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
arXiv:2610.14294v1 Announce Type: new
Abstract: Conditional diffusion models can generate high-quality images that align closely with a given prompt. However, samples generated from different random...
By Sylvain Dehayem Kenfouo, Aaron Zweig, Mingxuan Zhang, Elham Azizi
arXiv:2603. 28762v2 Announce Type: replace-cross Abstract: Modern Text-to-Image (T2I) diffusion models have achieved remarkable semantic alignment, yet they often suffer from a significant lack of variety, converging on a narrow set of visual solutions for any given prompt.
By Omer Dahary, Benaya Koren, Daniel Garibi, Daniel Cohen-Or
arXiv:2511.21415v2 Announce Type: replace
Abstract: We introduce DiverseVAR, a framework that enhances the diversity of text-conditioned visual autoregressive models (VAR) at test time without requir...
By Mingue Park, Prin Phunyaphibarn, Phillip Y. Lee, Minhyuk Sung
Visual Information-Guided Parallel Decoding for Diffusion Multimodal Large Language Models introduces the VIG‑Sampler, a method that prioritizes tokens for decoding based on their attention to image tokens and penalizes redundancy in image‑attention distributions. The approach aims to improve the quality of multimodal generation by selecting more informative tokens during diffusion decoding. Experiments on seven captioning and VQA benchmarks with three open‑source dMLLMs show that VIG‑Sampler outperforms the Info‑Gain Sampler by an average of 19.3 CIDEr points and achieves better COCO Caption results using only half as many decoding steps.
By Insu Lee, Wooje Park, Wonseok Shin, Jinwoo Son, Byonghyo Shim
arXiv:2610.15724v1 Announce Type: cross
Abstract: Denoising Diffusion Probabilistic Models have emerged as powerful generative priors across domains ranging from molecular design to high-resolution i...
By Hannah Janmohamed, Joao Correia, Penousal Machado, Antoine Cully
PAPT++ is a risk‑aware adversarial generation‑training framework designed to improve single domain generalization. It learns diverse semantic reference images per class and uses them as denoising targets in classifier‑guided diffusion synthesis, thereby generating challenging yet semantically consistent samples. These samples are iteratively combined with source data to update the classifier, progressively exposing it to difficult variations and enhancing generalization performance on standard benchmarks.
By Zhipeng Xu, De Cheng, Xinyang Jiang, Lingfeng He, Huaijie Wang, Dongsheng Li, Nannan Wang, Xinbo Gao
Single domain generalization (SDG) aims to learn a robust model, which could perform well on many unseen domains while there is only one single domain available for training. One of the promising directions for achieving single-domain generalization is to generate out-of-domain (OOD) training data through data augmentation or image generation.
arXiv:2610.01723v1 Announce Type: new
Abstract: Text-to-image diffusion models have achieved remarkable progress in image synthesis, yet can exhibit memorization by closely reproducing individual tra...
By Hyungjun Joo, Sehwan Kim, Hyeonggeun Han, Sangwoo Hong, Jungwoo Lee
The paper introduces an adaptive step schedule controller for text‑to‑image diffusion models, allowing the number of denoising steps to vary based on the complexity of the input prompt. By mixing step schedules of different sizes and monitoring error discrepancies at each timestep, the method switches schedules to maintain image quality while reducing inference time. Experiments on COCO and DiffusionDB demonstrate that this approach achieves faster generation without sacrificing visual fidelity.
By Kuluhan Binici, Cihan Acar, Shivam Aggarwal, Siying Liu, Tulika Mitra
arXiv:2607. 14580v1 Announce Type: cross Abstract: We present a novel system that integrates negative prompt optimization via a fine-tuned sequence-to-sequence LLM and latent-space classifier guidance to improve the quality of images generated by Stable Diffusion.
By Vaddi Charan Sai Nandan Reddy, Harini B, Chandana M S
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.