Instructions to use yang1232009/DC-ControlNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yang1232009/DC-ControlNet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("yang1232009/DC-ControlNet", 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:
- afdbd885b2b32ffd5063520c051495d39e35c244d9b673e9a052e279d8cf8c79
- Size of remote file:
- 5.67 GB
- SHA256:
- 1c5115a556ab2507110dde38c7070da3866c9d15417f6bf55f5e20de07a1893f
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