
+                idx = int(p)
+                while len(node) <= idx:
+                    node.append({})
+                if isinstance(default, list) and not isinstance(node[idx], list):
+                    node[idx] = default
+                node = node[idx]
+            else:
+                if p not in node or not isinstance(node[p], (dict, list)):
+                    node[p] = default
+                node = node[p]
+        if isinstance(node, list):
+            idx = int(parts[-1])
+            while len(node) <= idx:
+                node.append(None)
+            node[idx] = val
+        else:
+            node[parts[-1]] = val
+    return root