Depth Estimation
Transformers
Safetensors
prompt_depth_anything
prompt-depth-anything
metric-depth
custom_code
Instructions to use bigshanedogg/Prompt-Depth-Anything-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigshanedogg/Prompt-Depth-Anything-Small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="bigshanedogg/Prompt-Depth-Anything-Small", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("bigshanedogg/Prompt-Depth-Anything-Small", trust_remote_code=True) model = AutoModelForDepthEstimation.from_pretrained("bigshanedogg/Prompt-Depth-Anything-Small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 585 Bytes
29ebc1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | """Prompt Depth Anything processor: ``PromptDepthAnythingImageProcessor`` (the official load_image / load_depth) as its
only part."""
from transformers.processing_utils import ProcessorMixin
class PromptDepthAnythingProcessor(ProcessorMixin):
attributes = ["image_processor"]
def __init__(self, image_processor=None, **kwargs):
super().__init__(image_processor, **kwargs)
def __call__(self, images=None, prompt_depth=None, **kwargs):
return self.image_processor(images, prompt_depth=prompt_depth, **kwargs)
__all__ = ["PromptDepthAnythingProcessor"]
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