✨ Arthemy CLIP Tuner (SDXL)

Ever write a perfect prompt and get back an image that read it like a grocery list? SDXL models have moods - some flatten the composition, some ignore the pose, some act like adjectives don't exist. The ✨ Arthemy CLIP Tuner (SDXL) is a niche fix for exactly that: instead of rewriting your prompt or cranking CFG until the image screams, it tweaks the weights of the CLIP text encoder that turns your words into conditioning.

It sits between your CheckpointLoaderSimple and CLIPTextEncode. Feed it the CLIP object, move three sliders, and out comes a patched CLIP that interprets your prompt differently. No training, no API, no extra model files - it's a live weight remap that takes milliseconds and vanishes the moment you change a value. This is one half of the Arthemy Live Tuner pack, built by Aledelpho, the CivitAI author behind the Arthemy comics models. He announced it on r/StableDiffusion in early 2026, and it's his first ComfyUI extension - mercifully, the code is small and readable enough that the "first extension" part isn't scary.

How it works

The trick is that "CLIP" isn't one model. SDXL pairs a 12-layer CLIP-L with a 32-layer OpenCLIP bigG - the classic "two encoders, two vocabularies" setup from the SD 1.5 era. This node slices the encoder by layer depth into three bands and scales the weights in each band by your slider value, using the same patch machinery ComfyUI uses for LoRAs - just a plain multiplier instead of a trained delta.

• Layers 0–35% → syntax_rigidity (grammar, word order, how literally it parses)
• Layers 35–75% → semantic_focus (subject recognition, actions, attributes)
• Layers 75%+ → style_abstraction (global composition, vibe over words)

Cute detail: the 12-layer CLIP-L lands entirely in the syntax band by this arithmetic, which is roughly right - CLIP-L is the literal word-grammar encoder, and bigG is where the mood lives.

The inputs that matter

base_scale is a global multiplier across every layer - think "overall obedience dial," and usually leave it at 1.0. The three named sliders are the show. mode changes what slider numbers mean:

• Real Value - a straight linear multiplier. 1.2 means the band's weights run 20% hotter, and it is genuinely touchy. Stay in the 0.8–1.2 range and nudge by 0.01–0.02.
• Soft Value - maps your whole 0–2 slider range onto a gentler 0.8–1.2 band, so you can sweep without nuking the image. Good for "I think this area is wrong but I don't know by how much."

It outputs a CLIP (wire that into CLIPTextEncode) plus an info string that reports what the three bands resolved to - handy for a text display node, nothing downstream needs it.

Install

Clone it into custom_nodes and restart:

cd ComfyUI/custom_nodes
git clone https://github.com/aledelpho/Arthemy_Live-Tuner-SDXL-ComfyUI.git

That's it. There's no requirements.txt to speak of and no model download - pure Python leaning on comfy.sd and torch you already have. That's rare and worth appreciating. ComfyUI Manager usually finds it if you search "Arthemy"; the clone path above is the reliable one. The repo also ships a ready-made Athemy-Tuner-SDXL.json workflow - drag it into ComfyUI and you'll see both tuners wired in correctly.

Common issues

The most common "it's broken" is wiring: the patched CLIP has to be the one feeding CLIPTextEncode. If your text encoder is still hooked straight to the checkpoint loader, nothing you do here matters.

Beyond that, the real gotchas are the author's own warnings. The layer-to-meaning mapping is experimental, not gospel - in the announcement thread he explicitly asks for feedback to refine it, and the bands don't act in isolation. His own example is instructive: lowering Semantic Focus unstiffened poses but loosened style; raising Style Abstraction made the model work harder on composition but changed the character's face. Treat the sliders as starting points, change one at a time, reroll, compare.

And scope: this is an SDXL-family tool. Illustrious and NAI fine-tunes share the same bigG encoder, so they work great. Flux, Z-Image and the LLM-encoder generation have no CLIP layers to patch - the KB's text-encoder panel puts it bluntly: "the component was replaced." Don't expect this node to do anything for you there.