pith. sign in

arxiv: 2211.15462 · v1 · pith:IHAP3EKYnew · submitted 2022-11-21 · 💻 cs.CV · cs.AI· cs.CL

Investigating Prompt Engineering in Diffusion Models

classification 💻 cs.CV cs.AIcs.CL
keywords diffusionpromptsdesiredmodelsachieveappendixartisticartists
0
0 comments X
read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

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

  1. IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

    cs.CV 2023-08 unverdicted novelty 6.0

    IP-Adapter adds effective image prompting to text-to-image diffusion models using a lightweight decoupled cross-attention adapter that works alongside text prompts and other controls.