Instructions to use raulc0399/pixart-alpha-hed-controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use raulc0399/pixart-alpha-hed-controlnet with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("raulc0399/pixart-alpha-hed-controlnet") pipe = StableDiffusionControlNetPipeline.from_pretrained( "fill-in-base-model", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
These are the weights for the Pixart HED Controlnet, converted to be used with the pixart alpha controlnet pipeline.
To use:
git clone https://github.com/raulc0399/PixArt-alpha.git .
git checkout master_train_controlnet_diffusers
pip install -r requirements.txt
python controlnet/pipeline/run_controlnet_pipeline.py
(make sure to change the input_image_path and prompt)
the weights were created using the controlnet/convert_pixart_alpha_controlnet_to_diffusers.py from the github repo
python controlnet/convert_pixart_alpha_controlnet_to_diffusers.py --orig_ckpt_path=<full path to>/PixArt-XL-2-1024-ControlNet.pth --dump_path=./<output folder>/hed-controlnet
the training script has been implemented in controlnet/train_pixart_controlnet_hf.py to start it with the toy-dataset "fusing/fill50k" run the file ./train_controlnet_hf_diffusers.sh
more info can be found in this PR https://github.com/PixArt-alpha/PixArt-alpha/pull/164
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