The paper introduces CARE, a lightweight, plug‑and‑play regularization framework for diffusion models that dynamically adjusts feature distributions based on condition similarity. By leveraging built‑in conditioning signals such as labels or text prompts, CARE promotes tighter feature clusters for similar conditions without requiring explicit alignment losses or external supervision. Empirical results show consistent improvements in visual fidelity and convergence stability, achieving significant FID reductions and speed‑ups on ImageNet and text‑to‑image tasks, and it can be combined with existing regularization methods for further gains.
By Fengjia Guo, Zhuoyi Yang, Jie Tang
arXiv:2608. 14172v1 Announce Type: cross Abstract: Text-to-image diffusion models have two major drawbacks that severely limit their practical utility: (1) standard models lack an intrinsic mechanism for continuous, concept-specific guidance (e.
By Nikolai R\"ohrich, Isabell Hans, Felix Krause, Bj\"orn Ommer
arXiv:2609.36638v1 Announce Type: new
Abstract: Text-to-image users often provide concise and underspecified prompts, whereas generative models benefit from detailed textual conditions for reliable i...
By Mingfeng Lin, Chengfei Cai, Lin Xu, Chengqian Ma, Yuxiang Wei, Liang Han
arXiv:2603. 00133v2 Announce Type: replace-cross Abstract: Generative models have been shown to "memorize" certain training data, leading to verbatim or near-verbatim generating images, which may cause privacy concerns or copyright infringement.
By Kairan Zhao, Eleni Triantafillou, Peter Triantafillou
arXiv:2607. 08056v1 Announce Type: cross Abstract: Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks.
By Yidong Ouyang, Zhe Wang, Sourav Bhabesh, Dmitriy Bespalov
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.
arXiv:2606. 17979v1 Announce Type: new Abstract: Existing RL post-training methods for text-to-image generation usually convert the final-image reward into a single scalar advantage and apply it with the same strength to the entire generative trajectory.
By Jinjie Shen, Wei Deng, Xian Hu, Daiguo Zhou, Jian Luan
arXiv:2606. 14125v1 Announce Type: cross Abstract: Inversion-based image editing offers flexible and training-free control but still struggles with inversion accuracy and the trade-off between editing fidelity and background preservation.
By Zheyuan Zhan, Hongchen Li, Can Wang, Yinfei Ma, Mingzhen Huang, Ruoshi Bai, Jiawei Chen, Siwei Lyu, Defang Chen
arXiv:2507. 02288v2 Announce Type: replace-cross Abstract: Domain Generalization (DG) seeks to develop a versatile model capable of performing effectively on unseen target domains.
By De Cheng, Zhipeng Xu, Xinyang Jiang, Dongsheng Li, Nannan Wang, Xinbo Gao
arXiv:2607. 06432v1 Announce Type: cross Abstract: Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes, trademark constraints, and safety regulations, deployed systems must be able to suppress unwanted concepts after training.
By Naveen George, Naoki Murata, Yuhta Takida, Konda Reddy Mopuri, Yuki Mitsufuji
arXiv:2606. 29095v1 Announce Type: cross Abstract: Diffusion-based video relighting enables controllable relighting from a single input video, but modern video diffusion backbones are trained on short clips and applied to long-horizon videos through chunked sliding-window inference, often causing temporal discontinuities at chunk boundaries.
By Jing Yang, Mayoore Jaiswal, Zian Wang, Steven Zeng, Rochelle Pereira, Yajie Zhao, Jianyuan Min
arXiv:2609.37198v1 Announce Type: new
Abstract: Pretrained text-to-image models contain broad visual knowledge, yet they cannot reliably acquire or refine a specific visual identity from only a few r...
By Haoran He, Runyuan Cai, Yiming Wang, Lin Yu, Xiaodong Zeng