The paper studies classifier‑free guidance (CFG) in Flow Matching, showing that strong guidance can distort the generated distribution by shifting the mean and concentrating trajectories. By interpreting Flow Matching as a time‑varying gradient flow, the authors explain how CFG reshapes the underlying potential and propose a training‑free method, Posterior‑Mean‑Capped CFG (PMC‑CFG), that adaptively limits guidance to the strongest feasible level. Experiments on synthetic and large‑scale image‑generation tasks demonstrate that PMC‑CFG reduces distortion and concentration while improving the alignment–diversity trade‑off, especially when nominal guidance is large.
By Jishen Peng, Zheng Ma
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
The paper introduces Global Transport (GT), a class‑agnostic optimal‑transport coupling that can be computed without class labels. GT associates different conditions with distinct regions of the source noise, which degrades performance when used without guidance but consistently improves generation when combined with classifier‑free guidance across various domains, model scales, and sampling budgets. The authors argue that coupling design should be evaluated under guided flow conditions rather than unguided generation, and demonstrate GT’s benefits on both discrete class‑conditioned and continuous text‑conditioned image generation.
By Katarina Petrovi\'c, Zander W. Blasingame, Danyal Rehman, \.Ismail \.Ilkan Ceylan, Michael Bronstein, Stephen Y. Zhang, Lazar Atanackovic, Alexander Tong
arXiv:2610.20604v1 Announce Type: new
Abstract: World-action models aim to turn visual anticipation into driving decisions, yet typically train action experts on trajectory representations defined in...
By Ao Liang, Youquan Liu, Mingxiao Li, Wei Chow, Lingdong Kong, Yue Liao, Dongyue Lu, Yu Yang, Wei Yin, Runnan Chen
arXiv:2609.33167v2 Announce Type: replace
Abstract: We present FloodDiffusion 2 (FD2), an efficient and controllable framework that builds upon FloodDiffusion (FD1), a state-of-the-art streaming moti...
By Yiyi Cai, Yuhan Wu, Kunhang Li, Tu Fangyuan, Xiangyue Zhang, Qiaoge Li, Zhixiang Wang, Kaipeng Zhang, Haiyang Liu
VideoAWR introduces a decoupled-timestep advantage‑weighted rectification framework for video flow models, separating guided rollouts from the optimization anchor to avoid geometric guidance blow‑up and cold‑start collapse. The method uses an EMA reference policy for guided rollouts while rectifying against an unguided conditional anchor, proving that the centered advantage cancels the anchor in expectation and retains only reward‑correlated covariance. Experiments on Wan2.1‑1.3B and 14B models show stable training and improved visual and motion quality in both CFG‑free and CFG‑enabled settings.
By Zheming Fu, Wenbin Ning, Ruizhe He, Wei Shang, Xiaoxiao Ma, Lianrui Mu, Chang Liu, Lei Wang, Siming Fu