Magnific Generate Image

Yes, it really does call Magnific. The name isn't decorative here the way it is on some "Magnific-style" nodes you'll find elsewhere - this submits a job to Magnific's servers, waits, and hands the result back as an IMAGE batch. Nothing works offline, and every run is metered against your Magnific account.

So the question isn't "does it work", it's "why would I run a cloud generator next to a local one". The answer is the reference sockets. Four image inputs, one reference_type switch, and a model picker that spans Magnific's catalogue - that's a consistency toolkit (keep the same face, the same product, the same jacket) that's genuinely tedious to reproduce locally, especially when the model you want has no open weights.

What you set

prompt is a multiline text box. model defaults to auto, which lets Magnific choose; the list narrows once a model is selected, and resolution follows the selected model's tiers (auto uses the model's default). aspect_ratio covers the usual set plus auto, 2:1 and 21:9. count runs 1–8 and returns that many images in one batch. seed is sent to Magnific; a batch uses seed, seed+1, seed+2 and so on, and models that honour seeds reproduce the same result from the same settings.

The optional sockets are the interesting part. reference_image through reference_image_4 each take an image or a batch. One reference_type applies to all of them: image keeps a subject, product or person consistent, style borrows the look. Two things worth knowing before you build around it: a batch on one socket counts as one reference per image, and a generation accepts at most 12 references including Library ones - the node stops before uploading anything if you go over. Also, batching resizes everything to the first image's size, so use separate sockets when your references differ in size rather than feeding a mixed batch.

library_references takes the output of a Magnific Library Reference node: your trained characters, styles, elements or locations, or Magnific's public catalogue. Their @name tokens get added to the prompt if you forgot to write them.

folder comes from Magnific Save To. Without it, results land in your Personal project.

Outputs: images, creation_identifiers (one per image in the batch), and metadata - JSON with the model, prompt, seed, size and the link to the creation on magnific.com. Wire metadata into Magnific Metadata (unpack) and send the model output into a Save Image filename prefix, and your files will tell you which model made them. That's a nice habit, because ComfyUI re-encodes what a node returns, so the file itself carries no Magnific provenance.

Install

cd ComfyUI/custom_nodes
git clone https://github.com/Code2Collapse/ComfyUI-CustomNodePacks.git

Or install "CustomNodePacks" from ComfyUI Manager. The Magnific family is ported into this pack from Magnific's vendor pack magnific-comfyui 0.7.0, so a pack install is all you need - don't also install the vendor zip, or you'll have two nodes with the same name. Sign in once (the Magnific menu, or the Magnific Save To node's sign-in button); credentials live in ~/.magnific/comfyui_auth.json.

Before you point a pipeline at it

Three things, in order of how much they'll annoy you. It costs money per call and there's no free tier - this is a metered service, and a graph that quietly generates eight images per queue is a graph that quietly spends. Your prompt and every reference image are uploaded to Magnific's servers, and their filter applies, not yours. And auto model selection is convenient right up until you need reproducibility across weeks, at which point pin the model.

The honest comparison: for a subject you can describe well, a local model plus an IP-Adapter or a LoRA will do a lot of this for free and keep everything on your disk. Reach for this node when the consistency job is the whole point, or when you're already paying for Magnific and want it wired into the masking and compositing you do locally.