Metadata-Version: 2.4
Name: mlx-spatial
Version: 0.0.2
Summary: MLX-first primitives for 3D and spatial model inference.
Project-URL: Repository, https://github.com/appautomaton/mlx-spatial
Author: AppAutomaton
License: MIT License
        
        Copyright (c) 2026 AppAutomaton swarm of agents
        
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License-File: LICENSE
Requires-Python: >=3.11
Requires-Dist: fast-simplification>=0.1.13
Requires-Dist: mlx
Requires-Dist: numpy
Requires-Dist: pillow>=12.2.0
Requires-Dist: pyyaml>=6.0
Requires-Dist: safetensors
Requires-Dist: scipy>=1.17.1
Requires-Dist: xatlas>=0.0.11
Description-Content-Type: text/markdown

# mlx-spatial

MLX-native 3D and spatial inference tooling for Apple Silicon.

`mlx-spatial` is a practical runtime package for running modern 3D
reconstruction and image-to-3D pipelines locally with MLX. The package is
intentionally focused: keep weights outside the wheel, validate the assets you
downloaded, then run clear command-line paths that produce inspectable outputs.

This is not a training framework, and it does not bundle model weights.

## What Works Now

The package covers five model families:

| Pipeline | Input | Output | Weight setup |
| --- | --- | --- | --- |
| SAM 3D Objects | image + object mask | Gaussian PLY, optional GLB | `appautomaton` MLX bundle |
| TRELLIS.2 | object-centric RGB/RGBA image | shape OBJ or textured GLB | downloaded safetensors directly |
| HY-WorldMirror 2.0 | scene image or image frames | camera, depth, normals, point-cloud PLY | downloaded safetensors directly |
| LiTo | object-centric RGB/RGBA image | 3D Gaussian Splat PLY | `appautomaton` research MLX bundle |
| MapAnything | scene image views | scene `.npz` with depth, cameras, and world points | downloaded safetensors directly |

Choose by job:

- Use SAM3D when you have an object image plus an exact mask and want object
  reconstruction with Gaussian PLY output.
- Use TRELLIS.2 when you have an object-centric image and want a shape OBJ or
  textured GLB.
- Use HY-WorldMirror when the input is a scene or frame set and you need camera,
  depth, normal, or point-cloud outputs.
- Use LiTo when you want Apple's research image-to-3DGS path and can work with
  Gaussian splat PLY output instead of a mesh.
- Use MapAnything when you have related scene views and want image-only depth,
  confidence, masks, camera parameters, and dense world points.

Honest status:

- SAM3D is the strongest object reconstruction path in this package. It uses the public
  `appautomaton/sam-3d-objects-mlx` bundle.
- TRELLIS.2 generation works, including textured GLB export. The export path is
  usable, but still an area we keep improving for texture and mesh quality.
- HY-WorldMirror works for scene reconstruction with `camera,depth,normal,points`.
  The optional Gaussian head is not part of the release-ready path yet.
- LiTo runs checkpoint-backed image-to-3DGS inference with the public
  `appautomaton/lito-research-mlx` bundle. Outputs are Gaussian splat PLY files,
  not meshes; use a 3DGS-aware viewer.
- MapAnything runs checkpoint-backed scene generation with the public
  `facebook/map-anything` weights. The supported artifact is a scene `.npz`
  tensor bundle, not a mesh or Gaussian splat export.

## Install

For local development from this repo:

```bash
uv sync
uv run pytest -q
```

For package consumers:

```bash
uv add mlx-spatial
# or
pip install mlx-spatial
```

Requirements:

- Python 3.11+
- Apple Silicon recommended
- MLX installed through the package dependencies
- model weights downloaded separately under `weights/`

## Command Line Tools

The package installs five CLIs:

```bash
uv run mlx-spatial-sam3d --help
uv run mlx-spatial-trellis2 --help
uv run mlx-spatial-hyworld2 --help
uv run mlx-spatial-lito --help
uv run mlx-spatial-mapanything --help
```

The repository also includes readable script wrappers under `scripts/`. These
are the easiest starting point because they encode recommended settings.

