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🖼️ Prompting d'images🟢 Quality Boosters

Quality Boosters

🟢 This article is rated easy
Reading Time: 1 minute
Last updated on August 7, 2024

Sander Schulhoff

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.

Example

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

Now take at pyramids generated with this prompt:

Astronaut

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, artstation, stunning

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 CEO of HackAPrompt and Learn Prompting. He created the first Prompt Engineering guide on the internet, two months before ChatGPT was released, which has taught 3 million people how to prompt ChatGPT. He also partnered with OpenAI to run the first AI Red Teaming competition, HackAPrompt, which was 2x larger than the White House's subsequent AI Red Teaming competition. Today, HackAPrompt partners with the Frontier AI labs to produce research that makes their models more secure. Sander's background is in Natural Language Processing and deep reinforcement learning. He recently led the team behind The Prompt Report, the most comprehensive study of prompt engineering ever done. This 76-page survey, co-authored with OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions, analyzed 1,500+ academic papers and covered 200+ prompting techniques.

Footnotes

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