arXiv:2603.23463v2 Announce Type: replace-cross
Abstract: Recent diffusion-based models achieve photorealism in image inpainting but require many sampling steps, limiting practical use. Few-step text...
By Duc Vu, Kien Nguyen, Trong-Tung Nguyen, Ngan Nguyen, Phong Nguyen, Khoi Nguyen, Cuong Pham, Anh Tran
arXiv:2607. 10580v1 Announce Type: cross Abstract: AI models are increasingly trained on personal images scraped from social media and public platforms, often without consent, leading to serious privacy violations, such as unauthorized facial recognition and targeted advertising.
By Syed Irfan Ali Meerza, Oktay Ozturk, Amir Sadovnik, Jian Liu
arXiv:2502. 10389v2 Announce Type: replace-cross Abstract: Diffusion models (DMs) have become the leading choice for generative tasks across diverse domains.
By Ziming Liu, Yifan Yang, Chengruidong Zhang, Yiqi Zhang, Lili Qiu, Yang You, Yuqing Yang
The paper introduces D3S2, a diffusion‑guided dataset distillation framework tailored for semantic segmentation. It tackles long‑tailed class imbalance, pixel‑wise alignment, and high computational cost by first selecting class‑balanced masks and then synthesizing images with a pretrained diffusion model conditioned on those masks. Guided diffusion sampling further refines the data with segmentation‑consistency and class‑wise feature matching losses, achieving superior performance at a 1% compression rate on ADE20K and COCO‑Stuff.
By Wenjie Zheng, Haoji Hu, Jiali Lu, Xingze Zou, Jing Wang
EraseSAE introduces a surgical concept erasure method for text-to-video diffusion models, using sparse autoencoders to decompose activations into interpretable, monosemantic features. The framework employs a contrastive attribution mechanism to isolate concept-specific kernels and applies timestep-resolved masks during inference to remove target concepts while preserving unrelated content. Experiments show that EraseSAE achieves precise, robust concept removal with minimal quality loss, outperforming existing methods.
The paper demonstrates that a frozen generic text‑to‑image diffusion model can perform conditional inpainting across three natural‑image domains without any inpainting‑specific training or dataset adaptation. The proposed Step‑PI method augments known‑region projection with boundary‑interior latent feedback, persistent PI state, and a predefined four‑field release schedule, improving all evaluated metrics over baseline training‑free approaches. Experiments on CelebA‑HQ, AFHQ, and Places2 show consistent gains, with Step‑PI outperforming LanPaint and PILOT on all five macro metrics.
By Zhenhuan Wang, Fengyi Yuan