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Prompt engineering for image generation: Difference between revisions
Prompt engineering for image generation (view source)
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===Prompt variables=== | ===Prompt variables=== | ||
Liu and Chilton (2021) explored the challenges associated with generating coherent outputs using text-to-image generative models. The free-form nature of text interaction can lead to poor result quality, necessitating brute-force trial and error. The research systematically investigated various variables involved in prompt engineering for text-to-image generative models. It examined different parameters, such as prompt keywords, random seeds, and the length of iterations. It also explores the use of subject and style as dimensions in structuring prompts. Furthermore, they analyzed how the abstract nature of a subject or style can impact generation quality. The results of the study are presented as design guidelines to help users prompt text-to-image models for better outcomes. <ref name="”3”"></ref> | Liu and Chilton (2021) explored the challenges associated with generating coherent outputs using text-to-image generative models. The free-form nature of text interaction can lead to poor result quality, necessitating brute-force trial and error. The research systematically investigated various variables involved in prompt engineering for text-to-image generative models. It examined different parameters, such as prompt keywords, random seeds, and the length of iterations. It also explores the use of subject and style as dimensions in structuring prompts. Furthermore, they analyzed how the abstract nature of a subject or style can impact generation quality. The results of the study are presented as design guidelines to help users prompt text-to-image models for better outcomes. <ref name="”3”">Liu, V and Chilton, LB (2021). Design Guidelines for Prompt Engineering Text-to-Image Generative Models. arXiv:2109.06977v2</ref> | ||
Prompt engineering for text-to-image generative models is an emerging area of research. Previous studies have used text-to-image models to generate visual blends of concepts. BERT, a large language model, was utilized to help users generate prompts, and generations were evaluated using crowd-source workers on Mechanical Turk. Similar crowd-sourced approaches have been used in the past to evaluate machine-generated images for quality and coherence. <ref name="”3”"></ref> | Prompt engineering for text-to-image generative models is an emerging area of research. Previous studies have used text-to-image models to generate visual blends of concepts. BERT, a large language model, was utilized to help users generate prompts, and generations were evaluated using crowd-source workers on Mechanical Turk. Similar crowd-sourced approaches have been used in the past to evaluate machine-generated images for quality and coherence. <ref name="”3”"></ref> | ||