Style Model Apply Advanced

If you've used Flux Redux, you already know the frustration this node fixes. The stock StyleModelApply node has no strength control - you feed it your reference image and it slams the style in at full force, steamrolling your text prompt so hard that you basically just get variations of the input image. StyleModelApplyAdvanced is the same node with one thing added: a strength parameter. That's the entire pitch, and it's enough to make Redux actually usable.

The description is refreshingly honest about it: "StyleModelApply but with strength parameter." No frills. Kijai filed it under experimental in the pack, but it's been the go-to fix for Redux-overwhelm for a long time.

Why this matters

Redux (and style models generally) work by turning a reference image into conditioning via CLIP Vision, then injecting that into your generation. The problem is dosage. As one widely-cited community write-up on Redux put it, "running the default workflow in Comfy felt like I was just getting variations of the same image," and the fix was to control strength - with the finding that "any higher than like 0.07 and Redux will overwhelm the prompt and just produce a variation. 0.04 - 0.07 seems to be the sweet spot." Different style models scale differently, but the lesson is universal: at full strength the reference wins and your prompt loses. You need a dial.

This node is that dial. Turn it down and the reference becomes a nudge - a color palette, a vibe, a loose composition - while your prompt still drives. Turn it up for a faithful style transfer. Without the slider you only ever get the "up" setting.

The inputs and outputs that matter

All four inputs are required, and three of them are the standard style-model plumbing:

• conditioning (CONDITIONING) - your prompt conditioning, the thing you're layering style onto.
• style_model (STYLE_MODEL) - loaded via a style model loader (Redux, etc.).
• clip_vision_output (CLIP_VISION_OUTPUT) - your reference image run through a CLIP Vision encoder. This is what the style comes from.
• strength (FLOAT, default 1.0, range -10 to 10) - the whole reason you're here. Despite the wide range, the useful zone is usually low. Start well under 1.0 and creep up.

The output is CONDITIONING - plug it into your sampler's positive conditioning like you would any conditioning.

Note the range goes negative and up to 10. Negative and extreme values are there for experiments, not daily use; you'll live in the low positives.

How to install it

ComfyUI Manager: search KJNodes for ComfyUI, install, restart. Manually:

cd ComfyUI/custom_nodes
git clone https://github.com/kijai/ComfyUI-KJNodes
pip install -r ComfyUI-KJNodes/requirements.txt

Restart and it appears under KJNodes/experimental. The node itself downloads nothing - but you do need the actual style model (e.g. the Redux .safetensors) and a CLIP Vision model in your regular ComfyUI model folders for the graph to run.

Common issues & troubleshooting

The prompt still gets ignored. Your strength is too high. Drop it hard - for Redux specifically, people report the sweet spot down around 0.04–0.07, not 1.0. If 0.5 already overwhelms your prompt, that tells you where your model's usable band is; go lower.

The style barely shows. Opposite problem - nudge strength up. And check the clip_vision_output is actually encoding the reference you think it is; a wrong or empty CLIP Vision output gives you a weak, generic push.

Wrong CLIP Vision model. Style models expect a specific CLIP Vision encoder. Pair the wrong one and you get muddy or broken conditioning that no amount of strength tuning fixes. Match the CLIP Vision model your style model was trained against.

It's not for SDXL IP-Adapter. This is a style model node (Redux-style). If you're doing image prompting with IP-Adapter, that's a separate ecosystem with its own weight controls - this node won't slot into that graph.