openpose controlnet for flux.dev

(big thanks to oxen.ai for sponsoring the GPU for the training)

inference

an openpose controlnet for flux-dev, trained on https://huggingface.co./datasets/raulc0399/open_pose_controlnet

the controlnet model is trained for the xlabs ai pipeline https://github.com/XLabs-AI/x-flux

to install the pipeline, execute the following:

git clone https://github.com/XLabs-AI/x-flux.git
cd x-flux
python3 -m venv xflux_env
source xflux_env/bin/activate
pip install -r requirements.txt

to run the pipeline with controlnet:

python3 main.py \
 --prompt "person enjoying a day at the park, full hd, cinematic" \
 --image ~/open_pose_controlnet_dataset/validation_images/pose/3_pose_1024.jpg --control_type openpose \
 --local_path ./model.safetensors \
 --use_controlnet --model_type flux-dev \
 --width 1024 --height 1024  --timestep_to_start_cfg 2 \
 --num_steps 50 --true_gs 4 --guidance 4 \
 --save_path ~/gen_imgs

if the image has already been preprocessed comment out the line #146 from src/flux/xflux_pipeline.py

# self.annotator = Annotator(control_type, self.other_device)

training

oxen clone https://hub.oxen.ai/raulc/open_pose_controlnet_dataset
git clone https://github.com/raulc0399/x-flux.git
cd x-flux
git checkout open_pose_training
python3 -m venv xflux_env
source xflux_env/bin/activate
pip install -r requirements.txt
huggingface-cli login
accelerate config
mkdir images
rsync -r ~/open_pose_controlnet_dataset/train/images/ images/
cp train_configs/test_openpose_controlnet.yaml train_configs/openpose_controlnet.yaml
accelerate launch train_flux_deepspeed_controlnet.py --config "train_configs/openpose_controlnet.yaml"

note 1: check the file train_configs/openpose_controlnet.yaml before starting

note 2: rsync is needed, cp does not work with that many files

note 3: the oxen repo has the caption files as json as expected by the training script