SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Score

• URL: http://arxiv.org/abs/2506.10173v2
• Date: Thu, 30 Oct 2025 07:25:20 GMT
• Title: SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Score
• Abstract summary: Diffusion models have demonstrated remarkable success in high-fidelity image synthesis and prompt-guided generative modeling.<n>We propose the Scalable Prompt-Aware R'eny Kernel Entropy Diversity Guidance (SPARKE) method for prompt-aware diversity guidance.<n>We numerically test the SPARKE method on several text-to-image diffusion models, demonstrating that the proposed method improves the prompt-aware diversity of the generated data without incurring significant computational costs.
• Score: 22.364171814102487
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have demonstrated remarkable success in high-fidelity image synthesis and prompt-guided generative modeling. However, ensuring adequate diversity in generated samples of prompt-guided diffusion models remains a challenge, particularly when the prompts span a broad semantic spectrum and the diversity of generated data needs to be evaluated in a prompt-aware fashion across semantically similar prompts. Recent methods have introduced guidance via diversity measures to encourage more varied generations. In this work, we extend the diversity measure-based approaches by proposing the Scalable Prompt-Aware R\'eny Kernel Entropy Diversity Guidance (SPARKE) method for prompt-aware diversity guidance. SPARKE utilizes conditional entropy for diversity guidance, which dynamically conditions diversity measurement on similar prompts and enables prompt-aware diversity control. While the entropy-based guidance approach enhances prompt-aware diversity, its reliance on the matrix-based entropy scores poses computational challenges in large-scale generation settings. To address this, we focus on the special case of Conditional latent RKE Score Guidance, reducing entropy computation and gradient-based optimization complexity from the $O(n^3)$ of general entropy measures to $O(n)$. The reduced computational complexity allows for diversity-guided sampling over potentially thousands of generation rounds on different prompts. We numerically test the SPARKE method on several text-to-image diffusion models, demonstrating that the proposed method improves the prompt-aware diversity of the generated data without incurring significant computational costs. We release our code on the project page: https://mjalali.github.io/SPARKE

