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:
- 2c621a29cf636caeae3a1c1175631a223d50c4d0981d628feb09b5610ff2664b
- Size of remote file:
- 1 kB
- SHA256:
- 32a3406d2ef50f9b0ec90e408a1c13d2c8958de7599603cc4c0cc7154da6ebe9
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