MLcube implementation for Flux.1-schnell

[davidjurado]

MLCube for Flux.1-schnell

MLCube™ GitHub repository. MLCube™ wiki.

Project setup

An important requirement is that you must have Docker installed.

# Create Python environment and install MLCube Docker runner 
virtualenv -p python3 ./env && source ./env/bin/activate && pip install pip==24.0 && pip install mlcube-docker
# Fetch the implementation from GitHub
git clone https://github.com/mlcommons/training && cd ./training
git fetch origin pull/839/head:feature/mlcube_flux && git checkout feature/mlcube_flux
cd ./text_to_image/mlcube

Inside the mlcube directory run the following command to check implemented tasks.

mlcube describe

 Extra requirements

You need to download the torchtitan git submodule:

git submodule update --init --recursive

You also need accept the license for the FLUX schnell model on Hugginface.

Finally, to be able to download all the models you will need to get a token from Hugginface.

Note: Make sure that when creating the token you select:

• Read access to contents of all public gated repos you can access

After that you can set a new enviroment variable, like this:

export HUGGING_FACE_HUB_TOKEN="YOUR_TOKEN"

MLCube tasks

• Demo tasks:

Download demo dataset and models.

mlcube run --task=download_demo -Pdocker.build_strategy=always

Train demo.

mlcube run --task=demo -Pdocker.build_strategy=always

Execute the complete pipeline

You can execute the complete pipeline with one single command.

• Demo pipeline:

mlcube run --task=download_demo,demo -Pdocker.build_strategy=always

Note: To rebuild the image use the flag: -Pdocker.build_strategy=always during the mlcube run command.

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