arXiv Machine Learning
TPSO: Training-Free Diverse Image Generation via Semantic Prompt Embedding Optimization
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Diversifying Long Prompt Image Generation through Structured Prompt Embedding Space Sampling
arXiv:2511.20251v2 Announce Type: replace Abstract: Modern text-to-image models produce impressive visual results from richly specified prompts, yet their behavior under long prompts remains insuffic...
arXiv AI
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On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers
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.
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DiverseVAR: Balancing Diversity and Quality of Next-Scale Visual Autoregressive Models
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arXiv Computation and Language
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Visual Information-Guided Parallel Decoding for Diffusion Multimodal Large Language Models
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.
Hugging Face Trending Papers
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Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization
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 Machine Learning
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Advanced Image Generation: Negative Prompt Optimization and Latent Classifier Guidance
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.