Control image generation by edge maps generated with Edge Drawing Parameter Free (edpf). This is similar to ControlNet Canny but uses a modern algorithm for edge-detection which requires no parameter tuning (Canny was invented in 1986, Edge Drawing in 2012). You can use the gradio demo or edpf.py script to generate edge maps until a pre-processor is implemented in your favorite Stable Diffusion UI.
Feedback is welcome! I'm still improving this model and you can help me by generating simple usecases and discuss the results.
The case for a new edge model
Do you find all these settings for canny confusing and time-consuming?
Do your default edge maps have noise, artefacts and missing edges?
All your images come out a mess like they were drawn by a human?
Well look no further!
With Edge Drawing Parameter Free you can create a masterpiece with a single mouse click!
No tuning! No mistakes! No frustration!
By combining recent advances in deep learning, cloud computing and block chain technology(?) we created the EDPF control net model just for you.
And the best part: it's free! Parameter Free!
If you want to train your own control net see my article Play in Control - ControlNet training setup guide!
Description
Trained on clean image dataset, with non-square images and slightly better captions and batch size=32. Overall much better quality and improved no-prompt inference.
see https://huggingface.co/GeroldMeisinger/control-edgedrawing -> Experiment 6.1 for more information on training