ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting
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
67497408647c7aabef05d682
Chengyou Jia
claimed_verified
2024-11-29T09:30:09.129Z
6602548a68d519ed324b47c5
/avatars/5ab411f87440cc2a98c7a1c6a3ed5548.svg
ChengyouJia
user
ChengyouJia
67497408647c7aabef05d683
Changliang Xia
admin_assigned
2024-11-29T15:13:17.676Z
6749852ae60112b9012a1a74
/avatars/c5773f87da9f2bc460c24dbc01f812c5.svg
Changliang Xia
user
xcll
67497408647c7aabef05d684
Zhuohang Dang
admin_assigned
2024-11-29T15:13:23.532Z
672098263fb4d4644a0caed5
/avatars/ec321749642d8ec78352aedc8a6120a3.svg
Zhuohang Dang
user
Ericiand
67497408647c7aabef05d685
Weijia Wu
admin_assigned
2024-11-29T15:13:57.453Z
6345a93afe134dfd7a0cfabd
/avatars/65130ce06b1c72ab1066678419731d88.svg
wu weijia
user
weijiawu
67497408647c7aabef05d686
Hangwei Qian
67497408647c7aabef05d687
Minnan Luo
ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting
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
ChatGen-Evo, a multi-stage evolution strategy, automates text-to-image generation by improving accuracy and image quality through systematic evaluation on ChatGenBench.
text-to-image Automatic T2I ChatGenBench ChatGen-Evo multi-stage evolution strategy