Video diffusion and flow models require many sequential evaluations, making generation computationally expensive. Few-step distillation reduces this cost but poses a capacity allocation problem: a student must match the teacher's iterative generation with far less sequential computation. Existing trajectory methods ask...
Lang Feng, Péter Karkus, M. Igl et al.· 0 citations
Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajectory steering for reasoning and inference acceleration. To overcome this, we present Sigma, the first large-scale (3B/8B) continuous dLM built on steerable, low-dimensional...
Zhi-Han Yang, Wei Guo, Jean-Marie Lemercier et al.· 0 citations
This paper proposes a training paradigm where Mode Seeking meets Mean Seeking, decoupling local fidelity from long-term coherence based on a unified representation via a Decoupled Diffusion Transformer, and closes the fidelity-horizon gap by jointly improving local sharpness, motion and long-range consistency.
Shengqu Cai, Weili Nie, Chao Liu et al.· arXiv.org· 12 citations· ⚡2
This work proposes reward-based velocity matching (RVM), a simple trajectory-free update that acts directly on the velocity field and provides a general framework that recovers recent fine-tuning methods, including RAM and DiffusionNFT, as special cases.
Jaemoo Choi, Wei Guo, Yuchen Zhu et al.· 1 citation
Flex-Forcing is introduced, a unified training and inference framework that enables a video diffusion model to seamlessly operate under both bidirectional and autoregressive generation regimes, and achieves consistently better video quality, long-video stability than strong baselines with a rigid inference schedule.
Xinyin Ma, Julius Berner, Chao Liu et al.· arXiv.org· 1 citation
This work introduces a novel and efficient solver for the underlying HJB equation based on the functional tensor train (FTT) format, which leverages latent low-rank structures to efficiently approximate high-dimensional functions, enabling both model compression and rapid computation.
Robert Gruhlke, Julius Berner, David Sommer et al.· 0 citations
Generation in video diffusion or flow models is computationally expensive due to the slow and iterative sampling process. Current state-of-the-art (SOTA) acceleration methods heavily rely on variational score distillation (VSD) and adversarial losses to distill diffusion models into few-step generators. Albeit achievin...
Neta Shaul, Chao Liu, Arash Vahdat et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
