Training a Single Character style in Layer lets you teach the AI to consistently generate a specific character - with the right face, expressions, outfits, poses, and personality. Whether you’re creating a mascot, a main character, or an avatar style, this process helps lock in consistency across generations.
Start with Your Assets
For single character styles, your training images should include a variety of shots and expressions:
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This gives the model a broader understanding of your character’s visual identity - how they look, move, and emote.
When you’re creating prompts later, always include the character’s name to reinforce identity. For example:
“Luna, happy, wearing a blue jean, arms open on both sides”
This kind of input ensures your character stays visually consistent, even as you change outfits, expressions, or settings.
If you're looking for more details on the types of images you should be using to train your model, check out this video.
Why Captions Matter
(and Why Auto-Captions Aren’t Enough)
When you upload images for style training, Layer will automatically caption them, but for single character training, this isn’t ideal. The auto-captioning might miss key details or fail to structure descriptions consistently - which makes it harder for the AI to learn who your character really is.
That’s where LLMs like OpenAI’s ChatGPT or Google’s Gemini come in. They can help you write detailed, structured descriptions and keep things consistent. (They are also MUCH faster than doing it manually)
Use an LLM to Write Descriptions
You’ll want to kick off your LLM session with a clear prompt that defines the task, the format, the tone, and the length.
Here’s the full starting prompt you can use:
LLM Starting Prompt (for Single Character Captioning)
I’m working on training a LoRA for a single character. I will send you images and I need you to describe them to me. I need detailed descriptions that follow a consistent format for each image.
The descriptions should follow this format: [character’s name], [physical appearance], [pose], [expression], [shot type], and the [overall art style]. It’s important to maintain the same format and language across all images for this character. The text should be under 1024 characters, but aim for around 900.
Here is an example of what a good description looks like:
“Luna is a tall, slender woman with pale skin and long, flowing dark hair. She has striking blue eyes and a sharp jawline, wearing a sleek, black bodysuit with silver accents. She stands confidently with one hand on her hip and the other holding a glowing orb of light. Her expression is calm yet determined, with a slight smirk. Full body shot. Semi-realistic art style with detailed shading, emphasizing sharp contrasts between light and dark tones.”
Comparison: Auto-Caption vs LLM-Enhanced
Let’s compare what Layer’s auto-caption might give you vs. what you can get with a few minutes of help from an LLM.
Auto-caption (Layer):“girl with blue hair, wearing a T-shirt and a blue jean suit, standing pose with a wide smile, 3D cartoon style, full body shot.” | LLM-enhanced caption:“Luna is a stylized game character designed for a vibrant 3D animated world. She has fair skin, oversized teal-blue eyes full of curiosity, and long, flowing cobalt-blue hair that adds motion and flair to her silhouette. Her outfit balances charm and practicality: a fitted charcoal zip-up hoodie with a subtle texture, deep blue leggings that allow for agile movement, and navy lace-up shoes with mid-length socks—perfect for light adventure or platformer gameplay. Her idle pose is dynamic and expressive, with arms slightly raised and a face that shifts naturally between wonder and determination. The design features exaggerated proportions for appeal, with smooth, plasticky surfaces, clean edge lines, and soft lighting. Color palette is bold yet harmonious, ideal for standing out in colorful, saturated environments. |
That extra attention to detail — and structure — helps the model retain character identity and style much more accurately.
Yes, It’s a Bit Tedious (But Worth It)
We know this process can be slow — manually writing or refining captions, even with AI help, takes time. We’re actively working on product updates to support single character training better (as of April 2025). But right now, this is the best method to get great results.
Also keep in mind: even LLMs make mistakes. Sometimes they won’t follow your formatting exactly. You may need to lightly edit or re-prompt to stay consistent.
Adding Example Prompts
Once you’ve finished your captions and uploaded your assets, you’ll reach the example prompts step.
While these don’t impact the actual training, they do give you a quick, high-level preview of what the style might generate — so it’s worth doing thoughtfully.
You can reuse the same LLM session to generate prompts. Here’s what to say:
“I’m at the final stage of a LoRA training, and I need to generate 5 new ideas for my character Luna. These new ideas should belong to the same character in the data set, but they should introduce some variation, like different poses, outfits, emotions, context. The descriptions must follow the same format and consistency we’ve used throughout the project, with each description around 900 characters, not exceeding 1024.”
Once the LLM gives you 5 new ideas, copy and paste your favorite 3–5 as your example prompts, and you’re good to go.
While It’s Training: Set Up Prompt Prefix + Suffix
While your style is training, take a few minutes to set up your Prompt Prefix + Suffix.
This helps guide how the model behaves when you generate assets — for example:
Always inserting the character’s name at the start
Always appending a style description like “3D cartoon style with soft lighting”
Prefixes and suffixes are powerful ways to lock in tone, naming, and consistency once the model is ready to use.
We’ll explore prefix/suffix best practices more in a follow-up article.