FLUX-Reason-6M & PRISM-Bench: A Million-Scale Text-to-Image Reasoning
  Dataset and Comprehensive Benchmark
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/ .
68c37e4cfc1747b9124039ab
Rongyao Fang
claimed_verified
2025-09-12T16:08:45.111Z
65b8724123d948d884b379b1
/avatars/ce189d1d8d688c17912f9b869035b2d0.svg
Rongyao Fang
user
LucasFang
68c37e4cfc1747b9124039ac
Aldrich Yu
68c37e4cfc1747b9124039ad
Chengqi Duan
claimed_verified
2025-09-17T13:06:36.612Z
64a2b496e2e19de17db7de65
/avatars/241448ca487833d6cc5d57bb1fdb6ee5.svg
Duan Chengqi
user
gogoduan
68c37e4cfc1747b9124039ae
Linjiang Huang
68c37e4cfc1747b9124039af
Shuai Bai
68c37e4cfc1747b9124039b0
Yuxuan Cai
68c37e4cfc1747b9124039b1
Kun Wang
68c37e4cfc1747b9124039b2
Si Liu
68c37e4cfc1747b9124039b3
Xihui Liu
68c37e4cfc1747b9124039b4
Hongsheng Li
FLUX-Reason-6M & PRISM-Bench: A Million-Scale Text-to-Image Reasoning
  Dataset and Comprehensive Benchmark
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/ .
FLUX-Reason-6M and PRISM-Bench address the lack of reasoning-focused datasets and benchmarks for text-to-image models, providing a large-scale dataset and evaluation standard to improve model performance.
text-to-image (T2I) models FLUX-Reason-6M PRISM-Bench Generation Chain-of-Thought (GCoT) vision-language models prompt-image alignment image aesthetics