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
Scrip for custom training
#10
by anandguptacv - opened
Hi @instantx-admin As this model is open source, I’m currently working on a project where I need to train the ControlNet pipeline. Could you please provide guidance or share the training script? It would greatly help me understand how the model is trained, especially within the Flux Dev framework