Instructions to use XLabs-AI/flux-controlnet-canny-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use XLabs-AI/flux-controlnet-canny-v3 with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("XLabs-AI/flux-controlnet-canny-v3") pipe = StableDiffusionControlNetPipeline.from_pretrained( "fill-in-base-model", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
This repository provides a Canny ControlNet checkpoint for FLUX.1-dev model by Black Forest Labs
See our github for comfy ui workflows.

See our github for train script, train configs and demo script for inference.
Models
ControlNet is trained on 1024x1024 resolution and works for 1024x1024 resolution. We release v3 version - better and realistic version, which can be used directly in ComfyUI!
Please, see our ComfyUI custom nodes installation guide
Examples
See examples of our models results below.
Also, some generation results with input images are provided in "Files and versions"
Inference
To try our models, you have 2 options:
- Use main.py from our official repo
- Use our custom nodes for ComfyUI and test it with provided workflows (check out folder /workflows)
Please, try our workflow "canny_workflow.json"
License
Our weights fall under the FLUX.1 [dev] Non-Commercial License
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