Instructions to use MnLgt/depthpose with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MnLgt/depthpose with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MnLgt/depthpose", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad6623a0efe0e3c2adbec364733d6f686a4613455358c3380ba184bc581cfc78
- Size of remote file:
- 2.89 GB
- SHA256:
- b3a5ab862629ad67fdf8204956093f357ebca4a7545f10ae9b6a6a33f16ecbef
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