Instructions to use ckpt/FLUX.1-dev-Controlnet-Inpainting-Alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ckpt/FLUX.1-dev-Controlnet-Inpainting-Alpha with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ckpt/FLUX.1-dev-Controlnet-Inpainting-Alpha", 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:
- 5e05a5b3ce6021d6309bb9e3faf34cb24567253624e07642bae24961b1d83e65
- Size of remote file:
- 4.28 GB
- SHA256:
- 0e9d4891bb416584228b8fe16f35154ee462870f3798c108eef199d826c63ee2
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