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:
- a7bf6236faba7f9bd2e2adabe80854ab9141eef221edf5950e7a8c3300639d83
- Size of remote file:
- 2.89 GB
- SHA256:
- aabe13f814353de9192d3304ce7f3c9b62d13ec9193c160f3eb07d71829f74ac
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