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 achieving high-quality video generation, these training losses are notoriously hard to optimize and suffer from mode collapse, leading to loss of video diversity and lack of motion. In this paper, we introduce Parallel Decoding Distillation (PDD), a simplified and scalable trajectory-based distillation method for fast inference of diffusion and flow matching models. Our architecture and training procedure are compatible with any pre-trained model and support sampling with a varying number of function evaluations (NFE). PDD accelerates generation by predicting multiple denoising steps per network evaluation. Conceptually, it learns a representation of the mean velocity without regressing its derivative using JVPs or finite-difference approximations. Our method achieves SOTA performance with 4-8 NFE on LTX-2.3 Text-to-Video/Audio, Wan 14B Text-to-Video, and Qwen-Image Text-to-Image. Moreover, PDD presents a significant improvement in generated video diversity.
This work introduces the GenVC, a compression-oriented video diffusion model built on a video diffusion model trained from scratch for compression, and realizes this model directly in pixel space with a global-to-local hierarchy that recovers fine spatio-temporal details, enabling high-quality generative reconstruction from compressed representations.
Naifu Xue, Zhaoyang Jia, Haosen Li et al.· 0 citations
A novel post-training acceleration framework that exploits this redundancy by integrating dynamic structural sparsification directly into the distillation process, and introduces a Progressive Training Strategy coupled with an Output Rollout Mechanism that ensures the coherent learning of structural decisions across timesteps.
Yu Cheng, Siyue Yao, Zhongang Qi et al.· 0 citations
This work presents Dynamic Trajectory Initialization (DTI) paradigm for GFVSR, which reformulates GFVSR as an input-driven directional restoration and demonstrates the perception-distortion trade-off and that the LPIPS is the most convincing metric in this case.
Yingwei Tang, Chen Yan, Wending Liu et al.· arXiv.org· 0 citations
CineMobile adopts a three-fold optimization strategy, leveraging a distillation-guided pruning approach to derive a compact yet efficient model that retains the essential video generation capabilities required for cinematic effects, demonstrating its practical applicability for mobile-based image-to-video creation.
Xuyao Huang, Zelai Deng, Xu Wang et al.· 0 citations
MobileWan becomes the first 5B-scale video diffusion model deployable on a commercial mobile device and proposes a learnable attention head pruning method based on binary per-head gates optimized end-to-end using a noise-biased sparsity objective and distillation-based finetuning.
Mohsen Ghafoorian, Denis Korzhenkov, Adil Karjauv et al.· 1 citation
This work proposes FlashMo, a frequency-aware sparse motion diffusion model that prunes low-frequency tokens to enhance efficiency without custom kernel design, and introduces MotionSiT, a scalable diffusion transformer based on a joint-temporal factorized interpolant with Lie group geodesics over SO(3) manifolds, enabling principled generation of joint rotations.
Zeyu Zhang, Yiran Wang, Danning Li et al.· Advances in Neural Informati...· 11 citations