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:
- 161de03f365e3050074ee7cfb18f2483cef48ff6b10b234ea12d4f1c0a28c3bd
- Size of remote file:
- 2.89 GB
- SHA256:
- bea75e1ba23ca188a113477f01c923488836974f90147bba1dddbbafde6b9bba
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