LongLive: Real-time Interactive Long Video Generation
Authors: name: Shuai Yang, name: Wei Huang, name: Ruihang Chu, name: Yicheng Xiao, name: Yuyang Zhao, name: Xianbang Wang, name: Muyang Li, name: Enze Xie, name: Yingcong Chen, name: Yao Lu, name: Song Han, name: Yukang Chen
Organization: NVIDIA

We present LongLive, a frame-level autoregressive (AR) framework for
real-time and interactive long video generation. Long video generation presents
challenges in both efficiency and quality. Diffusion and Diffusion-Forcing
models can produce high-quality videos but suffer from low efficiency due to
bidirectional attention. Causal attention AR models support KV caching for
faster inference, but often degrade in quality on long videos due to memory
challenges during long-video training. In addition, beyond static prompt-based
generation, interactive capabilities, such as streaming prompt inputs, are
critical for dynamic content creation, enabling users to guide narratives in
real time. This interactive requirement significantly increases complexity,
especially in ensuring visual consistency and semantic coherence during prompt
transitions. To address these challenges, LongLive adopts a causal, frame-level
AR design that integrates a KV-recache mechanism that refreshes cached states
with new prompts for smooth, adherent switches; streaming long tuning to enable
long video training and to align training and inference (train-long-test-long);
and short window attention paired with a frame-level attention sink, shorten as
frame sink, preserving long-range consistency while enabling faster generation.
With these key designs, LongLive fine-tunes a 1.3B-parameter short-clip model
to minute-long generation in just 32 GPU-days. At inference, LongLive sustains
20.7 FPS on a single NVIDIA H100, achieves strong performance on VBench in both
short and long videos. LongLive supports up to 240-second videos on a single
H100 GPU. LongLive further supports INT8-quantized inference with only marginal
quality loss.