Instructions to use jschoormans/controlnet-densepose-sd2-1-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jschoormans/controlnet-densepose-sd2-1-base with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jschoormans/controlnet-densepose-sd2-1-base", 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:
- f558acf3a879b827f73d3d4a1785d72a997657c7d5135c86c0b4d5143dde216d
- Size of remote file:
- 2.91 GB
- SHA256:
- e93403b9965a78b7496fa576a5624fa997152dc4eb883aa72563f5a8e52ba3a9
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