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Prompt engineering: Difference between revisions
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===Prompt Modifiers=== | ===Prompt Modifiers=== | ||
Witteveen and Andrews (2022) | Witteveen and Andrews (2022) presented an evaluation of the Stable Diffusion model with chosen metrics on over 15,000 image generations, using more than 2,000 prompt variations. The results revealed that different linguistic categories, such as adjectives, nouns, and proper nouns, have varying impacts on the generated images. Simple adjectives have a relatively small effect, whereas nouns can dramatically alter the images as they introduce new content beyond mere modifiers. The paper demonstrated that words and phrases can be categorized based on their impact on image generation, and this categorization can be applied to various types of models. While the effects of each word or phrase may vary depending on the model used, the evaluation process described can establish baselines for future model evaluations. <ref name="”7”"></ref> | ||
Creating a prompt to generate an image can be challenging. The authors propose starting with a clear noun-based statement that contains the main subject of the image. Then, to record what seeds are effective, look for artists and key styles to emulate and add that to the prompt, and experiment with descriptors such as adding lighting effects phrases and repeating words. <ref name="”7”"></ref> | Creating a prompt to generate an image can be challenging. The authors propose starting with a clear noun-based statement that contains the main subject of the image. Then, to record what seeds are effective, look for artists and key styles to emulate and add that to the prompt, and experiment with descriptors such as adding lighting effects phrases and repeating words. <ref name="”7”"></ref> | ||