arxiv:2605.18739

LongLive-2.0: An NVFP4 Parallel Infrastructure for Long Video Generation

Published on May 18

· Submitted by

Wei Huang on May 19

Authors:

Ruihang Chu ,

Abstract

LongLive-2.0 presents an NVFP4-based parallel infrastructure for long video generation that addresses training and inference bottlenecks through sequence-parallel autoregressive training and diffusion model tuning.

AI-generated summary

We present LongLive-2.0, an NVFP4-based parallel infrastructure throughout the full training and inference workflow of long video generation, addressing speed and memory bottlenecks. For training, we introduce sequence-parallel autoregressive (AR) training, instantiated as Balanced SP, which co-designs the efficient teacher-forcing layout with SP execution by pairing clean-history and noisy-target temporal chunks on each rank, enabling a natural teacher-forcing mask with SP-aware chunked VAE encoding. Combined with NVFP4 precision, it reduces GPU memory cost and accelerates GEMM computation during training, the proportion of which increases as video length grows. Moreover, we show that a high-quality infrastructure and dataset enable a remarkably clean training pipeline. Unlike existing Self-Forcing series methods that rely on ODE initialization and subsequent distribution matching distillation (DMD), LongLive-2.0 directly tunes a diffusion model into a long, multi-shot, interactive auto-regressive (AR) diffusion model. It can be further converted to real-time generation (4 to 2 denoising steps) with standalone LoRA weights. For inference on Blackwell GPUs, we enable W4A4 NVFP4 inference, quantize KV cache into NVFP4 for memory savings, and boost end-to-end throughput with asynchronous streaming VAE decoding. On non-Blackwell GPU architectures, we deploy SP inference to match the speed on Blackwell GPUs, while the quantized KV cache can lower inter-GPU communication of SP. Experiments show up to 2.15x speedup in training, and 1.84x in inference. LongLive-2.0-5B achieves 45.7 FPS inference while attaining strong performance on benchmarks. To our knowledge, LongLive-2.0 is the first NVFP4 training and inference system for long video generation.

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Community

AaronHuangWei

Paper submitter 7 days ago

avahal

4 days ago

that balanced sequence parallelism in LongLive-2.0 is a clever way to fuse teacher forcing with SP execution, but how sensitive are the training gains to the exact chunk size and the clean-history vs noisy-target pairing when real long-form content has irregular shot lengths? the arxivlens breakdown helped me parse the method details (https://arxivlens.com/PaperView/Details/longlive-2-0-an-nvfp4-parallel-infrastructure-for-long-video-generation-9770-66c951dd), especially how the SP-aware chunked VAE encoding and per-rank memory reductions play with KV cache quantization. would love to see a small ablation where you vary the shot-length distribution or test on datasets with more abrupt scene changes to see if the gains persist.

Qasim5642

3 days ago

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"fps": "60fps",
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"Luxury Watches Collection",
"Shop Now",
"thisstyles.com",
"+92 319 7827504"
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