Suche: diffusion models

Mehr als 1.000 Paper passen: hier die 1.000 neuesten, nach Relevanz sortiert.

• Quantum Circuits in Diffusion Models: A Fair-Comparison Study and a Mechanistic Analysis of Angle-Embedding Failures

  Jaeuk Kim, Sanghoon Yoo · 13. Juli 2026 · Quantum Computing Algorithms and Architecture

  We study the integration of variational quantum circuits (VQCs) into diffusion models through a squeeze-and-excitation (SE) channel-modulation scaffold that isolates the quantum contribution. Using a role-matched classical control and multi-seed significance testing across DDPM and latent diffusion …

• ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models

  Sang-Hoon Lee, Ha-Yeong Choi · 13. Juli 2026 · Speech Recognition and Synthesis

  Representation alignment (REPA) has been investigated to accelerate diffusion training, but we observe that regularizing intermediate representations in diffusion Transformers (DiT) may implicitly entangle latents and limit generative capacity. To address this issue, we propose ReGen, a hierarchical…

• LongE2V: Long-Horizon Event-based Video Reconstruction, Prediction, and Frame Interpolation with Video Diffusion Models

  Cheng-De Fan, Chun-Wei Tuan Mu, Chen-Wei Chang, Chin-Yang Lin, Kun-Ru Wu, Yu-Chee Tseng, Yu-Lun Liu · 10. Juli 2026 · Image and Video Quality Assessment

  Recovering high-quality video from sparse event streams is a challenging task. Regression methods often blur textures, while existing generative models struggle with long-term stability. We propose LongE2V, a novel approach that leverages pre-trained video diffusion priors to jointly handle event-ba…

• Reinforcing the Generation Order of Multimodal Masked Diffusion Models

  Yidong Ouyang, Zhe Wang, Sourav Bhabesh, Dmitriy Bespalov · 10. Juli 2026 · Multimodal Machine Learning Applications

  Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks. Recent research demonstrates that adaptive token generation ordering can significantly improve performance in mathematical reasoning and code synthesis applications. In this work, we in…

• An exact information theory of generalization phase transitions in Bayesian diffusion models

  Henry Hunt, Mason Kamb, Surya Ganguli · 10. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  How diffusion models circumvent the curse of dimensionality to learn complex distributions over high dimensional spaces from a finite training set, instead of memorizing it, remains a fundamental mystery. To address this, we introduce analytically tractable Bayesian information restricted diffusion …

• Bifidelity Parameter Estimation Using Conditional Diffusion Models

  Caroline Tatsuoka, Minglei Yang, Dongbin Xiu, Guannan Zhang · 9. Juli 2026 · Probabilistic and Robust Engineering Design

  We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distribution. In the Bayesian inference setting, traditional parameter estimation methods rely on repeated simulations of pot…

• An Hybrid Quantum-Classical Diffusion Model for Image Generation

  Qipeng Qian, Keli Deng, Yuntao Qian · 9. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states. However, applying such models to classical high-dimensional data is constrained by the qubit cost of state encoding and the computational burden of simu…

• Generative Diffusion Models of Stochastic Graph Signals

  Yi\u{g}it Berkay Uslu, Samar Hadou, Sergio Rozada, Shirin Saeedi Bidokhti, Alejandro Ribeiro · 9. Juli 2026 · Advanced Graph Neural Networks

  Sampling stochastic signals supported on a graph underlies many graph machine learning tasks, including recommender systems, forecasting in financial markets, and wireless network optimization. In these settings, the target signals are realizations of unknown conditional distributions. However, prev…

• Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing

  Wanglong Lu, Lingming Su, Kaijie Shi, Minglun Gong, Xiaogang Jin, Hanli Zhao, Xianta Jiang · 8. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Recent diffusion-based generative models have shown impressive performance in image generation and editing. However, due to memory limitations and the high cost of collecting high-resolution training images, existing methods are typically restricted to inputs with linear resolutions below 1K. In con…

• Robust Face Super-Resolution and Recognition Through Multi-Feature Aggregation in Diffusion Models

  Marcelo dos Santos, Rayson Laroca, João Carlos Raposo Neves, David Menotti · 8. Juli 2026 · Advanced Image Processing Techniques

  Images acquired in surveillance environments often suffer from conditions such as low resolution, variations in pose, irregular illumination, and occlusions. Due to the low quality of these images, face recognition algorithms often struggle. This major limitation can be addressed by employing super-…

• FourTune: Towards Fully 4-Bit Efficient Post-Training for Diffusion Models

  Bowen Xue, Zihan Min, Xingyang Li, Zhekai Zhang, Haocheng Xi, Lvmin Zhang, Maneesh Agrawala, Jun-Yan Zhu, Song Han, Yujun Lin, Muyang Li · 8. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion models have become a dominant paradigm for high-quality generative modeling, while post-training is essential for adapting them to diverse downstream applications. However, post-training of large diffusion models is still challenging due to the prohibitive memory footprints and slow traini…

• CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training

  Max Van Puyvelde, Halil Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert · 8. Juli 2026 · COVID-19 diagnosis using AI

  Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning. We present CONFLUX, a latent diffusion model for chest computed tomo…

• Replication in Visual Diffusion Models: A Survey and Outlook

  Wenhao Wang, Yifan Sun, Zongxin Yang, Zhengdong Hu, Zhentao Tan, Yi Yang · 8. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Visual diffusion models have revolutionized the field of creative AI, producing high-quality and diverse content. However, they inevitably memorize training images or videos, subsequently replicating their concepts, content, or styles during inference. This phenomenon raises significant concerns abo…

