arXiv AI

CompArt: Operationalizing Aesthetic Alignment in Text-to-Image Generation via Principles of Art

CompArt introduces a new approach to aesthetic alignment in text-to-image generation by using the Principles of Art (PoA) such as Balance, Rhythm, and Emphasis to define explicit compositional constraints. The authors create a large dataset of 80,032 WikiArt images, each annotated with PoA analyses generated by a multimodal LLM, and present ArtDapter, a lightweight adapter that steers a pretrained diffusion model along ten PoA dimensions while preserving semantic fidelity. Experiments demonstrate that CompArt outperforms strong baselines in adhering to PoA controls under a dual evaluation protocol.

arXiv AI

Jun 17

Detail++: Training-Free Detail Enhancer for Text-to-Image Diffusion Models

arXiv:2507. 17853v2 Announce Type: replace-cross Abstract: Recent advances in text-to-image (T2I) generation have led to impressive visual results.

By Lifeng Chen, Jiner Wang, Zihao Pan, Beier Zhu, Xiaofeng Yang, Chi Zhang

diffusion benchmarks safety

arXiv AI

Jun 3

WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation

arXiv:2503. 07265v4 Announce Type: replace-cross Abstract: Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content.

By Yuwei Niu, Munan Ning, Mengren Zheng, Weiyang Jin, Bin Lin, Peng Jin, Jiaqi Liao, Chaoran Feng, Fanqing Meng, Kunpeng Ning, Bin Zhu, Li Yuan

diffusion multimodal benchmarks safety

arXiv Computer Vision

Sep 11

SenseNova-U1.5: Towards Native Unified Visual Intelligence

SenseNova-U1.5 is an 8B‑MoT native unified multimodal model that can understand, reason about, and generate visual content without using an encoder or VAE. It improves visual fidelity and text rendering through spatially coherent patch reconstruction, large‑scale training on curated generation and editing data, and native resolutions up to 4K. Post‑training, specialized experts for visual aesthetics, bilingual text rendering, infographic generation, and image editing are optimized and distilled into a multi‑expert framework, yielding advances in image fidelity, complex composition, multi‑reference editing, and instruction following.

By Haiwen Diao, Jiahao Wang, Chenjing Ding, Hanming Deng, Jiangnan Chen, Ruixi Zhang, Ruohui Wang, Wenwen Tong, Xiangyu Fan, Yubo Wang, Yue Zhu, Yuwei Niu, Zhengqi Bai, Zhiqian Lin, Zhitao Yang, Zhongang Cai, Bo Yang, Chen Feng, Chengguang Lv, Guangjia Liu, Guanlin Wang, Hanyu Zhang, Haojia Yu, Hongcan Xiao, Hongli Wang, Huan Wu, Huaping Zhong, Jian Fang, Jianan Fan, Jiaqi Li, Jiefan Lu, Jing Zuo, Jingcheng Ni, Junxiang Xu, Linjun Dai, Mutian Xu, Peishen Yan, Penghao Wu, Ruijie Mao, Ruisi Wang, Shihao Bai, Shuang Yang, Shuya Yang, Shuyan Zheng, Silei Wu, Siying Li, Tao Chu, Tianbo Zhong, Tongxi Zhou, Weichao Luo, Weichen Fan, Wenhao Jia, Wenjie Gao, Xiangli Kong, Yan Li, Yang Yong, Zimo Wen, Zixuan Qian, Wenxiu Sun, Ruihao Gong, Quan Wang, Lewei Lu, Lei Yang, Ziwei Liu, Dahua Lin

reinforcement-learning fine-tuning efficiency multimodal

arXiv Computer Vision

Sep 14

Unified Text-Image Generation with Weakness-Targeted Post-Training

The paper introduces a post‑training approach that enables a single inference process to transition from text reasoning to image synthesis, eliminating the need for explicit modality switching. Using the 14B BAGEL model, the authors demonstrate that targeted post‑training data and reward‑weighted training improve multimodal image generation across four independent T2I benchmarks. The study highlights the benefits of joint text‑image generation and strategic data selection for enhancing T2I performance.

By Jiahui Chen, Philippe Hansen-Estruch, Xiaochuang Han, Yushi Hu, Emily Dinan, Amita Kamath, Michal Drozdzal, Reyhane Askari-Hemmat, Luke Zettlemoyer, Marjan Ghazvininejad

llms diffusion multimodal benchmarks

arXiv Computation and Language

Sep 1

UReason: Benchmarking Reasoning-to-Generation Alignment in Unified Multimodal Models

UReason is a benchmark that evaluates how well unified multimodal models (UMMs) align textual reasoning with image generation. It contains 2,000 human‑curated instances across five reasoning‑intensive tasks—Code, Arithmetic, Spatial, Attribute, and Text—and compares direct generation, reasoning‑guided generation, and decontextualized generation. The study finds that while reasoning‑guided generation improves over direct generation, decontextualized generation consistently outperforms it, indicating that the visual semantics in textual reasoning are not reliably reflected in the generated images.

