FLUX-Reason-6M & PRISM-Bench: A Million-Scale Text-to-Image Reasoning
  Dataset and Comprehensive Benchmark
Authors: name: Rongyao Fang, name: Aldrich Yu, name: Chengqi Duan, name: Linjiang Huang, name: Shuai Bai, name: Yuxuan Cai, name: Kun Wang, name: Si Liu, name: Xihui Liu, name: Hongsheng Li

The advancement of open-source text-to-image (T2I) models has been hindered
by the absence of large-scale, reasoning-focused datasets and comprehensive
evaluation benchmarks, resulting in a performance gap compared to leading
closed-source systems. To address this challenge, We introduce FLUX-Reason-6M
and PRISM-Bench (Precise and Robust Image Synthesis Measurement Benchmark).
FLUX-Reason-6M is a massive dataset consisting of 6 million high-quality
FLUX-generated images and 20 million bilingual (English and Chinese)
descriptions specifically designed to teach complex reasoning. The image are
organized according to six key characteristics: Imagination, Entity, Text
rendering, Style, Affection, and Composition, and design explicit Generation
Chain-of-Thought (GCoT) to provide detailed breakdowns of image generation
steps. The whole data curation takes 15,000 A100 GPU days, providing the
community with a resource previously unattainable outside of large industrial
labs. PRISM-Bench offers a novel evaluation standard with seven distinct
tracks, including a formidable Long Text challenge using GCoT. Through
carefully designed prompts, it utilizes advanced vision-language models for
nuanced human-aligned assessment of prompt-image alignment and image
aesthetics. Our extensive evaluation of 19 leading models on PRISM-Bench
reveals critical performance gaps and highlights specific areas requiring
improvement. Our dataset, benchmark, and evaluation code are released to
catalyze the next wave of reasoning-oriented T2I generation. Project page:
https://flux-reason-6m.github.io/ .