Related papers

• DAK-UCB: Diversity-Aware Prompt Routing for LLMs and Generative Models [18.198091804236782]
  We introduce the Diversity-Aware Kernelized Upper Confidence Bound (DAK-UCB) as a contextual bandit algorithm for the online selection of generative models with diversity considerations.<n>We show that DAK-UCB promotes diversity-aware model selection while maintaining fidelity in the generations for a sequence of prompts.
  arXiv  Detail & Related papers  (2026-03-24T12:38:26Z)
• Masked Diffusion for Generative Recommendation [30.8737219110446]
  Generative recommendation (GR) with semantic IDs (SIDs) has emerged as a promising alternative to traditional recommendation approaches.<n>We propose to instead model and learn the probability of a user's sequence of SIDs using masked diffusion.<n>We demonstrate through thorough experiments that our proposed method consistently outperforms autoregressive modeling.
  arXiv  Detail & Related papers  (2025-11-28T09:36:26Z)
• G$^2$RPO: Granular GRPO for Precise Reward in Flow Models [74.21206048155669]
  We propose a novel Granular-GRPO (G$2$RPO) framework that achieves precise and comprehensive reward assessments of sampling directions.<n>We introduce a Multi-Granularity Advantage Integration module that aggregates advantages computed at multiple diffusion scales.<n>Our G$2$RPO significantly outperforms existing flow-based GRPO baselines.
  arXiv  Detail & Related papers  (2025-10-02T12:57:12Z)
• Post-training Large Language Models for Diverse High-Quality Responses [32.92680825196664]
  Reinforcement learning (RL) has emerged as a popular method for post-training large language models (LLMs)<n>We propose a novel training method named DQO (Diversity Quality Optimization) based on determinantal point processes (DPPs)<n>Our approach samples and embeds a group of responses for each prompt, then uses the determinant of a kernel-based similarity matrix to measure diversity as the volume spanned by the embeddings of these responses.
  arXiv  Detail & Related papers  (2025-09-05T03:47:06Z)
• FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation [50.438552588818]
  We propose textbfFindRec (textbfFlexible unified textbfinformation textbfdisentanglement for multi-modal sequential textbfRecommendation)<n>A Stein kernel-based Integrated Information Coordination Module (IICM) theoretically guarantees distribution consistency between multimodal features and ID streams.<n>A cross-modal expert routing mechanism that adaptively filters and combines multimodal features based on their contextual relevance.
  arXiv  Detail & Related papers  (2025-07-07T04:09:45Z)
• SIDE: Semantic ID Embedding for effective learning from sequences [1.2145532233226686]
  Sequence-based recommendations systems are driving the state-of-the-art for industrial ad-recommendation systems.<n>We propose a novel approach that leverages vector quantization (VQ) to inject a compact Semantic ID (SID) as input to the recommendation models.<n>The proposed enhancements when applied to a large-scale industrial ads-recommendation system achieves 2.4X improvement in normalized entropy (NE) gain and 3X reduction in data footprint.
  arXiv  Detail & Related papers  (2025-06-20T02:40:38Z)
• Evaluating the Diversity and Quality of LLM Generated Content [72.84945252821908]
  We introduce a framework for measuring effective semantic diversity--diversity among outputs that meet quality thresholds.<n>Although preference-tuned models exhibit reduced lexical and syntactic diversity, they produce greater effective semantic diversity than SFT or base models.<n>These findings have important implications for applications that require diverse yet high-quality outputs.
  arXiv  Detail & Related papers  (2025-04-16T23:02:23Z)
• Diversity Covariance-Aware Prompt Learning for Vision-Language Models [12.40822956896241]
  We present a Diversity Covariance-Aware framework that learns distributional information from the data to enhance the few-shot ability of the prompt model.<n>We propose a covariance-aware method that models the covariance relationships between visual features and uses anisotropic Mahalanobis distance, instead of the suboptimal cosine distance, to measure the similarity between two modalities.<n>Then, we propose the diversity-aware method, which learns multiple diverse soft prompts to capture different attributes of categories and aligns them independently with visual modalities.
  arXiv  Detail & Related papers  (2025-03-03T13:40:43Z)
• Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting [9.116108409344177]
  The source-free cross-domain few-shot learning task aims to transfer pretrained models to target domains utilizing minimal samples.<n>We propose the SeGD-VPT framework, which is divided into two phases.<n>The first step aims to increase feature diversity by adding diversity prompts to each support sample, thereby generating varying input and enhancing sample diversity.
  arXiv  Detail & Related papers  (2024-12-01T11:00:38Z)
• GANPrompt: Enhancing Robustness in LLM-Based Recommendations with GAN-Enhanced Diversity Prompts [15.920623515602038]
  Large Language Models (LLMs) are highly susceptible to the influence of prompt words.<n>This paper proposes GANPrompt, a multi-dimensional LLMs prompt diversity framework based on Generative Adversarial Networks (GANs)<n>The framework enhances the model's adaptability and stability to diverse prompts by integrating GANs generation techniques with the deep semantic understanding capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-08-19T03:13:20Z)
• Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding [90.77521413857448]
  Deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations. We introduce Generalized generative adversarial-Decoding Diffusion Probabilistic Models (EDDPMs) EDDPMs generalize the Gaussian noising-denoising in standard diffusion by introducing parameterized encoding-decoding. Experiments on text, proteins, and images demonstrate the flexibility to handle diverse data and tasks.
  arXiv  Detail & Related papers  (2024-02-29T10:08:57Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)
• Diversity of Thought Improves Reasoning Abilities of LLMs [26.149914503910235]
  Large language models (LLMs) are documented to struggle in settings that require complex reasoning. We discuss how one can create and leverage variations of the input prompt as a means of diversity of thought.
  arXiv  Detail & Related papers  (2023-10-11T00:01:41Z)
• Heterogeneous Multi-Task Gaussian Cox Processes [61.67344039414193]
  We present a novel extension of multi-task Gaussian Cox processes for modeling heterogeneous correlated tasks jointly. A MOGP prior over the parameters of the dedicated likelihoods for classification, regression and point process tasks can facilitate sharing of information between heterogeneous tasks. We derive a mean-field approximation to realize closed-form iterative updates for estimating model parameters.
  arXiv  Detail & Related papers  (2023-08-29T15:01:01Z)
• GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue [95.23775347605923]
  Building on the success of deep learning, Generative Adversarial Networks (GANs) provide a modern approach to learn a probability distribution from observed samples. GANs often suffer from the mode collapse issue where the generator fails to capture all existing modes of the input distribution. We take an information-theoretic approach and maximize a variational lower bound on the entropy of the generated samples to increase their diversity.
  arXiv  Detail & Related papers  (2020-09-24T19:34:37Z)
• Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization [73.45742420178196]
  We propose a novel multimodal response generation framework with exemplar augmentation and curriculum optimization. Our model achieves significant improvements compared to strong baselines in terms of diversity and relevance.
  arXiv  Detail & Related papers  (2020-04-26T16:29:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.