The idea behind the FaceID variant of IP-Adapter is that a face should be a first-class conditioning input, not something patched on afterwards. Instead of swapping a face into a finished image, the model is guided by both your text prompt and an ID embedding extracted from reference photos, so prompts like "a woman wearing a baseball cap playing sports" produce that specific person in that scene, in a chosen style, with identity that holds up across multiple outputs.
The site is a straightforward four-step playground: upload an avatar or portrait, type the prompt, pick a generation type, submit the job. Results are produced on shared GPU capacity, and the developers openly note that GPU resources are sometimes scarce, so a failed run may simply need to be retried. There is no account requirement in the main flow — open the page, generate, compare.
Alongside the demo, the site curates practical documentation: how to use IP-Adapter-FaceID through ComfyUI and the IPAdapter Plus nodes, and how to run it against SD1.5 and SDXL checkpoints, including the FaceID Plus variants. Links out to the upstream GitHub repository and model card are provided for people who want to self-host or read the methodology, and the site also discusses its limitations and bias considerations. It is an independent community project demonstrating an open research technique rather than an official product release.
