Instructions to use ucfzl/ControlNet_Hed_CPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ucfzl/ControlNet_Hed_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_Hed_CPO", 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:
- beac605ddfa2469cfa89ada69bfe165279b56a63e140eb9e79e9bf3548638ec8
- Size of remote file:
- 1.45 GB
- SHA256:
- da9561dfff716fc30d2f30275042cff7880f6d5fffaf310c8e1052c1b1ed22d3
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