Instructions to use xingyang1/Distill-Any-Depth-Large-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xingyang1/Distill-Any-Depth-Large-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="xingyang1/Distill-Any-Depth-Large-hf")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("xingyang1/Distill-Any-Depth-Large-hf") model = AutoModelForDepthEstimation.from_pretrained("xingyang1/Distill-Any-Depth-Large-hf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 008828aa72dd5eae091d0bfcd807cf542bc3a6456680ec2420ef6823a4cdbf34
- Size of remote file:
- 1.34 GB
- SHA256:
- d42d36e631fefa566f1be9f395cc20b8768b5b91a6408f56cc25e238004c7aa6
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