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Investigating Prompt Engineering in Diffusion Models

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arxiv 2211.15462 v1 pith:IHAP3EKY submitted 2022-11-21 cs.CV cs.AIcs.CL

Sam Witteveen , Martin Andrews This is my paper · ORCID
classification cs.CVcs.AIcs.CL
keywords diffusionpromptsdesiredmodelsachieveappendixartisticartists
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With the spread of the use of Text2Img diffusion models such as DALL-E 2, Imagen, Mid Journey and Stable Diffusion, one challenge that artists face is selecting the right prompts to achieve the desired artistic output. We present techniques for measuring the effect that specific words and phrases in prompts have, and (in the Appendix) present guidance on the selection of prompts to produce desired effects.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Synthesizing Reality: Leveraging the Generative AI-Powered Platform Midjourney for Construction Worker Detection

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Training YOLOv7 on 11,992 Midjourney-synthesized construction worker images transfers to real construction photos, with AP0.5 0.937 and AP0.5:0.95 0.642.

  2. RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A decoupled-attention adapter transfers image-pair edits to new photos in diffusion transformers, trained with a new 218-task visual editing dataset.

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