arXiv:2608.29107v1 Announce Type: new
Abstract: While modern generative models excel at modeling complex data, precise inference-time control in conditional generation remains a critical challenge. C...
By Avishag Nevo, Tamir Hazan
arXiv:2602. 20360v2 Announce Type: replace Abstract: Flow-based generative methods offer a simple and effective framework for high-fidelity generation, yet pretrained flow models are rarely used in their vanilla conditional form: in image generation, samples without guidance often appear diffuse and lack fine-grained detail.
By Runlong Liao, Jian Yu, Baiyu Su, Chi Zhang, Lizhang Chen, Qiang Liu
arXiv:2502. 08006v3 Announce Type: replace-cross Abstract: Training-free guided generation is a widely used and powerful technique that allows the end user to exert further control over the generative process of flow/diffusion models.
By Zander W. Blasingame, Chen Liu
arXiv:2512. 10339v2 Announce Type: replace Abstract: Inference-time steering adapts pretrained diffusion and flow models to new tasks without retraining, often utilizing ratio-of-densities constructions that reweight time-indexed marginals with fixed exponents.
By Ziseok Lee, Minyeong Hwang, Wooyeol Lee, Sanghyun Jo, Jihyung Ko, Young Bin Park, Jae-Mun Choi, Eunho Yang, Kyungsu Kim
arXiv:2606. 27377v1 Announce Type: cross Abstract: Modern image generation demands a single model that unifies diverse capabilities, including text-to-image (T2I), local editing, and global editing.
By Wei Zhou, Xiongwei Zhu, Zelin Xu, Bo Dong, Lixue Gong, Yongyuan Liang, Meng Chu, Leigang Qu, Lingdong Kong, Wei Liu, Tat-Seng Chua
Efficient Covariance-Aware Vicinal Latent Diffusion (CAViLD) is a latent diffusion framework designed for continuous conditional image generation. It builds a purified, condition-aware latent space using density-adaptive vicinal learning, auxiliary semantic regularization, and NIQE-guided negative data augmentation. CAViLD incorporates an EMA teacher to internalize conditional guidance, enabling single-branch inference, and employs covariance-aware progressive distillation to reduce sampling steps to four NFEs, achieving a balance between fidelity, condition consistency, and efficiency on datasets such as RC-49, UTKFace, and Steering Angle.