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:
- db05cb736b0db23f07f9a92005dfc5050604d94330fcb6bcec4841d566ba2c42
- Size of remote file:
- 1 kB
- SHA256:
- 26d12e9f2e83267071edd2d5d63069804ab0c8d876435e6130a966689bceb611
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