Fooocus-API RunPod serverless worker implementation
Repository consists of two branches: NetworkVolume and Standalone
The NetworkVolume expects you to install and prepare your own instance on the RunPod network volume, or to use our 3wad/runpod-fooocus-api:0.4.0.6-networksetup to do so. This is ideal if you want to change models, loras or other contents on the fly, let your users upload them, or persist generated image files right on the server. The downside of this solution is slower starts because everything has to be loaded over the datacenter's network. See network-guide for step-by-step instructions.
The Standalone branch is a ready-to-use docker image with all the files and models already baked and installed into it. You can still customize it to use your own content, but it can't be changed without rebuilding and redeploying the image. This is ideal if you want the fastest, cheapest possible endpoint for long-term usage without the need for frequent changes of its contents. See standalone-guide or simply use 3wad/runpod-fooocus-api:0.4.0.6-standalone as the image for a quick deploy with the default Juggernaut V8 on your RunPod serverless endpoint.
All prebuilt images can be found here: https://hub.docker.com/r/3wad/runpod-fooocus-api
request_examples.js contain example payloads for all endpoints on your serverless worker, regardless of the branch. But don't hesitate to ask if you need more help.
Feel free to make pull requests, fixes, improvements and suggestions to the code. Any cooperation on keeping this repo up-to-date and free of bugs is highly welcomed.
We're not always on the latest version automatically, as there can be breaking changes or major bugs. The updates are being made only after thorough tests by our community of Discord users generating images with the AI agent using this repo as it's tool. And only if we see that the new version performs better and stable.
Disclaimer: This repo is in no way affiliated with RunPod Inc. All logos and names are owned by the authors. This is an unofficial community implementation