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Quick Start with Styles Training

Learn what style training is, how to prepare your dataset, how long training takes, and how to create your first custom style in starryai.

Updated over 2 weeks ago

What Is Style Training?

Style training allows you to create your own custom AI style.

It works by fine-tuning an existing AI model using a smaller set of images (your dataset). A dataset is simply a collection of images that represent the look, aesthetic, or subject you want the AI to learn.

Once trained, the AI can generate new images that match the style or characteristics of your dataset.


Train Your First Style

1️⃣ Gather Your Training Data

  • Collect 5–60 images of a consistent style, object, or aesthetic

  • The more high-quality images you include, the better your results

  • Crop images to 1:1 (square) to avoid distortion after training

  • Make sure your images follow our Content Policy

  • Avoid mixing very different styles in one dataset

A strong, consistent dataset leads to stronger results.

🖼️Here’s an example of a dataset.

2️⃣ Upload Your Dataset

  1. Go to the Styles tab

  2. Select My Styles

  3. Tap Create Style

  4. Upload your images

You can crop your images directly inside starryai using the built-in crop tool.

After uploading:

  • Give your style a name

  • Select the most relevant category

  • Confirm to begin training

    Training a style costs 50 Lumens.

Here’s an example of an image that was created using this style

Prompt: Portrait of a handsome man

3️⃣ Start the Training Process

  • Training typically takes around 45 minutes

  • During busy periods, it may take up to 2 hours

  • If training fails after 2 hours, your Lumens are automatically refunded

Once training is complete, you’ll receive a notification.

After that, you can:

  • Use your style to generate new images

  • Publish your style to share it with other users


Example

Prompt: Portrait of a handsome man
(Using a trained custom style)

The output will reflect the aesthetic and characteristics learned from your dataset.

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