FLUX Image Generation with DigitalOcean

We have talked a lot about the capabilities and potential of Deep Learning Image Generation here on the Paperspace by DigitalOcean Blog. Not only are image generation tools fun and intuitive to use, but they are one of the most widely democratized and distributed AI models available to the public. Really, the only Deep Learning technology with a larger social footprint are Large Language Models.

For the last two years, Stable Diffusion, the first publicly distributed and functional image synthesis model, has completely dominated the scene. We have written about competitors like PixArt Alpha/Sigma and done research into others like AuraFlow, but, at the time of each release, nothing has set the tone like Stable Diffusion models. Stable Diffusion 3 remains one of the best open source models out there, and many are still trying to emulate their success.

Last week, this paradigm changed with the release of FLUX from Black Forest Labs. FLUX represents a palpable step forward in image synthesis technologies in terms of prompt understanding, object recognition, vocabulary, writing capability, and much more. In this tutorial, we are going to discuss what little is available to the public about the two open-source FLUX models, FLUX.1 schnell and FLUX.1-dev, before the release of any Flux related paper from the research team. Afterwards, we will show how to run Flux on a DigitalOcean GPU Droplet powered by an NVIDIA H100 GPU.