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:
- 4012924e6ee95e54c3bd3c46c0f0449943d56ef80b72ee538556acc1a9050c2c
- Size of remote file:
- 1.46 GB
- SHA256:
- a92fb9119e5f18948a609c7e9c2ce15bfaf13317fb070ac6e29fc3381c1e9c64
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.