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How to build a good style dataset?
How to build a good style dataset?
Updated this week

πŸ’‘A dataset is a collection of images you want the AI to learn a style from.

Creating a unique style starts with the foundation: your dataset. The final quality of your style will heavily depend on the quality of the images you provide. Here's how to get started:

1. Focus on High-Quality Images πŸ“Έ

  • The higher the quality, the better the results! Aim for images with a resolution of at least 1 Megapixel (MP) πŸ‘€.

  • For reference, a typical phone screenshot is around 3 MP.

  • The better the quality you provide to the model, the better results you'll see in return.

  • Any aspect ratio is fine, just ensure clarity and detail.

  • It's okay to use AI-generated images, but make sure they are free of artifacts like compression, noise, blurriness, or anatomical inaccuracies for the best results.

Good πŸ‘

These images are high quality with lots of details, properly rendered text and no visible artifacts.

Bad πŸ‘Ž

These images suffer from poor anatomy, pixelation due to low resolution, visible watermarks, compression artifacts, and poorly rendered text, all of which diminish the overall quality and usability of style creation.

2. Diversify Your Dataset 🌍

  • If your dataset mainly contains images of people, your style might struggle to generalize beyond that.

  • For a well-rounded style, mix in images of people, animals, objects, and/or landscapes. 🏞️🐾

  • Remember: Your style will reflect the variety in your dataset. If you want it to generate only flowers, for example, that's okay β€” just use a flower-focused dataset. 🌸 But if you want more versatility, include a wider range of subjects.

  • 🎨 Maintain Style Consistency: Ensure that the images in your dataset share a similar style. If the images are too varied, the model may struggle to interpret the desired look. For instance, if you want an 80s anime style, keep the entire dataset focused on that theme.

  • Avoid mixing in images with drastically different styles to help the model understand and replicate your vision accurately. 🚫🎨

Examples

Good πŸ‘

All the images share a consistent style, even though they feature different subjects like people, animals, objects, and landscapes.

Bad πŸ‘Ž

This dataset mixes different styles (e.g., graphic novel with photorealism) and features similar poses and subjects. Mixing drastically different styles confuses the AI, making it harder for it to learn and replicate a clear, coherent style. πŸ˜•

3. Use Existing Models as a Starting Point πŸ€–

  • Finding it difficult to build your dataset from scratch? Why not leverage our latest models?

  • Sometimes, a few well-chosen prompt keywords can generate images in a style you love. πŸ’‘

  • For example, try adding phrases like 'dark theme' or 'vertical streak lines' to your prompts to achieve a more unique look. Be creative! 🎨✨

  • By curating a dataset like this, you'll create a style that's more general, high-quality, more adaptable and fits your signature look.

These images were generated using the prompt β€œA woman” with a few special-effect keywords to create unique styles. Here’s how the keywords influenced each image:

  1. Cinematic Photo

  2. Water-splash painting with chaotic lines

  3. Thick line drawing, red and blue color, horizontal converging lines

  4. Glass Shatter Collage, light beams refracting in every direction

  5. Melting Canvas, paint dripping into a starry void

You can choose any Flux style and experiment with unique and interesting ideas. 🎨✨

With the right images and a bit of creativity, you're on your way to building a stunning custom style! πŸŽ‰πŸš€

Happy creating! ✨😊

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