Add Stable Fast 3D example (native MLX single-image-to-3D-mesh)

stable_fast_3d/LICENSE

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stable_fast_3d/NOTICE.md

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+# Notice
+
+**This folder (`stable_fast_3d/`) is licensed differently from the rest of this repository.** The rest of `mlx-examples` is MIT-licensed (see the repository root `LICENSE`); this folder is a Derivative Work of [Stability-AI/stable-fast-3d](https://github.com/Stability-AI/stable-fast-3d) and is licensed under the **Stability AI Community License** (see `stable_fast_3d/LICENSE`), which is not sublicensable to MIT terms.
+
+This Stability AI Model is licensed under the Stability AI Community License, Copyright © Stability AI Ltd. All Rights Reserved.
+
+Powered by Stability AI.
+
+## Changes made in this Derivative Work
+
+Reimplemented the full SF3D inference pipeline natively in [MLX](https://github.com/ml-explore/mlx) (Apple Silicon, unified memory):
+
+- Neural forward pass (`camera_embedder.py`, `dinov2.py`, `backbone.py`, `post_processor.py`, `decoder.py`): camera embedder, DINOv2 image tokenizer with per-layer AdaLN modulation, two-stream interleaved transformer backbone, pixel-shuffle post-processor, triplane query sampler, material decoder. Validated module-by-module against the original PyTorch model to float32 tolerance.
+- Mesh finishing (`isosurface.py`, `uv_unwrap.py`, `texture_baker.py`): marching-tetrahedra isosurface extraction, UV unwrapping, texture baking, reimplemented in MLX/numpy (previously PyTorch/CPU-only).
+
+No model weights are redistributed here; `weights.py` downloads and converts the checkpoint from the gated [stabilityai/stable-fast-3d](https://huggingface.co/stabilityai/stable-fast-3d) Hugging Face repo at runtime, and downloads the fixed tetrahedra grid (`load/tets/160_tets.npz`, a Stability AI-authored geometric asset, not a trained weight) from the upstream GitHub repo. Both remain under the original license terms.
+
+Standalone reference implementation (development history, module-by-module validation scripts, more detail): https://github.com/bahaehmimdi/stable-fast-3d-mlx

