Instructions to use jschoormans/controlnet-densepose-sdxl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jschoormans/controlnet-densepose-sdxl 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-sdxl", 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:
- d401134d8799d1fe6347fe6f2a03b32c5e7ccd7abe936c5d2fbcafcb7973c04c
- Size of remote file:
- 5 GB
- SHA256:
- 55d562f74bef022169227269760d49e1f64d1f578721ff4eceb7c02ccd1bc730
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