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...
arXiv:2603. 14294v3 Announce Type: replace-cross Abstract: Do video diffusion models encode signals predictive of physical plausibility?
We propose OPSD-V, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR video generators can produce long videos with low latency, but still suffer from error accumulation and weakened motion dynamics during long autoregressive rollout.
The paper introduces TRACK, a training‑free trajectory routing method that accelerates video diffusion by selectively switching between large and small models during denoising steps. A calibration process generates a disagreement score map, guiding the selection of the appropriate model at each step to maintain quality while reducing computational cost. Experiments on Wan 2.1, Cosmos 3, TurboDiffusion, and FastVideo show speedups ranging from 1.95× to 2.73× with comparable quality and diversity.
Accelerating Video Diffusion via Training-Free Trajectory Routing (TRACK) introduces a heterogeneous denoising strategy that switches between large and small diffusion models at selected steps, determined by a calibration process that measures disagreement between model predictions. By routing quality-sensitive steps to the large model and low-disagreement steps to the small model, TRACK achieves significant speedups—up to 2.73×—across several video diffusion benchmarks while maintaining comparable quality and diversity. The method requires no retraining, architectural changes, or online dual-model evaluation, making it a practical acceleration paradigm for video diffusion.
arXiv:2608. 11235v1 Announce Type: new Abstract: Diffusion language models (DLMs) update many tokens in parallel, yet practical decoders often use a fixed denoising horizon.
arXiv:2606. 27326v1 Announce Type: new Abstract: Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the ground-truth dynamics.
arXiv:2606. 15048v1 Announce Type: new Abstract: Diffusion models are typically trained with objectives that focus on local denoising targets at individual time steps (or adjacent pairs), which do not enforce consistency between predictions along the denoising trajectory.
LPA-CWM introduces a Learned Physical Adjudicator (LPA) to improve counterfactual world models (CWM) for motion reasoning by learning to weight candidate responses based on visual context and response structure. The 3.0M‑parameter LPA is trained on dense MOVi‑F trajectories while keeping the CWM predictor and intervention generator frozen. A new Completeness‑aware Motion Correspondence (CMC) protocol evaluates localization, trajectory completeness, visibility, and continuity, and LPA‑CWM achieves significant gains on DAVIS and Kinetics subsets.
arXiv:2512. 19311v2 Announce Type: replace-cross Abstract: This paper studies the training-testing discrepancy (a.
SelfLift is a progressive‑resolution framework that accelerates few‑step diffusion models by enabling late, self‑recovering transitions between low‑ and high‑resolution latents. It introduces a training‑free Artifact‑Aware Consistency Lift that uses disagreement between direct latent lifting and pixel‑VAE re‑encoding to detect and correct artifacts, and a self‑recovery policy that transfers high‑resolution guidance from an internal teacher. Experiments on FLUX.2‑Klein and Z‑Image‑Turbo show latency reductions of 41.5% and 44.1%, and overall speedups of 29.61× and 19.21× over 50‑step baselines while maintaining competitive generation quality.
arXiv:2609.37974v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) generate text by unmasking several tokens per step, but they are trained and sampled under different conditions. The m...