by Black Forest Labs: https://blackforestlabs.ai
This repo is intended to run FLUX text-to-image and image-to-image models as worker for the scalable AIME API Server.
cd $HOME && git clone https://github.com/black-forest-labs/flux
cd $HOME/flux
python3.10 -m venv .venv
source .venv/bin/activate
pip install -e ".[all]"Use AIME MLC to install in a dockerized container.
mlc-create flux1dev Pytorch 2.4.0
mlc-open flux1dev
git clone https://github.com/black-forest-labs/flux
cd flux
sudo apt install libgl1-mesa-glx libglib2.0-0
pip install -r requirements.txt
pip install -e .These models are avaible:
FLUX.1 [dev]guidance-distilled variantFLUX.1 [schnell]guidance and step-distilled variant
| Name | HuggingFace repo | License | md5sum |
|---|---|---|---|
FLUX.1 [schnell] |
https://huggingface.co/black-forest-labs/FLUX.1-schnell | apache-2.0 | a9e1e277b9b16add186f38e3f5a34044 |
FLUX.1 [dev] |
https://huggingface.co/black-forest-labs/FLUX.1-dev | FLUX.1-dev Non-Commercial License | a6bd8c16dfc23db6aee2f63a2eba78c0 |
The weights of the autoencoder are also released under apache-2.0 and can be found in either of the two HuggingFace repos above. They are the same for both models.
The weights will be downloaded automatically from HuggingFace once you start one of the demos. To download FLUX.1 [dev], you will need to be logged in, see here.
If you have downloaded the model weights manually, you can specify the downloaded paths via environment-variables:
export FLUX_SCHNELL=<path_to_flux_schnell_sft_file>
export FLUX_DEV=<path_to_flux_dev_sft_file>
export AE=<path_to_ae_sft_file>For interactive sampling run
python -m flux --name <name> --loopOr to generate a single sample run
python -m flux --name <name> \
--height <height> --width <width> \
--prompt "<prompt>"Run following command line:
python3 main.py --ckpt_dir {{directory where the model was downloaded}} --api_server https://{{ url to your api server}}This will register the worker to an AIME API Server to process distributed API job requests.