Image-to-3D
ONNX
Depth Pro
onnxruntime
gaussian-splatting
monocular-view-synthesis
novel-view-synthesis
sharp
oku3d
fp16
opset21
stereo
Instructions to use Jens-Duttke/Sharp-ONNX-HighPerf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Depth Pro
How to use Jens-Duttke/Sharp-ONNX-HighPerf with Depth Pro:
# Download checkpoint pip install huggingface_hub hf download Jens-Duttke/Sharp-ONNX-HighPerf --local-dir checkpoints
import depth_pro # Load model and preprocessing transform model, transform = depth_pro.create_model_and_transforms() model.eval() # Load and preprocess an image. image, _, f_px = depth_pro.load_rgb("example.png") image = transform(image) # Run inference. prediction = model.infer(image, f_px=f_px) # Results: 1. Depth in meters depth = prediction["depth"] # Results: 2. Focal length in pixels focallength_px = prediction["focallength_px"] - Notebooks
- Google Colab
- Kaggle
Download examples/sample2/sway.mp4 from Jens-Duttke/Sharp-ONNX-HighPerf: direct link, hf CLI and curl.
- Browser
- Download file 587 kB
-
https://huggingface.co/Jens-Duttke/Sharp-ONNX-HighPerf/resolve/main/examples/sample2/sway.mp4
- Command line
-
hf download hf://Jens-Duttke/Sharp-ONNX-HighPerf/examples/sample2/sway.mp4
-
curl -L -o sway.mp4 https://huggingface.co/Jens-Duttke/Sharp-ONNX-HighPerf/resolve/main/examples/sample2/sway.mp4
587 kB
- Xet hash:
- af4ff33c5bf83612cefb60bd1a9abe67d827e8003178a02afcc02f3cd12c8ea4
- Size of remote file:
- 587 kB
- SHA256:
- fe9c12000dcdb3ecaedbcc13f857eacbe9d981e35fdfaa30f46cd6a6c8edb5ad
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