Instructions to use diffusers/controlnet-canny-sdxl-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diffusers/controlnet-canny-sdxl-1.0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("diffusers/controlnet-canny-sdxl-1.0", 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
- Local Apps
- Draw Things
- DiffusionBee
Using non square images for training
#35
by demo001s - opened
Can I modify the code for resizing the image and use images with different scales for training, such as 3:2. I don't know if it's feasible?
I think that depends bit if the underlying base model would support good generations at the scale (SDXL UNet in this case). It might require fine-tuning the base model first and then doing the ControlNet training. I am not very sure, though.