Instructions to use Dung306080/Controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Dung306080/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("Dung306080/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:
- 6e44cd31704dccc2106a9f4b06c5b544c5d552e63987d51efa3e77559b784f0e
- Size of remote file:
- 2.51 GB
- SHA256:
- a9e13fd61f3193887791c8a0dd07a07202174dc47d5ddaea94ea1344f07c7467
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