• Erasing Without Collateral Damage: Precise Concept Removal in Diffusion Models

  Parth Upman, Nishita Jain, Shreyank N Gowda · 7. Juli 2026 · Domain Adaptation and Few-Shot Learning

  Training-free concept erasure is an attractive mechanism for controlling text-to-image diffusion models, but precise erasure often comes at the cost of damaging semantically related non-target concepts. Existing value-space methods remove the component of each cross-attention value along the target …

• LILAC: Layer-Wise Independent LoRAs and Cascaded Conditioning for Multi-Concept Customization of Diffusion Models

  Marian Lupascu, Sebastian Ripa, Mihai Trascau, Mariana-Iuliana Georgescu, Ionut Mironica · 7. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Personalizing text-to-image diffusion models to render several specific subjects in a coherent image remains challenging: the model must preserve each subject's identity while keeping the scene spatially and visually coherent. Methods that fuse independently trained concept adapters in a shared weig…

• Flash-BoN: Instant Drafts for Inference-Time Scaling in Diffusion Models

  Ruchit Rawal, Reza Shirkavand, Sayak Paul, Yuxin Wen, Heng Huang, Yizheng Chen, Tom Goldstein, Gowthami Somepalli · 7. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Inference-time scaling for text-to-image generation has progressed from simple Best-of-$N$ (BoN) sampling to guided search methods that verify and steer candidate trajectories at intermediate denoising steps. These approaches focus on when and how often to verify during denoising but largely treat t…

• DELTA-TTS: Adapting Autoregressive Model into Diffusion Language Model for Text-to-Speech

  Junwon Moon, Yejin Lee, Seungbeom Kim, Hoseong Ahn, Sewoong Park, Heeseung Kim, Kyuhong Shim · 7. Juli 2026 · Speech Recognition and Synthesis

  Autoregressive (AR) text-to-speech (TTS) models generate discrete speech tokens sequentially, which makes inference slow and can degrade robustness, since local errors propagate to later positions and can escalate into hallucination. This limitation stems from their left-to-right AR commitment: each…

• DICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models

  Chunnan Shang, Xin Zhang, Zhizhong Wang, Hongwei Wang · 7. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specific designs that can improve performance but limit generalization, and training-free loss guidance that compresses rich conditions into scalar objecti…

• Tightening the Score Matching Gap for Diffusion Models

  Benjamin Dupuis, Tyler Farghly, Maxime Haddouche, Alain Durmus, Umut Simsekli · 7. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion models (DMs) are a state-of-the-art generative method to approximately sample from an unknown distribution. Their training and evaluation primarily rely on an Evidence Lower Bound (ELBO), which relates the Kullback-Leibler (KL) divergence of model samples to the score matching loss along t…

• Benign Overfitting Does Not Occur in Diffusion Models

  Tyler Farghly, Benjamin Dupuis, Alain Durmus, Umut Simsekli · 7. Juli 2026 · Stochastic Gradient Optimization Techniques

  Benign overfitting and double descent have come to shape our understanding of generalization in deep learning, establishing that overfitting is not only compatible with good generalization but can actively benefit it. Diffusion models share much of the machinery of standard deep learning, so it is n…

• CDCP: Conditional Diffusion Model with Contextual Prompts for Multi-task Offline Safe Reinforcement Learning

  Jiayi Guan, Tianle Zhang, Li Shen, Ruiqi Zhang, Ao Zhou, Lusong Li, Guai Chen, Mengjie Li, Alois Knoll, Xiaodong He, Changjun Jiang · 7. Juli 2026 · Reinforcement Learning in Robotics

  Multi-task offline safe reinforcement learning (RL) promises to learn a shared optimal safe policy from offline data across multiple tasks. This paradigm provides an effective means for the widespread application of RL in multi-task scenarios with high risk and interaction costs. However, the triple…

• Mixture-of-Gaussians-Guided Schedule Design for Brownian Bridge Diffusion Models

  Ron Levi, Michael Elad · 7. Juli 2026 · Advanced Image Processing Techniques

  Brownian Bridge Diffusion Models (BBDM) offer an appealing framework for image restoration and inverse problems by constructing a stochastic bridge from the clean signal directly to the degraded observation, rather than to pure noise. Despite their promise, the choice of bridge schedule is typically…

• ELBO-T2IAlign: A Generic ELBO-Based Method for Calibrating Pixel-level Text-Image Alignment in Diffusion Models

  Qin Zhou, Zhiyang Zhang, Jinglong Wang, Xiaobin Li, Jing Zhang, Qian Yu, Lu Sheng, Dong Xu · 7. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion models excel at image generation. Recent studies have shown that these models not only generate high-quality images but also encode text-image alignment information through attention maps or loss functions. This information is valuable for various downstream tasks, including segmentation, …

• What Does a Discrete Diffusion Model Learn?

  Rodrigo Casado Noguerales, Bernhard Sch\"olkopf, Thomas Hofmann, Aran Raoufi · 7. Juli 2026 · Stochastic Gradient Optimization Techniques

  What does a discrete diffusion model learn: a denoiser, a score ratio, or a bridge plug-in predictor? At the level of jump rates, these are one object in different coordinates, and reading a neural network in the wrong coordinate changes the process being trained and sampled. Starting with a rigorou…

• Attention Dynamics in Diffusion Models: A Visual Analytics Framework for Human-AI Collaboration

  Yiran Xiao, George Legrady · 7. Juli 2026 · Data Visualization and Analytics

  Diffusion-based text-to-image models can synthesize complex and highly structured visual content, yet the emergence and evolution of semantic structure remain difficult to interpret. Many existing workflows rely on aggregated attention or scalar summaries that separate temporal change from image-spa…