TokenRepel: Generating Diverse Image Sets Without Compromising Quality
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
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...
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.
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...
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...
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.
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.