title: ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting
summary: Despite the significant advancements in text-to-image (T2I) generative
  models, users often face a trial-and-error challenge in practical scenarios.
  This challenge arises from the complexity and uncertainty of tedious steps such
  as crafting suitable prompts, selecting appropriate models, and configuring
  specific arguments, making users resort to labor-intensive attempts for desired
  images. This paper proposes Automatic T2I generation, which aims to automate
  these tedious steps, allowing users to simply describe their needs in a
  freestyle chatting way. To systematically study this problem, we first
  introduce ChatGenBench, a novel benchmark designed for Automatic T2I. It
  features high-quality paired data with diverse freestyle inputs, enabling
  comprehensive evaluation of automatic T2I models across all steps.
  Additionally, recognizing Automatic T2I as a complex multi-step reasoning task,
  we propose ChatGen-Evo, a multi-stage evolution strategy that progressively
  equips models with essential automation skills. Through extensive evaluation
  across step-wise accuracy and image quality, ChatGen-Evo significantly enhances
  performance over various baselines. Our evaluation also uncovers valuable
  insights for advancing automatic T2I. All our data, code, and models will be
  available in https://chengyou-jia.github.io/ChatGen-Home
paper_authors._id: 67497408647c7aabef05d682
paper_authors.name: Chengyou Jia
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-29T09:30:09.129Z
paper_authors._id: 6602548a68d519ed324b47c5
paper_authors.avatarUrl: /avatars/5ab411f87440cc2a98c7a1c6a3ed5548.svg
paper_authors.fullname: ChengyouJia
paper_authors.type: user
paper_authors.user: ChengyouJia
paper_authors._id: 67497408647c7aabef05d683
paper_authors.name: Changliang Xia
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-29T15:13:17.676Z
paper_authors._id: 6749852ae60112b9012a1a74
paper_authors.avatarUrl: /avatars/c5773f87da9f2bc460c24dbc01f812c5.svg
paper_authors.fullname: Changliang Xia
paper_authors.type: user
paper_authors.user: xcll
paper_authors._id: 67497408647c7aabef05d684
paper_authors.name: Zhuohang Dang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-29T15:13:23.532Z
paper_authors._id: 672098263fb4d4644a0caed5
paper_authors.avatarUrl: /avatars/ec321749642d8ec78352aedc8a6120a3.svg
paper_authors.fullname: Zhuohang Dang
paper_authors.type: user
paper_authors.user: Ericiand
paper_authors._id: 67497408647c7aabef05d685
paper_authors.name: Weijia Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-29T15:13:57.453Z
paper_authors._id: 6345a93afe134dfd7a0cfabd
paper_authors.avatarUrl: /avatars/65130ce06b1c72ab1066678419731d88.svg
paper_authors.fullname: wu weijia
paper_authors.type: user
paper_authors.user: weijiawu
paper_authors._id: 67497408647c7aabef05d686
paper_authors.name: Hangwei Qian
paper_authors._id: 67497408647c7aabef05d687
paper_authors.name: Minnan Luo
paper_title: ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting
paper_summary: Despite the significant advancements in text-to-image (T2I) generative
  models, users often face a trial-and-error challenge in practical scenarios.
  This challenge arises from the complexity and uncertainty of tedious steps such
  as crafting suitable prompts, selecting appropriate models, and configuring
  specific arguments, making users resort to labor-intensive attempts for desired
  images. This paper proposes Automatic T2I generation, which aims to automate
  these tedious steps, allowing users to simply describe their needs in a
  freestyle chatting way. To systematically study this problem, we first
  introduce ChatGenBench, a novel benchmark designed for Automatic T2I. It
  features high-quality paired data with diverse freestyle inputs, enabling
  comprehensive evaluation of automatic T2I models across all steps.
  Additionally, recognizing Automatic T2I as a complex multi-step reasoning task,
  we propose ChatGen-Evo, a multi-stage evolution strategy that progressively
  equips models with essential automation skills. Through extensive evaluation
  across step-wise accuracy and image quality, ChatGen-Evo significantly enhances
  performance over various baselines. Our evaluation also uncovers valuable
  insights for advancing automatic T2I. All our data, code, and models will be
  available in https://chengyou-jia.github.io/ChatGen-Home
paper_ai_summary: ChatGen-Evo, a multi-stage evolution strategy, automates text-to-image generation by improving accuracy and image quality through systematic evaluation on ChatGenBench.
paper_ai_keywords: text-to-image Automatic T2I ChatGenBench ChatGen-Evo multi-stage evolution strategy