Measured Sliders introduces a framework that defines continuous image controls via closed‑form differentiable measurements, enabling a unified measurement space for training and calibration. An observability test pre‑identifies usable supervision, while a measurement‑guided objective learns target movements and suppresses unwanted changes. The resulting controls, tested on SDXL and FLUX.1‑dev, are ordered, selective, and composable, achieving high monotonicity and selectivity across multiple attributes.
By Yijia Chen, Boyu Wei, Xuanhua Yin
arXiv:2611.05755v1 Announce Type: new
Abstract: Classifier-Free Guidance is a widely used inference-time guidance rule for conditional image generation with diffusion models, which exposes a guidance...
By Wuyang Zhang, Xiaolin Xu
The paper introduces Meta‑Diff, a method that learns a reusable source‑to‑target relation in a compact task space, enabling rapid adaptation of a diffusion model to new targets with minimal or no target data. By encoding each distribution into a low‑dimensional coordinate and transporting a source coordinate to its predicted target, the approach avoids costly fine‑tuning and achieves significant performance gains on CIFAR‑100 and Office‑Home benchmarks. Experiments show that when task identity is preserved, the relation generalizes across tasks, and the source‑target shift is manageable, Meta‑Diff can outperform target‑only modeling and full fine‑tuning.
whyItMatters:"The method offers a practical solution for few‑shot adaptation in data‑scarce settings, such as healthcare, by reducing computational cost and improving reliability compared to traditional retraining approaches."
By Jinjian Jiang, Jing Peng, Rehan Deen, Yu Cao, Ziquan Liu, Muhammad Salman Haleem, David Mguni
The paper argues that diffusion-based action policies can use a frozen, observation‑free backbone as a reusable trajectory prior, with task adaptation handled entirely by the conditioning pathway. By pretraining a general action head on forward‑kinematics data and then freezing it, the authors show that a single backbone can match or outperform normally trained models on MimicGen and LIBERO. Their experiments reveal that a small 5 M‑parameter MLP backbone can rival large U‑Net and transformer backbones, indicating that action backbones are often over‑parameterized and that image‑style architectures may not be the best fit for low‑dimensional action generation.
By Jian Zhou, Sihao Lin, Shuai Fu, Zerui Li, Gengze Zhou, Qi WU
arXiv:2605.15816v2 Announce Type: replace-cross
Abstract: Stipple patterns, point sets whose local density tracks a target image, are traditionally produced by per-density iterative optimizers, which...
By Ofir Gilad, Aleksander Plocharski, Przemyslaw Musialski, Andrei Sharf
arXiv:2608.14740v2 Announce Type: replace
Abstract: Unified image and video creation requires a model to follow diverse instructions while preserving identity, geometry, and temporal structure from v...
By Zhefan Rao, Bin Zou, Xuanhua He, Chong Hou Choi, Yanheng Li, Rui Liu, Haoxuan Che, Qifeng Chen
The paper introduces DiffusionOPSD, an on‑policy self‑distillation framework that transforms image‑level reinforcement learning rewards into explicit targets for intermediate denoising predictions in diffusion models. By generating trajectories with a frozen behavior policy and constructing bounded positive and negative targets around query states, the method trains a policy to fit these targets before updating the behavior policy via an exponential moving average. Experiments on SD 3.5‑M and Z‑Image‑Turbo show that DiffusionOPSD achieves the best held‑out scores in 19 of 20 reward‑matched settings, outperforms the strongest competitor by up to 44 % and cuts GPU‑hour usage by 40–63 % compared to DiffusionNFT.
By Wei Zhou, Xiongwei Zhu, Lingdong Kong, Bo Chen, Lei Zhang, Yongyuan Liang, Xiaoxia Hou, Ye Tian, Xian Sun, Yingshuo Wang, Linfeng Li, Shengqiong Wu, Leigang Qu, Feng Li, Wei Liu, Julian McAuley, Tat-Seng Chua
arXiv:2609.23967v1 Announce Type: cross
Abstract: Diffusion models are increasingly adapted from generation to conditional prediction, where a conditioning signal is combined with an evolving noisy r...
By Hengzhuo Yang, Yuming Zeng, Yuling Yang
Reinforcement learning can align diffusion models with human preferences and task-specific objectives, but endpoint rewards do not specify how an intermediate denoising prediction should change. We in...
Dynin‑Robotics introduces an omnimodal masked‑diffusion backbone, Dynin‑Omni, that jointly represents language, visual observations, goals, and actions as discrete tokens. By conditioning on different spans, the same model learns action prediction, next‑observation prediction, goal‑state prediction, and trajectory‑to‑instruction reconstruction, enabling test‑time scaling through goal prediction and action‑candidate evaluation. The system, pretrained on 1.33 million trajectories from 48 Open X‑Embodiment datasets, achieves competitive performance on LIBERO, zero‑shot LIBERO‑Plus, and a 78.4 % success rate on a Franka Research 3 robot, while a block‑parallel implementation speeds up action decoding by up to 29.2×.
By Hoeun Lee, Jaeik Kim, Jusang Oh, Jinhyeok Kim, Geon Choi, Hyeonggeun Kim, Jaeyoung Do
Text-to-image (T2I) diffusion models have achieved striking progress but still struggle to synthesize rare concepts involving unusual attribute-object pairings, often resulting in concept omission or semantic drift where a dominant entity overwhelms the generation. Tracing these failures to a lack of compositional balance during the denoising trajectory, we propose RADIANCE, a training-free framework that treats inference as a closed-loop feedback process.
arXiv:2609.05588v3 Announce Type: replace-cross
Abstract: World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-labeled inte...
By Renhang Liu, Wenzhi Zhao, Zhuo Yang, Liliang Chen, Pengfei Zhou, Shengcong Chen, Guanghui Ren, Youlun Peng, Rongjun Jin, Nan Wang, Sukai Wang, Xindong He, Jinyuan Feng, Ziyu Xiong, Linqing Zhong, Yifei Wei, Feng Han, Long Zhang, Da Huang, Nanshu Zhao, Chenghao Yin, Mo Wu, Zhaodong Yan, Kongtao Hu, Yuxiang Yan, Aogelijiang Niyazi, Yu Fang, Jia Zeng, Lizhu Meng, Daizhen Lv, Haoyu Cao, Zhiwen Hou, Lianjin Ye, Yuehan Niu, Zhikai Cai, Xuan Hu, Hui Min, Xiongfeng Cai, Yue Liao, Jing Wu, Soujanya Poria, Ye Li, Sanping Zhou, Maoqing Yao, AgiBot Research Team