Instructions to use furusu/acertainty_controlnet_identity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use furusu/acertainty_controlnet_identity with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("furusu/acertainty_controlnet_identity", 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:
- 8ea69f7c57f92e8ccee59572026488a85ec1e28301c140d2fae591f0038c8c18
- Size of remote file:
- 1.45 GB
- SHA256:
- 1bbe6e3e48d72352a6a6e4c256cc618c4eb4f4bc3c1d2903d1ab557fa3f7c62e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.