Instructions to use InstantX/FLUX.1-dev-Controlnet-Canny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use InstantX/FLUX.1-dev-Controlnet-Canny with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny", 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:
- 0506f65dde6db076eeec0fa39349cded6b6060749b8d8c2bc7c1e4225be386fa
- Size of remote file:
- 4.2 MB
- SHA256:
- fe8582bab1af07330bae6a2d98e626bbbc8380bc19f920c6e5b7b1c70b06ff73
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