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