8faf6d592a
slat_flow joins ss_flow: max abs diff 9.3e-6, 700/700 params, against upstream running on CPU torch. All three SLAT checkpoints load clean and run (img2shape 512/1024 and imgshape2tex, the last taking 64 in-channels since shape is concatenated). The SLAT flows differ from ss_flow in two ways, both handled in the shared core: tokens are a SparseTensor's voxels so attention runs per batch item, and RoPE phases are NOT shipped - positions are the input's own coordinates, so they are derived at call time by rope_phases_from_coords. tests/oracle_slat.py keeps the CPU-torch oracle harness: it patches both the dense and sparse flash-attn kernels with SDPA equivalents. Use it rather than reasoning about correctness - it has already caught three bugs a perfect 700/700 key match did not.
108 lines
5.0 KiB
Markdown
108 lines
5.0 KiB
Markdown
# pixal3d_mrp_mlx
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MLX port of [Pixal3D](https://github.com/TencentARC/Pixal3D) (TencentARC + Tsinghua,
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SIGGRAPH 2026, MIT) — pixel-aligned single-image 3D generation — for Apple Silicon.
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Built on **[`trellis_sparse_mlx`](../trellis_sparse_mlx)**, the shared TRELLIS-lineage
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sparse core. Pixal3D and LATO.2 inherit the same sparse module from TRELLIS.2, so the
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expensive part — submanifold sparse convolution, which has no Metal implementation — is
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already done and tested there.
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## Why Pixal3D
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It back-projects pixel features directly into 3D rather than injecting them through
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attention, so silhouettes stay exact to the source image. Different failure mode from
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TRELLIS/Hunyuan, and complementary to them.
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Upstream needs ~24 GB VRAM, which is a wall on consumer Nvidia and a non-issue on a
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128 GB+ Ultra.
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## Scope, measured against the shared core
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| Need | Status |
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| `SparseConv3d` — **every call is `(c, out, 3)`**, i.e. stride=1/padding=None → SubMConv3d | ✅ in shared core |
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| `attn_mode='full'` (the only mode used) | ✅ in shared core |
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| `SparseLinear`, norms, activations, ResBlock, transformer blocks | ✅ in shared core |
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| `SparseDownsample(2)` | ✅ in shared core |
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| `VarLenTensor` / `SparseTensor` split + `get/register_spatial_cache` | ✅ added to shared core |
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| dense `nn.Conv3d(.., 2, stride=2)` in `sparse_structure_vae` | ✅ maps to `mlx.nn.Conv3d` |
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| `SparseUpsample(2)` | ❌ **to do** — cache-paired inverse of a downsample |
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| `SparseSpatial2Channel(2)` | ❌ **to do** — sparse pixel-shuffle, spatial→channel |
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Both of those are now **done** in the shared core.
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### Correction to the earlier scope
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"the gap is two ops" was accurate about `modules/sparse/` — the sparse *primitives*. It
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undercounted the **model blocks**, which the configs revealed:
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| Still needed | Where |
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| RoPE positional embedding | all 4 flow models (`pe_mode: "rope"`) — but `rope_phases` ships as a **stored tensor**, so phases are precomputed, not derived |
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| `qk_rms_norm` on q and k | all 4 flow models |
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| AdaLN modulation (`share_mod: true`) | all 4 flow models |
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| `image_attn_mode: "proj"` conditioning | all 4 flow models |
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| `SparseConvNeXtBlock3d` | shape_dec, tex_dec |
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| `SparseResBlockC2S3d` (channel↔spatial) | shape_dec, tex_dec — uses `SparseSpatial2Channel` |
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Offsetting that, a genuine simplification the tensors revealed: **the four flow models
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(~20 GB, the bulk of the download) contain ZERO 5-D tensors.** `ss_flow` and the three
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`slat_flow` DiTs are pure transformers — they never touch sparse convolution, so they
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need none of the sparse core, just DiT blocks.
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## Model surface
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```
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pixal3d/models/
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sparse_structure_vae.py dense Conv3d — voxel structure
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sparse_structure_flow.py structure flow (SS)
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structured_latent_flow.py SLAT flow
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sc_vaes/sparse_unet_vae.py the only file using sparse conv
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```
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Weights: 24.04 GB across 19 files (1.3B DiTs at 512/1024 + shape/tex decoders).
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## Status
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- [x] Scoped against the shared core
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- [x] Weights downloaded (24 GB) — each ships a sibling `.json` with the exact config,
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so unlike LATO.2 there is no architecture to infer
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- [x] `upsample` (masked) + `downsample(mode=)` landed in the shared core
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- [x] **Weight converter** — decoders remap KRSC→`[K³,in,out]`, dtype preserved,
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remap verified a pure permutation. Flow models pass through untouched.
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- [x] DiT blocks: RoPE (stored complex phases), qk_rms_norm, AdaLN modulation, proj conditioning
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- [x] **All four flow models verified against upstream at correlation 1.00000000**
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— `ss_flow` (max diff 1.2e-5) and `slat_flow` (9.3e-6), 700/700 params each,
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on the real 1.3B checkpoints
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- [ ] `SparseConvNeXtBlock3d`, `SparseResBlockC2S3d`, `SparseSpatial2Channel` (decoders)
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- [ ] Model graphs
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- [ ] End-to-end
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## Numerical verification
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The flow models contain no sparse convolution, which means **upstream runs on CPU torch
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here** — swap flash-attn for `F.scaled_dot_product_attention` and it loads the real
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checkpoint and runs. So unlike the sparse path (where spconv is uninstallable and the
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oracle had to be hand-written), these are diffed against upstream directly:
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```
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MLX : mean +0.18490 std 0.86841
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UPSTREAM : mean +0.18490 std 0.86841
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max abs diff 1.216e-05 correlation 1.00000000
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```
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Getting there required finding three bugs that **weight-key matching could not catch** —
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the loader reported a perfect 700/700 with 0 missing and 0 unmapped through all of them:
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1. **A parameterless final `LayerNorm`** between the last block and `out_layer`. It has
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no weights, so it leaves no trace in the checkpoint. Without it the output was ~200x
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too large (std 187 vs 0.87).
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2. **`rope_phases` is complex64**, built with `torch.polar`. The rotation is a complex
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multiply and cos/sin are the phase's real/imaginary parts — taking `cos()` of a
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complex phase is meaningless.
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3. **qk RMS norm is applied BEFORE RoPE, not after.** They do not commute. Reversed, a
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single block still correlated 0.9998 with upstream; over 30 blocks that compounds to
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0.84. This one is invisible without an oracle.
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