Workflow templates

Four workflows ship with the node pack, in the workflows/ folder of the repository . Drag a .json file onto the ComfyUI canvas to load it.

Three need no account.

Each template carries a visible note on the canvas describing what to change, and declares its checkpoint downloads — a missing model offers a guided download instead of a red error. Seeds are set to randomize, so every queue produces a fresh image. Every template attaches Preview nodes to Apply’s outputs, so the generated positive and negative prompts are always readable on the canvas.

1. Quickstart

File: 01-quickstart.json · No account needed

The minimum useful graph: a gallery style, a subject, a sampler — plus two Preview nodes showing the compiled positive and negative prompts, so you can read exactly what StyleRef produced.

To run it: set your SDXL checkpoint on the Checkpoint Loader, then queue.

To make it yours: change subject on Apply, and swap style_ref on Load for any slug from the gallery  — or use the Search styles… button.

If you’re new to the nodes, start here — see install & quickstart.

2. Consistency grid

File: 02-consistency-grid.json · No account needed

One Load feeding three Apply nodes with three different subjects, each through its own sampler. Queue once and compare the three results side by side: the style holds steady while the content changes.

To run it: set your checkpoint and queue. Change any of the three subjects and queue again.

This is the template that shows what a style specification actually buys you: a coherent set from one definition, rather than prompt text you re-tune per image.

What this demonstrates, precisely: consistency within this model, across generations. It is not a before/after comparison, and it does not claim other tools will produce matching images from the same style. Each tool renders its own way — the style is what stays constant in the instructions.

3. Your own style

File: 03-your-own-style.json · Sign-in required

The full loop, with extraction where it belongs — on the web:

1. Extract a style from any image at styleref.io  (upload, preview, refine). It lands in your library.
2. Sign in — the template includes a Login node; queue it alone first if its status says signed out.
3. Put your style’s id in style_ref on Load — or pick it with Search styles…, which fills the field for you.
4. Set your checkpoint and queue.

Loading and applying your own style in ComfyUI is free — the extraction on the web is the only step that spends credits.

4. Facets

File: 04-facets.json · No account needed

Apply hands you one prompt string. Facets hands you the style’s sections one output at a time — 24 of them, exactly the sections on the style board at styleref.io, plus the six custom items.

Six sections are wired to Preview nodes here so you can read them on the canvas: colours, light and shadow, mood, surface and material, shape language, and guardrails. Every other output is on the node too — drag any of them wherever you need it.

To run it: set your SDXL checkpoint and queue. Swap style_ref on Load for any gallery slug to inspect a different style.

They’re plain text outputs, so they go anywhere text goes: a second CLIP encoder for regional conditioning, an IPAdapter or ControlNet prompt, a LoRA trigger line, or your own nodes. That’s the escape hatch — take the palette without taking the whole prompt.

The template also runs a normal Apply render alongside, so you can compare the assembled prompt against the parts it was built from.

Z-Image templates

A parallel set wired for Z-Image Turbo lives in workflows/z-image/. Z-Image is an S3-DiT model with its own loader stack — a UNET, a Qwen-3-4B text encoder, and a Flux-style VAE — rather than a single checkpoint; the three files download on first run. StyleRef compiles for it with the diffusion target, the same tag-weighted output SDXL and SD1.5 use.

The sampler is set to Z-Image Turbo defaults — 8 steps, CFG 1.0, euler/simple.

Z-Image takes no negative prompt. The model runs without classifier-free guidance, so there is no negative encoder on these canvases — the sampler’s negative input gets a zeroed conditioning, the same shape a FLUX graph uses. StyleRef still compiles the negative and previews it, so you can read it; put anything you need from it into subject on Apply as a positive phrase instead (“fully clothed”, “no text or watermark”). Raising CFG does not bring the negative back — it only oversaturates the image and slows the sample down.

Regenerating the templates

The templates are generated from a script rather than hand-edited, so node and link IDs stay correct:

python scripts/build_workflows.py

Edit scripts/build_workflows.py and re-run it — changes made directly to the JSON are overwritten.