Instructions to use ucfzl/ControlNet_DINO_Pose_CPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ucfzl/ControlNet_DINO_Pose_CPO with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ucfzl/ControlNet_DINO_Pose_CPO", torch_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:
- 900587823cf6185b5c3e8b2623b3d9afbf748dcea0761d76bd0d5f78326b3115
- Size of remote file:
- 1.53 GB
- SHA256:
- 3bd40cb9606425d3b1c5c423f647f9e4ea08857d1cc3d9c9d8f7f3578f88d04b
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