Learn PromptingStyle Modifiers
- Style Modifiers are descriptors that help generate specific artistic styles in AI image prompts (examples include art periods, artists, media, and technologies).
What are Style Modifiers?
Style modifiers are simply descriptors that consistently produce certain styles (e.g. 'tinted red', 'made of glass', 'rendered in Unity'). They can be combined together to produce even more specific styles. They can "include information about art periods, schools, and styles, but also art materials and media, techniques, and artists".
An Example of Style Modifiers
Here are a few pyramids generated by DALLE, with the prompt pyramid.
Here are a few pyramids generated by DALLE, with the prompt A pyramid made of glass, rendered in Unity and tinted red, which uses 3 style modifiers.
Here is a list of some useful style modifiers:
photorealistic, by greg rutkowski, by christopher nolan, painting, digital painting, concept art, octane render, wide lens, 3D render, cinematic lighting, trending on ArtStation, trending on CGSociety, hyperrealist, photo, natural light, film grain
Conclusion
Style modifiers are a simple way of adding specificity to your image prompts so that you can get a lot closer to your desired output.
FAQ
Why should I add style modifiers to my image prompts?
Style modifiers are descriptors that consistently produce certain styles and can be combined together to produce more specific styles. For example, you could prompt DALLE to generate an image of a pyramid and modify it by describing that it should be "made of glass, rendered in Unity, and tinted red."
Notes
Oppenlaender et al. describe the rendered in ... descriptor
as a quality booster, but our working definition differs since that modifier does consistently generate the specific Unity (or another render engine) style. As such, we will call that descriptor a style modifier.
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.