Instructions to use XLabs-AI/flux-controlnet-canny-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use XLabs-AI/flux-controlnet-canny-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("XLabs-AI/flux-controlnet-canny-diffusers") pipe = StableDiffusionControlNetPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
The method to convert v3 into Diffusers format
Hello, I have been greatly inspired by your work. I am currently working on creating a new ControlNet following the x-flux methodology. I have successfully created a ControlNet, but unfortunately, I have not been able to convert it into a Diffusers-compatible format.
While x-flux includes inference code, my goal is to make the ControlNet I created using x-flux compatible with the Diffusers library.
During this process, I noticed that you successfully converted canny-v3 into a Diffusers version. Could you share the steps or methods you used to achieve this?
When I inspected the safetensors file, I found that the v3 model contains 86 weight files, whereas the Diffusers version contains 102 weight files. Could you explain where this difference comes from?