Quality Boosters

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Reading Time: 2 minutes

Last updated on August 7, 2024

Takeaways

Quality Boosters are terms that enhance style-agnostic attributes, such as detail and resolution, in AI-generated images.

What are Quality Boosters?

Quality boosters are terms added to a prompt to improve certain non-style-specific qualities of the generated image. For example "amazing", "beautiful", and "good quality" are all quality boosters that can be used to improve the quality of the generated image.

An Example of Quality Boosters

Recall from the other page the pyramids generated with DALLE, and the prompt pyramid.

Now take at pyramids generated with this prompt:

Prompt

A beautiful, majestic, incredible pyramid, 4K

These are much more scenic and impressive!

Here is a list of a number of quality boosters:

High resolution, 2K, 4K, 8K, clear, good lighting, detailed, extremely detailed, sharp focus, intricate, beautiful, realistic+++, complementary colors, high quality, hyper-detailed, masterpiece, best quality, art station, stunning

Conclusion

Quality boosters, like style modifiers, demonstrate how small updates to an image prompt can lead to drastic improvements in generated outputs.

FAQ

How can quality boosters improve my image prompts?

Quality boosters can improve the non-style-specific qualities of an AI-generated image. As shown in this article's example, adding terms like "beautiful," "majestic," "incredible," and "4k" to our prompt leads to noticeable differences in the quality of the outputted image of a pyramid.

Notes

Similar to the note on the previous page, our working definition of quality boosters differs from Oppenlaender et al.. This being said, it is sometimes difficult to exactly distinguish between quality boosters and style modifiers.

Sander Schulhoff

Sander Schulhoff is the Founder of Learn Prompting and an ML Researcher at the University of Maryland. He created the first open-source Prompt Engineering guide, reaching 3M+ people and teaching them to use tools like ChatGPT. Sander also led a team behind Prompt Report, the most comprehensive study of prompting ever done, co-authored with researchers from the University of Maryland, OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions. This 76-page survey analyzed 1,500+ academic papers and covered 200+ prompting techniques.

Footnotes

Oppenlaender, J. (2022). A Taxonomy of Prompt Modifiers for Text-To-Image Generation. ↩ ↩²