arXiv:2610.19802v1 Announce Type: new
Abstract: Negative guidance of diffusion models generates samples that avoid an undesired condition, specified either by reference samples or by a conditional sc...
By Hirohane Takagi, Masashi Sugiyama
The paper introduces a method for specificity‑aware diffusion steering that suppresses undesired samples while preserving desired ones. By formulating the problem as a target‑design task, it derives a time‑dependent target distribution based on overlap between positive and negative reference distributions, and samples from it using a variance‑reduced Sequential Monte Carlo (SMC) sampler. Experiments on synthetic, class‑contrastive, text‑to‑image, and peptide‑MHC tasks demonstrate reduced mode shift, improved sampling stability, and better suppression of undesired regions compared to negative‑guidance baselines.
By Luran Wang, Linrui Ma, Hannes St\"ark, Regina Barzilay
The paper introduces Probability‑Flow Distillation (PFD), a new method for matching parameter distributions in diffusion‑based models. It extends the particle variational inference framework of Variational Score Distillation to Score Distillation Sampling (SDS) and Score Distillation via Inversion (SDI), revealing that SDS focuses on mode collapse while SDI converges to a contracted distribution. By replacing a single Euler step in SDI with a full reverse probability‑flow ODE solve and simplifying the gradient, PFD achieves distribution matching with only a forward ODE solve, and experiments on synthetic data, CelebA, and text‑to‑3D tasks confirm its effectiveness.
By Rohith Ramanan, A. N. Rajagopalan
arXiv:2602. 21429v3 Announce Type: replace Abstract: Flow-based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distributions.
By Darshan Gadginmath, Ahmed Allibhoy, Fabio Pasqualetti
arXiv:2606. 08953v1 Announce Type: new Abstract: Modern generative models often define an entire probability path from a simple prior to the data law, rather than only an endpoint map.
By Lei Luo, Yingzhen Zhang, Jian Yang
arXiv:2607. 27372v1 Announce Type: new Abstract: The deep learning revolution, kicked off by AlexNet, taught us that end-to-end training beats decomposing a problem into hand-designed stages.
By Alexi Gladstone, Heng Ji, Yilun Du