Weighted Terms

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Last updated on August 7, 2024

Takeaways

Weighting Terms allows you explicitly adjust the emphasis on a given feature of your image, in either direction.

What are Weighted Terms?

Some models (Stable Diffusion, Midjourney, etc.) allow you to assign weights to certain terms in a prompt. Such weighted terms can be used to emphasize certain words or phrases in the generated image. It can also be used to de-emphasize certain words or phrases in the generated image. Let's consider a simple example:

An Example of Weighted Terms in Image Prompts

Here are a few mountains generated by Stable Diffusion, with the prompt mountain.

However, if we want mountains without trees, we can use the prompt mountain | tree:-10. Since we weighted trees very negatively, they do not appear in the generated image.

Weighted terms can be combined into more complicated prompts, like A planet in space:10 | bursting with color red, blue, and purple:4 | aliens:-10 | 4K, high quality

Conclusion

Weighted terms allow you to explicitly tell the model which aspects of the output image or more or less important, giving you even more control in specifying your image prompts.

FAQ

How can weighted terms help my image prompts?

Weighted terms are a capability of certain models that allow you to explicitly define the weights of certain words or phrases in an input. In this way, you can emphasize or de-emphasize the things you'd like portrayed in the AI-generated image.

Notes

Read more about weighting in some of the resources at the end of this chapter.

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.