PUSA V1.0: Surpassing Wan-I2V with $500 Training Cost by Vectorized
  Timestep Adaptation
Authors: name: Yaofang Liu; name: Yumeng Ren; name: Aitor Artola; name: Yuxuan Hu; name: Xiaodong Cun; name: Xiaotong Zhao; name: Alan Zhao; name: Raymond H. Chan; name: Suiyun Zhang; name: Rui Liu; name: Dandan Tu; name: Jean-Michel Morel
Published: 2025-07-22 00:09:37

Abstract: The rapid advancement of video diffusion models has been hindered by
fundamental limitations in temporal modeling, particularly the rigid
synchronization of frame evolution imposed by conventional scalar timestep
variables. While task-specific adaptations and autoregressive models have
sought to address these challenges, they remain constrained by computational
inefficiency, catastrophic forgetting, or narrow applicability. In this work,
we present Pusa, a groundbreaking paradigm that leverages vectorized timestep
adaptation (VTA) to enable fine-grained temporal control within a unified video
diffusion framework. Besides, VTA is a non-destructive adaptation, which means
it fully preserves the capabilities of the base model. By finetuning the SOTA
Wan2.1-T2V-14B model with VTA, we achieve unprecedented efficiency --
surpassing the performance of Wan-I2V-14B with leq 1/200 of the training
cost (\500 vs. \geq 100,000) and leq 1/2500 of the dataset size (4K vs.
geq 10M samples). Pusa not only sets a new standard for image-to-video (I2V)
generation, achieving a VBench-I2V total score of 87.32\% (vs. 86.86\% of
Wan-I2V-14B), but also unlocks many zero-shot multi-task capabilities such as
start-end frames and video extension -- all without task-specific training.
Meanwhile, Pusa can still perform text-to-video generation. Mechanistic
analyses reveal that our approach preserves the foundation model's generative
priors while surgically injecting temporal dynamics, avoiding the combinatorial
explosion inherent to vectorized timesteps. This work establishes a scalable,
efficient, and versatile paradigm for next-generation video synthesis,
democratizing high-fidelity video generation for research and industry alike.
Code is open-sourced at https://github.com/Yaofang-Liu/Pusa-VidGen