Hugging Face Trending Papers June 22, 2026

Semantic Browsing: Controllable Diversity for Image Generation

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Modern text-to-image models excel in visual fidelity and prompt adherence. However, this strict adherence comes at the cost of diversity: generated samples tend to collapse into a single visual interpretation.

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arXiv Computer Vision

Sep 3

Diversifying Long Prompt Image Generation through Structured Prompt Embedding Space Sampling

The paper investigates how long, richly detailed prompts cause modern text-to-image models to lose diversity, even when many visual aspects are unspecified. It introduces PromptMoG, a training‑free method that samples prompt embeddings from a Mixture‑of‑Gaussians distribution to restore diversity while preserving semantic fidelity. The authors also present LPD‑Bench, a benchmark for evaluating fidelity and diversity under long, semantically dense prompts, and demonstrate PromptMoG’s effectiveness on four large diffusion models.

By Bo-Kai Ruan, Teng-Fang Hsiao, Ling Lo, Yi-Lun Wu, Hong-Han Shuai

rag diffusion benchmarks

arXiv AI

Jun 26

Semantic Generative Tuning for Unified Multimodal Models

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By Songsong Yu, Yuxin Chen, Ying Shan, Yanwei Li

computer-vision multimodal benchmarks

arXiv AI

Jul 1

Visual Semantic Entropy: Do Vision Language Models Recognize Visual Ambiguity?

arXiv:2606. 31407v1 Announce Type: cross Abstract: Vision-language models can produce confident answers on visually ambiguous inputs, resulting in biased predictions.

By Ta Duc Huy, Trang Nguyen, Townim Chowdhury, Ankit Yadav, Minh-Son To, Zhibin Liao, Johan W. Verjans, Vu Minh Hieu Phan

llms rag multimodal benchmarks

arXiv AI

Jun 4

On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers

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By Omer Dahary, Benaya Koren, Daniel Garibi, Daniel Cohen-Or

llms diffusion multimodal safety

arXiv Machine Learning

Sep 1

TPSO: Training-Free Diverse Image Generation via Semantic Prompt Embedding Optimization

arXiv:2511.19811v2 Announce Type: replace-cross Abstract: Image diversity remains a fundamental challenge for text-to-image diffusion models. Low-diversity generation often leads to repetitive output...

By Debin Meng, Chen Jin, Zheng Gao, Yanran Li, Ioannis Patras, Georgios Tzimiropoulos

rag diffusion

arXiv Machine Learning

Jul 2

Generated Contents Enrichment

arXiv:2405. 03650v4 Announce Type: replace-cross Abstract: We study Generated Contents Enrichment (GCE), a conditional image-generation task in which a sparse scene description is first enriched through an explicit scene representation and then rendered into semantically richer visual content.

By Mahdi Naseri, Jiayan Qiu, Zhou Wang

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