title: NeuroPrompts: An Adaptive Framework to Optimize Prompts for
    Text-to-Image Generation
summary: Despite impressive recent advances in text-to-image diffusion models,
  obtaining high-quality images often requires prompt engineering by humans who
  have developed expertise in using them. In this work, we present NeuroPrompts,
  an adaptive framework that automatically enhances a user's prompt to improve
  the quality of generations produced by text-to-image models. Our framework
  utilizes constrained text decoding with a pre-trained language model that has
  been adapted to generate prompts similar to those produced by human prompt
  engineers. This approach enables higher-quality text-to-image generations and
  provides user control over stylistic features via constraint set specification.
  We demonstrate the utility of our framework by creating an interactive
  application for prompt enhancement and image generation using Stable Diffusion.
  Additionally, we conduct experiments utilizing a large dataset of
  human-engineered prompts for text-to-image generation and show that our
  approach automatically produces enhanced prompts that result in superior image
  quality. We make our code, a screencast video demo and a live demo instance of
  NeuroPrompts publicly available.
paper_authors._id: 655d7fc5be545cd2456124c4
paper_authors.name: Shachar Rosenman
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-22T13:42:49.139Z
paper_authors._id: 64575110cd935d48a4773a38
paper_authors.avatarUrl: /avatars/9e5d08965efb316d9c3ffb7eb915aee1.svg
paper_authors.fullname: Shachar Rosenman
paper_authors.type: user
paper_authors.user: shacharrosenman
paper_authors._id: 655d7fc5be545cd2456124c5
paper_authors.name: Vasudev Lal
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-22T13:42:56.900Z
paper_authors._id: 6454021413dba495b2dfebd1
paper_authors.avatarUrl: /avatars/16f019f7c321aa24000364a50a1975fe.svg
paper_authors.fullname: Vasudev Lal
paper_authors.type: user
paper_authors.user: vasudevlal
paper_authors._id: 655d7fc5be545cd2456124c6
paper_authors.name: Phillip Howard
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-22T13:43:03.098Z
paper_authors._id: 63605f9388e41d249ecf13c4
paper_authors.avatarUrl: /avatars/7721e8039a107647e27cb3f7b4d665a0.svg
paper_authors.fullname: Phillip Howard
paper_authors.type: user
paper_authors.user: PhillipHoward
paper_title: NeuroPrompts: An Adaptive Framework to Optimize Prompts for
    Text-to-Image Generation
paper_summary: Despite impressive recent advances in text-to-image diffusion models,
  obtaining high-quality images often requires prompt engineering by humans who
  have developed expertise in using them. In this work, we present NeuroPrompts,
  an adaptive framework that automatically enhances a user's prompt to improve
  the quality of generations produced by text-to-image models. Our framework
  utilizes constrained text decoding with a pre-trained language model that has
  been adapted to generate prompts similar to those produced by human prompt
  engineers. This approach enables higher-quality text-to-image generations and
  provides user control over stylistic features via constraint set specification.
  We demonstrate the utility of our framework by creating an interactive
  application for prompt enhancement and image generation using Stable Diffusion.
  Additionally, we conduct experiments utilizing a large dataset of
  human-engineered prompts for text-to-image generation and show that our
  approach automatically produces enhanced prompts that result in superior image
  quality. We make our code, a screencast video demo and a live demo instance of
  NeuroPrompts publicly available.
paper_ai_summary: An adaptive framework automatically enhances text-to-image prompts to improve generation quality using a pre-trained language model and constrained text decoding.
paper_ai_keywords: text-to-image diffusion models prompt engineering adaptive framework constrained text decoding pre-trained language model Stable Diffusion image generation