stable_fast_3d/README.md

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+Stable Fast 3D
+==============
+
+[Stable Fast 3D (SF3D)](https://github.com/Stability-AI/stable-fast-3d) in
+MLX. SF3D is Stability AI's feedforward single-image -> textured 3D mesh
+model. This is a native MLX reimplementation of the full inference pipeline
+- no PyTorch, no MPS fallback.
+
+> **License note:** unlike the rest of `mlx-examples` (MIT), this folder is
+> licensed under the **Stability AI Community License** - see `LICENSE` and
+> `NOTICE.md`. The underlying model is also gated on Hugging Face; you must
+> request access before you can download the checkpoint.
+
+Why a separate port: the official PyTorch implementation's MPS backend on
+Apple Silicon is slow (full model reload + inference on every generation).
+On an M1 Pro, this MLX port brings a single generation from **7-9 minutes**
+down to **~12.4 seconds** end-to-end (~34-44x), with the neural forward
+pass and mesh finishing (UV unwrap, texture baking) both running in
+100% MLX/numpy.
+
+Installation
+------------
+
+```
+pip install -r requirements.txt
+```
+
+The model is gated at [Hugging Face](https://huggingface.co/stabilityai/stable-fast-3d):
+
+1. Request access [here](https://huggingface.co/stabilityai/stable-fast-3d).
+2. Create a read-access token [here](https://huggingface.co/settings/tokens).
+3. `huggingface-cli login` and enter the token.
+
+Usage
+-----
+
+```
+python image_to_3d.py chair.png output.glb
+```
+
+The checkpoint (~4GB) is downloaded and converted to MLX format on first
+run, then cached at `~/.cache/mlx-stable-fast-3d/`.
+
+Or from Python:
+
+```python
+from PIL import Image
+from stable_fast_3d import StableFast3D
+
+model = StableFast3D()  # loads weights once
+model.run(Image.open("chair.png"), "output.glb")
+```
+
+`StableFast3D.run` accepts `foreground_ratio`, `remove_bg` (background
+removal via `rembg`; set `False` if your image already has a clean alpha
+channel), and `bake_resolution` (texture resolution, default 512).
+
+What's not ported
+------------------
+
+- **CLIP-based material estimation** (`image_estimator` in the original) -
+  roughness/metallic use fixed constants instead of a real per-image
+  estimate. Not needed for a raw textured mesh.
+- **Illumination estimation** (`global_estimator`) - opt-in in the
+  original, not requested by default, not ported.
+- **Remeshing** (quad/triangle) - the original repo's optional
+  post-processing step; out of scope here, use the original repo's
+  `run.py --remesh_option` if you need it.
+
+Speed comparison
+-----------------
+
+Measured on an M1 Pro, single generation, model already loaded:
+
+| Version | Backend | Time | Detail |
+|---|---|---|---|
+| Original SF3D | PyTorch/CUDA (reference hardware) | 7-9 min | Full model reload every generation, no Mac-specific optimization |
+| This port, neural pass only | MLX (network) + PyTorch/MPS (finishing) | ~48s | 0.5s weight load + 5.3s MLX inference + 40.8s torch/MPS subprocess (isosurface, UV, texture) |
+| This port, full pipeline | 100% MLX + numpy | ~12.4s | 0.4s weight load + 5.4s neural pass + 7.0s MLX/numpy finishing |
+
+Validation
+----------
+
+Each module was validated against the original PyTorch model's intermediate
+tensors to float32 tolerance (max-abs-diff < 1e-4 throughout the neural
+pass), then checked end-to-end: feeding this port's `scene_codes` into the
+original (unmodified) PyTorch mesh-extraction code produces an identical
+mesh (same vertex/face counts, vertex positions matching to 2.2e-5 max /
+1.8e-7 mean) as a mesh produced by a fresh, real PyTorch forward pass on
+the same image. Full module-by-module validation scripts and development
+history: https://github.com/bahaehmimdi/stable-fast-3d-mlx.
+
+Citation
+--------
+
+```BibTeX
+@article{sf3d2024,
+  title={SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement},
+  author={Boss, Mark and Huang, Zixuan and Vasishta, Aaryaman and Jampani, Varun},
+  journal={arXiv preprint},
+  year={2024}
+}
+```

stable_fast_3d/image_to_3d.py

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+import argparse
+
+from PIL import Image
+
+from stable_fast_3d import StableFast3D
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Single-image to textured 3D mesh (SF3D, native MLX)."
+    )
+    parser.add_argument("image", help="Path to the input image.")
+    parser.add_argument(
+        "output",
+        nargs="?",
+        default="output.glb",
+        help="Output GLB path (default: output.glb).",
+    )
+    parser.add_argument("--foreground-ratio", type=float, default=0.85)
+    parser.add_argument(
+        "--no-remove-bg",
+        action="store_true",
+        help="Skip background removal (image already has a clean alpha/background).",
+    )
+    parser.add_argument("--bake-resolution", type=int, default=512)
+    parser.add_argument("--verbose", action="store_true")
+    args = parser.parse_args()
+
+    model = StableFast3D()
+    image = Image.open(args.image)
+    out = model.run(
+        image,
+        args.output,
+        foreground_ratio=args.foreground_ratio,
+        remove_bg=not args.no_remove_bg,
+        bake_resolution=args.bake_resolution,
+        verbose=args.verbose,
+    )
+    print(out)
+
+
+if __name__ == "__main__":
+    main()

stable_fast_3d/requirements.txt

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+mlx>=0.18
+numpy
+pillow
+huggingface-hub
+safetensors
+rembg
+onnxruntime
+trimesh

stable_fast_3d/stable_fast_3d/__init__.py

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+from .pipeline import StableFast3D
+
+__all__ = ["StableFast3D"]