By Cheng Yang, Chufan Shi, Bo Shui, Yaokang Wu, Muzi Tao, Huijuan Wang, Ivan Yee Lee, Yong Liu, Xuezhe Ma, Taylor Berg-Kirkpatrick

diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 3

Training-Free Multi-Concept LoRA Composition with Prompt-Aware Weighting

arXiv:2606. 03792v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) successfully enables personalization in text-to-image generation by adapting pre-trained diffusion models to specific visual concepts and styles.

By Georgios Tsoumplekas, Stella Bounareli, Vasileios Argyriou

llms diffusion fine-tuning benchmarks

arXiv Computer Vision

Aug 27

Towards Fine-Grained Text-to-3D Quality Assessment: A Benchmark and A Two-Stage Rank-Learning Metric

The paper introduces T23D-CompBench, a new benchmark for fine‑grained text‑to‑3D quality assessment that includes 3,600 textured meshes generated from ten state‑of‑the‑art models and 129,600 human ratings. It also proposes Rank2Score, a two‑stage rank‑learning metric that first trains with supervised contrastive regression and curriculum learning, then refines predictions using mean opinion scores to better align with human judgments. Experiments show Rank2Score outperforms existing metrics and can be used as a reward function for generative model optimization.

By Bingyang Cui, Yujie Zhang, Qi Yang, Zhu Li, Yiling Xu

benchmarks safety

arXiv AI

Jun 9

IEA: Amateur-Friendly Conversational Image Editing Agent via Three Stages of Multitask Alignment

arXiv:2606. 08016v1 Announce Type: cross Abstract: Current image editing software often hinges on fixed filters or expert tuning, leaving a gap between amateur users' intent and outcomes.

By Zichen Zhu, Yuheng Sun, Mingxuan Zhu, Wenjie Ma, Situo Zhang, Zhexiang Wang, Ziyue Yang, Danyang Zhang, Kunyao Lan, Zihan Zhao, Dingye Liu, Siqi Xiang, Lu Chen, Kai Yu

agents nlp fine-tuning safety

Hugging Face Trending Papers

Aug 26

VGA-BenchV2: An Expanded Unified Benchmark and Multi-Model Framework for Evaluating Video Aesthetics and Generation Quality

We introduce VGA-BenchV2, an extended human-aligned benchmark and optimization framework for jointly evaluating and improving video generation quality and aesthetic value. Built upon VGA-Bench, VGA-Be...

llms diffusion reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jul 31

Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives

arXiv:2607. 26735v1 Announce Type: cross Abstract: Prompt inversion, as a typical reverse engineering technique, enables text-to-image (T2I) diffusion models to generate the desired target images without extensive prompt engineering.

By Xiaolong Liu, Junjian Li, Yuan Xiao, Jiaqi Deng, Dayong Ye, Tianqing Zhu, Huan Huo

llms diffusion multimodal benchmarks safety

arXiv AI

Aug 19

From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Generalist Image Generation

The paper introduces a capability‑centric data infrastructure for generalist image generation, integrating task‑specific supervision with a curriculum that aligns with the dependencies among generative capabilities. It employs three interoperable data engines—text‑image grounding, inter‑image transformation, and image‑knowledge association—alongside caption experts to harmonize text‑to‑image and editing supervision. The system curates massive corpora (440M T2I images, 120M editing pairs, 27M image‑entity pairs) and trains multimodal diffusion models (3B and 6B parameters) from scratch, achieving broad visual coverage and versatile rendering as shown by CPI‑Bench and qualitative tests.

By Xingjian Wang, Zhao Wang, Taihang Hu, Jun Zheng, Qing Jin, Qinye Zhou, Zhengtao Wu, Yongchao Du, Zuan Gao, Chao Lin, Yefeng Shen, Xiaoli Xu, Zhengze Xu, Hao Yan, Yuhang Yu, Mingzhou Zhang, Mengting Chen

diffusion multimodal data-engineering

arXiv AI

Aug 13

TangPoetryBench: A Multi-Dimensional Benchmark and Rubric-Conditioned Evaluator for Poetry-to-Image Generation

arXiv:2608. 11452v1 Announce Type: cross Abstract: Text-to-image (T2I) models are increasingly asked to illustrate literary and cultural content, yet we cannot measure how well an image renders the meaning of a poem.

By Haoqi Hu, Tongji Luo, Li Zhang, Boning Zhou

llms diffusion benchmarks

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