## Model Assets

Weights are intentionally not committed and not shipped in the wheel. Keep them
under ignored local folders:

```text
weights/sam-3d-objects-mlx/
weights/lito-research-mlx/
weights/trellis2/
weights/rmbg2/
weights/dinov3-vitl16-pretrain-lvd1689m/
weights/hy-world-2/
weights/map-anything/
```

SAM3D uses the converted `appautomaton/sam-3d-objects-mlx` runtime bundle:

```bash
uv run hf download appautomaton/sam-3d-objects-mlx \
  --local-dir weights/sam-3d-objects-mlx
uv run mlx-spatial-sam3d validate weights/sam-3d-objects-mlx
```

LiTo uses the converted `appautomaton/lito-research-mlx` research bundle:

```bash
uv run hf download appautomaton/lito-research-mlx \
  --local-dir weights/lito-research-mlx
uv run mlx-spatial-lito validate weights/lito-research-mlx
```

TRELLIS.2, HY-WorldMirror, and MapAnything do not need SAM3D-style conversion.
They load the downloaded safetensors and JSON configs directly:

```bash
uv run mlx-spatial-trellis2 download-command --root weights/trellis2
uv run mlx-spatial-trellis2 rmbg-download-command --root weights/rmbg2
uv run mlx-spatial-trellis2 dinov3-download-command weights/dinov3-vitl16-pretrain-lvd1689m
uv run mlx-spatial-hyworld2 download-command weights/hy-world-2
uv run mlx-spatial-mapanything download-command weights/map-anything
```

Run the printed `hf download ...` commands, then validate:

```bash
uv run mlx-spatial-trellis2 validate --root weights/trellis2
uv run mlx-spatial-trellis2 rmbg-validate --root weights/rmbg2
uv run mlx-spatial-trellis2 dinov3-validate weights/dinov3-vitl16-pretrain-lvd1689m
uv run mlx-spatial-hyworld2 validate weights/hy-world-2
uv run mlx-spatial-mapanything validate weights/map-anything
```

Respect the licenses and access terms of the upstream model providers. The
Python package only provides runtime code.

## First Runs

### SAM3D Object Reconstruction

Use an image and the exact object mask you want reconstructed:

```bash
python scripts/sam3d/reconstruct.py inputs/sam3d/living-room/image.png \
  --mask inputs/sam3d/living-room/mask-3.png \
  --output-dir outputs/sam3d/living-room-script
```

Expected output:

```text
outputs/sam3d/living-room-script/
  gaussians.ply
  trace.json
```

Inspect the trace:

```bash
python scripts/sam3d/inspect_trace.py outputs/sam3d/living-room-script/trace.json
```

### TRELLIS.2 Textured GLB

Use an object-centric image. RGBA images use their alpha channel directly; RGB
images use RMBG to estimate the foreground:

```bash
python scripts/trellis2/generate_textured.py inputs/trellis2/cup-of-tea.jpg \
  --output-dir outputs/trellis2/cup-of-tea-script
```

Expected output:

```text
outputs/trellis2/cup-of-tea-script/
  model.glb
  trace.json
```

The default settings are quality-oriented for Apple Silicon: 512 pipeline,
model-config sampler steps, 1024 texture, 200k GLB face target, global xatlas
unwrap, and kdtree texture baking. Low-step runs are useful for smoke tests,
but they are not representative of output quality.

