Instructions to use ucfzl/ControlNet_DINO_Depth_CPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ucfzl/ControlNet_DINO_Depth_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_Depth_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:
- 9caf50b3c0df6d30f1e2e0988a75e54595d33aba23cf0773f6bf2fff8a882bf1
- Size of remote file:
- 1.53 GB
- SHA256:
- 79bea06dd6ba01b4b4eb24d787734fa950c552e067088df2e675dcf5a6275758
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