Instructions to use ucfzl/ControlNet_DINO_Lineart_CPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ucfzl/ControlNet_DINO_Lineart_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_Lineart_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:
- 7ec650b42f52d3aef642f3b0773dffd3966688f1b4eaf22081f4f57dd0a65034
- Size of remote file:
- 1.53 GB
- SHA256:
- 8ad9084ff928e67fb33617c2d7b81e4ec4c467471535f025581d77984d7d7402
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