Instructions to use mlx-community/moge-3-vitg-mlx-fp32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/moge-3-vitg-mlx-fp32 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir moge-3-vitg-mlx-fp32 mlx-community/moge-3-vitg-mlx-fp32
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
MoGe-3 (VITG) โ MLX
MLX port of MoGe-3: fine-detail monocular geometry estimation with self-guided sparse volumetric refinement. Predicts metric point maps, depth maps, normal maps, valid-pixel masks, and camera intrinsics from a single image.
Converted with mlx-vlm from the official
model.pt checkpoint (fp32).
Usage
from mlx_vlm import load
from mlx_vlm.models.moge3.generate import MoGe3Predictor, read_image
model, processor = load("mlx-community/moge-3-vitg-mlx-fp32")
predictor = MoGe3Predictor(model, processor)
output = predictor.infer(read_image("image.jpg"), resolution_level=9)
points, depth, mask = output["points"], output["depth"], output["mask"]
normal, intrinsics = output["normal"], output["intrinsics"]
Validated against the torch reference (CPU, fp32): identical valid-pixel masks and <0.2% median relative depth error.
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Model size
1B params
Tensor type
F32
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