Apply Style Model

Welcome to the secret sauce of stylistic coherence. The Apply Style Model node (internally referred to as StyleModelApply) takes your boring, unflavored conditioning and injects it with a concentrated blast of artistic flair using a reference image and a pre-trained style model.

So if you’ve ever thought, “This image looks cool, but can it look more like that?”, this is the node for you.

📦 What This Node Does

The Apply Style Model node enhances your conditioning data by applying a style model derived from a CLIP-encoded reference image. It doesn’t touch your prompt directly—it influences it. Think of it as whispering a visual moodboard into the AI’s ear before it starts painting.

By injecting style information into the conditioning stream, you get generations that feel more cohesive, more on-brand, and—let’s be real—just plain better.

🔌 Inputs

Name	Type	Description
conditioning	Conditioning	The base prompt conditioning. This is your control signal for the AI, before any styling is applied.
style_model	Style Model	The pre-trained style model (.ckpt or .safetensors) that knows what "style" looks like. Must contain a style_embedding.
clip_vision_output	CLIP Vision Output	Output from a CLIP Vision encoder node. This is the style reference image, encoded into a format the style model understands.

🎨 Outputs

Name	Type	Description
conditioning	Conditioning	Your original conditioning, now dressed in its Sunday best. Stylized and ready for sampling.

⚙️ Settings & Parameters (Explained)

Let’s break this down:

conditioning

This is the raw, unstyled conditioning information from your prompt or prior conditioning nodes. It’s the “what” of your generation, and this node helps shape the “how.”

• ✅ Required? Yes
• 📌 Comes from: A Prompt node, Style Conditioning node, or similar.
• 🔍 Why it matters: This is the foundation. If it’s weak, inconsistent, or noisy, styling it won’t help.

style_model

A pre-trained style embedding file. This is what transforms your vanilla conditioning into something worthy of a portfolio.

• ✅ Required? Yes
• 📁 Must contain: a style_embedding key
• 🧠 Think of it as: A compressed stylistic fingerprint trained from visual data
• ⚠️ If you get an error about a missing key, your file is probably not a real style model.

clip_vision_output

Output from a CLIP Vision Encode node. Represents a style image as an embedding vector that your style model can digest.

• ✅ Required? Absolutely
• 🎯 Purpose: This tells the style model which style to apply from its learned embedding space.
• 🖼️ Best practice: Use a clean, high-res image that strongly reflects the style you want to transfer.

🧠 Recommended Use Cases

• Applying the style of a reference image to generations across a series
• Maintaining consistent visual tone in a multi-image workflow
• Stylizing based on actual visual moodboards rather than 10 paragraphs of prompt copypasta
• Generating variations of a concept while preserving artistic identity

🔁 Example Workflow Setup

1. 🖼️ Load an image and encode it with CLIP Vision Encode
2. 🧠 Load your Style Model using the Load Style Model node
3. 📝 Create prompt conditioning (text prompt → conditioning)
4. 🎨 Apply the style with Apply Style Model
5. 🔄 Feed the new conditioning into a sampler like KSampler and render your styled image

💡 Prompting Tips

• Let the style model do the heavy lifting for aesthetic—don’t overcompensate with excessive prompt descriptors.
• Want a touch of style instead of full commitment? Consider blending styled and unstylized conditioning.
• Use consistent reference images if you’re going for a themed batch—AI doesn’t do nuance unless you spoon-feed it.

🧯 What-Not-To-Do-Unless-You-Want-A-Fire

• ❌ Don’t feed in a style model that’s not actually a style model (missing style_embedding)
• ❌ Don’t mismatch your clip_vision_output and your style model—the results will be ugly or broken (or both)
• ❌ Don’t pass a None or broken clip_vision_output or you’ll meet the dreaded 'NoneType' has no attribute 'flatten' error
• ❌ Don’t expect miracles if your base conditioning is garbage. Style can’t polish a turd (though it will try)

🧱 Common Errors & Fixes

Error Message	Translation	Fix
invalid style model <ckpt_path>	Your style model is missing a style_embedding	Use a proper style model file
AttributeError: 'NoneType' object has no attribute 'flatten'	clip_vision_output is empty or invalid	Check your CLIP Vision node; verify input image is loaded
RuntimeError: Sizes of tensors must match except in dimension 1	Your style model and conditioning tensors don’t align	Make sure all inputs come from compatible models/components

📝 Final Notes

• The Apply Style Model node is a powerful enhancement tool—think of it as applying makeup with an airbrush instead of a crayon.
• Different style models behave differently, and some are extremely opinionated. Try several before committing to one.
• For best results: Pair high-quality CLIP encodings with thoughtfully designed conditioning and prompts.
• You’ll get more reliable results if you ensure that your CLIP model and style model are aligned (i.e., trained to work together).