### HY-WorldMirror Scene Reconstruction

Use a scene image or a directory of scene frames. This pipeline does not take an
object mask:

```bash
python scripts/hyworld2/generate_scene.py inputs/sam3d/kidsroom/image.png \
  --output-dir outputs/hyworld2/kidsroom-scene-script
```

Expected output:

```text
outputs/hyworld2/kidsroom-scene-script/
  camera_params.json
  depth/
  normal/
  points/points.ply
  trace.json
```

The script uses the verified release path: real Tencent safetensors, `large`
memory profile, and `camera,depth,normal,points` heads. For frame directories,
use `--memory-profile balanced` when the `large` profile hits the attention
guard.

### LiTo Image to 3D Gaussian Splat

Use an object-centric image with a useful alpha mask when possible:

```bash
python scripts/lito/generate.py inputs/lito/sample.png \
  --weights-root weights/lito-research-mlx \
  --output outputs/lito/sample.ply \
  --memory-profile balanced \
  --print-metrics
```

Expected output:

```text
outputs/lito/sample.ply
outputs/lito/sample.safetensors
```

LiTo writes a Gaussian Splat PLY, not a mesh. Blender's native PLY importer can
read the container, but it does not render the 3DGS fields correctly. Use a
Gaussian-splat-aware viewer such as KIRI's Blender 3DGS add-on.

### MapAnything Scene Bundle

Use a directory of related scene views. The Desk example is a two-image scene:

```bash
python scripts/mapanything/generate_scene.py inputs/map-anything/desk \
  --output-dir outputs/mapanything/desk-script
```

Expected output:

```text
outputs/mapanything/desk-script/
  scene.npz
  trace.json
```

The script uses the upstream image-only inference settings: `fixed_mapping`
preprocessing, stride `1`, checkpoint-derived patch size, DINOv2
normalization, and mask/edge-mask postprocessing. `scene.npz` matches the original Torch scene layout
semantically: images, depth, confidence, masks, intrinsics, camera poses, and
world points. The MLX file uses clean top-level keys and also records
`extrinsics`.

## Repository Layout

```text
src/mlx_spatial/     package code
scripts/             readable user and maintainer wrappers
docs/                deeper setup, release, and architecture notes
tests/               unit and parity-oriented coverage
weights/             ignored local model assets
inputs/              ignored local sample inputs
outputs/             ignored generated results
vendors/             ignored upstream checkouts
```

## Documentation

- [docs/README.md](docs/README.md): documentation map and reader contract.
- [scripts/README.md](scripts/README.md): recommended inference scripts and their defaults.
- [docs/sam3d.md](docs/sam3d.md): SAM3D setup, inference, quality gates, PLY expectations, and coordinate notes.
- [docs/trellis2.md](docs/trellis2.md): TRELLIS.2 asset layout, no-conversion note, scripts, and export caveats.
- [docs/hyworld2.md](docs/hyworld2.md): HY-WorldMirror asset layout, scene inputs, memory profiles, and outputs.
- [docs/lito.md](docs/lito.md): LiTo setup, research-weight bundle, image-to-3DGS CLI, memory profiles, and PLY viewing notes.
- [docs/mapanything.md](docs/mapanything.md): MapAnything asset layout, scene `.npz` schema, parity notes, and viewer/export boundary.
- [docs/architecture.md](docs/architecture.md): module map and pipeline boundaries.
- [docs/development.md](docs/development.md): tests, local asset rules, and contribution constraints.
- [docs/model-publishing.md](docs/model-publishing.md): model bundles and model-card rules.
- [docs/release.md](docs/release.md): release checklist.

## Release Hygiene

Before publishing, build and inspect the artifacts:

```bash
uv run pytest -q
rm -rf dist
uv build
python scripts/packaging/check_release_artifacts.py \
  dist/mlx_spatial-*.tar.gz \
  dist/mlx_spatial-*-py3-none-any.whl
python scripts/packaging/check_release_artifacts.py --git-hygiene
```

The build must not include local weights, generated outputs, inputs, vendor
checkouts, caches, or agent state.

Publishing is handled by the trusted-publishing workflow in
`.github/workflows/workflow.yaml`. Do not publish from local shell credentials.
