Instructions to use williamberman/controlnet-model-3-12-learning-rates with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use williamberman/controlnet-model-3-12-learning-rates with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("williamberman/controlnet-model-3-12-learning-rates", 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:
- 24a8ced45bdbf96e88398a17d3a3aa53d6232227e0f1e2b37417646eb814ba7c
- Size of remote file:
- 2.89 GB
- SHA256:
- ec2d75c8a5cfb2a7622a1bec307d954abe563232ebd5acaf990286b441c464e3
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