Model reference · open weights

flux_dev_openpose_controlnet

Available as managed deployment Licence fee Image raulc0399 · community Text→image 1 variants 1k dl/mo

flux_dev_openpose_controlnet is an open-weight image model from raulc0399. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released byraulc0399
TypeImage models
TaskText→image
Parameters (lead)744M
Runs withdiffusers
Released2024-09-26
Popularity1k downloads / month
LicenceCommercial licence needed

About

What flux_dev_openpose_controlnet is

(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

Read the full model card

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

results

using these 2 images:

with these prompts:

"two friends sitting by each other enjoying a day at the park, full hd, cinematic" "person enjoying a day at the park, full hd, cinematic"

resulted in these images:

License

Weights fall under the FLUX.1 [dev] Non-Commercial License

From the published model card. Full card on the HuggingFace links in the sidebar.

How it works

How image models work

Text promptwhat to makeText encoderunderstands itDiffusion stepsdenoise to pixelsImagePNG / JPEGA diffusion model starts from noise and denoises it, guided by your prompt, into a finished image.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys flux-dev-openpose-controlnet for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (flux-dev-openpose-controlnet below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/images/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"flux-dev-openpose-controlnet","prompt":"a red bicycle","size":"1024x1024"}'

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms