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
canny thresholds used to train the model
#5
by ink10 - opened
Thanks for creating and sharing the model. Hope 1024^2 comes out soon.
What were the Canny thresholds you used for creating your training dataset? And were the images pre-processed before Canny was applied?
Thanks
probably random spread within a certain range