FreeMem: Enhancing Consistency in Long Video Generation via Tuning-Free Memory
Jibin Peng, Di Lin, Zhecheng Xu, Haoran Lu, Ruonan Liu, Wuyuan Xie, Miaohui Wang, Lingyu Liang
Abstract
Text-to-Video (T2V) generation has advanced greatly, yet maintaining consistency remains challenging, especially for tuning-free long video generation. We attribute the consistency problem to cumulative deviations for long video generation at three levels: the random noise lacking correlation results initial deviation between frames; discrepancy in semantic feature tokens between denoising network blocks gradually accumulates as the frame count grows, leading to greater deviations; attention mechanisms struggle to capture global relationships across distant frames in long videos. To address these, we propose FreeMem, a tuning-free framework leveraging hierarchical memory update and injection: the noise memory stabilizes consistency by manipulating low and high frequency components in the initial noise space; the token memory combats inconsistency through adaptive fusion of historical and current semantic feature tokens between denoising network blocks; and the attention memory establishes persistent cache to model long-range relationships within self attention layers. Evaluated on VBench, FreeMem improves subject and background consistency matrics across various methods, offering a practical solution for low-cost, high-consistency long video generation.
BibTeX
@inproceedings{aaai2026_freememenhancing,
title = {FreeMem: Enhancing Consistency in Long Video Generation via Tuning-Free Memory},
author = {Jibin Peng and Di Lin and Zhecheng Xu and Haoran Lu and Ruonan Liu and Wuyuan Xie and Miaohui Wang and Lingyu Liang and Yi Wang and Qing Guo},
booktitle = {AAAI 2026},
year = {2026}
}