Instructions to use mlx-community/Video-Depth-Anything-Base-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Video-Depth-Anything-Base-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Video-Depth-Anything-Base-MLX mlx-community/Video-Depth-Anything-Base-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Video Depth Anything Base (MLX)
MLX port of Video Depth Anything (ByteDance, CVPR 2025 highlight): consistent monocular depth estimation for arbitrarily long videos. Converted from the official checkpoint depth-anything/Video-Depth-Anything-Base.
Architecture: DINOv2 backbone + DPT head with AnimateDiff-style temporal motion modules. Outputs per-frame depth maps, not text.
Usage
from mlx_vlm import load
from mlx_vlm.models.video_depth_anything.generate import (
VideoDepthPredictor,
read_video_frames,
)
model, processor = load("mlx-community/Video-Depth-Anything-Base-MLX")
predictor = VideoDepthPredictor(model, processor)
frames, fps = read_video_frames("input.mp4", max_len=300, target_fps=15)
depths = predictor.infer(frames) # (T, H, W) float32
Notes
- Inputs are channel-last
(B, T, H, W, 3); H and W must be multiples of 14. - Metric model: no. Metric models output absolute depth (meters) and skip scale/shift window alignment.
- Weights are fp32. On GPU, output matches the PyTorch reference to ~1% relative (Metal fast-math); on CPU to ~1e-5.
License
CC-BY-NC-4.0 (same as the source checkpoint). Non-commercial use only.
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Model size
0.1B params
Tensor type
F32
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Hardware compatibility
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